dataesr/scientific-paragraphs-categorization huggingface dataset:
QUANTIFICATION AND STATISTICAL ANALYSIS Paths were digitised using the software Graphclick. Meander was calculated as the mean angular deviation in direction between successive 30 cm chunks of the ants' paths. For the 'Avoid vs. fell comparison' we used Fisher's Exact Test to look for differences between groups in the ratio of ants that circumvented or fell into the trap. For the 'Scan number and meander' comparisons ants were followed individually across successive trials. We compared scan number and meander across three situations: (i) Trial before trap; (ii) Trial with first trap-fall; (iii) Trial following first trap-fall for two sections of the route, before the trap and after the trap, separately. To analyse the number of scan per ants, we used a generalised linear mixed effects model with ants as a random effect for count data.
Acknowledgments We would like to thanks  Cody Freas  and  Jeanne Delor  for help on the field,  Cristian Pasquaretta  for statistical advice as well as the  Centre for Appropriate Technology Limited (CfAT Ltd, Alice Springs)  for field facilities and letting us run experiments on their grounds. The research was funded by the  European Research Council  (ERCstg:  EMERG-ANT 759817 ) to AW, a  Fyssen Foundation  fellowship to AW, the  European Union 's  Seventh Framework Programme  ( FP7 :  PIEF-GA-2013-624765 ) to CB and a grant from the  Australian Research Council  to KC ( DP110100608 ). PG was funded in part by  EPSRC  grant  EP/P006094/1.
Diagnosis. A species of General description and comparison. In dorsal view, the skull presents an ovoid shape, with its widest dimension being at the level of the frontal-parietal contact (Fig. 2A, B ). In lateral aspect, the skull is triangular and gradually tapers anteriorly (Fig. 2C ). The orbits are large, their anteroposterior length forms 1/3 of the complete anteroposterior length of the skull. The casque is flat and weakly elevated posteriorly, without a dorsally convex parietal. For detailed description and ilustration of each virtually isolated element, see Supplementary Data 1. The specimen KNM-RU 18340 shares the following features mainly with members of extant Calumma (see also Supplementary Figs. 14 15 16 ): (1) The contact of the premaxilla with the frontal. This is present in Calumma globifer, Ca.
boettgeri and Ca. gubei, but not in Ca. parsonii, Ca. brevicorne, Ca. nasutum, Ca. fallax, and Ca. juliae. It is present in Furcifer pardalis and occasionally in F. oustaleti (but not in F. bifidus and F. campani)  24, 25. Is is also present in Rhampholeon and Rieppeleon, but not in Bradypodion, Chamaeleo, Kinyongia, Trioceros and Brookesia. The premaxilla-frontal contact is absent in the outgroup and character optimization in Mesquite supports this character state as being the condition at the basal node of the Chamaeleonidae clade. Character optimization in Mesquite shows that the contact of these two bones evolved independently in Rieppeleon, Rhampholeon and some members of Calumma. Among Calumma, the absence of the contact is at the basal node of the clade. So its presence can be regarded as derived among of this clade.
2 , Supplementary Data 1). The morphological comparisons are in agreement with the results of the phylogenetic analyses that specimen KNM-RU 18340 can be distinguished from all extant genera, except from Calumma. Overall the morphology of the fossil skull most closely resembles that seen in the extant Ca. brevicorne (see Fig. 3d-f ), especially the shape of the parietal, the arrangement of protuberances on the parietal, the dorsal opening of the supratemporal fenestra (not visible in lateral aspect; this is also present in Ca. parsonii, Ca. globifer and Ca. ambreense), the presence of the prefrontal-postorbitofrontal contact over the orbit and the maximum width of the skull being located at the level of the frontal-parietal contact in dorsal view (the maximum width in Ca. parsonii, Ca. globifer and Ca.
