dataesr/scientific-paragraphs-categorization huggingface dataset:
Since every species (even the products of the reaction) are considered, the initial concentration must be defined for every species in all the areas and in the pit (a potential source of contamination). As only copper is considered in this study, the species that are considered for the reactions are Cu 2+ , Cu OH -(named Cu(  6 )) and CuSO 4 (named Cu(  7 )). There is also Cu+, which is called Cu(1) in the reaction database, which is formed due to the presence of electrons. Initial value for these species in the study area is defined in Figure  6. A concentration of 1. 0 × 10 -30 Mole/L is set in order to avoid numerical error because of a zero concentration for all the compounds. pH and pe also have to be defined. An arbitrary value of 6 is set for the pH because the media is rather acidic than neutral with heterogeneous values.
Conclusions Mount Morgan, a former gold, copper and silver mine in Queensland, Australia, has an impact on groundwater surface water quality. Indeed, because of the mixture of pyrite, water and oxygen in the site, Acid Rock Drainage is formed and, due to the hydrogeology of the site, is transported by groundwater through the aquifer to the Dee River, situated downstream of the mine site. This phenomenon is characterized by acid water rich in metals which polluted the ecosystem of the water systems present in the mine site. Following this problem of contamination, the aim of this study was to establish a transport model of the contaminants: the copper (II); and to validate this model in order to be able to use it for the identification of unknown pollution sources present in the site. For that, a three-dimensional reactive transport simulation model PHT3D, along with the groundwater flow simulation model MODFLOW (also available within the processing software GMS) has been used.
But how about the brain itself? It does not know the stimulus, it cannot look at its own anatomy, and it is unlikely to maintain databases on all of its neurons. Modern brain scientists consider the discovery of somatotopy to have rendered Lotze's problem a nonissue 11  (Kaas, 1990). We hold a rather different opinion on this and believe that the problem might be even more pressing than it was felt to be at the time. There are indications that an apparently physiologically intact visual brain might still lack local sign. We refer to the condition of tarachopia (''scrambled vision'') as reported by  Hess (1982). In a case of unilateral tarachopia, the tarachopic eye might have acuity and contrast sensitivity just as good as that of the normal eye, yet the amblyopia might be serious enough to prevent the owner making out today's newspaper's headlines.
Implementation Methods We use pseudocode to present our descriptions in an algorithmic language (Appendix B). We also make available a demolike implementation written in Processing  (Reas & Fry, 2010 ; Appendix A) that should run without much ado on most common platforms. A Matlab implementation is available at http://www. allpsych. uni-giessen. de/EidolonFactories/index. htm. A Python implementation is available at https://github. com/gestaltrevision/Eidolon.
In order to understand the scale-space data structure one needs a number of important insights. These are all based on the basic scale-space structure. Although we will not prove it here (textbooks quoted previously), scale-space is based on the Gaussian kernel as point. A convenient scale parameter is the half-width (standard deviation) of the Gaussian blurring kernel. In Figure  3  we show the (sampled) scale space for an image. Because the image at some scale can be computed from any image at some finer scale, this representation is inconveniently-and quite unbrainlike-redundant. It is preferable to find the differences between adjacent scale levels. A stack of such difference images will be our basic data structure. It represents the simplest possible nontrivial structure that has the desirable properties listed above: local, isotropic, translation invariant, and self-similar.
A structure like is a function that encapsulates a computation. Notice that the parameter section might be empty, like for a function COIN_FLIP() that returns head or tail. A program is a collection of functions. One of these is supposed to deliver the final result. Of course, there are numerous decisions in implementations that certainly may make a difference but can hardly be counted to be our business. Here is a simple example. Images are represented in numerous ways in computer memories. But how the bit planes are ordered, and so forth, is not our business. A monochrome point (''pixel'') can be represented by a byte, integer, float, double, and so forth. This is not our business either, but it makes a difference. We will need Fourier-based methods. Whether one uses the latest fast Fourier transform (FFT;  Heideman, Johnson, & Burrus, 1985)  implementation, the old-fashioned Filon integration  (Abramowitz & Stegun, 1972) , or something rolled oneself is not our business.
