dataesr/scientific-paragraphs-categorization huggingface dataset:
N-S crustal-scale section across the oblique/transform margin of Alboran basin To examine further the structural relationships between extension and strike-slip faulting across the Alboran margin, we explore 2D multichannel seismic lines acquired during the MARSIBAL 1-06 cruise  (Comas and MARSIBAL1-06 Scientific Party, 2007)  and ESCI cruises  (Comas et al. , 1995)  across the Eastern Alboran basin (EAB). The studied seismic dataset consists of ~300 km and are deep-penetration multichannel seismic reflection studies (12 s two-way travel time -TWTT). Here, we study two lines namely MSB08 and MSB07 (see location in Fig. 1 ). For stratigraphic and structural correlations between the studied seismic lines, we used the Andalucia-A1 well (Fig. 6a ) and results from ODP 977 and 978 legs (see location in Fig.
Acknowledgments Víctor Tendero Salmerón ,  Guillermo Booth-Rea , and an anonymous reviewers are warmly thanked for their comments that greatly improved the manuscript. Editors  J. Phethean  and  S. Buiter  are thanks for handling the manuscript. The stereogram results were obtained using Win-Tensor, a software developed by  Dr. Damien Delvaux ,  Royal Museum for Central Africa, Tervuren, Belgium  ( Delvaux  and Sperner, 2003). The processed seismic data were interpreted using Kingdom IHS Suite© software. This research benefited from discussions and support of  OROGEN  project, an academic-industry research consortium between  TOTAL ,  CNRS  and  BRGM.
Acknowledgements The authors gratefully acknowledge the generous nancial support of the  European Union Horizon 2020 Research and Innovation actions MSCA-RISE-ENGIMA  (No. 778072 ) and  MSCA-RISE-MELON  (No. 872631 ). Z. K. and M. S. acknowledge the  Slovenian Research Agency  program  P1-0125  and  P2-0091 , respectively.
The diffraction limit is defined at the highest frequency (100 GHz), determining the smallest radiated wavelength λ min. Considering a distance R = 25 cm and a square radiating aperture of L x,z = 220 mm side, the Rayleigh diffraction limit δ x,z is calculated as δ x,z = λ min R L x,z ≈ 3. 4 mm. ( 13 ) The imaged space r is defined by pixel size of 2. 5 mm, chosen slightly oversampled compared to the resolution limits in order to mitigate the effects of grid shift between the target position and the nearest pixels. In the absence of filtering of the subspaces according to their level of coherence, we first determine the field matrix E 0 ∈ C n ν ×n r. The singular value decomposition filtering is then performed directly on the tangential electric field E tan measured by the scanner. Using the notation introduced in the paper, the data are arranged to form two scalar near-field matrices E (1)   x and E (1)  z , both complex and of dimensions n ν × n r.
Current research on theoretical analysis focuses on the changes in the wind field of the UASS rotor and the movement of droplets affected by the wind field using calculations and simulations  (Zhu et al. , 2019; Tang et al. , 2020 Tang et al. , , 2021;; Zhang et al. , 2022). Experimental research is mainly carried out in wind tunnels or in the field combined, with present-day climate environment and crop types. Current experimental studies on drift include the characteristics of UASS drift, drift distance, and the influence of operating parameters or spraying systems on the drift  (Wang et al. , 2020 (Wang et al. , , 2020a (Wang et al. , , 2021)). However, current research on UASS drift is still scarce. Data on the spray drift of drones and their impact on the environment are scarce, and the factors affecting drift are still being studied.
Drift test database and modeling The development of drone spray technology is relatively new, and the UASS spray drift data base is still limited   (Wang et al. , 2020,b). According to the study developed in Part 3, the drift of UASS is simultaneously affected by multiple operating parameters. Current research focuses on the influence of a specific factor or a small number of factors on drift while ignoring the interactions between multiple factors  (Chen P. et al. , 2021). The choice of operating parameters can be easily affected by the experience level of the operator, and the use of models can reduce the risk of drift caused by human decision-making errors  (Chen et al. , 2022). The selection of operating parameters for drone sprayers relies on a large number of field trials. The conduction of further assessments on larger-scale application fields would help to optimize operating parameters and fully understand the potential and limitations of UASS spray technology.
