dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme n. 677706  ( WD3D ). This article is based on observations made in the  Observatorios de Canarias del IAC  with the WHT operated on the island of  La Palma  by the  Isaac Newton Group of Telescopes in the Observatorio del Roque de los Muchachos. Based on observations made with the  Gran Telescopio Canarias (GTC) , installed at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias , in the island of  La Palma. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC).
Funding for the DPAC is provided by national institutions, in particular the institutions participating in the Gaia Mul-tiLateral Agreement (MLA). B. T. G. was supported by the  UK STFC  grants  ST/P000495  and  ST/T000406/1. ARM acknowledges support from the  MINECO  under the  Ramón y Cajal programme  ( RYC-2016-20254 ), the  AYA2017-86274-P grant  and the  AGAUR  grant  SGR-661/2017. MRS thanks for support from  Fondecyt  (grant  1181404 ). DdM acknowledges financial support from  ASI-INAF I/037/12/0  and  ASI-INAF n. 2017-14-H. 0  and from  INAF "INAF  main streams", Presidential Decree  43/2018. RR has received funding from the  postdoctoral fellowship programme Beatriu de Pinós , funded by the  Secretary of Universities and Research (Government of Catalonia ) and by the  Horizon 2020 programme of research  and innovation of the  European Union  under the  Maria Sk lodowska-Curie  grant agreement No  801370.
2. Data based dimension: the trust is the confidence that a network node associates to the received data in a WBAN. 3. Human based dimension: the trust is the confidence level that the patient's WBAN can grant to the environment that surrounds it. Figure  3  gives an architecture of these different trust dimensions. The scope of this survey includes approaches dealing with these three trust dimensions. The differences appear only in the sides of the trust relationship (truster, trustee). The trust has many properties. The trust can be: • Direct: the truster and the trustee have a direct communication and the trust value is calculated and deduced from this direct relationship. • Indirect: the truster and the trustee do not have a direct link. The trust value about the trustee is calculated based on the recommendations propagated from different nodes of the network to the truster.
WBAN Trust Management Challenges In the WBAN architecture, different network nodes gather and exchange data between them and with the LPU. These data are then sent to the medical staff or to the medical database. During the whole process, we have to be sure that all the collaborating entities are honest and none of them represent a failure point for the whole system. The trust management is one of the solutions that may be used to achieve this requirement within the WBAN architecture. Thus, all the nodes as well as the LPU should be reliable, secure and trustworthy. In this context, WBAN present a set of challenges that we outline in the following and have to be addressed in order to make the use of WBAN as secure as possible.
They did not consider the various trust properties and so omitted the indirect and the history trust. Moreover, the composition and formation phases of the trust management process were not considered. However, the approach describes how the trust level is periodically updated. Besides, the approach evaluation is mainly based on the pattern of the bad mouthing attack. • Body Area Network trust (BAN-Trust): Authors in  [62, 63]  conceived an attack-resilient malicious node detection scheme for WBAN named "BAN-Trust". The WBAN node behavior will first be observed and analyzed in the data analysis process. Then WBAN nodes are classified into potential malicious nodes (examined by malicious node detection) or into evidence nodes, which can then be used by a trust management module. Second, each node computes a recommendation trust using collaborative filtering according to the following process: 1.
We demonstrate that the probability distribution function of surface slopes can be approximated with a parametric function whose single parameter can be used to characterise the degree of roughness. Facet azimuth orientation is isotropic at scales greater than 4. 3 km. The simulation results indicate that surface roughness is a minor factor in modelling the sea ice brightness temperature. The change in T B is most pronounced at incidence angles greater than 40 • and can reach up to 8 K for vertical polarisation at 60 •. Therefore current and future L-band missions (SMOS, SMAP, CIMR, SMOS-HR) measuring at such angles can be affected. Comparison of the brightness temperature simulations with the SMOSice 2014 radiometer data does not yield definite results.
