dataesr/scientific-paragraphs-categorization huggingface dataset:
We assessed swimming speed of SES relatively to surrounding water using sound  [65]  recorded by acoustic tag. This task was performed in MATLAB using custom code which is available on request. We estimated the swimming speed (v seal ) in descent and ascent phases from pressure changes (v z ) and pitch angle (α): v seal = v z /sin(α). Water flow noise level was calculated from the low-frequency noise extracted with a 110 Hz low-pass filter applied to acoustic data. Then, we calibrated the relationship between the water flow noise level and the swimming speed estimations and extrapolated it to predict swimming speed over the entire dive periods from noise level. The 3D path (Fig. 1 ) of each animal was calculated by dead-reckoning (dead_reckoning function of the   c  and d ), the dispersion explained by the first main component is particularly low.
The blue part represents the dive's bottom phase. The black points give location of starting points. The orange ellipses stand for the 2D projections of a 3D ellipsoid whose axis are the three main components. On the "Map views" (a and c) the orange line display the direction of the first main component which is used to construct the best "Profile Views" (b and d). More example, in interactive 3D plots, may be found at https:// github. com/SESman/SES_3Ddives animalTrack package  [64]  applied to body posture angles, swimming speed and GPS data). The observed GPS location during the surface period preceding the dives is used as the starting point of the reconstructed path. Finally, the reconstructed paths are corrected so that their arrival point matches the observed GPS location collected during the surface period following the dives.
Shape of bottom phase trajectories To describe how the SES used the 3D space during the bottom of their dives, we extracted the main components of the animals path during the bottom phases. This was achieved by implementing a Singular values Decomposition (calculation of the eigenvalues and eigenvectors) of the variance-covariance matrix of SES locations during dives' bottom phase. The variance-covariance matrix can be seen as a linear application that would transform Gaussian noise (in 3D, this would be a spherical cloud of data points with maximum density at its center) into the observed data. Such a transformation can be decomposed into simpler transformations, rotations whose characteristics are described by the eigenvectors, and scaling described by the square roots of the eigenvalues.
In our case study which relates to the extraction of main components of the 3D SES path, eigenvectors provides the direction of the main components and their eigenvalues quantifies how much of the dispersion (also called inertia) of SES locations each of these components can account for. All-equal eigenvalues would indicate that SES did not favour any direction of movement in their trajectories. The sum of the eigenvalues represents the total dispersion of the data. To describe the shape of the SES trajectories, we used the raw SES location data as well as eigenvectors and eigenvalues describing the main linear trends in the SES path. The variables that we calculated are listed and briefly described in Table  2. Additional explanations for the variables that require it is provided in the next paragraphs:.
These Poisson models indicated over-dispersion (θ = σ 2 /μ, θ 1. 5m = 2. 93, θ 9m = 2. 68, θ 18m = 2. 73) so we switched to the more flexible Negative Binomial distribution (MASS package  [69] ) which allows for higher variance/mean ratio. We observed a large proportion of zeros in our data (27. 44%) incorrectly predicted by the GLMs. We then tested the zero-inflated variants of Poisson and Negative Binomial models (using zeroinfl from the pscl package  [70] ). These models led to very significant improvement of Akaïke Information Criterion (AIC) and Vuong's test (p-values were < 0. 01 for all models). The explanatory variables considered for selection in the count part of the zero-inflated model were identical to the Poisson and Negative Binomial models. The choice of explanatory variable for the zero excess part was restricted to the non shape-description variables, individual identity, mean depth, swimming speed variability and log-transformed bottom duration (the latter was not included in the count part of the model).
Schooling behaviour The principal component analysis revealed that SES trajectories at the bottom of their dives are strongly dominated by a path in a single direction (Fig. 3 ) as it was noted by  [41]  on northern elephant seals. This results support the hypothesis that SES prey tend to not aggregate in large discrete schools. Indeed, in such a situation the predator is expected to adopt an overall sinuous and spherical trajectory. A spherical first passage time (SFPT,  [71] ) analysis performed on our dataset revealed that "Volume-restricted search" (VRS) could be detected in the bottom of half of the SES dives in which case they accounted for 36. 9% of the bottom phase duration but 67. 6% of PEE  [72]. The typical scale of VRS, 48. 2 ± 25. 7 m, was similar for all individuals and the average prey encounter rate was 1.
PEE density along depth The dominant direction in the trajectories of SES at dives' bottom was primarily horizontal (Fig. 4 ). This indicates that SES target specific layers of the water column during the bottom phase of their dive  [73, 74]. Shallower layers require less time and energy to be reached by SES and, therefore, are more accessible and more profitable. Performing dives' bottom phase at greater depth was found to be associated with smaller prey encounter density (Table  5 ). While the SES and their prey perform diel vertical migrations  [6, [8] [9] [10] , this process cannot explain our result because all the 3D dives in our dataset occurred during daytime. A previous study, based on likelihood of detecting bioluminescence  [75]  highlighted a similar pattern. The impact of the changes in the size or species composition of the mesopelagic community in relation with this decreasing prey encounter density could not be examined with the tools available to us but they could have substantial importance.
