dataesr/scientific-paragraphs-categorization huggingface dataset:
However, with a behavioural change whereby all tool builders are required or incentivised to specify their input and output parameters, the effort is quite feasible. An increasing set of modules could be provided that would automatically carry out the necessary transformations. Where this would not help it should become simpler to specify transformations. Our assessment, based on the many contributions and discussions at our meetings, is that we can be sure that new workflow technologies will be developed with the intent of making it easier for researchers to apply them in their daily work. This innovation will be ongoing. The technologies surveyed here have their own way of representing the workflow and in particular, the state of workflow processes. The CWFR initiative suggests a standardised approach, including an agreement on an interchangeable documentation format with FAIR Digital Objects as anchors.
Meanwhile, limited resources encourage increasing interest in integrated sensing and communication (ISAC) functions in the wireless communication community  [26] [27] [28]. Recently, the importance of sensing to enable wireless communication in dynamic smart metasurface-programmable radio environments Figure  1. Conceptual illustration and system configuration of the proposed intelligent indoor metasurface robotics. (a) Artistic sketch of the proposed intelligent indoor metasurface robotics paradigm. Therein, robotic limbs (in the sketch, a robotic arm, two motorized robotic vehicles and an airborne drone) execute tasks as instructed by the central robotic brain. The latter combines programmable-metasurface hardware with AI software to achieve microwave perception of the complex setting (including through the wall), to plan the necessary actions of its robotic limbs and to create high-capacity wireless links to the robotic limbs to communicate the instructions.
Instead, the I2MR's AI brain processes the sensor data that the brain acquires in real time to instruct the limbs how to act. If the brain concludes that action is required, it localizes its limbs and establishes a wireless link to transmit instructions at a rate of 10 Hz. The limb will then perform the demanded action via an ESP32 controller that is embedded with a 2. 4 GHz Wi-Fi module based on IEEE 802. 11 b/g/n standards. The brain's microwave perception, as well as the wireless communications between brain and limbs, pivotally rely on a programmable metasurface that is part of the brain. The metasurface's large aperture and massive number of degrees of freedom are used by the I2MR's brain sequentially in two different manners: first, to generate configurational diversity in order to illuminate the indoor environment with pseudo-random patterns for sensing and localization, and, second, to focus microwaves on the limb's wireless receiver to create a preferential highcapacity wireless link with boosted received-signalstrength indicator (RSSI).
Algorithmic overview I2MR orchestrates a multitude of AI-empowered algorithms to perceive and act in its environment. First, I2MR obtains a 3D point-cloud representation of its environment using the configurational diversity of 20 random metasurface patterns. Second, I2MR analyzes the 3D point-cloud neural network and the LSTM RNN to achieve the image and recognize the body gesture of the human subject. Third, if the need to bring medicine to the human is deduced, I2MR locates its limb using eight random metasurface configurations. Fourth, I2MR establishes a high-capacity wireless link between its brain and limb to wirelessly transfer instructions for the limb using an optimized metasurface coding pattern from a previously established code book. The location of the limb is constantly tracked, and the metasurface pattern, which is used to create.
In the two last test cases, we will consider homogeneous Neumann conditions. The surrounding domains O are always chosen as boxes aligned with the Cartesian coordinates and the background meshes T O h are obtained from uniform Cartesian grids, dividing the cells into the simplexes (semi-cross meshes in 2D). We always use the numerical quadrature of a high enough order so that all the integrals in (  6 ) are computed exactly. We have implemented φ-FEM both in FreeFEM  [13]  and in multiphenics  [2]. Both implementations give the same results in our test cases and we present here only those obtained with FreeFEM. The implementation scripts can be consulted on GitHub. 3 emark 3. Our method (6) features "mixed" terms, such as γ 1 Ω Γ h (y h +∇u h )•(z h +∇v h ) involving u h , v h defined on mesh T h and y h , z h defined on mesh T Γ h , a submesh of T h.
