dataesr/scientific-paragraphs-categorization huggingface dataset:
Empirical Noise Estimates Table  4  provides sensitivity limits based on our initial reduction of PHANGS-JWST data. We evaluated the depth of our data with respect to detection of point sources at the resolution achieved in each filter and measurement of diffuse emission on a fixed larger scale. For the point-source limit, we first identified the faintest area mapped that was also fully contained within the footprint of maximum exposure time. Inside each area, aperture photometry was performed for a set of 50 independent circular apertures sized to match the 50% encircled energy radius of each filter. Background estimation for each aperture was also filterdependent, using an annulus spanning a radial range from two to three times the aperture radius. The sigma-clipped standard deviation of the background-subtracted flux for these "empty" apertures was then scaled by a factor of 10 to estimate the 5σ point-source detection limit in each filter (5σ f ), which accounts for a factor of 2 for the choice of 50% enclosed energy radius and a factor of 5 for the choice of significance level.
We subtract off the median smoothed version from the original map to remove large-scale structure and produce an unsharp masked image. We then use a median absolute deviation estimator to measure the local noise in the unsharp masked image. We iteratively reject all values larger than the local 3σ noise measurement in each filter. After the data rejection converges, the local noise estimates are smoothed with a top-hat filter that has a size of 15× the PSF FWHM and interpolating over missing values. We compare this local estimate of the noise to the noise estimates provided by the JWST imaging pipeline. The resulting noise maps are within 10% of the pipeline-generated noise maps. We also estimated the local noise by calculating the median autocorrelation of the data as a function of scale and examining the distributions of the data in signal-free parts of the images (though some bands show emission across the field).
Color Composites and Public Outreach Since the first observations were taken for the PHANGS-JWST Treasury survey in 2022 July, color composites that combine JWST imaging from multiple filters have been developed and released for all of our targets by astronomers, professional public outreach teams, the press, and the public. The color images have broadly captured the attention of both the science community and general public, not only because the structures portrayed are aesthetically stunning but also because the images vividly illustrate and make qualitatively accessible the physics of star formation, feedback, and the ISM, particularly when the UV-optical imaging from HST is included. Figure  6  shows the first composite PHANGS-JWST image published for NGC 7496, which was observed shortly after the end of commissioning and among the first science data to be released from the mission.
Figures  7  and 8  show RGB color composites, now based on only the MIRI imaging, for all four PHANGS-JWST first results galaxies. The diversity of the dust emission structure motivates the study of a representative sample of galaxies and our specific science goals (next section). The four galaxies bracket the full range of star formation properties in the sample (with NGC 1365 having the highest star formation activity and gas surface density and IC 5332 among the lowest) and feature varied dynamical structures (e. g. , bars in NGC 1365 and NGC 7496, strong spiral arms in NGC 628, and weak spiral structure in IC 5332). The PHANGS-JWST team is collaborating with image processor Judy Schmidt to produce color composites as new data are received and with the public outreach offices at ESA and STScI to support the communication of new results.
3 μm PAH feature. The F1000W and F2100W capture the dust continuum, with a Together with data from HST, MUSE, and ALMA, the integrated PHANGS data set allows for multiscale, multiphase studies of the star formation cycle across a range of galactic environments. In Figure  2 , the five UV-optical filters used in the PHANGS-HST program  (Lee et al. 2022)  are also shown; these mainly sample the stellar continuum and provide a view of dust in extinction. The HST imaging provides high angular resolution (0 8), comparable to JWST NIRCam imaging at 2 μm. The PHANGS-MUSE program  (Emsellem et al. 2022)  provides integral field unit spectroscopy over 0. 475 < λ/ μm < 0. 935, covering many of the same bands as HST but with coarser angular resolution (∼1″). PHANGS-ALMA  (Leroy et al. 2021)  shows the distribution and kinematics of the CO that traces the star-forming molecular medium, also at ∼1″ resolution.
