dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Espadon was designed and authored by Cathy Fontbonne and Jean-Marc Fontbonne, with Nathan Azemar as contributor. It is a package written in R language, with some functions using C++ for better performances. The package uses the capabilities offered by other packages: for example qs  [6]  for data storage, rgl  [7]  for 3D visualization, Rvcg  [8]  for area calculation, stats [3] for statistical functions, but also sp [9], grDevices, graphics [3]….
Introduction Cancer research is focused on answering an important question: what treatment can kill cancer cells while preserving healthy organs? [1, 2]  Indeed, finding the influencing factors of the responses to a treatment is one of the key issues to optimize the treatment proposed for a specific patient and its post-treatment management. Espadon (acronym of Easy Study of PAtient Dicom data in ONcology) has been developed in the framework of this research. Its objective is to enhance clinical databases, generally containing the outcomes and pathways of patients, with objective data from imaging, dose distribution and treatment geometry or organ location in relation to the tumor. Espadon is a package proposed under the R  [3]  environment, which is also a very suitable language for statistical studies, data mining, machine learning and database creation.
It is available for free on the Comprehensive R Archive Network (CRAN) repository  [4]. Espadon is a coherent and logical set of functions that can be easily implemented in a study automation script. It allows to manipulate and visualize DICOM objects, and to systematically calculate most of the usual radiotherapy indices. Espadon can also manipulate the raw data of DICOM files: for example, it has been used in ArDCore  [5]  software that automatically pseudonymizes and sorts patient DICOM files. In this article, we will use simple examples to illustrate how the espadon package can be used to access or compute information of interest to research actors in radiotherapy. radiotherapy, the package is first able to decode a DICOM file into an R data. frame or R list. It also includes a function to modify the ASCII fields of the DICOM file.
Espadon incorporates a tag dictionary, which can be called using the dicom. tag. dictionary() function. The tag dictionary is defined by the nema standard  [10]. It can be completed, as needed, by constructor dictionaries. In addition to the NEMA standard dictionary, the espadon package provides the Raysearch Laboratories dictionary  [11]  by default. Espadon then interprets all the DICOM files by creating espadon objects that belong to specific classes. The class of an object ensures how the object is used by the espadon functions, and thus simplifies their use. Each object of a specific class contains information (fields) that are necessary for the use of that object. For example, RTdose dose distribution, CT and MRI scans create objects of the volume class. This class necessarily includes the number of voxels in the three dimensions of space, the size of the voxel in millimeters, the value (if requested) and the unit of each voxel, as well as the information to locate this volume in space.
The DICOM RTstruct files form struct class objects that necessarily include a table of information about the regions of interest (name, color of the RoI in the DICOM file, but also calculated data such as volume, barycenter, coordinates of the end points) and a list with the coordinates of the contours in space. In the current version there are other object classes, which are listed by calling the espadon. class() function and explained in the help page accessible by the command help(espadon. class). One of the important classes is the "patient" class. A patient object contains not only a description of all the patient's examinations, the examinations themselves sorted by modality, but also a t. mat class object managing the spatial registrations. To simplify the management of geometric reference frames, the espadon package automatically assigns to each reference frame defined by the DICOM tag (0020,0052) a pseudonym "ref1 ′′ then "ref2 ′′ … Each DICOM object loaded is then assigned a new alias by default, consisting of, among other things, its study date, the pseudonym of its reference frame, and its modality.
This new alias makes it possible to quickly visualize the chronology of a patient's examinations, as well as the links that exist between them. As the interpretation of DICOM data to create an espadon object can be a long process, the package proposes to convert DICOM files into *. Rdcm files with the function dicom. to. Rdcm. converter(). The backup of the object being performed with the qs package, the speed of access to the data as well as the amount of memory necessary for the backup are considerably improved. As a comparison, a database including the imaging and dose distribution data of 441 patients uses 239 GB of memory for 324,164 DICOM files, and uses only 79. 5 GB for 4499 Rdcm files. Considering now the data of a single patient including 2 CT-scan, 5 RTdose, 2 RTplan, 2 RTstruct, the data are loaded entirely in 130 s from the 907 DICOM files (717 MB) with the function load.
