dataesr/scientific-paragraphs-categorization huggingface dataset:
elwaspatid is a Python package for 1D elastic wave propagation dedicated to the computation of space-time diagrams. It is meant to model the propagation of elastic stress waves in slender bars (or rods) generated by the impact of another bar. Applications can include: mechanical testing (Split Hopkinson Pressure Bars, SHPB), mechanical systems that involve impact with a hammer, design of striker geometry to get a specific pulse shape, and understanding of wave reflection/transmission in bars in academical cases.
Numerical methodology for stability tools Results presented herein follow the same numerical approach adopted by  Fabre et al. (2018 ), Sierra, Fabre & Citro (2020a)  and  Sierra et al. (2020b). The calculation of the base flow, the eigenvalue problem and the normal form expansion are implemented in the open-source software FreeFem++. Parametric studies and generation of figures are collected by StabFem drivers, an open-source project available at https://gitlab. com/ stabfem/StabFem. Results shown in § § 3-5 have been computed with a numerical domain (see figure  1 ) of size z 2 = 50D, z 1 = 20D and r ∞ = 20D, in the streamwise and crosswise directions, respectively. For steady-state, stability and normal form computations, we set  On a standard laptop, every computation considered below can be attained within a few hours.
Acknowledgements. The authors are grateful to  A. Chatterjee  for useful discussions and the critical reading of the manuscript. Funding. This work has been partially financed by the  Junta de Andalucía ,  Universidad de Jaén , and European Funds under Project  FEDER-UJA 1262764. Declaration of interests. The authors report no conflict of interest. 942 A54-40 https://doi. org/10. 1017/jfm. 2022. 395Published online by Cambridge University Press Triple-Hopf bifurcation in the flow past a rotating sphere.
Von-Kármán Mixing Tank The experimental data set considered in this work was gathered in the Göttingen Turbulence Facility #3. The experimental rig consisted of a steel cylinder of diameter and height equal to 48 cm and 58 cm respectively. It featured 8 axial baffles, attached to its inside wall, and two counter-rotating impellers of diameter 25 cm driving the flow inside the cylinder. The rotation frequency was set to 0. 2 Hz. Water was used as the working fluid (kinematic viscosity of ν = 0. 98 mm 2 /s). The flow was seeded with PMMA microspheres with mean diameter 6. 0 µm and specific gravity ρ seeding /ρ = 1. 22 (the resultant particle Stokes number was estimated as 6 × 10 -5 ≪ 1). A high-speed Nd:YAG pulse laser was used as the source of illumination. Two high-speed Phantom v640 cameras were used to record the flow.
2017 , was used to reduce the residual divergence of the data). Each sequence consisted of 6 snapshots with the time separation below one-tenth of the Kolmogorov time scale. A vast collection of 2×10 5 such sequences was gathered throughout the experiment. They were considered independent samples of the velocity field as the time separation between sequences was of the order of the impeller revolution period. A Lagrangian filtering, similar to that proposed by  Novara & Scarano (2013) , was applied to the data in the post-processing phase. Each sequence was used to advect artificial tracers, whose initial positions coincided with the measurement grid points, forward and backwards in time. The resultant traces were utilised to evaluate the filtered velocity and Lagrangian acceleration vectors.
Direct Numerical Simulation A DNS data set of forced homogeneous and isotropic turbulence (HIT) provided by the Johns Hopkins University (JHU) turbulence database was used to provide numerical comparison  (Li et al. 2008 ). The DNS is triply periodic on a cubic domain of size length 2π with Reynolds number Re λ = 433 on a regular grid of 1024 3 points. In time intervals of one second, 256 spatially independent sub-cubes of side length 5λ (0. 4L) were extracted from the database at full resolution for a total of 2560 sub-cubes over 10 time units (or 5T L , where T L = u rms /L is the large eddy turnover time scale), for which thirdorder statistics (such as the structure functions reported in section 3. 4. 2) were found to satisfactorily converge. The DNS data was stored similarly to the experimental data, where each cube contained fields for all components of the velocity, the material derivatives (accounting for the numerical forcing), and the pressure field.
