dataesr/scientific-paragraphs-categorization huggingface dataset:
A. Initial HEVC encoder The proposed SHVC architecture rely on a multi-support real-time software encoder developed by ATEME  [17]. Video coding standards from MPEG-2 to HEVC are supported with a real-time coding rate. This performance is achieved thanks to parallelism at several level and Single Instruction Multiple Data (SIMD) on several operations. While used as an HEVC encoder, parallelism is performed over frame thanks to a pipeline architecture and over tiles  [18]  and over CTU lines in one slice per frame. In the rest of this paper, we refer to this real-time HEVC encoder as single layer encoder ATEME (SLE-A). The SLE-A encoding is based on a pipeline process at frame level. Pipeline operations are processed in independent processing threads. Each pipeline step ends with a synchronisation signal sending from the processing threads The last pipeline step is the coding decision which produces the coding information that can be used for inter-prediction on other frames.
V. CONCLUSION In this paper, we have presented a 2× spatially scalable software SHVC encoder achieving encoding bit-rate savings over simulcast at real-time coding rate. The proposed architecture allows parallelism over layer and introduces a cadence slow-down inferior to 19% of the simulcast equivalent total coding rate. Experiments show the proposed solution performs real-time encodings on 1080p60 and 1600p30 resolution in a low-delay set-up thanks to multiple parallelism employed and the limitations exposed in the Live configuration. The bit-rate savings over simulcast are less important than state of the art but remains interesting in a real-time set-up. The experiments also show that the more coding efficiency the BL performs, the more bit-rate savings the inter-layer prediction performs.
Azimuthal equal area projection. count Meddies. The previous year, also as part of CANIGO, but not part of CAMBIOS, tomography had been employed within the Strait of Gibraltar as a way to obtain integral measurements of mass and heat transports through the Strait itself  (Send et al. , 2002; Tiemann et al. , 2001). The entire CAMBIOS data set is publicly available. For this paper, our limited purpose is a description of the acoustic forward problem for the acoustic data obtained during the CAMBIOS experiment, drawing on the available measurements in support of that purpose. Understanding the nature of the observed arrival patterns is an essential part of the process of developing procedures to estimate temperature variations by inversion of the acoustic data  (Dushaw et al. , 2016) ; this is the forward problem of ocean acoustic tomography  (Cornuelle et al.
In sections 2 and 3, the CAMBIOS tomography program and associated acoustic, moored, and hydrographic data are described. In section 4, a computed acoustic arrival pattern is compared to the observations for an identification of the ray arrivals. In section 5, models for small-scale, stochastic variations in sound speed caused by internal waves and spice variations are employed to account for aspects of the acoustic arrival patterns. In section 6, a simple model for a Meddy is employed to illustrate the main travel time and arrival pattern signals of such a feature, as it would be observed by acoustic tomography. In section 7, the effects   (Dushaw et al. , 2013; Menemenlis et al. , 2008). ECCO2 was a global numerical model for the temporal evolution of ocean temperature, salinity, currents, etc.
CAMBIOS Tomography The CAMBIOS acoustic array consisted of five moorings denoted T1-T5 deployed across the Canary Basin between the Canary and Azores Islands (Figure  1 ;  Gaillard & Terre, 2003 , 2004; Terre & Gaillard, 2003). Because of a number of instrument failures, acoustic data were mainly obtained between the T1 and T2 moorings. The T1 mooring was located at 31:99888N, 19:80088W in about 4,150 m ocean depth, and the T2 mooring was located to the northwest at 34:24738N, 21:750028W in about 5,275 m ocean depth (Figure  1 ). The acoustic propagation was over 308. 692 km range over the smooth Madeira Abyssal Plain. In addition, the acoustic path was across the Azores Current (Fr€ undt & Waniek, 2012), characterized by 1-38C changes in temperature down to about 1,000 m depth. The acoustic sources, moored at 660 and 700 m depths, made reciprocal transmissions for 265 days, beginning on yearday 190 in June 1997 and ending on yearday 454 in early April 1998.
