dataesr/scientific-paragraphs-categorization huggingface dataset:
With this preparatory foundation addressed, the design phase could proceed in earnest. The overall architecture of the INSPEX system consists of three modules. There is the hardware module that attaches to the VIB person's white cane. This module communicates via Bluetooth with a software component running on a smartphone belonging to the user. The software component is able to use the phone's capabilities to communicate with the outside world, and to integrate information received from there with informaiton gained locally, in order to give the VIB user a richer and more informative experience. Finally, there is a pair of extraauricular headphones, connected to the smartphone module again via Bluetooth, that is able to paint a sound picture for the VIB user about their environment that is intended to be as vivid and informative as possible.
Fig. 4  shows these components in digramatic form. The account just given makes it clear that INSPEX belongs very much in the Internet of Things family of systems. An INSPEX system is ready to receive information made available by the environment, in order to assist its user to gain a more accurate picture of where, in relation to the rest of the environment, he or she might be. This potentially raises various ethical concerns. These days many smart apps are designed to divulge data about their users, whether to the app manufacturer or to connected nodes in the wider environment, and it is the users' (assumed informed) choice about whether or not they are prepared to have such information divulged. By contrast, from a legal perspective, the VIB use case of INSPEX is generally regarded as a Class 1 medical device  [12] ,  [13].
Because of the medical connection, by default, a much larger quantity of regulation comes to bear on the device (although the regulatory situation may change in future). Moreover, the details of the regulations concerned vary from jurisdiction to jurisdiction, so ensuring that INSPEX conforms to all required regulation forms a significant strand of the project (handled by partner Univ. Namur). By default, INSPEX implements a privacy by design policy. No data (that is intelligible outside the INSPEX system) is ever made available to the environment. Fig. 5  illustrates a prototype configuration of a VIB user deploying a white cane augmented with the kind of sensing abilities envisaged for the INSPEX white cane hardware clipon module. In fact the figure shows an early conceptualisation of the clip-on module, when having an additional module near the bottom of the white cane, to more precisely monitor conditions near the ground, was contemplated.
VI. THE INSPEX ADVANCED PROTOTYPE SYSTEM In the light of the many and varied environmental circumstances that a VIB person may meet outdoors, the hardware clip-on module features a collection of sensors, that are intended to harvest the maximum amount of useful data in the widest possible range of conditions, bearing in mind the limitations of size, weight and power consumption. In this section, we overview the various components that comprise the INSPEX advanced prototype. The heart of the INSPEX sensing system consists of four main sensors. There is a short range LIDAR, a long range LIDAR, a MEMS ultrasound sensor and a ultrawideband RADAR. These are supplemented with an inertial measurement unit and appropriate computation and communication facilities. The collection of sensors that have been incorporated into INSPEX complement each other in important ways, making up for each others' deficiencies under different circumstances in ways that genuinely add value  [14].
A. Overall INSPEX Architecture Fig. 6  shows all the components of INSPEX. In the main Mobile Detection Device we see not only the distance sensing module whose elements we have just discussed, but also other modules. There is an Environmental Sensing Module. This provides information about the ambient circumstances of use of INSPEX to improve the decision making capability of the overall system about how to best configure itself for optimal performance. Vital to effective working is the Attitude and Heading Reference Module. This contains an Inertial Measurement Unit, which regularly feeds information about the physical orientation in real space of the whole Mobile Detection Device to the system's decision making capability. This is indispensable as all the distance measurements obtained by the distance sensing devices, must be correlated to the direction in 3D space in which they were made, in order for them to be useful in constructing a 3D occupancy grid.
Energy is supplied to the whole Mobile Detection Device by the Energy Source Module, which is an interchangeable battery pack that can be quickly swapped for a spare when power is low, and which offers rapid recharge characteristics. The Generic Embedded Platform coordinates and controls the activities of the other modules in the Mobile Detection Device. As well as this it takes care of communication with what is referred to as the Mobile Device (Smart Phone) in Fig. 6. A separate major component is the Audio Headset. This features a pair of extra-auricular earbuds to project the audio signal from INSPEX into the user's ears. It also contains another Inertial Measurement Unit, so that the orientation of the user's head may be inferred, in order that the calculated INSPEX audio signal is perceived to be oriented stably with respect to a 3D space inertial frame.
