dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics have been computed on the whole dataset, i. e. , on every common epoch. The reference trajectory (provided by the LANDINS system) is the fusion result of an INS (Inertial Navigation System) based on a high-grade fiber optic gyroscope (FOG), a distance measurement instrument and a PPK (Post Processed Kinematic) trajectory from a high-performance GNSS receiver. Considering the errors of the "free" solutions (Figures  8  and 9 ), it appears that the LOS filter is equivalent to the SNR filter for the horizontal dimension. Meanwhile, when the vertical dimension is also considered, SNR filter seems to be better. This result can be explained by the fact that our circuit get at the borders of the 3D model currently available. Outside these borders, indeed, neither trees nor buildings are modelled and the NLOS test is performed obstacle-free, which potentially causes satellites not in direct view to be considered as LOS.
Conclusions This paper has shown that using NLOS detection and exclusion prior to incorporating data from a GNSS receiver was efficient for the localization in dense urban areas. Such a test needs (1) 3D maps of the urban environment, which are more and more easily available and (2) an a priori estimation of the position and orientation of the vehicle. The results have shown the prior importance of the 3D maps of the urban environment. First, it constrains the position and orientation estimations in coherence with the streets network, thus enhancing the accuracy of the localization. Moreover, it makes it possible to check the direct visibility of a given satellite from the estimated pose of the vehicle. The results shown in the paper lead to conclude that the NLOS test gives an equivalent accuracy to the SNR test when the estimated pose is not constrained by the map-aiding procedure after being updated by GNSS data (slightly worse when the vertical dimension is considered).
In contrast, when the map-aiding procedure is applied after having used the GNSS data with the NLOS (or SNR) test, the NLOS test gives slightly better accuracy than the SNR test. It thus confirms the feasibility study previously exposed in  [5]  where the accuracy of the localization was significantly better. NLOS detection and exclusion based on virtual image processing and a 3D map seems to be promising. This alternative to a simple SNR test on the satellites tracked by a standard automotive receiver has yielded better positioning.
Future Work Given that this dataset corresponds to a relatively short experimentation (5 min, 1 km in Paris), other tests will be realized to confirm our first conclusions. The GPS Positioning Algorithm was also very simple: no fault detection or exclusion of satellites (FDE strategy) based on the predicted position (and consequently predicted pseudo-ranges) was done in addition to the SNR or LOS tests. A future version of our algorithm will improve this Positioning Algorithm, including FDE through a χ 2 test. Doppler measurements, which are not employed in the current study, will also be used.
This study presents the +Cloud (masCloud) platform that offers Cloud services such as storage of information and files at the platform level, as well as a set of native software applications. The core of this platform is a MAS based on a Virtual Organization (VO), which makes it possible to automatically manage the computational resources of the system, adapting them in an elastic way according to demand. The present paper is structured as follows: the following section presents the stateof-the art of Cloud computing system and its relationship with MAS. The +Cloud architecture is then presented in detail. This study finalizes with a review of the initial tests of the system, conclusions and future research lines.
3 Proposed Architecture: +Cloud +Cloud is a platform based on the cloud computing paradigm. This platform allows offering services at the PaaS and SaaS levels. The IaaS layer is composed of the physical environment which allows the abstraction of resources shaped as virtual machines; however, the system does not offer this kind of service to the end users as shown in Figure  1. +Cloud has a layered structure that covers the main components of cloud computing: • The SaaS layer is composed of the management applications for the environment (control of users, installed applications, etc. ), and other more general third party applications that use the services provided by the next layer (PaaS). • The PaaS layer provides services through REST web services in API format. One of the more notable services among the APIs is the identification of users and applications, a simple non-relational database service and a file storage area that controls versions and simulates a directory structure.
