dataesr/scientific-paragraphs-categorization huggingface dataset:
The design phase and safety assessment of Generation IV liquid metal-cooled fast reactors calls for the improvement of fuel pin performance codes, in particular the enhancement of their predictive capabilities towards uranium-plutonium mixed oxide fuels and stainless-steel cladding under irradiation in fast reactor environments. To this end, the current capabilities of fuel performance codes must be critically assessed against experimental data from available irradiation experiments. This work is devoted to the assessment of three European fuel performance codes, namely GERMINAL, MACROS and TRANS-URANUS, against the irradiation of two fuel pins selected from the SUPERFACT-1 experimental campaign. The pins are characterized by a low enrichment (~2 wt. %) of minor actinides (neptunium and americium) in the fuel, and by plutonium content and cladding material in line with design choices envisaged for liquid metal-cooled Generation IV reactor fuels.
The code is equipped with both physical models and empirical engineering-level correlations, aiming at bridging the fuel grain scale (and related phenomena, e. g. , the fission gas behaviour) with the integral scale of the fuel pin. The TRANS-URANUS modelling choices for properties and phenomena of fuel, cladding and coolant, employed for the simulation of the SUPERFACT-1 irradiation experiment, correspond to the code recommended models to describe the behaviour under irradiation of fast reactor MOX fuels, 15-15Ti cladding steel and liquid sodium coolant [12,13] (e. g, correlation for MOX fuel thermal conductivity according to Philipponneau  [20] , employed also by GERMINAL). As for the code modelling of the main processes occurring in the fuel and cladding under irradiation, TRANSURANUS is currently equipped with suitable models for: Intra-and inter-granular fission gas behaviour  [21, 22] , accounting for grain boundary saturation  [23] , grain growth and grain boundary sweeping effects  [24].
The material parameters are derived from the GERMINAL validation with the SFR (Sodium Fast Reactor) experimental data base and updated based on the recommendations from the ESNII þ Project  [39]. The MACROS code was originally developed for the modelling and analysis of in-reactor behaviour of non-standard mixed oxide fuels (e. g. , inert matrices and targets with elevated concentrations of minor actinides [40e43]). The MACROS code consists of several modules: Neutronic module to model fuel depletion and/or build-up of actinides and the main fission products (e. g. , Cs, Mo, I)  [44]. Module to simulate material properties of homogeneous mixed oxides (based on thoria, urania-plutonia or zirconia) and heterogeneous targets (based on MgO, Mo, and SiC)  [40, 45, 46]. Module to simulate the in-pile properties of materials that are currently used or considered as claddings for LWR, LMFBR (Liquid-Metal Fast Breeder Reactor) and ADS (Accelerator-Driven System) pins  [45, 47].
Comparison against experimental data The overall comparison with the integral data shown in Table  4  for the Am-bearing pin is deemed satisfactory, although few specific issues arise. TRANSURANUS overpredicts the axial extent of the fuel central hole in both considered fuel pins, which can be ascribed to an overall overprediction of the temperature regime along the fuel column. Moreover, predictions of fuel column elongation do not compare well with the experimental data, being strongly overpredicted by TRANSURANUS and MACROS while underpredicted by GERMINAL. A correct estimation and assessment of fuel elongation remains difficult, considering the complexity of the mechanisms involved in the elongation process, which is also influenced by fuel axial relocation and by the constrained boundary conditions induced by the pellet-cladding gap closure.
2 , for the SF13 (Np-doped) and SF16 (Am-doped) fuel pins. The overall agreement of the simulation results with the experimental measurements is satisfactory. In particular, the agreement of the GERMINAL predictions with data from the Np-pin is excellent, thanks to its refined cladding swelling model based on the large experimental database available at the CEA  [9]. The cladding diametrical deformations at the end of life calculated by TRANS-URANUS and MACROS are slightly different for the Am-and Nppins, whereas no differences are observed in the GERMINAL results. These different predictions between codes, which remain within the experimental uncertainties  [14] , can be mainly explained by the initial pin outer diameters employed by the GERMINAL computations, which are similar and higher compared to the values reported in Table 1 (6.
