dataesr/scientific-paragraphs-categorization huggingface dataset:
V. CONCLUSIONS This work provides the most constraining measurements of the source of GW170817 to date. Without imposing strong astrophysical priors on the masses or spins, we show that the GW data constrains the masses to the range expected for BNS systems and constrains spin components parallel to the orbital angular momentum to be small. The GW data, however, do not significantly constrain the spin components perpendicular to the orbital angular momentum. If there is significant spin, it must lie near the orbital plane of the binary. Imposing a prior on the distance to GW170817 from the known distance to the host NGC 4993 allows us to constrain the inclination angle of the binary, providing insight into the nature of gamma-ray bursts. Our improved constraints on the tidal deformation of the binary components reduce the upper bounds on this deformation, further ruling out some of the stiffest equation-of-state models.
We have shown initial results with the RAPIDPE code and SEOBNRv4T and TEOBResumS waveform models, and found that the measured tidal parameters are consistent with the main results of the paper. Furthermore, updated instrumental calibration could improve constraints further. However, we do not expect these improvements to change the conclusions obtained here. We have also placed new, morphology-agnostic bounds on the postmerger signal and argue that the Advanced LIGO-Virgo network at design sensitivity could have potentially reconstructed the postmerger signal. There is still significant potential for improved constraints on GW170817 in the use of additional information in the priors for tidal deformation. In this work, we allow the component tidal parameters to vary independently, implicitly allowing each neutron star to have a different equation of state.
ACKNOWLEDGMENTS The authors gratefully acknowledge the support of the  United States National Science Foundation  ( NSF ) for the construction and operation of the  LIGO Laboratory and Advanced LIGO , as well as the  Science and Technology Facilities Council (STFC)  of the United Kingdom, the  Max-Planck-Society (MPS) , and the  State of Niedersachsen/Germany  for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the  Australian Research Council. The authors gratefully acknowledge the  Italian Istituto Nazionale di Fisica Nucleare (INFN) , the  French Centre National de la Recherche Scientifique (CNRS)  and the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , for the construction and operation of the Virgo detector and the creation and support of the EGO consortium.
Several newly introduced algorithms work well only with simulated or rig acquired data, usually with a single response of the first mode. Further efforts are needed to make them more mature to better deal with noise and weak or more complex responses. Validation against simulation is not accepted for certification of engines  [22]. BTT technique was already used in component certification tests, where the evidence to calculate a meaningful value of stress was produced  [23]. However, a documented validation of the measurement system and ability to estimate uncertainty are still missing in many BTT applications. This paper presents robust and efficient methodology of processing tip-timing data which produces vibration results of known and controlled uncertainty. It is demonstrated on two datasets acquired in a realistic test-cell environment.
Materials and Methods This work analyses forced vibrations of axial compressor blades, measured with tip timing system during the bench tests of the SO-3 turbojet. SO-3 is a first generation single-spool engine with the seven-stage axial compressor and no variable stator vanes. It rotates anticlockwise (looking from aft forward) with the maximum speed 15,600 rpm. There are 28 blades in the first stage with dovetail roots (Figure  1 ), made of 18H2N4MA steel and painted yellow for protection against corrosion. The stage is subsonic, preceded by three struts and 44 constant inlet guide vanes (IGV), and followed by 34 downstream vanes. The conducted engine tests were aimed to characterise blade vibrations i. e. , identify existing responses, especially high-order ones. This is often necessary when investigating blade failure in legacy compressors or turbines, when design data are not available or not reliable.
The purpose of fitting a felt blocker to the inlet (Figure  2 ) was not to force any particular order, but to excite as many responses as possible. It significantly increased blade vibration  [24]  and simulated icing, a bird, or another foreign object ingested and stuck into the inlet guide vanes. Its role was to excite a wide array of resonances, which are normally difficult to measure. Previous research showed  [25] [26] [27]  that operation with a foreign object in the inlet results in M1EO2 resonant vibrations exceeding critical stresses and can lead to their fatigue cracking even after several minutes. Two datasets (Figure  3 ) are analysed below: 1. Without inlet excitation (S001) 2. With the inlet blocker fitted (S349).
