dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, when the yaw angle of the turbine changes, the new generation of OPs will copy the new angle while old OPs still travel according to the previous angle. In the case of overlapping wakes, an OP travels into the wake of another turbine. It locates the closest upstream and downstream OPs from the foreign wake and interpolates their reduction factor at its location. The calculation of C T and C P is based on the lookup table generated via SOWFA (NREL, 2012) high-fidelity simulations for the reference turbine in the blind test. For the current benchmark study, the statistical properties of the wind field are matched (mean wind speed and turbulence intensity); however, no specific calibration to https://doi. org/10. 5194/wes-7-1791-2022 Wind Energ. Sci. , 7, 1791-1825, 2022 match to the CL-Windcon LES wake data was performed.
P16 For the P16 results, the same in-house wake modelling tool as described in Sect. 2. 2. 4 is used. It consists of a Gaussianbased wake model  (Bastankhah and Porté-Agel, 2016)  with a quadratic single-wake superposition model. Based on the calibration data provided, the wake model parameters k a , k b , α d , and β d are also optimized with the SGA optimizer of the "pygmo" library  (Biscani and Izzo, 2020)  and the same loss function, i. e. the sum of the average quadratic differences between the calibration states power and simulated power at every turbine of the wind farm. However, the optimization procedure is different compared to the SMV wind farm field data blind test. Instead of using a single global wake model parameter for all turbines, here each turbine has independent wake model parameters which are determined by the optimizer.
Multiple-wake results Similar to the single-wake results, the mean power estimated by the participating models is compared with the CL-Windcon LES blind test database for the multiple-wake cases. Accordingly, the power difference per turbine and the wind farm level power gain via Eqs. (  4 ) and (  5 ) are presented in Figs. 18  and 19. The turbine IDs for multiple-wake cases, however, are WT1, WT2, and WT3 for the three-turbine configuration and WT1, WT2,. , WT9 for the nine-turbine configuration. The representative layouts as well as the turbinespecific control settings within the investigated wind farm configuration are illustrated on the x axes of Figs. 18  and 19. Figure  18  shows P Ti and P GAIN for three turbines with 5 D spacing (where WT3 is laterally 0. 5 D apart) for several upstream and (first) downstream yaw control settings for the CL-Windcon blind tests listed in Table  7.
P Ti illustrates the dis-agreements in the power difference per turbine for that control setting, and the upstream power loss at -30 • steered turbines WT1, WT4, and WT7 is reproduced well. The error in the most downstream power predictions, however, is significantly higher for all except P12 at the turbines WT3, WT6, and WT9. This is also in line with the behaviour previously discussed for the other configurations in the blind test where the upstream yaw setting is -30 •. As opposed to P12, the wakes behind less steered turbines (Y-10_Y-123 and Y-123_Y000) are better captured by P11, P16, and P19, with overall less sensitivity to the yaw setting at the second turbines in the rows WT2, WT5, and WT8. This also implies an overall stronger wake deflection reported in the CL-Windcon  LES reference data set than the majority of the participating models.
As the reference LESs suggest a higher likelihood of power gain for those control settings, the need for better deflection models and/or (even) a more comprehensive calibration data set with higher deflection angles should be considered. -Most upstream misalignment is the main driver for the expected power gains. Within the FarmConners benchmark, the CL-Windcon blind test provides a unique opportunity with its diverse control settings, including the misalignment of the downstream turbine(s). For the investigated layouts with 3 × 1 and 3 × 3 turbine configurations, however, it is seen that the misalignment of the second row of turbines has a relatively lower impact on the expected power gains, compared to the most upstream. Similar results are reported in other LES studies with multi-turbine yaw misalignment (e.
g. Archer and Vasel-Be-Hagh, 2019). -Take all the conclusions with a grain of salt. As indicated earlier, high-fidelity simulations are very well suited for the proof-of-concept studies. Although it has many advantages, one of the main drawbacks of the reference CL-Windcon LES data set is its limited simulation period of 10 min. The impact of steering reported here and the overall model comparison (especially for the steady-state models), therefore, should be read as an initial comparison, not a comprehensive validation. Further discussion on the code comparison based on highfidelity simulations is provided in the next section with the TotalControl LES blind test results.