Acknowledgments KG and MV were supported by the  Deutsche Forschungsgemeinschaft   DFG SFB/TRR 135. MV was supported by the  EU   Marie Curie Initial Training Network ''PRISM''  ( FP7-PEOPLE-2012-ITN ; grant agreement  316746 ). JW, JK, and AvD were supported by the program by the  Flemish Government  ( METH/ 14/02 ), awarded to JW. JK was supported by a  Humboldt-Award  by the  Alexander-von-Humboldt-Foundation.
TRIPOLI-4 ® Monte Carlo transport code and FIFRELIN fission model have been coupled by means of external files so that neutron transport can take into account fission distributions (multiplicities and spectra) that are not averaged, as is the case when using evaluated nuclear data libraries. Spectral effects on responses in shielding configurations with fission sampling are then expected. In the present paper, the principle of this coupling is detailed and a comparison between TRIPOLI-4 ® fission distributions at the emission of fission neutrons is presented when using JEFF-3. 1. 1 evaluated data or FIFRELIN data generated either through a n/γ-uncoupled mode or through a n/γ-coupled mode. Finally, an application to a modified version of the ASPIS benchmark is performed and the impact of using FIFRELIN data on neutron transport is analyzed.
In the past, several works aimed at coupling various fission models with Monte Carlo transport codes or at integrating them, for example MCNP-DSP  [3] , MCNPX-PoliMi  [4]  or FREYA-TRIPOLI-4 ®  [5]. Related calculations were mainly dealing with nuclear instrumentation applications for which individual events are collected and postprocessed. However, in the context of shielding calculations in fission facilities, spectral effects of the use of a fission model for specific isotopes could be first analyzed in terms of average values provided by the Monte Carlo transport code. Secondly, an analysis based on non-average distributions could be performed in order to observe correlation effects between fission neutrons for example. These issues are addressed in the present paper by means of the TRIPOLI-4 ®  [6]  Monte Carlo transport code and the FIFRELIN fission model.
TRIPOLI-4 ® is a continuous-energy Monte Carlo transport code dedicated to shielding, reactor physics with or without depletion, criticality safety and nuclear instrumentation. A first coupling between TRIPOLI-4 ® and FREYA fission model has already been implemented  [5]  and consists in calling FREYA code by TRIPOLI-4 ® at each sampling of a fission event. In order to test another fission model, a new coupling with FIFRELIN code has recently been implemented starting from version 10 of TRIPOLI-4 ® code and works through external files between both codes. FIFRELIN is a Monte Carlo code that simulates the de-excitation process of primary fission fragments leading to an estimation of prompt fission observables such as neutron/gamma/electron multiplicities and spectra in correlation with fission fragments.
Primary fission fragments (after full acceleration and before prompt neutron emission) are completely determined by the knowledge of their mass A, charge Z, kinetic energy KE, excitation energy E* and spin/parity J π. The three first characteristics (A, Z, KE) are sampled from experimental data or can be provided by external codes. The description of the initial (E*, J) entry zone is performed through a mass dependent temperature ratio law R T (A) and a spin/parity distribution ruled by a spin cut-off parameter that can be modeled in different ways. Positive and negative parities are supposed to be equally likely. More details can be found in  [7]. De-excitation cascades are simulated following the Becvar's notion of nuclear realization  [8]. A nuclear realization is a random set of nuclear levels (energy, spin, parity) in association with partial widths for neutron, gamma or electron emission.
Experimental data related to electromagnetic transitions in the discrete level region are taken from RIPL-3 database  [9]. When nuclear level structure is completely unknown (in the continuum region), level density and strength function models are used. In between these regions, our partial knowledge of nuclear structure is completed by models up to a fixed maximum level density. In this way the whole available experimental information is accounted for. FIFRELIN is ruled by five free input parameters driving the primary fission fragment excitation energy and spin-parity distributions (the (E*, J) entry zone discussed above). These five free parameters are determined to match a target observable such as the total average prompt neutron multiplicity (nu-bar). Once this procedure is completed, the whole set of fission observables can be compared with experimental results.
