dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, thanks to the rotation, a change in offset can be distinguished from a change in the background field. A linear change in offset would translate into a linear trend in the data displayed in Figure  4 ; a change in background magnetic field would result in changing the amplitude of the sinusoidal oscillations. Therefore, with the fit and despin procedure, we are not removing any important information about the change in background field. It is to be noted that the linear trend in the spin-plane components, which we model as linear offset drift, can be also possibly explained by a MASCOT's precession motion with much larger period than the rotation. The two possibilities are hard to distinguish as the analyzed time period is too short compared to assumed precession. However, as this is only geometrical effect, it would also not change the resulting external field estimate.
The fit profile is shown by the red curve in Figure  4. The offset changes during the analyzed period by ∼2 nT. The fit parameters used for the despinning of the data yield the rotation period of 138. 9 s. The rotation axis of the MASCOT lander coming from the fit is a = (-0. 71, -0. 62, 0. 34) in MasMag internal coordinates and a = (0. 62, 0. 71, -0. 34) in MASCOT lander coordinate system. This converted to spherical coordinates means azimuth  = 49 • and elevation  = -20 •. 4. Data despin. The fitted parameters were used for despinning the data and for the offset removal. The linear offset, coming from the previous fit, was removed from all three components. It should be noted that the offset that was removed is completely recovered from the fit only in the rotational plane. The fit cannot distinguish between the DC value of the external magnetic field component in the rotation axis and an internal offset along the axis.
However, any constant contribution of the external magnetic field for the whole time period is not important for the further analysis. The data without offset were then despun; that is, each vector was rotated around the rotation axis by an angle corresponding to reversed rotation as given by the fit. The resulting data are in an inertial system that can be related to the asteroid body fixed frame, once the attitude of the MASCOT during the descent is known. For our analysis, however, the orientation of the system relative to Ryugu is not important. The result represents the best effort cleaned and calibrated data from the period just before (100 ms) the first contact with the surface. The final data are displayed in Figure  5. Here, the three components of the magnetic field are shown in the upper three panels, and the magnetic field magnitude is displayed in the bottom panel.
Although the data still probably contain some interference from the MASCOT flight system, the variation mainly reflects the background magnetic field. The data set starts when MASCOT is at a height of about 32 m above the surface (from the motion analysis by the MASCOT's flight dynamics team and the image analysis of the MASCOT's camera team) and the last data point is just before the first contact with the surface. The variation during this interval is in the order of 1 nT, and there is no increase visible in the magnitude with the decreasing distance to the surface. The magnitude of the observed external magnetic field is for the whole period between 1 and 3 nT, which is consistent with a typical interplanetary magnetic field at the distance of 1. 24 AU (heliocentric distance of Ryugu at the time of MASCOT operation).
Acknowledgments The lead investigator group at the Technische Universität Braunschweig is financially supported by the Deutsches Zentrum für Luft-und Raumfahrt and the Bundesministerium für Wirtschaft und Energie under Contract 50OW1501. The MasMag team would like to acknowledge extensive support and help during integration and testing from the DLR MASCOT system team in Bremen lead by Tra-Mi Ho and MASCOT operation preparation and control before and during the landing from the DLR team in Köln lead by Christian Krause. The analysis was supported by CNES flight dynamics team and DLR Berlin camera team providing the estimated MASCOT trajectory. B. Weiss thanks the NASA Emerging Worlds program (Grant NNX15AH72G) for support. The data used for the study are available on the Harvard Dataverse (under https://doi.
Acknowledgements. We thank  T. Boekholt  and  D. Carvalho  for discussions. This work was supported by  CFisUC  ( UIDB/04564/2020  and  UIDP/04564/2020 ),  PHOBOS  ( POCI-01-0145-FEDER-029932 ), and  ENGAGE SKA  ( POCI-01-0145-FEDER-022217 ), funded by  COMPETE 2020 and FCT, Portugal.
ESTIMATION RESULTS WITH REAL DATA The above approach has been tested with data recorded from our Gipsa-lab STM prototype  (Blanvillain et al. , 2014)    The corresponding numerical values for model (  5 ) are summarized in table 1 below. Gv 15 V /V fv 4 kHz G i 1e9 V /A f i 13 kHz k 16. 5nm -1 Gp 1. 2 nm/V Table  1. Numerical data For this system, measurements are obtained when operating the STM in a fixed position, under simple PI regulation of the distance between tip and sample (via output direct regulation). The corresponding output measurement and available control input are presented in figures 5 and 6 respectively, over 5 seconds (under a setpoint control during 3 seconds first, then followed by a square wave tracking configuration). The estimation for coefficient gV b with the above reduced order observer is then tested with those data, and considering some rough estimate for d 0 + z s here set to 52.
