dataesr/scientific-paragraphs-categorization huggingface dataset:
Gemini-South We acquired data with the AC on Gemini and reduced in a nearly identical fashion to the June 3rd event reduction. However, we used a larger 120×120 binned pixel window to include a reference star in the frame, resulting in a longer mean deadtime of 0. 178 s and an effective imaging cadence of 3. 6 Hz. We acquired a 45-minute imaging sequence centered on the nominal shadow passage time. The effective photometric SNR of the science target was 17. We did not detect any occultation event in the Gemini data. We computed minimum detectable size limits as done in Section 4. 6. Due to the increased deadtime and lower SNR for this sequence, we found a larger minimum detectable size of 5. 39 km.
SOAR We took data remotely at the SOAR telescope on Cerro Pachón using a Raptor photonics Merlin EM247 frame transfer CCD camera. The Raptor Merlin is a 658×496 pixel CCD camera with 10 µm pixels spanning a relatively narrow ∼60 arcsec by 60 arcsec FOV on the SOAR 4. 1-m telescope. The Raptor includes GPS-based timing. We took images continuously through an open filter wheel at a cadence of 500 ms per image from UT 2017-07-10 06:53:39 to 2017-07-10 08:45:39, and the signal-to-noise ratios near the middle of the observation window was 26. This station was 2310 km from the shadow centerline. No solid-body, rings, or diffuse occulting structures were seen in the data.
IRTF Although the 3. 0-m NASA Infrared Telescope Facility (IRTF; Mauna Kea, HI) was not near the centerline of the occultation prediction, we observed from here to look for extended rings or moons in the vicinity of Arrokoth. S. Benecchi with assistance from S. J. Bus and A. Zangari took data remotely. We used the MIT Optical Rapid Imaging System (MORIS), an Andor iXonEM+ DU-897 camera co-mounted with SpeX (which we did not use) in conventional readout mode. We operated without a filter to get the scale of 0. 11 arcseconds and a field of view of 1 arcmin square  (Gulbis et al. 2011). Our exposure time was 0. 5 seconds and we collected 3000 images over 49. 4 minutes from 2017-07-10 07:16:50 until 18 minutes past the predicted occultation time (UT 7:47). We stopped collecting data when clouds claimed the sky, obstructing the field.
The mayor also offered to provide large trucks as windbreaks. We tested all of these on the dress rehearsal night and found these made significant improvements in the data quality. The mayor and the University suggested shutting down RN3 during the time of our observations to address the stray light problem. Local authorities enforced a two-hour cessation of all traffic movement through the area where we had deployed telescopes. This level of help was absolutely essential to the success of our efforts on event night. Table  4  provides the final deployment locations. We recorded all of the positions from cell phone-based GPS applications and later confirmed these measurements with Google Earth. We experienced some variation in sky background signal among teams but variation in image motion due to wind dwarfed the variation in sky background signal.
Data Reductions We processed all the standard systems (T01-T22) data together, similar to the June 3 data. We copied images within ±1 minute of the predicted mid-time out of the full dataset for processing. There was no measurable need for bias, dark, or flat field calibration steps and therefore we did not apply these types of corrections. However, the raw images contained a low-level horizontal striping pattern. This striping is a feature of the bias pattern inherent in the detector readout and varied from frame to frame. We easily removed the pattern by computing a robust mean for each row and then subtracting that mean. Each image had a large number of stars (∼100) from which we generated a frameby-frame numerical point-spread function (PSF). We then fit the PSF to all the stars by adjusting the position and flux.
Taking full advantage of the PSF method required significant manual effort to guide the PSF building and fitting process. We only applied this extra effort as needed to critical images in and around the time of the actual event. Without this extra corrective step the target star may appear to drop out for a frame or two. We inspected these cases visually and saw a tortuous wind-driven PSF; however, the target star is still visible. For non-critical images, we noted that the star is still visible and a given dropout is not interesting. 6. 5. Fixed stations 6. 5. 1. SOAR We took data at SOAR using the Raptor Merlin camera described in Section 5. 3. 3. The occultation star was the brightest star within the field of view because of the smaller field of view of the Raptor camera. As a consequence, we kept the exposure time at 500 ms to accommodate the dim comparison stars available.