P8 The P8 methodology uses a preliminary version of HAWC2Farm to perform dynamic wind farm simulations on the blind test case. HAWC2Farm leverages the aeroelastic wind turbine software HAWC2  (Larsen and Hansen, 2007; Madsen et al. , 2020)  with the dynamic wake meandering (DWM) model  (Larsen et al. , 2008; Madsen et al. , 2010). While HAWC2Farm typically uses HAWC2 as the underlying wind turbine software, this preliminary implementation uses HAWCStab2 data  (Verelst et al. , 2018)  to synthesize a dynamic turbine model, from which the dynamic rotor induction is fed into the DWM model to generate the wakes. A flexible version of the DTU10MW is used to calibrate the rotor induction model to ensure the wake deficit is not overestimated, as described by  Liew et al. (2020a). The wake passive tracers advect in all spatial directions based on large-scale turbulence.
P10 The P10 model consists of wind turbine models based on the work of  Neilson (2010) , augmented with an individual blade model based on the work by  Gala-Santos (2018). The wind turbine model is validated against DNV Bladed in Gala-Santos  (2018). As an input, the wind turbine requires rotor-averaged effective wind speeds, which can be generated from input criteria of mean wind speed and turbulence intensity. The requirement for an effective wind speed rather than a point-wise wind speed means that the wind data are not identical as a time series to that used by the TotalControl LES results. However, the statistical properties of the flow are matched. A lateral wind speed is also generated that informs the meandering of wakes (see  Poushpas, 2016). Whilst previous work involving the P10 model has used the Frandsen wake model  (Frandsen, 2007) , the wake modelling was recently updated using a the Gaussian wake model of  Bastankhah and Porté-Agel (2014, 2016) , adapted for effective wind speed modelling (including load impacts) via a similar methodology to the wind shear and tower shadow modelling in  Gala-Santos (2018).
Further additions are made to account for wake steering effects, though these use an empirhttps://doi. org/10. 5194/wes-7-1791-2022 Wind Energ. Sci. , 7, 1791-1825, 2022   2010 ),  Allaerts and Meyers (2015) , and  Munters and Meyers (2018). h  Sørensen (1995). ical method. It is assumed that the reduction in power from a wake is cos 1. 9 (φ) based on Simley et al. (  2020 ) (where φ is the yaw misalignment). In order to model this change in power via wind speed, an adjustment to the wind speed of cos |φ k | with k = 1. 25 is applied. Though crude, this method has good agreement with the cos 1. 9 power adjustment up to yaw angles of between 20 and 30 •. Beyond ensuring that the statistical properties of the wind field are matched (the same turbulence and mean wind speed), no specific calibration to match to the TotalControl LES data was performed.
The turbines in the flow domain are parameterized using the aeroelastic actuator sector method (AASM)  (Vitsas and Meyers, 2016). Subgridscale stresses are modelled with a standard Smagorinsky model with Mason and Thomson wall damping  (Allaerts and Meyers, 2015). Wind farm simulations are run for a period of 75 min, which includes a 15 min start-up period for the settling of initial transients. A previously developed inflow database  (Munters et al. , 2019a, b, c, d)  is utilized to provide the inflow conditions for the wind farm through the concurrent precursor method  (Stevens et al. , 2014; Munters et al. , 2016) , and the entire wind farm is rotated in the flow domain to simulate different wind directions. The structural and aerodynamic properties of the DTU 10 MW turbine tower and blades and the DTU Wind energy controller are used to simulate the turbine operation  (Bak et al.
P ratio = n i=1 P i WFFC n i=1 P i Normal Operation and P GAIN = P ratio -1, ( 8 ) where P is the power and i is the turbine ID in the investigated subset of the layout (two turbines for single-wake and eight turbines for multiple-wake analysis behind the controlled turbines WT29 and WT32 in Fig. 20 ). Figure  22  shows the wake loss reduction for both WT32-WT28 and WT29-WT25 turbine pairs with -20 and -30 • upstream misalignment (anticlockwise) respectively. The time series on the left is illustrated at the frequency of the wind speed signals for all the (quasi-)dynamic models, including the validation data set TotalControl LES. Notably high fluctuations are seen for both of the control settings in the validation data set, which are very well captured by P8 with 5 s resolution. P20 is also in relatively good agreement, where the observed dynamics in the validation data set is closely followed.
