dataesr/scientific-paragraphs-categorization huggingface dataset:
Instrument and Data In January 2016 an Airbus A340 factory test aircraft conducted intentional thunderstorm-penetrating flights. The starting base was at Darwin Airport in Australia. The aircraft was equipped with a dedicated In-Flight Lightning Damage Assessment System called ILDAS. The system was developed by The Netherlands Aerospace Centre (NLR, www. ildas. nlr. nl) in cooperation with 12 partners, including Eindhoven University of Technology. Its main goals are to measure lightning strike parameters, to determine entry and exit points on the aircraft structure, and to assess the possible structural damage. It was comprehensively described in a series of publications  (de Boer et al. , 2011; Hervé et al. , 2014; Stelmashuk et al. , 2008 Stelmashuk et al. , , 2007;; van Deursen, 2011; van Deursen & Stelmashuk, 2011; Zwemmer et al.
The channels are shown in section 3. 3. The X-ray detectors were calibrated after the campaign during on-ground data processing using the internal LaBr 3 radioactivity. In the present campaign, in addition to ILDAS, a video camera was installed on board in the frame of another research project. The camera was monitoring the right wing at a rate of 25 frames per second. To locate the observed events, we used data from two ground-based lightning location networks. One is the global World Wide Lightning Location Network (WWLLN); another is a local Australian network LIAS (acronym for the Lighting Incident Archive Search) operated by The Global Position and Tracking Systems Pty. Ltd. WWLLN location accuracy is taken as 5 km radius over Australia  (Rodger et al. , 2004). The LIAS network claims 250-500 m accuracy for their data, usually reports more flashes, and provides additional data regarding their.
The linear distance between the two glows is 70 km. The aircraft traveled 4. 6 km inside the first glow and 7 km inside the second. Figure  2  shows the electric field profile and the total X-ray counts recorded by ILDAS continuous data file (CDF) algorithm. All X-ray pulses with energy higher than 60 keV for X14 and 100 keV for X15 are counted. The data are binned in 15 ms intervals. The E field data are stored as E max and E min , the maximum and minimum values during this interval. It can be seen that the aircraft was near intensive electrical activity before 07:37:00, then flew into electrically silent region between two clouds from 07:37:00 to 07:40:30, and finally approached and penetrated into the second cloud at 07:40:30. Figure  5. The glow-terminating flash as detected by ILDAS in high-resolution mode with the code 805D0DA4.
Journal of Geophysical Research: Atmospheres The ILDAS strike with the code 805D0DA4 captured the glow-terminating moment. Data of E00 electric field sensor, H13 magnetic field window sensor, and both X-ray detectors are shown in Figure  5. The electromagnetic (EM) pulse at t = 0 does not match any classified triggered or intercepted lightning discharges  (Kochkin et al. , 2015; Mazur & Ruhnke, 1998; Moreau et al. , 1992; Uman & Rakov, 2003). This means that the aircraft was not attached to the lightning leader directly. The EM pulse was induced by the distant lightning flash reported by WWLLN and LIAS. However, the induced pulse was strong enough to trigger a discharge from the right wing of the aircraft, as will be shown below. Individual X-ray pulses were recorded with 10 ns resolution but binned here in 15 ms intervals for better visualization and comparison with CDF data.
Spectrum The ILDAS continuously counts X-ray pulses in six energy channels for each detector. The channels are listed in Table  2. X-ray pulses are binned in 15 ms intervals and stored in so-called continuous data files (CDFs). This provides spectral information over the entire flight, in addition to individual X-ray pulses that are recorded in triggered mode with 10 ns resolution. Figure  11  shows a spectrogram of the glow period. The spectra of the two X-ray detectors are plotted together. Their energy channels are overlapping and are indicated on the left axes. Time is arbitrary and binned in 1 s intervals. Logarithmic number of particles is color coded and normalized on energy bin size. The first glow appears at t = 270 s relative time. The second glow appears 380 s later. Two more spectrum modifications can be visible after t = 800 s.