Results For this event, we obtained five positive occultation detections roughly in the center of the deployed stations. Table  5  lists the measured timings of these events. Figure  10  shows a plot of the combined geometry between the stations and the occultation timings. These observations clearly showed that Arrokoth was more complicated than a simple ellipsoidal object. Our first interpretation of these data was that Arrokoth was a contact binary shape. In the months following the initial data reduction, we began tracking two additional scenarios to explain the outline from this occultation. One extra option was that the occultation happened during a mutual event between two closely orbiting bodies and just look like a contact binary due to projection effects. This scenario received a lot of attention due to its implication for the New Horizons flyby: the spacecraft pointing was effectively set to look at the center of mass.
Prediction The prediction for this event was potentially better than for any of the 2017 occultations. The observational arc was extended by additional HST data; further, the successful occultation itself added a new astrometric constraint that was more than an order of magnitude more constraining than any single HST observation. The formal uncertainty for the occultation prediction was 13 km cross-track and 1. 8 sec (36 km) down-track. This uncertainty was a useful guide provided our assumption of a single body was true. The size shown by the 2017 data was roughly 30×14 km. We had no basis to predict what the projected shape would be in 2018 or what the orientation would be. The prior result could only then suggest the minimum (14 km) or maximum (30 km) cross-track extent. In the case of the binary scenario, the chance of observing during a mutual event a second time was low and we had to be concerned with the implications of there being two separated bodies to cover.
Data Reductions We reduced the data for these observations in essentially the same way as the 2017 data (see Section 6. 4). Figure  12  shows the data from teams whose data could be processed. T08 and T14 provided the two best datasets and both were straightforward to process requiring no special treatment. The T18 data were very challenging. All of the data suffered from extinction due to clouds at roughly 30% of the signal level found at the other two sites. Also, one of the frames during the middle of the occultation happened to be at an instant of a guiding correction or tracking glitch and the PSF looks like a dumbbell shape. For this case the automatic tools treated each end of the PSF as a separate source and the resulting stacked PSF was a very poor representation of the image. This problem required a manual edit of the source list to remove the second copy of each source giving a much better representation of the image PSF.
This new PSF was fit to the sources and then a new PSF stack was generated from the improved positions. This second-generation PSF was then fit to the stars and the target as usual. The extinction suffered by the challenging datasets was non-trivial. For instance, the signal from the target star was usually not even visible in the images. Normal photometric extraction tools that depend on measuring a position and a flux could not retrieve the occultation signal. However, we can compute an accurate location for the star with our astrometric knowledge of the image and the image positions of catalog stars. This computed position enables us to retrieve the flux with a constrained fit that treats the location as a given. The T18 data have a distinctive set of consecutive images where the fitted flux was consistent with zero, indicating an event.
Results We obtained two positive occultation detections and one unambiguous miss for this event. Figure  12  shows the useful data from the campaign. Table  7  lists the measured timings of the two chords. From this we see that the cross-track offset is comparable to the size of the object and is consistent with the uncertainty of the prediction (13 km). The miss was useful to rule out a second object at the location probed by T08, 14 km north of the predicted centerline. However, this constraint was weaker than the implication of getting the successful occultation observations so close to the predicted position. Additionally, both chord lengths are longer than the minimum dimension seen in 2017. These data had to have hit the larger lobe or body. With the continued improvements in the orbit estimate of Arrokoth the contact binary scenario was really considered to be a formal, but not likely, possibility.
