dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with real data The comparison of reconstructions using a real FDG dataset, reprocessed and reconstructed with the SB and DWB-1 protocol framings, is made in figure  7. Results on the real data showed similar evolution of CNR with increasing iterations for 4D and 3D reconstructions respectively. The 4D Spectral reconstruction using 6 basis functions provided the highest CNR values throughout all iterations, followed by the 4D Spectral reconstruction using 9 basis functions and 4D Patlak. Overall, CNR of all 4D reconstructions of DWB data was higher than that of 3D reconstruction of SB data and 3D reconstruction of DWB data. The liver SD versus tumour VOI average trade-off curves showed close behaviour between 4D reconstructions, resulting to SD values in the first 15-17 iterations of 4D reconstructions which were lower compared to 3D reconstruction of DWB data at matched tumour mean values.
Compared to 3D reconstruction of SB data, all 4D reconstructions of DWB data resulted in higher SD values (for matched tumour mean values where comparison is possible). Parametric K i images from 3D and 4D reconstructions at iterations with matched liver SD values of approximately 3. 1•10 -3 min -1 are shown in figure 8. The K i images show the degradation of the tumourʼs and heartʼs high-intensity definition in the transition from SB to DWB with 3D reconstruction based parametric imaging. The 4D reconstructions of DWB data improve on these definitions with 4D Spectral reconstruction providing a similar definition on the heartʼs structure to that of SB data with 3D reconstruction. The remaining differences in the heartʼs definition can be also attributed to differences in convergence between the presented iterations of each respective reconstruction.
For comparison against the simulation study, metrics of CNR and SD versus VOI average trade-off curves from simulation DWB-1 are shown in figure  9. Similar to the real data, values of CNR are highest for 4D Spectral reconstruction using 6 basis functions, followed by 4D Spectral reconstruction using 9 basis functions. Contrary to the real data, 4D Patlak reconstruction provided relatively lower CNR values and closer to the values of 3D reconstruction from SB data. The evolution of CNR with increasing iterations was similar to the real data, with maximum values attained at approximately 3 to 5 iterations for all reconstructions. Furthermore, the simulation data SD versus VOI average trade-off curves showed a larger separation of 4D Spectral reconstructions, compared to the real data, with the reconstruction of 6 basis providing lower SD values compared to 3D and 4D Patlak reconstruction of DWB data, but at higher bias values from other 4D reconstructions.
Discussion Our simulation study shows that the dynamic reconstruction of DWB FDG data resulted in a substantial reduction of Patlak K i image noise and more favourable convergence behaviour, compared to 3D reconstruction based parametric imaging. These results, limited to a single level of noise, are in agreement with the findings of  Karakatsanis et al (2016). Moreover, we directly compared against an SB dynamic protocol, processed with 3D reconstruction, and showed that comparable values of parametric image noise and bias can be achieved with DWB protocols by the use of a dynamic reconstruction. In this study, a SB location was used in the evaluations of reconstructions and comparisons between SB and DWB protocols. Although this study is limited to a single axial location, we anticipate the results to hold for FDG Patlak K i parametric imaging of other bed positions on both S&S and uni-directional CBM DWB protocols.
A high number of iterations of 4D reconstruction algorithms provided more stable VOI mean values, but at risk of resulting to higher parametric image noise than that of a 3D reconstructions on the same DWB data. The results obtained with one real dataset showed similar behaviour, with mean VOI values continuing to slightly increase even after 40 OSEM iterations and with 4D reconstruction parametric image noise surpassing that of 3D reconstruction at late iterations. This example illustrates that the relative aspects of 4D to 3D comparisons with simulated data for the tested DWB and SB protocols have the capacity to translate to studies with different levels of noise. Overall, the risks of excessive parametric image noise and under-converged K i values will be lesser for 4D based reconstruction methods than for a 3D reconstruction, which demonstrated considerably more instability with increasing iterations.
