dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmarking platform In order to benchmark tools that could be used in Digital Forensics, we design a software platform called G'DIP for GREYC Digital Investigation platform (see Fig. 3 ). This platform allows us to process data from any sources (device, computer, hard drive, file directory …). A python core applies any filter (like file type identification) to all files present in the data source. The results are stored in a database that can be processed by a graphical user interface. Statistics about the datasets, represented by diagrams, and evaluation results can be visualized by the user. The main objectives of this platform are 1) to benchmark tools for digital investigation, 2) to provide a software tool for teaching digital investigation and 3) to propose an operational and open-source tool for investigators on real data.
Dataset 17500 On this dataset, the fastest tool is ForENSIque with 59. 3% of correct identification while Fidentify, with 94. 4% is the best in terms of identification, followed by Autopsy, with 90. 6% and unix file command with 84. 7%. With lower identification rate, EnCase and detect-file-type are fast however. Which TrID is not, with more than 20 min of computation time. Lastly, we can see that guess-file-type is the less efficient tool with only 34. 1% of identification and more than 5 min in computation time.
Dataset 1M As it has been said, this dataset is meant to be a more realistic case with fewer challenging file types. We notice an improvement in terms of identification, observable for all tools except for Autopsy. Apart from that, there is no major change in their performances. Unix command File is the fastest while achieving 85. 8% of correct identification, with Fidentify that is still the best tool in this field, followed by Autopsy, TrID and EnCase.
Discussion Thanks to those results, we can draw some conclusions. First, filetype (python), file-type (node. js). guess-file-type, For-ENSIque, and TrID are not sufficiently efficient. The first four because of the lack of performance in retrieving the format of files and the last two because of too high an execution time. detect-file-type and EnCase are very similar in terms of performance and while quite fast, they are not accurate enough to be kept. Finally, there are only three modules left: Autopsy, File and Fidentify. Each is worth being considered, however, Autopsy and File are not as accurate as Fidentify.
Do existing tools use file extensions for their file type identification? In this experiment, we study how existing tools use the file extension to identify its type. To test that, we removed all file extensions in all datasets. As we can see in Table  4  describing results for the two datasets, all tested tools obtain the exact accuracy when extensions have been removed than in the case where extensions were present except for EnCase, Autopsy and guess-file-type. We observe this last tool having an important decrease in performance. The obtained results are not surprising as most tools use magic numbers to detect file types. This experiment shows that guess-file-type is not a good choice for digital investigation purposes (where file extensions could have been voluntarily altered). It is also surprising to have a decrease of performance for the EnCase commercial solution when file extensions have been removed.
Is there any relationship between the file size and computation time of existing tools? This experiment consists in running existing tools on each file in order to obtain the computation time and the associated file size. Since the two software EnCase and Autopsy are not suitable for this type of process, we did not include them in the study (they process files in a directory in a batch mode). We plot the file size versus the computation time in order to identify a potential correlation. We experimented on the two datasets to get a better representation, then the results were ordered by size for each dataset, which leads to two diagrams (see Fig. 5 ). The graphs tend to prove that the computation time does not depend on the file size. It  tends to be constant after 300 kb. We make the hypothesis that the computation time is linked to the size of the signature database, the search algorithm of a matching signature, and the programming language.
4. 6. Which formats are not recognized by existing tools? In this part, we study the file formats that are not correctly recognized. As for illustration, we consider two tools namely Fidentify and EnCase. We list for all datasets, extensions that are not recognized by these two tools. Fidentify: 7z, 8svx, aac, acbm, amv, anim, avro, cin, crx, deep, dmg, dpx, dwf, faxx, fbx, flif, fm, glb, mmf, parquet, tga, wbmp. C EnCase: avro, cin, crx, dwf, fbx, flif, glb, mmf, parquet. When looking at these lists, most extensions are not well known or may correspond to very different contents. Thanks to a web service called files. tips 1  , we can have some information on some of these extensions.