Measurement Setup The acquired data included signals from eight variable reluctance probes installed in the first stage of the compressor and the once-per-revolution sensor, organised in the form of 'shots' comprising slow manoeuvres of increasing and decreasing rotational speed. The inductive probes, developed by ITWL, were placed at the following circumferential positions (Figure  4 ): 0. 0, 19. 7, 83. 9, 93. 4, 103. 1, 113. 1, 122. 7, and 132. 7 degrees in the slots machined under a previous project. For that reason, sensor spacing could not be optimised. The data acquisition system was based on National Instruments PXI-1065 computer running a LabView application and two PXIe-6358 modules, which amplified and sampled sensor signals with the rate of 500 kHz. Waveforms were processed in real time by software edge detectors with linear interpolation so the resolution of blade arrival measurement (TOA) is not limited by the sampling rate  [28].
For synchronous vibration, Equation (  1 ) can be expressed in the following matrix form: d = Ma (2) where d = [d 1. d M ] T , a = [a 0 a 1 a 2 ] T , M -number of probes M = 1 sinEOθ 1 cosEOθ 1. 1 sinEOθ M cosEOθ M (3) The system was overdetermined because the number of probes M was higher then the number of the vibration parameters a, so the method of least squares can be applied to approximate the solution: â = (M T M) -1 M T d. (4) The algorithms for solving the numerical problem are described by Jousselin et al. [7, 22]  and  Russhard and Back [31]. The operation may be performed in a general-purpose package such as Matlab  [10]  or Python  [29]. The obtained vector â is substituted to Equation (2) to compare the calculated displacements d with the measured ones d. It is more convenient to use correlation between d and d to evaluate the goodness of fit instead of the residual d -d.
Where integral and non-integral displacements precede and follow on an integral response, or occur simultaneously, then that software fails. In this work, a new solver called EMTD Multitool  [35]  was used. It has the ability to operate conventionally or by using alternative methods of pre-processing the data, which can be manipulated into the form: d j = a 0 + a 1 sinEOθ j + a 2 cosEOθ j (9) where the value of EO is real number rather than an integer.
EO2 Forced Response The SO-3 turbojet had a low order resonance in take-off range which is an obvious design error. It was observed that the inlet blocker doubled the amplitude of M1EO2 and other responses occurring above 12,000 rpm. The deflection curve at the take-off speed 15,500 rpm showed the blade leaning as it went towards resonance (Figure  5 ). The stack pattern of the BTT data sets was used as a first level validation of the data. If carefully managed, it could be used as real time indicator of integral vibration reinforcing the dangers of looking at raw displacement curves to the untrained operator. In the case there are discrepancies, the analysis software should refuse to load incorrect data. For s349 data-set, the extreme forced vibration reaching 3 mm pp distorted the stack plot (Figure  7 ).
The mean values calculated for the stack deviated as a result of high integral vibration and consequently, the alignment checks on the stack pattern failed. Batch processor reported the stack pattern's error and denied loading and analysing the shot S349. There was no standard approach for cases when automated zeroing failed. Some software packages, including MultiTool, allow the data to be corrected for errors before allowing analysis. Both programs are unable to process this resonance properly without careful configuration requiring an understanding of BTT methods. The fitting results of the amplitude and phase of individual blades are available in spreadsheet files for further processing. Figure  8  shows the tracked order overlay for a single blade with and without the blocker. The same maximum speed was not reached for both setups, but a forced response was observed on the blade prior to approaching its resonant speed, as well as increased instability caused by the blocker.