2 , together with the contribution of fuel-cladding mechanical interaction in case of the gap closure predicted by GERMINAL and MACROS (Fig. 11 ). MACROS results show a general underestimation of the experimental profilometries. The calculated radial profiles of plutonium concentration at the peak power node are compared with corresponding experimental data in Fig. 3 , showing a remarkable agreement of TRANSURANUS predictions with the measurements. Also GERMINAL provides a satisfactory agreement at the pellet periphery. These codes predict a substantial redistribution of plutonium across the pellet, which is consistent with PIE outcomes. This effect is, however, overestimated by GERMINAL, i. e. , a stronger migration of plutonium from the intermediate pellet radii to the inner pellet region is observed.
g. ,  [60] ). The predictions of neptunium (SF13 pin) and americium (SF16 pin) radial concentrations at the peak power node are compared to experimental data in Fig. 4. Under fast reactor conditions, americium is subject to redistribution due either to solid-state diffusion or evaporation-condensation mechanisms because of the steep radial temperature gradient and high temperature levels. On the contrary, neptunium is considered to be less or even not affected by redistribution mechanisms  [26, 61, 62]. The TRANSURANUS model for actinide redistribution therefore considers only plutonium and americium redistribution  [26] , whereas the evolution of the neptunium radial concentration during irradiation is only determined by fuel burnup. The GERMINAL code also includes a model for neptunium redistribution  [9] , while the flat MACROS results are consistent with the fact that its burnup module is not yet coupled to an actinide redistribution model  [11].
The code predictions agree satisfactorily with the experimental results for both neptunium and americium, considering the experimental uncertainties  [16, 17]. The accuracy of GERMINAL calculations is remarkable, although   showing a concentration step close to the central void, due to a numerical artefact which allows moving from the finite volume mesh (used to solve the porosity migration equation) to the finite element mesh of the multi-physics formulation. TRANSURANUS overestimates the radial redistribution of americium and the average concentrations of Am and Np at the end of life, which could point to a required revision of some cross-sections of the code database. Comparisons of measured against predicted xenon and caesium radial concentrations in the two pins considered are presented in Fig.
5  and Fig. 6 , respectively. As for xenon, the concentration is well caught by MACROS and TRANSURANUS in the inner and outer regions of the pellet, while some discrepancies arise at the intermediate radii. This result indicates that the onset of fission gas release for the SUPERFACT-1 fuels simulated here is not evaluated properly by the two codes. Regarding GERMINAL calculations, the increase of Xe concentration at the pellet periphery is linked to a threshold temperature below which gas release does not occur (i. e. , the intragranular diffusion coefficient features a thermal activation term)  [9]. Only MACROS yields the same Xe concentration radial profiles between the Am-pin and the Np-pin, both underestimating the experimental data at the inner and intermediate radii. As for caesium, the code predictions are based on a simplified description of its production, evolution and release by fuel grains.
Indeed, the HBS is experimentally observed to start at local fuel burnups between 60 and 75 GWd/t, but its dynamics are still under investigations  [65, 66]. The assessment of the code predictions against PIE experimental data on the axial profiles of fuel inner diameter and columnar grain diameter at the end of life is presented in Fig. 7  and Fig. 8 , respectively. Focusing first on the inner void diameter predictions (Fig. 7 ), TRANSURANUS predictions of the inner void diameter are remarkable, but reveal clearly different profiles compared to GERMINAL and MACROS axial results. The central void formation in TRANSURANUS and GERMINAL is computed from a multi-physics formulation coupling thermo-mechanical aspects and fuel Fig. 4. Assessment of the calculated neptunium radial concentration (for the Npbearing pin SF13 -triangles, dashed lines) and of the calculated americium radial concentration (for the Am-bearing pin SF16 -circles, full lines), at the end of life, at the axial peak power node, against experimental measurements.
9 ). The inner void size evolution predicted by GERMINAL shows a gradual increase along irradiation and is discontinuous due to the discrete finite element mesh used for the computation  [9]. However, it follows the power cycles correctly. A similar stepwise central void behaviour is predicted by MACROS, although much less affected by the various power cycles, as in TRANSURANUS calculations after the first rise to power. In general, a slightly higher inner void size in time is predicted for the Am-pin, with the differences among the code results ascribable to the different densification and restructuring models available in the codes  [9, 11, 28, 31, 70, 71]. Fig. 11  showcases the predicted evolution of the fuel-cladding gap size at the peak power node. Again, significant differences arise in the code results, mainly linked to the discrepancies in the predicted temperature regimes (Fig.