According to recent observations, future research should incorporate more parameters, including system parameters, operating parameters, and environmental parameters, into the scope of the study so as to build a more accurate spray model. The existing AGDISP (Agricultural Dispersal) and CHARM (Comprehensive Hierarchical Aeromechanics Rotorcraft Model) were not originally designed for UASS sprayers. The previous models could not include all UASS platforms, rotor configurations, and spraying system types. However they could still act as a reference for UASS drift models  (Chen H. et al. , 2021). Therefore, a large accumulation of simulation or field data can provide the opportunity to establish a suitable model for UASS. Nevertheless, as the weather and crop structure in field experiments remain uncontrollable variables, it is challenging for these variables to be integrated into a decisive strategy  (Bartzanas et al.
Standardization of measurement methods Table  3  in section "Unmanned aerial spraying systems drift measurement method" highlights a lack of consistent test protocols in existing research projects. Many UASS spraying systems are available, and the types of sampling collectors and collection locations are also diverse. Therefore, a summary and comparison of existing research data are difficult to make. The ISO22866 standard provides a field drift test method, which can be used to compare the drift characteristics and environmental risks between different types of spray equipment. However, it may not be suitable for the UASS drift test. For example, it is necessary to determine a unified test method according to various typologies of UASS, including a number of spraying systems and spatial layouts.
Including the upwind drift data caused by the UASS wingtip vortex into the scope of the evaluation is a necessary step. NY/T 3213 is China's first agricultural UASS industry standard  (Zhang S. et al. , 2020). The standard determines the modeling rules, quality requirements, inspection methods, and rules of the UASS. However, the standard only defines the measurement of the UASS spray width, and the UASS drift test method is not mentioned. In order to further clarify the drift characteristics of UASS and to establish a drift model or database of UASS, it is necessary to first determine the corresponding field test method. Major difficulties still arise when testing drone spray in wind tunnels. The principle of drift testing in wind tunnel is that it should have a sufficient size so that the airflow is not disturbed by the inner wall or sprayer (or its installation)  (Iso 22856, 2008).
Finally, we analysed the contribution of these articles to provide theoretical distinction between ecosystem and supply chain. To find the main contributions in the field, we used Scopus as the major source of information. In September 2018 we searched all articles containing the word "ecosystem" in the title, abstract and keywords. Given the purpose of our research, we limited the perimeter to articles written in English and published in business, management and accounting journals, hence excluding conference proceedings, books and papers in other domains. We did not limit our search in time, so we could retrieve contributions as old as the Scopus database (the first paper extracted dates back to 1969 in World Energy Resources and it was excluded afterwards as it is not about business ecosystems).
Then we develop three additional propositions. The short cases were developed through the compilation of secondary data from trade magazines or company websites. They were then completed with interviews of industry representatives. For the 3D modelling, animation, and game design case we interviewed a software developer and a user of these products. For the automotive case we interviewed three industry representatives: one executive from an OEM, one from a first-tier supplier and a garage manager. For the RISC V ecosystem, seven interviews were conducted with different ecosystem members. The interviews were used for another research project fully focused on the RISC V ecosystem. The interviews lasted between 30 and 90 min. The number of interviews and their length were commensurate with the complexity of each ecosystem and supply chain, and the depth of analysis we needed to achieve.