The transmission schedule was partly designed to allow tidal signals to be filtered. After recovery, the recorded signals were processed by pulse compression techniques to form the equivalent impulse response signal. After pulse compression, the transmitted signals offered a time resolution of 10 ms. The range between the two moorings was limited to 300 km to allow for adequate received signal-to-noise ratio. The receiving arrays on each mooring consisted of two subarrays of four hydrophones, each comprising a receiver channel. That is, each of the two channels provided data from the combined signals of four hydrophones by hardware design. The four hydrophones on each subarray were spaced by a half wavelength with three points of measurement in a 1-2-1 weighting. The center point consisted of two of the hydrophones hardwired together.
The subarrays were spaced by a half wavelengths, or about 1. 875 m, so they overlapped by a half wavelength. While these two data channels could in principle be combined by beamforming techniques to provide a crude determination of ray arrival angle, for unknown reasons the first channel had significantly less signal and greater noise than the second channel. For this reason, only data from channel 2 are employed here, without beamforming. In processing these data, a beamforming capability would Sound Speed (km/s) The travel times were corrected for the motion of the source and receiver moorings  (Gaillard et al. , 2006). The instrument clock times were checked at deployment and recovery, but time keeping during the experiment relied on temperature compensated quartz oscillators. Clock errors affecting measurements of currents at low frequencies are therefore possible from the clock frequency errors.
Acoustic propagation along the T1-T2 path over the Madeira Abyssal Plain was bottom limited to around 4,000 m. On this section the depth varied only gradually. We employed the Smith-Sandwell global topographic database version 12. 1  (Smith & Sandwell, 1997). The bottom topography of the region consists of a flat abyssal plain, ideally suited for well-behaved bottom reflections. The timefront (the arrival pattern at a given range as a function of travel time and arrival depth) was computed using a sound speed section computed from WOA09 (Figure  9 ). The arrivals in the timefront at the depth of the CAMBIOS receiver can be aligned to the observations. This alignment, requiring an advance in the predicted travel time of 220 ms (the climatology had a cold bias), shows that ray arrivals following the overture can be readily identified.
Horizontal Refraction Meddies represent a large horizontal gradient in sound speed, hence may potentially be causing horizontal refraction of the tomographic signals  (Voronovich et al. , 2005). Such effects can be readily assessed by first computing the field of acoustic mode phase speed associated with a Meddy, and then computing the horizontal refraction of acoustic modes  (Dushaw, 2014). The increased phase speed associated with Meddies serves to refract acoustic paths away from the Meddy. Such phase speeds were computed using the acoustic mode code KRAKEN  (Jensen et al. , 1994)  and WOA09 climatological sound speed for the CAMBIOS region. The Meddy perturbation was that given by the example above, 12 m s 21 maximum sound speed, and the acoustic frequency was 400 Hz. The lowest acoustic modes were centered on the lower sound speed minimum at about 1,700 m, and they were unaffected by the presence of the Meddy at 1,000 m.
The hydrographic conditions applied to the mode displacements then determine the sound speed variations. Lastly, the acoustic ray sampling determines the travel time variations associated with the sound speed variations  (Dushaw, 2006; Dushaw et al. , 1995 Dushaw et al. , , 2011)). These computations were done using both the WOA09 climatology and the hydrographic data acquired along the acoustic path (Figure  7 ), but there was little difference between in the two estimated travel time variations. The comparison between the observations and predictions over a 30 day section of data is shown in Figure  16 , top. The sum travel times suffered the same noise problems as the differential travel times, but the tidal signal is evident. Note that by historical precedence, the sum travel times are the average of reciprocal travel times while the differential travel times are just the difference.