The substantial deviations observed between code predictions of the fuel-cladding gap evolution under irradiation confirm the need for a more accurate fuel mechanical description, which is under investigation in INSPYRE through dedicated experiments and model developments  [75, 76]. Concerning fission gas behaviour, the comparison of the radial distribution of xenon in the fuel (Fig. 5 ) and of the integral quantities of fission gases and helium released (Table  4 ) shows that improvement is also needed on this side, both in the modelling and in the experiments. A mechanistic model of fission gas and helium behaviour in nuclear fuels, including a dedicated treatment of gas release in fast reactor conditions, has recently been developed  [34, 77, 78]  and implemented in the SCIANTIX grain-scale code  [79] , which is currently coupled with the TRANSURANUS and GERMINAL fuel performance codes.
In close relationship to this, the modelling of the oxygen potential and oxygen-to-metal ratio radial evolution in fuel performance codes, which strongly impacts the volatile fission product behaviour, is also necessary. The current assessed versions of the GERMINAL, MACROS and TRANSURANUS codes only consider the effect of the oxygen radial concentration in the fuel on the thermal properties (e. g. , thermal conductivity, melting temperature), although TRANSURANUS also considers the coupling between the oxygen and actinide redistribution  [26, 27]. Developments ongoing for the GERMINAL code concern the coupling of the multi-physics computational scheme with the OpenCalphad code for thermodynamic computations  [38] , while the TRANSURANUS code is being coupled with the MFPR-F code for similar purposes  [82].
During this period, the researchers performed software adjustments to switch the assistance level and review the stored data's quality. Moreover, the users only interacted with the Smart Walker during the execution of the path. A researcher moved the Smart Walker from the end of the last route to the beginning of the next one. After each trial, a researcher stored software logs in the VICON Workstation and the AGoRA Smart Walker's external CPU. The software Nexus 2 (VICON, Oxford, UK) [39] allowed the storage of VICON data and ROS bags stored walker's data  [40]. Each subject was only required to participate in one session.
Outcome Measures and Data Analysis The Nexus 2 software allowed biomechanical data curation and pre-processing. Then an inverse kinematics approach, using the full-body model reported by  [38] , was executed with a multi-body kinematics optimization algorithm OpenSim software  [41]. The Matlab R2018a Software and the Biomechanical ToolKit Library (BTK) allowed the computation of interaction and kinematic indicators  [42, 43]. Finally, a resulting outcome was obtained by averaging the kinematic and interaction indicators from the six repetitions (i. e. , three repetitions in each direction of the path) for each subject and each assistance mode. This study proposes several quantitative indicators to measure the users' performance during trials and the effects of the assistance levels on the physical interaction between users and the AGoRA Smart Walker.
Physical Interaction Results The information provided by the tri-axial force sensors on the platform's deck was used to estimate the physical interaction between the smart walker and the users. In this sense, Table  3  summarizes the values of resulting force and torque values for each assistance level, where AM stands for Assistance Mode, PM stands for Passive Mode, and RM stands for Resistance Mode. Moreover, peak values for these signals are also reported. In this case, no data were reported for the unassisted trials (i. e. , without the SW). Considering that significant differences were found for some parameters, pairwise comparisons were performed. Table  4  summarizes the results of the posthoc tests. p-values in bold were found to be statistically different. In addition to the above, to illustrate the behavior of the resulting force and torque signals' behavior under each assistance level, Figure  5  shows the recorded signals for one representative subject.
Kinematic and Additional Results Several indicators of the users' performance were estimated by employing the data collected by the motion capture system. On the one hand, to characterize users' gait, several parameters such as gait speed, cadence, average gait cycle duration, and the number of gait cycles were calculated. On the one hand, to assess the effects of the assistance levels on the lower-limb kinematic chain of users, the range of motion (ROM) was estimated for ankle, knee, and hip joints. Likewise, the trunk's angle was also estimated using the marker located at C7 (i. e. , at the 7th Cervical Vertebra) and the two markers located at the posterior of the iliac crest. Moreover, data related to trials' mean duration were also calculated. A summary of this information is reported in Table  5 , where the robotic platform's linear and angular speed are also reported.
