dataesr/scientific-paragraphs-categorization huggingface dataset:
(Uddin et al. , 2020). Moreover, an annual mean concentration was used, not considering wastewater discharge seasonal variability, which might be important, particularly in more touristic areas. This model limitation might partly explain the deviation in some cases (e. g. , Saronikos and Gabes Gulf) between the simulated and observed variability of MPs abundance during the two sampling campaigns. In the Ligurian Sea, untreated wastewater was most likely overestimated, possibly due to upgraded treatment facilities along the Italian coast, with regard to those reported in the UNEP/MAP (2011). Despite those limitations, using a comprehensive wastewater discharge dataset (UNEP/MAP, 2011) contributed to the reasonable agreement of the simulated microplastics distribution with in situ data on basin scale (see also  Tsiaras et al.
Depth (x) of hydrogen penetration is the depth at which the normalized H intensity is equal to a value calculated using the Eq. (  8 ) given below. Normalized H intensity ¼ C H avg: gel layer ð Þ þ 1 2 (8) NMR spectroscopy NMR data were collected on a Bruker 500WB Avance II spectrometer operating at a magnetic field of 11. 72 T, using a 4-mm Bruker CP-MAS probe at a spinning frequency of 12. 5 kHz. For 27 Al,  23  Na and 11 B, a short single pulse excitation of 1 µs in length (tip angle of about 15°-20°) was used to acquire quantitative spectra with a recycle delay of 1 s for  27  Al,  23  Na and 2 s for 11 B. 17  O MAS NMR spectra were acquired using a rotor-synchronized spin echo pulse sequence (i. e. , with a spin echo delay of one rotor period and soft selective pulses on the central transition) in order to minimize the baseline distortion due to the ringing signal.
ACKNOWLEDGEMENTS We would like to thank  Jean-Pierre Mestre  (CEA) and  Marie Fenart  ( CEA ) for the SEM images,  Myriam Chartier  ( CEA ) for XRD patterns,  Laurent Dupuy  and  Elodie Chauvet  ( TESCAN Analytics ) for the ToF-SIMS profiles,  Géraldine Parisot  (CEA) and FILAB for the ICP-OES solution concentration analysis. The study was financially supported by  CEA  and  EDF (Electricité de France ).
The first incarnation of this book was a dissertation defended at the department of South Asian Languages and Civilizations (salc) of the University of Chicago in November 2019. I thank the University for supporting my research with a Graduate Aid Initiative, and with fellowships from the Committee for Southern Asia Studies and the Nicholson Center for British Studies. I also received a Social Science Research Council International Dissertation Research Fellowship, and a Junior Fellowship of the American Institute of Indian Studies. These funds enabled yearly visits to Tamil Nadu from 2013 onwards, an extended period of archival research and fieldwork in 2016-2018, and also trips to Italy, France, and the UK. Further archival work in Rome has been funded by a Marie Skłodowska-Curie fellowship, currently supporting my new research project at the Centre d'Études de l'Inde et de l'Asie du Sud in Paris.
Funding: This research was funded by the  Basque Government , grant number  IT 1503-22. J. M. acknowledges the Grant  PID2021-123438NB-I00  funded by  MCIN/AEI/10. 13039/501100011033and  by " ERDF A way of making Europe ". J. M. also acknowledges the financial support of  Eusko Jaurlaritza , code:  IT-1566-22.
25/54. 75/25, respectively. The solution is very stable over time; detailed synthesis protocol has been the subject of earlier publications  50, 51. The residual pressure before plasma deposition was in the order of 10 -3 mTorr, and the working pressure was 3 mTorr with a total gas flow of 24 SCCM. Pressure variations due to the pulsed liquid injection were recorded using an MKS capacitance gauge. Time-resolved optical emission spectroscopy was carried out using an Avantes AvaSpec-3648-2-USB2 dual-channel spectrometer, scanning between 500 and 900 nm with an integration time of 100 ms. Film growth was monitored by in situ spectroscopic ellipsometry using a J. A. Woollam M-2000 ellipsometer. Samples were modeled as heterogeneous structures with a Bruggeman effective medium approximation, comprised of data from Herzinger et al.