650 UT (see Table  7  for disappearance and reappearance times). With the then assumption and now knowledge of a single body, these occultation-based positions provided an exceptionally strong constraint on the mean motion of Arrokoth, or equivalently the semi-major axis of its orbit. This result gave us the most important piece of information needed for a successful spacecraft encounter and that is the heliocentric distance. This constraint also helped to better separate out bad astrometry from the HST data set. The consequences of this improvement are discussed in the next section as well as final astrometry that takes advantage of the post-encounter shape model for Arrokoth. Buie et al.
The final four rows of Table  8  show the formal uncertainties of the reconstructed post-dictions using all of the HST and occultation data as well as the orbit estimate delivered to the New Horizons project. The orbit ID "may25a" refers to the orbit based on all HST data up through and including data taken on 2017 May 25. This was the final orbit solution prior to the first observing campaign. "May25a" was based on 83 good observations providing a total astrometric arc of almost three years. This was our first orbit reduced against the Gaia DR2 catalog (first pre-release version). The orbit with ID "lc1" was the first to include all of the HST lightcurve campaign data and was used to target SOFIA for its flight. This version was based on 187 HST data points. Subsequent work in the next couple of days led to improved bad-point filtering largely driven by the requirement that the photometry associated with the astrometry must all be consistent rather than filtering based on astrometric residuals alone.
This led to the orbit denoted "lc1gr" based on 197 data points which was used to target the third occultation campaign. The marked reduction in the prediction errors was crucial to the success of the July 17 effort. The orbit with ID "ey3jul1" included numerous improvements in data reduction and new observations. This orbit was based on 230 observations from HST up through 2018 July 1 and included the 2017 July 17 occultation measurement. The final Gaia DR2 release  (Gaia Collaboration et al. 2018)  further improved the orbit along with a new pipeline image product from STScI that eliminated trails in the images that can strongly affect measurements of all sources, especially those on the faint end of the detectable range. We discovered these "flc" image products as an unanticipated bonus during a data-processing detour working on astrometric measurements of 1I/'Oumuamua  (Micheli et al.
By leaving out the occultation constraints, the four occultation datasets (detections and non-detection) were much harder to explain. In the end, the inclusion of two occultation points led to the removal of some data that were having a systematic erroneous effect on the orbit solution. This analysis also helped us understand the reference systems used for astrometry and orbit analysis. We were concerned about unrecognized systematics between "ground-based" data and spacecraft navigation (radio tracking and optical navigation). This concern often leads to significant underweighting of ground-based priors, and places higher demands on the spacecraft navigation data, leading to more conservative uncertainty estimates for spacecraft encounters. For New Horizons, we had very little time to observe Arrokoth with spacecraft instruments prior to encounter and consequently these priors became unusually important.
In particular, the constraint on the heliocentric distance of Arrokothwas dominated by the orbit estimate based on HST and occultation data and could not be independently derived from optical navigation until it was too late to use the information. One element of the crosscomparison centered on whether the Inertial Coordinate Reference Frame (ICRF) was the same for the astrometric support catalog used for ground-based measurements and the ICRF used for spacecraft navigation. The Gaia mission team's attention to this issue  (Lindegren et al. 2018)  was very important to how the New Horizons mission used our orbit fitting results. Even so, we used very conservative estimates of the time-of-flight uncertainties for the encounter planning. However, this process indicates that for future missions the Gaia ICRF is close enough to that used for radio tracking to allow treating them as indistinguishable.
Our experience shows this to have been an effective strategy and one that is especially important for mapping out the shape of small objects such as Arrokoth. Objects in this size range are particularly well-suited to centrally organized deployments. This project provides an interesting example for the use of the Gaia star catalog by providing a homogeneous set of data that are all referenced near catalog epoch star positions. The challenge of both successful occultation observations and the New Horizons encounter was met despite the short observational arc on Arrokoth. The full details of the orbit and error determination are discussed in  Porter et al. (2018). However, some general lessons learned from that work are worth summarizing here. The event prediction uncertainties provided here are dependent on capturing all sources of noise and proper handling of any priors.