Final Remarks and Future Works One of the main findings of this study is related to the kinematic and interaction parameters during the resistive mode (RM). Although the RM opposes the user's intention to move, it might induce muscular training during rehabilitation processes; the level of resistance could be configured to meet each user's specific needs. This assistance level also induces slower gait patterns compared to the reported studies in the literature. However, these could be interpreted as a safety strategy, where slower gait patterns might help users to avoid collisions and stumbling during walking. Additionally, the force data gathered during the RM provided insights into possible applications of muscular training. This study was carried out employing the AGoRA Smart Walker, which is mounted on a commercial robotic platform.
The effectiveness of NBGOA was evaluated using 20 datasets with various sizes taken from the UCI datasets repository in comparison with five well-regarded optimization techniques in the feature selection field. Simulation results revealed that NBGOA achieves better results compared to the tested methods in terms of fitness function and average classification accuracy. Mafarja et al. [67]  proposed binary variants of GOA for tackling the feature selection problems. Two mechanisms were used to obtain the binary version of GOA. The first mechanism is based on Sigmoid transfer function (BGOA-S) and V-shaped transfer function (BGOA-V). While the second one is based on the mutation operator and best solution found so far (BGOA-M). These techniques were evaluated based on 25 standard UCI datasets in comparison with eight well-known approaches.
[86]  for tackling the Feature Selection Problem (FSP). The effectiveness of OLCGOA was tested using 30 IEEE CEC2017 benchmark functions. It was also applied for solving the FSP. According to the experiments, OLCGOA can reach optimal solutions for solving benchmarking and feature selection problem. Saxena and Kumar  [87]  proposed 10 enhanced chaotic GOA (ECGOAs) variants for tackling the protein structure prediction problem. The efficiency of ECGOAs was validated using artificial and real protein sequences. Test results revealed the effectiveness of the chaotic variants (ECGOA) compared to the original GOA. Dwivedi et al. [88]  suggested an enhanced model (ECAGOA) based on chaotic adaptive GOA with Ensemble of Feature Selection (EFS) for tackling the intrusion detection problem. ECAGOA was assessed using 3 datasets including ISCX 2012, CIC-IDS2017, and NSL-KDD.
3) GAUSSIAN GRASSHOPPER OPTIMIZATION ALGORITHM An improved GOA (IGOA) was proposed by Luo et al. [70]  for solving the financial stress prediction problem. Three strategies including Gaussian mutation, opposition-based learning, and Levy-flight were introduced in GOA to provide a good balance between exploitation and exploration. IGOA was evaluated using datasets collected from some Japanese financial statements from 1995 to 2009 compared to GA, FA, PSO, MFO, DE, SCA, and standard GOA. According to the experiments, IGOA provided good classification accuracy comparing with other existing approaches. Li et al. [89]  proposed an enhanced GOA (EGOA) for modeling multivariable systems with support vector regression. Gaussian mutation operator and coevolution strategy were introduced into GOA to enhance the search ability.
Experimental results showed the effectiveness and superiority of EGOA compared to standard GOA, GA, PSO, and GSA. Algamal et al. [90]  suggested an improved GOA (PGOA) by adding Gaussian function into GOA for feature selection and optimizing SVR parameters. PGOA was tested and assessed using four chemical datasets taking into account three metrics: prediction, number of features, and computational time. According to the simulation, PGOA outperforms other nature-inspired algorithms such as PSO, FA, BA, WOA, and the standard GOA.
Alhejji et al. [93]  suggested an adaptive GOA (AGOA) by adding a Levy-flight distribution mechanism into the original GOA for tackling the optimal power flow (OPF) problem. The robustness of AGOA was verified using 26-bus and IEEE 30-bus systems as well as 57-bus system. According to the experiments, AGOA gave better results when compared with other optimization approaches reported in the literature. Chhikar and Kumar  [94]  developed a Multi-Islands Levyflight GOA model, named MI-LFGOA, for spatial image steganalysis. MI-LFGOA was tested using SPAM (686-D) and SRM (34671-D) subsets taken from the BOSS base 1. 01 image database. Test results revealed that MI-LFGOA gives promising solutions when comparing with other feature selection approaches. Mokeddem  [95]  proposed an improved method (LGOA) by incorporating the Levy-flight mechanism into GOA for parameter extraction of solar photo-voltaic models.