The two variables therefore differ in scale by a factor of 2. The predicted and observed internal tide variations agreed in phase, but the observed amplitudes varied irregularly and were larger than predicted. A clearer comparison can be made by comparing estimates of mode-1 amplitude derived from the travel times to equivalent mode amplitude derived from the altimetric model (Figure  18 ). A simple inverse employing baroclinic sound speed modes as vertical basis functions was employed for this purpose  (Dushaw, 2006; Dushaw et al. , 1995). These modes were derived from the CAMBIOS hydrographic data obtained along the acoustic path. While the data suffered Journal of Geophysical Research: Oceans 10. 1002/2017JC013356 from considerable noise, as noted above, it is also apparent that there were considerable irregular variations in the tidal amplitude.
Some of the variations may be Meddies, others may be more complicated manifestations of Meditarranean water. As a practical matter, the dominance of acoustic scattering effects precludes the acoustical measurement of the small signals of Meddy currents or relative vorticity. (The 20 cm s 21 current speeds within Meddies would produce travel time variations of a mere fraction of a millisecond. ) In  Barotropic tidal currents were not detected in the tomography data acquired during CAMBIOS, both because of the small signal expected and the considerable noise in the differential travel times obtained. Internal tide signals were measured, though a tidal analysis of the time series of mode-1 amplitude estimated from the travel times accounted for only 20% of the variance. The coherent tidal variability estimated from that tidal analysis was predicted reasonably well by an independent empirical global model derived from satellite altimetry.
Such variations can be mostly accounted for by latitudinal motions of the Azores Current. From the CTD data obtained during CAMBIOS, the 0-2,000 m average temperature change across the Current is 0:78C. A movement of the Current over the latitude range of the CAMBIOS moorings therefore would correspond to this temperature change, which sets the scaling factor between observed temperature and latitude change. Relative to mean, the temperature variations corresponded to latitudinal variations from about 21:18 at the start of the experiment to 11:18 in winter 1998. Such shifts in Current latitude are well within the monthy variations obtained from numerical ocean state estimates determined by the Simple Ocean Data Assimilation Parallel Ocean Program (SODA POP; Fr€ undt & Waniek, 2012; Figure  21 ).
Acknowledgments B. Dushaw was supported by  ONR  grant  N00014-15-1-2186  and  NASA  grant  NNX13AE27G. This work is a contributrion to the  CAMBIOS  integrated project which was financed by the  European Union  in the 6th  Framework Programme for Research and Development. F. Gaillard ,  T. Terre , and the CAMBIOS experiment were supported by the  MAST III CANIGO programme  (contract  MAS3-CT96-0060 ). We also acknowledge the efficient, professional support for the deployment, and recovery of the moorings, and for the hydrographic surveys, provided by the crew of the N/O Thalassa operated by  IFREMER. The data supporting the conclusions of this analysis can be obtained through the cited references and their associated web links. The direct link to the CAMBIOS data set is http:// campagnes.
While the original GreenLITE system was designed for remote monitoring of GCS sites, similar needs exist for other applications, such as facilities monitoring and urban areas. Currently, tower-based in situ instruments and other point measurements, along with static and dynamic inventories, are being used to help constrain models that provide dynamic estimates of changes in urban concentrations and fluxes. 2, 10, 11 Placing towers within the urban environments often presents difficulties due to siting challenges, and point measurements are often adversely impacted by nearby highly localized sources. This susceptibility to local variations presents a significant challenge in assimilating these data into transport models with grid scales on the order of 1 to 4 km. Since the initial deployment of the first GreenLITE system, a second system that directly addresses the urban scale covering areas of ∼25 km 2 has been deployed in Paris, France.
In Sec. 2, we describe the hardware, software, and data processing algorithms of the GreenLITE system. In Sec. 3, we describe deployments of the GreenLITE system at controlled test sites, at an active GCS site, and in an urban environment. In Sec. 4, we present analyses and results based on the aforementioned deployments, and in Sec. 5, we summarize our work and offer possibilities for future system improvements and applications. While procedures are in place to calibrate the measurements taken with the GreenLITE system, in particular to address measurement precision and drift, a true reference measurement-and thus bias estimate-are difficult to obtain due to the complexities associated with measuring a 1 to 5 km nearly horizontal column of air in a nonhomogeneous atmosphere that contains spatially varying gaseous mixing ratios, temperatures, and pressures and includes constituents other than the species of interest.