Fourth-order differentiation in physical space was applied directly to the fields as necessary to calculate gradient quantities used in the analysis. As the JHU database enforces the divergence-free condition via spectral methods, a small but non-negligible divergence residual was found for the gradients in physical space. This error was found to propagate to higher-order derivatives; impacting the energy, helicity, and enstrophy budget residuals at approximately 2%, 5%, and 15% respectively (appendix A). Though this error is non-negligible, it is not believed to overshadow the correlation-based conclusions drawn in this study that are primarily rooted in phase information. For the VK data, the error propagation of gradients in physical space is more significant as a result of the limited sub-pixel accuracy.
The universal constant for the analogous longitudinal second-order structure function is C 2 ≈ 2. 0 ± 0. 2  (Sreenivasan 1995; Pope 2001) , however the energy structure functions feature a sum over two transverse components (that scale as 4 3 C 2 ) and one longitudinal component. Therefore, in the inertial range, the energy structure function is expected to plateau at a value of approximately 7. 3 ± 0. 7. For the VK data, this value is achieved only at the largest available separation. This is likely due to the limited range of separations, but may also be attributed to the Reynolds number (Re λ = 199) for which the inertial range is expected to be limited in breadth  (Ishihara et al. 2009). For the JHU data the plateau is more evident, however it is also considerably limited due to the sizes of the extracted sub-cubes of side length 5λ.
On the other hand, defining the cosine of the angle between δu and -δ∇p as cos ϕ u = ( δu • r)(-δ∇p • r) + ( δu × r) • (-δ∇p × r) (4. 1) where • denotes unit norm, we find that the correlation of T q 2 p with cos ϕ u is substantial. This is shown in fig. 6 (a) for the JHU data but similar results were found for the VK data (these are omitted for brevity). From both the dot product and cross product contributions of eq. (4. 1),  Yasuda & Vassilicos (2018)  found that the mean was small but positive. This implies that in the averaged picture the convergence events (i. e. δu • r < 0) occur simultaneously with compressing forces (i. e. -δ∇p • r < 0), and vice-versa, divergence events are coupled to expanding pressure forces. Importantly, one may conclude based on the identity between the (scale-space) volume integral V R Π q 2 dV R and surface integral ∂V R δu • rδq 2 dS that the convergence events facilitate the forward δq 2 cascade, whereas divergence events facilitate the inverse cascade  (Yasuda & Vassilicos 2018).
This motivates the interpretation of Π q 2 > as a transfer of energy to larger scales whose average is a complement to Π q 2 <. The establishment of an overall balance between the triple product and the dissipation of energy in the inertial range (eq. (5. 4)) motivates further insight into the alignment of Σu and (δω × δu). This is quantified using the angle defined by cos θ = Σu • (δω × δu) ||Σu|| ||δu × δω||. (5. 5) The PDF is presented for both data sets in fig. 10. Note that an extra separation at r = 4. 7λ (the maximum separation attainable using the sub-cubes extracted from the JHU database) is presented for further insight. As required by eq. (  5. 4) to maintain dissipation downscale, the alignment is found to be biased towards positive values of cos θ for all separations such that cos θ > 0.
Since the VK data is the noisier of the two sets, it follows that the measured value of E ω 2 is larger, in agreement with fig. 11. Taking into account the above considerations, any conclusions regarding terms A ω 2 t and E ω 2 based on the VK data are questionable. This ambiguity is avoided in the manuscript by discarding the VK data and relying purely on the JHU data for the enstrophy transfers.
Acknowledgements The authors wish to acknowledge  J. Dawson  for kindly sharing experimental data. D. W. C. wishes to thank  F. Coletti ,  A. Alexakis , and  B. Ganapathisubramani  for support in early stages. This work is the product of exchanges of ideas at the  Eleventh International Symposium on Turbulence and Shear Flow Phenomena in Southampton, UK.