Conclusion Here in this article we present Part I of the results of the FarmConners benchmark for code comparison under controlled operation. The benchmark brought together four data sets generated under several European WFFC projects: (1) SMV wind farm field data, (2) CL-Windcon wind tunnel experiments, (3) CL-Windcon LES, and (4) TotalControl LES databases, where data sets 1, 3, and 4 focusing on the large rotors are investigated in this study. The wind tunnel experiments are the focus of Part 2 of the series. Although the original benchmark included more control strategies (i. e. axial induction) and quantities of interest (i. e. load channels), the analysis presented here is limited to wind speed and power behaviour under wake steering WFFC. The results from 11 participating models in total are then presented separately under these three blind tests.
The highlights of the blind test exercises are summarized individually in their corresponding sections through the article. A compilation of the observations/reference simulations and participating model trends for the overall benchmark is listed below. -Customizable WFFC-oriented models. The overwhelming majority of the participating models in the Farm-Conners benchmark are parametric, typically modular frameworks, indicating their popularity within the field. Using similar approaches to resolve the wake behaviour, the main difference among them is the calibration procedure. Accordingly, the importance of variety in terms of control set points in the calibration data set is underlined in all the blind tests. Similarly, a clear description of the calibration procedure with a list of parameters when disseminating the results is crucial for reproducible and credible assessment of the potential gains via WFFC.
Within the benchmark, two separate LES frameworks developed in different institutes are also compared, and high correlation observed in their results is found to be reassuring for the technology readiness level (TRL) of WFFC where high-fidelity simulations are considered to be the key enabler for further field implementation. Although an already extensive analysis, the presented FarmConners benchmark results are limited in the applied control strategy and the investigated quantities of interest. It should therefore be read as the first step on which other benchmarks can be built. With increasing availability of field tests, wind tunnel experiments, and reference high-fidelity simulation databases, future work should include larger wind farms, different control strategies, and other control objectives such as potential load alleviation and profit maximization.
It should also be noted that P10 submitted the time series for P ratio directly and is therefore excluded in the illustrations below. Data availability. All the data used in FarmConners benchmark blind tests can potentially be made available for non-commercial purposes. Please contact us (per blind test data set) using the details provided under the FarmConners benchmark wiki page https: //farmconners. readthedocs. io/en/latest/contact_us. html  (Göçmen et al. , 2022). Author contributions. The FarmConners benchmark has been a comprehensive collaborative effort within the WFFC community worldwide. TG is the main coordinator of the FarmConners benchmark for the presented blind tests and the lead author of this article. She has led Sects. 2, 3, and 4. FC has contributed significantly to the FarmConners benchmark organization and led CL-Windcon wind tunnel blind tests to be presented in Part 2 of the series.
TD, IE, and SJA have also contributed significantly to the FarmConners benchmark organization and aided with the data preparation, description, and analysis of the results for the SMV wind farm field blind test in Sect. 2, CL-Windcon LES blind test in Sect. 3, and TotalControl LES blind test in Sect. 4 respectively. VP has also been an active member of the FarmConners benchmark organization team and, together with all the other members listed above, has co-written the introductions and helped disseminate the benchmark further to achieve a high(er) number of participants. The rest of the authors, namely LI, RB, JF, JL, MB, MPvdL, GQ, MAS, RGL, VVD, MB, MvdB, JWvW, AS, MC, RR, EB, NR, SS, JS, LV, FB, IS, and JM, are the benchmark participants who have provided model runs for the blind test(s) they have registered for, descriptions in the corresponding sections for their participating models, and extensive reviews of the analysis.
They are listed in no particular order to preserve anonymity as much as possible. Competing interests. At least one of the (co-)authors is a member of the editorial board of Wind Energy Science. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgements. The FarmConners benchmark is organized and conducted under the FarmConners project, funded by the European Union's Horizon 2020 research and innovation programme with grant agreement no. 857844. The wind farm field data used in the benchmark exercise were obtained through the French national project SMARTEOLE, supported by the Agence Nationale de la Recherche (grant no.
