dataesr/scientific-paragraphs-categorization huggingface dataset:
The local wind-wave relation is established by either a linear or an energyconserved decomposition. We also find that the local wind-wave relation is a power-law when the wind speed U 10 is above 4 m/s. The proposed method is first validated by applying the SEP method to buoy collected wave spectra data. The global pattern of the swell and the local wind waves is retrieved by applying the proposed method to a 17-year wind and wave database from the JASON satellite. Strong seasonal and spatial variations are obtained. Finally, a prediction model based on the empirical wind-wave relation is shown to successfully retrieve the wave field when the wind field is available.
5 ∘ N and 118 ∘ W, with the water depth of 1844 m) for nearly 18 years (i. e. , from 6 November 2003 to 31 December 2021) are used to illustrate the new swell identification method. The typical measurement accuracy for the NDBC buoy are 0. 55 m/s and 0. 2 m respectively for wind speed and significant wave height  (Evans et al. , 2003). Due to technical reasons, some data are missing, and there are a total of 149,089 valid pairs of wave height and wind speed values. The wind speed is measured by an anemometer located at z=4. 1 m above the sea surface, a conversion from U 4. 1 to U 10 is required before further studying the relation between wind and waves. Assuming that the marine atmospheric boundary layer is in a neutral stability logarithmic state, the surface wind profile is, U z = u * k ln z z 0 ( 9 ) where k is the von Kaŕmań constant and z 0 is the roughness length, which is usually set as 0.
4 and 9. 7 ×10 -5 m, respectively; z is the height above the sea surface; u * is the friction velocity which can be experimentally derived from U 10 and the drag coefficient C d (always set as 1. 2 ×10 -3 ) as follows, u * = ffiffiffiffiffi ffi C d p U 10 (10) Combining the above two equations, U z for z>0 can be reexpressed introducing U 10. By considering the measured wind speed at a height z 1 we finally have  (Ribal and Young, 2019) , U 10 = U z 1 ffiffiffiffiffiffi k 2 C d s 1 ln (z 1 =z 0 ) (11) Note that, to keep the self-consistency of this work, the parameters C d and z 0 are the same as those in the second dataset  (Ribal and Young, 2019). Finally, applying z 1 =4. 1 m in the above equation, the converted wind speed at 10 m can be expressed in a simplified way as U 10 =1. 084U 4.
1. A further test shows that a variation of the height, e. g. , 3≲z 1 ≲5 m, will have less than 2% difference in the estimation of U 10. Figures  2A, B  show the hourly U 10 and Hs data collected by the NDBC buoy 46086, where annual cycles for wind speed and wave height are visible. Moreover, the corresponding hourly wave spectra with frequency on the range 0. 02≤f≤0. 485 Hz are also used to perform the SEP algorithm to retrieve the swell wave and wind waves: see the example of five days wave spectra during 27-31 December 2021 in Figure  2C. Two regimes are distinguished, the one with high frequency (e. g. , f≥0. 15 Hz) corresponds to the wind waves, and another one with low frequency (e. g. , f≤0. 15 Hz) indicates the swell waves. Another dataset is a 17-year (from 2002 to 2018) calibrated JASON altimeters observed global measurements of U 10 and Hs  (Ribal and Young, 2019).
Different pulses are received by the satellite altimeter, coming from reflections at the ocean's surface. These different backscattered echos are classically treated using a model of the ocean's rough surface  (Brown, 1977) , seen as the convolution of a point source, a flat sea surface, and an assumed probability density function of sea elevation. Based on the fitting of the Brown model echo to the recorded waveforms and the using of a maximum likelihood estimator, the Hs information could be retrieved. The U 10 is then estimated by a mathematical relationship with the backscatter coefficient and the Hs using the Gourrion algorithm  (Gourrion et al. , 2002)  and Collard table  (Collard, 2005). This dataset was carefully quality controlled and calibrated to ensure long-term stability and cross-mission consistency.