H. Software and Firmware in the Generic Embedded Platform The Generic Embedded Platform within the INSPEX Mobile Detection Device has the responsibility of appropriately integrating all the sources of information provided by the family of sensors embedded in the Mobile Detection Device, and communicating suitably with the INSPEX Smartphone Application. Considering the variety of the information received, the integration of all this information is a non-trivial task, and a considerable quantity of sophisticated software is needed to accomplish an optimal result. A major portion of the job is the receipt and husbanding of the raw data coming from the sensors. These all work at their own rate, modulated by the commands sent to them by the GEP. The data has to be received and timestamped so that fresh data may be given priority over older stale data.
K. The INSPEX Smartphone Application The INSPEX Smartphone Application, developed by partner CIT, binds the functionality of the INSPEX modules discussed above into a consistent whole. One major task is the integrated handling of all the 3D information coming from different parts of the system. The mobile detection device is physically independent from the audio headset, so each is independently situated in Euclidean space. The information coming from the two IMUs in these modules has to be integrated so that a single consistent picture of the INSPEX system's position in 3D space can be determined. The calculations for this, though not trivial, are relatively familiar from the robotics world  [15] ,  [16]. Another major responsibility of the smartphone application is context awareness. In the growing world of IoT, especially as realised in the growing number of smart cities, the environment is capable of being, of itself, informative in the cyberspace domain.
VIII. VERIFICATION, VALIDATION, EVALUATION A system as complex as our presentation of INSPEX has elucidated, evidently throws up highly non-trivial issues regarding the ensuring of correct behaviour under all operating conditions. This challenge must be approached in a disciplined and structured way, otherwise little assurance is derived. At the lowest level, individual project partners are responsible for the testing and verification of their individual submodules. Usual best practice in each individual physical domain is applied in each case. At the next level up, the behaviour of the community of sensors must be integrated via the software and firmware of the generic embedded platform. Here, software complexity must be confronted. In this sphere, formal modelling and verification techniques are brought to bear.
Each sensor node piggybacks its wake-up interval in data packets. The sink keeps an updated table that associates for each node its wake-up interval and polls each node at the right time. The sink sets for each node a timer for the wake-up interval piggybacked in data packets and polls the node every time the timer expires. Sensing operations are performed by each node at anytime between two sink polls, ensuring that data are ready to be sent when the sink sends the wake-up beacon. Compared to traditional receiver-initiated protocols, this approach reduces the energy consumption of the sink and the nodes as no rendezvous process is required. The sink energy consumption is further reduced as useless periodic WuBs sending is avoided. Because the wake-up interval is typically a 16-bit integer, minimal overhead is incurred by the piggybacking of this information.
SNW-MAC: Collisions between packets sent by nodes belonging to the same SNW-MAC-based network are impossible as the sink specifically polls each node. Nonetheless, as receiving a packet requires a non-null duration, the receiving rate is still bounded. The total time required to receive a packet is denoted by τ R and is defined by: τ R = τ d + τ o , (13) where τ d is the time required to receive the data payload and τ o is the overhead incurred by the hardware and the protocol at each packet reception (WuB sending, radio setup, turn-around time, software overhead). The maximum receiving rate in packets per minute is thus: Γ = 60 τ R , ( 14 ) where • is the floor function. We assume that the packet generation rates of the nodes are independent of each other and are modeled by Poisson distributions of mean λ packets per minute, and we denote by A the aggregate rate.
Experimental Setup The position and force controllers with the proposed adaptive predictive control scheme were validated using the KUKA LWR real experimental identification data  [34] , models, MATLAB, SYMORO+  [36] , and V-REP simulation platforms. For the experimental identification, the KUKA LWR was working on a custom-built painting wall (height: 0. 8 m, width: 0. 6 m). A six-axes force sensor (ATI, Mini45) was employed to measure the reference force data, and it was positioned at the center of the wall. The desired external forces were set as F d equal (-30, 0, 0) N. Fig. 6  presents the KUKA environment when the exciting motion was executed for the identification of the parameters. The identification and control were validated using two different types of painting rollers. Table  2  presents the physical parameters of these tools, and Table  3  presents the identified parameters.
