dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses We used multiple correspondence analysis to investigate the underlying structure of the dataset, which was fully categorical. The aim was to investigate whether the various chemical compounds reported in the literature formed groups based on some categorical variables. This would help to identify experimental conditions in which they would be more likely to be detected. We included the year of publication (expecting that certain molecules would only be detected in recent years due to technological improvement in detection methods), the reference of each paper (to test for potential effects of publications reporting a large number of molecules), the cancer type (expecting different molecules to be observed for different cancers), and the detection methods (expecting different molecules would be detected with different methods).
c) Breast cancer Breast cancer is the most frequently diagnosed cancer in women worldwide, with more than two million new cases per year, and it is the first cause of cancer death in women, with 626,700 deaths world-wide (data from 2018,  [257] ). Despite current diagnostic techniques based on mammograms and biopsy when mammograms shows breast change, breast cancer is often fatal when detected too late  [262]. Despite the importance of finding early breast cancer biomarkers, only 8% of studies looking at odors linked to cancer have studied breast cancer. Among the 19 studies that investigated volatile emissions associated with breast cancer, 113 compounds were isolated and identified. Only four types of samples were used to study breast cancer odors: exhaled breath, urine, salivary and cell culture.
Multiple correspondence analysis The multiple correspondence analysis only explained 4. 1% of the variance of the dataset. This indicates that, overall, there are very few compounds in the dataset that are specific to certain publications, cancer types, year of publication, or detection method. This also suggests that many VOCs can be detected using multiple methods. If this is the case, it could lead to the discovery of potential biomarkers that can be relatively easily detected using the wide range of machines available in pathology laboratories and hospitals.
It also aims to estimate the effectiveness of development and management policies in the marine coastal zone. The regular acquisition of data allows the establishment of a long-term monitoring of the evolution of coastal water quality, as well as the observation of the consequences of large-scale alterations and modifications that are more related to regional anthropogenic activities. This paper provides an overview of the main characteristics of SRN data (descriptive statistics and data series main patterns) as well as an analysis of temporal trends using specific numerical tools available as an R package to help make use of such data. Main results of several research projects are also highlighted providing the readers with examples of what is doable with such a data set.
The objective of this monitoring is to evaluate the influence of continental inputs on the marine environment (nitrogen, phosphate, silicate) and their consequences on possible eutrophication processes. It also aims to estimate the efficiency of wastewater treatment plants and policies for the development and management of the coastal zone and more generally the possible elimination of such discharges. The regular acquisition of data allows the establishment of a long-term monitoring of the coastal waters along three transects located off Dunkerque, Boulogne-sur-Mer and in the Bay of Somme, making it possible to pretend to be able to deconvolute the effects of large-scale changes from those linked to more local anthropic activities  (Lefebvre et al. , 2011). Since 1992, the SRN dataset has included long-term time series on marine phytoplankton and physico-chemical measurements along the east coast of the English Channel and the French section of the southern bight of the North Sea (Dunkerque).
SRN data are complementary to REPHY and REPHYTOX datasets  (PHYTOBS, 2021; REPHY, 2021; REPHYTOX, 2021). Phytoplankton data cover microscopic taxonomic identifications and counts, up to the species level, and pigments measures (Chlorophyll-a and pheopigment). Physico-chemical measurements include temperature, salinity, turbidity, suspended matters (organic, mineral), dissolved oxygen, and dissolved inorganic nutrients (ammonium, nitrite+nitrate, phosphate, silicate).
Objectives The objective of this paper is to present the SRN dataset, from the sampling strategy to data collection (including associated Quality Assurance/ Quality Controls Steps) and data investigation and storage. The characteristics of the different datasets as well as a general interpretation of their variability will be presented. Based on existing valorisations of the SRN dataset, we will demonstrate that these data are of relevance not just for furthering understanding in marine phytoplankton ecology, but also for public policy needs such as assessment of environmental or ecological status as requested by EU directives or Regional Sea Conventions. We also propose some numerical tools based on an R package available for the scientific community and developed specifically to rapidly process such data and therefore to valorise the findings.