The surface facet orientation is characterised by both the slope (α) and the azimuthal angle in which it is facing (γ ). In this paper, we address the knowledge gap regarding the influence of large-scale surface roughness on L-band T B. The paper comprises four main sections. The introduction, presenting the context, is in Sect. 1. Section 2 introduces the experimental data collected during the SMOSice 2014 campaign (Sect. 2. 1). Among them are the EMIRAD2 L-band radiometer T B measurements, which will serve as a reference for the T B simulations. Another instrument is the airborne laser scanner (ALS) used for surface elevation measurements. The surface elevation measurements are used to construct a digital elevation model (DEM) of sea ice surface. From the DEM we derive the facet surface slopes and their orientation.
SMOSice 2014 campaign The SMOSice 2014 campaign took place between 21 and 27 March 2014 in the area between Edgeøya and Kong Karls Land, east of Svalbard. Hendricks et al. (2014)  and  Kaleschke et al. (2016)  described the campaign extensively. In this study we analysed the data acquired during the flights on 24 and 26 March. From this point onwards, we focus solely on the parts relevant to the presented work. In the period preceding the experiment from late January until early March the meteorological conditions in the region deviated strongly from the climatological means. The air temperature measured at Hopen Island meteorological station was on average 9 to 12 • C higher than the climatological value for the period 1961-1990 (Strübing and Schwarz, 2014). Prevailing southerly winds pushed sea ice against the coasts of Nordaustlandet and into Hinlopen Strait, leaving a small strip of compacted ice along the coasts of Edgeøya.
Sea ice surface roughness In this subsection we will analyse the data from the airborne laser scanner (ALS) that we presented in Sect. 2. 1. 2. We use the ALS data to measure the decimetre-scale surface roughness. The ALS is a laser instrument that measures the distance to the surface. If snow covers the ice the ALS will register the snow-air interface as the elevation. Therefore, the relief of the ice is modified by snow cover. During the SMOSice 2014 campaign the snow measurements were unavailable. We assume that snow cover is a plane-parallel layer over sea ice. This assumption does not account for snow dunes and drifts that may form on the ice. The implications of snow thickness on the radioactive transfer modelling are discussed in the section presenting the sensitivity analysis (Sect.
Facet azimuth orientation In the previous section we used the DEM to calculate the vectors normal to the surface facets. In this subsection we analyse the orientations of facet azimuths. In order to evaluate the distribution of the facet azimuths, we define parameter f R (Eq. 2). This parameter is calculated from a histogram of azimuth orientation. In Eq. (  2 ) the denominator is the total number of measurements expressed as the number of angular bins multiplied by the mean number of samples per bin (total number of samples). The numerator is a sum of the differences between the mean number of samples per bin and the actual number of samples in each bin. There are 36 bins. The number of bins was determined with N bins = 5log 10 (N data ) considering the maximal number of samples in the 25 km flight track.
Simulation of brightness temperature of rough sea ice In the previous sections, we described the sea ice surface as composed of facets with an orientation described by two The Cryosphere, 14, 461-476, 2020 www. the-cryosphere. net/14/461/2020/ angles: the slope α and the azimuthal direction γ. Subsequently, we analysed the ALS data to extract information about statistical distributions of slopes and their orientation. We concluded that the exponential function is suitable to describe the probability density function of surface slopes for a range of ice surfaces with different degrees of surface roughness. In this section, we describe how we integrate the probability description of faceted sea ice surface with the MILLAS emission model. We will start by describing the coordinate system that we used in the T B simulations.
5 K % -1. During the preprocessing of the airborne laser scanner (ALS) data we excluded the 70 m sections with more than 5 % missing values. The missing values are caused by the instrument setup (rotating mirror, edge of swath) or by the lack of return reflection from open water or thin ice. We estimate that the uncertainty associated with the sea ice concentration is up to 7. 5 K. To put the partial sensitivities into perspective, the expected changes in the T B caused by the strongest surface roughness measured during the SMOSice 2014 campaign do not exceed -2. 2 K for nadir and 1 K and -5. 6 K for the horizontal and vertical polarisation of the 45 • antenna, respectively. To conclude, the sensitivity analysis of the geometrical roughness model leads to the conclusion that the surface roughness effects will be hard to observe in the SMOSice 2014 flight data with the current emission model setup.