In part II, we simulate the propagation of the produced τs with GEANT4. 9, using parameterizations of PDFs of the τ path length and proper time. We simulate τ decays with the TAUOLA package  [17]. For the above two parts, 1D tracking of a primary ν τ is performed down to the converted τ decay. We have refined our previous calculation of part III  [18]  with numerical simulations. We first simulate a set of ν τ showers with ZHaireS  [19]  at different energies, and determine a conical volume inside which the electric field is above the expected detection threshold of the GRAND antennas. Based on a parametrization of the shape (including angle and height) of the detection cones as a function of energy, we compute the expected shape and position of cones for each neutrino-initiated EAS in our simulation (so far in the 30-80 MHz band).
Acknowledgments The  GRAND  and  GRANDproto  project are supported by the  France China Particle Physics Laboratory , the  Institut Lagrange de Paris , the  APACHE  grant ( ANR-16-CE31-0001 ) of the  French Agence Nationale de la Recherche , the  Natural Science Foundation of China  (Nos. 11135010 , 11105156 ,  11375209  and  11405180 ), the  Chinese Ministry of Science and Technology , the  Youth Innovation Promotion Association of Chinese Academy of Sciences , and the  São Paulo Research Foundation FAPESP  (grant  2015/15735-1 ).
Acknowledgements This work has been supported by the  ESI GROUP Chair at Ecole Centrale of Nantes  as well as by the  Spanish Ministry of Economy and Competitiveness , through grants number  CICYT DPI2014-51844-C2-1-R  and  DPI2015-72365-EXP  and by the  Regional Government of Aragon  and the  European Social Fund , research group  T88. This support is gratefully acknowledged.
The observational era of gravitational-wave astronomy began in the Fall of 2015 with the detection of GW150914. One potential type of detectable gravitational wave is short-duration gravitationalwave bursts, whose waveforms can be difficult to predict. We present the framework for a new detection algorithm for such burst events -oLIB -that can be used in low-latency to identify gravitational-wave transients independently of other search algorithms. This algorithm consists of 1) an excess-power event generator based on the Q-transform -Omicron -, 2) coincidence of these events across a detector network, and 3) an analysis of the coincident events using a Markov chain Monte Carlo Bayesian evidence calculator -LALInferenceBurst. These steps compress the full data streams into a set of Bayes factors for each event; through this process, we use elements from information theory to minimize the amount of information regarding the signal-versus-noise hypothesis that is lost.
oLIB is an attempt to implement this optimal scheme. The implementation is carried out by first flagging, in each detector, subsets of data that have excess power, which we refer to as "events". This step is carried out with a time-frequency decomposition based on the Qtransform  [34] [35] [36]  that we will refer to as Omicron  [37]. This first step is followed by a time coincidence of such excess power among the network of detectors. The resulting set of coincidences are handed to a follow-up, performed with LALInference Burst (LIB)  [24, 38, 39] , that analyzes all data streams simultaneously and compresses them into a set of Bayes factors. Applying a likelihoodratio test (LRT) to these Bayes factors produces a single search statistic, which is then mapped into an estimate of the GW detection significance.
A. Information-theoretic justification of oLIB's design While we have motivated oLIB's design with the idea of preserving information, we have yet to rigorously define this concept. Here we provide the framework for an optimal search in an information-theoretic sense. While other GW-burst search algorithms utilize components of this optimal framework, oLIB is the first to implement it in its entirety. First, we quantify the qualitative concept of information by utilizing elements from information theory, defining the information in the data stream x regarding the signal-versus-noise binary hypothesis H to be their mutual information I(H; x) = H(H) -H(H| x) (1) where H(H) and H(H| x) are the entropy and conditional entropy, respectively, of the probability distributions for H (see Appendix A 2 for explicit definitions of entropy).