GreenLITE System The GreenLITE system includes both data collection and data processing components. The data collection portion includes transceivers, retroreflectors, and software necessary to operate the transceivers and facilitate data collection. The data processing portion includes algorithms to convert measured differential transmission to integrated CO 2 column concentration and estimate 2-D tomographic reconstructions of CO 2 concentration fields, as well as a web-based user interface for real-time monitoring. The following two sections provide an overview of these two components.
15 The GreenLITE system leveraged the best aspects of these prior works, resulting in a robust and capable design. The GreenLITE transceiver is divided into two main components: (1) the electro-optical box and (2) the optical head, as shown in Fig. 1. The electro-optical box is a thermally stabilized box that houses the laser transmitters, optical modulators, detector chain for reference and science channels, computer, data acquisition, and other associated electronics. The optical head is also thermally controlled and houses both transmit and receive telescopes. The optical head is mounted on a mechanical scanner that is used to point the optical head at individual retroreflectors. The system is designed to operate in remote locations for long periods of time with minimal interaction. The current systems operate on an absorption line near 1571.
The system requires a clear line of sight from the transceivers to the retroreflectors, which can pose challenges. However, the system testing to date has shown that the algorithms discussed in Sec. 3 provide a robust solution   for nonoptimal layouts and sites with a limited number of obstructed chords. This finding enables GreenLITE to be considered for a number of applications where the optimal layout may not be practical, such as an urban environment where the layout is driven by existing buildings and structures suitable for mounting the retroreflectors. All hardware control and monitoring, data acquisition, preliminary data processing, and data transmission to the remote database are performed by the software running at each transceiver. The transceiver software controls the movement of the mechanical scanner and coordinates the acquisition of data with the scanner movement.
GreenLITE Data Processing The GreenLITE system computes CO 2 densities and near-real-time 2-D maps of CO 2 concentrations. Specifically, this process continuously: (1) computes integrated column CO 2 concentration (ppmv) from observed differential laser absorption measurements along horizontal lines of sight between a transceiver and a reflectors (chords) and (2) estimates 2-D distribution of timevarying CO 2 concentrations based on an analytic plume model and an ensemble set of temporally adjacent integrated chord values. Currently, this process has been implemented in modular fashion where the applications defined above run asynchronously in a multithreaded environment coupled to one another through a common database structure. Near-real-time monitoring of CO 2 concentrations and 2-D distribution estimates are made possible through a web-based analysis tool.
Data processing and data dissemination GreenLITE utilizes an integrated cloud-based data collection, data processing, and data dissemination approach. At the center of this design is a common database implementation based on open-source software, MySQL, and a python web framework, hosted at Amazon Web Services (AWS). The core database provides common queryable secure storage and enables deployment of standard mechanisms for data ingest, data access, and instrument and site monitoring, as well as data dissemination for real-time analysis. Raw instrument data, instrument metadata, and in situ measurements from each transceiver, as well as data from colocated weather stations, are transferred to the cloud-based applications via cellular data services using secure web-based protocols. Portions of these data are distributed in autonomous fashion to downstream processing elements, which are hosted in a distributed fashion and compute the column CO 2 concentration values and 2-D distributions described above.
The results are also stored and indexed in the same centralized database implementation. Finally, access to instrument, in situ, and processed data are provided to end users and applications via common web interfaces. An example of the end-user oriented web interface is shown in Fig. 5. This web interface is configured in an automated fashion based on site-specific information, e. g. , transceiver geographic location and sample times, and provides both user-oriented real-time dynamic site-specific decision support information, as well as global remote access  to up-to-the-minute instrument monitoring, quality control, system statistics, and ancillary information. This system provides top-level summary information that includes current integrated concentration values on a chord-by-chord basis, georeferenced 2-D reconstructions, and indicators of anomalous conditions based on user-defined thresholds.
