dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work has been supported by the  Spanish State Research Agency  under contracts  PID2019-108257GB-I00 ,  PID2022-137444NB-I00  and the  María de Maeztu "Programme for Units of Excellence  in R&D″  CEX2018-000805-M. P. G acknowledges funding from the  French Agence Nationale de la Recherche  under grant  ANR-20-CE09-0022  (UltraNanOSpec). The authors acknowledge the contribution of  Beatriz H. Juarez  on the dispersion of the fluorescence submicron particles and the TEM images.
Materials and Methods Bio-logging dataset. We used Global Positioning System tracking data from a long-term study of juvenile golden eagles in the Central European alps, where the species is sedentary. We used data collected for a total of 55 juveniles tracked over 2017-2023 (Table  S1 ) with a regular sampling schedule of one GPS fix every 20 minutes during daytime (see  [32]  for details on fieldwork procedure and data sampling schedules). Nestlings were fitted with solar powered loggers manufactured by e-obs GmbH (Munich, Germany) using a leg-loop harness. The weight of the harness and logger (in total maximum 60g) was well below the recommended maximum 3% of the birds' body mass. We subset the data to correspond to the transient phase of natal dispersal up to three years after emigration.
We selected a classification using a 10 m inner radius and 200 m outer radius on the basis of the thin size of the identified ridge lines. Then, we calculated the distance from each pixel to the ridge line pixel using the function proximity raster from SAGA  [39]  to obtain a distance map to the ridge line. For both TRI and distance to ridge lines, we averaged neighboring cell values to achieve a 100 m cell size. Modeling the energy landscape. We used a step-selection approach  [21]  to summarize the environmental conditions in which the birds chose to commute. Each pair of consecutive points in the tracking dataset was considered as one step. For each step, we generated 50 random steps that originated at the observed steps' start point, but had alternative end points to represent alternative movement decisions that the bird could have taken.
The location of the alternative end points were selected by drawing from a gamma distribution fitted to the step lengths and a von Mises distribution fitted to the turning angles of all the empirical tracking data. The result was a stratified dataset with one used and 50 alternative points in each stratum. All used and available points were associated with TRI and distance to ridge line values. We found no autocorrelation (r < 0. 5) among our predictor variables TRI, distance to ridge line, step length, and week since emigration (as a continuous variable). The predictor variables were z-transformed prior to modeling to ensure comparability of the coefficient estimates. We used a conditional logistic regression to build a step-selection function to model and predict the energy landscape. We included step length as one of the predictors in our model, to take the movement characteristics of the growing juveniles into account  [21].
In the new datasets that we generated to make these predictions, we set the variables that were not represented in the interaction term to their average values and set the grouping factor that represented the stratum as missing data. We made the predictions on the scale of the link function and converted them to values between 0 and 1 using the inverse logit function  [44]. We considered these predicted values as the Perceived Energy Availability index, a quantity representing the potential energy available in the landscape based on the uplift proxies (TRI and distance to ridge line) and the movement capacity (step length) of the birds included in the model.
Energy landscape maps To investigate the changes in the energy landscape across the ontogenetic axis, we used our model to create prediction maps for weekly increments during the first three years after emigration. We made our prediction maps for the entire extent of the Alpine region ("Perimeter of the Alpine Convention" layer available via Permanent Secretariat of the Alpine Convention). For each week since emigration, we created a new dataset to make predictions for each 100 m x 100 m cell. We assigned the mean step length value for each given week in the new datasets. To explore the changes in flyable areas over time, we defined landscape flyability as Perceived Energy Availability index value above 0. 7. We then estimated the total flyable areas for each week since emigration. The Alpine region covers 190,544.
Data, Materials, and Software Availability The tracking data used for this study will be made available via Movebank upon acceptance of the manuscript. All data needed to replicate the findings of this paper can be accessed via an Edmond repository "Energy landscape Ontogeny"  [45]. The code needed for data analysis and plotting can be found at https://github. com/kamransafi/GoldenEagles/tree/main/WP3_Soaring_Ontogeny/ MS2_flight_landscape/public. DOIs will be generated for these repositories when the manuscript is accepted.