The resolution was 18. 83 µm. The micro-CT data were analyzed using VG Studio Max 3. 2 and Avizo 8. 1. The right side of the skull is in better condition and more completely preserved than the left side. For this reason, we focused our reconstruction efforts on the right side, and supplemented certain areas with elements from the left side, when better preserved. For the reconstruction of the whole skull in dorsal view, missing areas were restored by mirror imaging. phylogenetic analysis. A morphological data matrix (see Supplementary Data 2) was developed based on characters taken primarily from Rieppel and Crumly 24 (see Supplementary Data 1). Nineteen characters of relevance for chameleons were added. The matrix comprises 43 characters scored for 23 extant chameleon ingroup taxa (Archaius tigris, Bradypodion ventrale, Bra.
pumilum, Brookesia superciliaris, Bro. stumpffi, Calumma globifer, Ca. parsonii, Ca. brevicorne, Ca. nasutum, Chamaeleo zeylanicus, Ch. chamaeleon, Ch. dilepis, Furcifer campani, F. oustaleti, F. pardalis, Kinyongia fischeri, Rhampholeon spectrum, Rh. platyceps, Rieppeleon kerstenii, Ri. brevicaudatus, Trioceros melleri, T. johnstoni and T. jacksonii), in addition to the extinct taxon represented by the Kenyan material described here. Uromastyx was used as the outgroup. The principal goal of this analysis is to understand the relationship of the Kenyan early Miocene taxon among Chamaeleonidae. The data matrix was analysed using maximum parsimony as an optimality criterion in the program TNT and the NT (New Technology) search (with ratchet) with 1000 iterations  48. All characters were treated as unordered and were equally weighted.
Geometric morphometric analyses. We ran a 3D geometric morphometric analysis on 27 species of chameleons in addition to the fossil. Extant species covered all genera with most genera represented by two or three species. Specimens were micro CT-scanned and surfaces were cleaned in Geomagic Studio and imported in the 'Landmark' software  51. Forty-three landmarks (see Supplementary Data 1 -Supplementary Tab. 1) were taken on the right side of each skull to describe the overall shape (Supplementary Data 1 -Supplementary Fig. 17 ). A general Procrustes analysis (GPA)  52  was run and was followed by a principal component analysis (PCA) performed on the Procrustes residuals. We constructed a neighbor-joining tree on the PC axes that jointly explained 99. 6% of the variance to evaluate phenotypic proximity between the fossil and the extant genera.
Acknowledgements For access to the specimen KNM-RU 18340, we thank  Dr. Fredrick Manthi  ( National Museums of Kenya, Nairobi ). We are indebted to  Dr. Kudakwashe Jakata  ( University of the Witwatersrand, Johannesburg, South Africa ) for his help with the µCT. We are also indebted to  J. D. Daza  ( Sam Houston State University, TX, USA ) for his help with Bremer value processing in TNT and to the  Willi Hennig Society  for making the TNT program freely available. A. H. thanks  Anne-Claire Fabre  for help with the morphometric analysis. We thank  NASA  for the public domain regarding the map in Figure 3g. We acknowledge  Dr. Georgios Georgalis  ( Dipartimento di Scienze della Terra, Università degli Studi di Torino, Italy ), and an anonymous reviewer for their comments and revisions on the manuscript.
F1, F2, and F3 were bought from Faraday Defense LLC, USA. F4 fabric was bought from Innovative textiles, Italy. In the second part of experiment, two different types of embroidered skin dry electrodes were developed (E1 and E2 in Table  1 ). These electrodes were used for ECG measurements in real-time, data transmission, and analysis. Embroidered skin electrodes were prepared based on previous work  [18]. These electrodes were embroidered using two different types of yarns: Shieldex (117f17 HC+B) (E1) and Madeira (HC-40) yarns (E2). Four to six samples for each type of electrode and test were prepared. Different instruments have different sampling capacities. For example, in the case of the abrasion test, five samples can be tested at the same time, but this is limited to four in case of the pilling box test.
ECG Measurement ECG measurement was performed using the SHIELD-EMK-EMG card from OLIMEX. This card was connected to an Arduino board. Figure  2  shows the ECG electrodes for different types of materials. For fabricated samples (F1-F4), the size of the electrodes was 3 cm × 6 cm. For E1 and E2, circular electrodes with 3 cm diameter were embroidered. The sampling frequency was 250 Hz and the data were processed and analyzed using MATLAB software. A notch filter was used to remove powerline noises at 50 Hz and a passband filter was used to filter 0. 5 to 100 Hz. Signals were recorded in still sitting conditions for at least 45 seconds. Before measurement, subjects were in a resting position for at least 10 min. These experiments were conducted on three healthy male subjects aged between 26 to 38 years.