In this software segment, many product benchmarks and product comparisons available online show that Cinema 4D from Maxon and Blender are competing options for users. Cinema 4D is sold by a company named Maxon, which has established a supply chain to deliver the product to users. Maxon use bilateral contracts to hire resources to work on the development of the software and then they can sell the software to a distributor who can package it for clients along with additional services. In the same product and market segment, Blender is an opensource software that leverages an ecosystem. In 2002, the "Free Blender" crowdsourcing campaign orchestrated by the non-profit Blender Foundation collected €100,000 allowing to release Blender as a community based open-source project. Add-ons are created by an ecosystem made up of individuals, but a few companies, such as Ubisoft and Epic Games or organizations such as NASA, also use Blender and contribute to its development.
Our analysis of Maxon and Blender shows that a supply chain (Maxon) and an ecosystem (Blender) can compete. None of these organizations have yet created decisive advantages in terms of cost or in functionalities offered to clients. Consequently, no governance form has the upper hand. Neither collective shared assets, nor investment in complementary assets create advantages. Two different governance forms can therefore coexist. This is best described as the result of a historical development and of path dependence processes. The two governance forms have evolved differently, but neither has a dominant position. A specialist of this software, who we interviewed, commented on the case: "You will find some people who are very keen to have an open-source model available on the market and others who have been trained on a proprietary software and would never consider to switch to another tool.
We report an improved measurement of the degree-scale cosmic microwave background B-mode angular-power spectrum over 670 deg 2 sky area at 150 GHz with POLARBEAR. In the original analysis of the data, errors in the angle measurement of the continuously rotating half-wave plate, a polarization modulator, caused significant data loss. By introducing an angle-correction algorithm, the data volume is increased by a factor of 1. 8. We report a new analysis using the larger data set. We find the measured B-mode spectrum is consistent with the ΛCDM model with Galactic dust foregrounds. We estimate the contamination of the foreground by cross-correlating our data and Planck 143, 217, and 353 GHz measurements, where its spectrum is modeled as a power law in angular scale and a.
It consists of the 2. 5 m aperture Huan Tran Telescope with 1274 transition-edge-sensor bolometers sensitive to the 150 GHz band  (Arnold et al. 2012; Kermish et al. 2012). In 2014, we installed a continuously rotating half-wave plate (HWP) at the prime focus  (Takakura et al. 2017). The HWP modulates incoming linear polarization signals and therefore reduces low-frequency noise due to both the atmosphere and the instrument. In POLARBEAR Collaboration (2020; hereafter PB20), we reported a measurement of the degree-scale B-mode angularpower spectrum using data from three years of observations from 2014 to 2016. The HWP modulation results in a relatively low knee in the noise spectrum at ℓ knee = 90, where the contribution of the low-frequency noise to the power spectrum uncertainty becomes comparable to that of detector white noise.
Calibration of the pointing model and each detector's properties (pointing offset, relative gain, and relative polarization angle) is the same as in PB20. Calibration using observed power spectra, i. e. , absolute gain, beam smearing due to pointing jitter, and absolute polarization angle, is updated and found to be consistent with PB20. We confirm that the new data set passes the same set of null tests as PB20. We assume that the systematic uncertainties are the same as PB20 because we use the same seasons of data and calibration. Note that statistical uncertainties are still dominant even with the additional data. Finally, we cross-correlate the POLARBEAR map with Planck 143, 217, and 353 GHz maps and estimate constraints on r considering Galactic dust foregrounds as in PB20. In Section 2 we explain the glitches due to the HWP angle error and improvements in the data processing.
Example of Data with the Angle Error Figure  1  shows an example of HWP encoder data causing the angle error Δθ(t) and the resulting glitch in the detector timestream. To understand this, it is necessary to explain how the encoder works. We measure the angle of the HWP using an encoder plate with 360 precisely machined teeth and one index hole. Optocouplers regularly sample whether the gate is open or closed at 40 kHz. Synchronizing with detector sampling at 191 Hz, we store the count of the edges and the timing of the last edge. Since the timings of the edge and detector sampling are asynchronous, the raw encoder count (the blue points) has a quantization noise of 0°. 5. We fix this quantization error by interpolating the middle angle at the detector sampling between edges using the timestamp information (the blue line).