Acknowledgments EN was supported by the  PRIME program  of the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research (BMBF). LF was partially supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy   EXC 2117 422037984  (grant  M21-20  to EN). HB was partially supported by the  DFG  ( Emmy Noether Fellowship   463925853  to  Andrea Flack ) and the  International Max Planck Research School for Quantitative Behaviour, Ecology and Evolution (IMPRS-QBEE). We thank  Michael Chimento  for scientific and technical discussions. We are grateful for the crucial assistance of the mountain rescue teams of the  Guardia di Finanza  as well as the forestry and game wardens of  South Tyrol/Alto Adige  and the national park team in Gesäuse, as well as the  Stelvio national park Italy  and the  Fish and Game Department of the Canton of Grisons (AJF)  and many game keepers.
Fourier-based Fluid-Structure Interaction solver: FluSI The developed FluSI (Fourier-based Fluid-Structure Interaction solver) open source software package is a fully parallel Fourier pseudospectral method for high resolution computation of 3D flapping flight in viscous flow  [41]  on massively parallel computers with distributed memory architectures. The penalized incompressible Navier-Stokes equations (2) are discretized with a classical Fourier pseudospectral method with adaptive time stepping  [44, 45]. Solving a Poisson equation in Fourier space thus becomes trivial and the incompressibility constraint can be easily satisfied, since the Riesz projector is diagonal in Fourier space. Artificial numerical diffusion and dispersion are absent, but the no-slip (and outflow) boundary conditions are imposed with the volume penalization method and modeling error arises.
The computational complexity is almost entirely determined by the FFT, for which excellent scaling can be obtained on supercomputers using highly optimized libraries, e. g. , P3DFFT. Nevertheless Cartesian equidistant grids limit the number of grid points and the maximum domain size by the available computational resources. The inherent periodicity can be alleviated using penalization-based sponge techniques for wake removal. Moreover the numerics (and the physics) implies small time steps, ∆t < C η , even though Runge-Kutta-Chebychev (RKC) schemes could be included, particularly helpful for low Reynolds number simulations where the penalization method imposes prohibitively small time step sizes. Different modules are available in the FluSI software package. Flapping flight with rigid wings, either tethered, but a free flight option is likewise available, however without active control so far.
Semi-implicitly coupled FSI is also implemented and for handling the solid part of the deformable wings the mass spring-model described in Section 2. 5 is used. The modular code structure allows easily to take into account different complex geometries, and thus insects with wings of different shape can be simulated easily. The wing kinematics and flight mechanisms are defined in further modules. Extensive details on the numerical method including thorough validation and benchmarking can be found in  [41]. The FluSI code has been used in particular for computing bumblebees in turbulence  [46] , for free flight in turbulence  [47] , FSI of Calliphora wings  [48]. In Figure  2  we show a high resolution computation of a tethered bumblebee with FluSI and the corresponding strong scaling test on an IBM Blue Gene with up to 16384 CPUs.
Wavelet-Adaptive Block-Based solver: WABBIT Motivated by the limitations of the FluSI package, the Fourier discretization requires equidistant grids, which make large scale computations extremely expensive, we developed a second fully parallel code called WABBIT, which stands for Wavelet-Adaptive Block-Based solver for Interactions with Turbulence  [49]. It has been designed for computing 3D multiscale flows in complex, time-varying geometries using a similar modular structure as in FluSI. The key feature is adaptivity with cubic blocks and locally equidistant grids endowed with an efficient implementation on last generation massively parallel architectures using tree-like data-structures. The major difference relative to FluSI is that we relax the incompressibility condition and we use instead artificial compressibility.
For further details on the numerical method, its numerical analysis including thorough validation and benchmarking of the paralellization efficiency, see  [49]. Figure  3  illustrates an adaptive 3D computation of a bumblebee together with the locally refined grid. The corresponding strong scaling test on different machines of GENCI (Irene SKL and Irene AMD and IDRIS Zay SKL) assesses the speed-up going up to a factor 4. The WABBIT open access software package is available on Github at the following address, https://github. com/adaptive-cfd/WABBIT.