ECG Measurement ECG measurement was performed using the SHIELD-EMK-EMG card from OLIMEX. This card was connected to an Arduino board. Figure  2  shows the ECG electrodes for different types of materials. For fabricated samples (F1-F4), the size of the electrodes was 3cm × 6cm. For E1 and E2, circular electrodes with 3 cm diameter were embroidered. The sampling frequency was 250 Hz and the data were processed and analyzed using MATLAB software. A notch filter was used to remove powerline noises at 50 Hz and a passband filter was used to filter 0. 5 to 100 Hz. Signals were recorded in still sitting conditions for at least 45 seconds. Before measurement, subjects were in a resting position for at least 10 minutes. These experiments were conducted on three healthy male subjects aged between 26 to 38 years.
Influence of decision-making dynamics on the relationship between brain activation and numerical problem-solving abilities Moderation analysis as implemented in the R-package (Version 3. 4. 1; 2017) was used to assess how decision-making dynamics influence the relationship between brain activation and numerical problem-solving abilities. We first demeaned individual Numerical Operations scores and decision parameters using the grand mean of the combined ASD and TD samples. This analysis was conducted in the combined group to avoid circularity and under the assumption that some proportion of children with ASD may have profiles similar to TD children, while others may differ significantly. This allowed us to more fully characterize sources of heterogeneity in the ASD group. Numerical Operations scores were entered as the dependent variable; brain activation for 'addition' vs.
'control' problems was entered as the independent variable, and HDDM scores that distinguished the ASD and TD groups were entered as the moderator variable. One participant was excluded based on outlier detection procedures (i. e. > 3SD based on Cook's Distance). A total of 31 subjects were therefore included in the moderation analysis. Robustness of moderation effects was assessed using Cohen's f 2  (Cohen, 1988 ) (R-Version 3. 4. 1; 2017), and evidence against the null hypothesis (i. e. no moderation) using BF 10  (Kass and Raftery, 1995; Wetzels and Wagenmakers, 2012)  implemented in the "Bayes Factor" R Package (R-Version 3. 4. 1; 2017).
Funding This work was supported by  NIH  grants  MH084164 ,  HD059205 , and  MH121069  to VM, and a  Postdoctoral Award  from the  Stanford Child Health Research Institute  and the  Stanford NIH-National Center for Advancing Training Sciences-Clinical and Translational Science Awards program  ( TR001085 ) to LC. TI is currently supported by grants from the  Agence Nationale de la Recherche  ( ANR-18-CE28-0010 ) and from the  Universit� e de Paris (IdEx).
The theoretical description of fiducial cross sections and kinematic distributions has been greatly improved by the calculation of NNLO QCD corrections, which are nowadays necessary to reliably describe the experimental data away from the soft and collinear regions. Fully differential predictions at NNLO QCD in the Born kinematics are available by now for essentially all 2 → 2 colour-singlet production processes, including HZ and HW ± production  [1] [2] [3] [4] [5] [6] , HH production  [7] [8] [9] , γγ production  [10] [11] [12] , Zγ and W ± γ production  [13] [14] [15] , W + W -production  [16, 17] , ZZ production  [18] [19] [20] [21]  and W ± Z production  [22, 23]. It is well known that in kinematical regimes dominated by soft and collinear QCD radiation the perturbative expansion in the strong coupling constant α s does not provide a physical description of differential observables.
A. 1 Compilation and setup of a process We assume that the MATRIX+RadISH_v1. 0. 0. tar. gz package has been downloaded and extracted, and that a working installation of Lhapdf  [160]  is present on the machine such that the shell command lhapdf-config is recognised when it is run in a terminal. We also require a working Python 2. 7 installation. 18 If these requirement have been met, the command $. /matrix --radish executed inside the MATRIX+RadISH_v1. 0. 0 folder will launch the Matrix shell with the RadISH interface enabled. 19 An interactive Python session for the compilation will appear, with instructions printed on the screen. This is followed by the usual steps in the Matrix shell to select a process via its ${process_id}, 20 e. g. for Drell-Yan production.
|===>> y This requires you to acknowledge the work of various groups that went into the computation performed by Matrix+RadISH for the present process by citing the references collected in the file CITATION. bib, which is provided with the results in every run. The compilation shell will then execute the following steps: • linking to Lhapdf  [160] ; • download and installation of OpenLoops  [87] [88] [89]  (skipped if already installed); • installation of CLN  [163]  (skipped if already installed); • installation of GiNaC  [164]  (skipped if already installed); • installation of Hoppet  [165]  (skipped if already installed); • installation of Chaplin  [166]  (skipped if already installed); • installation of RadISH  [43, 45, 55]  (skipped if already installed); 18 Currently the scripts necessary to run the Matrix shell are not yet compliant with Python 3 standards.