Coupling method 2. 1 General principle As already mentioned, the FIFRELIN-TRIPOLI-4 ® coupling was carried out through external files per fissile isotope of interest and therefore works in two consecutive steps: first, FIFRELIN code is run to produce a large amount of fission events for a given fissile isotope. For each fission event, specific data are stored in an output file that can be read by TRIPOLI-4 ® afterwards. This step can be repeated independently for several fissile isotopes of interest. Then, during the Monte Carlo neutron transport simulation, whenever a fission event needs to be sampled, TRIPOLI-4 ® reads the data stored in the appropriate FIFRELIN file, if it exists, at the current reading position for a new fission event (otherwise the evaluation files are used). Fission neutrons are directly produced accordingly in TRIPOLI-4 ® instead of sampling the number of fission neutrons and their energy and angle according to the evaluation distributions.
Validity and contents of a FIFRELIN file The actual FIFRELIN list-mode (event-by-event) data file is simplified to contain an even number of lines (two lines for a fragment pair). Each line contains the nuclear charge, the mass number, the initial fragment kinetic energy, the characteristics of the cascade and the final (post-neutron) fragment kinetic energy. The characteristics of a cascade depend on the calculation scheme. In the n/γ-uncoupled mode it contains the neutron kinetic energy for every emitted neutron, while in the n/γ-coupled mode it contains the energy, spin and parity of every nuclear level and the kinetic energy in the laboratory frame of every neutron, gamma and conversion electron. In a very first step the emission is supposed to be isotropic in the laboratory frame. These events can be used to estimate a distribution like the prompt fission neutron spectrum allowing a comparison with estimators available in the code.
Related distributions as retrieved by TRIPOLI-4 ® As expected, using FIFRELIN instead of evaluated data induces modifications to the fission neutron multiplicity distribution and to the fission neutron spectrum. To make this clear, for both modes of FIFRELIN we compared several distributions related to fission neutron emission, obtained with two TRIPOLI-4 ® simulations (using either FIFRELIN data or JEFF-3. 1. 1 library) and involving thermal neutron induced fission on 235 U. Three quantities of interest were collected by specific TRIPOLI-4 ® tallies which do not take into account neutron transport simulation. Figure  1  compares prompt fission neutron multiplicities, Figures  2  and 3  present prompt fission neutron spectra and Figure  4  shows the distributions of the energies summed up for all neutrons emitted at a same fission event.
Description We have selected the ASPIS benchmark from the SINBAD Database  [11]. This configuration allows us to study the propagation of neutrons with energies higher than 40 keV from a fission plate into an iron bulk. Three different dosimeters are installed at various depths of penetration (from 5 cm to 1. 2 m): • 103 Rh(n,n'), threshold = 40 keV • 115 In(n,n'), threshold = 339 keV • 32 S(n,p), threshold § 1. 6 MeV. Reaction rates are calculated for all these dosimeters. Several modifications were made on this benchmark for the purpose of this study. With the aim of simulating fission events, a 10 eV-source was placed in the fission plate (instead of the usual Watt-spectrum together with fission sampling turned off). When running TRIPOLI-4 ® with FIFRELIN data or with the evaluated data, the same following options were activated.
1. 1 results. It should be noted that the convergence was better for the n/γuncoupled mode, due to a higher total number of fission events present in the related FIFRELIN files. In the case of the n/γ-uncoupled mode of FIFRELIN, significant differences with JEFF-3. 1. 1 results are observed for all responses, particularly at the surface positions closest to the fission plate. This suggests that the spectral effect of FIFRELIN data noticed in the previous section, at the emission of fission neutrons, has still an impact on results after neutron transport to distances up to 34 centimeters. As for the n/γ-coupled mode of FIFRELIN, Tables  2  and 4  show no difference for the first two responses: JEFF-3. 1. 1 and FIFRELIN results are within 3 sigma of uncertainties for 103 Rh and 115 In dosimeters.
Further investigations Further investigations were performed through specific modifications based on JEFF-3. 1. 1 nuclear data file, so as to better determine the origin of the differences noticed in Tables 1, 3, 5 and 6. First, the same trend in results was observed when running TRIPOLI-4 ® with modified evaluated data of the prompt fission spectrum that consist in suppressing the dependence on the incident neutron energy by extending the evaluated data for thermal incident neutron energies to the whole incident energy range of the prompt 235 U fission spectrum. This means that the previously noticed differences are not due to the fact that FIFRELIN data, as used in this coupling work, do not depend on the incident neutron energy but are valid for a given energy range only. Then, in another test, when replacing the prompt fission spectrum by the average prompt neutron fission spectrum, as illustrated in Figure  2 , from respectively both modes of FIFRELIN, all TRIPOLI-4 ® results turned out to be very close to the results obtained with TRIPOLI-4 ® -FIFRELIN coupling (within 3 sigma of uncertainties).