4. 7. Can we combine existing tools to enhance their performance? Instead of looking for a perfect tool, it could be more efficient to combine multiple ones. In this work, we did not consider all the combinations of the 10 tested tools. As an illustration, we propose some combinations, starting with both Fidentify and ForEN-SIque tools. Fidentify provided the best result and ForENSIque has been designed by some of our students. The prototype is developed in Python. It takes as input a directory path and analyze all files in the directory. The strategy is to first apply Fidentify on every file since it is the most efficient module, and to use ForEN-SIque as a fallback when Fidentify fails to identify the type. Algorithm 1 details the combined method we implemented called GreycFiletype.
Algorithm 1. Combining Fidentify and ForENSIque: GreycFiletype Following the same principle, we have combined Fidentify with Autopsy and TrID. In order to determine the performance of these new tools, we tested them with the same evaluation protocol. The results are shown in Tables  6  and 7. The combination of Fidentify and ForENSIque demonstrates a significant improvement in terms of accuracy. The computation time is increased compared to Fidentify which can be explained by the naive implementation of this proof of concept. Conversely, the increase due to the other two combinations is less, if any. These results show that Fidentify can be improved but that it is difficult to do so, particularly on a dataset with few uncommon file types.
Summary In Fig. 7 , we show the cumulated accuracy on all datasets for each tool. We can draw the following conclusions. C filetype, file-type, detect-file-type, File and For-ENSIque tools provide globally insufficient performance and should not be used alone. C guess-file-type, TrID and Encase tools provide good results in general but in case of changed extension (possible attack), the accuracy decreases (a lot for guess-file-type),  C File is clearly the quickest solution with a good performance, C Fidentify and GreycFiletype provide very good results and are robust to a modification of file extensions.
Conclusion and perspectives In Digital Forensics, investigators have to mine data storage medium in order to recover information or to assess evidence. Open source investigation tools are available and can be very useful to handle cases. However, they mostly lack rigorous quality evaluation. Starred projects and popular tools should be rigorously evaluated to determine whether they can be used in a forensic context, or not. In this study, we make a literature review of existing tools for file type identification. We consider both academic and non-academic forensic tools, including commercial tools used in digital investigation cases. We proposed an objective protocol involving significant complementary datasets and performance metrics for the comparative study of the file identification tools.
However, our experiments tend to show that it still remains a very slow tool. The comparative evaluation tends to prove that the three file type identification tools designed for investigation purposes are more accurate than open source repos available on GitHub, even well-rated ones. Finally, we showed that the combined tool, called GreycFiletype, can improve the accuracy of file type identification (near 98%). As perspectives, we plan to open the used datasets and codes for the research community to stimulate studies in this area. We also plan to consider the feasibility of merging the improvements in Fidentify and Photorec. In order to enhance accuracy results of file format identification, we intend to apply some machine learning techniques by combining different file features such as MIME type, magic numbers, file size, or entropy.
Paper Year Principle #types Dataset Accuracy Sester et al. (Sester et al. , 2021) 2021 SVM linear kernel 6 6000 91. 4% Al Neaimi et al. (Al Neaimi et al. , 2020) 2020 deep learning 8 4000 99% Karampidis et al. (Karampidis et al. , 2018) 2018 byte frequency distribution þ neural network 4 2200 97. 6% Beebe et al. (Beebe et al. , 2016) 2016 K-Means, Hierarchical classification 50 2600 74. 1% Evensen (Evensen, 2015) 2015 n-gram analysis with naive Bayes classifier 6 60000 99. 5% Amirani et al. (Amirani et al. , 2013) 2013 PCA þ Neural Networks feature extraction, SVM 6 1200 99. 1% Cao et al. (Cao et al. , 2010) 2010 Gram Frequency Distribution 4 1000 90. 3% Dhanalakshmi & Chellappan (Dhanalakshmi and Chellappan, 2009) 2009 Feature Selection þ KNN Classifier 10 5000 90. 5% Software Year Principle #types Language Licence File (Unix) 1973 magic number þ language 338 C open-source Fidentify (Grenier, 2019) 2012 magic numbers þ binary 440 C open-source ForENSIque 2021 magic numbers 155 Rust open-source Guess-file-type (Kosinix, 2018) 2018 magic numbers þ extension 985 Javascript open-source detect-file-type (Paloskin, 2016) 2016 magic numbers 94 Javascript open-source filetype (h2non, 2016) 2016 magic numbers 64 Python open-source EnCase (Shawn and McCreight, 1998) 2015 magic numbers 406 EnScript/Cþþ commercial file-type (Sorhus, 2014) 2014 magic numbers 135 Javascript open-source Autopsy (Corp, 2012) 2012 magic numbers þ extension 1524 Java open-source TrID (Pontello, 2003) 2003 magic numbers 14374 Python commercial.