They rise serious VEC technical issues and challenges such as latency, scheduling, load balancing, offloading, resource management, and security/privacy. The authors survey the main challenges and opportunities when vehicle network meets edge computing. However, the work missed an overall architecture that defines the most appropriate network modules and edge techniques. Moreover, the authors should take into consideration the V2X communication protocols in presence of the NFV/SDN paradigms. They also missed optimization techniques related to the network/edge convergence according to safety indicators when designing the allocation loop of autonomous vehicles. In  [19] , the authors present an edge-cloud computing model for autonomous vehicles using the open-source software platform Autoware  [20].
They believe that their proposed edge-cloud computing model for Autoware-based autonomous vehicles reduces the execution time and the total deadline miss. Among the main missing modules in their platform, the work consider neither the in-vehicle computing resources management, nor the Vehicle to Edge (V2E) communications. In  [21] , the authors propose surrogate: an edge architecture for self-driving cars with OpenStack and ETSI open-source MANO. It aims at virtualizing the in-vehicle OBUs at the distributed edge platform and managing Multi-Access Edge Computing (MEC) layers that process realtime vehicle requests. The work suffers from optimal virtual OBU (vOBU) management and orchestration algorithms at the virtualized edge surrogates. Moreover, vOBU manager module needs to take into account solver instances related to the IoAV network scale and driving conditions.
It assists autonomous vehicles to detect/avoid obstacles such as pedestrians and other vehicles that may not be directly detected by the AV sensors. It helps autonomous vehicles to plan efficient paths. Then, the authors introduce a cloud-based planner module along with request, sender, and receiver modules. They implement the planner module within the cloud using the Robot Operating System (ROS). The cloud-assisted system tracks request messages from the cloud, and accordingly transmits the sensor information in the form of UDP packets to the cloud. Their proposed metrics are the response time and the distance to pedestrian. We believe that the work is of practical interest, however, it missed virtualization techniques and VNF manager module that ease the allocation procedure of the autopilot chain on the cloud.
• Minimum server's constraint: Constraint (11) assures a minimum selection of MEC servers available to process autopilot VNFs. y mec ≤ ea,vnf x mec ea,vnf , ∀mec ∈ MEC (11) • AV-RSU/gNB link constraint: Constraint (  12 ) guarantees the 5G quality on link capacities between MEC servers sources mec and autonomous vehicles sinks av. Indeed, network links should allow IoV data to be sent and control commands to be received. b Lea av ≤ L mec,av ∀mec, av ∈ AV ∪ MEC (12) • Latency constraint: Constraint (  13 ) assures that the autopilot VNFs are processed before the deadline d ea∈EA x mec ea,vnf t ea,vnf ≤ d ∀ea ∈ EA, vnf ∈ VN F (13 ) where t ea,vnf is the processing time matrix of the autopilot VNF (ea, vnf ). • Non-negativity constraints: Used variables x and y are binary in order to decide efficiently (without relaxation) about the autopilot VNFs allocation.
Tables  3  and 4  show the different configurations used in the evaluation. It is worth mentioning that we evaluated the proposed algorithms in small and large scale using simulation. We simulate the datacenter using the "x86" architecture, the "Linux" operating system and the "Xen" hypervisor. For the sake of better clarification, network and system parameters are shown in Table  5. Recall that the simulation considers the offloading of sensor data to the edge. This latter creates the world model according to the vPerception VNF. Then, the vLocalisation adds the position of the vehicle in the environment. The vPlanner traces the chart between the source and the destination and the vControl executes the path by sending the control command to the embedded vehicle.
2006) , the HI/OH Recombinationline Survey of the Inner Milky Way (THOR;  Beuther et al. 2016) , the Southern Galactic Centre Survey  (McClure-Griffiths et al. , 2012) , and surveys of the Magellanic System  (Kim et al. , 1998; Staveley-Smith et al. , 1997; Stanimirović et al. , 1999)  by increasing the angular resolution to arcsecond scales (a factor of two increase relative to SGPS and VGPS), increasing the spectral resolution to 0. 5 km s - 1  (useful to probe kinetic temperatures of ∼6 K), and expanding the instantaneous uv-coverage to characterize a virtually continuous range of spatial frequencies. Combining these novel GASKAP-HI data with data from the 64 m Parkes single dish telescope (Murriyang) will allow, for the first time, a view of nearby H i emission at similar physical scales as dust and molecular gas in the Magellanic System and supplement the aforementioned surveys of the Galactic Plane.