Although null results from multiple experimental searches are in tension with this interpretation  [5, 6] , detailed analyses demonstrate the large * Deceased January 2017 sensitivity to theoretical assumptions in the comparison of WIMP search results between different nuclear targets  [7]. In this Letter, we explore with the same nuclear target the parameter space that corresponds to the CDMS event excess. Throughout 2017-2018, DAMIC acquired data for its dark matter search with a tower of seven 16-megapixel CCDs (6. 0 g each) in the SNOLAB underground laboratory. Each CCD is held in a copper module that slides into slots of a copper box that is cooled to ∼140 K inside a cryostat. The top module (CCD 1) was made from high-radiopurity copper electroformed by Pacific Northwest National Laboratory  [8] , and is shielded above and below by two 2.
5-cm-thick lead bricks. The other modules (CCDs 2-7), made from commercial copper, populate the bottom segment of the box. The box is shielded on all sides by ∼20 cm of lead and 42 cm of polyethylene to stop environmental γ rays and neutrons, respectively. The innermost 5 cm of the lead shield and the bricks inside the box are ancient (smelted more than 300 years ago) and have reduced radiation from 210 Pb (τ 1/2 =22 y) contamination. Boiloff from a liquid nitrogen dewar is used to purge the volume around the cryostat from radon, whose level is continuously monitored. The overburden of the laboratory site (6010 m water equivalent) suppresses cosmic-muon backgrounds to a negligible level. Details of the DAMIC infrastructure can be found in Ref. [9]. The DAMIC CCDs were developed by Lawrence Berkeley National Laboratory MicroSystems Lab  [10].
The image contains a two-dimensional stacked history (projected on the x-y plane) of all ionization produced throughout the exposure. For details on the readout of DAMIC CCDs see Ref. [11]. The DAMIC detector was commissioned in the summer of 2017 with a red (780 nm) light-emitting diode installed inside the cryostat. Images were acquired with varying light exposures to confirm the efficient charge transfer and to calibrate the output signal of each CCD in units of eV electron equivalent (1 e -=3. 8 eV ee ) following the procedure in Ref. [11]. For the dark matter search, data images were acquired with 3×10 4 s or 10 5 s exposures, immediately followed by "blank" images whose exposure is solely the 130 s readout time. Image quality was monitored throughout data acquisition, including visual inspections.
Images with visible gradients from transients of leakage current after restarting the electronics or caused by temperature changes, or with visible patterns from readout noise, were discarded before processing. Images acquired when there was a measurable level of radon (>5 Bq m -3 ) around the cryostat were also excluded because they have an increased background from penetrating γ rays emitted by short-lived radon daughters. A total of 5607 images from 801 exposures, together with their corresponding blanks, were considered for this analysis, with an integrated exposure time of 308. 1 d. Image processing started with the pedestal removal and correlated-noise subtraction procedures of the DAMIC analysis pipeline described in Refs. [11]  and  [12] , respectively. We used the images for this analysis and images from higher-temperature data runs acquired in early 2017 to identify spatially localized regions of high leakage current due to lattice defects and generated "masks" following the procedure in Ref.
A selection on ∆LL as in Ref. [11]  was then used to reject clusters compatible with noise. We started with blank images, which contain only readout noise, and added leakage charge according to the value measured in the corresponding exposed image. The likelihood clustering algorithm was run on the simulated images to obtain a sample of simulated clusters. We determined from the ∆LL distribution of all simulated images that a selection of ∆LL≤-22 results in <0. 1 clusters from noise in the final dataset. The detection efficiency for ionization events as a function of energy was estimated with the CCD-response Monte Carlo following the procedure that was validated with γ-ray calibration data in Ref. [11]. We simulated pointlike ionization events with uniform distributions in energy and depth (z) and added the pixel values directly on the blank images.
