dataesr/scientific-paragraphs-categorization huggingface dataset:
The archaeoseismological study of a number of contexts affected by historical earthquakes provides a more complete understanding of the seismic impacts had on architecture, as well as restoration techniques used in the aftermath of these events. The PROTECT project is part of this line of research, funded by the Horizon 2020 research and innovation program of the European Union, and aims to apply, on an experimental basis, methods of archaeoseismological analysis to a portion of the historic center of Siena (Tuscany), in order to acquire further knowledge on the context of study from the point of view of seismic prevention. In this paper only the first steps of the project, begun in December 2021 and focused on the study of the Terzo di Città, are presented. The contribution focuses in particular on the proposal of a theoretical framework for the creation of a database, linked to a GIS platform, designed to answer precise chrono-typological queries.
The other important premise regards on-site work. Given the rather extensive size of the area, featuring a wealth of well-preserved and stratigraphically readable Medieval architecture, the recording of evidence was based on the autoptic analysis of restoration interventions with preventive functions, correlating these last to the constructive features of buildings. Compatibly with the type of structures documented, tools and methods characteristic of the archaeology of architecture and the more recent discipline of archaeoseismology have been chosen, suitable for the identification and typological characterization of these interventions  (Arrighetti, 2015). Once evidence was documented through survey drawings, field datasheets and photographs, the next step involved the registration and geolocalization of all data acquired on a GIS platform database 8.
Cartographic and typological restitution of the archaeosesimological evidence After completing the survey of evidence related to the seismic history of Siena and the recording of restoration interventions with preventive functions, the data obtained was organized and returned in the form of digital cartography (figg. 8-9) through the use of QGIS software. In application terms, an initial response of content structuring and drafting the datasheet was followed by the graphic and geographical restitution of this information. The data obtained from the field survey was then interpolated in the software through a join operation of the field "ID presidio", specific for each intervention and present both in the vector file drawn in GIS and the datasheets of the four categories of interventions, separately pre-compiled in an Excel file.
Background Clinical decisions are mainly driven by the ability of physicians to apply risk stratification to patients. However, this task is difficult as it requires complex integration of numerous parameters and is impacted by patient heterogeneity. We sought to evaluate the ability of transplant physicians to predict the risk of long-term allograft failure and compare them to a validated artificial intelligence (AI) prediction algorithm. Methods We randomly selected 400 kidney transplant recipients from a qualified dataset of 4000 patients. For each patient, 44 features routinely collected during the first-year posttransplant were compiled in an electronic health record (EHR). We enrolled 9 transplant physicians at various career stages. At 1-year post-transplant, they blindly predicted the longterm graft survival with probabilities for each patient.
Methods Study design and participants. From the 4000 kidney transplant recipients of a qualified prospective multicentric cohort, 400 patients with an evaluation available at one-year post-transplant were randomly selected as detailed in the flow chart (Fig. 1 ). This cohort includes consecutive patients over 18 years of age prospectively enrolled at the time of kidney transplantation from a living or deceased donor at Necker Hospital, Saint-Louis Hospital, Foch Hospital, and Toulouse Hospital between 1 January 2005, and 1 January 2014, in France and has been previously reported and used to develop the iBox prognostication system  9. All data from this cohort were anonymized and prospectively entered at the time of transplantation, at the time of posttransplant allograft biopsies, and at each transplant anniversary by using a standardized protocol.
Data were retrieved from the database on March 2018 and allograft outcomes were prospectively assessed in the Paris Transplant Group cohort up to 1 January 2021. All patients provided written informed consent at the time of transplantation. The institutional review board of the Paris Transplant Group approved the protocol of the study (NCT03474003, IRB: #000119258). This database has been approved by the National French Commission for Bioinformatics, Data, and Patient Liberty: CNIL registration number: 363505. Patient anonymized electronic health record (EHR). Patient risk evaluation, which was performed at one-year post-transplant according to the centers' practices, comprised 44 transplant features included in the anonymized electronic health record (EHR). The features are detailed in Supplementary Table  1  and comprise: (i) Recipient characteristics including comorbidities, age, gender, number of years on dialysis, (ii) Donor characteristics including age, deceased/living, cause of death, history of hypertension or diabetes, (iii) Transplant characteristics including the number of HLA mismatches, cold ischemia time, induction therapy, delayed graft function, (iv) Biological features at time of evaluation including the kidney allograft function which was assessed by the glomerular filtration rate estimated (eGFR) by the Modification of Diet in Renal Disease Study equation  22  , and the proteinuria level using the urine protein/creatinine ratio 23 , (v) Immunological features at the time of transplant and evaluation, including circulating anti-HLA donor-specific antibodies against HLA-A, HLA-B, HLA-Cw, HLA-DR, HLA-DQ and HLA-DP which were assessed using single-antigen flow bead assays  25  (mean fluorescence intensity and specificity), and (vi) Allograft pathology data according to the 2017 Banff international classification for allograft pathology  24  (including elementary lesion scores and diagnoses).