Furthermore, coupling the detailed view of the H i emission structure with high spectral resolution H i absorption measurements will facilitate accurate measurements of the spin temperature, placing lower limits on the kinetic temperatures of the WNM and LNM. The high number densities in CNM thermalise the H i line in this phase such that absorption measurements will provide the true kinetic temperature. Each antenna element of the Australian Square Kilometer Array Pathfinder (ASKAP;  Hotan et al. 2021)  telescope is outfitted with a Phased Array Feed (PAF) receiver and a matched set of digital equipment that forms 36 simultaneous primary beams on the sky, expanding the instantaneous field of view (FoV) from ∼1 deg 2 to ∼25 deg 2. The unique wide-field imaging capability of ASKAP presents several unique data processing challenges that the GASKAP-HI survey must address before beginning full survey operation.
For example, it is paramount to remove the inherent instrumental pointspread function (PSF) from a sky brightness distribution that extends over the boundaries of multiple beams without degrading the recovered structure. It is also crucial to combine ASKAP data with data from a single dish to fill in the missing short-spacings filtered out by the fixed antenna distribution  (Stanimirović et al. , 1999; Stanimirović, 2002). ASKAP surveys, including GASKAP, were selected in 2009 based on scientific merit during the construction phase of the telescope. The plans and specifications of each survey have been progressively refined in the intervening years to match the telescope capabilities and increased computing power, as described in  Hotan et al. (2021). In late 2019, ASKAP commenced pilot observations for each survey, allocating 100 hours of observing time to each survey team to test full survey operations.
In this paper, we present the first GASKAP-HI pilot observations of the SMC, obtained in December 2019. The observations are one part of a multi-faceted pilot survey, covering an extended region of the SMC, the starts of the Magellanic Stream and Bridge, as well as two fields in the Milky Way Galactic Plane. These images are also the first to be produced by our custom imaging pipeline built around WSClean, a command-line application first introduced by  Offringa et al. (2014)  that is specifically designed to perform efficient wide-field imaging. This software allows us to perform joint deconvolution distributed over a computing cluster, which ensures accurate reconstruction of the extended H i emission intrinsic to the Galaxy and Magellanic System while also managing the overall memory footprint.
The center frequency of each measurement set is 1419. 81 MHz in the topocentric reference frame. Our observations of the SMC total 20. 9 h of integration, split equally into two separate sessions. Each session is known as a schedule block (SB) and identified with IDs (SBIDs) 10941 and 10944, making it easier to identify observations in the CSIRO ASKAP Science Data Archive (CASDA 1 ). The PAF footprint, known as closepack36, was centered on J2000 RA = 00 h 58 m 43. 280 s , Dec = -72 d 31 m 49. 03 s. This particular PAF footprint places 36 simultaneously formed beams in a 6×6 hexagonal grid such that the beam centers are separated by 0. 9 deg to provide more uniform sensitivity  (McConnell, 2016; Hotan et al. , 2021). Throughout the observation, every 10 minutes the PAF footprint cycles through three pointing centers provided in Table  1.
ASKAPsoft Pipeline ASKAP observations are calibrated and imaged using a high-performance processing pipeline  (Whiting, 2020; Hotan et al. , 2021)  that runs on the Galaxy supercomputer at the Pawsey Supercomputing Centre. This typically includes all of the steps necessary to calibrate the data and produce images and spectral cubes of the observation, using the ASKAPsoft package  (Guzman et al. , 2019). For the GASKAP-HI processing, since the imaging is done in the joint-imaging pipeline described below, the ASKAP pipeline was limited to bandpass calibration, flagging, and self-calibration of the time-dependent gains.
Bandpass and Flux Scale Calibration and Flagging Prior to self-calibration, the ASKAP pipeline prepares the data through calibration of the bandpass and flagging of unwanted signal. The bandpass and overall flux scale is calibrated using the primary flux calibrator PKS B1934-638. The target and flux calibrator data are then flagged to remove radio-frequency interference (RFI). This employs a dynamic flagging algorithm operating in the time-domain for each channel, which flags discrepant time ranges based on the Stokes V flux. Data is then averaged to form 1 MHz-wide channels (that is, averaging 864 channels together), which will be used to create the continuum images used in the selfcalibration. Those channels that are identified to contain HI emission are then flagged for this step, so that only continuum emission contributes to the resulting images.