Acknowledgements. The authors acknowledge the institutions providing the data and people involved in carrying out the measurements. The TerraSAR-X and TanDEM-X teams provided the SAR data for this study. Special thanks are due to  Yann Karr , PI of the SMOS mission, for his suggestions and comments. We are also grateful for the financial support that covered the publication costs. Financial support. This research was funded by the  HGF Alliance ,  Remote Sensing  and Earth System Dynamics. The  European Space Agency co- financed the AWI research aircraft Polar 5 and helicopter flights (ESA contract  4000110477/14/NL/FF/lf ;  PI Stefan Hendricks ) and the development and validation of SMOS sea ice thickness retrieval methods (ESA contracts  4000101476/10/NL/CT  and  4000112022/14/I-AM ;  PI Lars Kaleschke).
ALLOCATION The patients will be randomized by an investigator named in the protocol. After checking the patient's inclusion criteria and consent, the investigator connects to the secure (SSL) website https://recherche-clinique. chu-amiens. fr/CSOnline/ and creates a patient le in the database (assignment of the patient code). The investigator will have to enter the requested data in order to check the inclusion and non-inclusion criteria. If all the requested data are entered and are consistent, the investigator will be given the result of the randomization as either "standard management" or "respiratory exchange ratio". If any of the data are erroneous or data, an error message will indicate which corrections must be made so that the patient can be randomized.
Plans and collection of outcomes {18a} All data will continuously recorded a case report form by a clinical data manager who is blinded to the group allocation. All the information required by the protocol must be recorded as and when it is obtained in the electronic case report form and any missing data must be explained: "NK" for "not known" "ND" for "not determined" "NA" for "not applicable". Any access to the study data and any changes will be tracked via the Clinsight® software (Ennov Clinical). At each visit participants will be reminded of the importance of their participation in the study and the need to be continuously contactable and available for monitoring until the end of the follow-up period. We will make sure that appointments for study visits at different times of day and on different days of the week are available.
Data management {19} All data will be continuously recorded on a case report form by a clinical data manager who is blinded to patient allocation. Data collected directly during the patient's follow-up will be entered (single-entry) into an electronic case report form by the principal investigator (or by authorized persons referenced on the task delegation list) on the https://recherche-clinique. chu-amiens. fr/CSOnline/ website. The pre-con gured consistency tests will check the quality of the data. If inconsistencies are detected, queries will be sent to the investigator. The data coding will be given on a separate sheet within the electronic case report form. Data will be entered initially as the actual numerical value or the actual categorical variable. At the end of the study, the Clinsight® database (Ennov Clinical) will be frozen by a data manager at Amiens University Medical Center's Clinical Research and Innovation Directorate.
Con dentiality {27} In with the legislative provisions in force (articles L. 1121-3 and R. 5121-13 of the French Public Health Code), people with direct access to source data will take all the necessary precautions to ensure the con dentiality of information relating to the study drugs, research, participants (particular the latter's names) and results. Like the investigators, these people are subject to a professional duty of con dentiality. During or after the study, the data collected on the participants will be sent to the sponsor by the investigators (or any other specialist staff) will be pseudonymized. They must in no case show the study participants' names or address. Data con dentiality will be ensured by coding the participants' information as follows: -the participant's initials: the rst letter of the family name and the rst letter of rst name.