Next, we fully analyzed time-lapse movies from four different Tg(gfap:hZO1-mKate2);Tg(deltaA:egfp) fish and could follow the fate of 828 NSCs. Most of these NSCs (634 tracks, 76. 6% of all) are resting in a long quiescent phase, i. e. , not dividing nor delaminating during up to 43 days of imaging. 194 cells divided at least once, enabling to explore their cell lineage. Using the criteria above, 125 of these were activation and division events from quiescence (divisions following a qNSC to aNSC transition). Finally, 97 delamination events were observed. Delaminating cells are the smallest cells tracked, with a median AA of 8µm 2 and 98% express deltaA, strongly suggesting that they are aNPs (Figures S3B). They usually express deltaA at highest levels, confirming our fixed data analyses on the link between AA and deltaA expression (Figure  S3C ).
All tracks showing a division, a delamination and/or a change in deltaA expression over time are shown in Figure  S3D. Hereafter, considering only divisions following a quiescence event, we will refer to the first tp after division as DC (Daughter NSC), and respectively name the preceding and following tp MC (Mother NSC) and DC+1 (one tp after the appearance of two DCs) (Figure  2B ). Successive imaging tp are then referred to as DC+2, DC+3, etc. (Figure  2C ). These dataset and measurements put us in a position to explore the temporal and quantitative relationship of NSC quiescence exit, division and fate with deltaA expression and AA dynamics.
The behavior of deltaA pos NSCs is biased towards proliferation and differentiation. We next assessed the overall dynamics of deltaA expression across time and NSC decisions. As in our static dataset (Figure  1G ), we confirmed a negative correlation between deltaA expression and AA, and we retrieved qualitatively and quantitatively similar qNSCs and aNSCs subtypes (Figure  3A ). In nondividing tracks (n=703), deltaA expression appears continuous with only 2% of NSCs changing their expression profile during a time-lapse (9 NSCs switching deltaA OFF, 5 NSCs switching it ON). In dividing tracks (n=125), deltaA expression before division is also largely stable, either always ON (77 tracks) or always OFF (40 tracks), with only 9. 4% of dividing NSCs changing deltaA level before dividing (8 tracks switch deltaA ON before division out of 85 dividing deltaA neg MCs, and a single deltaA pos track switches deltaA OFF) (Figure  S3D ).
Importantly, in between these extremes, we identify a key lineage transition linked to both parameters and impacting fate: the division of deltaA neg NSCs. This event systematically generates differently fated daughter NSCs, of which the deltaA neg daughter has the potential to regrow its AA and behave identically to its mother, while the deltaA pos daughter does not regrow, and engages into more frequent subsequent divisions and ultimately differentiation. Altogether, these results, supported by quantitative measures of division rates and AA growth, allow inferring relative temporal and hierarchical NSC states from in vivo images and interpreting static datasets staining NSCs in their niche.
Processing and analysis of time-lapses images We mounted the movies composed of z-stacks with two channels (ZO1 and deltaA) taken on different days using a custom Fiji macro. We performed translational and rotational spatial registration manually using the correct drift function on Imaris software. Then we used the CSBDeep toolbox to denoise the apical membrane channel (ZO1). CSBDeep is a content-aware image restoration (CARE) that requires to be trained with a set of high resolution and low-resolution images. CARE is an open-source Python algorithm  65  and it has been adapted and trained with images from the notum of the Drosophila embryo. The restored channel, together with the second channel (deltaA) are MAX projected in 2D. To improve the tracking we first applied slight deformations over the image frames using the Image J's plugin bUnwarpJ  66.