patient. from. dicom(), compared to 21 s from the 11 Rdcm files (248 MB) with the function load. patient. from. Rdcm(). In this second case, it is nevertheless necessary to perform a one-time 114 s conversion from DICOM files to Rdcm files beforehand with the function dicom. to. Rdcm. converter(). The measurements were preformed on computer with Intel Core i9 CPU (2. 40 GHz) and 64 GB of system memory, running 64-bit Microsoft Windows 10 Professional. The complete content of a *. Rdcm file can be accessed with the function load. Rdcm. raw. data(). It is a list composed of, among other things, a "header" list containing interpreted data (e. g. name of the patient, number of voxels or number of contours, etc. ), an "address" list giving the addresses of the content of the DICOM tags in the raw data of the DICOM file, and a "data" list giving the list of exports in the R-list of the various DICOM files making up the object.
All these indices are compiled by Kaplan et al. [12]  and Patel et al. [13]  and referenced in the reference manual of espadon package. With the functions rt. gamma. index() and rt. chi. index(), the espadon package can also compute, on a 3D volume or 2D image, the γ-index and χ-index, as defined by Low et al. [14]  and Bakai et al. [15]  respectively.
Loading and displaying patient imaging and dose distribution data The data from patient A is loaded into memory by creating a "patient" class object, either by the simple function load. patient. from. dicom (), or, if it is preferable to convert the data into pre-formatted Rdcm data for faster later use, by using the functions dicom. to. Rdcm. converter() followed by load. patient. from. Rdcm(). To manage memory space, objects can only be partially loaded, depending on whether the data argument is TRUE or FALSE. If FALSE, the objects of the volume class, for example, will be missing the values of the voxels ($vol3D. data field), which can then be loaded using the load. obj. data() function. In all cases, the links between the objects contained in the espadon class object "patient" can be represented using the display.
Conclusions The espadon open-source code is an R package available for free on the Comprehensive R Archive Network (CRAN)  [4]. It allows radiotherapists, medical physicists and students to access an R tool, independent of the treatment planning system (TPS), to extract data from DICOM files in order to enhance clinical data a posteriori. By forming a coherent set of functions, it allows users to write their     own script in R, which may eventually enrich the package. The applications are numerous: from the creation of new contours to the automatic calculation of indices, it opens the way to multi-TPS analysis, the analysis of inter-operator practices, intra-and inter-centric comparisons and dose calculation or contouring challenges. The espadon package will improve and develop over time: we plan to include TCP/NTCP and geometric index calculations.
Acknowledgments Espadon was developed as part of the  PMRT  project, co-funded by the  European Union  and the  Normandy Region (ERDF-FSE  fund  2014-2020 , under the grant agreement n  • 18P03532/ 18 E01765 ). We would like to thank all our collaborators who shared their needs with us and allowed us to test the espadon package:  A. Chaikh  ( Institut de Radioprotection et de Sûreté Nucléaire France ),  Dr. S. Guihard  ( Institut de Cancérologie Strasbourg Europe, France ) ,  C. Legrand  ( Institut de Cancérologie de l'Ouest [Angers], France ),  C. Mazzara  ( Union Mutualiste pour la Gestion du Groupe Hospitalier Mutualiste de Grenoble, France ),  A. Moignier  ( Institut de Cancérologie de l'Ouest [Nantes], France ),  Pr. J. Thariat  ( Centre François Baclesse, Centre de Lutte contre le Cancer, Caen France ).
Since 2008 a network of five sea-level monitoring stations was progressively installed in French Polynesia. The stations are autonomous and data, collected at a sampling rate of 1 or 2 min, are not only recorded locally, but also transferred in real time by a radio-link to the NOAA through the GOES satellite. The new ET34-ANA-V80 version of ETERNA, initially developed for Earth Tides analysis, is now able to analyze ocean tides records. Through a two-step validation scheme, we took advantage of the flexibility of this new version, operated in conjunction with the preprocessing facilities of the Tsoft software, to recover corrected data series able to model sea-level variations after elimination of the ocean tides signal. We performed the tidal analysis of the tide gauge data with the highest possible selectivity (optimal wave grouping) and a maximum of additional terms (shallow water constituents).