Interim analyses {21b} An interim analysis is planned half way through the inclusion process (n=175 out of 350). Methods for additional analyses (e. g. subgroup analyses) {20b} No additional analyses are planned. Methods in analysis to protocol non-adherence and any statistical methods to missing data {20c} For the primary outcome, we expect the proportion of missing data to be below 5%. We will not perform multiple imputations for the primary outcome unless more than 5% of the data are missing. The secondary outcomes will be assessed in the full analysis set. Plans to access to the full protocol, participant level-data and statistical code {31c} Adverse event reporting and harms {22} The investigator assesses each adverse event in terms of its seriousness. The investigator should notify the sponsor without delay from the day on which he/she becomes aware of any serious adverse event or new development, if it occurs: -after the date on which the consent form is signed.
The full protocol is available from ClinicalTrials. gov (Identi er: NCT03852147. February 25, 2019) Participant-level data and statistical code are available on request from the Clinical Research and Innovation Directorate at CHU Professor Jean-Marc CHILLON CHU Amiens-Picardie F-80054 Amiens cedex 1 France Tel: +33-322-088-371 (ext. 88390); Fax: +33-322-089-645 Composition of the data monitoring committee, its role and reporting structure {21a} Data Monitoring Committee: Momar DIOUF, Biostatistician Pauline MORIN, Data Manager Salah MATTOUG, Project Manager Clinical Research and Innovation Directorate CHU Amiens-Picardie Amiens-Picardie. F-80054 Amiens cedex 1 Oversight and monitoring France Composition of the coordinating center and trial steering committee {5d} Tel: +33-322-088-371 Fax: +33-322-089-645 Sponsor: Amiens-Picardie Clinical Research and Innovation Directorate F-80054 Amiens cedex 1 France Tel.
Withdrawal In accordance with the Declaration of Helsinki, patients, or upon their legally authorised representatives' decision, will be free to withdraw from the study at any time if they wish to, for any reason specified or unspecified. Withdrawal from the study will not affect or detriment the patient's further care or treatment. Patients have the right to withdraw their consent at any time, partially or totally; however, all data collected until the time of withdrawal will be used in the analyses. Potential criteria for discontinuing or modifying allocated interventions include: occurrence of adverse events that justify the withdrawal from the study; the decision of a subject to withdraw from the study; major protocol deviations; pregnancy.
Sample size The results from this study will be exploratory in nature; hence, there is no hypothesis testing comparing outcomes between treatment arms. As little is presently known about the outcome measures used in the study of stem cells, SEPCELL will provide the initial dataset necessary for determining endpoints and enable preliminary estimates of effect size for the design of future efficacy-finding studies of Cx611 used as add-on therapy in patients with sCABP requiring mechanical ventilation and/or vasopressor administration.
Data management Data management and handling will be conducted according to the study-specific Data Management Plan in accordance with International Conference on Harmonization guidelines  [31]  and clinical research organisation standard operating procedures (SOPs). Data entry, validation and data queries will be handled by the Trial Form Support (TFS) data management team. Data will be subjected to validation according to TFS SOPs in order to ensure accuracy in the collected case report form data. Before database closure, reconciliation will be performed between the SAEs entered in the safety database and the study database. Any deviations, i. e. , discrepancies and additions from the process defined in the Data Management Plan, will be described in a study-specific data management report.
General statistical methods In general, data will be reported by means of summary statistics. Continuous data will be presented with the number of observations, mean value, standard deviation, and minimum, median and maximum value. Categorical data will be presented as counts and percentages when applicable. Individual patient data will be listed. The results of all laboratory test results, physical examination findings, ECGs and vital signs will be presented in data listings; safety laboratory data will be presented by absolute and changes from baseline values by visit. All abnormalities will be assessed for potential clinical relevance.
