dataesr/scientific-paragraphs-categorization huggingface dataset:
The closely related restricted linear energy transfer (LET), defined as energy imparted locally to the medium per unit distance, is a main factor determining the effectiveness of the radiation in inducing biological damage, together with the spatial patterns of energy deposition, the nature of the ion and other chemical and biological aspects  (EJ, 1978; Paganetti, 2014; Chen, 2010; Ilicic et al. , 2018). Known electronic stopping power tables, such as SRIM  (Ziegler and Biersack, 2008)  and PSTAR (NIST), are used as preinitialized values given in input for the low-energy regime to Monte Carlo (MC) condensed history codes for macroand micro-dosimetric quantities  (Ivanchenko et al. , 2017; Apostolakis et al. , 2015) , and high-to-low energy datasets are provided as international recommendations, such as the ICRU 49 dataset  (ICRU Report 49, 1993) , to benchmark track structure calculations in water  (Francis et al.
, 2011a; Incerti et al. , 2018a; Bernal et al. , 2015). However, at low energies, SRIM suffers from several limitations and uncertainties  (Wittmaack, 2016) , such as the use of Lindhard's theory at energies too low for the linear regime to be valid and the use of the Bragg's additivity rule (BAR)  (Thwaites, 1983) , according to which the stopping power of a compound is the stoichiometric summation of the stopping powers of its constituent elements, neglecting electronic structure and chemical bonding aspects that become relevant at low energies. The ICRU 49 dataset of data  (ICRU Report 49, 1993 ) is built such that at low energies (below approximately 0. 5 MeV for protons) the tabulated collision stopping powers are based on experimental data and copied directly from the book by Andersen and Ziegler  (Andersen and Ziegler, 1977)  (see also  (Ziegler, 1999) ), on which SRIM is based.
Thus, the ICRU 49 dataset actually inherits many of the limitations of SRIM. In particular, the use of the Bragg's additivity rule at energies around the Bragg peak has shown to overestimate the stopping power by up to 25%  (Thwaites, 1992). In practice, a scaling factor is often applied to the Bragg curve, so that the scaled curve coincides with available experimental data for the compound, or with calculations at high energies based on Bethe-Bloch theory  (Sigmund, 2014). To include chemical bonding contributions explicitly, Both et al. (Both et al. , 1983)  proposed to partition the stopping into Core and Bond (CAB) contributions. In this scheme, the electronic stopping in compounds is obtained as the superposition of stopping by atomic cores, supplemented with the stopping corresponding to the bonding electrons to consider the "connectivity".
, 2020) , thus providing information beyond the "connectivity and conformation" perspective of the CAB approximation. We chose to focus this study on water vapor for two reasons. Firstly, the available experimental data on water vapor has been, for a long time, the basis for studying energy deposition in liquid water by some first-generation track structure codes  (Kyriakou et al. , 2017) , and and they still constitute the cross section databases for several well established codes  (Nikjoo et al. , 2006; Kyriakou et al. , 2022). Even the codes which have models for liquid water implemented, actually use a mixture of semi-empirical models for liquid and for water vapor. For example, the proton-induced low energy excitations/ionization in Geant4-DNA are based on semi-empirical models parametrized on water vapor data  (Miller and Green, 1973; Rudd et al.
, 1985 Rudd et al. , , 1992;; Villagrasa et al. , 2011). Dimilarly, processes of electron capture and electron loss by impacting protons and other ions are currently described by analytical parameterizations based on experimental data in the vapor phase  (Incerti et al. , 2018b). Simulations of cross sections in water vapor are also currently performed in (or are the basis of) the recent TILDA-V code  (Alcocer-Ávila et al. , 2019). As currently there are no direct experimental data for excitation and ionization cross-sections for liquid water  (Kyriakou et al. , 2022) , comparisons with existing experimental data in water vapor provide routinely qualitative appreciations of the plausibility of the simulation models. Secondly, the main goal of this study is to understand the validity of additivity rules for computing stopping in water from that in oxygen and hydrogen.