Retrieval The pressure-broadened ozone line spectra can be inverted into ozone profiles from approximately 20 to 70 km. The retrieval of an ozone profile from the calibrated spectrum is known as the inverse problem. For the ozone profile retrieval of GROMOS, the Atmospheric Radiative Transfer Simulator ARTS  (Eriksson et al. , 2011)  and the accompanying Matlab package Qpack  (Eriksson et al. , 2005)  are used. ARTS is a modular program simulating atmospheric radiative transfer. It calculates an ozone line spectrum for a model atmosphere through radiative transfer calculations using an a priori ozone profile. Qpack takes advantage of ARTS and compares the modelled ozone spectrum with the observed ozone spectrum at 142 GHz. Using the optimal estimation method (OEM), as formulated by  Rodgers (1976) , Qpack derives the best estimate of the vertical profile of ozone volume mixing ratio with consideration of the uncertainties of the measured ozone spectrum and the a priori profile.
7 K and brightness temperature at the ozone line peak is around 20 K). The a priori ozone profiles used in the GROMOS retrieval consists of monthly varying climatologies from ECMWF until available (70 km), extended by an Aura/MLS climatology (2004 to 2010) above. Figure  1  shows mean ozone profiles from GROMOS FFTS for January 2011 (blue) and July 2011 (magenta), together with the a priori profiles (dashed lines) used in the retrieval for these two months. In the middle and right panel, one finds the mean AVK matrix for January 2011 and July 2011 respectively. The line shape used in the retrieval is the representation of the Voigt line profile from  Kuntz (1997). Spectroscopic parameters to calculate the ozone absorption coefficients were taken from the JPL catalogue  (Pickett et al. , 1998)  and the HITRAN spectroscopic database  (Rothman et al.
, 1998). For correction of a low bias in ozone found in the lower stratosphere, the line intensity and pressure broadening parameters have been reduced to 90 % of the values given in the catalogues. The modified spectral line parameters are kept constant for the whole retrieval of the ozone time series. In difference to the 110 GHz ozone line, the spectral parameters of the 142 GHz ozone line have not been measured in a laboratory yet. The spectroscopic modification may reduce a systematic error in the line parameters or it renders an unknown baseline perturbation of the ozone line spectrum. The atmospheric temperature profiles are taken from 6 hourly ECMWF Operational Analysis data and are extended above 80 km by monthly mean temperatures of the CIRA-86 Atmosphere Model. The continuum is fitted by a straight line.
The smoothing term is due to the limited altitude resolution. Further information can be found in the microwave chapter of the data user guide of the NORS project (Demonstration Network Of ground-based Remote Sensing Observations in support of the Copernicus Atmospheric Service (http://nors. aeronomie. be/). The lidar is an active remote sensing instrument: laser pulses of specific wavelengths are emitted into the atmosphere and interact with atmospheric particles and molecules. A small part of the radiation is reflected back by those objects and this backscattered radiation is collected by a telescope and transmitted to the detector. The ozone lidar measurements at OHP are performed according to the Differential Absorption Lidar (DIAL) technique, which requires the emission of two laser wavelengths (308 nm and 355 nm) with different ozone absorption cross sections.
One wavelength is in the region of high absorption (308 nm) and the other wavelength is less absorbed and considered as the reference wavelength (355 nm). Details of the derivation of the ozone number density from the OHP lidar measurements are described in  Godin-Beekmann et al. (2003). Measurements are performed during night-time under clear sky conditions. The typical integration time of a stratospheric ozone profile is 4 h. The altitude range of measurements is between the tropopause and 45-50 km. The vertical resolution ranges from 0. 5 km at 20 km to about 2 km at 30 km, and it increases to perature  (Godin-Beekmann and Nair, 2012). As in  Nair et al. (2011) , a composite of temperature profiles, made from nearby radiosonde data (in the lower stratosphere), the National Center for Environmental Prediction (NCEP) (from 25 to 50 km) and the COSPAR International Reference Atmosphere 1985 (CIRA-85) climatology (in the upper stratosphere) is used to compute the ozone cross section.