Also tests that are no longer of interest will be removed to allow flexibility to test for specific biomarkers of most interest nearer the time of sample analysis 3. Study unblinding will occur after day 90 data has been collected and analysed for all subjects To allow the investigators to be unblinded earlier, efficacy assessments post-day 90 will be removed (previously at day 180 and day 365). Safety assessments at day 180, day 365, month 18 and month 24 will remain 4. Safety and efficacy data will be summarised via descriptive statistics only Due to early closure of enrolment, the total number of enrolled subjects will be too low to detect any safety and efficacy signals, and, therefore, any statistical inference including 95% CIs and p values may be misleading AEMPS Agencia Española de Medicamentos y Productos Sanitarios, CI confidence interval, DMSO dimethyl sulfoxide, eASC expanded adipose derived mesenchymal stem cells, ICF informed consent form, IMP investigational medical product, IRT interactive response technology, MoA mode of action, SAE serious adverse event, sCABP severe community-acquired bacterial pneumonia a As of July 2020, the trial is active but no longer enrolling participants and well-tolerated (NCT02421484  [34] ;).
A phase II trial to assess the efficacy and safety of MSCs in the treatment of septic shock is now planned (NCT03369275). Furthermore, results from a phase I and phase IIa clinical trial (NCT01775774; NCT02097641) have shown stem cell therapy to be well tolerated in patients with acute respiratory distress syndrome (ARDS)  [35, 36] , with two phase II trials currently ongoing in ARDS in the US and the Republic of Korea (NCT03818854, STAT; NCT02112500, STELLAR, respectively). The current trial will help advance our knowledge of the anti-inflammatory effects of eASCs, such as effects on T-cell response, RNA expression profiles of blood leukocytes and plasma concentration of biomarkers in this patient population, building on the promising results provided by preclinical and phase I data for Cx611  [19] [20] [21].
SEPCELL will also provide insights on the efficacy of Cx611, including its impact on the need for use of mechanical ventilation and/or vasopressors, time to clinical cure and rate of pneumonia recurrence/re-infection after clinical cure in patients with sCABP. Although mortality will not be assessed during the SEPCELL study, data on the safety and tolerability of eASCs in patients with sCABP will provide key information to facilitate the design of subsequent clinical trials. To conclude, SEPCELL is a multicentre, double blind, randomised controlled trial assessing the safety, tolerability and efficacy of eASCs (Cx611) as adjunctive therapy for patients with sCABP. The novel MoA offered by eASCs may address the underlying immune dysregulation caused by sCABP, and, thereby, offer another treatment option for patients with this disease and ultimately reduce patient mortality  [37].
Availability of data and materials The datasets, including the redacted study protocol, redacted statistical analysis plan, and individual participants data supporting the results of the study, will be made available after the publication of study results within 3 months from initial request, to researchers who provide a methodologically sound proposal. The data will be provided after its deidentification, in compliance with applicable privacy laws, data protection and requirements for consent and anonymization. A completed SPIRIT checklist is available in Additional file 1; the World Health Organization Trial Registration Data Set is available in Additional file 2.
Funding Authors are partners of the SEPCELL Consortium. This study is funded by the European Union's Horizon 2020 Research and Innovation Program under grant agreement number 681031; funding from this grant covered approximately one third of the total estimated costs of the SEPCELL trial. Peer-review of the study protocol was performed by experts from the Horizon 2020 Research and Innovation Program. The study sponsor is TiGenix SAU (a wholly-owned subsidiary of Takeda Pharmaceutical Company Limited) who funded approximately two thirds of the total estimated trial costs. The funding from TiGenix SAU/ Takeda Pharmaceuticals covered costs associated with (but not limited to) appointing the contract research organization, the manufacturing and shipping of Cx611, rental and shipping of cryofreezers for storage of Cx611 at study sites, internal personnel costs for all departments involved in the SEPCELL trial, regulatory submissions and interactions, and performing non-clinical studies.
Dataset The The methods for annotation were not consistent across the dataset. The location and size of the nodules were labelled as: • a mark of a single voxel in the middle of the nodule • a larger spot in the middle of nodules on one single two-dimensional (2D) CT slice • a circle surrounding nodules on one single 2D CT slice • a 3D volume to segment the selected nodules • A few DICOM also annotated nodules smaller than 100 mm³ while it was below the challenge detection criteria.