The hardest part of this process is capturing or constraining systematic errors. We now see that the missed occultation on our first attempt was clearly the result of such unrecognized systematics. Our best estimate of the source of these early systematics is the filtering applied to reject bad astrometric measurements. Bad point rejection became easier as our dataset increased; however, we lacked sufficient data prior to 2017 June 3 to know for sure which measurements were bad. To check this conjecture, we ran a test case that used the final filtering used for "ey7" but restricted the orbit fit to the data available prior to June 3. Had that prediction been used with the exact deployment strategy from that attempt, we would have been successful. We suspected this was the case at the time but there was little we could do about it.
Trying to get full size and shape information on this first event makes the task even harder by requiring many more stations. Instead of trying to get it all on the first attempt, we can chose to cover an event with relatively sparse coverage with the expected goal being to get just a single chord. This single chord can provide a much higher precision astrometric measurement than can be achieved with the usual direct imaging observations. For instance, one ground-based image can be expected to collect a position good to 50 mas. A successful occultation, even a single-chord dataset, can easily get a position good to a factor of 100 better at 0. 5 mas. With simple √ N scaling, getting a factor of 100 improvement to match would require 10,000 such images. Most of these small TNOs can only be seen with large (4-10 m) telescopes.
cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The local logistical support from  Argentina , especially from the municipality of Comodoro Rivadavia, was critical to the success of the observations on 2017 July 17. Support from the  Government of Sénégal  and the  Ministry of Higher Education, Research and Innovation (MESRI)  was critical to the success of the observation on 2018 Aug 4. This paper uses observations made at the  South African Astronomical Observatory (SAAO). Josselin Desmars acknowledges the funding from the  European Research Council  under the European Community's  H2020  ( 2014-2020/ERC Grant  Agreement No.
669416  " LUCKY STAR "). The French participants were supported by the  French Space Agency (CNES) , the  2014-2020/ERC Grant  Agreement No. 669416  " LUCKY STAR " and the  French National Research Institute for Sustainable Development. Salma Sylla  acknowledges support from the  Africa Initiative for Planetary and Space Science  (http://africapss. org) and the  Uranoscope de France  (https://uranoscope. org). Pablo Santos-Sanz  acknowledges financial support by the  European Union 's  Horizon 2020 Research and Innovation Programme , under Grant Agreement No. 687378 , as part of the project " Small Bodies Near and Far " (SBNAF) and also acknowledges financial support from the  State Agency for Research of the Spanish MCIU through the "Center of Excellence Severo Ochoa " award for the  Instituto de Astrofisica de Andalucia  ( SEV-2017-0709 ).
Identification Assessment of LOD Using a range of viral spikes from 10 5 to 10 3 genome copies per mL, most viruses were consistently detected at least at 10 4 genome copies per mL in viral vaccine crude harvest (Table  2 ). Reproducibility of the number of reads detected by the HTS test across three replicate datasets in the viral vaccine crude harvest Reproducibility of the test was evaluated using three independent replicates (over two spiking studies) at a spiking level of 10 4 genome copies per mL. The mean number of reads at 10 4 genome copies per mL is shown in Table  2 ; the coefficient of variation ranged from 11 to 107%.
Preparation of virus panel Two vials of each of the 16 virus stocks were received from the NIH; the viruses are described in Supplementary Table  2. The genome copy (GC) of each stock was determined by qPCR by Clean Cells (https://clean-cells. Fig. 2  Overview of HTS for adventitious virus detection. Extraction of the sample in order to recover all types of nucleic acid, followed by second-strand synthesis and sequencing library preparation. Data analysis was carried out by PhyloID (a Sanofi Pasteur developed analysis pipeline). ds double-stranded, ss single-stranded. com/), a contract organization for Sanofi Pasteur, using plasmids containing synthetic nucleic acids of the targeted sequence to establish a standard curve; the resultant GC was used in addition to the infectious titer to convert the in vivo and in vitro LOD values established in the previous NIH study  9  (expressed in infectious units) into equivalent genome copy numbers (the unit used HTS detection) (Supplementary Table  3 ).
