dataesr/scientific-paragraphs-categorization huggingface dataset:
The objective is to detect which parts of the messages have been modified. Rendez-vous hiding with trajectory modification: A dataset created using FDI-T's trajectory modification feature to mimic real-life scenarios in which two vessels hide a rendez-vous by modifying their trajectory. Indeed, two vessels staying close to each other in the middle of the sea is an easily detectable behavior. Spoofing: A dataset in which each sample contains multiple vessels' histories; the objective of the model is to spot fake vessels among genuine ones.
Conclusions This paper presents an end-to-end toolchain that is capable of creating full datasets that can be used to train and test AI-based anomaly detection systems for air and sea transportation quickly, easily and with a high degree of control. First, a surveillance data acquisition and processing module is created using surveillance historical databases and existing extraction libraries. The module helps to collect relevant data, automatically clean it from outlier messages (e. g. , faulty sensors) and transform it into datasets that are readily feedable to models. Second, a synthetic anomalous data generation module is proposed that covers the whole spectrum of seen and anticipated attack scenarios. It is built upon an existing FDIA testing tool designed for surveillance systems testing, which relies on a DSL-based scenario design module to automatically generate test cases.
Several extensions were developed for the DSL to facilitate the training and testing of ML models. Finally, we show the possibility to create datasets to train or test ML anomaly detection models as well as discuss different use-case scenarios for both ATC and VTS. Future experimentation in the ATC domain includes testing on Olive and Basora  [24]  to further validate our approach and explore more local flows of trajectories around airports, as well as to refine our own model detection for ADS-B data. Future work in the VTS domain will consist of finalizing the discussed datasets to train and evaluate the associated models.
Acknowledgments: This work is part of an ongoing research initiative toward the generation of FDIA test scenarios, partially carried out in partnership with the  French company Kereval. It is partially supported by a  UBFC-ISITE-BFC grant , the  EIPHI Graduate school  (contract  ANR-17-EURE-0002 ), and the  ANR ASTRID   GeLeaD  project grant. Most computations have been performed on the supercomputer facilities of the  Mésocentre de calcul de Franche-Comté.
3 , 4 here appears to be a gender difference in outcomes after aortic valve interventions. 0] [11] [12] [13]  In the WIN TAVI registry (the first all-female TAVR registry with collection of female sex-specific baseline parameters), a total of 1019 intermediate to high-risk women with severe symptomatic aortic stenosis were enrolled. At 30-day and 1-year follow-up, the VARC-2 composite safety endpoint was 14. 0% and 16. 5%, respectively with a low incidence of early mortality and stroke. Prior revascularization and EuroSCORE I were independent predictors of the VARC-2 efficacy endpoint, whereas EuroSCORE I, baseline atrial fibrillation, and prior percutaneous coronary intervention were independent predictors of death or stroke at the 1-year follow-up. 14 ,15 urthermore, data from meta-analyses of randomized trials comparing TAVR with SAVR for the treatment of symptomatic severe aortic stenosis reported TAVR to be associated with a survival benefit compared with SAVR in women but not men.
Procedures and data collection Patients whose eligibility is confirmed will be randomized 1:1 to undergo treatment by either transfemoral TAVR or conventional SAVR. Randomization will be performed centrally by an electronic system. Valve implant procedures will occur within 14 days of randomization. TAVR will be performed by transfemoral access using the SAPIEN 3 (model 9600TFX) or SAPIEN 3 Ultra (model 9750TFX) transcatheter heart valve and associated delivery systems, in accordance with the relevant Instructions for Use for device sizing, preparation and recommended implant procedure. SAVR will be performed using a commercially available surgical valve and associated components, in accordance with the standard of care at the institution. The protocol also includes new generation sutureless or rapid deployment valve and does not exclude surgical annulus enlargement.