Acknowledgments We thank  Massimiliano Grazzini ,  Pier Monni  and  Paolo Torrielli  for fruitful discussions and useful comments on the manuscript. We are grateful to  CERN, Max-Planck-Institut für Physik , and  Università degli Studi di Milano-Bicocca , where part of this project was carried out, for hospitality. The work of SK and LR is supported by the  ERC Starting  Grant  714788 REINVENT. SK  and LR acknowledge the  CINECA award  under the  ISCRA initiative  for the availability of high-performance computing resources needed for this work.
First results from WIPAFF aircraft operations have been reported by  Platis et al. (2018). This publication is designed to give an integrative overview on the results of WIPAFF. Results from the evaluations of the different measurement and modelling efforts in the project (see  Emeis et al. , 2016; Djath et al. , 2018; Platis et al. , 2018; Siedersleben et al. , 2018b; Siedersleben et al. , 2018a; Siedersleben et al. , 2020; Lampert et al. , 2020; Platis et al. , in review; Djath and Schulz-Stellenfleth, 2020; Cañadillas et al. , 2020)  are put into a common perspective. The airborne data set of the WIPAFF project is accessible to the community via the PANGAEA database  (Bärfuss et al. , 2019; Lampert et al. , 2020). Section 2 gives the initial hypotheses of the project and Section 3 briefly reviews methods and their state of the art at the beginning of the project.
Airborne Data Airborne in situ data were collected with the research aircraft Dornier DO-128 operated by the Technische Universitat Braunschweig, Germany. 41 measurement flights over the German Bight during the WIPAFF project during 2016 and 2017 delivered (in general at a sampling frequency of 100 Hz) 3D wind vector, temperature, humidity, and sea surface-temperature. A detailed explanation of the used measurement instruments and the aircraft can be found in  Corsmeier et al. (2001) ;  Platis et al. (2018) ;  Lampert et al. (2020). The starting points of the flights were Wilhelmshaven, Borkum or Husum airport, respectively. A typical flight pattern to capture the wakes is displayed in Figure  1  is the socalled "meander pattern", with several flight legs at hub height (at about 100 m above sea level) positioned downstream of the wind-park cluster.
SAR measures near surface wind fields in an indirect way through the small scale roughness of the sea surface. The wind influences the sea surface by generating cm-scale roughness, which is captured by active microwave sensors due to the Bragg-scattering principle. Low image intensities thus indicate areas of reduced wind speed. Sentinel-1A (launched in 2014) and Sentinel-1B (launched in 2016) are twin satellites that provide SAR data in the German Bight on a regular basis. They were launched into a sun-synchronous orbit and operate at C-band (5. 405 GHz) at vertical receive and transmit polarization (VV). Each satellite has an exact repeat cycle of 12 days for the same imaging geometry (i. e. , incidence angles and area covered are identical), but can provide data for a particular spot at one or two day intervals, if different imaging geometries are acceptable.
The use of wind speeds in neighbouring areas as a proxy leads to certain errors, which depend on spectral properties of the background wind field and which are discussed in more detail in  Djath and Schulz-Stellenfleth (2020). A spectral analysis of wind fields in the German Bight was conducted as follows. SAR scenes from both satellites were collected for the period September 2016 to December 2017. The occurrence of wakes around the wind park Amrumbank West was analysed (Figure  1 ). Only the scenes that included the entire Amrumbank West were considered and overall, 177 scenes were collected. Wave-number spectra were then computed from the SAR derived 10 m-wind speed maps. Wind field data were classified considering the stability conditions based on the thermal stratification from FINO1 data.
Appearance of wakes from SAR and airborne data 28 out of the total 41 flights during the WIPAFF project included a flight strategy which allowed for the determination of wakes behind wind parks. For 12 out of the 28 flights, the wake length, defined as the distance to 95 % recovery of the wind field, exceeded the length of the flight meander pattern and for two flights the wake length was observed to be shorter than the downwind distance of the first measured leg. The observed wakes within the WIPAFF campaigns range from nearly 0 km in unstable atmospheric conditions to over 65 km in atmospheric stable conditions (see for further results and discussion Section 4. 1. 2). The flow inside the wakes was reduced up to 43 % compared to the undisturbed flow, two examples are shown in Figure  4  and 10a ).
