dataesr/scientific-paragraphs-categorization huggingface dataset:
8), which has been specifically developed to improve the data quality for the detection of diffuse, low-SB emission in MUSE datacubes  22, 23, 51, 52. In detail, we improved the flat fielding by using the tool CubeFix 37 , which performs an additional correction to homogenize the illumination across the field and as a function of wavelength. We performed the sky subtraction using CubeSharp 37 , which can reach a better accuracy than the MUSE pipeline  22. We then combined the corrected and skysubtracted individual exposures from both datasets using an average 3σ-clipping. The final datacube samples the instrument FOV of 1 0 × 1 0 in pixels of size 0. 2″. Each pixel contains a spectrum covering the wavelength domain 4750-9350 Å, and individual channels have a spectral resolution of 1. 25 Å.
We compute the spatial extension of such emission using the tool CubEx (from the CubExtractor package  37  ). CubEx is extensively used to perform automatic detection and extraction of extended sources in MUSE datacubes  22, 23  , and it is based on a 3D extension of the connected-labeling-component algorithm with union finding, usually used in classical binary image analysis  22. We feed CubEx with a subcube that we extract to have a wavelength range of Δλ~100 Å centered around the peak of Lyman-α emission (4388 Å), and from which we remove continuum sources by adopting a median-filtering approach. We also apply a spatial Gaussian filtering of 2 pixels (0. 4″) and a spectral smoothing with a Gaussian filter size of 1 layer (1. 25 Å) to bring out extended but narrow features. Running CubEx with a SNR threshold for detection of SNR > 3 in individual datacube elements (voxels) we detect a nebula consisting of approximately 1600 connected voxels and a maximum spectral width, as defined in the 3Dsegmentation-mask, of Δλ ~15 Å.
This values is consistent, within the uncertainties, to the predicted comoving density of halos hosting central massive galaxies like W0410-0913, with maximum velocities of v max = 500 ± 70 km s À1 (see the radial profile of the rotational velocity in Fig. 4b ) at z = 3. 631, which we find to be n halo ~10 -3. 8±0. 5 Mpc -3 , as we compute integrating the halo mass function provided by the large N-body simulation MDPL2  63  , included in the open-access CosmoSim database (https://www. cosmosim. org/cms/simulations/mdpl2/).
ALMA data In this work, we use archival ALMA observations of W0410-0913 targeting the emission-line spectra arising from rotational transitions of carbon monoxide CO(4-3), CO (6-5), and CO (7-6), and the atomic carbon transition [CI] 3 P 2 -3 P 1 (hereafter [CI]). CO(4-3) 461GHz, redshifted to 99. 5GHz at z = 3. 631, was observed with ALMA in Band 3 on January 2018 (Program ID: 2017. 1. 00123. S), with a total integration time of 11:5 min. Deep observations of CO (6-5) 691. 4GHz, redshifted to 149. 3GHz, were taken in Band 4 in December 2017 (Program ID: 2017. 1. 00908. S) exploiting a total integration time of about 3. 6h. CO (7-6) 806. 6GHz and the adjacent [CI] 809. 3GHz, redshifted to 174. 2GHz and 174. 75GHz respectively, were observed in Band 5 on September 2018 (Program ID: 2017. 1.
00123. S), with a total integration time of about 10 min. See the Data Availability Section for information on the data repositories. We performed a standard calibration of the data using the Common Astronomy Software Applications package (CASA  67  ), running different versions of the software per each dataset, according to the Quality Assurance report provided by the ALMA staff. The pipeline calibration outputs did not show any issue, and thus we did not perform any additional flagging. We obtained continuumsubtracted visibilities using the CASA task uvcontsub, and we generated the continuum-subtracted datacubes of the spectral windows containing the emission lines by running the CASA cleaning algorithm clean with 500 iterations and a SNR threshold of 3. In order to maximize the sensitivity, we adopted a natural weighting of the visibilities for all the three datasets.
Study of the CGM environment with ALMA Unfortunately, none of these datasets, designed to observe the cold gas emission from the hyper-luminous W0410-0913, provides information about the rest-frame FIR line properties of the companion Lyman-α emitters that we discover with MUSE in its CGM. However this is not surprising, and it is mainly due to a combination of insufficient sensitivity and too high resolution (that dilutes the flux over several beams), as we test by running simulations of the estimated telescope time on the ALMA Observing Tool (OT; version released for Cycle 8). In detail, we first estimate the total molecular gas mass (M mol ) from the SFR measured with HST by using standard M mol -SFR scaling relations calibrated at high-z  68. We then convert M mol into total CO fluxes by adopting a conservative Milky-Way-like conversion factor between M mol and the CO(1-0) luminosity, α MW CO = 4:3, and standard Milky-Waylike intensity ratios between CO(1-0) and the rotational transitions observed by ALMA  69.