However, the highly flexible wing appears to be less efficient than the flexible wing. This can be interpreted as the existence of an optimized zone of wing flexibility, which is ideal for flying. Furthermore, the wing inertia contributes to the damping of fluctuations in the aerodynamic forces and hence stabilizes the insect during flight. Experimental data on insect wing stiffness is essential for modelling correctly wing flexibility. To this purpose, an experimental setup for measuring the wing elasticity of female (Calliphora vomitoria) was designed by Wehmann et al. [65]. The measured data were used for training a data-driven model of insect wings as proposed in  [48]. The new approach ensured that the model had the same behavior as the real wing. We obtained overall nine sets of stiffness parameters corresponding to the nine measured individuals.
Acknowledgments Financial support from the  Agence Nationale de la Recherche (ANR)  (Grant  15-CE40-0019 ) and  Deutsche Forschungsgemeinschaft (DFG)  (Grants  SE824/26-1  and  LE905/17-1 ), project  AIFIT , is gratefully acknowledged. The authors were granted access to the  HPC resources of IDRIS  under the allocation No. A0102A01664  attributed by  Grand Équipement National de Calcul Intensif (GENCI ). DK gratefully acknowledges financial support from the  JSPS KAKENHI  Grants Number  15F15061  and  18K13693. Centre de Calcul Intensif d'Aix-Marseille, Global Scientific Information  and  Computing Center  at  Tokyo Institute of Technology and Center of Shared Research Facilities at Lomonosov Moscow State University  are acknowledged for granting access to their high performance computing resources.
These constraints necessitate the design of appropriate novel frameworks to optimize the performance of modern wireless communication systems, since classical centralized optimization techniques fail to address the scalability requirement and the continual evolution of IoT networks. To enhance the capacity of the system, non-orthogonal multiple access (NOMA) has been introduced as a promising multiple access technique to accommodate a larger number of devices. Specifically, power-domain NOMA (PD-NOMA) consists of scheduling multiple users on the same resource block by multiplexing them in the power domain  [3] -  [6]. Therefore, NOMA can achieve a higher spectral efficiency and higher data rates when compared to orthogonal multiple access (OMA) schemes  [7]. To decode signals at the receiver level, successive interference cancellation (SIC) is employed.
The fundamental challenge of the matching problem is to assign resources from players of one set to those of the other set in an optimal manner. To achieve this, each player builds a preference list based on a utility function, in order to rank players of the opposing set. In the context of this paper, the first set of players corresponds to the devices, while the other corresponds to the available network resources  [15]. Added to NOMA, relaying can be quite useful in massive communication settings to reduce network congestion at the central base station (BS) level, extend cellular coverage and increase achieved rates. Mobile relays that can be deployed in a quick manner, such as unmanned aerial vehicles, can also restore communication in damaged networks infrastructure  [16] ,  [17]. In order to achieve high data rates, reduce latency and enhance spectral efficiency, full-duplex relays can be considered.
Framework for multidisciplinary conceptual aircraft design During the aircraft design phase, analyses and optimizations have been carried out using the multidisciplinary conceptual robust design optimisation framework FRIDA (FRamework for Innovative Design in Aeronautics), of the Aerospace Structures and Design group of Roma Tre University  [12, [28] [29] [30]. Since the FRIDA framework was developed for the conceptual design of both conventional and unconventional aircraft concepts, most of the implemented models are based on fundamental physics, with specific assumptions to reduce the order of complexity. JThe accuracy of the models is capable to capture the main aircraft dynamics with a computational effort compatible with the optimal design processes. The FRIDA simulation modules are briefly summarized: (a) Geometric processor, including a multi-patch parametric kernel with structured-grid generator; (b) Sketch definition for the initial grossweight assessment and inner layout definition; (c) Structural module for the modal analysis using booms and skins idealization of the wingbox to build an equivalent beam model; (d) Weight breakdown based on iterative schemes, enhanced to account for the BWB layout; (e) Aerodynamic analysis based on a quasi-potential flow with a boundarylayer integral model for the viscosity effects, coupled with a transonic correction for the wave drag effects estimation; (f) Performance analysis of take-off, cruise and landing phases using flight-mechanics equations; (g) Flight simulation by means of inverse flight mechanic to derive the time history of all the relevant variables; (h) Aeroelastic analysis with a finite state reduction, using a matrix-fraction-based Reduced Order Model (ROM); plus modules for noise assessment and finances.