Hydrophobicity Evaluation The hydrophobicity of the mortar surface during bacterial colonization was evaluated using a dropshape scanner (KRÜSS, DSA 30), which measures the angles and diameters of contact between a drop of water and a surface  [30]. After each period of incubation in seawater, 3 mortar specimens were drained for 2 h. Then, a drop (3 µL) of water was placed on mortar surface. Absorption of the drop was monitored using a camera (8 images per second). The contact angle between the drop and the material was measured using the Advance machine software, and a minimum of three tests were carried out at different points.
The thrust coefficient of the tower and the nacelle was also measured and it yields c T,n ≈ 0. 12 so that the turbine operates close to the ideal thrust coefficient of c T,id = 8/9 derived from the Betz limit. As shown in figure  1 , an array of six 1d hot-wire probes with sensor lengths of 1. 25 mm and a sensor diameter of 5 µm was used to measure the wake downstream of three different wind turbine array configurations at the hub height with a very high downstream resolution of ∆X/D = 0. 17. The data was collected using a StreamLine 9091N0102 frame with 91C10 CTA Modules. A temperature correction, as proposed by  Hultmark and Smits (2010) , was applied to the data. At each position, 1. 2 • 10 6 data points were collected with a sampling frequency of f s = 15 kHz. A hardware low-pass filter with a cut-off frequency of 10 kHz has been used.
Acknowledgements. The authors would like to thank the  German Environmental Foundation, DBU , for funding the project with a PhD scholarship (nr. 20014/342 ) for Ingrid Neunaber. Further, the authors would like to thank  Christos Vassilicos  for the fruitful discussions. A special thanks goes to the  research group Turbulence, Wind Energy and Stochastic at the University of Oldenburg  for helpful discussions and experimental cooperation, in particular  Michael Hölling ,  Jannik Schottler  who built the model wind turbines, and  Vlaho Petrović  who designed the control.
Acknowledgments The authors would like to thank  Hervé Roussel ,  Grenoble, France  for his assistance in the XRD experiments. P. G. was supported by a doctoral fellowship from the  CDP Eco- SESA  project ( ANR-15-IDEX-02 ). V. C. gratefully thanks the financial support from the  French National Research Agency  through the project  DOSETTE  ( ANR-17-CE24-0003 ). They further acknowledge the facilities, and the scientific and technical assistance of the  CMTC characterization platform of Grenoble INP , which is supported by the  Centre of Excellence of Multifunctional Architectured Materials (LabEx CEMAM)  under the contract  ANR-10-LABX-44-01  funded by the  "Investments for the Future" Program. Funding by the  Carnot Institute Energies du Futur  through the project  ECOLED  is also acknowledged.
The authors acknowledge the MAEDI and  MENESR French Ministries  and the  Estonian Research Council  for a financial support through the  Parrot program  (n°42401QH). The study was also funded by the  Estonian Research Council  project  PRG627 , the  Estonian Centre of Excellence  project  TK141 (TAR16016EK ) "Advanced materials and high-technology devices for energy recuperation systems", and the  European Commission 's  H2020 program  under the  ERA Chair  project  5GSOLAR  grant agreement No  952509.
We compute the first difference in Equation (  29 ) using dedicated simulations with the 4HEX action. We use the harmonic-mean-square (HMS) to set the physical point: -2 ,HMS ≡ 1 16 -2 , , defined as an average over the masses of the 16 pion tastes, ,. We set ,HMS to the physical value of the pion mass, which requires lowering the Goldstone-pion mass to 110 MeV. This way of fixing the physical point results in much smaller lattice artefacts than the usual setting with the Goldstone-pion, at least for an observable like the finite-size effect. To generate the 4HEX data set, we performed simulations with two different Goldstone pion masses: = 104 MeV and 121 MeV. To set the physical point as described above, we perform an interpolation from these two pion masses to = 110 MeV. To compute light from the current propagator in our 4HEX simulations we use the upper and lower bound technique described in Section 4.