To ensure convergence of the K i values while suppressing the increase of parametric image noise, further regularisation techniques can be used with methods such as 4D MAP reconstruction  (Wang et al 2008, Reader and Verhaeghe 2014)  or kernel 4D dynamic reconstruction  (Novosad and Reader 2016, Gong et al 2018). Our nested optimization tests using NNLS instead of multiple MLEM sub-iterations did not provide any differences in the acceleration of the convergence and showed comparable behaviour to a previous study on the use of NNLS with the spectral model  (Matthews et al 2010). NNLS did provide computing acceleration by a factor of approximately two for our datasets, but resulted in an increase of the parametric image noise compared to MLEM sub-iterations for similar bias characteristics.
For the higher numbers of basis functions, with 9 and 17, almost identical behaviour was seen on the DWB-1 dataset (of 8 frames). Overall on the choice of the number of basis functions, results indicate a greater risk in image bias when using a too small number of basis functions, and a lesser risk in image noise when using more basis functions than strictly needed to properly model the underlying kinetics. In any case the number of basis needs to be tuned for every DWB protocol, depending on the number of frames within the dataset and the range of underlying kinetics as well as the level of noise in the PET data. In this study, the investigation between S&S and CBM DWB protocols was limited to aspects of sampling frequency and uniformity within the total examination time. Our results showed small differences in parametric image bias but a noticeable reduction in parametric image noise when utilising CBM acquisition with uniform sampling.
Acknowledgments This project has received funding from the  European Unionʼs Horizon 2020  research and innovation programme under the  Marie Skłodowska-Curie  grant agreement No  764 458. This work was performed on a platform of  France Life Imaging  network partly funded by the grant  ANR-11-INBS-0006. The authors would like to thank  Dr Florent Besson  for providing the FDG Dynamic dataset and the delineation of the tumour volume.
Indeed, in some judicial system, as in France, detention or custodial sentences are impossible below the age of 10 years old and there may be an increased punishment for adults exploiting a child under 6 years old to beg on public road. This division at the age of 13 years old was also justified by the evolution of the correlation between age and the hand bones biometric measurements observed in a precedent paper: this relation became less linear from the ages of 13-15 years as the inter-individual variability became more important  [19]. This observation may be explained by the non-synchronous maturation observed from this age period  [20, 21]. Respectively For each individual, measurements were collected following the protocol previously developed  [19]  with the ORS ® (Object Research System) Visual ® software specializing in medical image processing, and the ''HandBones'' plug-in, also developed in the same previous publication  [19].
1. Fig. 1  Representation of six measurements collected for each metacarpal and proximal phalange of the five hand rays (from top to bottom: the length, proximal width and distal width of the second metacarpal and of the third proximal phalange)from Remy et al. [19]  b. Statistical analyses The study sample was divided into two subsamples using the surveyselect procedure of the SAS 9. 4 software (SAS Institute Inc. , Cary, NC, USA): one to build the discriminant function and age estimation formulas, and another for their evaluation (named respectively training and test samples). These subsamples were obtained with a stratified sampling method according to age and sex, with a sampling rate equal to 75%. Thus, the proportions of individuals aged under 13 and those aged between 13 and 21 years old were respected.
DISCUSSION Due to the increase in transnational movements of people without any identification documents, proof of the age of migrants is a problematic issue in the judicial context. The most popular methods available for age estimation, the GPA  [9]  and the TW method  [16, 17] , are not reliable enough: because they are based on a simple reading of the hand radiographs, the results are observer-dependent  [10] [11] [12]. Yet, studies focusing on the evaluation of skeletal maturation through measurements are still rare  [23] [24] [25]. Likewise, the development of software programs for estimating hand bones maturation is quite recent  [26]. We previously proposed an estimation of age based on the biometric information of hand bones: four formulas were developed for the 0-21, 0-13, 13-18 and 18-21 years-old subgroups using measurements of the metacarpals and proximal phalanges  [19].
