dataesr/scientific-paragraphs-categorization huggingface dataset:
Determining D and v is therefore critical; v is determined from the orbit. Mass m can either be determined directly in rare cases23 or derived from a combination of D and . The thermal IR capability provided by NEOSM allows diameters to be determined for hundreds of thousands of objects. 24 Roughly 1/3 of NEOs are extremely dark,9,11 so achieving >90% completeness for asteroids with D >140 m (corresponding to absolute magnitude H<23 magas opposed to the previously used H<22 mag) requires finding the dark objects25; NEOSM, with its IR camera, is well-suited to this task. The density of a potential impactor could be anywhere between that of water and metal, so measuring density is also key for assessing impact energy. Where optical and thermal IR data are available, albedo can also be determined, which helps constrain composition.
Visible and near-IR colors can also provide links to albedo if detections in multiple filters are available with a cadence suitable for resolving rotational and shape degeneracies. The Rubin will return mostly visible and near-IR color photometry for larger NEOs that are observed repeatedly over the survey. 26 The combined capability offered by both NEOSM and the Rubin Observatory will provide an unparalleled dataset for finding NEOs and characterizing the population.
Future Survey and Warning Strategies The next surveys that might be undertaken would depend on the statistics revealed by the Rubin and NEOSM. These surveys will quantify the slope of the size-frequency distribution (SFD) down to tens of meters, including any breaks, allowing for robust comparison with bolide data in the ~1 m range27,28,29 and studies of lunar cratering. The SFD slope is very uncertain in the range 10 m < D < 100 m, leading to large (factor of ~30) uncertainty in the total number of objects in this size range. 9,30 NEOSM and Rubin will also reduce the uncertainty in comet SFD measurements; present-day knowledge comes from studies of dozens of objects to ~150 objects. 31, 32, 33  Should small NEOs turn out to be more numerous than expected, surveys focused on providing short-term warning on timescales of hours or days (e.
Characterization 4. 3. 1 Remote Sensing in the Visible and Infrared Remote sensing is the most effective method to rapidly characterize of NEOs, allowing researchers to sift through large numbers of objects quickly to identify the most hazardous objects. The combination of thermal IR data from NEOSM and optical colors from the Rubin will provide basic characterization for most detected NEOs. NEOSM will produce highly reliable NEO detections and robust diameters and constraints on shape and spin state within days or weeks of a discovery. From thermal IR observations in two bands, it is sometimes possible to infer a metallic composition and/or derive an estimate of thermal inertia. 38,39 As discussed in Section 3, when visible light observations are available for a NEOSM-detected object, the albedo can be determined as well, which loosely correlates with taxonomic type.
46 Radar can also provide measurements of spin state, radar albedo, porosity, and satellites. Sometimes, radar can resolve objects well enough to detect boulders and craters. Repeated radar ranging observations over years to decades enables measurement of the orbit-altering Yarkovsky effect47. When combined with thermal data obtained from an IR telescope such as NEOSM, these data can constrain the mass of an asteroid. 22,48 The advanced surveys will provide a wealth of targets for future examination with radar. Maintaining the funding of the only two key radar facilities, Arecibo Observatory and Goldstone Solar System Radar, is crucial for planetary defense. See white papers by Rivera-Valentín, Virkki, Taylor, and Lazio.
What Would We Do If a High-Probability Potential Impactor Were Discovered? In the event that an object with a significant chance of a major impact is discovered, the next steps will depend critically on what is known about it, and when these data becomes available. The most appropriate mitigation option depends on the lead time and asteroid mass/size. 4 NEOSM is specifically designed to discover and characterize NEOs years to decades in advance of a potential impact, facilitating a range of possible mitigation strategies assuming it is funded. For kinetic impactors, NASA's Deep Impact mission63 probed impact processes on a comet, and the upcoming DART technology demonstration mission, coupled with Europe's Hera reconnaissance mission, will shed light on the effects of kinetic impacts on ~100-m sized S-complex asteroids.