The information of all image channels was used to compute the transformation matrix between two consecutive frames. We chose the middle frame as the reference and aligned all the other frames from the referenced frame. The transformation matrices were saved in text files and served as registering other movies and performing the revert transform. The segmentation and tracking of the cell contours and deltaA from the registered image frames were done in Tissue Analyzer  67. We then aligned the segmented apical surfaces back to the pre-registration state in order to compute their original apical areas. The revert transform of the image frame was deduced from its transformation file. We provide the scripts that wrap up all the steps. Dividing and non-dividing tracks are processed separately, and further analyses are performed on R and PRISM (tabular dataset are also provided).
QUANTIFICATION AND STATISTICAL ANALYSIS The same experimenter carried out all the segmentation and corrections for a given experiment. Balanced ratios of females and males were included in the different experimental groups as much as possible. For the IHC and intravital dataset, means and medians are per hemisphere per animal. The size of the analyzed regions differs from fish to fish. Thus we collected different numbers of tracks per fish (data available on Zenodo). Statistical analyses were carried out using PRISM, R and Python. The normality of the residuals of the responses was assessed using normality probability plots and the homogeneity of the variance was inspected on a predicted versus residual plot. Non-parametric tests were used when the responses show a deviation of the residuals from a normal distribution and/or heterogeneous variances.
Data sources and modelling process Each NSC track was labelled individually according to whether the NSC underwent division during the study period. For NSCs that divided, their AA and deltaA expression prior to the division timepoint were recorded, while for NSCs that did not divide, the average AA and most prevalent deltaA expression (off or on) over the study period were used. For NSCs that divided, the deltaA expression of the two daughter cells was also tracked, with three possible values: deltaA neg /deltaA pos (for an asymmetric division), deltaA pos /deltaA pos , or deltaA neg /deltaA neg. Estimation of the proliferative capacity and fate of the NSCs was carried out using logistic regression models, with AA and deltaA expression as covariates. To account for heterogeneity between fish and between treatments (WT vs LY), the models were adjusted by adding fixed effects to the intercept and testing for interactions with other covariates.
Labex Revive), La Ligue Nationale Contre le Cancer, CNRS, Institut Pasteur, DIM ELICIT (with E. B. ) and the European Research Council (ERC) (AdG 322936). L. M. was recipient of a PhD student fellowship from Labex Revive and the Fondation pour la Recherche Médicale (FRM). M. L. was recipient of a 4 th year PhD fellowship from the FRM. Multiphoton equipment in E. B. 's laboratory was partly supported by the ANR (ANR-11-EQPX-0029 Morphoscope2, ANR-10-INSB-04 France BioImaging). Work in Y. B. 's lab was supported by CNRS and the Institut Curie.
( Labex Revive), La Ligue Nationale Contre le Cancer, CNRS, Institut Pasteur, DIM ELICIT (with E. B. ) and the European Research Council (ERC) (AdG 322936). L. M. was recipient of a PhD student fellowship from Labex Revive and the Fondation pour la Recherche Médicale (FRM). M. L. was recipient of a 4 th year PhD fellowship from the FRM. Multiphoton equipment in E. B. 's laboratory was partly supported by the ANR (ANR-11-EQPX-0029 Morphoscope2, ANR-10-INSB-04 France BioImaging). Work in Y. B. 's lab was supported by CNRS and the Institut Curie.
They were then assessed for their ability to simulate global modern observations of salinity and temperature (compared to the NOAA World Ocean Atlas salinity and temperature database;  Locarnini et al. , 2019; Zweng et al. , 2019)  as an objective and quantitative basis for selecting the best-performing parameter configuration to be our control (Fig. 1 ). From this analysis, XPDAA returned the lowest root mean square error (and hence best performance using this metric; Fig. 1 ) for simulating both salinity and temperature and so was used as our control simulation (Fig. 2 ). The simulated steady state AMOC strength in FAMOUS under fixed pre-industrial boundary conditions is approximately 17 Sv (Fig. 3a ), which we consider to be in excellent agreement with direct modern AMOC observations from the RAPID AMOC array at 26.