Prior to the start of the study, investigators will undergo guided review of the Instructions for Use of the devices, to ensure correct device usage. Study sites will be provided with uniform guidance on anticoagulation/ antithrombotic regimens. All patients who receive a SAPIEN 3 or SAPIEN 3 Ultra transcatheter heart valve or undergo SAVR will continue in the study throughout 1-year post-procedure (including patients who are randomized to TAVR but are subsequently converted to SAVR). Data will be collected prospectively according to the schedule presented in Table  II. Assessments will include medical history, physical examinations, electrocardiograms, echocardiograms, laboratory tests, neurological assessments, medications, adverse events, and quality-oflife questionnaires. Echocardiograms will be performed according to a standardized protocol and images will be analyzed by a central echocardiography core laboratory.
Data will be entered onto electronic case report forms by study site and central laboratory personnel, and checked for completeness and accuracy by automated systems and by on-site monitoring. An independent clinical events committee, which will include cardiologists and cardiothoracic surgeons experienced in the field of aortic stenosis, will adjudicate endpoint events (unblinded) and assess serious adverse events and device/procedure relatedness. An independent Data Safety Monitoring Board, which will include at least one cardiothoracic surgeon experienced in aortic stenosis, will monitor all adverse events.
Sample size Event rate estimates for the primary endpoint were based on data from the PARTNER 3 study. 18 The sample size estimation assumes an event rate for the composite primary endpoint of 16% in the SAVR arm and 8% in the TAVR arm. Using a one-sided alpha = 0. 05 and a specified non-inferiority margin of 6. 0%, a sample size of 132 patients with 1-year data would provide 80% power to fulfill non-inferiority (Figure  2 ). A sample size of 402 patients would provide 70% power (two-sided alpha 0. 05) or 80% for a one-sided slpha of 0. 05) to fulfill superiority. Because the feasibility assumptions are uncertain in an all-comer (i. e. including low to intermediate surgical risk) population and patients dropping out, an actual sample size of 440 has been chosen.
Statistical analysis The primary population for endpoint analysis will be the As Treated population (patients in whom the index procedure is begun, whether or not the procedure is completed). The As-Treated population was chosen as patients dying before the intervention, with protocol violations and withdrawing their consent would not make sense. Cross-overs are not expected in this context, as TAVR is not an option for patients with initiated surgery. SAVR in patients undergoing TAVR is considered a potential solution for procedural complications. All results are also reported for the Intent to Treat Population (all randomized patients). Echocardiogram data will be analyzed in the Valve Implant population (the subset of the As Treated population who receive and retain the intended valve during the index procedure).
Justification for the study 0] [11] [12] [13]  In the WIN TAVI registry the VARC-2 composite safety endpoint was 16. 5% at 1 year with a low incidence of early mortality and stroke. 14 ] [11] [12] [13]  Moreover, data from two meta-analyses have indicated that TAVR may provide a survival benefit compared with SAVR in women, whereas similar outcomes have been observed with either intervention among men. 16 , 17 To date, most data are derived from subgroup analyses of large trials, and randomized trials comparing outcomes after TAVR or SAVR specifically in women are lacking. Moreover, most trials involved patients at intermediate to high surgical risk, whereas there is increasing evidence that TAVR may also be suitable for low-risk patients. 3 , 4 ecent randomized trials in low-surgical risk populations, i.
Study design and organization The RHEIA study is a prospective, multicenter, randomized, controlled trial. It is designed to determine whether TAVR is non-inferior, or potentially superior, compared to SAVR, with respect to safety and efficacy in the treatment of women with severe symptomatic aortic stenosis. Data will be collected from a range of hospitals in Europe. The multinational nature of the study increases the applicability of the study findings to clinical practice across Europe. The study sites and investigators have been selected to ensure that the personnel and their institutions are suitably qualified, experienced and equipped to undertake the planned interventions. The trial management structure has been designed to provide disciplined oversight of trial activities. Patient eligibility will be confirmed by a Case Review Board, echocardiograms will be analyzed by a central imaging laboratory, and endpoint events will be adjudicated by a nonblinded clinical events committee.
