dataesr/scientific-paragraphs-categorization huggingface dataset:
Through such availability counts the dashboard approximates the technological readiness of each language (see Chapter 3). There is an urgent need for monolingual, bilingual and multilingual domain-specific corpora. Such data can only rarely be found via available resources, mostly because it simply is not there, but also because of incorrect or missing documentation of data and metadata. Manually annotated data is lacking; although the quality of automated and semi-automated annotations is increasing, manual annotation by human experts in a certain field is still the best means of acquiring high-quality data. Overall, there are missing open LRs. Domain-specific LRs are required to be available for scientific purposes with open licences. If the FAIR principles were systematically applied, this would be a huge benefit where data and metadata is concerned, but they are not really being rolled out properly.
Although Europe has benefited from a strong open data movement for about 15 years now, there is still a gap in the provision of clearly specified licences for data. At the moment, benchmark approaches are not harmonised or standardised, and benchmarks on domain-specific vocabularies and annotated data and corpora are often missing. Metadata provides only very limited data provenance. Overall data quality is weak and so it often happens that use-cases cannot be realised as specified as labeled data is not available for the use-case at hand. Non-existing policies around data and metadata management 44 https://internationaldataspaces. org/use/reference-architecture/ 45 https://datamarket. at 46 https://live. european-language-grid. eu/catalogue/dashboard that should be part of a data governance model often result in low data and metadata quality.
reportsanddata. com/report-detail/enterprise-metadata-management-market • NLP pipelines of tokenisers, taggers, parsers etc. , which require labelled linguistic datasets (e. g. , treebanks) and evaluation sets; • interfaces and content should be available in all languages via the web, i. e. , the information available on a specific object, person or event provides the same amount of information in all languages; • knowledge and content available in the form of audio files should be available in all languages so that it can be easily consumed; • appropriate data required to train and develop monolingual, bilingual and multilingual models that cover the type of knowledge (domain-specific) and the type of language required for MT, (multi)document summarisation and speech-totext technologies; • efficient APIs required to integrate organisation-specific data and systems with social media platforms; • pseudonymised or anonymised data for all EU languages, as well as domainspecific annotated corpora; • data and models which address gender bias or minority bias etc.
Existing LT data infrastructure providers, such as ELG, ELRC-SHARE, CLARIN, META-SHARE, and ELRA as well as industrial and national initiatives can provide the seeds for a kind of federated data infrastructure, i. e. , a data space that enables seamless and trusted interactions between data providers and data consumers, and enables cross-fertilisation by means of interoperability, aided among others by semantic KG technologies. Interoperability challenges can be broadly classified in four different layers: • technical interoperability, enabling technical components (i. e. , data space connectors) to communicate with each other; • semantic interoperability, ensuring that attributes and policies have the same meaning; • organisational interoperability, ensuring that the different (business) procedures and operations are compatible; • legal interoperability, ensuring that contractual statements are legally equivalent.
Different federation architectures can be designed for building data spaces ranging from architectures with some central components (e. g. , a data space catalogue) to fully decentralised ones. Whatever the architectural choice, data spaces will promote data sovereignty, enhance data exchange and trading, and enable the creation of value from data. The Language Data Space, coupled with the data space-inherent data integration capabilities, and developments in machine learning, deep learning, transfer learning and federated learning is expected to help fill in the gaps. Of key importance in the development of language data spaces is the compliance of data and operations with the rules, regulations and values of the European Union. LTs themselves are expected to play a crucial role in ensuring such compliance.
Privacy preservation technologies, such as data anonymisation technologies and ethics compliance (through bias detection technologies, say), will be important tools in the hands of data providers, data consumers and data space operators. By its nature, the Language Data Space is conceived of as one of the horizontal data spaces in the data space ecosystem designed by the European Commission. In addition to the intra-data space interoperability, the Language Data Space will have to ensure interoperability with vertical data spaces (e. g. , health, manufacturing, skills, mobility, etc), enabling cross-fertilisation, data discovery, exchange and trading at the inter-data space level. Zooming out of the data spaces discourse and moving to technology visions regarding data access and sharing in general, one of the top-10 data and analytics technology trends identified by Gartner Research is the notion of a Semantic Data Fabric.