g. ,  Larsen 2009; Chandar et al. 2017; Krumholz et al. 2019; Adamo et al. 2020a Adamo et al. , 2020b)). Due to dust extinction, it has been difficult to obtain a truly complete census of clusters across all galactic environments and ages. The PHANGS-JWST infrared imaging will now allow us to study the clusters in dusty galaxy centers, spiral arms, and bar-end regions. These are sites where super star clusters tend to be found, the cluster formation efficiency is expected to be highest (e. visible cluster population, we expect to detect 100 new embedded clusters in each galaxy, or ∼2000 across the sample, with the greatest gain in the dustiest and most active parts of the sample. Analysis combining JWST with HST data will also allow us to break the age-reddening degeneracy for optically detected clusters and derive robust constraints on age, mass, and reddening for both embedded and visible cluster populations (e.
g. , Figure  2 ). These investigations use the full set of PHANGS-JWST data for measuring the properties of embedded clusters. In particular, the near-IR bands provide key constraints on stellar properties. The F200W observations provide good visibility through the dust in the region of the SED still dominated by stellar photospheric emission. The excellent angular resolution (0 07) at 2 μm is required to resolve star clusters, which have effective radii between 0. 5 and about 10 pc  (Portegies Zwart et al. 2010; Ryon et al. 2017). Rodriguez et al. (2023)  and  Whitmore et al. (2023b)  took the first steps in identifying embedded clusters, estimating their physical properties, and examining the incompleteness in optical samples. Hoyer et al. (2023)  studied the nuclear star cluster in NGC 628 and the nature of the mid-infrared structure surrounding it.
Schinnerer et al. (2023)  showed that massive, dusty clusters can provide a sensitive diagnostic of the very dusty star-forming environments in nuclear regions when comparing ALMA data and simulations. Some of the clusters detected in the PHANGS-JWST survey push current stellar and dust models used by CIGALE to their limits due to their very young ages, high dust content, and/or low mass. Including dust models computed in the optically thick case and stellar populations with a stochastically sampled initial mass function may prove necessary to model such clusters.
Measuring the Duration of Embedded Star Formation PHANGS-JWST data enable characterization of the poorly studied embedded phase of star formation before feedback disrupts the natal cloud. Observations that resolve galaxies into discrete star-forming regions, clusters, and molecular clouds have enabled statistical measurements of the timescales associated with the major phases of the star formation cycle (e. g. ,  Kawamura et al. 2009; Kruijssen & Longmore 2014; Meidt et al. 2015; Corbelli et al. 2017; Grasha et al. 2018; Kruijssen et al. 2018 Kruijssen et al. , 2019a;; Grasha et al. 2019; Hannon et al. 2019 Hannon et al. , 2022;; Chevance et al. 2020a; Kim et al. 2022; Ward et al. 2022). Over the last decade, the (anti)correlation between clouds and H II regions or optically identified clusters has constrained the total lifetimes of clouds to 10-30 Myr (where the range represents the physical variation rather than the uncertainty; see, e.
2007; Aniano et al. 2020). This variability hampers the use of PAH emission as a star formation tracer  (Calzetti et al. 2007; Whitcomb et al. 2020 ) and leads to critical uncertainties when modeling their role in the ISM. The PHANGS-JWST Treasury data will yield the first highly resolved atlas of PAH properties-abundance, size distribution, and charge inferred from mid-IR band ratios-in the nearby galaxy population. These data can be used to build an empirical model to predict the abundance and characteristics of PAHs as a function of local metallicity, ISM phase, density, and radiation field (from HST, MUSE, and ALMA). The measurements underpinning this model must be highly resolved because radiation field, density, and ISM phase can vary by factors of ∼1000 on 10-100 pc scales, altering the PAHs  (Chastenet et al.
Williams et al. (2022)  leveraged the high-resolution images to make a careful measurement of evolutionary timescales in a spiral arm spur. The PAH imaging also shows us where the neutral ISM is not found. Rings of PAH emission with faint centers can be used to define the wall of "bubbles" or feedback-driven shells. While bubbles from feedback have been long studied (e. g. ,  Oey & Clarke 1997; Weisz et al. 2009 , and references therein), the exquisite resolution, sensitivity to structure on many scales, and wide dynamic range of the PHANGS-JWST images make them an excellent tracer of bubble walls. PHANGS-JWST data can be used to produce inventories of bubbles and shell features. These structures can be connected to the sources of feedback identified from PHANGS-MUSE and PHANGS-HST catalogs of star clusters and associations and to the kinematics of the cold ISM as traced by PHANGS-ALMA.