The likelihood clustering algorithm was run, and from the fraction of simulated clusters of a given energy that survive the selection criteria, we reconstructed the acceptance for ionization events as a function of energy. The acceptance starts at 10% at 50 eV ee , increasing to 50% at 77 eV ee , until it plateaus at 90% above 120 eV ee because of the fraction of clusters that touch the mask. To obtain background predictions that can be compared to the likelihood clustering output, we produced images with simulated events sampled randomly from the (E, z) templates of the baseline background model and an additive systematic correction to account for the PCC region on the back side, treating CCD 1 and CCDs 2-7 separately. We applied the same clustering, reconstruction, and selection procedure as in the data to construct probability density functions (PDFs) in (E, σ x ) space normalized to 1 in the fit region E ∈ [0.
05, 6] keV ee and σ x ∈ [0, 1. 2] pixel, excluding Si K fluorescence E ∈ [1. 6, 1. 8] keV ee. For a statistical test to check the consistency between the background model and the data, we assumed a decaying exponential with characteristic decay constant convolved with the detector energy response as a generic signal PDF obtained from the (E, σ x ) template of uniformly distributed events in (E, z) space by scaling the amplitude as a function of energy and normalizing to 1 in the fit region. We defined a two-dimensional (E, σ x ) unbinned extended likelihood function following the formalism in Ref. [11]. Clusters from CCD 1 and CCDs 2-7 were considered independent datasets with their own background PDFs. We minimized the joint -ln L using MINUIT with the PDF amplitudes b 1,2-7 , c, and s (baseline background, PCC correction and generic signal), and as free parameters.
L. exclusion limits from our previous result with a 0. 6 kg d exposure  [11] , other experiments  [4, 6] , and the 90% C. L. contours for the WIMP-signal interpretation of the CDMS silicon result  [4]. esis with a p value of 2. 2 × 10 -4. If we perform the fit to the data from CCD 1 or CCDs 2-7 separately, the resulting bulk component is statistically consistent between the two datasets with a higher statistical significance in CCD 1, which has the lowest background. The statistical significance of the exponential bulk component is driven by an excess of events at low energies with σ x ∼0. 2 pixel. We explored the possibility that these events arise from an improper modeling of front-surface (z∼0) events, which can also populate this region of parameter space. We removed from the data and in the generation of the PDFs clusters where only one pixel has a value greater than 1.
ACKNOWLEDGMENTS We are grateful to SNOLAB and its staff for support through underground space, logistical, and technical services. SNOLAB operations are supported by the  Canada Foundation for Innovation and the Province of Ontario Ministry of Research and Innovation , with underground access provided by  Vale  at the Creighton mine site. The CCD development work was supported in part by the Director,  Office of Science, of the U. S. Department of Energy  under Contract No. DE-AC02-05CH11231. We acknowledge financial support from the following agencies and organizations:  National Science Foundation  through Grant No. NSF   PHY-1806974  and  Kavli Institute for Cosmological Physics at The University of Chicago  through an endowment from the  Kavli Foundation ;  Gordon and Betty Moore Foundation  through Grant No.
GBMF6210  to the  University of Washington ;  Fermi National Accelerator Laboratory  (Contract No. DE-AC02-07CH11359 );  Institut Lagrange de Paris Laboratoire d'Excellence  (under Reference No. ANR-10-LABX-63 ) supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program  under Reference No. ANR-11-IDEX-0004-02 ;  Swiss National Science Foundation  through Grant No. 200021 153654  and via the  Swiss Canton of Zurich ; Mexico's  Consejo Nacional de Ciencia y Tecnología  (Grant No. 240666 ) and  Dirección General de Asuntos del Personal Académico-Universidad Nacional Autónoma de México (Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica  Grants No. IB100413  and No. IN112213 );  STFC Global Challenges Research Fund (Foundation Awards  Grant No.
Statistics and graphical representation All figures were produced using GraphPad Prism software version 7. 0 (Graphpad Software TM , USA) Comparisons between the different experimental conditions were performed using a non-parametric Mann-Whitney test. In case of multiple groups which are all compared to each other, a Kruskall-Wallis test (KW) was first performed, and in case of positivity of KW test (threshold p < 0. 05), a Mann-Whitney non-parametric test was then practiced in a side-by-side comparison, with a post-hoc correction using the method of Benjamini and Hochberg. Statistical analyzes were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC, USA). A p-value < 0. 05 was considered to be statistically significant.