We produce a diagnostics report to describe the quality of the observation. 2 It summarises and visualises outputs produced by the ASKAPsoft  (Guzman et al. , 2019)  pipeline up to the production of calibrated measurement sets, along with the continuum image and source catalogue of the field. The report provides a snapshot of the overall quality and is generally sufficient to determine whether to proceed with imaging or re-observe. The first two sections of the report provide details of the observation and the continuum image. In the observation we need to know where the field is and how it was observed -duration, beam pattern (or PAF footprint), and the correlator mode. The footprint is also used later to determine the layout of beam based plots. Noting that the continuum data is well covered by the ASKAP continuum validation report, the continuum image report is limited to basic details.
To allow assessment of the calibration we visualise the median calibration bandpasses both by beam and by antenna. In the plots we highlight up to 10 bandpasses that either have a) a median more than one standard deviation from the median of all bandpasses, or b) an amplitude range that is more than one standard deviation from the median amplitude range of all bandpasses. The first rule highlights antenna sensitivity issues and the second calls out structure in the calibration bandpasses. As the raw ASKAP visibilities are not retained to manage disk space, recalibration is not possible, so any problems reflected here would generally mean the dataset needs to be rejected and the field re-observed. In the diagnostics section, we largely focus on what data have been flagged by the ASKAPSoft pipeline (see Section 3.
1. 1) within the measurement sets and the effect this has on the suitability of the dataset for different science cases. The flagging of the measurement sets is done in four dimensions: by baseline (or combinations of antennas), by beam, by channel, and by time. Flagging is generally used to remove bad data from the dataset, whether for an antenna that is performing incorrectly, an antenna that is in the shadow of another antenna with respect to the target source, or for radio frequency interference. We provide plots of the flagging a) by baseline, (see Figure  1 ), which shows if there is any bias in coverage by angular scale, b) by time to show if our u-v coverage is even, and c) by percentage of each baseline flagged to highlight frequency based flagging. For example, the histogram of the baseline coverage across all beams and all channels for the entire obser-vation for SBID 10944 in Figure  1  shows a majority of the available baselines remain unflagged during this scheduling block.
e. , 12 m), fill in gaps between all the short baselines, and deconvolve the images deeper, we must utilize a joint deconvolution approach. Here, a large image is created from the ourier inversion of the sampled visibilities from all beams (i. e. , the 'dirty image') before proceeding with deconvolution. Since the calibrated visibilities from all of the beams must be managed simultaneously in a joint deconvolution approach, the computation requirements -specifically the memory footprint -are immense relative to other imaging techniques. As of publication, ASKAPsoft 1. 3. 0 3 , the primary processing software utilized to image all other ASKAP data, is not fully optimised to run a distributed joint deconvolution scheme. We instead developed a custom imaging pipeline 4  that utilizes a combination of Common Astronomy Software Application (CASA;  McMullin et al.
The pipeline takes advantage of a high-performance computing (HPC) environment, while also managing the memory footprint, by submitting simultaneous batch imaging jobs of individual channels. A majority of the processing was performed with the Avatar computing cluster located at the Australian National University Research School of Astronomy and Astrophysics. The configuration of WSClean imaging mode and resource allocation is performed by bash scripts specific to both the Portable Batch System (PBS) and Simple Linux Utility for Resource Management (SLURM) scheduling software. Figure  2  summarizes the work flow of our jointimaging pipeline to image a single GASKAP-HI field. We download from CASDA 108 measurement set files that contain the gain-calibrated visibilities for a field observed in the closepack36 beam configuration.
We use the miriad task, IMMERGE, to feather a cube extracted from the Parkes Galactic All-Sky Survey (GASS 6 ; McClure-Griffiths et al. 2009) with our deconvolved ASKAP image. This cube was centered on the SMC and re-gridded to be on the same spectral and spatial grid before combination. IMMERGE combines the two data sets by Fourier transforming both the deconvolved ASKAPonly and Parkes images. The Parkes data are then scaled by the ratio of the solid angle of the two beams, α = Ω ASKAP Ω Parkes , ( 2 ) where we find α to equal 9. 77×10 -4 , assuming the Parkes beam is a Gaussian with a full-width at half maximum (FWHM) of 16 at 1. 4 GHz  (Kalberla et al. , 2010). This factor accounts for the difference in flux density of the two data sets based solely on the differences in resolution. The ASKAP uv data are then scaled by the factor β = 1 -F [B Parkes ], (3) where the second term represents the 2D Fourier transform (F ) of the Parkes beam, B Parkes.