The median calibration error was assessed as the median of the absolute differences, for each quantile, between the predicted allograft failure, and the fraction of surviving allografts. Additionally, a decision curve analysis was performed to assess the clinical value using the net benefit of the iBox system compared with the physicians to predict allograft failure at seven years post-evaluation (dcurves package in R). Evaluation of feature importance in the prediction. To identify the features driving each physician's predictions, a random survival forest was performed for each physician. The mean decrease in accuracy was used to determine the relative importance of the first ten features that led to their predictions. We then ranked all features to obtain the overall top 10 features driving the physician's judgment (randomForestSRC package in R).
Fleiss kappa was used to measure inter-rater agreement between each physician's ranking (irr package in R)  28. Evaluation of physician reliability to predict the risk of long-term allograft failure. The inter-rater reliability was investigated using intraclass correlation (ICC) to evaluate the physician's agreement to predict each individual patient percentage of allograft survival at seven years post-evaluation. A two-way random effect for multiple raters/measurements ICC model was fit using Stata  29. To assess the intra-rater reliability, the relationship between the two predictions made by each physician (probabilities and risk scores of allograft failure) were compared using a linear regression model for each physician. In addition, the inter-rater reliability to score each individual patient was also tested using a two-way random effect for multiple raters/measurements ICC model in Stata  29.
Acknowledgements We thank  MSD Avenir  and the  Fondation AP-HP , which provided financial support to this project. A. L. received financial support from  INSERM-Action thématique incitative sur programme Avenir (ATIP-Avenir ),  RHU KTDInnov  ( 17-RHUS-0010 ) and  H2020   EUTRAIN  (No. 754995 ); O. A. received a grant from the  Fondation Bettencourt Schueller ; G. D. received a grant from the  French Foundation for Medical Research and the French speaking Society of Nephrology, Dialysis , and Transplantation. We thank the  Cibiltech company  for developing the online platform and the  EHR.
Data availability All source data to reproduce the main Figs. 2 3 4 and the Supplementary Figs. 3 4 5 are deposited into the synapse public repository  40. Additional data to reproduce Supplementary Fig. 2  are available upon reasonable request. Technical appendix is available from the corresponding author at alexandre. loupy@inserm. fr. Study protocol is available on clinicaltrials. gov: NCT04918199.
Aerosol characterization with Particle counters Four online particle counters were installed in the chamber system: a Condensation Particle Counter (CPC), an Optical Particle Sizer (OPS), a Differential Mobility Particle Sizer (DMPS) and an Aerosol Particle Sizer (APS) (Table  1 ). The OPS, DMPS and APS measure the particle number size distribution, and the CPC measured the total aerosol concentration in the experiments. The flow rate of each instrument was measured regularly (TSI Mass flow meter 4143), DMPS raw data was inverted to final particle size distributions as described by  Wiedensohler et al. (2012)  (FMI DMPS) and compared with the total CPC numbers. The specifications of the used aerosol measurement devices are listed in Table  1. To ensure that only dry particles were measured, the sample air reaching the particle counters was dried with silica-gel based diffusion driers (Topas DDU 570).
Acknowledgements Total concentration and size distribution of the particles released by the bubble bursting are analyzed in optical particle sizer (OPS), condensation particle counter (CPC), Aerodynamic Particle Sizer (APS) and differential mobility particle sizer (DMPS). DMPS consists of two equipment: differential particle analyzer (DMA) and CPC. Silica driers are inserted in the system before the air reaches the particle analyzers for only calculating dry particles size and the RH meters are installed to monitor this. All the particle counters and the exhaust air tube on the lid are connected to the inhouse air removal, assuring that no air is released into the indoors.
The Data Collection Exclusions Annex VI of the END establishes the data to be sent to the Commission by each Member State for each major airport and for each agglomeration affected by airport noise. Eionet (European Environment Information and Observation Network) is the Central Data Repository for the EEA countries. The END data reporting must adhere to required rules and the EEA has developed Support guidance for reporting under the Environmental Noise Directive 2002/49/EC (END) that the countries must take into account. The reporting mechanism is clear and well explained. However, checking the reported data per country reveals the following: • Some countries have been late filing the data. In some cases after 15 years, there is no data. • The data is not well reported by the countries so that the data are available in some documents but it is not available in the maps presented or in the database.