6 What Can Be Learned About the Solar System in the Era of Advanced Surveys? NEOSM and Rubin will deliver a wealth of data informing us about the origins and evolution of our solar system. In addition to delivering data on hundreds of thousands of NEOs and thousands of comets, these surveys will discover millions of more distant small bodies. Populations of rare objects such as Earth Trojans and interior-to-Earth-orbit objects can be studied in detail. By finding the most hazardous population of asteroids the advanced surveys will identify a much larger set of accessible asteroids. The OSIRIS-REx and Hayabusa2 missions have revealed in exquisite detail the strange top-shapes and rugged, boulder-rich surfaces of primitive asteroids Bennu and Ryugu (both of which were discovered by NEO surveys), and pristine samples returned from them will yield new insight into their compositions.
The oscillations could be recorded by imaging, at a magnification of ×1000, the top of the longest stereociliary row onto a displacement monitor that included a dual photodiode. Calibration was performed by measuring the output voltages of this photometric system in response to a series of offset displacements. Here, we analyzed 182 spontaneously oscillating hair bundles from data previously published  [24]. Spontaneous hair-bundle oscillations were described by a published model of active hair-bundle motility  [2]  that rest on a necessary condition of negative hair-bundle stiffness, on the presence of molecular motors that actively pull on the tip links, and on feedback by the calcium component of the transduction current. Hair-bundle deflections affect tension in tip links that interconnect neighbouring stereocillia of the bundle.
ACKNOWLEDGMENTS The electron micrograph of the hair bundles shown in Fig. 1A was obtained by  Atitheb Chaiyasitdhi , a PhD student in  P Martin 's group. We acknowledge stimulating discussions with  Roman Belousov ,  Izaak Neri ,  Andre C. Barato ,  Simone Pigolotti ,  Johannes Baumgart ,  Jose Negrete Jr ,  Ken Sekimoto ,  Ignacio A. Martínez ,  Patrick Pietzonka  and  A. J. Hudspeth.
Time projection chamber design The homogeneity of  ⃗ is essential for obtaining a straight projection of the muon's path and to minimize artifacts, specially near the perimeter of the TPC. The shape of the electric field inside the TPC has been calculated for different field cage candidates with two different software packages: QuickField for electrostatic and COMSOL Mul-tiphysics® for particle tracing, both yielding comparable results (see Figure  2 ). The initial conditions, seen in Figure  2a , consider a TPC without electric field homogenizers, a drift cathode polarized at -3. 000 V, a grounded Micromesh and a resistive layer polarized at +440 V. The field lines allow visualization of the most probable path of the electrons generated inside the drift volume. Figure  2b  shows the retained field cage, composed of 6 polarized copper bands printed over a circuit board and connected from the drift electrode to the ground trough resistors (see Figures  2e  and 2f ).
We present the results regarding the analysis of the fast X-ray/infrared (IR) variability of the black-hole transient MAXI J1535-571. The data studied in this work consist of two strictly simultaneous observations performed with XMM-Newton (X-rays: 0. 7-10 keV), VLT/HAWK-I ( s band, 2. 2 m) and VLT/VISIR ( and  2_2 bands, 4. 85 and 11. 88 m respectively). The cross-correlation function between the X-ray and near-IR light curves shows a strong asymmetric anti-correlation dip at positive lags. We detect a near-IR QPO (2. 5 ) at 2. 07±0. 09 Hz simultaneously with an X-ray QPO at approximately the same frequency ( 0 = 2. 25±0. 05). From the cross-spectral analysis a lag consistent with zero was measured between the two oscillations. We also measure a significant correlation between the average near-IR and mid-IR fluxes during the second night, but find no correlation on short timescales.
X-ray data. XMM-Newton X-ray data were collected with the EPIC-pn camera. Observations were taken in Burst mode (OBS ID: 0795712001 and 0795712101). The data reduction was carried out using the XMM Science Analysis System (SAS v15). In particular, the source counts were extracted in the range RAWX: 28-48 (corresponding to an angular size of ≈86 arcsec along RAWX). Due to the high absorption of the source, X-ray events were extracted only between 2 and 10 keV. The mean count rate of the two observations was found to be very similar: 234 ± 1 cts s -1 for the first night, and 226 ± 1 cts s -1 for the second. Data were then barycentered with the barycen software and binned in a light curve of 5. 7 ms.