However, all of this lossless compression is useless if we do not have an optimal way of extracting the information from the compressed data. Just having a compressed statistic (Λ) containing the maximal amount of information about a model (H) does not guarantee that any arbitrary estimator Ĥ(Λ) will be optimal. Fortunately, the Neyman-Pearson lemma  [42]  argues that a likelihood-ratio test (LRT) maximizes the probability of detection at a given false-alarm probability, so it is an optimal means of information extraction. As we will see in § II E, once we have a likelihood ratio, evaluating an LRT is straightforward. The implementation of this information-theoretically optimal scheme in oLIB is as follows: 1. Use Omicron to flag stretches of the detector's data streams that contain excess power, which will serve as "events" in our further analysis.
B. Omicron Omicron provides fast and accurate identification of statistically significant deviations from Gaussian noise in a single interferometer's data stream. It is based on the Q transform, which varies the duration of data used within a Fourier transform to maintain a constant quality factor Q ∝ τ • f 0 , meaning the duration τ is inversely proportional to the targeted frequency f 0  [34] [35] [36] [37]. By repeatedly decomposing a data stream into several planes of constant Q, Omicron can search for excess power with different characteristic aspect ratios in the time-frequency plane. In effect, the Q transform is similar to matchedfiltering with a bank of sine-Gaussian waveforms, each of which has a characteristic shape in the time-frequency plane and is well-localized. In this way, oLIB uses Omicron to flag interesting stretches of data and later uses the results of the Omicron analysis to perform all of the down-selection.
C. Coincidence As mentioned, Omicron matched-filters a bank of sine-Gaussian templates with the entire stretch of data. Any template that has a signal-to-noise ratio (SNR) greaterthan-or-equal-to a threshold value is recorded as an event. However, the presence of excess power alone does not provide strong evidence of a GW because it can also be generated by loud, non-Gaussian noise fluctuations resulting from instrumental or environmental causes. We will refer to these fluctuations as noise "glitches". Furthermore, many of the events are redundant because any excess of power in the data stream can have significant overlap with multiple sine-Gaussian templates, so there are routinely multiple events of different f 0 and Q recorded at nearly identical times in the data stream. For computational reasons, we are motivated to only followup the most "GW-like" events with LIB.
The strategy of this down-selection naturally falls out of how we define "GW-like". Even though the oLIB algorithm is designed to detect unmodeled GW bursts, we hypothesize that detectable burst signals exhibit several qualities. For example, we expect a GW to leave a specific signature in the data streams of all detectors. More precisely, Omicron models these signatures as single sine-Gaussians, so we hypothesize that the events produced by a single burst-like GW will cover similar ranges of f 0 and Q in each detector. In addition, from General Relativity, we expect GWs to travel at the speed of light, meaning there is a given time window, defined by the physical separations of the detectors, in which GWs can leave this signature. As a result, we choose to pass to LIB only the Omicron events whose f 0 and Q values are identical across all detectors and whose detection times are consistent with this time-of-flight time window  1.
Using the Likelihood-Ratio Test for Detection The primary purpose of oLIB is to optimally extract the information contained within the data regarding the signal-versus-noise hypothesis and to use this information to make a detection statement. As we argue in § II A, our working assumption is that the Bayes factors produced by LIB compress the dimensionality of the raw data streams while still preserving a sufficiently large fraction of the original information. With any n-dimensional set of compressed search statistics x B = BSN,BCI,. , the problem of optimal information extraction immediately suggests the use of an LRT. The motivation for this approach comes from the Neyman-Pearson lemma  [42] , which states that the LRT is the optimal method of binary hypothesis testing in that it maximizes the probability of successfully detecting a signal at a given false-alarm probability.
F. Summary of how optimality breaks down in oLIB's implementation Although we justified the optimality of oLIB's design in § II A, such optimality is not acheived in practice. Here we will briefly review and discuss the ways in which oLIB's implementation can lead to sub-optimal performance. 1. As previously mentioned, oLIB models the GW signals as sine-Gaussians, and the noise as Gaussian with potential sine-Gaussian glitches. If the signals and detector noise only ever take these forms, 4 There are subtleties involved with timeslides with regards to which 0-lag coincidences to remove from the data before doing the timeslides in order to reduce GW contamination. For this paper, we do not remove any 0-lag coincidences, although it has become a common procedure to remove high-confidence GW detections from the timeslided data.
then oLIB's data compression should lose no information concerning H. However, in most scenarios, these models are only approximations, so information loss is introduced. Including a wider range of models in our vector of Bayes factors x B could help to suppress this information loss, but as we will see in § III C 1, oLIB can detect a wide-range of morphologies regardless, suggesting that this information loss is not significant. 2. Although we are treating GW bursts as unmodeled, in practice we need to enforce a minimal set of assumptions in order to distinguish GW signals from noise. We can obtain populations of noise events through timeslides, but we must make assumptions regarding the population of GW burst signals. These assumptions involve choosing the set of signal morphologies on which to train (e.