Combined, our observational database represents a total of 6,048 [Nd]d and 3,278 εNd measurements, making it the largest compilation of seawater Nd data used to date to validate the performance of an Nd isotope enabled model. Notably, we omit measurements of [Nd]d > 100 pmol kg -1 from the model data comparison because they represent very localised signals which we do not attempt to resolve. These include extreme surface concentrations present in restricted basins such as fjords, the Baltic Sea and the Gulf of Alaska  (Chen et al. , 2013; Haley et al. , 2014) , or input from hydrothermal activity  (Chavagnac et al. , 2018) , which is not believed to govern global marine Nd distributions due to the immediate removal of hydrothermal Nd at the vent site  (Goldstein and O'Nions, 1981). The location and spatial distribution of all observational records used in this study are shown in Fig.
Acknowledgements SMR was funded by the  Natural Environment Research Council (NERC)   SPHERES Doctoral Training Partnership  (grant number:  NE/ L002574/1 ). LJG was funded by  UKRI Future Leaders Fellowship 'SMBGen ' (grant number:  MR/S016961/1 ). JCT was supported through the  Centre for Environmental Modelling and Computation (CEMAC) ,  University of Leeds. FP was supported by the  European Union 's  Horizon 2020 program  (grant number:  101023443 ). TvdF acknowledges funding from the  NERC  grant  NE/ P019080/1. Our heartfelt thanks go to  Dr Jamie D Wilson  for valuable discussions when implementing the reversible scavenging scheme, and to  Professor Andy Ridgwell  for helpful discussions during the design of code developments.
The project has developed three main strategic objectives: • define the best sampling strategy for waste production optimization through new/improved statistics approaches for sampling plan establishment; feasibility of realistic cases, including safety and waste management impact; assessment of cost, duration, and impact on waste production. • Assess the performance of available measurement techniques (methods & tools) to establish a scientific basis for decision-making through validation of quick/costeffective analytical methods (in lab and in-situ); performance assessment; reference materials production. • Establish common methodologies to deploy reference guidelines for selected use cases through the first mapping guide for potential support to standardisation commissions, a database of analytical methods and reference materials, training and software module offer.
It is also an aim of PLEIADES to provide evidence for its added value and cost-saving through realistic pilot applications in different facility types. In order to achieve these goals, PLEIADES will develop a platform and demonstrate a new methodology for costing, planning, radiation protection and waste management. The platform will include a set of innovative modules, developed by project partners, including 3D simulation-based solutions. Using this platform, PLEIADES will implement use cases based on data from three different nuclear sites: the Santa María de Garoña nuclear power plant in Burgos (Spain), the Halden research reactor in Norway and the BCOT nuclear maintenance facility in Bollène (France). To structure the data, PLEIADES proposes a decommissioning-oriented ontology that extends the standard Open BIM ontology applied in the building industry.
PLEIADES aims at demonstrating a BIM-like modular software ecosystem based on the connection of frontline support tools through an interface built on this decommissioning-specific ontology. As mentioned in the introduction, the nuclear dismantling community needs mature technologies for efficiently performing a large number of foreseen dismantling operations. PLEIADES will integrate mature solutions from partners that were, individually, already tested in the nuclear or other sectors. The exploitation strategy of PLEIADES is based on capabilities and services offered through the integration and interoperability of the partner's solutions. The expected results of the project are foreseen to: • improve safety, specifically by providing improvements in radiological protection, communication between stakeholders and training of workers.
The DEMplus R for Nuclear software is a digital tool which allows simulation scenarios in a 3D virtual environment, enriched by radiological, physical, waste streams and labour costs information. It is also used in PLEIADES, as one of the applications of the Platform for enhanced Decommissioning processes. In INNO4GRAPH, DEMplus R for Nuclear has been upgraded with a new module dedicated to Graphite reactors which will allow to capitalisation graphite core data, to use if needed, external calculation models and to perform sensitivity study on the decommissioning scenario. 3D models of each reactor (Latina, Chinon A2, G1, Ignalina, Vandellos 1 -see Fig. 2 ) have thus been integrated into the DEMplus R software. This will allow graphite retrieval simulation in the second period of the project.