dataesr/scientific-paragraphs-categorization huggingface dataset:
We thereby obtain an orbital-resolved video of the SF process. In the experiment, pentacene is excited with a 1. 81 eV pump pulse polarized along the crystal a axis, thus resonantly populating the lowest bright singlet exciton. The non-equilibrium state is then probed by a p-polarized ionizing extreme-ultraviolet (XUV) probe pulse (Fig. 1c, d )  38  with a system response function <50 fs. Figure  2a  shows snapshots of the 3D PE intensity at three important time steps: before and directly after excitation, and after the primary step. In pentacene, each band features two branches due to the two inequivalent molecules in the unit cell. The dispersion of the well-separated electronic bands is apparent over the several Brillouin zones (BZ, k BZ ≅ 0. 9 Å -1 , compare Extended Data Fig. 1a ) within our momentum range (k ≈ 2.
3  and 7 ). In the HOMO contribution (Fig. 4c ) the X is predominantly visible, whereas S and TT appear in the LUMO contribution (Fig. 4d ). Hence, the decomposition separates energetically overlapping states on the basis of their orbital character intrinsically imprinted in momentum maps. We furthermore applied the decomposition procedure to a dataset in which the crystal was rotated by 10°. This yielded correspondingly modified momentum maps (Extended Data Fig. 4 ) and the decomposition resulted in the same dynamics. Similar dynamics result when integrating over the HOMO or LUMO characteristic features (Extended Data Fig. 5 ). Having successfully disentangled the excited states, we turn to the kinetic analysis of SF. The orbital-resolved dynamics of the individual PE signals (Fig. 4e ) demonstrate that S and X show the same dynamics (for non-normalized kinetics, see Extended Data Fig.
Nonetheless, the significant Davydov shift in tetracene indicates that in PE, the CT-induced transition S 1-1 should be visible. Hexacene features CT states that lie lower than in pentacene. We expect them to lie in between Frenkel and bitriplet states, leading to a stronger CT character of S 1 and a similar one of 1 TT. In conclusion, we have decomposed the excited states occurring during ultrafast SF in crystalline pentacene in momentum space by their orbital character. The orbital resolution allowed us to infer a large CT character of the bright singlet exciton and rule out the coherent mechanism with an instantaneous population of the bitriplet exciton. Our analysis proves that the depth of information contained in trARPES data allows disentangling the dynamics of ultrafast molecular processes: excited states can be unambiguously assigned to and separated by their orbital character imprinted in momentum maps.
Methods trARPES We used a homebuilt high-harmonic generation setup operating at 500 kHz to provide XUV pulses for PE  38  with the following parameters: photon flux was up to 2 × 10 11 photons per second on target, spectral width was 110 meV full-width at half-maximum (FWHM), photon energy was 21. 7 eV and pulse duration 35 fs. As the visible (VIS) pump beam, we used the compressed output of a non-colinear optical parametric amplifier with the following parameters: incident fluence of roughly 0. 2 mJ cm -2 and photon energy of 1. 81 eV. The instrument response function of the main dataset (Fig. 4e ) was 43(5) fs, obtained by fitting the kinetic model mentioned in the main text. A longer instrument response function (60 ± 15 fs) is obtained for the long data sets shown in Fig. 4f. These data sets were acquired under slightly different experimental conditions.
Both VIS and XUV pulses were incident in parallel at 65° with respect to the surface normal. The VIS pulses were s-polarized along the a axis of the crystal and the XUV pulses p-polarized. Under these conditions, we estimate that roughly 2. 5% of molecules are in an excited state. The pentacene crystal was glued with conductive ultra-high vacuum glue to a copper sample holder and then mounted on a six-axis cryogenic manipulator (SPECS GmbH) and cleaved at a base pressure of 5 × 10 -11 mbar. The data were acquired at room temperature using a time-of-flight momentum microscope (METIS1000, SPECS GmbH), allowing detection of each photoelectron as a single event and as a function of pump-probe delay. The resulting 4D PE intensity data is, hence, a function of the two components of the parallel momentum, the electron kinetic energy and the pump-probe time delay: I(k x , k y , E, Δt).