However, they mainly focus on the monitoring of tsunamis, the only exception being the UH tide gauge of the Papeete harbor, also equipped with a GPS receiver. We used also for comparison purposes the tide Gauges of the University of Hawaii at Mangareva (RikiteaUH). Our goal was to provide corrected data series and modelled ocean tides signal to compute tide free sea-level variations. Two data collection packages, titled "SEA LEVEL collected from TIDE STATIONS in South Pacific Ocean from 2009 to 06e13 to 2021-01-28 (ref. 0244182)", have been accepted by the NOAA National Centers for Environmental Information (NCEI)  [1]. The first one is relative to the original sampling rate, and the second one is relative to the hourly averaged data, both under NCEI accession number 0244182. Ocean tide modelling and prediction have always been important for the safety of seafarers, but the determination of the mean sea level (MSL) and its variations, due to climate changes, is now a hotspot of oceanographic research  [2].
In French Polynesia, the situation is complex, with inter-annual modulations on one side and vertical subduction of many volcanic edifices  [3, 4]. The early detection of tsunami waves is a high priority for protecting human populations in coastal areas. We present here, first, our methodology of sea-level modelling, and how to process and analyze noisy and gappy data with a low signal-to-noise ratio. In a second step, the tidal analysis results are used to illustrate the principal characteristics of the ocean tides signal in this area of the Pacific Ocean, as well as local distortions of the tidal signal in the lagoons. The tidal models derived from this analysis allow the tides prediction with a centimeter precision at different stations. Finally, we present preliminary non-tidal variations DRSL of the relative sea level (RSL), measured with respect to the tide gauge setting.
Data preprocessing and validation All the five tide gauges operated in French Polynesia by OGT obey the guidelines set up by SHOM  [6]. They are equipped with: GNSS antenna and receiver, radar and pressure gauge sensors for sea-level monitoring, and a satellite GOES (Geostationary Operational Environmental Satellite) radio transmitter. We use the RA-DAR seal level sensor QHR104 of Vaisala (Fig. 2 ). Measurement is done in pulses, the so-called pulse procedure, where a transmitter sends out a short microwave pulse, followed by a period when the receiver picks up the signals reflected by the water. The accuracy is better than 5 mm. At RikiteaUH an encoder has been installed on an old tide gauge with float sensor in parallel with the RADAR and the pressure gauge. The sea level sampling corresponds to measurements taken every second and averaged over 1 min.
All the data are stored locally and transferred to the UPF database when maintenance takes place. They are also transferred via satellite at least every 2 min by a UHF radio-link to the NOAA (National Oceanic and Atmospheric Administration of the USA) through the GOES satellite constellation, but without neither GPS in situ time-tagging nor GPS positioning data, to save transmission bandwidth. They are ex-situ time-tagged again upon arrival at the NOAA and stored at the Sea Level Station Monitoring Facility site  [7]  (VLIZ/IOC). Additional information about the stations and the data are available in Refs. [8, 9]. Sometimes the local recording at the tide gauges was not operating, so the only source of data was the VLIZ/IOC database, sometimes, the GOES data link was not operating, so the only source of data was the local records on the tide gauges.
We first did a cross-comparison of the VLIZ/IOC data sets versus the UPF data set based on the coherence of the tidal phases after preliminary analysis. We found that the time re-tagging by VLIZ/ IOC was often off by several minutes with respect to the GPS time tagging done at the tide gauges, and sometimes shifts up to 1 h were found. We, therefore, elected to retain the UPF data sets whenever possible. When the UPF and VLIZ/IOC time series have no overlapping, it was always possible to correct any timing error by considering the coherence of the phase of the main tidal waves, as explained in section 2. The observation time corresponds to the middle of the time interval, i. e. it is shifted of 30 s for a 1-min integration period. This time shift is negligible for long period phenomena which are the main goal of the sea level measurements in French Polynesia.