We note that the adopted value of α CO has been calibrated at solar metallicities, and should be considered conservative in our case since α CO typically increases at lower metallicities  70  , and high-z galaxies tend to be less metal-rich at a fixed stellar mass  71. Assuming projected sizes of about 1″ and line-widths of 300 km s -1 , we find that the estimated total ALMA time to observe the integrated CO spectrum in a galaxy with SFR = 30 M ⊙ yr -1 (the median SFR of the HSTdetected companions), with at least 3σ per channel in at least 3 spectral bins, at the same angular resolution of the available observations, would be about 1. 6h for CO(4-3) and CO (7-6), and about 24. 5h for CO (6-5), between a factor of 6-8 longer than the observed datasets. Other additional limitations derive from the ALMA smaller fields of view (the primary beams range from about 30″ in Band 5 to about 45″ in Band 3), and the narrower spectral coverage (sidebands ranging from 5000 km s -1 in Band 5 to 9000 km s -1 in Band 3) with respect to MUSE data.
The combination of these effects reduces the number of companion galaxies that could be possibly detected (up to 60%, in the best case) and, together with the sensitivity issue discussed above, limits the information that can be derived through stacking analyses. Indeed we find signals that are consistent with noise in the stacked images that we produce, for each dataset, by combining moment-0 maps centered at the expected positions of the galaxies (using the peak of Lyman-α emission as a prior), obtained by collapsing three different velocity ranges, 300, 400, 500 km s -1 , centered around the expected systemic frequencies (using the redshifts derived from the Lyman-α as a prior).
The resulting model datacube is then convolved with the ALMA synthesized beam and rescaled so that the velocity-integrated intensity of each spaxel in the data and model cubes are equal. Then, the values of the pixels in the data and model datacubes are compared, and each parameter is varied until the absolute residual (i. e. , |model-observation|) is minimized. When this process reaches a minimum, the next ring is analyzed. During the first run 3DFIT provides best-fit results for the rotational velocity, velocity dispersion, systemic velocity, inclination, and position angle. The three latter variables are averaged across all the rings, and the whole fit routine is run again, with only the rotational velocity and velocity dispersion as variables. Among the final 3D Barolo outputs, we obtain morphological parameters (e.
Acknowledgements ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. The CosmoSim database used in this paper is a service by the  Leibniz-Institute for Astrophysics Potsdam (AIP). The  Cosmic Dawn Center (DAWN)  is funded by the  Danish National Research Foundation  under grant No. 140. G. C. J. acknowledges funding from  European Research Council (ERC)  Advanced Grants  695671  " QUENCH" and 789056 "FirstGalaxies"  under the European Union's  Horizon 2020 research and innovation programme , as well as support by the  Science and Technology Facilities Council (STFC).
Data availability The MUSE data used in this work (programs: 0102. A-0729, 106. 2184-001) are available at http://archive. eso. org/scienceportal/home. The archival HST (program ID: 12930) and ALMA (programs: #2017. 1. 00123. S, #2017. 1. 00908. S) data are available at https://archive. stsci. edu/and http://almascience. eso. org/aq/respectively. All data generated and/or analyzed during the current study are available from the corresponding author upon request.
Code availability The MUSE pipeline recipes, used to reduce the MUSE data, are publicly available and the software distribution can be obtained at https://www. eso. org/sci/software/pipelines/muse/. The software CASA, used to process the ALMA data, is publicly available and can be downloaded at https://almascience. eso. org/processing/science-pipeline. The code used to model the molecular gas kinematics, 3D Barolo, is publicly accessible through github (https://editeodoro. github. io/Bbarolo/), with a full documentation hosted at https://bbarolo. readthedocs. io/en/ latest/. The package used for the 3D extraction of faint diffuse emission in the MUSE datacube, CubExtractor, is not publicly available, though the reader interested in using this code can contact Sebastiano Cantalupo at the mail address sebastiano.
An example of a system that employs the coincidence approach is the STELLA apparatus  (Heine et al. 2018) , which exploits several specific technical developments to maximise sensitivity and background rejection, tailored to the challenging application of measuring carbon-fusion at very low cross sections. STELLA combines double-sided silicon strip detectors for charged-particle detection with an array of LaBr 3 (Ce) gamma-ray detectors  (Roberts et al. 2014 ) that offer excellent timing resolution  (Heine et al. 2018). Data are collected using a triggerless data acquisition system. To support the high beam currents required for such studies, a rotating target mechanism is employed to efficiently dissipate heat in the thin self-supporting target foils, allowing for beam intensities of several microamps to be safely handled.