This result hints at two distinct kinetics driving plasma deposition, one for each period. Figure  1  further shows that the post-injection pressure deviates from a simple first-order decay. A good fit was reached with a sum of two exponential decays: for any number of pulses and many pressure datasets, the time constants were τ1 = 1. 6 ± 0. 3 s and τ2 = 8. 6 ± 1. 0 s. Although assigning a definite physical meaning to these values is still delicate at this stage, it can be noted that the order of magnitude of a few seconds is consistent with the droplet evaporation times calculated by Ogawa et al. 39  in low-pressure environments. Since the sole evacuation of excess pressure at maximum pumping capacity would have led to first-order kinetics with much shorter timescales, the pressure decrease seems evaporation-limited (as opposed to pumping-limited).
Data collection approach In-depth interview, as a key bottom-up approach for policymaking process, has been a long-standing method to understand individuals' behaviour, experience, value and needs. A total of 30 long-distance commuters were recruited as interviewees in Beijing who volunteered to register in an online interview platform. Each participant was asked a series of open questions upon their commute conditions, experiences, concerns, values and needs through 40-60 min of telephone calls. In this research, interviewees were divided into three categories based on their frequency of green trips per week. Participants comprised males and females aged between 21 and 55 from various backgrounds and with differing longdistance commute experiences. Thus, they were representative of a wide range of individuals.
Following the recommendations of the 2013 European Strategy for Particle Physics, the Future Circular Collider (FCC) conceptual design study was launched in February 2014, and comprised the study of both a hadron and a lepton collider. The Conceptual Design Reports  [13, 14, 15]  were published by a large collaboration of 1500 authors. The overarching FCC document submitted to the European Strategy Update  [16] , describes the FCC integrated programme (FCC-INT), a staged implementation scenario in which the lepton collider (FCC-ee) is the first step to be installed in a new 100 km underground circular infrastructure, providing a great part of the infrastructure towards the 100 TeV collider, as can be seen in Fig. 1. FCC-ee would be able to start taking data soon after the completion of the HL-LHC, for a duration of O(15 years).
Our aim is to demonstrate that along with climate-change-related threats, Mediterranean ecosystems, present in sharply contrasted environmental gradients, are laboratories of high significance when considering adaptation to climate change. We did not aim at an exhaustive review of all Mediterranean ecosystems. We focused our work on species with important ecological roles (such as engineer species) and for which sufficient data on adaptation are available in different ecosystems. Our case studies are also mainly species without the possibility of important active dispersal, such as sessile species or planktonic species (passive dispersal). Such species are directly susceptible to the modifications of their environment, making them flagship species for studying adaptation to face environmental changes.
Semiarid and arid steppes Plants of semiarid and arid steppes are emblematic Mediterranean ecosystems, with data on response to climate change lacking for important species. A modeling study on Stipa tenacissima (alfa grass) has nevertheless shown that suitable habitats for this species in Tunisia may decrease importantly along with climate change  (Ben Mariem & Chaieb, 2017). The seed germination of S. tenacissima is likely to be impacted by the interaction among temperature, drought, and salinity, which raises the question of the evolution and conservation of local populations of this species  (Krichen et al. , 2017). More generally, where steppes result from land exploitation such as grazing, a change of practice could lead to a transformation into shrub land or sparse forest, with water availability as a key issue  (Fady & Médail, 2004).
ACKNOWLEDGMENTS This work is a contribution to  Labex OT-Med  (n  ANR-11-LABX-0061 ) and has received funding from  Excellence Initiative of Aix-Marseille University -A*MIDEX, a  French "Investissements d'Avenir" program. Part of the results synthesized here was obtained thanks to the  ADACNI program  of the  French National Research Agency (ANR)  (project no. ANR-12-ADAP-0016 ) and to the  ECCOREV Research Federation  ( FR 3098 ). This project has received funding from  European FEDER Fund  under project  1166-39417. This work was funded by the  French government  through the program Investments for the future, Initiative of  Excellence of Aix-Marseille University A*Midex, Mediterranen Institute for Environmental Transition (ITEM ,  AMX-19-IET-012 ). The author thank  Marie-Charlotte Bellinghéry  for drawing figures and illustrations for this paper.