Near-IR data. VLT/HAWK-I Near-IR sub-second data were collected with HAWK-I  (Pirard et al. 2004 ) mounted at the VLT UT-4/Yepun under Program: 099. D-0068(A). 1-hour long observations were taken in the s band (effective wavelength 2. 2 m) with the time resolution of 0. 125 s. Images were stacked in "data-cubes" of 250 frames, separated by a gap of ≈3 s. A fraction of the cubes was affected by frame-losses. For cross-correlation analysis cubes with frame losses were therefore discarded in order to avoid to introduce spurious lags. During the observation, (especially in the second part of the first night), the target ended up few pixels from the edge of the detector, introducing spurious features in the lightcurve. To be conservative we decided not to include those cubes in the analysis (hence the large gaps seen in Fig.
1 ). The telescope was pointed to 15:35:41. 6, -57:11:22. 9 (RA, DEC) at an angle of 70 • in order to have the target on the lower-left quadrant (Q1) together with the reference star with a s magnitude of 11. 55±0. 02. The data reduction was performed using the ULTRACAM data reduction software tools  (Dhillon et al. 2007). Parameters for the reduction were derived from the bright reference star point spread function and position. To account for seeing and transmission variations, the ratio between the source and the bright reference star count rate was used. Individual power spectral densities (PSDs) of the non-variable reference and comparison star showed that the variability induced by the seeing is extremely low, as compared to the intrinsic variability of the target, and was successfully removed by taking the ratio between the target and the bright reference star.
Mid-IR data. VLT/VISIR Mid-IR observations were obtained with VISIR  (Lagage et al. 2004 ) mounted at the VLT-UT3 Melipal under Program 099. D-0884(A). The instrument was set in small-field imaging mode, with a pixel scale of 45 mas pixel - 1  , and the and 2_2 filters were used on September 14th and 15th, respectively (covering the 4. 85-11. 88 m wavelength range; see Table  1 ). Every observation consisted of 1000s of time on source, characterized by 44 nodding cycles. In total, considering chopping and nodding between sky and source, the total exposure time was 1800-1900 s per observation. Reduction of data was performed using the VISIR pipeline, available in 1. Sensitivities were estimated thanks to the observation of two standard stars (HD161096 and HD163376) on the same night and telescope configuration.
After recombination of the chop/nod cycle raw images, aperture photometry was performed by means of an aperture that was large enough to avoid that possible seeing variations could affect the portion of the flux falling in the aperture. We note that the target of the observations was bright enough to allow a detection in individual observations. The VISIR pipeline requires that observations are combined in groups of multiples of two. In order to achieve a flux from each observation for this work, we therefore had to combine each image with the first observation on each night, then take into account the flux of this first observation to obtain the light curve. Using this method we were able to sample the data twice as fast with respect to  Baglio et al. (2018). In particular, each time bin has 27.
X-ray and near-IR power spectral densities The properties of the X-ray and near-IR variability were studied through Fourier power spectral analysis. The X-ray and near-IR light curves were divided in 16384-bin and 512-bin long segments, respectively. We computed the power spectral density (PSD) of each segment in squared root-mean-square (rms) normalization (see e. g. Belloni & Hasinger 1990; Miyamoto et al. 1991; Vaughan et al. 2003)  and then applied a logarithmic binning factor of 1. 05 to the resulting average. The gaps between the cubes in the near-IR data were filled with simulated data, following the procedure described in  Kalamkar et al. (2016). For the near-IR case, in order to reach lower frequencies without affecting the statistics of the higher frequencies, we also computed a PSD with the light curve rebinned to 10 s (much longer than the ≈3 s gaps) using 16 bins per segment.
3 ). We quantified the possible noise contribution to the observed CCF by simulating = 10 3 couples of uncorrelated light curves with the same power-spectral properties of our dataset. The resulting distribution of of the uncorrelated CCFs has a standard deviation of 0. 016 (grey-shaded area in Fig. 3 ). The anti-correlation dip appears statistically significant, while the peaks observed at lags larger than ±5 s -as well as those at negative lags -are consistent with noise. To investigate further such correlated variability, Fourier crossspectral analysis was applied. In particular, intrinsic coherence and lags were computed using the procedure described in  Uttley et al. (2014)  (see also  Vaughan & Nowak 1997; Ingram 2019, and references therein). The presence of frames losses forced us to dis- card a significant fraction of the data-cubes in the cross-correlation analysis.