Area-restricted search are supposedly periods where SES meet high prey densities and are likely to exhibit larger horizontal sinuosity in their bottom phase 3D path. As such, the ARS appear like interesting periods to address this particular issue. The 3D reconstruction of the SES path underwater by dead-reckoning assumes that the direction of travel of the animal is always parallel to the body orientation. More sophisticated methods such as the one developed by  [92]  which is free of this assumption, attest that this approximation can have an impact on the reconstructed tracks. However, such methods require large computation times and are not suitable for datasets of several hundreds of dives. Dead-reckoning is also subject to cumulative errors. Therefore, uncertainties about the shape of the SES trajectories at the bottom of their dives increase with the dive depth and the duration of bottom phase.
We suggest that the prey density decrease with depth but that their distribution tends to standardize yielding higher predictability. The extent of the vertical exploration performed by the SES during their bottom phases related negatively to prey encounter density, seemingly indicating that the thickness of the layers targeted by SES mechanically impacts micronekton density. These results underline the primary importance of the vertical dimension into the spatial organization of the micronekton. The 3D trajectories in our dataset were essentially linear paths. While the vertical deviations from this path were of the the same order of magnitude of the horizontal ones, they better related to the prey encounter density. Under such circumstances, the widely used time-depth recorders can be considered as an effective simplification of the SES movements at scales of few-hundreds meters.
Methods Materials. The studied specimens are housed at the Entomology Department of the Muséum national d'Histoire naturelle, Paris, France. The inventory numbers are detailed in Suppl. Table  2 , see MNHN collection data base at https://science. mnhn. fr/institution/mnhn/collection/eo/). Imaging. The four extant specimens were imaged under X-ray, with phase contrast, at the microtomograph of the University of Poitiers.
Acknowledgements The present study was part of the Master Systematics, Evolution and Paleontology performed by the first author in the Ecole Doctorale of the MNHN. Scans were performed at the  MNHN platform AST-RX  ( UMS 2700 CNRS ) supported by the  ATM "Formes possibles, formes réalisées" (V Bels and JP Gouyon) ; and at the  University of Poitiers  and supported by grants from  "Région Poitou-Charentes " ( 16-17-79-86 ). This work was also supported by a grant from  Agence Nationale de la Recherche  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program " Investissements d'avenir " n  ANR-11-IDEX-0004-02. 3D reconstructions were realized in the 3D platform of the EvoFonc team of the UMR 7205. We thank  Miguel Garcia-Sanz  ( UMS2700   CNRS ) for the scans of the insects,  Patricia Wills  ( UMS2700   CNRS ) for her help during tomography reconstructions, and  Anthony Herrel  ( UMR 7179 CNRS ) for his technical help.
Furthermore, MELODI supports the availability of key infrastructures in Europe as an essential basis for research activities, and the long-term maintenance of competences in radiation research and health risk assessment by means of an integrated European approach for training and education. Consequently, MELODI established three working groups (WGs): one on the MELODI SRA, one on education and training, and a third on infrastructures. The European Network of Excellence DoReMi (2010-2016) funded by Euratom FP7 Radiation Protection Programme served as an important initial operational tool for establishing MELODI  (Salomaa et al. 2015). Currently, major parts of European radiation protection research are being organized within the CONCERT European Joint Programme Co-fund Action (EJP). CONCERT brings together funding agencies from the European Commission (EC) and the member states in an effort to integrate European radiation protection research and launches research calls in radiation protection on behalf of the EC.
Research line 3: impact of radiation exposure characteristics The impact of external versus internal emitters, dose inhomogeneities and radiation quality on individual radiosensitivity related to different doses and dose-rates has not been defined for relevant environmental, medical and occupational exposures. In case of internal contamination, individual radio-sensitivity could be dependent on localized dose distributions, but currently no mechanistic understanding, relevant experimental models, or valid datasets exist for these relationships. Similarly, radiation quality is gaining importance because of the more widespread availability of external beam hadrontherapy, where scattered neutrons are of concern, and the increasing clinical use of radionuclides  (Belli et al. 2010). Individual sensitivity should be analysed as a function of exposure and not only dose, because the same exposure can result in very different doses and spatial dose distributions in different individuals.