Acknowledgments This work was supported by the  BLRI Labex  ( ANR-11-LABX-0036 ),  Institut Convergence ILCB  ( ANR-16-CONV-0002 ), and the  CHUNKED ANR project  (# ANR-17-CE28-0013-02 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are grateful to  Jeremy Yeaton  for their helpful comments on a previous version of the manuscript.
Funding: AR. - BLRI Labex  ( ANR-11-LABX-0036 ),  Institut Convergence ILCB  ( ANR-16-CONV-0002 ), https://fundit. fr/en/institutions/labex-brainlanguage-research-institute-blriA. R. -CHUNKED  ANR  project (# ANR-17-CE28-0013-02 ), https://anr. fr/en/funded-projects-and-impact/funded-projects/. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
It is also very important to develop other types of research and to synthesis the trial's research in individual patient data meta-analysis. These types of analysis are very useful, particularly to identify the patients who are more likely to benefit from the treatment. Some journals, such as The BMJ and PLOS Medicine, have a very strong policy in that they require data sharing as a condition for publication of clinical trials in their journals. A study led by Florian Naudet published in The BMJ in 2018  19  evaluated whether we actually had access to this data. He selected 37 published randomised control trials, requested the individual patient data from all of the authors, but could access the data in only half of the trials. I would like to finish this presentation with a quote from Professor Doug Altman, who was a Professor of Statistics at the University of Oxford, founder of the Equator Network  20  and one of the great leaders in the field of transparency and meta-research.
The institutions also need to assist researchers in open research practices because they are demanding and resource-intense. A good example are so-called data stewards who can help scientists to share their data in a FAIR, that is findable, accessible, interoperable, and reusable manner. Maybe even most importantly, institutions need to incentivise and reward open practices in hiring, in tenure, in promotion and also in intramural programmes. In essence I think we need to change from a situation where researchers are basically mostly evaluated and rewarded on the basis of where they publish -very often it is just the impact factor as a very abstract number or the h-index -and by the sum of their third-party funding to a situation where we, in addition at least, value open and responsible practices.
The first phase consists of a binary classifier that predicts a binary label, i. e. kissing or non-kissing scenes and the second phase is to aggregate the binary labels for contiguous non-overlapping segments into a set of kissing scenes. The project was experimented with a variety of 2D and 3D convolutional architectures such as ResNet, DenseNet, and VGG to develop a highly accurate kissing detector. The performance of the conv-LSTM model generated 97% accuracy, while the standalone CNN model can perform 95% of accuracy. The other high-performance pre-trained CNN models such as densnet, alexnet, resnet, VGG, squeezenet, googlenet were proposed by previous researchers to solve various problem domains  [9] -  [12]. From a previous study in  [13] , the multi-task CNN model for attribute prediction was used and the study used two categories of the dataset: the clothing attributes dataset and the AwA dataset.
The clothing attributes dataset consists of 1856 images with 23 binary attributes that have been annotated; while the AwA dataset is the animal with attributes dataset consisting of 30475 images of 50 animal classes. The annotation was made by the animal's class level and provided 85 binary attributes for each class. The dataset has nine groups. Another research on violence detection shows that the dataset's specification used consists of 50 frames (720x536 pixels) and it was manually labeled as fight or non-fight scenes  [14]. Then, the research about online deep learning for action recognition also seems to use a lot of datasets consisting of 720x480 pixels of resolution with 10 frames per second. Somehow, the dataset also undergoing downgrading due to fit the frame patches  [15]. However, the higher resolution and a greater number of samples will give better performance of the trained model.