Two engine variants (Eng. 1 and Eng. 2) were predesigned with the software GasTurb (GasTurb  [21] ), following the methodology outlined in Kurzke and Halliwell  [37]. The two variants differ with respect to their design bypass ratio (BPR) of 8 and 12, and their maximum static thrust of kN and 560 kN, for Eng. 1 and Eng. 2, respectively. They share the same key cycle parameters at their design point top of climb, namely, an overall pressure ratio of 60 and 1700 K burner exit temperature. Design, sizing and performance analysis are described in more detail in LeGriffon et al. [40]. Fig. 3  shows the two engine cross sections. Eng. 2 is substantially larger and heavier than Eng. 1, but also about 5 % more fuel efficient at its design point, with 13. 95 vs. 14. 71 g/kNs specific fuel consumption.
Flight trajectories Flight trajectories and corresponding dynamic engine data for the BWB aircraft have been calculated using the flight simulation environment within FRIDA. The technique is based on solving the inverse flight mechanics problem, which yields the relevant flight mechanics variables (characteristic angles of attack, aircraft orientation, high-lift devices settings, landing gear deployment and engine operating point). Take-off and landing maneuvers are tailored to the current BWB configuration. The starting point is a present ICAO flight procedure (as used for the reference aircraft) related to an aircraft with a comparable mass, in terms of number of trajectory segments and maximum altitude (last node for the departure and first node for the approach). This baseline operation is modified in accordance with the performances of the BWB under considerations, in order to derive a BWB-specific flight trajectory.
The implemented semi-empirical models provide directivity and spectra in the far field. A thorough presentation of the most common noise source models used in CARMEN and PANAM is given in Bertsch et al. [6]. The source prediction is followed by a module adding shielding effects to the spectral source directivities. The assumed source locations for the fan inlets and outlets, the jets, and the landing gears are shown in LeGriffon et al. [40]. Insertion losses are predicted for point sources, e. g. , to assess the shielding of fan inlet and exhaust noise, but also to assess reflection of landing gear noise by the aircraft body. An example of predicted insertion losses is shown in Fig. 5. The setup of the system noise simulation tools is in general very similar, with remaining differences in the individual code implementation  [4].
CARMEN simulation tool CARMEN  [44]  is a software tool that has served for parametric evaluation of new aircraft concepts  [39]  for the last few years. Experimental data were used to validate the noise source models (e. g. [41] ) and the computational chain (e. g. [38] ). The method used for calculation of reflection and diffraction by the aircraft geometry depends on the frequency range under consideration (hybrid approach). For low and medium frequencies, a BEM is used, through the solver BEMUSE  [59, 60]. This method gives an exact solution of the Helmholtz equation in the frequency domain by a surface integral method. Insertion loss is provided for each source location and 1/3 octave band on a lower hemi-sphere surrounding the aircraft in the acoustic far field. The higher the computed frequency, the stronger are local changes in insertion loss due to interferences.
PANAM simulation tool PANAM  [3]  is a software tool that has previously been used to design and study novel low-noise aircraft concepts, e. g. [36, 50, 67]. It was validated by comparison to flyover field measurements  [5]. For the reflection and diffraction of the different noise sources by the aircraft geometry, the tool SHADOW is applied  [43]. SHADOW was developed to investigate different engine installation locations during design phase with respect to noise shielding at low computational costs. In contrast to CARMEN's approach, this method is applied to the entire frequency range of interest. SHADOW is based on a high-frequency approximation of the linearized Euler equations. The pressure field is calculated by solving ordinary differential equations along rays in space, which originate from one point representing the source.