Validation using the SEP algorithm With the aforementioned probability analysis, a nominal swell Hs sw can be identified, thus the wind-wave Hs L (U 10 ). To verify the extracted swell and wind-wave, the SEP analysis is also performed for the wave partitioning of the data collected by the NDBC buoy 46086. The PM64-based algorithm (e. g. , f s =0. 8f m ) is considered  (Gilhousen and Hervey, 2002; Portilla et al. , 2009). Note that when the wind speed is less than 2. 1 m/s, the calculated f s is then larger than 0. 485 Hz. Therefore, in such cases no local wind-wave is extracted. The measured joint PDF of U 10 and Hs sw (U 10 ) derived by the PM64-based SEP approach is shown in Figure  4A , in which the solid red curve is the conditional mean Hs sw ( U 10 ), and the maximum statistical difference between two subsets with equal sample size is indicated by an error bar.
According to the joint PDF, the Hs sw for NDBC buoy 41049, 46006 both equal to 1. 25 m. The Hs sw extracted from JASON data which is close to the buoy 41049 is also 1. 25 m, and the one corresponding to buoy 46006 is equal to 1. 18 m. The identified wind-wave is used to estimate the scaling exponents b for the local wind wave relation. Two decomposition methods are both used here, the results are illustrated in the insets in Figure  6 , in which the squares and crosses are the wind-wave derived from the linear decomposition method and the energy conservation based approach, respectively. The colors green and black are used to indicate the results from buoy and satellite data. Visually, the derived wind waves are in a good agreement between the buoy and satellite observed data, where a similar scaling feature is obtained.
More precisely, for the linear decomposition, the measured b l for the data recorded by the buoy 41049 and nearby JASON data are 2. 03 and 2. 11; the results for the buoy 46006 and JASON data are 1. 71 and 1. 53. On the other hand, for the energy conservation approach b e for the buoy 41049 and JASON data are 1. 37 and 1. 49; b e for the buoy 46006 and JASON data are 1. 25 and 1. 22. These results show that the JASON data have remarkable performances in the swell identification and the construction of a local wind-wave relation.
Though the concepts for Hs are different, the magnitudes are close to each other. Thus, the extension of the proposed method from buoy data to altimeter data is reasonable. One advantage of the current proposal is that the influence of anomalous values will be automatically excluded since the skeleton of the joint PDFs is considered. However, to accurately estimate the skeleton (i. e. , the nominal swell), a minimum sample size is required. One consequence is that the analysis is performed on a coarse time period, e. g. , seasons in this study. Note that both the atmospheric and oceanic movements are driven either directly or indirectly by solar radiations. Therefore, daily and annual cycles due to earth rotation and revolution are expected. The former one is hard to be detected using the current satellite database since it requires a much larger dataset to capture the daily variation, while the latter one is confirmed by our results.
The swell Hs sw derived from the proposed probability-based method is validated against the SEP analysis, see the comparison between Hs 0 in small winds and Hs sw (U 10 ) extracted from SEP approach in Figure  5. Thus, the introduced new method can be used to distinguish the swell Hs sw from collected Hs(U 10 ) without knowing the wave spectra information a priori. The global view of Hs sw was then extracted for the 17-year JASON data. The extracted seasonal variations and spatial distribution pattern for Hs sw are the same as the ones extracted by the SEP analysis of reanalysis data  (Semedo et al. , 2011). For instance, large Hs sw are found in midlatitudes during the boreal winter time, and the values of Hs sw in ACC are larger than 2 m. The local wind-wave Hs L (U 10 ) are then identified by either the linear decomposition or energy conserved approach.
5 m. The differences are acceptable for most regions when U 10 ≤12 m/s. Therefore, the model is suitable for moderate wind speeds to predict the instantaneous wave field. While the marine environment is complex, retrieved instantaneous wave height with the wind wave relation model may deviate systematically from the observations for the high-intensity wind speeds (figure not shown here). This systematical discrepancy is partially due to the underestimation of the high wind speed  (Hauser et al. , 2020). It might be corrected by either systematically checking the U 10 dependent bias or using a machine learning based model when the accumulation of the CFOSAT data is enough in the future.
Conclusions In summary, a probability-based swell and local wind-wave decomposition is proposed in this work, which does not require the knowledge of the wave spectrum information a priori. Its methodology is first illustrated by the joint probability analysis of a buoy collected wind and wave data, then validated by a comparison with the classical SEP analysis using wave spectra data. The new method is then applied to the 17-year JASON data to retrieve the spatial and seasonal patterns of the swell. Both spatial and temporal patterns agree well with the previous study by  Semedo et al. (2011). In this work, two different local wind-wave decomposition approaches are proposed. The first one is a linear decomposition, and the other one is based on an energy conservation hypothesis. Comparisons of the identified wind-wave by the two methods are made.