Information Query All the instances of information corresponding to a specified graph pattern may be found within a network of information, through the information query function. The specialization relationship between the query and the data graphs imply that the structure of the query graph must be entirely found in the data graph. The query function relies on the search for injective homomorphism between the query graph and the data graph. Information Fusion When a model of a situation of interest (e. g. an activity involving a specific person at a specific date) is available, one may want to monitor the situation and trigger further processes if an instance of such a situation is happening. Therefore, different observations, potentially coming from different sources, are filtered out in order to keep observations of interest only.
On can achieve a search for specific sources of information, or statistics of the fusion operations used and the global level of similarity of individual information items, for instance, using well known, tried and tested graph algorithms. Furthermore, representing the concept's lineage as an nested graph has an other advantage, regarding the easiness of manipulation of information. Indeed, even if the traceability information and the knowledge are integrated into a single nested graph, they can easily be isolated in two different smaller conceptual graphs [situation, lineage]. This would be achieved by somehow "cutting" the nested situation graph at the first level of lineage, that is to say cutting the lineages of the situation concepts and storing them in a separated graph data base. Finally, if we chose to use a reduced vocabulary in order to express the trace information in the lineage graphs, our approach can be enriched using other ontology (for example  [8] ).
These tools are defined as an approach involving any techniques, models, frameworks (one project's framework can be seen in Figure  3 ), or methodologies that strategically manage and support the decision-making  [26]. Moreover, decision-making tools help to evaluate and monitor the co-benefits systematically  [27]  and processes for connecting, reflecting and investigating, exploring, and modeling while suggesting proper solutions  [28]. One such example of these tools is the Adaptation Planning Support Tool (APST), which is specifically designed to focus on the impacts due to climate change. This toolbox has been proven to be useful for policy-makers and has been applied practically in many cities  [30]. The Mitigation Impact Screening Tool (MIST) is another decision software-based tool developed by the US Environmental Protection Agency (EPA) for an assessment of the impacts of UHI mitigation strategies' (mainly albedo and vegetation) increase on the reduction in urban air temperatures, ozone, and energy consumption for over 200 US cities  [31].
The tool is currently unavailable as it was disabled by the EPA due to the update of One such example of these tools is the Adaptation Planning Support Tool (APST), which is specifically designed to focus on the impacts due to climate change. This toolbox has been proven to be useful for policy-makers and has been applied practically in many cities  [30]. The Mitigation Impact Screening Tool (MIST) is another decision software-based tool developed by the US Environmental Protection Agency (EPA) for an assessment of the impacts of UHI mitigation strategies' (mainly albedo and vegetation) increase on the reduction in urban air temperatures, ozone, and energy consumption for over 200 US cities  [31]. The tool is currently unavailable as it was disabled by the EPA due to the update of the methodology and data inputs.
and easy access to forestry, heat examples for planning and action. Developed and (Germany) information. stress. implementation of heat published in 2013. stress measures. • Input data WBGT are WBGT decision support tool High school athletes adjust practice schedules based on heat threat through the week Heat stress • • • temperature, dew point temperature, wind speed, relative humidity, pressure. Forecasting data from National Digital Forecast Database (NDFD). Past 24 h: Real-Time Meso-scale Analysis (RTMA). WBGT estimations are compared to from Kestrels at 2 sites measurements taken English Online tool • Publicly accessible tool North Carolina. assesses hourly WBGT which helps to avoid heat stress exposure by making informed decisions about when to schedule outdoor • • Spatial coverage is for WBGT risk categories.
Modelling the seismic potential of active faults and the associated epistemic uncertainty is a fundamental step of probabilistic seismic hazard assessment (PSHA). We use SHERIFS (Seismic Hazard and Earthquake Rate In Fault  Systems), an open-source code allowing us to build hazard models including earthquake ruptures involving several faults, to model the seismicity rates on the North Anatolian Fault (NAF) system in the Marmara Region. Through an iterative approach, SHERIFS converts the slip rate on the faults into earthquake rates that follow a magnitude frequency distribution (MFD) defined at the fault system level, allowing us to model complex multi-fault ruptures and off-fault seismicity while exploring the underlying epistemic uncertainties. In a logic tree, we explore uncertainties concerning the locking state of the NAF in the Sea of Marmara, the maximum possible rupture in the system, the shape of the MFD and the ratio of off-fault seismicity.