ANR-14-CE05-0034). The high-fidelity simulations used in the other two blind tests were produced under the CL-Windcon and TotalControl projects, funded by the European Union's Horizon 2020 research and innovation programme with grant agreement no. 727477 and grant agreement no. 727680 respectively. Financial support. This research has been supported by the Horizon 2020 (FarmConners (grant no. 857844)). Review statement. This paper was edited by Rebecca Barthelmie and reviewed by two anonymous referees. https://doi. org/10. 5194/wes-7-1791-2022 Wind Energ. Sci. , 7, 1791-1825, 2022.
Additional Constraints from LICIACube and Hera Further in situ data acquired after the DART impact will serve to refine the understanding of the geophysical and compositional properties of the Didymos system, and will help connect the constraints on the ejecta response obtained from the test to observable geophysical signatures of the underlying material properties. Images of the ejecta plume from LICIACube will potentially add constraints on the ejecta momentum, especially the ejection angle α. Images of the newly forming crater, if visible through the plume, will provide additional constraints on the nearsurface porosity and help to constrain the other ejecta response parameters. The full LICIACube data set should provide information on surface properties of Dimorphos, such as geological features, color differences and variegation, boulder distribution, and mass movements, if present  (Pajola et al.
We simulated 620 kg spherical projectiles impacting porous basaltic regolith targets at 7 km s -1 , at various impact angles. The target porosity was kept constant at 20% and was modeled using the P -α model  (Jutzi et al. 2008 ) with a simple quadratic crush curve with the input parameters defined in  Luther et al. (2022). The target was modeled as a half-space with a cohesive strength between Y 0 = 0. 1 and 10 kPa. The material response to shear deformation was described by a simple pressure-dependent strength model  (Collins et al. 2004) , which asymptotes to a certain strength at high pressures. The resolution of the simulations was about 5 million SPH particles in the target. Also shown for comparison are results using the iSALE-3D code for the case of Y 0 = 0. 1 kPa  (Raducan et al. 2022a) , which are similar to the corresponding SPH results and illustrate the level of agreement between the two approaches.
2006) , reported in  Raducan et al. (2022a), and CTH (McGlaun et al. 1990; Hertel et al. 1995). Both iSALE-3D and CTH are grid-based codes; in brief, CTH is a two-step Eulerian finite-difference code with an adaptive mesh refinement capability, while iSALE is an explicit Arbitrary Lagrangian Eulerian (ALE) mesh. Both codes rely on a continuum representation of materials and allow for multiple materials and rheologies with the option to apply strength/ damage models. The three different codes were used to simulate ∼600 kg projectiles impacting at 6 km s -1 into homogeneous targets of different geometries and different material mechanical properties. The Bern SPH simulations considered: (1) spherical (75 m) targets with cohesions between 0 and 50 Pa and 40% porosity; (2) oblate (85 × 85 × 56 m) and prolate (100 × 65 × 65 m) ellipsoidal targets, cohesionless (0 Pa) and 40% porosity  (Raducan & Jutzi 2022) ; and (3) half-space targets with a cohesion between Y 0 = 100 and 10 kPa and 20% porosity (reported above).
g. ,  Benner et al. 2015; Brozovic et al. 2017). For the Goldstone Solar System Radar (DSS-14), currently the world's most powerful planetary radar, the limiting distance would be approximately a factor of 2 smaller. Also, Equation (  22 ) assumes a rotation period of 2. 1 hr; faster rotation would further decrease the limiting distance. Radarderived volumes can be accurate to within a few percent  (Nolan et al. 2013; Barnouin et al. 2019; Daly et al. 2020 ) if viewing geometries are optimal. Observation of a satellite could enable determination of the mass of the system and its mean density. Without a satellite, if the semimajor axis drift caused by the Yarkovsky effect is measurable, mass estimates within a few percent could be possible  (Chesley et al. 2014; Scheeres et al. 2019). These data would also collectively constrain density and bulk porosity.
When the first consonant was a stop, that consonant was 20 ms in duration and the following vowel was 80 ms in duration. For fricative and affricate onsets, both consonants and vowels were 50 ms in duration. The nasal was always 100 ms in duration. The syllables were synthesised in four different Mbrola voices (us1-English, fr4-French, jp2-Japanese and pl1-Polish; https:// github. com/ mbrola/ mbrola-voices) with the fundamental frequency (F0) of 180 Hz. The noise-vocoded non-speech versions of the Polish voice was created by extracting the broadband envelope of the syllables between 0-120 Hz and modulating white noise in the envelope. This 1-channel noise vocoded stimuli can be considered non-speech as previous studies have shown that speech identification scores for such stimuli is close to 0%  53.