Uncertainties in the Estimation Data Uncertainties in the exposure level. Data collected in the 3rd round under the END do not cover all aviation sources as discussed earlier, nor do they cover populations exposed to levels of noise below 50 dB Lnight and 55 dB Lden -although Lden/Lnight 45/40 were suggested by WHO  [9]. Noise data from different countries, regions or cities may be modelled using different methodologies, and are often reported late or in different ways (due to a lack of internal coordination in some countries). All of these issues introduce some inconsistencies into the EU wide combined dataset. The completeness of the aviation data reported for the 2017 round of noise mapping is about 66% -75%. Therefore, gap-filling data were used to estimate the total area covered by the END following a clear methodology to estimate missing data.
Despite being only a third, budget wise, of the Silence(R) project, more than 50 engine and airframe oriented technology developments took place in OPENAIR, all executed around a number of key technologies identified at project start. From this set, the technologies described above achieved TRL4/5 through large scale testing in wind tunnels and/or dedicated engine fan or exhaust rigs. Part of the 50 technology developments were as risk-reduction, backup or long term solution related to the key technologies. A total of 144 low TRL ideas were collected before project start in an "innovative concepts" database from which the project selected 19 for inclusion in OPENAIR. This approach was perceived as very successful for OPENAIR and other related projects and proposed for best practice in the future.
Over the following decade, FRIDA has been extensively used and improved within the context of FP7 and H2020 projects COSMA (FP7, 2009-2013), OPENAIR (FP7, 2009-2014), ANIMA (H2020, ongoing), and ARTEM (H2020, ongoing) in order to address the increasing complexity of the requirements with a state-of-the-art, multi-objective, robust and reliable approach suited to the most advanced Simulation-Based Design Optimisation (SBDO) frameworks. The conceptual layout of FRIDA is depicted in Fig. 6. A parsing shell acts as an interface with the external world and manages the I/O structure, as well as the link with external simulation tools (under development). The Simulation modules library includes all the disciplines relevant for the design of a sustainable, innovative concept that can be activated through an appropriate description of the workflow.
Benefits of the Box-Wing Architecture for Passenger Aircraft-The H2020 Project "PARSIFAL" Dr. Vittorio Cipolla and Prof. Aldo Frediani PARSIFAL stands for "PrandtlPlane Architecture for the Sustainable Improvement of Future Airplanes". Funded by the EU under the Horizon 2020 Programme between 2017 and 2020, the PARSIFAL project has investigated the feasibility and performed a comprehensive impact assessment of the application of the box-wing architecture to short-to-medium range aircraft. The box-wing configuration derives from the studies carried out in 1920s by Ludwig Prandtl, who described the box-wing, i. e. two horizontal wings connected at their tips by properly designed vertical wings (Fig. 9 , left), as the "best wing system". According to Prandtl studies published in 1924  [23] , in fact, among all the possible lifting systems the box-wing is the one capable to minimise the induced drag, once lift and wingspan are given.
In the H2020 project RUMBLE (2017-2020), different sonic boom prediction tools were developed and assessed  [32]. The need for reliable sonic boom prediction has already been addressed in three workshops at the AIAA SciTech conference in 2014, 2017, and 2020  [33]. Regarding the human response one has to find an appropriate metric  [34]  but also conduct listening tests and sleep studies. In 2021, two H2020 EU projects started with the aim to further improve the detailed assessment of supersonic aircraft on the one hand regarding LTO noise and on the other to investigate sonic boom. The projects will support the establishment of new certification standards ensuring sustainability and public acceptance of novel supersonic aircraft models. In the project SENECA (LTO noise and emissions of supersonic aircraft, 2021-2024), supersonic aircraft concepts will be designed and multidisciplinary optimised regarding airframe and engine architecture as well as the LTO trajectories.
The aim is to reduce LTO noise and emissions as well as the global climate impact. In total, four different aircraft concepts will be examined: two business jets with cruise Mach numbers of 1. 4 and 1. 6 and two airliners cruising at Mach 2. 0 and 2. 2. The certification authorities will be given the scientific evidence to change the LTO procedure by handing over a comprehensive and reliable database of virtually flown landings and takeoffs with varying climb out speeds and thrust reductions before cutback and also with different aircraft and engine architectures, seeking for the lowest community noise. In particular various engine bypass ratios, the number of engines, the position of the engines as well as the nozzle and inlet geometry will be examined in terms of their tradeoffs between flight range and their noise reduction potential.
Acknowledging this, the ICBEN question makes an attempt to capture the multidimensionality of the concept via only one single-item question that combines the aspects "annoyance", "disturbance", and "bother". Whether these concepts can really be subsumed under the general construct of annoyance, is disputable. It could be valuable in further research to complement the ICBEN-question by additional standardised questions to better match the concept of annoyance  [46].