Funding This work was supported by the  National Natural Science Foundation of China  ( 91958203 ,  U22A20579  and  11732010 ). This research has been performed in the frame of the  MultiW2  project, funded by the  CNES/ TOSCA program. Funding of YG's  Cotutella doctoral research  project by  Reǵion Hauts-de-France  and  Xiamen University  is acknowledged. YH is partially supported by  State Key Laboratory of Ocean Engineering (Shanghai Jiao Tong University)  (Grant No. 1910 ). A copy of Python codes to perform the analyses presented in this work can be found at https://github. com/lanlankai.
| METHODS We collected cross-sectional data from employees working in organizations in different sectors located in 22 LEED-certified green office buildings (silver, gold, or platinum) across six Indian cities: New Delhi, Mumbai, Bangalore, Hyderabad, Kolkata, and Bhubaneswar. This study used probability sampling methods to select the 22 LEED buildings. The buildings were chosen based on accessibility; geographic diversity was included as a variation. According to IMDMESDI (2023), all these cities experience temperatures between a minimum of 7. 7 C-15. 3 C and a maximum of 32 C-33 C. However, the weather and climate conditions of these locations did not impact the topic of our study. Survey data were collected using convenience sampling because it could be collected via several avenues, such as email, online, and in-person, and usually has a high response rate  (Babin & Zikmund, 2015).
To minimize bias, the questionnaire was designed with methodological separation, where predictor variables were collected separately from criterion variables, and the items were randomly ordered for different constructs  (Podsakoff et al. , 2012). The items in the survey questions were measured with a five-point Likert scale. A pilot study was conducted online with 39 employees to ensure the survey's readability, remove errors if necessary, and ensure respondents understood the questions correctly. No cases of extreme values were found in the pilot study, and the data were normally distributed. A total of 460 surveys were selected for analysis after datasets with missing data were removed. Our study investigated employees' perceptions of GHRM practices that could influence their pro-environmental behaviors and job performance in green buildings.
Based on Islam et al. (  2022 ), we followed a power analysis to calculate the sample size of 460. Before data collection, the researchers gained approval for a low-risk ethical study from the Human Ethics Advisory Group (HEAG) at Curtin University to ensure that the research design met ethical standards. Environmental proactivity (EPr) was measured using Liu et al. 's (2017) six-item scale and a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). GHRM was measured using  Zibarras and Coan's (2015)  16-item scale with a five-point Likert scale ranging from 1 (never) to 5 (always). Job performance (JP) was measured with  Gong et al. 's (2009)  four-item scale with a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Pro-environmental behaviors (PB) and environmental performance (EP) were measured using Paillé and Mejía-Morelos' (  2014 ) five-item scale on a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
Introduction Mechanical wave propagation is a physical phenomenon upon which rely various industrial applications. Examples are numerous, across different scientific and technical fields: soil imaging in geophysics, medical imaging in healthcare applications, or ultrasonic testing experiments. The main advantage of mechanical waves is their capacity to propagate over large distances, and during their course to be sensitive to the fluctuations of the material characteristics (variations in sound velocity or in geometry, presence of defects. ). After propagation, signals bearing capital information on the material at hand are recorded and analyzed. Recovering quantitative data from this analyzing phase is a complex task. A typical family of configurations generating complex signals is guided wave propagation in plate-like geometries, where the propagation domain occupies a volume with a small thickness compared to the transversal wavelengths.
Acknowledgments This experiment was performed within the FAIR Phase-0 beam-time. The work has been supported by the  Spanish Ministry for Science and Innovation  under grants No. PGC2018-099746-B-C21 ,  PGC2018-099746-B-C22 ,  PID2019-104390GB-I00 ,  PID2021-125771NB-C21 , and  PID2021-125771NB-C22 , by the  Regional Government of Galicia  under the program " Grupos de Referencia Competitiva "  ED431C-2021-38 , by the " María de Maeztu" Units of Excellence program   MDM-2016-0692 , by the  German Federal Ministry of Education and Research (BMBF , project  05P2015RDFN1 ) and by the  GSI-TU Darmstadt  cooperation agreement. J. L. R. -S. is thankful for support from  Xunta de Galicia  grant No. ED481D-2021-018. I. G, I. L. , D. J. M. have been supported by  Croatian Science Foundation (HRZZ)  under porject numbers  IP-2018-01-1257  and CEMS at RBI.