Database To manage coastal monitoring data, Ifremer has developed the Quadrige² information system (https://wwz. ifremer. fr/envlit/Quadrige-la-base-de-donnees; last access on 28 April 2022), which combines a database with a variety of interpretation and information product development tools. As an information system, Quadrige² plays a crucial role in: (1) storing basic monitoring data, such as the results of analyses from all monitoring networks, in a safe, optimal, supervised, and scalable manner, and (2) interpreting and valuing the data. Once the data has been stored and a quality level assigned to it, it is ready for use in a wide range of applications. As a result, this system is the required link for monitoring data between data collection in the field and its availability in various formats.
Data Validation The data are collected in the field and/or laboratory, then enter into the Quadrige² database using an application with the same name. The control entails modifying the data entered (results and metadata) to ensure that it is consistent with the bench book (or field sheets). After this check is completed and any necessary corrections are made, the data are validated: -Confirmation of the technical validity of the data which corresponds to the result of the analysis, -Data locking, so that it can no longer be edited, even by the person who entered it, -Data distribution: verified data may be taken and disseminated by all Quadrige² users who have access to the database.
Discussion and conclusion The SRN data, which have been collected since 1992, represent one of the longest long-term datasets in the English Channel and the southern bight of the North Sea. It enables the study of phytoplankton dynamics and diversity, as well as changes in particular composition in response to anthropogenic and/or climatic change. Indeed, this data have been used by scientists from several backgrounds, and for a variety of research purposes. In the following paragraphs, main outputs from these research activities will be highlighted. This review can help the scientific community interested in this SRN dataset to identify topics that can be addressed or those that have not been addressed. It also allows for new topics to be discussed in more detail since the general patterns have been identified.
+ - We also confirmed from SRN data that periods of Phaeocystis globosa dominance are generally associated with high concentrations of Pseudo-nitzschia complex. The simultaneous presence of P. globosa and Pseudo-nitzschia complex will cause the creation of structures resembling mini-bearings (Pseudo-nitzschia needles planted in Phaeocystis colonies) that will be passively swallowed by the fish. These structures can cause mechanical aggression of the gill and/or digestive tissues opening the way to viral and bacterial infections. This mechanical aggressiveness will become even more crucial as new needle-shaped species or those with pointed spicules have been found in our investigations, contributing to the creation of these irritating assemblages. These are Chaetoceros sp. , some Thalassiosira sp.
Acknowledgments The acquisition of this dataset was performed in the framework of the SRN network, created and implemented by  Ifremer  and the  Artois Picardie Water Agency  in 1992. The people cited in the team list and contributors include regional coordinators of the SRN, key analysts and experts in phytoplankton observations, key analysts in physico-chemical measurements, those who contributed to structuring SRN data in the Quadrige 2 database, and individuals who contributed to the data through their https://doi. org/10. 5194/essd-2022-146 Preprint. Discussion started: 28 July 2022 c Author(s) 2022. CC BY 4. 0 License. personal expertise. Other contributors such as the REPHY and REPHYTOX teams, crewmembers, students and PhD who have contributed to valorisation of the SRN data are also gratefully acknowledged.
μm data is 18-35 arcsec, so the sources identified in these surv e ys are unresolv ed, their e xact spatial locations are poorly constrained, and it is likely that many sources are confused within the Herschel beams. Urquhart et al. ( 2022 ) , in the Bright Extragalactic ALMA Redshift Surv e y (BEARS), used ALMA to observe a set of 85 gravitational lens candidates from the Herschel Bright Sources (HerBS;  Bakx et al. 2018 Bakx et al. , 2020b ) )  sample that lie within the South Galactic Pole field observed by H-ATLAS. The primary goal of the observations, which were spectral scans co v ering most of ALMA Bands 3 and 4, was to determine the spectroscopic redshifts of the sources in these fields. Ho we ver, since the spectral line emission, when detected, is typically found within a very small fraction of the observed spectra, the rest of the data can be used for serendipitous measurements of the continuum at the observed frequencies.