The branches of the logic tree are weighted according to the match between the modelled earthquake rate and the earthquake rates calculated from the local data, earthquake catalogue and palaeoseismicity. In addition, we use the result of the physics-based earthquake simulator RSQSim to inform the logic tree and increase the weight on the hypotheses that are compatible with the result of the simulator. Using both the local data and the simulator to weight the logic tree branches, we are able to reduce the uncertainties affecting the earthquake rates in the Marmara Region. The weighted logic tree of models built in this study will be used in a following article to calculate the probability of collapse of a building in Istanbul.
Since SHERIFS only uses the fault data as input, it is possible to compare the modelled rates with rates calculated from the earthquake catalogue and palaeo-earthquakes which can be considered as independent data from the SHERIFS input. Branch weights in a logic tree are usually based on the scientific value of each hypothesis explored in the logic tree but not on the capacity of the modelled earthquake rates in each branch to reproduce the data. The weight of an individual branch of the logic tree is simply the product of the weights of each hypothesis used for the branch. In this discussion, we propose a novel approach to set the weight of each branch of the logic tree accounting for both the input hypotheses used and the ability to reproduce the independent data. We set up a quantitative scoring system in order to set the weight of each individual branch of the logic tree.
For each branch, four scores are calculated (Fig. 11 ). One score judges the ability of the combination of input parameters and hypotheses to spend the slip-rate budget of the faults as earthquake rate and not generate a large amount of NMS (S1). Two scores compare the model to the data, judging the capacity of the modelled earthquake rates to reproduce the earthquake rates deduced from the catalogue (S2) and to reproduce the earthquake rates deduced from the palaeoseismic records (S3). The last score rates a priory the input hypotheses based on the analysis of the RSQSim synthetic catalogue (S4). The final weight of each branch of the logic tree is given by the combination of the four scores (Fig. 11 ). The code allowing the computation of the scores and the excel file showing the computation of the weights are provided in the electronic Supplement.
Scoring based on the fit to the rate of the palaeo-earthquake Figure  9  exposes the underestimation of the rate of earthquakes on the Ganos section compared to data when considering the creep branch of the logic tree. The comparison with the palaeoseismicity is done at all sites where palaeoseismicity is available for each model of the logic tree (see above). At each site, the rate of palaeo-earthquake is extracted from the literature ( magnitude and annual rate of occurrence. At each site, the modelled rates are compared to this distribution. The closer the participation rate for a given section is from the maximum of the palaeo-earthquake rate distribution, the larger the score. The score of the branch is the average score for each palaeo-site. The average scores on the palaeo-earthquake fit for the creep, partial creep, deep creep and fully locked models are 0.
Code and data availability. All the data and input parameters used for the earthquake rate calculation with SHERIFS are available in the electronic Supplement. The SHERIFS code is available at https: //github. com/tomchartier/SHERIFS (Chartier et al. , 2019)  and in the electronic Supplement. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-21-2733-2021-supplement. Author contributions. TC, OS and HLC were responsible for the collection and organization of the databases, construction of the SHERIFS and the physics-based models, and redaction of the article. KRD, JHD and BES were responsible for the construction of the physics-based models. Competing interests. The authors declare that they have no conflict of interest. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. This project was partly founded by the  Axa Research Fund. We thank  Aurelien Boiselet from Axa  for his involvement in the project. We also thank  José A. Alvarez-Gómez  and another anonymous reviewer for their valuable reviews and comments that contributed greatly to the improvement of this article. Financial support. This has been supported by the  AXA Research Fund  (grant no. Joint Research Initiative  "Earthquake geology and seismic hazard assessment: tools for risk-informed decision-making. "). Review statement. This paper was edited by  Filippos Vallianatos  and reviewed by  José A. Alvarez-Gómez  and one anonymous referee.