For tidal analysis, a time shift of 30 s can be introduced during the analysis procedure. We routinely preprocessed and analyzed the RADAR and pressure gauge data of the 6 stations. The preprocessing data flow, divided into 3 levels, is shown in Fig. 3. LEVEL 1 data are obtained by rewriting the original records (locally stored UPF or radio transferred VLIZ/IOC data) in the socalled PRETERNA format. It is impossible to directly use these raw data to compute a final data set with an hourly sampling rate due to several remaining errors, either incorrect data producing spikes, jumps, and gaps or timing problems. During the first step, between LEVEL 1 and LEVEL 2, the data sequence is checked, and preliminary tidal analyses are performed with the software ET34-ANA-V80 designed by K. Schueller [12e14] , in order to prepare a preliminary tidal model and detect timing errors.
It is always possible to detect any timing error by considering the coherence of the phase of the main tidal waves, with the phase computed from the UPF data archived with the correct in situ timing. In the semi-diurnal band, a timing error of 2 min produces a phase difference of one degree at the frequency of 2 cycles-per-day (cpd). If necessary, a time shift is applied. The outliers, mainly unwanted "spikes", are identified and removed using the software Tsoft  [9, 15]  and a "remove-restore" procedure. The preliminary tidal model is subtracted from the raw data, the corrections are applied on the residues and the corrected tidal signal is reconstructed, summing up the removed tidal model again. Finally, the small interruptions (gaps) are tagged and interpolated with the same procedure (Fig.
e. 1440 or 720 samples for a 1 min or 2 min original sampling rate. This reduction presents the advantage of filtering out the higher Fig. 3. Data flow during preprocessing. The corrective actions listed in the square brackets are performed with the TSoft software  [10]. The PRETERNA and ETERNA formats are described in  [11]. Fig. 4. Example of remove-restore procedure for automatic correction of gaps for the RADAR signal at Rangiroa. Top: raw signal (mm) with numerous gaps; Middle: residues (mm) after subtraction of the theoretical tides from the raw signal (remove step). Gaps detected by the "auto detect gaps" corrector indicated by white dots are linearly interpolated; Bottom: corrected signal (mm) obtained by the addition of the theoretical tides to the corrected residues (restore step).
frequency noise present in the original data, while keeping the tidal signal up to 12cpd. This third-level database is convenient for most applications, except detection of transient signals such as tsunamis or swells. Tidal analyses are performed to compute and subtract the ocean tides signal. The computed residues are a preliminary representation of the variations of the Mean Sea Level due, in the first order, to meteorological phenomena and in the second-order to climate-related effects.
Tidal analysis procedure Validated and preprocessed data have been analyzed with the ETERNA system software  [16]. The latest version of the software ET34-ANAL-V80  [12]  proposes a special option for ocean tides analysis and prediction in agreement with oceanographic conventions (i. e. phases refer to Greenwich and phase lags are counted positive). Moreover, the changes of signs of the tidal potential with latitude are not taken into account by oceanographers. The program can be downloaded from http://ggp. bkg. bund. de/eterna/. Two useful new features have been implemented in ET34-ANA-V80: An automatic detection of spectral peaks not included in the tidal analysis model. The computation of the correlation between the different unknowns. The program performs a spectral analysis of the tidal residues and proposes a list of the peaks exceeding a given threshold level.
We can only consider the RADAR data for the determination of the relative sea level (RSL). For dataset presenting jumps and long-term variations, it is helpful to apply the filter N1H1KSHW (Tsoft manual, p68), which eliminates at least 99% of the periodic signals with periods larger than one month. In our case, the tidal analysis provides a much lower RMS error on one observation using the filtered data than with the unfiltered data set, as shown in Table  5. It is due to large long-term fluctuations from meteorological origin on the sea level. It means that these long term fluctuations should be modelled, or at least averaged over a very long time span. The longest time span does not exceed 13 years and is thus too short to assess "secular trends". Moreover, some station records present gaps, and the mean RSL values of the consecutive blocks can be different.