For the air absorption simulation, a homogenous atmosphere was assumed with temperature and relative humidity set to local conditions at 2 m height, i. e. 15 • C and 70 %, respectively. For the ground reflection simulation, grassland with an airflow resistivity of 250 kPa s/m 2 was assumed. Ground impedance was modelled using the Miki model  [45]. Turbulence parameters were estimated from a weather forecast database (validation, Section 5), or set to defined reference conditions (evaluation, Section 6).
Validation cases The four validation cases are listed in Table  3. The case data was taken from a prior field measurement campaign. The field measurements were conducted around Zurich airport in the years 2013 and  [71, 72]. A departure and an approach of the two current types of jet aircraft Airbus A320 (narrow-body vehicle) and A340 (wide-body vehicle), both commonly used aircraft in Europe, were selected. For these flights, flight deck recording (FDR) data were provided by the airline (SWISS), yielding the necessary input data like flight trajectory,  low compressor speed N1 and flap setting. Meteorological data were taken from a nearby monitoring station. Flights with calm atmospheric conditions, i. e. no precipitation, low wind and low turbulence, were chosen. The measurement microphones were located close to the noise certification points for take-off and landing, respectively  [27].
The data was analyzed to assess whether syntheses and recordings are classified into mutually exclusive classes, and whether they are perceived as equally or differently plausible. In the direct comparison task (2-AFC), if measurements and simulations would not be discriminable from each other, both classes would have the same relative frequency of 50 %, indicating random selection by chance. Here, the recordings were correctly identified in 70 %, i. e. , the simulation was taken for the recording in ~30 % of the comparisons, meaning that slightly less than half of the cases were not discriminable. Nevertheless, the differences in the relative frequencies of the two classes are significant (χ 2 -tests, p < 0. 001). In the ranking task (3-AFC), the ideal distribution of the measurements and two simulations would have the same relative frequency of ~33 %.
Acknowledgements The authors are grateful to the participants of the laboratory experiments. They would further like to thank  Corinne Gianola  for conducting the listening experiments as the experimenter, and  Fotis Georgiou  and  Markus Haselbach  for their help in preparing the experimental laboratory setup. The authors appreciate that  SWISS International Airlines  supplied their flight data recorder data for this study and thank the colleagues who conducted the field measurements. This study was performed within the research project Aircraft noise  Reduction Technologies and related Environmental iMpact (ARTEM ), which has received funding from the  European Union 's  Horizon 2020 Research and Innovation Program  under grant agreement no. 769350.
We reconstruct the 3D-images using CS considering each pixel in the FPA as a singlepixel detector. As in previous works on single-pixel imaging  [18, [24] [25] [26] [27] [28] [29] [30] [31] , we use a Digital Micro-mirror Device (DMD)  [34]  to modulate the incoming light onto the FPA in a binary spatial-modulation scheme which we call patterns. Hereafter, we define a complete pipeline to apply CS to GmAPD sensor acquisition and constraints. This pipeline is fully described in Section 2. Then we numerically demonstrate in Section 3 its feasibility using simulated data and, discuss its possibilities capabilities and limits.
Simulation of 3D-LiDAR signal Simulated data are used to design, test and validate our method. The MATLIS software -an ONERA software  [49]  allows us to simulate the signal from a GmAPD sensors paired with a laser source. This endto-end tool models 3D laser imagers, using physically realistic sensor models, validated through laboratory and field experiments. It relies on a description of the optronic scene, including the geometry and the optical properties of the materials for each facet of the target  [50, 51]. Moreover, simulation provides full control of input parameters and information unavailable on field data to design, test and validate our approach: • The same mathematical model  [3]  can be used to both simulate detection and reconstruction of the correct waveform; • Realistic signal features are artificially accessible eg.