Conclusion FIFRELIN fission model and TRIPOLI-4 ® Monte Carlo transport code have been coupled through external files, so as to simulate a detailed model of fission with an accurate transport of the fission neutrons. Calculations on a modified version of the ASPIS benchmark, either with JEFF-3. 1. 1 data or with the coupling with FIFRELIN code for the fission sampling on 235 U, showed significant differences on reaction rates which turned out to be mainly due to the slight difference of average prompt fission spectrum. However, the n/γ-coupled mode of FIFRELIN actually gave average reaction rates closer to those obtained with the evaluated data than its n/γ-uncoupled mode. A complementary analysis was performed so as to clarify the real impact of the fission model after neutron transport, apart from the average trend: the distribution of reaction rates per fission event turned out to be very different with or without the use of FIFRELIN, highlighting the capability of the present coupling to take into account coincidences between neutrons emitted by a same fission event, which are still visible after neutron transport.
Musculoskeletal model Opensim 3. 3 software  (Delp et al. , 2007)  was used to develop hand models versions for the three taxa: H. sapiens, P. troglodytes and A. afarensis. Opensim is an open-source software platform especially suited for developing models of musculoskeletal structures and creating dynamic simulations of movement. All models were adapted from an original model available on the Opensim repository (https://simtk. org/projects/hand muscle) based on  Lee et al. (2015). The initial model gathers works from numerous studies from the anatomic, biomechanical and physiological fields including those from  An et al. (1979). Several levels of modelling are considered. The reference to the kinematic model specifically refers to the mechanical interpretation of the DoF of a joint (e. g.
For example, the long and robust hamulus of A. afarensis  (Bush, 1982)  was interpreted as an origin of the opponens digiti minimi located slightly more palmarly compared to H. sapiens. Muscle properties (muscle mass, fibre length, pennation angle, etc. ) and resultant force-length-velocity relationships implemented in the Opensim (Hill-type) muscle model originate from several imaging and experimental studies. Given the limited literature, and even if the influence of muscle properties on model output has been deemed modest  (Bolsterlee et al. , 2015; Valero Cuevas et al. , 2008) , three databases of PCSA were investigated (Table  1 ): PCSA-Homo1, the database used in the original model  (Lee et al. , 2015)  that gathers data from different collections.
Data analysis Dependant variables analysed are simulations outputs of : • muscle forces to exert a unit external force ; • maximal external force a model can exert. Numerous simulations based on combination of constitutive parameters of the musculoskeletal model (CMC-V kinematic model, anatomical model, PCSA database) and input data parameters (CMC-V joint posture and external force direction) were run. Results are synthesised in terms of sensitivity of simulation outputs and relevance from an evolutionary perspective.
Sensitivity to PCSA database Before running the simulations and calculating muscle forces, muscles PCSA of the three databases (Table  1 ) were compared. On average, PCSA-Pan are 6% higher than PCSA-Homo2 and 51% than PCSA-Homo1, with notable differences for some muscles such as the FDSL (2. 1, 0. 4 and 1. 54 cm 2 for PCSA-Homo1, PCSA-Homo2 and PCSA-Pan, respectively) and the ID4 (1. 1, 0. 91 and 2. 3 cm 2 for PCSA-Homo1, PCSA-Homo2 and PCSA-Pan, respectively). The root mean square deviation (RMSD) between PCSA-Homo1 and PCSA-Homo2 database is higher (2. 07) than between PCSA-Homo1 and PCSA-Pan (0. 99) and between PCSA-Homo2 and PCSA-Pan (1. 98). As a matter of course,.