Mira Didic MD, PhD Memory Resource and Research Centre of Marseille, CHU de Marseille, Hôpital La Co-investigator ACKNOWLEDGMENTS Timone, F-13000, Marseille, France Maritchu Doireau MD The MEMENTO cohort is funded by the Foundation Plan Alzheimer Memory Resource and Research Centre of Bordeaux, CHU de Bordeaux, Hôpital Co-investigator (Alzheimer Plan 2008-2012), through the Plan Maladies Neu-Pellegrin, F-33000, Bordeaux, France Antonio Dos Santos MD rodégénératives (2014-2019), and the French Ministry of Research Institute of Co-investigator (MESRI, DGRI 2020-2024). This work was also supported by CIC Patrice Douillet MD 1401-EC, Bordeaux University Hospital (CHU Bordeaux, sponsor of Memory Resource and Research Centre of Montpellier, CHU de Montpellier, Hôpital Co-investigator the cohort), Inserm, and the University of Bordeaux.
Study limitations Our study suffers from several limitations. From a methodological point of view, the bias from misclassification of covariates cannot be excluded as data were collected from prehospital and in-hospital medical reports. Moreover, because data abstraction was collected by a single investigator, the data accuracy can be compromised  [65]. The statistical analysis performed does not allow any causal conclusion between iPP < 40mmHg, positive PP and 30-day mortality. In this study, we only included adults, consequently our conclusions are not directly transposable to a pediatric population. This is a retrospective study; because no therapeutic goal was a priori defined, we cannot define which mean arterial pressure was targeted nor when was prescribed norepinephrine administration before or after fluid expansion failure.
Methods Te study was performed between October and December 2016 at the University Hospital of Zurich, Switzerland, with a clinical follow-up until January 2020 (up to 40 months). Te study population consisted of 30 clinically stable chronic outpatients on maintenance HD using a FXCordDiax 100 flter (Fresenius Medical Care GmbH, Bad Homburg, Germany) in the haemodiafltration mode. Te original study was designed to analyse circulating plasma biomarkers at diferent levels of congestion and consisted of four visits with collection of clinical data and blood samples, as described previously  [4]. Previous studies showed lower intraindividual and higher interindividual biological variation of GDF-15  [5, 6]. However, to overcome the impact of congestion on circulating GDF-15 in HD patients as previously shown by our group  [7] , for the purpose of this study, we measured GDF-15 only in samples from the frst study visit and collected after a routine HD session.
Te measuring range of the assay extended from 400 to 20,000 ng/L. Follow-up for survival was performed using the electronic medical record with direct contact by phone if needed. Data are presented as median (quartiles) or number (percentage), as appropriate. Group characteristics were compared with the Fisher's exact test or the Mann-Whitney U test, as appropriate. Survival was plotted with the Kaplan-Meier curve, and diferences between groups were assessed by the log-rank test. Te prognostic performance of GDF-15 was assessed by receiver operating characteristic (ROC) analysis and expressed as area under the ROC curve (AUROC). Correlation analysis was performed using Spearman's correlation coefcient (rho), and the agreement between the two diferent assays is plotted on the Bland-Altman plot.
Te study was performed according to the standards of the Declaration of Helsinki and approved by the local ethics committees of Zurich (2016-00451 and 2020-02087). All patients signed written informed consent forms. Te study is registered in clinicaltrials. gov (NCT02962635). Te null hypothesis was rejected with a two-sidedP value <0. 05. All analyses were performed with the use of IBM SPSS Statistics, Version 28. 0 (IBM Corp, Armonk, NY, USA).