Acknowledgements. J. N. acknowledges support from the  Interdisciplinary Thematic Institute QMat , as part of the  ITI 2021 2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , which was supported by  IdEx Unistra  ( ANR 10 IDEX 0002 ), and by  SFRI STRAT ' US  project ( ANR 20 SFRI 0012 ) and  EUR QMAT ANR-17-EURE-0024  under the framework of the  French Investments for the Future Program. S. M. , G. M. , and S. E. acknowledge the STAREX grant from the  ERC   Horizon 2020 research and innovation programme  (grant agreement No  833925 ), and the  COST Action ChETEC  ( CA 16117 ) supported by  COST (European Cooperation in Science and Technology). This work was supported by the  Fonds de la Recherche Scientifique-FNRS  under Grant No IISN  4. 4502. 19.
The improvement of driving comfort and vehicle stability performance is essential for the vehicles, which can be actualized by adaptive semi-active suspension control. Cloud computing allows several features for autonomous vehicles. Implementing the adaptive suspension control using historical road data gathered in the cloud database is one of these features. This paper deals with the adaptive semi-active suspension control from the perspective of a Vehicleto-Cloud-to-Vehicle integration. Measured and historical performance(vertical acceleration and tire deformation) and velocity data in different locations and road irregularities from other vehicles have been stored in the cloud database and used to design the dedicated scheduling variable. The novelty of this paper is developing the adaptive semi-active suspension control method with different scheduling parameter design approaches based on cloud application for the road adaptation capabilities of the suspension system.
INTRODUCTION Cloud computing is a significant technology for the application of advanced vehicle control and optimization computation. Autonomous vehicles have advanced technology consisting of numerous embedded electronic control units, and control of these units needs high computing power and storage. But this computing power and storage is not cost-effective, due to this reason, cloud computing is an effective solution for the high algorithm load and database complexity. There are several criteria for the autonomous driving, while safety is the most important one. Nevertheless ensuring driving comfort is also a significant performance development on the control design. Controlling the vertical dynamics of the vehicle increase comfort level of passenger. Due to that a well-designed suspension system radically improves both road holding and driving comfort.
The road condition and irregularity are needed to be known and analyzed in order to design adaptive vertical control of autonomous vehicles. The road condition can be estimated with several methods  Qin et al. (2018) ;  Göhrle et al. (2014)  or the cloud-based database can be used in order to know the road conditions  Basargan et al. (2021b). The reconfigurable control method is needed in order to change controller behavior for different external factors such as velocity, road condition, and road irregularity, etc. This control approach finds the trade-off between vehicle safety and driving comfort. The reconfigurable control is based on the velocity of vehicle, road condition and performance importance of the vehicle. There are numerous researches where the development of the semi-active suspension system based on the quartercar model has been studied with different approaches and techniques.
The road condition and irregularity are needed to be known and analyzed in order to design adaptive vertical control of autonomous vehicles. The road condition can be estimated with several methods  Qin et al. (2018) ;  Göhrle et al. (2014)  or the cloud-based database can be used in order to know the road conditions  Basargan et al. (2021b). The reconfigurable control method is needed in order to change controller behavior for different external factors such as velocity, road condition, and road irregularity, etc. This control approach finds the trade-off between vehicle safety and driving comfort. Abstract: The improvement of driving comfort and vehicle stability performance is essential for the vehicles, which can be actualized by adaptive semi-active suspension control. Cloud computing allows several features for autonomous vehicles.
Implementing the adaptive suspension control using historical road data gathered in the cloud database is one of these features. This paper deals with the adaptive semi-active suspension control from the perspective of a Vehicleto-Cloud-to-Vehicle integration. Measured and historical performance(vertical acceleration and tire deformation) and velocity data in different locations and road irregularities from other vehicles have been stored in the cloud database and used to design the dedicated scheduling variable. The novelty of this paper is developing the adaptive semi-active suspension control method with different scheduling parameter design approaches based on cloud application for the road adaptation capabilities of the suspension system. The control architecture is founded on the Linear Parameter-Varying framework, where the scheduling variable allows the trade-off between driving comfort and vehicle stability.
The most common control strategy is a skyhook  Savaia et al. (2021) ;  Negash et al. (2021). The implementation of this method is easy; however, it disrupts the dynamic tire load. Abstract: The improvement of driving comfort and vehicle stability performance is essential for the vehicles, which can be actualized by adaptive semi-active suspension control. Cloud computing allows several features for autonomous vehicles. Implementing the adaptive suspension control using historical road data gathered in the cloud database is one of these features. This paper deals with the adaptive semi-active suspension control from the perspective of a Vehicleto-Cloud-to-Vehicle integration. Measured and historical performance(vertical acceleration and tire deformation) and velocity data in different locations and road irregularities from other vehicles have been stored in the cloud database and used to design the dedicated scheduling variable.