We call the resulting corrections taste improvements, because they improve the continuum extrapolation of our lattice data without, in principle, modifying the continuum-limit value. Indeed, these corrections vanish in that limit, as taste-breaking effects should. These improvements are applied on light-quark observables at the isospin-symmetric point, whose taste violations have the largest impact on our final uncertainties. The models NNLO XPT, MLLGS and RHO describe the long-distance physics associated with finite-volume effects, as measured in our simulations. One can also define corresponding models describing the taste violations, they are denoted NNLO SXPT, SMLLGS and SRHO. We find that they describe the physics associated with taste violations, at least at larger distances. This is illustrated in Figure  4 , where cutoff effects in the integrand of light are plotted as a function of Euclidean time.
Thus, massive and representative training data are required to achieve good accuracy. The Constant Speed (CS), Constant Acceleration (CA), SUMO (Simulation of Urban MObility) model, and Intelligent Driver Model (IDM), are four typical models that apply the parametric approaches  [17]. Unlike parametric approaches, non-parametric methods do not require prior fixing of the model structure, as it could be computed from historical data. For instance, Authors in  [18]  have conducted a comparison study between parametric and non-parametric approaches for vehicle speed prediction. In their work, they evaluated six models by implementing both parametric and non-parametric approaches, and they run 10-fold cross-validation for different prediction horizons to test the models' accuracy. Reported results showed that parametric models perform well for short-term prediction, while advanced parametric models are more accurate for long-term prediction.
Recently, more attention has been given to machine learning techniques for vehicle speed forecasting. It is worth noting that, these techniques can be classified under datadriven approaches, as they rely mainly on the historical data of model training. For instance, the authors in  [19]  proposed a deep learning approach to predict the future short-term speed values using the collected dataset. This latter includes historical vehicle speed values, acceleration, steering information, locations, and driving dates. In order to test the generalization ability of their approach, the authors used four standard driving cycles and analyzed the influence of driving factors on the accuracy of vehicle speed prediction. Similarly, in  [20] , the authors proposed a deep learning algorithm for vehicle speed prediction featuring big data analytics and Adaptive Neuro-Fuzzy Inference System (ANFIS).
The proposed algorithm can predict the vehicle's speed on both freeway and urban traffic roads. The big data analytic program is used to extract the data of a specific trip, and the ANFIS system is utilized to extract the features of the speed dynamics through supervised learning. Other studies investigated Recurrent Neural Networks (RNN) by implementing the Long Short Term Memory algorithm (LSTM). For instance, the authors in  [21]  proposed an ego-vehicle speed prediction model based on LSTM. This model is used as a microscopic and non-parametric approach to apply various urban driving conditions. The proposed model can predict up to 15 seconds of vehicle speed values. They mainly compared three driving scenarios to determine the impact of the inputs' combinations, which are: the internal information of the ego-vehicle, the relative speed, the distance to the vehicle ahead, and the GPS coordinates.
Also, the authors in  [22]  discussed several machine learning algorithms for speed prediction, and they proposed an algorithm, which is based on Back Propagation Long Short Term Memory (BP-LSTM) for on-road individual long-term average speed prediction. Furthermore, the authors in  [23]  proposed a novel speed predictor that is based on a K-means algorithm and radius basis function neural network (RBF-NN). The proposed method contains various predictive sub-models to cope with different input drive states. First, the principal component analysis (PCA) is applied to the data to extract the key factors. The dataset is then preprocessed into sub-datasets using the Kmeans clustering technique. Finally, the RBF-NN is trained upon the sub-datasets in order to predict future speed values. Certainly, the use of machine learning has become very popular for speed prediction due to its ability to link multiple parameters.
It is mainly due to the fact that the speed relies on a lot of factors and, therefore, multivariate models are suitable for this case. The study was performed using a dataset collected from the Vehicle Simulation Runtime Infrastructure (VSimRTI) simulator  [34] , which is, a simulator dedicated to developing ITS applications. To further investigate the development and deploy these algorithms in real-setting scenarios, we have investigated, in this work, recent advances in artificial intelligence (AI), IoT, and Big Data technologies. In fact, an IoT/Big data platform is first introduced to enable the development and deployment of data-driven approaches for vehicle speed prediction. A multivariate LSTM algorithm is then developed and deployed together with other algorithms from the literature, mainly ARIMA, CNN, and ConvLSTM.