The overall energy and momentum resolutions of the experiment are ΔE = 150 meV and Δk = 0. 08 Å -1 , respectively  46. Owing to the damage that the XUV radiation causes in pentacene, we maximized the field of view (field aperture diameter of 500 μm) and therefore the number of probed molecules, while still maintaining the momentum resolution at the relevant kinetic energies. We constantly operated at the count rate of roughly 1 × 10 6 cts s -1 to acquire the data sets in reasonable time. The two main data sets used in this publication have been acquired over a period of roughly 30 h, respectively. The first dataset, used in Figs. 2a  and 4g , was acquired in a 'snapshot' mode, in which the time delay was not continuously changed but fixed to certain values. The second dataset, used in all other figures, was acquired in a 'continuous' mode, in which the time delay was continuously changed.
Sample degradation Just like many other organic materials, pentacene experiences light-induced damage when irradiated by XUV light. Throughout the measurement of one full dataset, the VB intensity reduces by 25%, indicating a chemical modification. This leads to a slight blur of the momentum map at the end of the measurement. However, the overall shape and features of the momentum maps are conserved (Extended Data Fig. 6b-d ). Whereas it would be, in principle, desirable to reduce the degradation, we note that there is a trade-off between acquiring enough signal in the excited states requiring a long measurement time and reduced degradation requiring a short measurement time (Extended Data Fig. 7 ). We optimized these parameters and further reduced the XUV flux experienced by the pentacene sample by enlarging the field aperture of the momentum microscope.
Extended Data Fig. 7 | Statistics and decomposition. Orbital-projected dynamics for a range of measurement times and respective total counts in the VB and the singlet exciton. All plots are based on the main dataset and normalized to the total count rate in the VB for each row. Extended Data Fig. 8 | Additional photoemission transitions. State diagram of the different initial states and the photoelectron and cationic final states after photoemission. a, Bitriplet exciton with charge-transfer character and b, separated, non-interacting bitriplet exciton. Transitions from the LUMO are shown in red, transitions from the HOMO in blue. Extended Data Fig. 9 | Position of the higher energy signal. Time-dependence of the energetic position of the higher energy signal of the main high-statistics dataset.
Acknowledgements We thank  H. Seiler  for helpful discussions. We gratefully acknowledge funding from the  Max-Planck-Gesellschaft , the  Deutsche Forschungsgemeinschaft  within the  Emmy Noether program  (grant no. RE 3977/1 ) and through  SFB951  (grant no. 1820877777 , project  B17 ) and the priority program  SPP2244  (project  443366970 ), the  Max Planck-EPFL Center for Molecular Nanoscience and Technology  and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant no. ERC-2015-CoG-682843  and grant agreement No  899794 ). S. B. acknowledges financial support from the  NSERC-Banting Postdoctoral Fellowship Program. T. P. acknowledges funding from the  Alexander von Humboldt Foundation. S. H. and J. P. acknowledge financial support by the  Bavarian State Ministry of Science, Research , and the  Arts (Collaborative Research Network 'Solar Technologies Go Hybrid ').
There is a significant added value for science should one take advantage of the large number of data such a group could produce. Building bridges between Occupational Scientific Diving and Citizen Science Diving is becoming more and more of an operational necessity. Developing cooperative ventures will make science more effective and open up an increasingly important field of action for recreational diving, one of the positive consequences of which will be a growing awareness of the underwater environment. Occupational Scientific Diving should not be confused with diving for Citizen Sciences (Citizen Scientific Diving). Figure  1  distinguishes the processes in the Academic Sciences and illustrates feedback loops between Occupational Scientific Diving and Citizen Scientific Diving. The modern public awareness of the protection of the environment and/or the global warming  (Lucrezi et al.