Besides machine learning approaches for sentiment classification, in the literature we find lexicon-based approaches. While finding or constructing those lexicons is not always an easy task and the difficulty might vary depending on the language, these methods allow us to use a list of words (dictionary of subjective words)  [22] , where each word is associated with a specific sentiment; emoticons are used the same way as well. Yadollahi et al. [48]  discuss the ability to use more than one dataset to take into account multiple subjective perspectives of the word and to modify the existing dictionary in order to satisfy the topic sentiment characteristics. Asghar et al. [1]  proposed to classify sentiments in reviews by combining an emoticon classifier, a modifier and negation classifier and a classifier based on the opinion lexicon SentiWordNetwork (SWNC) in a sequential, then input the text to a domain specific classifier (DSC) that takes into account the polarity of domain specific words both existing or unknown in SWNC.
Working Principle In the European context airport noise models have to be compatible with the methodology described in ECAC Doc29  [1]. Therefore the NMT has been developed around the SONDEO model  [2] , which implements the latest version of this methodology. The design of SONDEO is such that it separates the complex noise calculations required to obtain the noise levels for each individual aircraft (the so-called "single events"), from the generation of the map that reflects the noise exposure of the full fleet operation, representative for a certain scenario. After this, impact related features can be calculated. Figure  1  provides a schematic representation of the workflow of the NMT. The first step (generation of the single events database) inevitably requires in-depth technical knowledge of airport noise modeling.
To avoid exposing the end-user to this part, the single-event database will be generated "off-line" by specialists (here called the NMT Administrator). This database will contain the individual noise footprint of each combination of aircraft type, flight track, flight profile, etc. that may be considered later on in the scenarios. With the support of additional simulation tools, the single events for any not yet existing aircraft can also be generated here. It is noted that the single event database will need to be generated only once. The resulting single event database will be available at the start of the on-line user experience. The user can build his own scenarios by selecting the relevant combinations of single events and in this way generate the full set of operations for which the noise results shall be calculated.
g. to design an intervention and assess its effect on the noise exposure and impact. Registration is required to obtain access to this advanced version of the NMT. As a first step, an authorised person (here called the Airport Owner) shall request inclusion of an airport in the NMT database, following the procedure described on the NMT website. This person shall have the permissions required to publish the information relevant for the noise calculations at that airport. The Airport Owner shall provide a dataset with which the NMT Administrator can generate the single event database and configure the system for inclusion of the new airport. This dataset will include information on the runway(s), standard flight routes (tracks), aircraft fleet, populated areas, etc. Once this database is ready, the airport will be available in the NMT and the Airport Owner can invite additional users to register for the new airport.
Working Principle Figure  3  displays the overall workflow of the tool. There are two main inputs that are required for the computations performed by the Virtual Community Tool. First, an airport database must be available. This database contains the ground acoustical data (i. e. acoustical footprints) of all possible flight operations of a single airport. The dataset is sampled over a geographical grid that covers a given area in the vicinity of the given airport. The airport database may also be supplemented by additional regional information, containing a demographic map of the population density, an insulation map of the buildings in the area and further auxiliary data. The second input is called a flight schedule and it contains a list of actual operations performed in a given frame of time.
A typical schedule may contain all operations of a week or a fortnight, whereas short schedules of a single day, or long schedules containing the traffic over a whole year are also handled by the tool. Once a schedule is available, all of its flights are matched to the airport database in order to establish the connection of the actual operation with the corresponding acoustical footprint. By means of the metric computation engine, the tool enables the analysis of various scenarios for one airport, i. e. , a given airport database. A scenario contains the flight schedule together with the traffic modifications defined by the user as well as the land use plan (LUP) areas and the settings of the computations. The user is able to edit the traffic composition through the graphical user interface.
Acknowledgements The authors of this book and the guest editors would like to express their warmest thanks to  Ms. Jordis Wöthge ,  Mr. Sam Hartley  and  Mr. Tim Johnson , who performed a careful and fruitful review of the work introduced therein. This book refers to numerous projects granted by the  European Union  under its successive  Framework Programme  for research and innovation. The authors and guest editors of this book would like to acknowledge the contribution of the following projects: Acknowledgements The main authors wish to thank all contributors to this chapter -most coauthors are mentioned directly in conjunction with their respective contributions. The funding provided by the  EC  for the project ARTEM under grant number  769350  is gratefully acknowledged. Also, many other projects receive funding by the  EC  (grant numbers are given above).
Acknowledgements Figures 4, 5, 6, 7, 8, 9, 10, 12, 15 and 16 are using OpenStreetMap data. OpenStreetMap ® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the  OpenStreetMap Foundation (OSMF). https://www. openstreetmap. orghttps://ope ndatacommons. org.