These models are called Atmosphere-Ocean General Circulation Models (AOGCMs). They include a physical representation of the water cycle and can be used to calculate continental runoff on a 2D-spatial grid covering the continents, together with the spatially resolved air temperature. The model developed by  Donnadieu et al. (2006)  is called GEOCLIM, and it has been used for a large variety of studies. It was found that supercontinental configuration (for example, the Pangaea supercontinent that existed during the later Paleozoic and into the Mesozoic) would strongly limit the impact of changes to CO 2 on continental runoff because of the development of arid conditions over large continental surfaces. These periods are thus characterized by high steady-state CO 2 levels. Conversely, a wide dispersion of the continents should facilitate an increase of runoff with CO 2  (Goddéris et al.
2014). In addition to this, the climate sensitivity to atmospheric CO 2 (e. g. , global warming per doubling of CO 2 concentration) is dependent on the continental configuration, ranging from about 4°for a CO 2 doubling during times of supercontinental configuration, or when large continental masses are located on the South Pole (such as in the early Paleozoic), to 2°for the present-day configuration. Finally, still using GEOCLIM, Goddéris &  Donnadieu (2019)  found that the paleogeographic control of the evolution of the carbon cycle and the climate is more pronounced in the Paleozoic. In the Mesozoic and Cenozoic, the continental dispersion appears to be large enough to force the Earth system into a glacial stage since the breakup phase of Pangaea in the Jurassic. As this was not the case, higher solid Earth degassing is required to keep CO 2 at a high level, especially during the Cretaceous.
Following the method employed in GEOCLIM, the SCION (Spatial Continuous Integration) model  (Mills et al. 2021)  extends the COPSE global biogeochemical model (which itself is a modification of GEOCARB) to include a spatial land surface informed by the FOAM GCM, which is interpolated over time as the continental configuration changes. The effects of continental distribution on the modeled atmospheric CO 2 evolution are similar to those observed in GEOCLIM, and the model predicts higher CO 2 levels during the time of the Pangaea supercontinent when compared to COPSE. An advantage of this new model is that the continuous whole-Phanerozoic simulations typical of COPSE and GEOCARB can now be run using a spatial representation of climate. Adding a 2D representation of the continental surfaces allows for the simulation of the weathering fluxes accounting for the lithology.
FUNDING European Union's  Horizon 2020  ( 713694 );  Volkswagen Foundation (NeuroQNet II) ;  Agence Nationale de la Recherche  ( ANR-17-EURE-0002 ,  ANR-15-IDEX-0003 );  Region Bourgogne Franche-Comté ;  French Investissements d'Avenir ;  French RENATECH network and FEMTO-ST technological facility.
Acknowledgement The authors would like to thank the helps from  Prof. Mengxing Tang  and  Prof. Christopher Dunsby  at  Imperial College London  in the completion of this article and the reviewers for reviewing this article. This work was supported by  Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology  (No:  OHIC2022Y02  and No:  OHIC2022G06 ).
Methods FDTD. 3D Simulation were conducted using the commercial software FDTD LUMERICAL. For the results reported in the FDTD simulations sections, simulations conditions were chosen as follows. A plane wave of light is normally incident along the z-axis and the electric field is x-polarized. Symmetric and Perfect Match Layer (PML) boundary conditions are used in x and y directions. PML boundary conditions are chosen in the z direction. The optical constants for silver are extracted from Johnson & Christie's experimental data  61. Non-uniform mesh is used. For the simulation results reported in Fig. 7 , simulations conditions were chosen as follows. A plane wave of light is normally incident along the z-axis and the electric field is x-polarized. PML boundary conditions are set on the top and bottom of the computational domain (in the z direction), simulating a highly absorbent material.