Frequency Estimation In theory, vibration phase changes the sign when crossing the resonance but a 180-degree phase shift is observed in rare cases and cannot be used reliably to confirm the location of resonances. In a real test environment, examining each phase response visually is impractical even for low-order responses, which need many revolutions to sweep. If the phase shift was used as the only indicator defining the resonance, then less than 1% of existing resonances would be found in real engine datasets. Mostly, it is assumed that the blade resonant frequency occurs at the point where amplitude reaches a maximum value. The quality factor of metal blades is high (e. g. , 200 or more) so the resonance peak is a good approximation of the resonant frequency. The frequency measurements presented in the next section are obtained in this way.
Asynchronous Vibration The compressor is not equipped with guided vanes, so there are rotating stall and other intensive asynchronous vibrations observed below 12,000 rpm that disappear at higher speeds. This is shown in the all-blade spectrum plotted for the s1 dataset (Figure  16 ). When the speed changes (the left and right part of Figure  16 ), vibrations of individual blades are visible in the spectrum in the form of a crossing of the engine order axis of the diagram. Asynchronous vibration is presented as a coherent trace over the blue background denoting the noise floor. The average assembly amplitude and nodal diameter of travelling wave responses can be found by observing the vibration from two different angular positions  [46] [47] [48] : ND = φ 2 -φ 1 θ 2 -θ 1 ( 12 ) φ j is the phase of the spectral line observed by probe j.
We report on a method to fit time-trace data from a terahertz time-domain-spectroscopy system enabling the extraction of physical parameters from a material or metamaterial. To accomplish this we developed a Python-based, open-source software, called Fit@TDS that functions on a personal computer. This software includes commonly used methods where the refractive index is extracted from frequency-domain data. This method has limitations when the signal is too noisy or when an absorption peak saturates the spectrum. Thus, the software also includes a new method where the refractive indices are directly fitted from the time-trace. The idea is to model a material or a metamaterial through parametric physical models (Drude Lorentz model and time-domain coupled mode theory) and implement this in the propagation model to simulate the time-trace.
I. INTRODUCTION The method of terahertz time-domain spectroscopy (THz-TDS), enabled by the progress in short-pulse lasers, begun rapid development about 30 years ago  [1, 2, 3, 4]. This wellestablished technique is now mature and has been commercialized by several companies. Today, THz-TDS is the main tool for broadband terahertz (THz) spectroscopy, offering more than one-decade of bandwidth with a standard resolution up to ~ 1 GHz. THz-TDS has shown the capability to study different materials such as semiconductors  [5] , ferroelectrics, superconductors, liquids  [6] , gases  [7, 8] , "This work was partially supported by: i) the international chair of excellence "ThOTroV" from region "Hauts-de-France" ii) the welcome talent grant NeFiStoV from European metropole of Lille and iii) the French government through the National Research Agency (ANR) under program PIA EQUIPEX ExCELSiOR ANR 11-EQPX-0015.
This process has shown promising results, but has several drawbacks as previously described. In this work, we present a robust and generic optimization approach  [29, 30]. The Fit@TDS software is based on a direct comparison of the initial time-domain data of the measured THz pulse with a mode similarly to the work presented to measure thickness of paint layer by Van Mechelen et al. [19]. We introduce this software and show that it enables one to model both simple materials, such as silicon; as well as more complex ones, such as carbohydrates and even metasurfaces. The remainder of this article is organized as follows. The basics of the implemented optimization is explained in section II. The two different models used are briefly described in section III. Sections IV and V present an analysis of the method's performance on fictitious and real samples, respectively.
III. MODELS To perform the optimization, we coded the methods as given in repository from Ref. [31]  in a mainstream language (Python) that allows the use of different optimization libraries. Since our goal is to offer a broad tool to the community, that is usable regardless of the type of sample under study or the subsequent problem to solve, we implemented a function from a research library  [32]  that includes many different optimization algorithms-giving users the opportunity to choose the ideal one for their samples. For the choice of the algorithm itself, we chose the versatile augmented Lagrangian particle swarm optimizer (ALPSO)  [33]. This algorithm is an improvement on the basic "swarm particle optimization" approach used in constrained engineering design and the optimization problem.