DoF Palmar force (N) Palmar-radial force (N) Radial force (N)  these discrepancies lead to notable differences in the estimated muscle forces. Fig. 4  illustrates muscle forces (as a percentage of their maximal isometric force) to exert a 1-N external force for 2 PCSA databases: PCSA-Homo1 (left) and PCSA-Homo2 (right) and 2 external force directions (palmar and radial). A large difference can be observed for the FDSL muscle force (there is also a large difference in the PCSA of FDSL between the two models: 2. 1 cm 2 for PCSA-Homo1 vs. 0. 4 cm 2 for PCSA-Homo2). In general terms, and beyond specific differences, results gathered from the numerous simulations exhibited a similar muscle forces distribution pattern whatsoever the PCSA database used. The influence was mainly on the amplitude of the maximal external force the model is able to exert: PCSA-Homo1 consistently leads to a smaller maximal force.
Influence of the anatomical model of A. afarensis The %RMSD between muscle forces estimated from simulations based on the anatomical model of H. sapiens and simulations based on an anatomical model of P. troglodytes is 13% (average across force direction; CMC-V posture and PCSA database conditions). Overall, simulations run by using an anatomical model based on P. troglodytes demonstrated a higher capacity of external force, but muscle force patterns were rather similar (the greatest differences were observed for ECU and ADM).
Sensitivity of the simulation to PCSA Facing the absence of soft tissue data for extinct species, databases from an extant analogue has to be used  (Hutchinson, 2004; Nicolas et al. , 2009). In order to ensure that the influence of this parameter on simulation outcomes was limited, we tested three PCSA databases related to H. sapiens (PCSA-Homo1 and PCSA-Homo2) and P. troglodytes (PCSA-Pan). Surprisingly, intrataxic variations (between the two H. sapiens PCSA databases) are larger than intertaxic variations (between the two H. sapiens PCSAs and PCSA-Pan). Using PCSA-Pan leads to the highest potential of external grip force but, overall, muscle force patterns remain similar for the three databases. The difference between the two H. sapiens PCSAs highlights the difficulty to accurately estimate this parameter and the need for a unified database, rather than data gathered from different works and specimens.
Muscle attachment sites influence muscle moment arm and thus its moment potential (capacity to exert a "rotation force" at a given DoF). Attachment sites are expressed in a bone reference system (see Fig. 2 ), thus, the consistency between muscle attachment sites and the kinematic model of the joints (rotation axes location/orientation) is as critical as the precision of the joint model itself. The use of an accurate and consistent database, even if obtained on a single individual (see  Mirakhorlo et al. , 2016) , would likely contribute to more accurate simulations for H. sapiens. For extinct taxa, such as A. afarensis, a more accurate morphofunctional analysis of the bones  (Chaudhari et al. , 2014; Daver et al. , 2012; Orr et al. , 2010)  associated with a validation procedure on extant taxa, should also help refining putative muscle attachment sites.
Acknowledgements We dedicate this contribution to the memory of Laurent Puymerail, our friend. We are also indebted to  F. K. Jouffroy , who inspired this work and unfortunately recently passed away. We also thank the guest editors,  Roberto Macchiarelli  and  Clement Zanolli , for their invitation for submitting a manuscript to this thematic issue as well as  Sonia Harmand ,  Sandrine Prat  and the two reviewers for their valuable comments on a previous version of the manuscript. For access to original osteological material used in the present study, we are grateful to the  Authority for Research and Conservation of Cultural Heritage (ARCCH) ,  Ethiopian Ministry of Culture and Tourism  and the curatorial staff of the  National Museum of Addis Ababa. Funding was provided by the  Actions Incitatives of the Université de Poitiers  and the  Conseil Général de la Vienne  ( ACI 2013 ( ACI -2014 )), the  French National Research Agency  (Project  ANR ARCHOR 12-CULT-0006 , dir.
nearby illumination changes. Moreover, our approach is viable using only this single sensor, in contrast to other methodology where data coming from various kinds of sources is fused. Furthermore, data acquired in a non-intrusive manner allows for full expressiveness in contrast to data coming from sensors attached to the user's body. The process as a whole is feasible and realizable in real-time insofar as that once the model is generated after training, it can be simply transferred onto a mobile device and utilized with no negative impact on the device's performance. The remaining sections are organized as follows: Work presented in this contribution is contrasted to state of the art methodology within the domain of dynamic freehand gesture recognition (Sect. 1. 1). The Machine Learning models are trained on a database described in Sect.