SABER onboard TIMED SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) is one of four instruments on NASA's TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics) mission. Its goal is to explore the mesosphere and lower thermosphere globally  (Russell et al. , 1999; Remsberg et al. , 2008). The TIMED satellite was launched in 2001 and is in a non-sun-synchronous orbit at 628 km mean altitude. Due to its drifting orbit, TIMED spans nearly all local times every 60 days. The data used in this publication comprise ozone profiles from version 1. 07 retrieved from the infrared emission of the 9. 6 µm band of ozone. In their validation,  Rong et al. (2009)  found a SABER positive bias in all regions other than the lower stratosphere. They note that biases in the stratosphere vary from 5 to 17 % with the largest bias found in equatorial to middle latitudes and between 30 and 50 km.
They note a persistent high bias of ACE-FTS in the mesosphere (45-60 km) with mean relative differences of up to 40 %. Ozone profiles from the new version 3. 0 of the ACE-FTS retrieval software, used in this comparison, are in the process of being validated  (Adams et al. , 2012). Dragani (2011) compared ERA-Interim ozone analyses with ozone profiles from a number of satellite instruments (SAGE, HALOE, UARS/MLS, Aura/MLS and POAM) for different latitudinal bands. They selected four pressure levels in the stratosphere (at 65, 30, 10 and 5 hPa) for comparison: at 65 hPa, they find relative differences between ERA-Interim with satellite measurements up to around 20 %. Between 10 and 5 hPa, the comparisons show good agreement and the relative differences are mostly within ±5 % at all latitudinal bands.
Collocation and coincidence For the satellite-GROMOS intercomparison, the selected criterion for coincident profile pairs are differences less than 1. 80 • in latitude, 10. 50 GROMOS provides ozone profiles with a time step of 30 min. The percentage of missing ozone profiles due to high tropospheric opacity or instrument operation problems was 22 % in the time interval from 2009 to 2011. Thus, the probability is 88 % to find for a satellite overpass above Bern a coincident ozone profile in the GROMOS database. For the annual, seasonal and monthly mean profiles, all profiles that satisfied the spatial collocation criteria were averaged. For the mean difference profiles, additionally to the collocation criteria, only GROMOS FFTS profiles that satisfied the following temporal coincidence were taken into account: ∆t max = ±15 min.
The number of collocated and coinciding pairs used in determining the average difference profiles of Sect. 5 is summarized in Table  2. It has to be noted that in the (lower) mesosphere, the diurnal cycle in ozone has a strong amplitude (up to 30 %) and one has to be careful of including day-and nighttime measurements for all instruments when averaging to mean profiles is performed. Day-and nighttime data were available for all instruments except for ozonesonde and lidar measurements. Ozonesondes are always launched at 12:00 UT and only daytime profiles are available. In the case of comparison with lidar in Sect. 5, only coincident GROMOS profiles.
Averaging kernel smoothing To perform a comparison between two different instruments, one has to take into account the different vertical resolution and a priori data contribution. For this purpose, the vertical resolution of measurements from lidar, satellites and ozonesondes is reduced to the effective GROMOS altitude resolution. This is realized by convolving each profile with the corresponding averaging kernel matrix of GROMOS  (Tsou et al. , 1995). The convolved profile is expressed as: x low = x a + AVK • (x high -x a ), (1) where x low is the convolved profile from lidar or satellite, x a is the microwave a priori profile, AVK is the matrix of GROMOS averaging kernel and x high is the measured lidar, satellite or ozonesonde profile interpolated to the GROMOS retrieval grid. For the comparison, all profiles were further interpolated to a reference pressure grid p as a function of log(p).