The values of these parameters, such as BEV load, SoC, and velocity, are required for developing energy management techniques. The onboard device is FIGURE 2: The holistic architecture of the HELECAR platform also equipped with GPS and GPRS/4G shields. The GPS module is used to maintain BEV tracking/tracing including detailed information about trips. This enables enriching our data models with information, which is related to roads' parameters, in particular, type (urban/freeway), slope, and speed limitations. The GPRS/4G ensures the transfer of data streams to the server side of the platform. In case of a lack of network coverage, the monitoring device switches to local archiving mode and executes deferred transfer accordingly. Similarly, charging station monitoring relies on an embedded device, which reports information about the charging station's exploitation.
B. DATA PROCESSING SUBSYSTEM The data processing subsystem is composed of four main components axed on IoT nodes interconnections, streams and batch data processing, and AI-based techniques for forecasting BEV endogenous and exogenous parameters. The IoT component provides requirements, software, and hardware to support the interconnection of both BEV and charging station onboard devices. Apache Kafka and Node-red are combined in order to support batch and streaming data flows. This component helps gather the required data from electric vehicles, charging stations, and also from other services, such as weather and traffic data. This component comes on top of the deployed monitoring subsystem. As this latter collects required data, the IoT component ensures that collected data are first preprocessed and transferred to related components for further processing.
More precisely, data reported by IoT nodes are first processed as streams using Apache Kafka streams. The data streams are then analyzed to detect data drift and to forecast important features, such as SoC, remaining driving range, and charging stations' occupancy. These features are keys for performing BEV predictive control to achieve efficient energy management. Data streams are also archived in the Mongo database for further batch processing. It's worth noting that batch processing comes in handy when, for example, the accuracy of the used in-vehicle forecasting model decreases. In this case, a new model is generated accordingly (i. e. , trained based on the stored data). The decision of requesting a new model is not obvious, in fact, the accuracy of the forecasting model depends on the data history as well as on the newly collected data.
IV. MATERIALS AND METHODS In this section, we focus on materials and methods we have followed to enable BEV's speed forecasting. The aim is to deploy a low-cost embedded software/hardware (Sw/Hw) subsystem for speed forecasting. This subsystem is a part of the HELECAR platform, it was developed and deployed for collecting needed data and for real-time speed forecasting as well. Speed forecasting was performed using a neural network algorithm following two steps; training a model upon historical data for being used to predict future speed values. However, model training can be a tedious task, which needs a powerful computer and a huge historical dataset. In fact, due to the computational limitations of the low-cost existing embedded devices, the deployed subsystem focuses only on data gathering/communication as well as the execution of the established pre-trained model for speed forecasting.
To capture different traffic and road conditions, three main trajectories have been selected in Sala Al Jadida and Rabat region, Morocco, as shown in Fig. 4. Each trajectory is between 30 min and 2 h long, and each trajectory is repeated many times in different periods, mainly, morning, noon and afternoon. The aim is to mimic a typical real-life scenario of a person going from home to work and from home to another place in the city. Also, all the trajectories have the same starting and end point (S), which is our university, and sometimes, the electric vehicle is recharged in point (D). An example of the speed profile of each trajectory is depicted in Fig. 5. The second and third trajectories, as depicted in Fig. 5b  and Fig. 5c  are done on an urban road, which explains the stops and many slowdowns in the dataset due to the traffic lights, speed bumps, and heavy traffic.
Fig. 6  represents the in-vehicle embedded device, which was used to collect the data from the Twizy. An Arduino nano development board is used with a Controller Area Network Bus (CAN Bus) module to read the data from the electric vehicle through the On-board Diagnostics (OBD) connector. The CAN protocol, widely used in the automobile industry, allows embedded components to communicate with each other without the need for a host computer or server. The CAN bus is a message-based protocol, each message is transmitted in a frame and each frame has a unique identifier. When a component transmits a message, all other components receive it including the message emitter. The data collected is raw data, which is encoded in Hex values. This data is transmitted to the NVIDIA Jetson nano, in which a developed program, using a Node-red framework, performs raw data decoding.