During the winter sea ice accumulates snow, which may thaw during the summer melt season to form slush and melt ponds atop the ice. Sea ice thickness together with its overlying snow controls ocean-ice-atmosphere exchanges of heat, momentum and gases at the ocean's surface. Systematic and long-term observations of the major sea ice variables is only possible using past, present and future satellite Earth Observation (EO) data. Sea ice data from satellites has been collected for almost five decades, starting with the first passive microwave Nimbus-5 satellite Electrically Scanning Microwave Radiometer (ESMR) data in 1972. Sea ice observation has been one of the most successful applications of EO data in climate change studies as well as in other polar research topics. Several sensors and retrieval methods have been developed and successfully utilized to measure sea ice area, concentration and drift (e.
Thickness of Thin Sea Ice Thin sea ice develops during the freeze-up season over large areas in the marginal ice zone and coastal areas of the polar seas. During the cold season thin ice also develops in leads and coastal polynyas. In areas of thin ice, with thickness varying from a few cm to half a meter, the salt fluxes generated by freezing and heat fluxes from ocean to atmosphere are important process affecting the weather and ocean circulation in the polar regions. Algorithms for retrieval of thin sea ice thickness (SIT) from passive microwave data was developed using the L-band  (1. 4  GHz) on ESA's SMOS mission. At L-band the full microwave penetration into the sea ice is used to retrieve SIT of up to 0. 5-1. 0 m (Tian-  Kunze et al. 2014; Huntemann et al. 2014) , where the maximum retrievable SIT depends on ice temperature and salinity.
Sea Ice Displacement Since the 1990's, several methods have been developed to retrieve sea ice displacement from scatterometer data provided daily over the ice-covered oceans. Retrieval of ice displacement can be done during the cold period from October to April. During the melt season, the retrieval methods are not reliable because feature recognition is more difficult. The ice displacement is estimated at spatial scale of tens of km and time intervals from one to six days. All the retrieval methods assume that the structures tracked have a spatial dimension higher than the pixel resolution. Several methods have been tested including wavelet analysis, which is similar to Fourier transform in both time and frequency  (Liu et al. 1999) , and methods mainly based on tracking common features in pairs of images.
The Maximum Cross Correlation is a commonly used method to estimate translatory displacement of ice structures (e. g. ,  Emery et al. 1997). The method has been further developed by, e. g. ,  Lavergne et al. (2010). Lagrangian trajectory data has been provided from drifting ice buoys since the early 1990s operated under the International Arctic Buoy Programme where new buoys are deployed every year providing near real-time meterological and sea ice data (https:// iabp. apl. uw. edu/ overv iew_ histo ry. html). The trajectory data are applied for validation of ice displacement data from both active and passive microwave data. Displacement datasets are derived from scatterometer or radiometer observations separately, or as merged data sets from both sensors. By merging scatterometer data with passive microwave data the spatial resolution of the ice displacement vectors can be increased by ≈ 15% and the length of the retrieval period from autumn to spring can be extended.
5. The ice displacement data sets and derived products are made available for the scientific community and operational monitoring and forecasting services on regular basis by processing and archiving centers (e. g. , https:// cersat. ifrem er. fr/, https:// nsidc. org/ home, https:// www. eumet sat. int/ osi-saf) and numerous research institutes. One example of a derived product is sea ice volume exiting the Arctic basin through the Fram Strait (Fig. 6 ). Sea ice displacement data are also used for studying ice-ocean-atmospheric processes. Examples are provided by  Itkin et al. (2017)  who studied the impact of winter storms on the Arctic sea ice distribution,  Ricker et al. (2018)  showed that sea ice thickness anomaly can be explained by a sea ice displacement anomaly, and Bi et al. (  2019 ) evaluated the Baffin Bay sea ice inflow and outflow since the 1970's.