Annual mean A comparison of annual mean profiles between GROMOS FFTS, GROMOS FB, satellites, ozonesondes and ERA-Interim is shown in black line, ozonesondes in red dashed and finally ERA-Interim can be found in grey dashed. The general agreement is within 10 % between 50 and 0. 1 hPa. Exceptions are SABER around 2 hPa and ERA-Interim above 3 hPa. The large difference between GROMOS FFTS and SABER can be explained by a previously reported, systematic high bias of 10-20 % for SABER's ozone values from the middle stratosphere to the lower mesosphere  (Rong et al. , 2009). ERA-Interim and GROMOS FFTS agree well up to 3 hPa and the mean relative difference ∆O 3,rel is ±5 %, while above ERA-Interim has about 10 % less ozone. The oscillatory nature of the relative standard deviations is a noticeable feature and visible in all data sets.
Acknowledgements. Simone Studer  is funded by the  Swiss National Science Foundation SNF  under grant no. 200020-134613. The work was further supported by the  EU  project  NORS  ( FP7-SPACE-2011-284421 ) and the  International Space Science Institute  (http://www. issibern. ch/teams/ozonetrend/). We thank the MLS-and SABER-team for the ozone data used in this study which was retrieved from the  NASA Goddard Space Flight Center , the MIPAS-team from  Karlsruhe Institute of Technology (KIT)  for provision of MIPAS ozone data. The ACE-FTS/SCISAT-1 ozone profiles are mainly supported by the  Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. The author would further like to thank  MeteoSwiss  for providing us with ozonesonde data from Payerne.
FoFdation SMC Focusing on the central low-level task execution of the SMC, the original vision in FoFdation for the SMC is to implement an advanced machine controller based on an open-architecture and standards such as a PC driven by Linux RTOS and Enhanced Machine Controller (EMC2), including the XML standard enabling data access, and data visualization application. Additionally, an extended STEP standard will be seamlessly integrated to bring CAD-CAM data down to the shopfloor level, thus enabling intelligent and selflearning manufacturing process as defined by 'Smart Machining'  [2]. A smart machine is defined here as a machine equipped with an advanced controller that knows the capabilities of the machine to be driven  [3]. Such a machine is able to define the most efficient method, e.
This condition is necessary to support the transformation of prevalent brand name controllers mostly based on ISO G&M code into STEP-NC compliant controllers, thus allowing companies to increase their production performance without heavily investing in new expensive machines and timeconsuming training  [4]. Breakthrough innovations for the legacy mandate will be based on a STEP-NC SMC interface for part programming tightly integrated with the SMO optimization module and running on a dedicated Windows PC. The controller will be able to generate cutter trajectory and optimize the cutting strategy in realtime. This is possible with an embedded STEP-NC Windows-based SMC interface on the FHMI including its own tool-path generator, e. g. for surface milling. The STEP-NC interpreted programming makes it possible to use the STEP-NC standard with the existing machine tools and NC controllers, which still understand G-code programming  [5].
To address this issue, two distinct alternatives are proposed for the SMO developments. This proposal extends the tools and methods used to define the signature of the machine tool and machining processes from the above discussion. Thus enabling the capture of the signature in real-time as well as in-situ condition monitoring during the manufacturing process. The final goal of this is to attain reliable adaptive control of the manufacturing process. Based on the Consortium background, a prototype is expected that will verify and test the adaptive intelligent framework using Delcam's CAM and NC software platforms with supporting ARTIS Monitoring technologies  [13]. The integrated marriage of these two software (CAD/CAM) and hardware (Monitoring) platforms ensure that once isolated knowledge from either is easily and effectively transferrable.
Today, the problem of designing suitable multiprocessor architecture tailored for a target application field raises the need for a fast and efficient multiprocessor system-on-chip (MPSoC) design environment. Additionally, the implementation of image processing applications on MPSoC system will need to exploit the parallelism and the pipelining in algorithms with the hope of delivering significant reduction in execution times. To take advantage of parallelization on homogeneous MPSoCs and to reduce the programming effort, the proposed design methodology offers more opportunities for accelerating the parallelization of sequential processing image algorithms on pipeline architecture. Our approach provides rapid prototyping tool as a graphic programming environment (CubeGen). Further, it offers a set of parallel software skeletons as a communication library, providing a software abstraction to enable quick implementation of complex image processing applications on field-programmable gate array (FPGA) platform.