, 2012). Moreover, there are measures of the magnitude of turbulence, not considering any thermal stratification at all. Most common in ABL science is the turbulence kinetic energy (TKE) per volume and unit mass k = 1 2 • u 2 + v 2 + w 2 (4. 2) taking the turbulent part of the wind speed components into account. We used SAR and flight data in the WIPAFF project to investigate the correlation between stability and wake length. In a first step SAR scenes taken over the offshore windpark Alpha Ventus were analysed and wake lengths were estimated using the technique described in Christiansen and  Hasager (2005). This technique consists of estimating the velocity deficit from two parallel transects defined such that one transect encloses the wake area and the second transect is defined outside the wake, which is characterised as freestream conditions.
Looking at the distinct wind park clusters (Figure  5 ), the correlation between wake length and stronger stability, as observed for the wind parks GO (Godewind) and AW, can be approximated by two different exponential functions. This is an indication that there are more parameters that govern the wake length, in addition to stability -possibly also the wind-park architecture as considered in the analytical model. Moreover, strong stability coincides with the absence of short wakes. In addition, there is a correlation between higher wind speed deficit with more stable atmospheric conditions (not shown), while small wind deficits occur during more convective conditions. Using the airborne data set of the WIPAFF project, a distinct correlation between the wake length and the other stability parameters L and Ri B (not shown) was not apparent.
Wave-number spectrum vs. stability Wave-number spectra were computed from the 10 mwind speed derived from the SAR dataset taking into account the stability conditions based on the thermal stratification from the FINO1 data. The spectral analysis is performed for the period September 2016 to May 2017 and considered stable and unstable stratification cases. The results for all stability cases are shown in Figure  11. Each spectrum represents the average over the considered period. Although their spectral forms appear to be different at high wavenumber, the shapes look quite similar in general at low wavenumber. The slope is quite close to the k -5/3 power law  (Pond et al. , 1966; Kaimal et al. , 1972; Nicholls and Readings, 1981; Chin et al. , 1998; Wikle et al. , 1999; Cho et al. , 1999b; Cho et al.
Conclusion/Outlook A unique dataset from airborne in situ data, remote sensing by laser scanner and SAR gained during the WIPAFF project proves that wakes up to several tens of kilometers exist downstream of offshore wind farms. The wind speed deficits in the wakes and their length tend to be larger in stable than in unstable conditions. The results show that the average wake lengths under stable conditions exceed 50 km, while under neutral/unstable conditions, the wake length amounts to 15 km or less. Data also indicates that a denser wind park layout increases the wake length additionally due to a higher initial wind speed deficit. Turbulence occurs at the edges of the wakes due to shear between the reduced wind speed inside the wake and the undisturbed flow. The intensity depends on the strength of the wind speed gradient and is further enhanced for denser wind park geometries.
In contrast, within the wake, turbulent kinetic energy is reduced even compared to the undisturbed flow. The observational data of the WIPAFF project was further compared to industrial, analytical and mesoscale models. The respective models show in general a good correlation with the measured wake lengths, nevertheless, they also show deficiencies: • As a first order approximation the analytical model seems to work well, however it has to be optimized to be able to account for the park layout and turbine turbine-induced turbulence left over from the wind park. This has to be improved in the future. • The engineering model WindFarmer underestimates the wake length during stable conditions when using the default settings. Therefore, default settings of the engineering model WindFarmer have been modified to account for a slower wind speed recovery in stable stratification  (Cañadillas et al.
, 2020). • The mesoscale WRF model enables the simulation of a complete area like the German Bight. However, the results of the simulations show that the WRF model is highly sensitive to the upwind conditions. During offshore winds (advecting warm air over the ocean) strong inversions developed at rotor height that are challenging for a mesoscale model. Hence, the upstream wind speed was sometimes underestimated due to a wrongly predicted stratification in the model. To accomplish accurate predictions of the wind energy production by numerical models, further effects have to be taken into account, e. g. the blockage effects of wind parks or how the state of the operation of wind parks influences the wakes, which requires the analysis of operational data. As such data was not available in the WIPAFF project, this is a topic of future research and implementation in numerical models.