Conclusions This work provides cleaned, calibrated hourly seal-level data, with a verified time-tagging, for the five tide gauge geodetic stations installed in French Polynesia by UPF and maintained by SHOM for 2008e2020. This data set can be accessed at NCEI  [1]. In Mangareva, the UPF station results are also compared with the University of Hawaii station (RikiteaUH). The tidal pattern is purely semi-diurnal with a large annual modulation of M2 amplitude. The amplitude and the phase of the main semi-diurnal waves M2 and S2 is in agreement with the values predicted by the ocean tides model FES04, except in Rangiroa and Makemo, where the lagoon is damping the tidal signal. The annual meteorological wave is generally close to 3 cm but reaches 7 cm in Makemo, where large sea swell events produce sea level rise up to 7.
Acknowledgements We thank all the successive Chief Officers of the local outposts of  SHOM in French Polynesia  for their dedication in the maintenance of the network of the tide gauges, and we are grateful to the anonymous reviewer who enriched the bibliography concerning the sea level variations in tropical Pacific islands. We thank  Dr. Benoit Stoll , from the  geomatics department of UPF , for Fig. 1. Pr. Jean-Pierre Barriot  acknowledges many sources of funding over the years from several institutions belonging to the central government of France (CNRS,  CNES ,  ANR ,  SHOM ,  Minist ere de l'Outre-Mer, FondsPacifique)  and the local authorities of  French Polynesia (UPF, Haut-Commissariat de la R epublique, local government of French Polynesia ). Dr. Fangzhao Zhang  acknowledges funding from the  "Talent Introduction Scientific Research Start-Up Fund" of Shandong University of Science and Technology  (Grant number  0104060510217 ), and the  "Open Fund of State Key Laboratory of Geodesy and Earth's Dynamics " (Grant number  SKLGED2021-3-5 ).
IV. FORMAL VERIFICATION OF SAP The formal verication of security protocols can be per- search technique which empowers it to explore all type aws and innite state spaces  [14]  and (3) According to a study conducted in  [15] , Scyther is the fastest tool among the existing state-of-the-art tools. In case of an attack, it gives an attack scenario which provides a better understanding of the aws in the protocol. In the subsequent subsections, we explain the adversary model and security claims used in Scyther, and further, we discuss the modeling and verication of SAP using Scyther. Scyther uses spdl (Security Protocol Definition Language) format for describing the semantics of a protocol, which is explicitly invented for Scyther.
Our method uses several abstractions and Integer Linear Programming (ILP) formulations, and thus it is still limited by the NP-hard complexity of ILP, just as in the general case of task scheduling  [47]. However, and despite this computational complexity limit, we demonstrate that our method runs in about one second for a scheduling problem with hundred tasks. In contrast to many scheduling approaches, the Aine DataFlow Graph (ADFG) tool  [14]  synthesizes near-optimal and always valid periods of the real-time tasks to execute, without any simulation of the complete schedule. We say that the ADFG tool synthesizes a schedule, which actually means that it computes all needed characteristics of the tasks to schedule: period, priority, mapping. The periods are near-optimal in the sense that they are computed with the objective to maximize the throughput of the scheduled application; they are valid since the schedulability of the system is guaranteed by formal methods adapted from synchronous languages  [72].
As a tool, ADFG has been used to validate a subset of Architecture Analysis and Design Language (AADL)  [7, 32]  and to generate code compatible with the Real-Time Executive for Multiprocessor Systems (RTEMS) real-time operating system  [79]. This article is the irst to provide a general overview of both theoretical and practical aspects (up to the execution of the synthesized code) and it describes and evaluates so far unpublished details of the synthesis algorithms. In a decade a few bugs have been ixed, and updated evaluations still show the eiciency of our approach. The article is organized as follows. Next Section 2 introduces related work. Section 3 recalls the main concepts of SDF graphs and FP scheduling. Then the Section 4 provides an overview of the ADFG theory and the UCSDF graph model; and previously unpublished contributions are also detailed.