Funding The second and third authors are partially supported by the project MTM2016-77642-C2-1-P of Ministerio de Economía y Competitividad, Spain; the second author is partially supported by Dirección General de Investigación e Innovación, Consejería de Educación e Investigación of Comunidad de Madrid (Spain), and Universidad de Alcalá under grant CM/JIN/2019-010.
Each fiber is reconstructed into the 3D space using a custom MATLAB code. First, the extremities of each fiber are detected in all two images. Then, those extremities are triangulated into the 3D space. Finally, the centroid location (x c,i ) and the orientation (p i ) of each fiber are optimized such that the difference between the projection of the fiber onto each camera and the actual image is minimized. The measurement volume is that of a cube of ≈ 13 cm length centered at the center of the tank. N ≥ 5, 000 individual trajectories are stored and used to compute the rotation statistics for each case. The characteristic time of the dynamics of a random signal is given by the correlation function. The correlation function of ṗi (t) is defined as, C ṗi (t) = ṗi (t)ṗ i (t + τ ). ( 5 ) Here there is no summation over i.
FUNDING This work was carried out in the framework of  FlexFiT Project  ( ANR-17-CE30-0005-01 ) funded by the  French National Research Agency (ANR) , the  Labex MEC Project  (No. ANR-10-LABX-0092 ) and of the A* MIDEX  Project (No. ANR-11-IDEX-0001-02 ), funded by the  Investissements d'Avenir French Government program  managed by the  French National Research Agency (ANR). In addition to NSF, this work and its open-access publication was supported by the  University of California.
Acknowledgements. M. E. D. acknowledges financial support by a visiting fellowship from the  Ecole Nationale Supérieure de Lyon, Université de Lyon. DF and RW acknowledge support from the  French National Program of High Energy  (PNHE). Simulations were performed with the EPOCH code financed by the grant  EP/P02212X/1  on resources provided by the French supercomputing facilities  GENCI  through the grant  A0030406960.
67. The MUSE catalogue includes 171 LAEs 1 before multiple image removal, with redshifts in the range 2. 9 < z < 6. 7. All LAEs were detected with Muselet 2  , a detection software for emission lines in MUSE cubes. Table  1  summarizes the effective lens-corrected volume surveyed by the MUSE observations behind A2744. The detection fluxes of the LAEs are measured with SExtractor  (Bertin & Arnouts 1996)  using the MAG_AUTO segmentation on Lyα Narrow Band (NB) images. These NB images are continuum subtracted, and their spectral width is manually adjusted to the spectral extent of each LAE to ensure the best recovery of the total Lyα flux. The automated segmentation is efficient in dealing with a wide variety of spatial profiles, including high distortion induced by lensing. All technical details regarding the LAE extraction method including some examples can be found in Sect.
3. Selection of LBG and LAE galaxies at z∼2. 9-6. 7 Two different methods were used to select LBGs in this field among the sources detected by Astrodeep in the MUSE field of view: the usual three-band dropout technique applied to HFF data, and a method based on pure photometric redshifts and probability distributions. For the dropout (two-color) technique, we adopted the same criteria as proposed by  Bouwens et al. (2015b)  for the selection of galaxies at z ∼3. 8, 4. 9, 5. 9, and 6. 8. Photometric redshifts and redshift probability distribution (noted P(z)) were computed with the code New-Hyperz 3 , originally developed by  Bolzonella et al. (2000) , based on the fitting of the photometric Spectral Energy Distributions (SED) of galaxies. The input Astrodeep catalog presented in Sect. 2 also includes photometric redshifts.
Acknowledgements. Partially funded by the  ERC  starting grant CALENDS (JR), the  Agence Nationale de  la recherche bearing the reference  ANR-13-BS05-0010-02  (FOGHAR), and the  "Programme National de Cosmologie and Galaxies" (PNCG)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES, France. This work has been carried out thanks to the support of the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'Avenir" French government program  managed by the  ANR. This work also recieved support from the  ECOS SUD Program   C16U02. NL acknowledges support from the  Kavli foundation. FEB acknowledges support from  CONICYT  grants  CATA-Basal AFB-170002  (FEB),  FONDECYT Regular 1190818  (FEB), and  Programa de Cooperación Científica ECOS-CONICYT C16U02 the Chilean Ministry of Economy, Development, and Tourism's Millennium Science Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics, MAS.