III. A SAMPLE ANALYSIS We perform a proof-of-concept simulation in order to demonstrate more illustratively how oLIB functions. Performance comparsions with other search algorthims are an integral part of the real GW-burst searches that have been  [23, 49]  and will be completed in the advanced detector era. Completed comparisons have shown that oLIB is competitive with other GW burst search algorithms in terms of sensitivity, and is the most sensitive search algorithm in certain regions of the short-duration GW burst parameter space  [49]. In order to illustrate typical features of oLIB's endto-end performance, we undertook the analysis of three days worth of data from the sixth science run of initial LIGO (S6)  [50]. Specifically, we ran on science time segments produced for the Hanford (H1) and Livingston (L1) detectors between 14-17 September 2010.
IV. SUMMARY In this paper, we introduced the justification and methodology for a new end-to-end search algorithm targetting GW bursts called oLIB. This algorithm takes in calibrated strain data and compresses it into a set of search statistics that can be used to make a detection statement independently of other algorithms. Specifically, the compression involves several steps. First, Omicron is used to flag stretches of excess power in each detector, which we refer to as events. For computational reasons, these events are down-selected by imposing constraints such as event clustering, vetoing based on data quality, and requiring a time-of-flight time coincidence across the network of detectors. Once this incoherent down-selection is complete, these coincident events are compressed into a set of Bayes factors with LIB, an MCMC algorithm used to calculate Bayesian evidences.
Because Bayes factors are sufficient statistics for binary hypothesis testing, we expect the information loss concerning the signal-versus-noise hypothesis to be minimal as long as the set of oLIB's hypotheses model the actual data sufficiently well. We further compress this vector of Bayes factors into a scalar likelihood ratio, which preserves all of the information regarding the signal-versus-noise hypothesis contained within the set of Bayes factors. Finally, we use a likelihood-ratio test to assign a detection significance to each event. This LRT allows us to optimally extract this signal-versus-noise information that we have been preserving in our compression and make a detection statement. In order to demonstrate the validity of the algorithm's implementation, we ran oLIB over a stretch of real interferometer data taken from the initial LIGO S6 science run.
15 km s -1 and 1. 0 km s -1 , respectively. Once the V LSR is determined, the near/far distance ambiguity is solved by matching the source positions with a catalogue of sources with known distances (H ii regions, masers and others) or, alternatively, with features in extinction maps (in this case, the near distance is assigned). In cases for which none of the aforementioned data can be used, the ambiguity is always arbitrarily solved in favour of the far distance, and a "bad quality" flag is given to that assignment. The additional use of extinction maps to solve for distance ambiguity  (Russeil et al. 2011 ) (where applicable) can be a source of error, whose magnitude typically increases with increasing difference between the near and far heliocentric distance solutions. For the present paper, we rely on the use of extinction maps for practical reasons and also because, for most of the sources, no spectral line emission has yet been observed other than what can be extracted from the two CO surveys.
On the one hand, for many sources, these data can be complemented with line observations to obtain a more detailed picture. On the other hand, Hi-GAL produced an unprecedentedly large and unbiased catalogue containing many thou-sands of newly detected cold clumps, for which it is important to provide a first classification. The criteria we provide to separate different populations, although somewhat conventional in the Herschel literature, remain probably too clear-cut and surely affected by biases we introduced in this section and discuss also in the following sections of this paper. Reciprocal contamination of the samples certainly increases overlap of the physical property distributions obtained separately for the different populations, as will be seen in Sections 6 and 7.