Therefore our choice is suitable for our purpose. Due to the low statistics, and given the similar timing properties during the two observations, cross-spectral analysis was performed combining the data from both nights together. We also checked the data for the individual nights but no significant variation from the described results was found. The results are shown in Fig. 5. The intrinsic coherence of the two datasets is very low for almost all the probed frequencies. Nevertheless the phase lags, though scattered, seem to show a clear trend. At low frequencies there is evidence for a (nearly) constant phase lag at ∼ -/2, while at frequencies higher than the QPO the phase lags are unconstrained, thus oscillate betweenand. A somewhat higher coherence of 0. 25 ± 0. 1 (1 error) is measured over the frequency range where the QPO is detected in the X-ray light curve.
g  Coriat et al. 2019; Miller-Jones et al. 2019) , support the possibility for such precession. We notice that a combination of the two scenarios is also possible. For instance, the stronger IR variability observed at lower frequencies may indicate the presence of a significant contribution from the jet  (Veledina et al. 2011; Malzac et al. 2018). Therefore, the weak QPO signal measured in this dataset, could then be simply be reproduced by assuming a higher and B (i. e. smaller radii with higher density and magnetic energy density), as the IR band would be falling in the self-absorption regime. A detailed simulation is however necessary in order to disentangle the contribution from these two components and to better understand the observed differences with respect to GX 339-4.
2018). We notice, however, that although we do not find correlation on timescales of tens of seconds, this does not necessarily point against this scenario. As mentioned above, neither X-ray nor near-IR emission display strong variability on timescales comparable with VISIR's time resolution: thus, a low connection is somehow expected in this case. New detailed simulations which include the low-frequency end of the X-ray fluctuation PSD may help to quantitatively test this scenario with this dataset. Mid-IR jet, near-IR hot inflow: On short timescales, the expected correlation strongly depends on the physical parameters of the system, which can affect the responses of the two components. The shape of the CCF may have a complex shape difficult to predict without proper modelling. Nevertheless, if the same input massaccretion rate fluctuations travel through the hot-inflow (where it emits in the near-IR) and then in the jet (emitting in the mid-IR), a lag of the order of seconds is expected to appear.
On longer timescales, instead, the two bands should be correlated, according to the well known strong inflow-outflow connection of these sources (see e. g. Gallo et al. 2018, and references there in). Given that both scenarios can reproduce the observed correlation on longer timescales, a self consistent modelling with both the components is necessary in order to actually quantify the contribution of hot-inflow and jet in the near-IR. More importantly, the two scenarios have strongly different predictions when probing (sub-)second timescales, which could not be reached with the current dataset. Thus, new higher time resolution observations will provide crucial physical information.
In our case, from the trend visible in the lower panel of Fig. 1 , we can only say that the oscillation has a characteristic timescale ≫1000s. Fast multi-wavelength variability is revealing to be one of the most powerful tools to study the innermost regions around accreting stellar-mass BHs, allowing us to put constraints on the accretion geometry of these systems. The analysis of an increasing number of datasets, together with the development of theoretical models, are starting to shed light also on the physical processes which take place inside the jet. In particular, the results found in our analysis will provide a solid test for the state-of-the-art models. New high time-resolution multiwavelength observations, especially at longer wavelengths (e. g. with JWST;  Gardner et al. 2006) , will help to address many remaining open questions on these objects.
ACKNOWLEDGEMENTS The authors are extremely grateful to the VLT and XMM-Newton astronomers, who allowed us to successfully collect strictly simultaneous data from 3 different telescopes. The authors would like to thank the referee for the useful comments which significantly improved the paper. The discussion of this paper benefited from the meeting 'Looking at the disc-jet coupling from different angles' held at the  International Space Science Institute in Bern, Switzerland. FMV acknowledges support from  STFC  under grant.