C. Implementation The proposed models can involve more than thirty parameters for complex samples, thus an exhaustive error calculation to reach the global maximum for each set of parameters is too demanding. As stated above, the strategy we use is to implement an optimization algorithm to solve this nonlinear problem. A tremendous number of algorithms have been developed-giving birth to several fields of research. We implemented a library offering several optimization algorithms (the Python-based optimization package called PyOpt  [32] ), which is designed to formulate and solve nonlinear constrained optimization problems. The main advantage of this package is that it includes 20 different optimization algorithms, allowing users of Fit@TDS to change the algorithm for one that is more efficient for his or her specific problem.
IV. VALIDATION AND PERFORMANCE ASSESSMENT WITH SIMULATED FACTITIOUS SAMPLES To validate and assess the performance of the proposed methods we first used a simulated sample. To do so, we recorded the reference spectrum with the TeraSmart THz-TDS spectrometer by Menlo Systems GmbH (1000 accumulations) [40] and then numerically simulated the response of the system with a sample using the equations described above. The data without a sample were windowed at the end of the time-trace on a segment length corresponding to the time delay introduced by the sample to remove folding effects due to the periodicity of the FFT. For the data with the simulated sample, we convolved the measured pulse by the transfer function of equation (  1 ) with the Drude-Lorentz model to define the permittivity. Then, we added Gaussian white noise to the time-trace with a magnitude equivalent to the level of the high frequency noise one can observe in FIG.
A. First validation: one oscillator Drude-Lorentz model To perform a first validation we tested Fit@TDS with a simulated 5-mm-thick sample with a dielectric constant build with the one-oscillator Drude-Lorentz model with the following parameters: ; ; ;. The time-trace of the sample is shown in FIG. 3 , and the resulting permittivity is plotted in FIG. 4. The spectral data clearly features a dip at the frequency of the oscillator. Then, we implemented our software Fit@TDS, with the ALPSO algorithm using the swarm-size of 1000, 6 inner iterations and 20 outer iterations. The bounds for the thickness were ±1% around 5 mm. The bounds for the four other parameters were -50% and +100% of the parameter value. Although we optimized neither the choice of the algorithm, nor the parameters (swarm size, inner and outer iterations, etc.
VI. CONCLUSION In this paper, we presented a new method, and an associated software, to fit THz-TDS data with material and metamaterial models based on time-domain fits. The goal of the software is to provide an improved, robust tool that gives more precise insight into an unknown sample, as well as to accelerate the analysis of a known sample, for instance, in the case of quality control. This software is freely available, and we provide a link to the source code in Ref. [31]. We first explained the methods, then tested it on fictitious samples and finally on real ones: a semiconductor, a molecular crystal and a metamaterial. Compared to other available software and methods, Fit@TDS has five main advantages. (i) A precise measurement of the thickness of the sample is not needed since it plays the same role as the other fit parameters.
System overview The CMS Phase-1 pixel detector has three disks on both ends of the forward regions (FPIX) and four barrel layers in the central region (BPIX). An overview of the Phase-1 pixel detector DAQ and -2 -control system architecture, including auxiliary components required to interface with the central CMS services, is shown in figure  1. The CMS Phase-1 pixel detector contains one type of sensor, bump bonded to 16 ROCs  [5]. The active area of the module is 16. 2 × 64. 8 mm 2. The pixel size has remained the same as in the original detector, 100 × 150 µm 2. The same n + -in-n technology as for the original detector is used for the silicon sensors. A high density interconnect (HDI) is glued on top of the sensor. The HDI provides signal and power distribution for the ROCs, and it carries the token-bit manager chip (TBM) and decoupling capacitors.
The TBM chips are glued onto and wire-bonded to the HDI. The TBM chip is described in detail in section 3. 2. They orchestrate the transmission of the data from the ROCs to the back-end electronics. The Phase-1 pixel detector features a fully digital readout system including new back-end electronics. The new ROCs with digital readout operate on a 40 MHz  [6]  clock1 and have a 160 Mb/s serial output data stream. This stream is encoded and multiplexed by the TBM using a 4b/5b encoding scheme, to reduce the impact of bit-errors during transmission  [7]  and for DC balancing. The TBM outputs one or two 400 Mb/s data streams. A dedicated ROC was designed for the sensor modules of the innermost layer of BPIX (layer 1) to cope with the higher hit rates. Layer 1 sensor modules require two TBMs to manage the higher data rates, while all other modules have one TBM.