EEG responses for these two target sound sequences were not analysed. Participants from all the groups listened to the same set of stimuli. Sound sequence analysis. While our stimuli were constructed so that the foot, syllable and mora levels could all be perceived, it should be noted that our sequences were simplified compared to complex natural speech, so that there were no acoustic/prosodic cues to the foot level (contra what is found in natural speech), all our syllables had the same structure (CVn), and we used only two types of morae (CV and n). Therefore, to determine the rhythmic structure present at the acoustic level, the envelopes of the different 30-s-long sequences were extracted using the Hilbert function implemented in MATLAB (MathWorks, Natick, MA, USA) to obtain a time-varying estimate of the instantaneous amplitude of the sound envelope.
EEG recording, EEG analysis, and determination of EEG peaks. The recording conditions were identical across the two testing locations (Sydney and Paris), using the same equipment, same stimuli (English, French, Japanese, Polish and vocoded Polish) and same calibration of the intensity of the stimuli. The EEG was recorded using the 129-channel (128 channels plus reference electrode at Cz) Hydrocel Geodesic Sensor Net (HCGSN), the NetAmps 300 amplifier and NetStation 4. 5. 7 software (EGI Inc. ) at a sampling rate of 1000 Hz with the reference electrode placed at Cz. The electrode impedances were kept below 50 kΩ. The continuous EEG was saved for offline analyses. The recorded EEG was analysed offline using fieldtrip  55  (version: 20200409), EEGLAB  56  (version: 2019. 1) and letswave 6 (https:// www.
letsw ave. org/) toolboxes in MATLAB 2017a. Multiple toolboxes were used for the analysis since different toolboxes have different features and strengths in EEG analysis (filtering and bad channel rejection were performed in fieldtrip; ICA, re-referencing and time domain averaging were performed in EEGLAB; all the subsequent analyses were performed in letswave). The EEG was not downsampled for any of the analyses. The EEG was first band pass filtered between 0. 3-30 Hz (using a windowed sync finite impulse response filter) and then divided into epochs between 0 to 30 s relative to sound sequence onset. Noisy EEG channels were identified by visual inspection and removed (average: 3 channels; range 0-12). Independent component analysis (ICA) was then performed on the EEG data and components with stereotypical features of eye blinks and eye movements removed (average 3.
3  and 4 ). As the z-scores showed there were significant EEG peaks at 5 Hz and 10 Hz, but not at 2. 5 Hz, statistical analyses were conducted for the 5 Hz and 10 Hz data but not for the 2. 5 Hz data. Statistical analysis. Linear Mixed Effects (LME) models were fitted to the data. The continuous variable (Amplitude) was scaled and centred around zero to assist with model convergence, and simple coding was used for all the categorical variables (Group, Frequency, and Condition). Analyses were conducted in R using the lme4 package for conducting the LME analyses  64  and the lmerTest package to calculate p-values (using the Satterthwaite approximation) and to conduct post hoc pairwise comparisons  65. All models were first constructed including the maximal random effects structure  66  , and random slopes were removed until achieving model convergence.
Acknowledgements In Australia, this work was supported by a Transdisciplinary and Innovation Grant from  Australian Research Council Centre of Excellence in Dynamics of Language. In France , the work was funded by an  ANR-DFG  grant ( ANR-16-FRAL-0007 ) and funds from  LABEX EFL  ( ANR-10-LABX-0083 ) to TN, and a  DIM Cerveau et Pensées  grant (2013 MOBIBRAIN). In Japan, the work was funded by  JSPS   grant-in-aid for Scientific Research S ( 16H06319 ) and for Specially Promoted Research ( 20H05617 ),  MEXT  grant for Innovative Areas # 4903  ( 17H06382 ) to RM. We would like to thank  Sylvie Nozaradan  for useful discussions, and  Annabel Vici ,  Saya Kawase  for the assistance in data collection. We would also like to thank three anonymous reviewers for their helpful comments on an earlier version of the manuscript.