The information-rich nature of the datasets provided by such an integral field spectrograph will represent a resource with considerable legacy value for the scientific community. Additionally, these observations will open up completely new areas of the parameter space, allowing the proposed mission to have a great potential for serendipitous discoveries. In the coming years, when most of the new large facilities such as the Extremely Large Telescope (ELT) and the James Webb Space Telescope (JWST) will focus on the infrared (IR) wavelength range, and the Hubble Space Telescope (HST) will not be operational anymore, a mission in the UV with the capability of observing spectroscopically large areas of the sky will be unique. In synergy with the Atacama Large Millimeter Array (ALMA), the ELT instruments, and Square Kilometer Array (SKA), but also with other space-based missions such as the Advanced Telescope for High Energy Astrophysics (Athena) and the Laser Interferometer Space Antenna (LISA) it will allow us to push further our current understanding of the Universe.
2004 , Peng et al. 2010). The environmental Fig. 3 : Example of a local cluster member undergoing ram pressure stripping observed with VLT/MUSE: ESO137-001 at z = 0. 016  (Fumagalli et al. , 2014). Left: HST/ACS image in the F475W filter with, superposed, the MUSE field of view at the two locations targeted by the observations. Right: RGB color image obtained combining images extracted from the MUSE data cube in three wavelength intervals (λ = 500 -600 nm for the B channel, λ = 600 -700 nm for the G channel, and λ = 700 -800 nm for the R channel). A map of the Hα flux is overlaid in red using a logarithmic scale, revealing the extended gas tail that originates from the high-velocity encounter of ESO137-001 with the intra-cluster medium. dependence seems to indicate that not only internal, but also external, physical processes play a role in shaping the star formation of galaxies at all cosmic ages  (Boselli et al.
Acknowledgments The first two authors contributed to this paper equally. All authors acknowledge helpful discussions with  S. Conti ,  M. Jędrychowski ,  N. Greneche ,  J. Weiss ,  P. Zhang ,  S. Queyreau  and  I. R. Ioanescu. The work was partially supported by the grants  ANR-17-CE08-0047  (O. U. S. and L. T. ) and  ANR-19-CE08-0010-01  (O. U. S). The possibility to access computational resources (MAGI) provided by  Université Sorbonne Paris Nord  is highly appreciated.
ACKNOWLEDGMENTS We acknowledge discussions with  Xujun Ma. This work is supported by the  National Science Foundation  under  EAGER  Award No. 2022629  and by  PSC-CUNY  under Award No. 63822-00 51 , and by  MCIU (Spain)  under the Project number  PGC2018-094684-B-C22. L. A. R. -L. thanks  UCA JEDI  for the award of predoctoral studies fellowship. † Email: gopar@unizar. es.
ACKNOWLEDGMENTS We would like to thank  Dr. Richard Miles  for his helpful discussions during the preparation of this manuscript. Additionally, we would like to thank our two anonymous reviewers, who's thorough review of the manuscript provided improvements to the presentation of the analyses. We also thank the  NASA Earth and Space Science Fellowship  ( NNX16AP41H ) and the  Center for Lunar and Asteroid Surface Science (CLASS ,  NNA14AB05A ) for support of this work. This work is based on observations made with the  Spitzer Space Telescope , which is operated by the  Jet Propulsion Laboratory, California Institute of Technology  under a contract with  NASA. This research made use of Tiny Tim/Spitzer, developed by  John Krist  for the  Spitzer Science Center. The Center is managed by the  California Institute of Technology  under a contract with  NASA.
The sample length (D) is determined from radiographic images that are collected in situ. The P-wave velocity follows from     / 2. P V D For both sample powders, we collected multiple measurements in a series of heating cycles at a range of pressures. The experiment is ended when the sample is thinned too much by compression for obtaining useful ultrasonic data. A full description of the V P experiments and the data analysis is available in text C of supporting information. Both the Fe-17 wt% Si (FeSi17) sample and the Fe-10 wt% Si (FeSi10) sample are analyzed by electron microprobe at the Westfälische-Wilhelms University Münster, Germany (Text B of supporting information).