Robust Mid-IR Star Formation Diagnostics from Cloud to Galaxy Scales Mid-IR emission from hot dust is widely used to trace star formation at both low and high z (e. g. ,  Kennicutt & Evans 2012). Unfortunately, the calibration of mid-IR star formation tracers has a strong dependence on both spatial scale and environment (e. g. ,  Boquien et al. 2015) , which likely stems from a variable heating contribution by old stars and uncertain dust geometry (e. g. ,  Groves et al. 2012; Nersesian et al. 2019). PHANGS-JWST imaging, combined with supporting PHANGS data sets, enables a data-driven description of the origin and variations in mid-IR emission. Mid-IR emission is due to the heating of dust and PAHs by both young and old stars, but the bright thermal dust emission in the mid-IR continuum bands (F2100W and F1000W) is expected to respond differently to the local dust-heating radiation field than the PAH emission  (Draine et al.
from the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  in the form of an  Emmy Noether Research Group  (grant No. KR4598/2-1 ; PI: Kreckel). J. S. acknowledges support by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  through a  Canadian Institute for Theoretical Astrophysics (CITA)  National Fellowship. S. D. is supported by funding from the  European Research Council (ERC)  under the  European Unionʼs Horizon 2020 research and innovation program  (grant agreement No. 101018897  CosmicExplorer). H. A. P. acknowledges support by the  National Science and Technology Council of Taiwan  under grant  110-2112-M-032-020-MY3. (and its affiliated packages) (Astropy Collaboration et al. 2018, 2022). SAOImage  DS9 (Smithsonian Astrophysical Observatory 2000;  Joye  & Mandel 2003).
G. A. B. acknowledges the support from ANID Basal project FB210003. E. C. acknowledges support from ANID Basal projects ACE210002 and FB210003. K. G. is supported by the Australian Research Council through the Discovery Early Career Researcher Award (DECRA) Fellowship DE220100766 funded by the Australian Government. M. C. gratefully acknowledges funding from the DFG through an Emmy Noether Research Group (grant No. CH2137/1-1). COOL Research DAO is a Decentralized Autonomous Organization supporting research in astrophysics aimed at uncovering our cosmic origins. J. M. D. K. gratefully acknowledges funding from the ERC under the European Union's Horizon 2020 research and innovation program via the ERC Starting Grant MUSTANG (grant agreement No. 714907). A. D. B. acknowledges support by NSF-AST2108140 and award JWST-GO-02107.
As a consequence, it is of paramount importance to have tools that can help public authorities in contrasting undeclared work. In this respect, labour inspections play a crucial role. However, inspections are costly for the policymakers. Therefore, the issue related to the analysis of their efficiency deserves a deep exploration. We contribute to the debate on the topic by employing the Benford's law (BL, hereafter) as a methodology for evaluating the labour inspections efficacy. In so doing, we also provide a guide for policymakers on the firms' characteristics to be monitored with care. BL describes the frequency distribution of leading digits in many natural and human phenomena. Such a data regularity property has been discovered in  [3]  and better formalized by  [4]. In particular, BL states that the leading first digit 𝑑 in a set of numbers from many real-life data is not uniformly distributed, as one might naively expect, but it follows a logarithm-type law given by: 𝑝 𝑑 = log 10 ( 1 + 1 𝑑 ) , 𝑑 = 1, 2, … , 9 (1) where 𝑝 𝑑 is the probability that a number from the dataset has its first digit equal to 𝑑.
More recently,  [22]  elaborates on the worthiness of this law, quoting also some historical facts about its introduction. BL can be viewed a ''natural property'' of some datasets. The violation of the BL points to the presence of distortions and possible manipulations of the data. For this reason, such a statistical law is often exploited in the context of fraud detection. This is particularly true in the context of accounting, where BL is an acknowledged instrument of fraud detection (see e. g. [5, [23] [24] [25] [26] ). Indeed, the violation of the BL can mirror misreporting or cheating behaviours when dealing with datasets composed by balance sheet and income statements entries (on this, see also  [27] ). This paper moves from this premise. We test the compliance to BL of firms' income statements in the context of labour inspections.