We used these new ALMA continuum data to address two specific science questions in this paper. First, since the ALMA Band 4 data have angular resolutions of ∼2 arcsec, we used the images to study the multiplicities and morphologies of the sources so as to further understanding the nature of the sources within these fields. Resolved or multiple sources could be lenses, protocluster cores or simply sources that are confused along the line of sight. Unresolved sources could be gravitational lenses with small Einstein radii or even individual hyperluminous infrared galaxies (HLIRGs) with intrinsic luminosities of > 10 13 L. Second, the ALMA Band 3 and 4 data are particularly useful for constraining the Rayleigh-Jeans side of the dust spectral energy distribution (SED) from these galaxies, even for galaxies at redshifts up to 5, and hence for characterizing the colour temperatures and emissivities of the dust.
A L M A O B S E RVAT I O N S A N D DATA P RO C E S S I N G The details of the sample selection, observations, and the data calibration are described by  Urquhart et al. ( 2022 ) ; we only provide brief summaries of those topics here. Ho we ver, because the continuum imaging differs from the spectral line imaging, we provide full details about the continuum imaging here. As stated abo v e, the BEARS sample consists of a subset of 85 fields from the HerBS sample. The HerBS sample, which was selected by  Bakx et al. ( 2018 ) , consists of unresolv ed Hersc hel sources with 500-μm flux densities > 80 mJy and with photometric redshifts of > 2 derived from fitting the Herschel data with the template from  Pearson et al. ( 2013 ). Efforts were made to remo v e nearby ( z ≤ 0. 1) spiral galaxies and blazars that may have otherwise satisfied these selection criteria.
BEARS used a combination of the Atacama Compact Array (ACA; also called the Morita Array) and the main ALMA 12-m array to observe these fields. Data for 12 fields were acquired with the ACA during ALMA Cycles 4 and 6 in programmes 2016. 2. 00133. S and 2018. 1. 00804. S. Each target was observed using single pointings larger than the Herschel 500-μm beam. The observations co v ered a frequenc y range from 86. 6 to 115. 7 GHz (with the exception of HerBS-49, which is missing data from 97. 0-98. 6 and 108. 9-112. 2 GHz) with multiple spectral windows, each of which had a bandwidth of 2 GHz and 256 channels. In programme 2019. 1. 01477, which was e x ecuted in Cycle 7, all 85 fields were observed with the 12-m array in ALMA Band 4 in the frequency range 139. 0-162. 2 GHz (with a small gap in coverage at 150.
2-150. 9 GHz). Additionally, 75 fields that (with the exception of HerBS-37 and HerBS-39 1  ) had not been previously observed with the ACA were observed with the 12-m array in ALMA Band 3 within the frequency range 89. 6-112. 8 GHz (with a small gap in co v erage at 100. 8-101. 5 GHz). This frequency set-up was designed to impro v e our efficienc y in measuring robust redshifts  (Bakx & Dannerbauer 2022 ). Each field was observed using single pointing that were larger than the Herschel 500-μm beams. Every spectral window used to co v er these frequenc y ranges had 1920 channels and co v ered a bandwidth of 1. 875 GHz. The ACA visibility data were manually calibrated using the COMMON ASTRONOMY SOFTWARE APPLICATIONS ( CASA ) package version 5. 6. 1  (McMullin et al. 2007  ; CASA Team 2022 ), while the 12-m Array data were pipeline-calibrated with the same version of CASA.
This included all the standard calibration steps applied to the phases and the amplitudes of the data. The manual calibration included visual inspections of every data set to identify and remo v e data with any irregular amplitude or phase values. Continuum images were created using TCLEAN interactively within CASA. The characteristics of the final images are listed in Table  1. We used all data from all spectral windows excluding any channels that contained potential spectral lines. Each field was imaged separately. The central position of each image is the same as the phase centre of each field, which in turn is equi v alent to the coordinates of the source originally identified in the Herschel images. Different pixel scales and image sizes were used for the ACA Band 3 observations, the 12-m Array Band 3 observations, and the Band 4 observations.