Data Selection We applied the same PB20 data selection method to the data processed with the new encoder correction. The results of data volume and data selection efficiencies are summarized in Tables  1  and 2. The final volume of data available increased by 81. 6% from PB20. The total observation time has slightly increased from PB20 by 3. 1%. We use observations from 2014 July 25 until 2016 December 30. We also recovered some missing observations by reconstructing the database of all observations. The detectors and their calibrations (pointing offset, relative gain, and relative polarization angle) used in this analysis are identical to those in PB20. Thus, we have no increase in detector yields. The main data increase comes from improvements in data selection efficiency. To explain how the encoder correction affects the data selection, we briefly explain our data selection procedure.
Comparison with the Results of PB20 Here, we compare the new results with those of PB20 and discuss their consistency. Since both data sets pass the null tests, the results are statistically valid. However, the new data set significantly overlaps the PB20 data set, and the change should be the result of the additional data recovered in Section 3. In this section, we evaluate the probability of the consistency using a Monte Carlo (MC) simulation as follows. First, we evaluate the effective data increase between PB20 and the new data set. Next, we simulate expected shifts of measurements from PB20 due to the increase of statistics. Finally, we evaluate the probability by comparing the actual new measurement with the MC simulations.
Effective Increase in the Data Volume First, we evaluate the effective data increase by comparing the noise power spectrum, N b. The estimate of fractional data increase of 81. 6% in Section 3 assumes uniform weights among detectors and observations. In practice, however, each observation has a different noise performance depending on the weather and other instrumental conditions. In the map-making and coadding steps, we average data using the inverse-variance weighting. If the recovered data have a better noise performance than the PB20 data set, the effective data increase can exceed the naive estimation. In the analytic power spectrum uncertainty estimation known as the Knox formula  (Knox 1995) , the inverse of the noise power spectrum is proportional to the sensitivity-weighted data volume per solid angle as p s = - + N n t N f ℓ ℓ 1 2 4 exp 1 , 17 ℓ det obs NET 2 sky beam 2 [ ( ) ] ( ) where n det is the total number of detectors, t obs is the total observation time, N NET is the instantaneous sensitivity of a single detector, 4πf sky is the solid angle of the observing patch, and the exponential term is the Gaussian beam smearing effect.
Conclusion We perform an improved analysis of the PB20 data, the three seasons of POLARBEAR observations on a 670 deg 2 patch of the sky. We successfully recover 80% more data by improving the angle error correction of the rotating HWP. By processing the data using the same analysis pipeline as was used in PB20, we measure the CMB B-mode power spectrum at the multipole range 50 < ℓ < 600. We find no excess signal beyond that expected from the combination of a ΛCDM model and a Galactic dust foreground. We place an upper limit on the tensor-to-scalar ratio r < 0. 33 at a 95% confidence level. The change in the POLARBEAR B-mode power spectrum from PB20 is consistent with statistical expectations due to the additional data. Our result demonstrates the possibility of measuring degreescale CMB anisotropies with a ground-based telescope located in the Atacama desert of Chile.
The rotating HWP is a key technique for separating contamination from atmospheric fluctuations and achieving a good noise performance at low frequencies. The low-frequency noise performance enabled by the continuously rotating HWP is one of reasons why some future experiments, including the Simons Array experiment  (Suzuki et al. 2016 ) and the Small Aperture Telescope of the Simons Observatory experiment  (Ade et al. 2019) , plan to employ rotating HWPs. As we show in this paper, accurate angle encoding is a key to the success of this methodology. The POLARBEAR project is funded by the National Science Foundation under grants AST-0618398 and AST-1212230. The analysis presented here was also supported by Moore Foundation grant No. 4633, the Simons Foundation grant No. 034079, and the Templeton Foundation grant No.