The novelty of this paper is developing the adaptive semi-active suspension control method with different scheduling parameter design approaches based on cloud application for the road adaptation capabilities of the suspension system. The control architecture is founded on the Linear Parameter-Varying framework, where the scheduling variable allows the trade-off between driving comfort and vehicle stability. The real data simulation demonstrates the operation of the introduced method in the TruckSim simulation environment and Matlab/Simulink. The results show that both vehicle stability and driving comfort has been improved.
Abstract: The improvement of driving comfort and vehicle stability performance is essential for the vehicles, which can be actualized by adaptive semi-active suspension control. Cloud computing allows several features for autonomous vehicles. Implementing the adaptive suspension control using historical road data gathered in the cloud database is one of these features. This paper deals with the adaptive semi-active suspension control from the perspective of a Vehicleto-Cloud-to-Vehicle integration. Measured and historical performance(vertical acceleration and tire deformation) and velocity data in different locations and road irregularities from other vehicles have been stored in the cloud database and used to design the dedicated scheduling variable. The novelty of this paper is developing the adaptive semi-active suspension control method with different scheduling parameter design approaches based on cloud application for the road adaptation capabilities of the suspension system.
System architecture and cloud application The capabilities of the cloud, database, and computing technologies are used in order to improve the comfort and safety of such equipped vehicles in this study. The system consisft of three different subsystem, that are semiactive suspension system with MR damper, controlled by the LPV controller explained in the previous section; the cloud system communicates with the vehicle and gathers, processes, stores and distributes the data. The architecture of the proposed system is shown in Fig. 1  The general-purpose V2C2V architecture is illustrated in Fig. 2. Vehicles are connected to the internet with the wireless networks. The cloud system provides access to the database as needed for cloud computing and designing the scheduling variable. The cloud system can provide huge computing power, its use for vehicle control is limited by the availability of the network.
Sensors provide required data from vehicles, raw and processed data can be transmitted to the cloud system for computing and reaching the database  Li et al. (2015). The process of decision making for the scheduling parameter in the cloud system is the following: • numerous measurement data is collected by the onboard unit from the passive suspension system and this measurement data is uploaded to the cloud system. • the database, which is consist of the road information is build by gathering information from vehicles by the developed cloud application. • the performance index is defined in order to find importance between driving comfort and road holding in the cloud application. • according to road irregularity, vehicle velocity, performance results and performance index, the corresponding scheduling variable is calculated in the related location by the decision algorithm in the cloud.
• the calculated scheduling variable is used in the LPV controller to modify the behavior of the LPV controller. • the corresponding damper force is forwarded to the damper of the vehicle. The cloud part of the system is implemented on a private infrastructure cloud. It is also possible to implement the introduced architecture on the other clouds with minor modifications. The selected database engine is MongoDB due to its handling geospatial information feature. The cloud application uses a Flash framework for providing a RESTful API to the cloud functionality and written in Python  Basargan et al. (2021b). TruckSim software is used to simulate the simulation vehicle, while the environment has been integrated with Matlab/Simulink. Here, a test of controller and cloud connection via web API is possible.
Scheduling variable design The scheduling variable design is based on the comparison of performance results in corresponding velocity and road irregularity in the cloud database. First, the performance index is calculated based on the previous measurement of the vehicles that have passive suspension. The importance of one of vertical acceleration and tire deformation performances is selected depending on their value. Once new data arrives, the cloud application calculates the dedicated scheduling variable for the controller. Cloud-based road database has numerous measurements and historical data with different velocities and road irregularities. These measurement and data consist of type and size of road irregularity, the location of road irregularity, velocity of the vehicle, tire deformation value and vertical body acceleration value at each time step.
ACKNOWLEDGEMENTS The research presented in this paper, carried out by Institute for Computer Science and Control was supported by the  Ministry for Innovation and Technology  and the  National Research, Development and Innovation Office  within the framework of the  National Lab for Autonomous Systems. The research was partially supported by the  Hungarian Government  and cofinanced by the  European Social Fund  through the project " Talent management in autonomous vehicle control technologies " ( EFOP-3. 6. 3-VEKOP-16-2017-00001 ).
Zin et al. (2005)guarantee the ride comfort and vehicle stability performances, where it is allowed by H ∞ control methodZin et al. (2005); Rossi and Lucente ( (2014) or the cloud-based database can be used in order to know the road conditions Basargan et al. (2021b). The reconfigurable control method is needed in order to change controller behavior for different external factors such as velocity, road condition, and road irregularity, etc. This control approach finds the trade-off between vehicle safety and driving comfort. The reconfigurable control is based on the velocity of vehicle, road condition and performance importance of the vehicle. There are numerous researches where the development of the semi-active suspension system based on the quarter- car model has been studied with different approaches The road condition and irregularity are needed to be known and analyzed in order to design adaptive vertical control of autonomous vehicles.