Kissing dataset preparation Datasets are a crucial part of the machine learning process. It determines the quality of machine learning. In this study, the video clip dataset had been collected from various sources such as YouTube and movies. It had gone through some clipping and editing processes. The supervised machine learning-based conv-LSTM had been trained with 300 cartoon clips with 30 frames/second. The dataset consists of kissing and non-kissing scenes with a duration range of three seconds. Two frame sizes are selected: 80x60 pixels and 320x240 pixels. Figure  1  shows the clipping process of a kissing scene in a video. The CNN promises to generate a good representation of each video frame, while a recurrent neural network (RNN) is required to encode the temporal changes. Since we are focusing on the encoding of spatial and temporal dimensions; thus, the conv-LSTM will be an appropriate option in this study, which may result in a better analysis of video representation.
Therefore, for this dedicated video scene detection application, the pre-trained Darknet-19 model and the LSTM models were used to represent the CNN and RNN network; respectively. Darknet-19 is made up of 19 convolutional layers, five max-pooling layers, and a soft-max layer for classifying objects. This model is pre-trained on the ImageNet database and is considered as fast execution with good classification performance in object detection which is very important for predicting in real-time  [21]. Several studies demonstrated that the model has higher generalization and performs well in object and action recognition  [22] ,  [23]. Figure  3  illustrates the conv-LSTM architecture. Figure  3. The model is made up of layers that alternate between convolutional (blue), normalization (white), and pooling (red), the final hidden state of conv-LSTM (yellow) is used for kissing and non-kissing scenes classification The model's network consists of a CNN which comprises a set of convolutional layers followed by max-pooling operations in series for collecting discriminant features.
RESULTS AND DISCUSSION This chapter will discuss the result of the experiment that has been carried out. The objectives of the experiment are to find out which trained model gives the best performance in terms of accuracy and losses. The accuracy is the number of correctly predicted by the model. The accuracy rate was evaluated by using the confusion matrix. While the loss rate is cumulative of the mean square loss of the dataset. Two sample sizes were selected (320x240 and 80x60) with identical dataset split proportion (training: 70%; validation: 10%; test: 20%). Every model will be trained in epochs and three models had been built for each sample size category. The best model selection criteria for each model were depending on its best-fitting epoch from 1-30 epoch(s). The evaluation performance of each model had been carried out as tabulated separately in Tables 1-2 by sample frame size categories.
Sentinel-1 data can offer convective wind gust estimates at a high spatial resolution while the other sources (SMAP, Windsat, and ASCAT-A/B/C) only estimate wind gusts at a scale larger than 0. 25° grid. Also, thanks to the LEO data collocation, one can observe the displacement direction of surface wind patterns and their intensity variabilities (15-25 m/s). Surface wind patterns move according to the horizontal displacement of deep convective clouds aloft observed by the Meteosat geostationary satellite. Likewise, the wind intensity variability and deep cloud brightness temperature evolve accordingly. Using simultaneous rainfall and wind speed measurements from Windsat, this study showed that the highintensity radar backscattering from C-band Sentinel-1 SAR data is mainly induced by convective winds rather than precipitation.
In particular, they may occur suddenly and become more intense very quickly, and that feature makes the CS observation and prediction more complicated, notably at a high spatial resolution. However, this work has been improved in recent years thanks to a complete network of GEOstationary (GEO) satellites (Meteosat, GOES, Himawari, Gaofen) covering all the Earth. Meanwhile, the observation and estimation of strong surface wind speed and intense precipitation associated with CSs are still significant challenges due to the lack of remote sensing and in-situ data at high spatial and temporal resolutions. Heavy rain may be particularly dangerous for land-use applications since it may cause flash floods, while strong wind gusts may significantly damage many onshore, offshore, and coastal activities.
In addition to the Windsat radiometer, this paper uses data acquired by the SMAP L-band radiometer  [23] [24]  to observe convective wind gusts. Like Windsat, surface wind speed is retrieved from the SMAP brightness temperature (BT) measurements. However, the L-band radar signal is not impacted by precipitation, even under heavy rainfall  [14]   [23]. Therefore, the surface wind speed estimated by SMAP can be used as a reference to evaluate the impact of rain on surface wind speed retrieval at the other frequencies (above C-band). As suggested in  [24] , the SMAP wind data should be used for moderate wind speed above 12 m/s. The potential of data acquired from Sentinel-1 SAR, ASCAT scatterometers, and Windsat and SMAP radiometers to estimate and observe the evolution of surface convective wind gusts is actual under the collocation conditions of these datasets.