Traditionally, C-and Ku-band data were produced by different satellites. Therefore, it is a challenge to establish homogeneous time series over several decades where multiyear and firstyear ice are separated. However, several of the planned scatterometers will have a combination of C-and Ku-band. These scatterometers will continue to be operated under Eumetsat and NOAA's polar programmes, providing C-or Ku band data primarily for ocean wind observations, but also for sea ice. Other scatterometer satellites have been launched by China (HY-2 satellites) and the China-France Oceanography Satellite (CFOSAT). These programmes will ensure that scatterometer data will be available for sea ice observations in the coming years.
575 GHz (Ku Band) and 3. 2 GHz (S band). Since the radar altimeter on Envisat was similar to that employed on the earlier ERS satellites the methods described in  Laxon et al. (2003)  were also used to derive sea ice thickness from Envisat data, albeit with a small additional correction for the radar travel time through the snow pack  (Giles et al. 2008a , Hendricks 2018). A major limitation of the ERS-1/2 and Envisat satellite radar altimeter datasets, however, is that only partial observations exist for the Central Arctic due to satellite orbit constraints which limit coverage to 81. 5° N. Since the early 2000s, NASA and ESA committed to obtaining measurements of changes in sea ice thickness via the use of high-resolution satellite laser and radar altimeters dedicated to polar observations.
12. While this is useful for evaluating ridge densities, first experiments with interferometric SAR (i. e. using the phase difference between two coherent SAR signals received at different sensor positions, where the distance between both is being called the interferometric baseline) showed a potential for the retrieval of ridge heights over fast or temporally stationary ice  (Yitayew et al. 2018). For applications over drifting pack ice, however, this technique requires data from satellite tandems with very small temporal gaps between the received signals but sufficiently large baselines for achieving the required sensitivity to height variations. Interferometric SAR has also been used for mapping fast ice extent and the assessments of fast ice stability  (Dammann et al. 2019). The timing of melt-onset and freeze-up is determined from significant changes in backscattering characteristics taking place within a few (1-4) days.
In fact, ice navigation has been one of the drivers for SAR satellites and delivery of data in near realtime to users. In particular the RADARSAT and the ERS/ENVISAT programs were instrumental in developing SAR-based ice monitoring to support ice navigation. From 2014 the Sentinel-1 satellites have extended to provision of SAR data significantly, by delivering regular coverage of the shipping lanes in the Arctic. The interval of the images is from 1 to 3 days with dissemination to users within a few hours. The capability to deliver repeated SAR images in the same areas provide valuable information about the dynamics of the sea ice in particular ice motion, leads, ridges, rough ice areas, fastice, large floes, etc. However, the requirements for timely ice information will depend on the type of vessel and the ice conditions along the sailing routes.
2018 ) and many additional sea ice parameters like ice type, ice temperature and thin ice thickness  (Scarlat et al. 2020). As the observations are done in the range from 1. 4 to 36. 5 GHz, the uncertainty cause by atmospheric influence will be much lower than, for example, in the 89 GHz ASI retrievals (Sect. 2. 1). Also, sea ice motion and snow on sea ice will be retrieved at higher spatial resolutions than today. Moreover, based on the L-band passive microwave data from CIMR, the time series of thin ice thickness from SMOS and SMAP will be continued.
ii. Performance analysis This analysis is used to validate the forecasted output of the SVR model. We adopted the root mean square error for this work. Thus expressed: (18) where is the true or measured value of the output, is the forecasted output and n is the total number of data set. where v is the wind speed (m/s), c and a are the Weibull scale and shape parameters. Different methods of calculating the two parameters of Weibull distribution of wind speed are available in literature  [8]. From which the obtainable average wind power Pwind from a turbine is estimated by: (2 0) where Pr is rated power of the turbine, υr is rated wind speed and υci is the cut-in wind speed, υco is the cut-out wind speed respectively, a is Weibull shape parameter.