In addition, the same interface provides user-selectable time-series-based displays and analyses of retrieved as well as raw sample information and metadata. Finally, it provides real-time displays of ancillary information from instrument health and safety sensors, colocated weather stations, and webcams. Some of these functions are illustrated in the displays shown in Fig. 6. Top-level information is available to authorized users through the main page shown in Fig. 5. The database structure allows individualized access to additional measurement details through the other next-level webpages like those shown in Fig. 6. The system administrator can provide access to different levels on a per user basis as required. Additional information is also available through the web interface such as: 2-D reconstruction time series, instrument telemetry, raw signal and differential transmission data, and server administration details, each accessible in near-real-time or historic views.
Following completion of the second transceiver, the farm test site was used to do side-by-side comparisons between the two transceivers for a number of retroreflector configurations. Additionally, this site provided a location to test various configurations of the full GreenLITE system prior to deployment to the ZERT site and the Illinois Basin-Decatur Project (IBDP) site, discussed in the following sections. The primary objectives for testing at this site were to verify that the transceivers met design specifications, demonstrate consistent calibration between the two transceivers, and evaluate potential drift. The site also enabled the first tests of the algorithms to generate the 2-D concentration estimate maps and the interfaces between the hardware and the AWS database, retrievals, and data dissemination aspects of GreenLITE.
Boulder Test Site The extended-range GreenLITE system was sent to Boulder, Colorado, in August 2015, with the objective of demonstrating both the longer-range capabilities of the transceivers and the segmented retrievals for slant paths as described in Sec. 2. 2. 1. This site also allowed for a well-characterized and calibrated in situ system to provide some estimate of the accuracy of the extended-range GreenLITE. It was tested over a 3-week period adjacent to the NOAA BAO tower near Boulder. The BAO tower is a 300-m tower instrumented with a highly calibrated LI-COR-based measurement system. The LI-COR system samples at three ports, located at 22, 100, and 300 m AGL, and then follows this sampling with the sampling of a test gas traceable to WMO standards. In addition, the tower provides high accuracy meteorological data for calibration and validation of our portable weather stations.
02 ppmv for this instrument. The instrument is  calibrated with three reference gases every 6 months and with one reference gas twice per day. All reference gases are calibrated at LSCE with a suite of six NOAA/WMO cylinders (WMO-CO2-X2007 scale). The raw data are transferred once per day and processed at LSCE. For the colocated field campaign in Jussieu, a Thermo Fisher Delta Ray isotope ratio IR spectrometer was installed at the QUALAIR Laboratory adjacent to the Jussieu tower at a height of 122 m above sea level (25-m AGL). It was deployed with two pure CO 2 standards for carbon isotopes and a concentration reference gas. Before deployment, the typical performance of the Delta Ray was assessed using long-term target measurements and revealed a reproducibility of better than 0. 1 ppmv.
Analysis and Results The GreenLITE system deployments described in Sec. 3 have produced a significant amount of test and validation data over the past several years, including observations from the ZERT test facility in Bozeman, Montana, a GCS site in Decatur, Illinois, as part of a field campaign in Boulder, Colorado, and an ongoing installation in Paris, France. Each of these deployments exercised and validated various aspects of the GreenLITE system hardware, data collection system, retrieval algorithms, and 2-D reconstruction algorithms. The resulting observations include both an extensive set of retrieved CO 2 concentration values for a number of geographically diverse locations and environmentally significant conditions as well as the corresponding 2-D reconstructions. As part of these efforts, additional data have been obtained to cross-validate the resulting data products, and a number of field experiments have been undertaken to quantify the performance of the 2-D reconstruction algorithm.