In each case, the pixel scales were set to o v ersample the beams, and the image sizes were set to encompass the whole of the primary beams. Natural weighting was used primarily to optimize the data for source detection. The Hogbom algorithm  (H ögbom 1974 )  was used as the deconvolver because it provided the best detections for the low signal-to-noise ratio unresolved sources. Two set of images were created with and without the primary beams corrections that adjust the signal levels for off-centre sources. The images without the primary beam corrections were used for identifying sources and for measuring the noise levels in the ACA data; the images with the primary beam corrections were used for measuring flux densities for all of the sources. The calibration uncertainty is expected to be 5 per cent  (Privon et al.
Photometric measurements We identified sources as locations in the images without primary beam corrections where the surface brightnesses peaked at ≥5 σ ( ≥0. 20 mJy beam -1 in the 12-m data and ≥0. 55 mJy beam -1 in the a This refers to the diameter of the region where the primary beam is 0. 2 × its peak value, and it is also the diameter of the fields imaged by ALMA. ACA data). We also measured 151-GHz flux densities for any source with associated line emission detected at the ≥5 σ level by  Urquhart et al. ( 2022 ). Note that some sources observed with the ACA at 101 GHz were separated into multiple sources when observed with the 12-m array at 151 GHz. Flux densities were measured using aperture photometry within the images with the primary beam corrections. For most sources, we used elliptical apertures with axis ratios and position angles equi v alent to the beam shape for each image.
In the 12-m data, nine detected sources are located > 15 arcsec from the centres of the primary beams where the sensitivity levels drop notably relative to the central regions. To measure the local background noise levels around each source within the primary beam corrected images, we used relatively small circular annuli centered on each source. The diameters of these annuli were set to 20-25 arcsec for the Band 3 data and 15-20 arcsec for the Band 4 data. In the ACA data, all detected sources lie relatively close to the centres of the fields where the responsivity of the telescope is still 85 per cent, but because of the size of the beam, it is not possible to measure the background noise in regions with the same responsivity. We therefore measured the background noise levels in ACA images without the primary beam corrections using circular annuli with diameters of 90-120 arcsec positioned at the centre of each image, which should yield representative noise levels for sources centred in these fields.
M U LT I P L I C I T I E S Submillimetre and millimetre interferometers, including ALMA, have been essential for locating or resolving individual infrared and millimetre sources that had been detected with single-dish telescopes, including Herschel , the Atacama Pathfinder Experiment, the Atacama Submillimeter Telescope Experiment, the James Clerk Maxwell Telescope, the Large Millimeter Telescope, and the South Pole Telescope. When the infrared, submillimetre, and millimetre sources initially detected in deep fields with 15-arcsec resolutions are then re-observed using interferometers with angular resolutions of ࣠ 3 arcsec, the new data often show that a significant fraction (potentially up to 70 per cent) of the 15-arcsec submillimetre sources have multiple counterparts (e. g. Hodge et al.
S P E C T R A L E N E R G Y D I S T R I B U T I O N S For a subset of the objects where the spectroscopic redshifts were measured by  Urquhart et al. ( 2022 ) , we constructed SEDs based on the Herschel 250-500-μm flux densities (from H-ATLAS Data Release 2) and the ALMA 151-and 101-GHz flux densities (as inte grated o v er the entire fields) and then performed an analysis of the SEDs. The 101-and 151-GHz data in particular can be used to constrain the emissivity index β of the dust emission, but the SED data can also be used to search for temperature variations versus redshift and to compare the results of photometric and spectroscopic redshifts. The SEDs for fields with single sources will represent single objects and are therefore straightforward to work with. The flux densities for these fields from Herschel correspond to the same sources seen in the ALMA data (assuming that any lower redshift galaxies in the foreground contribute negligible amounts of emission at these wavelengths), so the Herschel and ALMA data can be straightforwardly combined to create individual SEDs for individual sources.