Acknowledgements The authors acknowledge fruitful discussions with  L Yi  and the rest of the PLIONA team. This work was supported by the  Knut and Alice Wallenberg Foundation , the  European Research Council  ( ERC-2014-CoG grant 647121 ) and by the  Swedish Research Council , Grant No. 2016-05012. The simulations were performed on resources provided by  Grand ÃĽquipement National pour le Calcul Intensif (GENCI)  (projects  A0040507594 ,  A0050506129 ) and at  Chalmers Centre for Computational Science and Engineering  ( C3SE ) provided by the  Swedish National Infrastructure for Computing  (grants  SNIC2018-2-13  and  SNIC2018-3-297 ).
Acknowledgments The authors would like to thank  Thierry Baron  and his team from  CNRS-LTM  for the InGaAs epitaxy on InP. This work was supported by the  French National Research Agency (ANR)  under the  "Investissements d'avenir" programs :  ANR 10-AIRT-0005  ( IRT NANO-ELEC ),  ANR   10-EQPX-0030  ( EQUIPEX FDSOI 11 ) and by the  ST-LETI Alliance Program.
II. EXPERIMENT Powder x-ray diffraction data were collected at beamline ID15B of the European Synchrotron Radiation Facility (ESRF). A polycrystalline sample of LiV 2 O 4 was prepared by a high-temperature solid state reaction and portions were loaded into two identical symmetrical-type diamond anvil cells (DACs) prepared with 300 μm anvil culets and stainlesssteel gaskets indented to 30 μm. Using helium as the pressuretransmitting medium, the pressure was controlled online using a gas membrane and calibrated using online ruby fluorescence  [12]. One DAC was used for measurements at 300 K, with the pressure increased to 21. 0 GPa before the gasket failed. The second DAC was cooled to 20 K using a He cryostat and a first set of measurements were made at this temperature upon compression to 15.
3 GPa. The DAC was then warmed to 100 K with pressure maintained and a second set of measurements were made upon decompression from 15. 5 GPa at this higher temperature. The diffraction pattern at each pressuretemperature step was recorded on a Mar555 2D detector using a 1 s exposure of 30 keV radiation (λ = 0. 411536 Å), during which the DAC was rocked by ±3 •. The collected diffraction patterns were processed using DIOPTAS  [13]. The intensities and profiles of the measured Bragg reflections were found to be strongly affected by texturing and strain effects, preventing meaningful Rietveld refinement of the structure from being carried out, although the lattice parameters of the unit cell could be determined through Le Bail fitting done using GSAS  [14]. The region 2θ = 6. 0 • -7. 8 • was excluded from all fits to mask a broad background feature.
We analyze the first eight of Juno's orbits around Jupiter (designated as PJ1, PJ3, PJ4, PJ5, PJ6, PJ7, PJ8 and PJ9), with each perijove probing a cross-section of Jupiter's atmosphere at a different longitude and spanning latitudes from the north pole to the south pole (no MWR data were obtained during PJ2). Dates and longitudes of each perijove are summarized in Supplementary Table  1. The raw data (antenna temperatures) were processed through a deconvolution algorithm (See Methods and Janssen et al. , 2017  19  ) that removes synchrotron radiation and microwave cosmic background radiation to recover calibrated atmospheric brightness temperatures whose emission angle dependence is parameterized by three coefficients with spatial resolution constrained by geometry and the antenna beam. The subset of nadir brightness temperatures thus obtained from PJ1 to PJ9 is displayed in Fig.
1. The spatial resolution is highest near the equator (~0. 5 degree) and lowest toward the poles because the spacecraft is closer to the planet near the equator (~ 4000 km above 1 bar level) and further away near the poles. Despite an approximate 45-degree separation in longitude between each of the PJ1 to PJ9 orbits, the observed nadir brightness temperatures show extremely good consistency, especially at latitudes near the equator, at mid-latitudes, and at pressure levels larger than 10 bars. Compared to the simulated brightness temperatures of a reference adiabatic atmosphere, the observed nadir brightness temperatures are warmer than expected at all channels everywhere except within a narrow latitudinal range of a few degrees located near the equator. Our new analysis has extended the latitudinal coverage to the poles, while our previous analysis based on PJ1 data 20 was confined to within 40 degrees of the equator.