The same color code is used for both cases. The dotted line at ∩ = 0. 95 is shown as a reference for discussion. The inset shows the PDFs using 25 trajectories for geometry pre-sampling, compared to the reference distributions generated by 50,000 randomly selected trajectories (solid black lines). The color code is the same as above. As shown in Fig. 2 , for both hydrogen and oxygen gas, only 10 pre-sampled trajectories are required to achieve an overlap index of 0. 95, while the same overlap index requires about 70 randomly selected trajectories. For water vapor, a few more pre-sampled trajectories (11 for the oxygen PDF, and 16 for hydrogen one) are required to reach ∩ = 0. 95. The observation that more trajectories are required for water vapor can be explained by the fact that, for multi-atomic or multi-species targets, the algorithm selects trajectories that optimize the PDFs of two or more species simultaneously.
Acknowledgements This work has received funding from the  Research Executive Agency  under the EU's  Horizon 2020 Research and Innovation program  ESC2RAD (grant ID  776410 ). We are grateful for computational support from the  UK  national high performance computing service,  ARCHER , for which access was obtained via the  UKCP consortium  and funded by  EPSRC  grant ref  EP/P022561/1. This work benefited from networking activities carried out within the  EU  funded  COST Action TUMIEE  ( CA17126 ) and represents a contribution to it.
92 ± 0. 01) to allow measurements of the outer wall temperature within the adiabatic section by a medium wave infrared (MWIR) camera (FLIR ® SC5500, sampling frequency: 50 Hz, pixel resolution: 320 × 256). The opaque paint emissivity, along with its uncertainty, was empirically estimated by performing dedicated IR acquisitions on a coated isothermal surface (same opaque paint, at controlled temperature and monitored through thermocouples). The emissivity parameter set in the IR camera software (Altair © , version 5. 91. 010, FLIR, Wilsonville, OR, USA) was adjusted until a match between the temperature measured by thermocouples and the one by the infrared camera was reached in the range of uncertainty of the camera (±1 °C), resulting in an emissivity of 0. 92 ± 0. 01. The estimated value is in accordance with the emissivity of longer waves of most opaque, commercial paints  [29].
92 ± 0. 01) to allow measurements of the outer wall temperature within the adiabatic section by a medium wave infrared (MWIR) camera (FLIR ® SC5500, sampling frequency: 50 Hz, pixel resolution: 320 × 256). The opaque paint emissivity, along with its uncertainty, was empirically estimated by performing dedicated IR acquisitions on a coated isothermal surface (same opaque paint, at controlled temperature and monitored through thermocouples). The emissivity parameter set in the IR camera software (Altair © , version 5. 91. 010, FLIR, Wilsonville, OR, USA) was adjusted until a match between the temperature measured by thermocouples and the one by the infrared camera was reached in the range of uncertainty of the camera (±1 • C), resulting in an emissivity of 0. 92 ± 0. 01. The estimated value is in accordance with the emissivity of longer waves of most opaque, commercial paints  [29].
Local Heat Flux Estimation Procedure The heat flux locally exchanged at the fluid-wall interface was estimated by using the acquired IR temperature maps as inputs for the IHCP resolution approach. For the data processing, each FPPHP branch in the adiabatic section was considered as a planesymmetric geometry (Figure  3a ) due to the same boundary conditions on both outer surfaces of the device (free convection coupled with radiation). In the present analysis, the only channel walls having thickness s = x extx int = 0. 5 mm were considered (red dashed lines in Figure  3a ) to better understand the heat transfer phenomena involving internal interactions between the working fluid and the device wall.
Local Heat Flux Estimation Procedure The heat flux locally exchanged at the fluid-wall interface was estimated by using the acquired IR temperature maps as inputs for the IHCP resolution approach. For the data processing, each FPPHP branch in the adiabatic section was considered as a planesymmetric geometry (Figure  3a ) due to the same boundary conditions on both outer surfaces of the device (free convection coupled with radiation). In the present analysis, the only channel walls having thickness s = xext -xint = 0. 5 mm were considered (red dashed lines in Figure  3a ) to better understand the heat transfer phenomena involving internal interactions between the working fluid and the device wall. For such a thin wall element (Figure  3b ), the transient energy balance equation without heat generation reads as: 𝑘∇ 2 𝑇 = 𝜌𝑐 𝑝 𝜕𝑇 𝜕𝑡 (1) where k, ρ and cp are the solid wall thermal conductivity, density and specific heat, respectively.