Ho we ver, we can also work with fields containing two sources that are at the same redshift. The Herschel data can be combined with the sum of the ALMA flux densities for the sources in those fields to create SEDs that are still useful for determining the average dust temperatures and emissivities of the detected objects, although note that, if the sources in these fields have significantly different average dust temperatures, the SEDs may appear unusually broad. With fields containing two or more sources with different or unidentified redshifts, combining the integrated ALMA flux densities with Herschel data will not produce SEDs that hav e an y meaningful physical interpretation. It is also very difficult to accurately disentangle the contributions of the different sources in these fields to the total integrated flux densities measured in the Herschel data.
For the subset of fields that either contain a single detected source with a spectroscopic redshift or that contain two detected sources with the same spectroscopic redshift (as based on the data from Urquhart et al. (  2022 )), we performed some analyses on the SEDs of the Herschel and ALMA data. The SEDs for this subset are largely consistent with dust described by single modified blackbodies with colour temperatures ranging from 26 to 38 K and dust emissivity indices β of 2. We also demonstrated that the ALMA (observed frame) 151/101-GHz ratios provided a more reliable measurement of β than the single modified blackbodies that are fitted to the Herschel and ALMA data. With rare exceptions, we found no evidence of other sources of emission in the ALMA bands. We found that the relation between colour temperature and redshift for our sample was relatively weak.
Both these photometric results and the spectroscopic data from  Urquhart et al. ( 2022 )  have allowed us, to some degree, to separate individual infrared-bright objects at high redshift, associated galaxies at high redshift, and confused sources. The individual infrared-bright high-redshift objects are potentially gravitationally lensed galaxies or HLIRGs and should be studied further in follow-up ALMA observations to confirm the phenomenology of these objects, while some of the associated highredshift sources could actually be parts of protoclusters and should be examined more carefully at multiple wavelengths to understand more about how these structures are forming. MNRAS 522,  2995-3017 (2023)  (La evoluci ón de los c íumulos de galaxias desde el amanecer hasta el mediod ía c ósmico) with reference (PID2019-105776GB-I00/DOI:10.
13039/501100011033) and acknowledge support from the ACIISI, Consejer ía de Econom ía, Conocimiento y Empleo del Gobierno de Canarias and the European Regional Development Fund (ERDF) under grant with reference PROID2020010107. Herschel is an ESA space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from NASA. This paper makes use of the following ALMA data: ADS/JAO. ALMA#2016 ALMA#. 2. 00133. S, 2018 ALMA#. 1. 00804. S, and 2019. 1. 01477. 1. 01477. S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, A UI/NRA O, and NAOJ.
AC K N OW L E D G E M E N T S The authors thank the anonomous re vie wer as well as  Robert Ivison  and  Shuowen Jin  for their comments on this paper. GJB acknowledges support from  STFC  Grant  ST/T001488/1. SS was partly supported by the  ESCAPE  project;  ESCAPE -The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures  has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement no. 824064. SS also thanks the  Science and Technology Facilities Council  for financial support under grant  ST/P000584/1. SU w ould lik e to thank the  Open University School of Physical Sciences  for supporting this work. TB acknowledges funding from  NAOJ   ALMA Scientific Research  Grant Numbers  2018-09B  and  JSPS KAKENHI  No.
In the second phase, the numerical simulation methods were matured for the simulation of the deflecting Krueger device. This aimed to gain of some best practice in terms of mesh generation and solver settings. In parallel the wind tunnel experiments were setup and performed, including modifying the wind tunnel models by incorporating a controlled moveable Krueger device into the leading edge of existing high-lift airfoil models, and maturing and synchronizing the time-resolved measurement techniques applied. The final phase is dedicated to numerically simulate the preformed experimental conditions including the measured deployment schedules of the Krueger flap motion and to compare with the detailed experimental database obtained in the wind tunnel experiments for validating the numerical tools.