S d ). We perform the numerical experiments by using MatLab software. To compute the integral of a bivariate function over a face of the simplex S d (for example, the evaluation of the enrichment functionals at the enrichment functions, needed in the construction of the matrix G ) and the integral of a bivariate function over S d (for example, the L 1 -norm of the approximation error) we use the command integral2. The results are reported in Figs. 1 2 3. We notice that for a fixed function f , not every set of enrichment functions significantly improves the accuracy of the approximation realized by the enriched finite element. The accuracy of the approximation depends on the chosen set of enrichment functions. the standard simplicial linear finite element (red dashed line), and the enriched finite element (S d , P enr linear finite element are realized by using the functionals defined in (37) and the sets of enrichment functions E 1 and E 2 for the left and the right picture, respectively.
Acknowledgment This research has been achieved as part of  RITA  " Research ITalian network on Approximation " and as part of the  UMI group "Teoria dell'Approssimazione e Applicazioni". The research was supported by  GNCS-INdAM 2022  projects and by the grant " Bando Professori visitatori 2022 " which has allowed the visit of  Prof. Allal Guessab  to the  Department of Mathematics and Computer Science of the University of Calabria  in the spring 2022.
The X7 printer uses a range of materials, including onyx, onyx FR, onyx FR-A, and white nylon, which can be reinforced with continuous carbon, glass, or Kevlar fibres. It has several printing variables accessible via its Eiger slicing software, such as layer thickness, number of walls, orientation and geometric positioning of the parts on the printing platform, number of fibre layers, fibre orientation (0 • , 45 mode (concentric, isotropic), and type of printable patterns (triangular, rectangular, hexagonal, gyroid, or solid)  [21, 22]. The tensile specimen studied was modelled using Inventor software and then exported in STL format before being imported into Markforged's slicing software (Eiger). In this study, all specimens were printed with a density of 100% solid pattern, with successive layers printed at ± 45 • (see Fig.
Uniaxial tensile test The experimental test conducted in this study was a uniaxial tensile test, which was carried out using an Instron 5569 tensile machine with a 50 KN force cell. The strain stress was measured with a 12. 5 mm gauge length extensometer and 40% of maximum elongation. The test speed was determined based on several tests according to ASTM D638, and a displacement speed of 10 mm/min was chosen for the tests. The data acquisition was performed using Instron's Bluehill Universal software, and the data was processed in accordance with the recommendations of the standard used. Young's modulus was calculated by considering the linear part of the nominal stress-strain curve, as was also done by S. Hasanov et al. [24]  in their work. In this study, a linear regression was used in Excel software with a regression coefficient R2 > 0.
Numerical simulation Finite element simulation was used to perform a numerical simulation of a tensile test, using the commercial software Abaqus Standard, with the aim of predicting the mechanical properties of a part containing a variable number of walls. In this case study, the mechanical properties of a specimen containing four walls were predicted. The walls and the solid were assumed to be isotropic and homogeneous. In their study, D. Krzikalla et al. [18]  considered identical properties for the walls and the solid parts, and assumed onyx as an isotropic material. When the specimen is reinforced with long fibres, a laminated shell or 3D homogeneous composite strategy can be used for the numerical simulation, as used in Refs. [18, 28]. The elastic parameters used for the simulation are shown in Table  2 ,  and Poisson's ratio (with a value of 0.
λ] do 8 x (i) ← a copy of x; 9 Flip ℓ bits in x (i) chosen uniformly at random; Choosing λ from a power-law distribution and taking p = λ n and c = 1 λ lets the (1 + (λ, λ)) GA optimize OneMax in linear time  [ABD20]. Also, as it was mentioned in the introduction, with randomly chosen parameters (but with some dependencies between several of them) the (1 + (λ, λ)) GA can optimize Jump k in time of e O(k) ( n k ) (1+ε)k/2 [AD20]. For the LeadingOnes it was shown in  [ADK20b]  that the runtime of the (1 + (λ, λ)) GA is Θ(n 2 ) and that any dynamic choice of λ does not change this asymptotical runtime. In our proofs we use the following language (also for the (1 + (λ, λ)) GA with static parameters). When we analyse the (1 + (λ, λ)) GA on Jump and the algorithm has already reached the local optimum, then we call the mutation phase successful if all k zero-bits of x are flipped to ones in the mutation winner x ′.