We use the collected information about source masses and surface densities to place them in the Σ vs M plot of  Tan (2005) , in which different classes of structures populate different regions. Our Figure  8  is analogous to Figure  11  of  Molinari et al. (2014) , but the "temporary" data set used for that plot is replaced here with the values from the final Hi-GAL physical catalogue. The Hi-GAL sources are found to lie in the regions quoted by  Tan (2005)  for Galactic clumps and local IRDCs. In the upper-right part of their distribution they graze the line representing the condition for ionised gas to remain bound. This plot summarises the nature of the sources in our catalogue: clumps spanning a wide range of mass/surface density combinations, with many of them found to be compatible with the formation of massive Galactic clusters.
To identify possible additional sub-classes within the proto-stellar population, the correlation with external evolutionary tracers can be used (as we intend to do in future papers based on this catalogue). For example, in the RMS survey  (Lumsden et al. 2013)  an effort has been made to identify massive YSOs and ultra-compact H ii regions by means of multi-wavelength ancillary data, both photometric and spectroscopic (e. g. Urquhart et al. 2009, and references therein). Similarly, for Hi-GAL  Cesaroni et al. (2015)  studied, in the longitude range 10 • < < 65 • , the counterparts of CORNISH  (Hoare et al. 2012; Purcell et al. 2013)  sources, treated as bona-fide young H ii regions, and distributed across the range 2 L /M < L bol /M < 270 L /M. From the data of  Cesaroni et al. (2015)  we estimate the peak of this distribution to lie around (L/M) P ≡ 22.
These appear not to be confined to the H ii-region locus and are generally more spread out. This indicates that this choice of Herschel colours does not appear to be correlated with the presence of one or more MSX/WISE/Spitzer counterparts. Another diagnostic used by Paladini et al. (  2012 ) is F 24 /F 70 vs F 160 /F 250 diagram, thus involving the Spitzer -MIPS flux at 24 µm, which is shown in Figure  17 , top panel. As sources with F 24 2 Jy tend to be saturated in MIPS  (Carey et al. 2008) , we also construct a F 22 /F 70 vs F 160 /F 250 diagram using WISE 22 µm data (Figure  17 , bottom panel). In both panels, data appear highly scattered, though the sub-sample of sources identified as H ii-region candidates still occupy a smaller area of this space, where Log(F 160 /F 250 ) > 0, similarly to  Paladini et al.
In conclusion, the aim of this paper is to give a first look at the huge amount of information contained in the Hi-GAL physical catalogue of the inner Galaxy. Papers based on data taken from this catalogue have been already published  (Ragan et al. 2016; Persi et al. 2016) , as well as observational programs submitted for (and also partially observed with) ALMA. A number of papers exploiting this catalogue, or aiming at completing it in the outer Galaxy, are in preparation at present, and will contribute to deepen and refine the conclusions of this work. [24], DF250 : the same as columns [7], [8], and [9], respectively, but for the SPIRE 250 µm band. • Columns [25], DESIGNATION 350, [26], F350, and [27], DF350 : the same as columns [7], [8], and [9], respectively, but for the SPIRE 350 µm band.
ACKNOWLEDGEMENTS The authors thank the anonymous referee for her/his careful reading of the manuscript and insightful comments. This work is part of the  VIALACTEA  Project, a  Collaborative Project under Framework Programme 7 of the European Union , funded under Contract # 607380  that is hereby acknowledged. Herschel Hi-GAL data processing, maps production and source catalogue generation is the result of a multi-year effort that was initially funded thanks to Contracts  I/038/080/0  and  I/029/12/0  from  ASI ,  Agenzia Spaziale Italiana. Herschel is an  ESA  space observatory with science instruments provided by European-led Principal Investigator consortia and with important participation from  NASA. PACS has been developed by a con-sortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KUL, CSL, IMEC (Belgium) ;  CEA, OAMP (France) ;  MPIA (Germany) ;  IAPS, OAP/OAT, OAA/CAISMI, LENS, SISSA (Italy) ;  IAC (Spain).