Step (1) was performed through the Comsol Multiphysics© software (version 5. 6, COMSOL Inc. , Stockholm, Sweden) by the finite elements method. More than 150,000 elements were found to guarantee the grid-independency of the model. To ensure the reliability of the method validation, the q exact given in step (1) has to be as similar as possible to the thermofluidic interactions occurring in the real device. Hence, according to the expected physical behavior in experimental cases  [3, 18] , the convective heat flux is assumed to exhibit a sinusoidal shape described by time-space variations, representative of the working fluid oscillations through the adiabatic section: q exact,A = Amp•cos(2πt• f -ϕ) + Amp 2 - z H •Amp ( 20 ) where H is the length of the considered portion of the adiabatic section, f is the oscillation frequency of the sinusoidal signal and z is the axial coordinate.
Sci. 2022, 12, x FOR PEER REVIEW 10 of 28 Step (1) was performed through the Comsol Multiphysics © software (version 5. 6, COMSOL Inc. , Stockholm, Sweden) by the finite elements method. More than 150,000 elements were found to guarantee the grid-independency of the model. To ensure the reliability of the method validation, the qexact given in step (1) has to be as similar as possible to the thermofluidic interactions occurring in the real device. Hence, according to the expected physical behavior in experimental cases  [3, 18] , the convective heat flux is assumed to exhibit a sinusoidal shape described by time-space variations, representative of the working fluid oscillations through the adiabatic section: 𝑞 𝑒𝑥𝑎𝑐𝑡,𝐴 = 𝐴𝑚𝑝 • 𝑐𝑜𝑠(2𝜋𝑡 • 𝑓 -𝜑) + 𝐴𝑚𝑝 2 - 𝑧 𝐻 • 𝐴𝑚𝑝 ( 20 ) where H is the length of the considered portion of the adiabatic section, f is the oscillation frequency of the sinusoidal signal and z is the axial coordinate.
e. the exploitation of massive amounts of connections to collect, transmit and process enormous data volumes within low end-to-end (E2E) delays, possibly with a sustainable perspective. The aforementioned perspectives are also naturally bonded into an end-to-end view in which AI/ML, and in general adaptive mechanisms, are exploited to manage radio, computing and control resources, to finally enable reliable and sustainable AI/ML on data collected at the edge  [2]. A key enabler of EI is Multi-access Edge Computing (MEC)  [3] , which foresees the deployment of distributed computing resources close to end users, namely in Mobile Edge Hosts (MEHs) that are, e. g. This work has been partly funded by the European Commission through the H2020 project Hexa-X (Grant Agreement no. 101015956). co-located with radio Access Points (APs).
We propose an adaptive resource allocation algorithm aimed at enabling energy efficient image classification with E2E delay and inference reliability guarantees. We consider a dynamic scenario, in which data (images) are continuously collected by end devices (e. g. cameras), and uploaded to an MEH through the wireless connection with an AP. To cope with time varying context parameters, such as radio channels and data arrivals, our method dynamically, adaptively, and jointly optimizes; i) Data encoding scheme; ii) Radio resources; iii) Local and remote computation resources. We propose to use a specific metric of inference reliability, whose dynamic behaviour is measured through formally defined state variables that can be updated online, and controlled by the encoding scheme with a closed-loop.
IV. NUMERICAL RESULTS In this section, we assess the performance of our algorithm. Let us briefly present the learning related setting, followed by the communication-computation scenario and parameters. Learning setting: We recall that, since we focus on inference, we assume a pre-trained model, pre-uploaded in the MEH. For our numerical results, we consider the CIFAR-10 dataset  3. CIFAR-10 consists of 60000 32x32 color images divided in 10 classes, with 6000 images per class. The training set is made of 50000 instances, while the test set is made of 10000 instances. We exploit a Deep Convolutional Neural Network trained via usual backpropagation with an l 2penalized Cross-entropy loss. The CNN architecture is made of six convolutional layers divided in three blocks with 32, 32 (end of the first block), 64, 64 (end of the second block), 128 and 128 (end of the third block) 3×3 filters, respectively, with final flatten and dense layers; SAME padding, eLu nonlinearities and Batch Normalization are applied after each convolutional layer, 2 Max Pooling and Dropout is performed after each block and a final Softmax non-linearity is applied after the flatten and dense layers  4.