The two scaled uv data sets are then summed and Fourier transformed back to the image plane. The scaling depicted in Equation 3 ensures the effects of the poorly sampled low spatial frequencies in the ASKAP data are smoothly removed before the well-sampled low spatial frequencies provided by the Parkes data are added. The effective beam of the resultant image is the same as the original ASKAP restoring beam. The above scaling assumes the two data sets are perfectly flux calibrated; however, technical issues introduced by stray radiation and spillover 6 https://www. astro. uni-bonn. de/hisurvey/gass/ make single dish data notoriously difficult to correctly flux calibrate. We take an interactive approach to determine the ideal flux calibration factor, henceforth referred to as the single dish factor (SDF), by setting the factor keyword in IMMERGE to be 0.
Quality Assessment The spectral line cubes produced from GASKAP-HI observations also need to be validated, both to determine their suitability for science use and also to compare different processing techniques. We aim to validate the representation of the diffuse H i of the Galactic plane and the Magellanic System in the following ways: • that large scale emission is present in velocity ranges where it is expected and not present where not expected, based on previous surveys; • that the noise level in the emission-free velocity range is close to theoretical, but not below, and is even across the cube; and • that spectra taken at the locations of selected targets, such as against bright sources, do not show any periodic features. We validate the spectral line data at the ASKAP-only stage rather than after they have been combined with Parkes data.
This allows us to compare the ASKAPonly data with data from GASS;  McClure-Griffiths et al. 2009 ) in our tests for expected presence and absence of emission. It also provides a more rigorous test of noise levels in the ASKAP data. To check for the presence or absence of emission, we define two ∆v ≈ 40 km s -1 regions where H i line emission is either present or not present in the GASS cube. We extract a slab from the ASKAP cube covering each velocity region and use BANE (part of the Aegean suite;  Hancock et al. 2018) , to produce a large-scale emission line image of each slab. The large-scale emission line images exclude H i point sources and smooth small-scale fluctuations based on the synthesised beam size. We assess the maximum flux density level of these images against the thresholds in Table  4.
A typical GASKAP-HI observation of t 0 = 20 hours with ∆ f = 5 kHz channels gives σ F = 1. 89 mJy/beam. Lastly, we validate spectra extracted both from the locations of the 15 brightest sources (as identified in the continuum catalogue) and the centre of each beam. At each position we extract a single pixel spectrum and plot these spectra for visual inspection. We assess the spectra using a partial autocorrelation function (PACF) to identify any periodic features in each spectrum, requiring a 3-sigma result to classify a feature as periodic. We rate the dataset based on the number of spectra with periodic features as shown in Table  4. We can use these metrics on the ASKAP-only image cube for the SMC field to assess its quality. Emission is present (21 Jy beam -1 km s -1 ) where expected in the range 119 ≤v LSRK ≤ 165 km s -1 and absent (0.
Metric Good Uncertain Bad Units On-line emission ≥ 20 20 -12 < 12 Jy beam -1 km s -1 Off-line emission There is high noise at the edges of the cube and a notable cross-hatch low level noise pattern in the main area of the cube. However, it shows a generally low and consistent noise level across the main area of the cube, with a median noise level of 2. 34 mJy beam -1 , 1. 24 times theoretical. < 5 5 -12 ≥ 12 Jy beam -1 km s -1 Spectral Noise compared to theoretical noise < √ 2 √ 2 -2 √ 2 ≥ 2 √ 2 - Spectra with periodic features 0 1 -4 ≥ 5 - Identifier (DOI) 7. We measure the rms noise using the source finding software, SoFiA 2;  Serra et al. 2015; Westmeier et al. 2021 , which fits a Gaussian to the negative half of the histogram of pixel values. This method is more robust against the presence of strong, extended emission or artifacts than measuring the standard deviation of emission-free channels.