Augmented SSM creation for comparisons To fulfill the objectives of the study, three augmented SSMs were created: aSSMproposed, aSSMset1, aSSMset2. To create an augmented SSM, the SSMnon-augmented was supplemented with additional set of 16 anatomical landmarks and their deformation field in the training dataset  [6]. The aSSMproposed was augmented with a proposed set of 16 clinically relevant anatomical landmarks to map scapular shape  [6]  with previously evaluated inter-and intra-observer reliability for each landmark selection  [6]. This landmark set consisted six anatomical landmarks in the glenoid cavity region and four landmarks on the acromion region (Figure  1 ). To illustrate the best augmentation strategy to improve fitting quality and clinical relevance, two more sets of augmented SSMs (aSSMset1 and aSSMset2) having 16 anatomical landmarks each were created (Figure  1 ).
A 130 km 2 tabular iceberg calved from Petermann Glacier in northwestern Greenland on 5 August 2012. Subsequent fracturing generated many individual large "ice islands", including Petermann ice island (PII)-A-1-f, which drifted between Nares Strait and the North Atlantic. Thinning caused by basal and surface ablation increases the likelihood that these ice islands will fracture and disperse further, thereby increasing the risk to marine transport and infrastructure as well as affecting the distribution of freshwater from the polar ice sheets. We use a unique stationary and mobile ice-penetrating radar dataset collected over four campaigns to PII-A-1-f to quantify and contextualize ice island surface and basal ablation rates and calibrate a forced convection basal ablation model. The ice island thinned by 4.
7 m over 11 months. The majority of thinning (73 %) resulted from basal ablation, but the volume loss associated with basal ablation was ∼ 12 times less than that caused by areal reduction (e. g. wave erosion, calving, and fracture). However, localized thinning may have influenced a large fracture event that occurred along a section of ice that was ∼ 40 m thinner than the remainder of the ice island. The calibration of the basal ablation model, the first known to be conducted with field data, supports assigning the theoretically derived value of 1. 2×10 -5 m 2/5 s -1/5 • C -1 to the model's bulk heat transfer coefficient with the use of an empirically estimated iceocean interface temperature. Overall, this work highlights the value of systematically collecting ice island field data for analyzing deterioration processes, assessing their connections to ice island morphology, and adequately developing models for operational and research purposes.
This forced convection model is based on the transfer of heat across a flat plate due to the turbulent flow of the underlying water. A bulk heat transfer coefficient is used to estimate an ablation rate based on the differential velocity between the ice island and the ocean current as well as the water temperature across the length of the ice island  (Ballicater Consulting, 2012; Bouhier et al. , 2018; Weeks and Campbell, 1973). Previous efforts to calibrate basal ablation models have relied upon remotely sensed datasets as well as potentially inaccurate modelled environmental data  (Bouhier et al. , 2018; Jansen et al. , 2007). This is largely due to the challenges associated with collecting field data for determining the individual rates of surface and basal ablation. We overcame these challenges to collect a unique dataset from Petermann ice island (PII)-A-1-f between November 2015 and September 2016.
PII-A-1-f was grounded on the continental shelf of Baffin Island and had a surface areal extent of 13 km 2 when it was first visited. The fieldwork included repeat mobile ice-penetrating radar (mIPR) transects and the collection of a long-term in situ dataset of ice island thinning by a stationary IPR (sIPR). These were the first such field data to be collected from an ice island or iceberg in either the Arctic or Antarctic. The measurements are used to calibrate the forced convection basal ablation model, which had previously not been validated or calibrated with direct field observations. In addition, the IPR data were used to assess the spatial and temporal variation in ice island thinning and ablation rates. Using remotely sensed imagery to monitor areal reduction, the ablation magnitudes are put into context with respect to other processes (e.
g. fracture) that contributed to the deterioration of PII-A-1-f. 2 Study site PII-A-1-f was a fragment of the 130 km 2 PII that calved from Petermann Glacier in northwestern Greenland on 5 August 2012  (Crawford et al. , 2018a). Using tracking data derived from RADARSAT-2 satellite images in the Canadian Ice Island Drift, Deterioration and Detection (CI2D3) Database  (Crawford et al. , 2018a)  and by the Canadian Ice Service (CIS; Environment and Climate Change Canada) we were able to trace the origins of PII-A-1-f as the PII broke up and drifted through Nares Strait and Baffin Bay between August 2012 and November 2014. As this piece drifted south, it further fragmented and experienced periods of stagnation while grounded in Kane Basin and northern Baffin Bay (Fig. 1a ). The PII-A-1-f fragment entered northern Baffin Bay in late 2013.