A multidisciplinary design analysis and optimization process is developed at ONERA for the design of tube and wing and blended wing-body aircraft configurations. This process is composed of different disciplinary modules (geometry, propulsion, aerodynamics, structure, handling qualities and flight mission), and the overall process considers different fidelity levels for these modules at each step of the design process. This article describes the low-fidelity aerodynamic module used during the preliminary design optimization process. Analytical formulations retained for lift and drag components are presented in the first part. Then, the performances estimated by the aerodynamic module on some reference configurations are compared with both numerical and experimental data, showing a quite good agreement for both tube and wing and blended wing-body configurations not only for global performance but also for individual drag components.
The modules implemented are easily exchangeable to adapt the workflow for specific configurations or a higher level of fidelity in specific domains if different methods are needed. This design tool evolves continually due to the enrichment of the disciplinary modules integrated or the addition of new disciplinary modules. The last version is composed of six main disciplinary modules: aircraft geometry, propulsion, aerodynamics, structure and weight, mission and performance and, finally, handling qualities. Additionally, some acoustic analysis can be performed offline of the multidisciplinary process for the selected configurations (Figure  2 ). Those modules are integrated in the NASA OpenM-DAO  [6]  framework to constitute the multidisciplinary design analysis and optimization (MDAO) process.
The last version is composed of six main disciplinary modules: aircraft geometry, propulsion, aerodynamics, structure and weight, mission and performance and, finally, handling qualities. Additionally, some acoustic analysis can be performed offline of the multidisciplinary process for the selected configurations (Figure  2 ). Those modules are integrated in the NASA OpenMDAO  [6]  framework to constitute the multidisciplinary design analysis and optimization (MDAO) process. The modules implemented are easily exchangeable to adapt the workflow for specific configurations or a higher level of fidelity in specific domains if different methods are needed. This design tool evolves continually due to the enrichment of the disciplinary modules integrated or the addition of new disciplinary modules.
The last version is composed of six main disciplinary modules: aircraft geometry, propulsion, aerodynamics, structure and weight, mission and performance and, finally, handling qualities. Additionally, some acoustic analysis can be performed offline of the multidisciplinary process for the selected configurations (Figure  2 ). Those modules are integrated in the NASA OpenMDAO  [6]  framework to constitute the multidisciplinary design analysis and optimization (MDAO) process. figurations are evaluated during the exploratory phase of the overall aircraft design (OAD) process with a large variety of architectures and planforms. As an example, Fig ure  3  presents some BWB planforms evaluated for building up a multi-fidelity surrogat model for the first pre-design optimization loop, showing a large variety of architecture to be considered (from  [4] ).
A first constraint for the aerodynamic module is to be able to manage 100% of these different architectures. Then, for each configuration, the mission module generates an aerodynamic data base. As an order of magnitude, these databases consider about 5000 aerodynamic flow conditions to be computed for each planform, which makes the use of an aerodynami disciplinary module with a very fast computing time and a reliable performance leve prediction mandatory. Therefore, among the different numerical methods available for aerodynamic mod eling, the ones belonging to the "low-fidelity" family (L0 or L1) are often used for OAD phases. For the present application, a dedicated L0 aerodynamic module based on ana lytical formulations derived from the theory or from the data analysis of past or presen aircraft is developed for a fast evaluation of the aerodynamic performance for standard "Tube and Wing" (T&W), "Flying Wing" (FW) or "Blended Wing Body" (BWB) configu rations for typical subsonic cruise flight conditions.
Standard Atmosphere Model To characterize the evolution of the ambient static flow conditions throughout th atmosphere, the aerodynamic module integrates an analytic formulation of the Interna tional Standard Atmosphere (ISA)  [7]. ISA is a mathematical model that divides the at mosphere into several layers (troposphere, stratosphere, mesosphere, etc. ) that gives th evolution of the ambient pressure, temperature and density with the altitude. The fligh altitudes considered in the module belong to the troposphere and the stratosphere Within each layer or sublayer, the temperature is assumed to have a linear evolution with respect to the altitude, the pressure is calculated by the hydrostatic balance from Equation  (1) , and the air density is calculated assuming the air as a perfect gas from Equation (2): Then, for each configuration, the mission module generates an aerodynamic database.