Citizen Science is sometimes described as public participation in scientific research or participatory monitoring. Those participatory actions of research conduct often advance scientific research by improving the scientific communities' capacity. It also contributes to the public's awareness and understanding of science and, thus, respect for the environment. This agrees with the definition of BioDiversa reported by  Goudeseune et al. (2020) : "the involvement of the non-academic public in the process of scientific research -whether communitydriven research or global investigations". The fields of action are so broad and different, as are the data collection and processing, that it is challenging to define Citizen Science  (Haklay et al. , 2021a). Haklay et al. (2021b)  comprehensively explore the diverse perceptions of Citizen Science.
It is to be noticed that the main fields of development of Citizen Scientific Diving are ecology and the environment  (Dickinson et al. , 2012; Fraisl et al. , 2022) , including pollution  (Hyder et al. , 2017)  or biological invasions  (Delaney et al. , 2008; Encarnacão et al. , 2021) , monitoring, and archaeology  (Smith, 2014; Viduka, 2020a and Viduka, 2020b; Flemming, 2021a). However, underwater projects are increasingly being developed (e. g. Tessier et al. , 2013video   From a general point of view, and despite the difficulty in defining the various objectives and practices of Citizen Science, the advantages are the collection of often large amounts of data and the opening of science to society. This is essential to enrich research and to reinforce society's trust in science and innovation.
There are four aspects involved in Citizen Science practice: (1) anyone can participate, (2) participants use the same protocol so data can be combined and be of usable quality, (3) data can help professional scientists reach conclusions, and (4) a broad community of scientists and volunteers work together and share data to which the public, as well as professional scientists, have access  (Cavalier and Kennedy, 2016; Flagg, 2016). Citizen scientists typically are not professional scientists. The training of volunteer recreational divers in scientific specialties (marine biology or oceanology certifications or even geology or archaeology) as organized by recreational diving agencies such as the World Underwater Federation  (CMAS, 2000a)   suggests a more careful and detailed consideration of the data that must be published to be completely accessible to scientists in available databases.
It is also possible to automatically filter a considerable amount of data using machine learning models, for example, to identify images. Combining machine learning tools and citizen participation can increase the analysis capabilities. One can use hundreds of hours of video, which was impossible before these techniques became available  (Anton et al. , 2021; Westphal et al. , 2022). While increasing the potential for attracting and retaining volunteers on programs, it is noted that the involvement of professional scientists is compulsory to ensure success in this process. Academic Science is increasingly aware of this very positive aspect of the potential contribution of Citizen Science and the interest in feedback loops between volunteers, society, and professional scientists. Citizen Science projects concerning coastal and oceanic research have been recorded by the European Marine Board  (Garcia-Soto et al.
, 2017). Few of them involve scuba diving. Taking examples in the EU, some are "pure" Citizen Science actions (e. g. ANEMOON, The Netherlands, recreational divers are engaged in sea mollusks inventory; COMBER, Greece, divers and snorkelers identify fish species and enter observations into the database). Others are associated with scientific teams or programs (e. g. GHOST FISHING, Norway; Collaboration between the Norwegian Diving Association and the Institute of Marine Research to provide standardized reports for every set of ghost fishing gear found; RED POSIDONIA, Spain, volunteer divers assist scientists in monitoring seagrass meadows or REEF LIFE SURVEY PROGRAM, worldwide, trained volunteer divers work with scientists to survey reef life). Finally, some citizen actions are fully integrated into a scientific project and are the subject of a specific work package (WP) of a research program (e.
This need is particularly acute in the marine realm, where threats are out of sight, under-mapped, cumulative, and often poorly understood, generating inefficiently managed impact. Recent advances in macroecology, statistical analysis and the compilation of very large datasets will play a central role in improving conservation outcomes, provided that regional and local data streams can be integrated to produce locally relevant and interpretable results. As  Edgar et al. (2016)  summarize, progress will be facilitated by (1) expanded deployment of systematic surveys that quantify species patterns, including some surveys conducted with the help of citizen scientists, (2) coordinated experimental research networks that use large-scale manipulations to identify the mechanisms underlying these patterns, (3) a better understanding of the consequences of threats through the application of recently developed statistical techniques to analyze global species distribution data and associated environmental and socio-economic factors, (4) the development of reliable ecological indicators for accurate and understandable monitoring of threats, and (5) the improvement of data processing and dissemination tools.