Education and training (E&T) The HLEG report of 2009 (http://www. hleg. de/fr. pdf) identified a problem with the maintenance in Europe of the range of expertise essential to an effective programme of research into the risks to humans from low-dose radiation. The report advised that specific programmes aiming at knowledge management across generations need to be designed in order to achieve sustainable continuity and development. It is important that sponsored courses and workshops are provided to attract students into the radiation research area. A successful example is the annual series of short courses funded by CONCERT (http://www. concert-h2020. eu/en/Concert_info/Education_Training). A large proportion of the groundwork of research is carried out as student projects and thesis work. For this reason, the research effort relies on a continuing relationship with universities, and on a healthy stream of high-level students.
Infrastructures One of the roles of MELODI is to ensure the availability of and to facilitate ready access to the state-of-the-art research infrastructures required to support the research efforts in low dose research. The priority is to promote the use of mature and up-to-date infrastructures and avoid unnecessary duplication. Furthermore, an effort should be made to harmonize practices amongst multiple facilities. Finally, the sustainability of rare but necessary facilities (e. g. for internal contamination) needs to be guaranteed. This should include recommendations on the provision of the financial means to harmonize, sustain and access these facilities. Infrastructures include so-called large infrastructures such as exposure facilities including those for animal experimentation, as well as the collection and storage of cohort data, databases, biobanks, and analytical platforms.
2017). Within the EU-funded project STORE (https://www. storedb. org/store_v3/), an internet-based platform for sharing data from epidemiological studies, as well as data and biological samples from radiation experiments (new and past), has been developed, further carried forward and supported first by DoReMi then by CONCERT. Going forward, it will be necessary to promote activities to further update and continuously expand the content of the database, and to elucidate to what extent data from other radiation protection platforms (ALLIANCE, NERIS and EURADOS) can be incorporated into STORE or whether a comparable database would be more appropriate. Activities to identify valuable materials and archives that could be included in the database and the tissue bank, as well as to maintain relevant biobanks and rescue material from endangered biobanks, need to be supported.
for valuable input into previous versions of the SRA. Similar thank goes to the  Scientific Committee of MELODI , and all MELODI members and participants in the MELODI workshops for providing comments on the SRA. Funding The research leading to these results has received funding from the  European Atomic Energy Community Seventh Framework Programme   FP7/2007-2011  under Grant agreement no. 249689  (DoReMi), the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant agreement no. 604984  (OPERRA) and from the  Euratom  research and training programme 2014-2018 under Grant agreement no. 662287  ( CONCERT ).
During High Dose Rate Brachytherapy (HDR-BT) the spatial position of the radiation source inside catheters implanted into a female breast is determined via electromagnetic tracking (EMT). Dwell positions and dwell times of the radiation source are established, relative to the patient's anatomy, from an initial X-ray-CT-image. During the irradiation treatment, catheter displacements can occur due to patient movements. The current study develops an automatic analysis tool of EMT data sets recorded with a solenoid sensor to assure concordance of the source movement with the treatment plan. The tool combines machine learning techniques such as multi-dimensional scaling (MDS), ensemble empirical mode decomposition (EEMD), singular spectrum analysis (SSA) and particle filter (PF) to precisely detect and quantify any mismatch between the treatment plan and actual EMT measurements.
Implant geometry, hence indirectly radiation source localization, can be determined with an electromagnetic tracking (EMT) device. Thereby a field generator (FG) produces a magnetic field extending across the female breast. The EMT system used in this study applies a three-pole transmitter to generate an electromagnetic field. Inside the catheters then a five degrees of freedom (5DoF) solenoid sensor is moved in accord with a pre-defined treatment plan. The sensor induces a distance-dependent induction voltage which allows to determine its spatial position (dwell position) and mean residence time (dwell time). The precision of the EMT system itself, and the accuracy of the registration between the field generator (FG) (EMT data set) and the CT imaging (Digital Imaging and COmunications in Medicine (DICOM) data set) coordinate systems, have a strong impact on the performance of an EMT system in HDR-BT catheter reconstruction.
In addition, it relies on empirical mode decomposition techniques to decompose the recorded non-stationary time series and remove breathing artifacts in real clinical applications with patients. The newly proposed method robustly and almost perfectly reconstructs the shape of various catheters involved in any HDR-BT treatment, quantifies any deviations from the treatment plan and conveniently visualizes the dwell position tracks of both, the CT data set and the various EMT data sets. The manuscript is organized as follows: Section 1 provides an Introduction and a discussion of related work. In Section 2, entitled Materials and Methods we discuss details of the Data Acquisition process and proposes a new Data Analysis Methodology based on a singular spectrum analysis (SSA) for denoising and high amplitude measurement artifact removal followed by an EEMD to isolate and identify the breathing mode contribution and remove it from the signal.