5) DYNAMIC GRASSHOPPER OPTIMIZATION ALGORITHM An enhanced GOA (GOA_EPD) based on the use of Evolutionary Population Dynamics (EPD) and selection operators was proposed by Mafarja et al. [65]  for tackling the feature selection problems. GOA_EPD was evaluated based on 22 real benchmark datasets taken from the UCI machine learning repository. Comparative results and analysis showed the robustness of GOA_EPD comparing with GA, PSO, BGSA, BBA, and bGWO. Zhao et al. [96]  proposed a Dynamic Improved GOA (DJGOA) by integrating dynamic weight strategy and random jumping concept for tackling the single objective numeric optimization problem. DJGOA was evaluated using 13 benchmark functions in comparison with PSO, DA, and standard GOA. It was demonstrated that DJGOA gives better performance compared to the standard GOA, PSO, and DA.
Wang et al. [97]  suggested a dynamic quantum binary GOA (DQBGOA_MR) based on quantum computing concept, mutual information strategy, and rough set mechanism for tackling the feature selection problem. DQBGOA_MR was tested using twenty UCI datasets by considering three metrics such as average classification accuracy, average feature subset size, and average fitness value. According to the extensive experiments, DQBGOA_MR provided better solutions comparing with other swarm intelligence techniques.
An adaptive search and skip method were introduced. GHO was tested on five different cases of weather conditions in comparison with Perturb and Observe (P&O), ABC, PSO, DA, PSO Gravitational Search (PSOGS), and CS algorithms. Shukla  [103]  suggested a self-adaptive GOA by adding the concepts of mutation and perceptive into the original GOA for anomaly intrusion detection. To test the performance of self-adaptive GOA, three datasets such as NSL-KDD, CIC-IDS 2017, and AWID were used. According to the experiments, the self-adaptive GOA provided better results comparing with PSO, GA, DE, and ABC in terms of accuracy, detection rate, and false-positive rate.
Tiwari and Jain  [100]  suggested a combined model (EGOA-FC) by combining GOA with fuzzy clustering technique for cells segmentation in histopathological images. To test the efficiency of the EGOA-FC model, two histopathological images (i. e TNBC patients cancer and UCSB bio segmentation images datasets) were used in the simulation. According to the experiments, EGOA-FC showed good results compared to K-means and fuzzy c-means techniques in terms of aggregated Jaccard index value and F1-score.
It was demonstrated that MGOA gives better results compared to other well-regarded optimization approaches existing in the literature. In the work of Zakeri and Hokmabadi  [112] , a real-valued GOA (GOFS) was proposed for tackling the feature selection problem. GOFS was validated using 10 UCI datasets and compared with 12 well-regarded feature selection methods. The effectiveness of GOFS was proved compared with other optimization algorithms by achieving the highest classification accuracy in 7 out of 10 datasets. Yue and Zhang  [113]  proposed an enhanced GOA (PCA-GOA) for global optimization problems. Principal Component Analysis (PCA) and novel inertia weight were incorporated into GOA to enhance its search space ability. in a simiar work, Feng et al. [114]  proposed an enhanced GOA (EGOA) for solving the Bin Packing Problem (BPP).
111, 0. 85, and 11, respectively. Bala et al. [119]  suggested an improved GOA by incorporating the Echo State Network (ESN) strategy into GOA for predicting faults in airplane engines. The improved GOA was validated using time-series data collected from the GitHub repository with mean squared error (MSE) as an objective function. According to the experiments, the improved GOA provided accurate solutions comparing with CS, DE, PSO, BPSO, LSTM, ESN, Deep ESN, and the classical GOA. Goel et al. [120]  developed a modified GOA by combining GOA with the random forest approach for Autism Spectrum Disorder (ASD) detection. The modified GOA was validated using three ASD screening datasets of different age groups including children, adolescents, and adults. According to the experiments, the modified GOA provides better results when comparing with other state-of-the-art approaches by achieving an accuracy of 100% at all stages of life.