Data analysis Data analysis was undertaken using PhyloID (a Sanofi Pasteur in-house bioinformatics tool developed for adventitious virus detection) and has been described previously  26. Sequence data were trimmed, assembled into contigs and identified using a phylogenomic approach based on the profile of matches against reference data. Sensitivity was determined as the lowest concentration that a virus was conclusively positive (here, defined as >2 reads in >1 contig or in 1 contig >200 nt from the PhyloID analysis). Limit of detection (LOD) The limit of detection of adventitious viruses using HTS was compared to the data obtained from the NIH study  9. The LOD achieved by the in vivo test methods, as described in the NIH study, was converted to genome copies based on a determination of genome content of the NIH stocks by Clean Cells to facilitate the comparison with the HTS studies described here.
ACKNOWLEDGEMENTS The authors would like to thank  Dr. Rebecca Sheets  from the  NIH , the  QBI  and the  Vaccine Research Program, Division of AIDS, NIAID  who provided the viral strains through  Quality Biological, Inc. , the  Vaccine Research Program  (Contract No. HHSN272201100023C ) and  Jim Gombold  for in-depth discussion regarding LOD expression. Editorial assistance with the preparation of the manuscript was provided by  Ella Palmer , inScience Communications, Springer Healthcare Ltd. , and was funded by  Sanofi Pasteur. The authors also thank  Jacek Remani  and  Lauren Rodrigues  for support and oversight of the maintenance of the Illumina sequencer, and  Sandrine Buisson , for editorial assistance and manuscript coordination on behalf of  Sanofi Pasteur. This study was funded by  Sanofi Pasteur.
a windsurfer's mast broke about 1 km offshore during a severe Scirocco storm in the Northern Adriatic Sea. He was drifting in severe marine conditions until he eventually beached alive and well in Sistiana (Italy) 24 hours later. We conducted an interview with the survivor to reconstruct his trajectory and to gain insight into his swimming and paddling strategy. We then attempted a Lagrangian simulation of his trajectory in two ways. Firstly by performing a leeway simulation using the OpenDrift tracking code using two object types: Person-in-Water-1 and Person-powered-vessel-2. Secondly, we model the trajectory using our own Lagrangian tracking code FlowTrack. In both cases a high-resolution (1 km) setup of NEMO v3. 6 circulation model was employed for the surface current component and a 4.
Coastal buoy Vida The oceanographic buoy Vida is a coastal observation platform, operated by the Marine Biology Station at the National Institute of Biology (NIB). It is located in the southern part of the Gulf of Trieste at (13. 55505 E, 45. 5488 N), see b) panel of Figure  1  (marked with a cyan cross). Data from the buoy are multifaceted (air temperature, air humidity, currents, waves, sea temperature, salinity, dissolved oxygen, chlorophyll concentration, etc. ) and are publicly available (http://www. nib. si/mbp/en/buoy/) in near real time. Ocean currents are acquired by a Nortek AWAC acoustic Doppler current profiler, mounted on the sea bottom at a depth of 22. 5 m, to monitor vertical current profiles (at 1 m intervals along the water column). The top most cell of the ADCP measurement corresponds to a depth around 0.
OpenDrift Model and Setup OpenDrift is an open-source Python-based Lagrangian particle modelling code developed at the Norwegian Meteorological Institute with contributions from the wider scientific community. It is described in detail in  Dagestad et al. (2018). It supports a wide range of offline (i. e. with precomputed currents and winds) predictions from oil spills and drifting objects to microplastics and fish larvae transport. Particle seeding is very convenient to use and its Leeway module supports a wide range of object types with different lift and drag behaviour under current and wind forces  (Dagestad et al. , 2018). Object drift is decomposed into downwind and crosswind components  (Breivik and Allen, 2008)  based on empirical observations collected in  Allen and Plourde (1999).