As an order of magnitude, these databases consider about 5000 aerodynamic flow conditions to be computed for each planform, which makes the use of an aerodynamic disciplinary module with a very fast computing time and a reliable performance level prediction mandatory. Therefore, among the different numerical methods available for aerodynamic modeling, the ones belonging to the "low-fidelity" family (L0 or L1) are often used for OAD phases. For the present application, a dedicated L0 aerodynamic module based on analytical formulations derived from the theory or from the data analysis of past or present aircraft is developed for a fast evaluation of the aerodynamic performance for standard "Tube and Wing" (T&W), "Flying Wing" (FW) or "Blended Wing Body" (BWB) configurations for typical subsonic cruise flight conditions.
Module Validation The following chapters present the validation of the aerodynamic model on standard tube and wing or blended wing-body configurations. The results obtained by the module are compared to the CFD results or experimental data in terms of overall performance and drag components when possible. Note that all the drag coefficients presented are expressed in drag counts. The first configuration considered is the Nextgen ONERA Versatile Aircraft (NOVA)  [19]  (Figure  14 ), which is designed with a downward winglet element and different engine configurations. The total wave drag of the wing can be found by adding the contributions of NDY wing subsections.
ONERA NOVA Configuration The first configuration considered is the Nextgen ONERA Versatile Aircraf VA)  [19]  (Figure  14 ), which is designed with a downward winglet element and di engine configurations. The aerodynamic flow conditions for this test case are a Mach number of M = an altitude of Z = 11,300 m. For this configuration, drag decompositions using th tools  [20]  of a different configuration are available, which makes it possible to co the lift-induced drag for the clean wing and the wing equipped with the winglet negative cant angle δWLT = -18°. The comparison with the prediction from the aerodynamic module (Figu shows a quite good prediction of this drag component, even though only fou segments are used for the wing definition. The aerodynamic flow conditions for this test case are a Mach number of M = 0.
For the evaluation of the performance, the module considered only six wing sections (Figure  18 ). Note that for the CRM configuration, the reference area used for the force coefficients is not the geometrical wing one, as considered by the module (SWing = 411. 806 m 2 ), but a modified trapezoidal one ("Wimpress" area = 383. 69 m 2 ). For the present comparisons, the force coefficients considered for the CRM database (from CFD or experiments) are therefore corrected in order to deal with this difference in SREF with a factor of 0. 93172. The first validation exercise considered the effect of the fuselage on the CL(α) curve. Figure  19  compares the CL(α) curves from the aerodynamic module, with or without a fuselage taken into account, with the CFD results from ONERA obtained within the DPW framework.
The module considers a zero-lift incidence of α0 = -1. 55° to improve the comparison. A quite good agreement can be observed for the estimation of the lift slope of the wing-fuselage arrangement by the module with the CFD data. For the evaluation of performance, the module considered only six wing sections (Figure  18 ). For the evaluation of the performance, the module considered only six wing sections (Figure  18 ). Note that for the CRM configuration, the reference area used for the force coefficients is not the geometrical wing one, as considered by the module (SWing = 411. 806 m 2 ), but a modified trapezoidal one ("Wimpress" area = 383. 69 m 2 ). For the present comparisons, the force coefficients considered for the CRM database (from CFD or experiments) are therefore corrected in order to deal with this difference in SREF with a factor of 0.
93172. The first validation exercise considered the effect of the fuselage on the CL(α) curve. Figure  19  compares the CL(α) curves from the aerodynamic module, with or without a fuselage taken into account, with the CFD results from ONERA obtained within the DPW framework. The module considers a zero-lift incidence of α0 = -1. 55° to improve the comparison. A quite good agreement can be observed for the estimation of the lift slope of the wing-fuselage arrangement by the module with the CFD data. Note that for the CRM configuration, the reference area used for the force coefficients is not the geometrical wing one, as considered by the module (S Wing = 411. 806 m 2 ), but a modified trapezoidal one ("Wimpress" area = 383. 69 m 2 ). For the present comparisons, the force coefficients considered for the CRM database (from CFD or experiments) are therefore corrected in order to deal with this difference in S REF with a factor of 0.