Reliability Analysis of Drone Swarm Review As shown in the introduction above, reliability analysis and evaluation are the principal domains in research on the design and use of drones. However, most of these studies are implemented for lone drones. At the same time, the intensive development of drone-based applications provokes research in drone swarm reliability analysis. The increase of interest in research in the field of drone swarm reliability analysis can be illustrated by the growing number of publications in the scientific database Scopus (Figure  2 ). The development of the drone swarm structure, its optimization, elaboration of the communication platform, and other problems for the effective performance of the swarm are the subjects of many studies. The problem of reliability analysis of a swarm of drones, as well as its safety and maintenance, is considered much less frequently, in contrast to the mentioned problems or these problems in relation to a single drone (Figure  1 ).
In this study, a systematic strategy was adopted to search and analyze publications related to drone swarm reliability assessment from four scientific databases (Science In this study, a systematic strategy was adopted to search and analyze publications related to drone swarm reliability assessment from four scientific databases (Science Direct/Scopus, Web of Science, IEEE Explore, and Wiley Online Library). In this analysis, a group of drones (UAVs) for the implementation of one mission, or some interconnected missions, is interpreted as a drone swarm or drone fleet. The expressions "drone swarm", "drone fleet", and "multi-drones" (multi-UAVs) are considered equivalent (Figure  2 ). The keywords used were: drone swarm, drone fleet, multi-drones, reliability, availability, failure, and crash, in the search query.
Eighty-two papers were selected for analysis from the indicated scientific databases, and most of them are considered below in the review of innovations in the reliability analysis of drone swarms. In three papers  [30, 38, 42] , the review methods for the reliability analysis of a single drone and a swarm (fleet) of drones were made, and systematic approaches to their reliability analysis and evaluation have been indicated. The reliability analysis of a drone swarm (fleet) is considered depending on several parameters in one paper  [38]. This study  [38]  introduces a systematic platform in the consideration of important parameters that influenced the reliability of drone swarms. This platform takes into consideration the reliability (Figure  3 ) and structural (Figure  4 ) characteristics of drone swarms.
Direct/Scopus, Web of Science, IEEE Explore, and Wiley Online Library). In this analysis, a group of drones (UAVs) for the implementation of one mission, or some interconnected missions, is interpreted as a drone swarm or drone fleet. The expressions "drone swarm", "drone fleet", and "multi-drones" (multi-UAVs) are considered equivalent (Figure  2 ). The keywords used were: drone swarm, drone fleet, multi-drones, reliability, availability, failure, and crash, in the search query. Eighty-two papers were selected for analysis from the indicated scientific databases, and most of them are considered below in the review of innovations in the reliability analysis of drone swarms. In three papers  [30, 38, 42] , the review methods for the reliability analysis of a single drone and a swarm (fleet) of drones were made, and systematic approaches to their reliability analysis and evaluation have been indicated.
For the irredundant swarm and each type of redundant swarm, the flight time is taken into consideration, which includes the switching-on time of redundant drone(s) and the time of flight to change the failed drone: redundant drones can have the same flight time from a starting position to a failed drone/drone fleet and a different flight time. a group of drones (UAVs) for the implementation of one mission, or some interconnected missions, is interpreted as a drone swarm or drone fleet. The expressions "drone swarm", "drone fleet", and "multi-drones" (multi-UAVs) are considered equivalent (Figure  2 ). The keywords used were: drone swarm, drone fleet, multi-drones, reliability, availability, failure, and crash, in the search query. Eighty-two papers were selected for analysis from the indicated scientific databases, and most of them are considered below in the review of innovations in the reliability analysis of drone swarms.
This research has also been funded by the  Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan , under Grants:  AP14869972  " Development and Adaptation of Computer Vision and Machine Learning Methods for Solving Precision Agriculture Problems Using Unmanned Aerial Systems ", Grant  BR10965172   «Space monitoring and GIS for quantitative assessment of soil salinity and degradation of agricultural lands in South Kazakhstan» ,  BR18574144   «Development of a Data Mining System for Monitoring Dams and Other Engineering Structures under the Conditions of Man-Made and Natural Impacts».