(2016)  reported. A large part of the solution concerns Citizen Science, particularly Citizen Scientific Diving, which must organize itself (in connection with academic scientists) from data collection to processing and dissemination to the environmental management decision-making bodies. Access to wrecks, among other things, has been a powerful driver for the development of recreational divers' interest in this branch of archaeology, creating an important pool of volunteers for underwater archaeology research, which is traditionally much needed. Such programs enable greater monitoring and recording as more and more people are introduced to the methodology  (Viduka, 2020b). However, in the occupational sector, as for other scientific disciplines, becoming an underwater archaeologist takes several years of education and training beyond several years of college.
One must be specially trained in Occupational Scientific Diving, mastering specific on-site skills. From a general perspective, while underwater archaeology workcamps need to draw in many volunteers, long-term data that are key to understanding how species, communities, and habitats change over time are increasingly staff-intensive. Citizen Science programs can support data collection at larger spatial and temporal scales than other scientifically collected data, which tend to be project-specific and are often tied to short funding periods  (Campbell et al. , 2022). Citizen Science organizations face trade-offs when balancing volunteer commitment with methodological complexity. The results provide essential information for managers and researchers considering using volunteer data or protocols to assess biodiversity in aquatic systems, helping to quantify the value of thousands of existing surveys  (Holt et al.
Differences in diver profiles could affect their willingness and ability to contribute to Citizen Science. Conducting a transect requires intensive activities during the dive, and relevant diving experience. Projectoriented training are required  (Roelfsema et al. , 2016; Gatti et al. , 2022) , which reduces the pool of potential volunteers. Another point is that transect protocols are often incompatible with most dives conducted by a dive center. Therefore, such a task should either be assigned to an occupational scientific diver or a highly trained amateur. If this is impossible, simplified protocols should be developed  (Gerovasileiou et al. , 2016)  and accounted for in data processing  (Freitag et al. , 2016). The same analyses of the situation are made on the sites of underwater archaeology  (Viduka and Edney, 2022).
Study methodology This work is part of a larger study investigating various parameters related to the wind tunnel: turbulence level, flow uniformity, and wind speed. The focus of this paper is on wind speed. We conducted this study on the wind tunnel through various wind tests using wind speed sensors and multiple numerical simulations. This is to verify the reliability of sensors and the accuracy of the data they provide and then to identify the CFD model that fits the experimental results. We performed the wind tests for 6 minutes at each wind speed range and recorded the measured wind speed values every 0. 2 seconds. Since the values measured by the sensors are instantaneous, we used statistical functions such as the standard deviation and the mean values to represent and compare them.
Conclusion This work has been carried out by adopting experimental and numerical investigations. The first experiment investigation led us to develop a preexperimentation methodology. Indeed, before any experiment, this methodology will be used to evaluate the accuracy of sensors and the dispersion of wind in the wind tunnel test chamber. The findings of the intercomparing tests allowed us to conclude that the disparities in measurements are related to the sensors rather than the wind tunnel. Furthermore, the results of the comparison tests revealed strongly correlated data and R-squared values close to 1, allowing us to confidently apply the adjustment to the wind tunnel characterization measurements. Secondly, the numerical investigation was devoted to developing a numerical model of the wind tunnel using the computational fluid dynamics of the Ansys Fluent environment.
IV. PROPOSED METHOD A. COMPENSATION AT THE TRANSMITTER In order to compensate for part of the HPA memory effects, a matched filter precoding is considered as a compensation block in Figure  1 , ensuring a flat spectrum for all data subcarriers in the transmitted signal. The precoded QAM data symbols can be written as X ′ i,k = X i,k H FIRk , (9) where H FIRk is the frequency response of the FIR filter for the k-th subcarrier, i. e. , it corresponds to the fast Fourier transform (FFT) of ω, and X i,k presents the QAM data symbols for all k subcarriers within each i-th symbol. This strategy assigns a compensation step with low complexity to the transmitter. The result is shown in Figure  3 , where we can observe a flat spectrum except for the 12 inactive subcarriers, alleviating signal degradation.