58724. The James Ax Observatory operates in the Parque Astronómico Atacama in Northern Chile under the auspices of the Comisión   ( ), which agrees with the above discussion about the data volume. The first measurement D b ˆobtained above, however, is random. In order to evaluate the conditional probability having the first measurement as PB20, we repeat a trial of drawing a set of X b,i until D b ˆbecomes sufficiently close to the PB20 result. On the other hand, we do not apply any condition on Y b,i and the resulting ¢ D b ˆ. To reduce the computational cost, we reuse the same X b,i for 100 draws of Y b,i. The total number of MC realizations is 100,000 with 1000 draws of X b,i. Next, we extend this method to the cross-spectrum with Planck. Although the Planck data are the same for the first and second measurements, the change of POLARBEAR data affects the cross-spectrum.
Human Subjects Survey Data Household Income and Expenditure Survey The face-to-face survey, broken up into 40 rounds of collection, was implemented over a period of 10-months (May 2019-March 2020) in order to capture seasonal fluctuations in consumption, production and income. In addition to staff from KNSO, a total of 10 interviewers and 5 supervisors-divided into 5 teams-were employed to implement the survey. The survey questionnaire was developed in English using the World Bank Survey Solutions Computer Assisted Personal Interview (CAPI) software. The HIES questionnaire was divided into a series of modules (Supplementary Table  S1 ). Beyond the household-level food recall, there was also a module including a semi-quantitative food frequency questionnaire, where individual respondents within households were asked about the frequency of their consumption of 79 foods.
The sixth section detailed the availability of physical infrastructure, such as sewage and waste disposal. The final section focused on broad changes experienced in the village within the past 10 years. Of the 147 villages in Kiribati, the HIES was implemented in 111 and it was intended that one VRS would be conducted in each village (i. e. , VRS target was one interview to be conducted in 111 different villages). In total, 109 villages were included in the VRS with 111 interviews being completed (i. e. , some villages were interviewed more than once). The VRS questionnaire was administered to village authorities. Respondents included elected village leaders  (51) , unimane/unaine (elders; 41), teachers (1), pastors (2), and others  (5). The data were collected via interview of the HIES team supervisors using Survey Solutions Software.
Data Analysis The first module included general demographic and descriptive analyses at the individual, household, village, and island levels. These analyses utilized key basic indicators characteristic of HIES surveys, including variables such as geographic location, age, sex, poverty and food security status, and educational distributions, and provided foundational context for subsequent research modules on marine ecology (module 2), social, institutional, and economic dynamics (module 3), and human health outcomes (module 4). Further, these analyses provided key criteria for comparisons between islands and villages within our study (e. g. , along gradients of population density), and across time scales (e. g. , with past Kiribati HIES data).
Construction of the Consumption Expenditure Aggregate Food Consumption. The monetary value of food consumption was attributed to the quantity consumed over the past 7 days for each food consumed by each food acquisition source (cash, own production, gifts, and exchange). The market survey was used to convert quantities of consumption expenditure that were reported in non-standard units of measurement to grams. Whole food acquisition was converted into edible quantities and nutrients using the Pacific Nutrient Database  (72). Caloric availability was derived using the Atwater equation where 1 gram of protein = 4 calories, 1 gram of carbohydrate = 4 calories, 1 gram of fat = 9 calories, and 1 gram of pure alcohol = 7 calories  (72). Only food consumed by the household was included, whether it was sourced from cash transactions, own-account production, gifting, or through exchange.
Deductions were made from the imputed rent for maintenance costs of owner occupiers. Dietary Intake Food Frequency. Each of the 79 food groups were coded into their respective frequencies of consumption based on a provided serving size. All foods reported in "other" categories were translated, coded, and incorporated during the data cleaning and analysis stages. For each of the food groups, participants reported the frequency with which they consumed one serving based on the following options: (1) never/almost never; (2) once per month; (3) once per week; (4) three times per week; (5) daily; (6) more than daily; (7) more than three times per day.