Using the ESA Grid Processing On Demand (GPOD) SARvatore chain on GPOD service  (Dinardo et al. , 2014) , we compute four Cryosat-2 freeboard solutions over the period 2013-2017. All these solutions include zeropadding and they only differ by the retracking method (TFMRA or SAMOSA+) and the activation or not of the Hamming Window (HW). This approach enables to calculate the different solutions from the same source of data (ESA FBR) and through the same freeboard processing chain. Only the processing options differ. Using this methodology we evaluate separately the impacts on the heights of floes and the heights of leads. Compared to other studies, which only compare the impact on the freeboard, this approach is quite original. To assess the consistency of our results, we also investigate the data of the Copernicus Sentinel-3A mission (2016-), which carries an altimeter similar to that of CryoSat-2.
Algorithm and freeboard solutions The previous section described the main difficulties inherent to the SAR mode freeboard estimation and solutions to get around them. Here, we analyse the effects on the freeboard of these various solutions. For that purpose, we have computed four radar freeboard data-sets presented in Table  1. The Fig. 5  gives an overview of the processing chain used to produce the freeboard data-sets. Level L1B and L2 data are processed in the GPOD processing cluster. Among all the processing options offered by the SARvatore interface, only two combinations are used in this study: the zero-padding only (noted Z) or the zeropadding combined with the Hamming Window (noted HZ). The ranges output by the retrackers are converted into surface height anomaly by applying the oceanic and atmospheric corrections  (Quartly et al.
The pan-Arctic mean radar freeboard time series of the four output products are presented in Fig. 4  for the period 2013-2017. The along-track data are gridded for each month with an EASE-2 grid with 12. 5 km resolution. A median smoothing filter with a radius of 25 km is applied in order to statistically filter out outliers. Since all these freeboards are processed with the same algorithms, differences are only due to the retracker (SAMOSA+ or TFMRA50) or the side-lobe filtering methodology (Hamming or SLD. ). Fig. 4  shows that the differences do not depend on the period. The aim of the next sections is to explain these differences and to point out the most adapted solution.
A regional influence is still perceptible in marginal ice zone and North East of Greenland where À4 cm biases can be locally reached. Indeed, along-track height anomaly comparisons between HZ and Z computed with SAMOSA+ show that HZ data are more sensitive to off-nadir errors than Z data in these thin ice fractured regions. Over the leads, a global and regionally constant bias of 0. 4 cm for TFMRA50, and of À1. 9 cm for SAMOSA+ is found. The TFMRA small bias indicates that the shape of specular waveforms (over leads) is not impacted by the Hamming Window. This is an expected result as the side-lobe effect impacts only the lower back-scattering surfaces. Thus the À1. 9 cm bias for SAMOSA+ cannot be explained other than by a problem with the implementation of the physical retracker when Hamming is enabled.
3 he same studies performed with Sentinel 3-A data leads to similar conclusions. Mean biases are presented in Table  2. The resulting impacts of the Hamming window on the mean freeboard for March 2015 are of 1. 6 cm for TFMRA50 and 1. 1 cm for SAMOSA+. The order of magnitude of these biases are in agreement with the mean freeboard differences presented in Fig. 4. 7(a)  shows the freeboard differences between SAMOSA_Z and TFMRA50_Z for the period 2013-2017. The mean difference for pan-Arctic sea ice is 1. 3 cm. When separating the FYI and the MYI (using OSI-SAF sea ice type product), a mean difference of respectively +4. 2 cm and À2. 6 cm can be found. This disparity, depending on the ice type, is illustrated in Fig. 7 (b). Fig. 7(c ) represents the trend of the mean freeboard difference between SAMOSA+ and TFMRA50 function of the mean PP for each pixel.