Data 2. 1 Gridded in-situ data: the CANEK database We used an optimal interpolation product generated by  Durante et al. (2023)  from observational mooring array data. The dataset provides ocean currents (speed and direction) in the Yucatan Channel throughout the water column. The instrumentation on the moorings is a combination of Acoustic Doppler Current Profilers (ADCPs) and current meters in a configuration with up to ten moorings in the array as shown in Figure  1. The data cover the time period from July 2012 to July 2020, achieving more than 8 years with 99. 9% of temporal coverage from hourly measurements. The mooring array was serviced approximately once per year, configurations changed slightly between deployments, and more moorings were added in the 2018-2020 period. See  Candela et al.
For a specific case for the year 2016, the authors managed to predict the Loop Current pattern evolution 3 months in advance. This 1 to 3 months window of predictability fits the operational scope for the objectives of the phase 3 of the Understanding Gulf Ocean Systems program (UGOS-3), and one challenge for the near future will be to combine the published approaches and datasets in order get the best possible predictability skill of the Loop Current extension and eddy shedding. The main strength of the predictive model developed here is identifying the regions that have high forecast skill, firstly from altimetry, and then adding those deep flow measurements that effectively contribute to improve the predictability. Another strength of the predictive model is that the deep flow measurements can be reduced to a few locations which could be locations of moored observations, away from the near-surface intense flow that represents a challenge for operational mooring arrays.
High-frequency predictability implies also a challenge due to the limited resolution of the altimetric data, especially for predicting the detachment/reattachment events when the Loop Current is highly extended (>2000km) reaching north of 26°N. Although the Loop Current extension itself can act as a predictor, these events seem to be driven mainly by mesoscale eddy interactions in the North of the Gulf of Mexico, rather than by substantial changes in the flow anomalies in the Yucatan Channel over the previous weeks. However, this is a subject of intense discussion in the community since many detachments and separations have been linked with perturbations (eddies) coming from the Caribbean  (Abascal et al. , 2003; Athiéet al. , 2012; Athiéet al. , 2015; Sheinbaum et al. , 2016). Predicting trajectories of the cyclonic eddies that constrain the Loop Current using TOEddies or any related eddy tracking algorithm could contribute to improve the predictability of these detachment/ reattachment events in the future, especially if the upcoming data from the SWOT satellite allows us to better resolve the small cyclonic structures.
Acknowledgements The authors thank  Mohammad Reza Azizian ,  Arthur Dupuy ,  Stéphanie Sauze ,  Alexandre Heintz  and  Thierno Mamoudou Diallo  for scientific discussions, and providing many helpful comments and suggestions throughout the project. The authors appreciate helpful advice from  Olivier Marconot  and  Hubert Pelletier concerning XRD , and are grateful to  René Labrecque ,  Julie Ménard  and all the technical staff of  3IT  for the technical support. The authors thank  Umicore, Saint-Augustin Canada Electric (Stace) ,  Innovation en énergie électrique (InnovÉÉ) , the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  Fonds de recherche du Québec (FRQNT) ,  Mitacs , for the financial support. Abderraouf Boucherif  is grateful for a Discovery grant supporting this work.
Acknowledgments The authors thank the IPP Garching and LPGP-Orsay teams for the collaboration, their contributions and support during the development of the ONIX code. This work was supported by a grant from the  Swiss National Supercomputing Centre (CSCS)  under project ID s1061. We acknowledge  PRACE  for awarding access to the  Fenix Infrastructure resources , which are partially funded from the  European Union 's  Horizon 2020 research and innovation programme  through the  ICEI  project under the grant agreement No. 800858.