One should note that some intense mesoscale CSs may exist for several hours  [27]. So, through the combination of Sentinel-1, ASCAT-A/B/C, Windsat, and SMAP, this paper presents the observations of surface wind pattern evolution for short and long terms, which has not been illustrated yet in the literature. In addition to the Low-Earth Orbit (LEO) satellites discussed above (Sentinel-1, ASCAT, Windsat, and SMAP), this paper uses the Meteosat GEO images (SEVIRI data-0 degree, channel-10-IR 12. 0) corresponding to the LEO data to detect deep convective clouds. The Meteosat sampling time of 15 minutes eases the investigation of the relationship between deep convective clouds and surface wind patterns. The time lag between Meteosat and LEO data is less than 10 minutes in this paper. Likewise, the Meteosat images are resampled on the grid of Sentinel-1 ones to facilitate comparisons.
A. Data Preparation and Collocation Fig. 1  presents the Gulf of Guinea imaged by Sentinel ASCAT-A/B/C, Windsat, and SMAP for direction, February 4, 2019 (Fig. 1a ), and ascending orbit direction, December 19, 2019 (Fig. 1b ). In the two cases, the footprints of the five devices are superposed. Sentinel the narrowest swath but the highest spatial resolution. The others have a coarse spatial resolution but a large swath. general, a footprint matching between satellite instruments remains complicated footprint matching cases between the five this paper presents two cases of descendi orbit directions, as shown in Fig. 1. In both cases, the ASCAT A/B/C, Windsat, and SMAP data are resampled on the Sentinel-1 grid to facilitate the observation of surface wind patterns. Likewise, the Meteosat data are col Sentinel-1 data to observe the corresponding locations between deep convective clouds and surface wind patterns.
Such a study is significant to estimate convective rainfall intensity, then forecast flash floods. The short-and long-term observations of sea surface wind patterns under deep convection based on the collocation of various satellites and the strong relationship between strong surface winds and deep convective clouds bring a significant perspective for the forecast of convective wind gusts. This work can be performed by using Artificial Intelligence (AI) combined with GEO images as input data. The remote sensing data (Sentinel-1 SAR, ASCAT-A/B/C, Windsat, and SMAP) are used as training and validating data for the AI models.
ACKNOWLEDGMENT The Sentinel-1A/B images are given by the  European Copernicus Program. The ASCAT-A/B/C data are downloaded from https://osi-saf. eumetsat. int. The Windsat and SMAP data are offered by www. remss. com/. Meteosat images are provided by the  European Organization for the Exploitation of Meteorological Satellites (EUMETSAT)  via EUMETSAT Data Center. The authors would like to thank the Editors and Reviewers for the valuable comments.
Such ageing interventions, sometimes compared with hardware defragmentation, might benefit from the exponential growth of pharmacological approaches (see, for example, the DrugAge database of ageing-related drugs at https://genomics. senescence. info/) and the appearance of an anti-ageing biotech sector. In silico-based screening approaches could also be developed to identify more rapidly potentially active molecules, given the time and costs of validating these interventions  [83]. Additional cancer prevention strategies might also emerge from the better identification of specific ecosystems that impact cancer emergence, either in individuals (such as genetic and epigenetic alterations, disabilities and socioeconomic disadvantages) or in populations (such as exposure to chemicals, pathogens and radiations).
However, ageing-associated changes such as immune-senescence require specific attention in this patient population  [109]. Available evidence indicates that the clinical efficacy and toxicity of most of these drugs is comparable in fit older and younger adults included in clinical trials. By contrast, their effectiveness and toxicity among frail older adults is still poorly understood as these patients were excluded from the landmark clinical trials  [110] [111] [112]. More inclusive clinical trials have now been designed to specifically address how immunosenescence in frail patients could alter the therapeutic response to ICIs or increase their side effects (as with NCT04533451). In addition to the harms identified in younger patients, these clinical trials will evaluate the impact of new drugs on the functional status and quality of life of older patients.