Thinning (temporal assessment) To ascertain the magnitudes and rates of surface and basal ablation, a series of ablation stakes, a 40 MHz sIPR, and a small meteorological station with a sonic ranger and camera were installed on PII-A-1-f on 20 October 2015 (Fig. 2 ). The ice island position was recorded hourly with a Garmin 6X-HVS GPS (Garmin International, Inc. ), and air temperature (T a ) was measured with a 109 thermistor (Campbell Scientific Canada Corporation -CSCC) in a radiation shield at 1 min intervals and logged as hourly averages. All meteorological station data were recorded on a CR1000 data logger (CSCC) and telemetered with an Iridium L-Band modem (9522B; CSCC). Ice thickness was recorded and telemetered daily by the sIPR (Blue System Integration, Ltd. ) until 27 September 2016.
No snow was present on the date when the stakes and instruments were installed, and the average weekly ablation (or accumulation) at these stakes was calculated with ImageJ (v. 10. 8. 0) software using the markings for scale  (Abramoff et al. , 2004). Increases in surface height due to the accumulation of snow are presented in this study as negative values. A linear interpolation was applied to estimate daily ablation or accumulation between these observations so that the sIPR, SR50A, and ablation stake data were available over a consistent time span. Basal ablation was calculated by subtracting the interpolated ablation magnitudes derived from the stake data from the ice thickness time series. These values are therefore approximations of daily basal ablation rates, as they were based on values of ice thickness that were linearly interpolated across longer time periods.
Oceanographic data collection and comparison The derived basal ablation was used to calibrate the forced convection model described in the Introduction. In addition to the general paucity of ice island thinning measurements, there is a dearth of in situ oceanographic data available for the validation and calibration of iceberg and ice island numerical deterioration models. Here, we use two oceanographic field datasets to validate and calibrate a time series of modelled ocean temperature, salinity, and current data that spans the full duration for which basal ablation rates were available. Using the oceanographic model data greatly extended the time span over which the model calibration could be conducted. The first oceanographic field dataset was collected by the CCGS Amundsen during its annual ArcticNet science research cruise.
CTD (conductivity, temperature, depth) profiles were conducted around PII-A-1-f on 28 July (four casts) and 29 September (five casts) 2016 (Amundsen Science Data Collection, 2016; Fig. 1c, d ). CTD profiles were also collected on 29 July 2016 (two casts) and 28 September 2016 (one cast) close to the location of a sea ice camp that was situated 20 km northwest (67 • 29 N, 63 • 47 W) of the grounding location of PII-A-1-f (Fig. 1a , bottom inset). This sea ice camp was operated by the GreenEdge project based out of Université Laval, Québec. The second oceanographic field dataset was composed of 39 CTD profiles (SBE 49 FastCAT CT; Sea-Bird Electronics, Inc. ) acquired at the GreenEdge sea ice camp between 20 April and 22 July 2016. Absolute salinity (S A ; g kg -1 ) and conservative temperature ( ; • C) measurements were reported at 1 m depth bins for all CTD casts.
Current speed (u; m s -1 ) was calculated as an average of the current vectors measured every 30 s over 2 m depth bins and recorded every 30 min at the ice camp with a Teledyne RDI Workhorse Sentinel 300 kHz acoustic Doppler current profiler (ADCP; Teledyne RD Instruments;  Oziel et al. , 2019). A linear interpolation was applied to fill missing values before daily average u was calculated for the individual depth bins. Data from the Copernicus Marine Environment Monitoring Service (CMEMS) Global Ocean Physical Reanalysis (GLORYS12V1) product were used for the calibration of the forced convection basal ablation model. This reanalysis product, henceforth referred to as the CMEMS data, had a spatial resolution of 1/12 • and 50 depth levels. Potential temperature, practical salinity, and the horizontal current velocity components were extracted from the model grid cells that corresponded with the locations of PII-A-1-f and the GreenEdge sea ice camp at the model depth bin in which the keel of the ice island was located over the course of sIPR data collection.