Patient cohort This investigation is intended to serve as proof-of-principle of the proposed automatized data processing chain. Hence it relies on data that was collected in a recent study from four female patients with an age of 49 -68 years. All women have been thoroughly informed and have given their written consent to the treatment which, furthermore, has been approved by the institutional review board of the Friedrich-Alexander-University Erlangen-Nu ¨rnberg (Nr. 355 -14B, 2014). The study comprised 4 patients between 49 -68 years of age. The women underwent a high dose rate brachytherapy treatment of a breast cancer that was excised in a surgical intervention (Lumpectomy) before the radiation treatment. For the HDR-Brachytherapy radiation treatment, catheters were implanted in the breast of the patients following a pre-defined geometry designed by a experienced radiologist.
The number of catheters varied from case to case with a mean number of implanted catheters of 18. 25. Based on an initial X-ray computed tomography (CT), a treatment plan was created with an Oncentra program (Oncentra 1 Brachy v4. 3, Elekta, Veenendaal, The Netherlands) which stores all data in the Digital Imaging and Communications in Medicine (DICOM) format. The contours for the treatment plan were defined by a medical expert according to a prescription given in  [25]. For each catheter, a different number of dwell positions was defined in the treatment plan. The mean number of dwell-positions per patient was 633. 5. Altogether, for each patient, only the first session of EMT data collection was recorded on the CT bench. This imaging data was used for treatment planning and formed the reference data set to which all EMT-based data sets had to be compared.
Image acquisition and treatment planning Computed tomography X-ray images (CT image) have been acquired either with breathing command, where an X-ray image is recorded while patients stopped breathing. For the treatment plan, the catheters were reconstructed from CT image slices by a medical physicist. Hence, the reconstruction of the catheters depends on experience and attention of the medical physicist. This data set is designated as CT data set. The catheters were implanted on the first day of the treatment period. Before the CT is acquired, the catheters are cut to a length of 287. 5 [mm]. During treatment, the sensor is inserted into the catheter to its very end. From the very end of the catheter, a default margin of l 0 = 5 [mm] is left. There the first dwell-position is set, and all subsequent dwell positions are chosen in steps of 2.
The distance between FG and the sensor critically influences the signal sensitivity. Additionally, three 6 DoF fiducial sensors are placed on the thorax of the patient to measure the breathing motion. All data are made available at https://osf. io/kd6ta, DOI: 10. 17605/OSF. IO/KD6TA for the convenience of the reader. Phantom data. The catheters implanted into the phantom were connected to the afterloader via transfer cables. The treatment plan defined for the phantom was up-loaded to the software of the Flexitron 1 afterloader prototype (Electa AB, Stockholm, Sweden). The sensor was moved to each dwell position inside the catheters according to the treatment plan and remained at the dwell positions for the defined dwell time. To investigate the possibility of a breathing compensation, preparative measurements with a phantom have been performed.
The measured sensor movement then consisted of a superposition of the chest motion and the sensor motion. Thereby, the chest motion was in a direction roughly perpendicular to the sensor motion inside the catheters. Patient data. After the preparatory phantom measurements, data measured from four patients was analyzed. Depending on the location of the tumor, in each of the patients, the catheters were implanted in different anatomical planes. A patient specific treatment plan was defined for each of the four patients by an experienced medical physicist. For each of the patients, EMT data was recorded in an HDR Brachytherapy treatment room on a specifically prepared patient table. The movement of the solenoid sensor according to the treatment plan was tracked with the NDI Aurora 1 (Northern Digital Inc.
Data analysis methodology Following we describe a data analysis methodology to automatically analyze the electromagnetic tracking (EMT) measurements obtained during a HDR-BT treatment. The induction voltage signals are obtained from a solenoid sensor moving inside various catheters implanted into the breast of female patients, and three fiducial sensors fixed to the chest of the patient, respectively. The movement of the 5 DoF solenoid sensor in the inhomogeneous magnetic field of a field generator is characterized by the sensor's dwell positions. An independent measure of the breathing mode is obtained with EMT measurements of signals from the fiducial sensors. Such measurements contain a number of artifacts, mainly those from the breathing motion of the patients during the measurement. The proposed data processing chain encompasses the following steps: • Sensor tracking: A particle filter (PF) technique is employed to track the dwell positions of a solenoid sensor inside various catheters implanted in a female breast for a radiotherapy treatment.
• Fiducial signal First of all, an average breathing signal was calculated by averaging over the three fiducial signals. This signal was not tracked by a particle filter. Consequently, it was very noisy. To get rid of the noise on this signal, an SSA was applied to the latter. For further computations, only the first SSA component was considered  [32]. In  • Outlier detection Despite a particle filter tracking of the sensor signal, the data still contained outliers due to technical malfunctioning of the EMT measurement apparatus. If, for technical reasons, a measured value is missing, the software puts zeros as entries of the data records. Employing particle filter tracking, such anomalous measurements will then lie closer to the regularly measured points but still will not lie on a smooth line.