More in detail, we consider a high-quality dataset of Italian firms' performance indicators composed of two subsamples. One of them contains companies audited in 2005 by the National Institute of Social Security while the other one relies to randomly selected firms. Firms are clustered at Italian regions level, to have a clear view of the different regional realities. In this framework, we pursue two different targets. On one side, we aim at identifying the items breaking more evidently the compliance with the BL; on the other side, we make an ex-post assessment of inspections' plausibility. The latter statement needs further clarification. Labour inspectors visit several firms each year, either based on their knowledge of the economic and production fabric of the territory they are in charge of, or because they receive whistle-blowing.
Description of the dataset In this work we make use of two samples of data drawn from the population of Italian building and construction companies (NACE section: F) operating in 2005. The size of the population is 529,757. Both samples come from an administrative source, that is data gathered by public authorities with a typical aim of control/knowledge of the statistical units. This kind of data are a very precious sources of information because they allow to study phenomena that would remain otherwise unknown. The first sample consists of the 14,777 building and construction companies (bcc henceforth) audited in 2005 by the National Institute of Social Security inspectors and the second is a random sample of 75,382 bcc. The two samples overlap by 2,539 units. The first sample contains information on firms' main economic characteristics as retrieved from the so-called ''Studi di Settore'' (SS in the following) of the Italian Revenue Agency 2  It is worth noting that this sample is non-random because inspectors choose to visit firms that are suspicious -i.
Methodology Given a dataset, the empirical probability that the first digits of one of its elements is 𝑑 will be denoted by p𝑑 , for each 𝑑 = 1, 2, … , 9. In order to check the validity of the BL, we employ two concepts of distance between the empirical probabilities p's and the theoretical probabilities 𝑝's in Eq. (  1 ) coming from the BL. The Minimum Absolute Deviation (MAD) is defined by 𝑀𝐴𝐷 = 1 9 9 ∑ 𝑑=1 |𝑝 𝑑 -p𝑑 |. (2) MAD is evaluated as the most reliable test for checking the validity of the BL, with a value below 0. 006 as close conformity, between 0. 006 and 0. 012 as acceptable conformity, marginally acceptable conformity for values between 0. 012 and 0. 015 and nonconformity otherwise (see  [5] ) The Sum of Squared Deviations (SSD) is defined as follows: 𝑆𝑆𝐷 = 9 ∑ 𝑑=1 (𝑝 𝑑 -p𝑑 ) 2.
, 2017) , partly because oxygen data are relatively sparse and sampling has been heterogeneous in this region over the last decades  (Ito et al. , 2017). Abstract Observations suggest that the tropical Pacific Ocean has lost oxygen since the 1960s leading to the expansion of its oxygen minimum zone (OMZ). Attribution to anthropogenic forcing is, however, difficult because of limited data availability and the large natural variability introduced by the Pacific Decadal Oscillation (PDO). Here, we evaluate the PDO influence on oxygen dynamics and OMZ extent using observations and hindcast simulations from two global ocean circulation models (NEMO-PISCES, MOM6-COBALT). In both models, the tropical Pacific oxygen content decreases by about 30 Tmol. decade -1 and the OMZ volume expands by 1. 3 × 10 5 km 3.
Identifying PDO Signal We used the monthly PDO index data distributed by NOAA (https://psl. noaa. gov/pdo/) to track PDO phase changes. We retained decadal variations associated with the PDO using a temporal butterworth low-pass filter with a cutoff period of 16 years that removes high frequency variations associated with shorter variability modes such as ENSO (Figure  S1  in Supporting Information S1). During the simulation period, the PDO index is characterized by two negative phases  (1958-1976 and 1998-2015)  and a positive phase  (1977 ( -1997, Figure S1 , Figure S1  in Supporting Information S1). Oxygen fields (oxygen concentration, OMZ volume and oxygen budget terms) were filtered using the same approach to remove frequencies higher than 16 years and retain the signal associated with the PDO.