Keel depth was calculated using ice thickness assuming ice density (ρ i ) = 873 kg m -3  (Crawford et al. , 2018c)  and hydrostatic equilibrium. Potential tempera-ture and practical salinity were converted to and S A , respectively, using the Gibbs SeaWater functions provided in the R "gsw" package  (Kelly, 2017). The CMEMS data were compared against the in situ oceanographic data to justify its use for calibrating the forced convection basal ablation model and to identify any bias in the modelled oceanographic data. Comparisons were conducted between (1) the full CCGS Amundsen CTD profiles collected at the location of PII-A-1-f and near the GreenEdge sea ice camp location in July and September 2016, (2) the full CCGS Amundsen profiles and the CMEMS data profiles, and (3) the CMEMS data time series and the mean S A , , and u values of all CTD casts and ADCP measurements that fell within the CMEMS depth bin in which the ice island keel was located.
The driving force was derived from CMEMS modelled oceanographic data (S A , , and u) at the ice island keel. p was set to the pressure at the keel depth. L was assigned as the median of all distances between the sIPR location and all vertices of the outline of the areal surface extent of PII-A-1-f that was digitized from RADARSAT-2 Fine Quad (FQ; 8 m nominal resolution) SAR imagery acquired on 27 July 2016 (S2). A second calibration for each interval was conducted with the CMEMS data after corrections were applied based on the comparisons against the in situ oceanographic data (Sect. 3. 2. 1). A final calibration of C was obtained based on the basal ablation and driving force over the total duration over which basal ablation was derived. This final calibration provided a single value of C as opposed to the previous calibrations where values of C i were obtained for each calibration interval.
An analysis was conducted to determine the sensitivity of the basal melt magnitude predicted by the forced convection basal ablation model to variations in u, , and C. The two variables and one parameter were individually perturbed across eight equally spaced intervals that covered their observed and calculated ranges. The non-perturbed variables were held constant at their median values. All assigned values were based on the corrected CMEMS data series. The sensitivity of the forced convection basal ablation model was assessed as the average percent increase in cumulative basal ablation predicted for the duration over which basal ablation was derived with each incremental increase in the value assigned to a given variable or parameter. The sensitivity was analyzed over this longer time period due to the certainty in the bulk basal ablation derived from total ice thinning and surface ablation.
Thinning (spatial assessment) A repeat transect was conducted to assess spatial variation in thickness and thinning and to verify that the sIPR data were representative with respect to other locations across the ice island. An initial thickness profile was collected with a 25 MHz mIPR (Blue System Integration Ltd. ) that was towed by snowmobile over an approximately 3 km transect on 8 May 2016. A series of 10 ablation stakes were also installed along the profile route. Thickness data were collected over 2. 4 km of the original transect when the same mIPR system was towed on foot on 28 September 2016. Eight of the stakes were also remeasured at this time. Further details of the mIPR system can be found in S1 and  Mingo and Flowers (2010). All mIPR processing was conducted with Radar Tools (release 0.
In the depth interval of interest, the values of and S A were reasonably similar and not consistently different be- tween sites. This supported our use of the S A correction for the CMEMS data that were derived from the difference between the CMEMS data and the longer time series collected at the GreenEdge sea ice camp. This S A correction was applied to the CMEMS data associated with the location of PII-A-1-f, which was used for the model calibration (Fig. 4b ). www. the-cryosphere. net/14/1067/2020/ The Cryosphere, 14, 1067-1081, 2020 The full CMEMS and S A profiles were also plotted (not shown) against those acquired by the CCGS Amundsen to ensure that the CMEMS data were generally representative of the water column. The CMEMS profiles were reasonable in form, and, in the depth interval of interest, CMEMS consistently overestimated the S A recorded by the CTD casts acquired by the CCGS Amundsen.