The design of homogeneous network of communicating processor is presented from the hardware and software specification down to synthesizable hardware description. Then, we extend our approach to support more complex applications by implementing a soft multiprocessor for 'multihypotheses model-driven approach for road recognition' and show the impact of various configuration choices (hardware and software) to match the specific application needs. Using the images of a real road scene, the performance results of the road recognition algorithm on a Xilinx Virtex-6 FPGA platform not only achieve the desired latency but also further improve the tracking performance which depends mainly on the number of hypotheses.
More precisely, our goal is to develop rapid prototyping tools for image processing applications, using parallel homogeneous architecture, as will be described in the next section. In response to that, we have proposed a new MPSoC approach  [1]  that aims at raising the abstraction level of the specifications for both software and hardware providing the necessary tools supporting the design from the specification down to the embedded implementation. In addition, our methodology proposes a complete generic architecture from which code can be generated automatically. Furthermore, our new design methodology is able to support parallelization of complete image processing applications using multiple instruction multiple data processors (MIMD). From a sequential application, the proposed graphic programming environment (CubeGen) leads us to generate MPSoC system implementation on an FPGA with the parallelized application implemented onto it only in few hours.
The rest of this paper is organized as follows. We briefly present in Section 2 the state of some design methodologies that are able to automate the hardware implementation of an application algorithm on MPSoC architecture. We describe in Section 3 the proposed fast prototyping methodology which helps the software and hardware designer to obtain quickly an efficient implementation of this application on HNCP architecture. In Section 4, we use a motivating example of image processing application multihypotheses recognition approach to illustrate the proposed design flow. Section 5 describes the parallel structure of the algorithm and the choices made while porting the application software to the embedded system. We show in Section 6 the results of the implementation of the road recognition into Xilinx FPGA following the proposed design methodology.
Related work Creating an embedded multiprocessor system which meets its performance, cost, and design time goals is a hardware-software co-design problem where the design of the hardware and software components influences each other. Actually, co-design  [2]  is usually used as the http://asp. eurasipjournals. com/content/2013/1/153 design method for embedded systems. For example, the co-design approach MoPCoM  [3]  allows the use of generic unified modeling language(UML) tools based on the modeling and analysis of real-time and embedded systems (MARTE) standard  [4]. In fact, the MoPCoM methodology is performed using three abstraction levels: abstract, execution, and detailed modeling levels (AML, EML, and DML, respectively). Whereas, we have to respect multiple design rules to build UML models for embedded systems in order to perform automatically VHDL (VHSIC hardware description language) code generation.
Given a complete algorithm specification(C or C++), the designer identifies where to introduce parallelism through software skeletons for data and/or task parallelism which are completely different from hardware skeletons. Traditionally, the concept of hardware skeletons has been first presented by  [12]. In their study, they have discussed the use of the notion of hardware skeletons, specific to application field which is novel to the hardware domain. They implemented their hardware skeleton library as a hierarchy of three levels of hardware blocks: arithmetic core library, basic image operation library, and finally, highlevel (compound) skeleton library. However, this approach employed high-level descriptions of task-specific architectures specifically optimized only for Xilinx XC4000 FPGAs.
Indeed, designing an image and video processing system can be complex and time-consuming; as a result, the design process can take months depending on the system's complexity. The adopted method developed to ease this bottleneck is to create a graphical tool called 'CubeGen' which automatically generates the hardware for a given application. In practice, with this framework, the system is built entirely hiding unnecessary hardware complexities and you can only focus on optimizing the sequential code and investigating improvements of the parallel code. The user does not need to know anything about hardware language; its application can be designed in C language and implemented automatically into our hardware architecture. For rapid prototyping of complex sequential applications, we address these issues by providing: • Hardware architectures supporting the simultaneous use of data parallelism and task parallelism.