01 μmol. kg -1. decade -1 , Figure  2c ). This later period is, however, the least sampled, with on average 2,034 observations per year versus 3,525 per year for the earlier period (Figure  2d ). These observed oxygen changes are almost four times larger than those simulated in the models (±0. 25 μmol. kg -1. decade -1 in MOM6, and ±0. 23 μmol. kg -1. decade -1 in NEMO). We assess the effect of heterogeneous sampling in space and time by subsampling the simulated oxygen fields at the location of available observations (solid colored lines in Figure  2c ). Once subsampled, the simulated oxygen changes amplitude increases by a factor two during the first two PDO phases and is almost zero during the last and least sampled period, improving the match with the data (Figure  2c , r 2 = 0. 59 vs.
For high-temperature JET and TFTR discharges, electron cyclotron emission (ECE) measurements of central electron temperature were systematically found to be up to 20% higher than those taken with Thomson scattering. In recent high-performance JET discharges, central T e measurements, performed with LIDAR Thomson scattering and the X-mode ECE interferometer, have been studied in a large database, including deuterium (DD), and deuterium-tritium plasmas (DT). Discrepancies between T e measurements have been observed outside of the experimental uncertainties. ECE measurements, at high T e , have been found to be higher or lower than those of LIDAR, depending on the specific plasma scenario. In addition, discrepancies between the peaks of the second and third harmonic ranges of the ECE spectrum have been interpreted as evidence for the presence of non-Maxwellian features in the electron distribution function.
In the past years, JET has worked to develop high-performance plasma scenarios after the installation of the ITER-like wall  [13]. These efforts have culminated in the first experimental campaign using DT fuel since almost thirty years, which has marked a new record in generated fusion energy. This occasion presented an excellent opportunity to investigate the issue of ECE-TS discrepancies. For this, the core temperature measurements on JET have been collected in a large database, that covers different plasma conditions and isotopic compositions including DT plasmas. Study of this database highlighted different behaviours for various plasma scenarios. For T e >5 keV, cases with T e,ECE > T e,T S and with T e,ECE < T e,T S have both been observed. Related ECE spectra seem to display features that could be attributed to the presence of EEDFs with non-Maxwellian characteristics.
ECE radiometer ECE interferometer HRTS LIDAR TS Also measurements from the ECE radiometer have not been included in this study. Due to the relatively low repetition rate of LIDAR, in fact, the high temporal resolution of the ECE radiometer is not necessary for comparing the two techniques. Furthermore, the radiometer does not allow comparison of second and third harmonics. In conclusion, the diagnostics that will be referred to in the rest of the paper for ECE and TS measurement are the X-mode ECE interferometer and the LIDAR Thomson scattering system respectively. 3 Pulse selection for the database ECE and TS measurements have been compared for pulses from several JET campaigns from 2018 to 2022. These experiments include DD and DT plasmas covering a variety of high-performance plasma scenarios, designed to demonstrate high fusion power for about five seconds with the ITER-like wall.
During the DT experiments, some of these pulses were designed to optimize non-thermal fusion reactions, with T-rich plasmas heated by D-only NBI. Finally, the database also includes pulses belonging to experiments exploring advanced scenarios, studying internal transport barriers, energetic particle modes and afterglow  [23]. For each pulse, LIDAR times were selected where the laser energy was above 0. 4 J and T e >1 keV. For each of these points, the closest ECE time, with a maximum difference of 10 ms, was identified. ECE spectra were converted into radial profiles using a magnetic reconstruction based only on magnetic measurements with no further constraints. For each ECE and LIDAR T e radial profile, a representative value of the centre is determined by averaging the measurement points included between 2.
85-3. 15 m. This averaging window helps to smooth out the effects of the different lines of sight for the two diagnostics (see Figure  1 ) and the uncertainties in the localization of the plasma axis position. The values used to compare the second and third harmonics of the ECE spectra were calculated averaging T e in the interval +/-5% around the frequency corresponding to the plasma centre according to the equilibrium reconstruction. The points that make up the database cover the evolution of the pulses from ramp-up to ramp-down or disruption. During the flat top phase for these pulses, the combinations of toroidal magnetic field (B t ), plasma current (I p ) and electron density (n e ) and temperature (T e ) covered by the different scenarios are shown in Figures  2  and 3   spectively.