The sensor modules are connected to the on-detector auxiliary electronics (portcards) via flex (FPIX) or twisted pair (BPIX) cables. The portcards are located in the 3 m long service cylinders, which also serve as mechanical support for all detector services (power, cooling and optical links) routed to and from the outside of the 6 m long pixel support tube. There are two different types of optical hybrids on the portcards: the Pixel-Opto-Hybrid (POH) and the Digital-Opto-Hybrid (DOH), which facilitate communication with back-end electronics via optical links. The POH converts the electrical signal from the TBM to an optical signal and delivers it to the FED. The FED handles decoding and deserialization, and builds event fragments, which are sent to the Central DAQ Front-End Readout Optical Link-40 (FEROL40) card  [8] , the first stage of the CMS Central DAQ chain, via a small form-factor pluggable (SFP+) 10 Gb/s S-Link Express transceiver (Tx).
The number of optical readout links has increased with respect to the original detector from 448 to 672 for FPIX and from 1152 to 1696 for BPIX, resulting in a total of 2368 readout links. The first and second layer of BPIX use four and two links per sensor module, respectively, to cope with the higher occupancy and data rate. The third and fourth layer of BPIX, as well as the FPIX disks, use one link per sensor module. -4 -.
Two output data streams are non-return-to-zero-inverted (NRZI) and encoded with a 4b/5b scheme and multiplexed by a -6 -block called the DataKeeper into a 400 Mb/s stream which is transferred to the FED. There are three versions of the Phase-1 pixel detector TBM (summarized in table  1 ). The TBM08, used in FPIX disks and BPIX layers 3 and 4, combines two groups of ROC data, while the TBM09 and TBM10, used in BPIX layer 2 and layer 1, respectively, combine the output of four groups of ROCs into two 400 Mb/s data streams. The TBM09 and TBM10 differ in their timing settings, which are optimized to match the PSI46dig and PROC600, respectively. The data format for Phase-1 sensor modules is as follows: TBM Header, followed by ROC Headers and pixel-level event information, followed by TBM Trailer.
Pixel Opto Hybrid (POH) The POH is a printed circuit board (PCB) mounted on the detector service cylinders. Figure  2  shows the POH4 (left) used in BPIX and the POH7 (right) used in FPIX. The optical characteristics of the two variants are the same. The overall system requires 424 POH4 and 96 POH7. The design of the POHs uses the Transmitter Optical Sub-Assembly (TOSA) component provided by the Versatile Link project  [23]. The POH receives electrical signals from the TBM and converts them into optical signals to be transmitted to the back-end receiver on the FED installed in the counting room, about 65 m away from the detector. Each POH houses single-mode Fabry-Perot laser TOSAs operating at 1310 nm, as well as Digital Level Translators (DLT) and Linear Laser Drivers (LLD)  [24]. The DLT chips convert the signals received from the TBM to levels compatible with the LLD and introduce a gain and an offset to the input signal.
Phase-1 Tracker FEC The Tracker FEC is responsible for programming the auxiliary detector electronics, like the DC-DC converters, which is independent from the control of the sensor modules. Each Tracker FEC controls a control/token ring via semi-redundant connections that carry clock and data signals. The -9 - control is done via a token-ring protocol. For the CMS Phase-1 pixel detector there are four control rings each for FPIX and BPIX. The Tracker FEC firmware is designed to implement four control ring firmware blocks (CTRL_RING) independently from each other. Each CCU control ring is addressed by one control ring firmware block. The firmware is link compliant with the CCU communication protocol specified for the original detector  [18, 32]  and access compliant with the control software of the original detector.