To obtain the calibration parameters some simultaneously seasonal measurements of W , from independent sources, taken over a large range of solar zenith angle and covering a wide range of W , are needed. In this work yearly GNSS/GPS datasets were used for obtaining a table of photometric calibration constants and the methodology was ap-plied and validated in three European ESR-SKYNET network sites, characterized by different atmospheric and climatic conditions: Rome, Valencia and Aosta. Results were validated against the GNSS/GPS and AErosol RObotic NETwork (AERONET) W estimations. In both the validations the agreement was very high, with a percentage RMSD of about 6, 13 and 8 % in the case of GPS intercomparison at Rome, Aosta and Valencia, respectively, and of 8 % in the case of AERONET comparison in Valencia.
For both the above stations dataset with time interval of 30 s was used to retrieve W GPS with a temporal resolution of 15 min. The VALE system (0. 3376 • W, 39. 481 • N; ellipsoidal height: 77. 7 m; altitude: 26. 88 m) is located on the roof of the Escuela de Cartografia y Geodesia of Universidad Politecnica de Valencia, Spain, about 7 km east from the sun-sky radiometer position. This system is part of the EUREF Permanent GNSS Network, and RINEX data are available from the EUREF website (http://www. epncb. oma. be). For this station W GPS was retrieved with time interval of 1 h.
GPS receivers Dry air and water vapour molecules in the troposphere affect GNSS signals by lowering their propagation velocities with respect to vacuum  (Saastamoinen, 1973; Bevis et al. , 1992). A diminished speed results in a time delay in the signal propagation along the satellite-receiver path, which multiplied by the vacuum speed of light, adds an extra distance to the satellite-receiver geometrical one. It is worth reminding here that the tropospheric delay (the word delay is usually referred to the extra distance and is expressed in metres) due to the dry air and water vapour molecules is just one out of many other systematic errors affecting GNSS observations that must be accounted for in order to achieve sub-centimetre accuracy positions. During GNSS data processing, the contribution of dry air and water vapour to the total delay is separated and estimated in the zenith direction.
The height difference between receivers and weather stations has been corrected according to  Realini et al. (2014). Latitude and altitude of receivers, read in the RINEX files, are also used to convert ZTD to precipitable water content W GPS using the procedure by  Bevis et al. (1992). This technique allows calculating the ZHD and the ZWD. Finally, according to  Askne and Nordius (1987) , Eq. (  1 ) returns the amount of IWV (integrated water vapour): IWV = K(Tm) • ZWD, (1) where the coefficient K depends on the vertically integrated mean temperature (Tm)  (Davis et al. , 1985)  and can be obtained either from meteorological models or by the linear relationships proposed by  Bevis et al. (1992) : Tm ∼ 70. 2 + 0. 72 Ts, where Ts is the measured temperature. In the analysis of Aosta GPS data, the Bevis equation was used, whereas ECMWF data were used for VALE.
Once parameters V 0 , a and b have been determined, the values of precipitable water content W P can be calculated according to the Eq. (  6 ): W P = 1 m • 1 a • (ln V 0 -y) 1 b. (6) In order to calculate x values in Eq. (  4 ), an independent dataset of columnar water vapour W , measured by other instrumentation (such as radiosondes, microwave radiometers or GPS receivers) taken over a large range of solar zenith angle simultaneously with the sun-sky radiometer irradiance measurements, is needed. If seasonal dependent measurements of W are available, it is possible to calculate a table of calibration constants (a, b) as a function of the amount of columnar water vapour typical of the site under consideration. This table can be used for the calculation of W P until the instrument is moved to another location or its status is deteriorated.
AERONET/CIMEL In the AERONET methodology  (Pérez-Ramírez et al. , 2014) , the transmittance T at 940 nm is fitted to those generated from the HITRAN 2000 spectral database  (Rothman et al. , 2003)  using the Spherical Harmonics (SHARM) radiative transfer code  (Lyapustin, 2005) ; then the coefficients a and b are computed by a curve-fitting procedure of T as a function of W. The output of the HITRAN 2000 spectral database is convolved with recently measured filter response functions  (Smirnov et al. , 2004). Each AERONET instrument has its own unique set of a and b values depending on the filter configuration, and these coefficients are considered fixed until the filter is changed. Since only one pair of a and b parameters is used, the dependence of T on the vertical profile of temperature, pressure and moisture that can seasonally happen at each site is neglected, introducing uncertainties in their retrieval.
The use of a different database for the determination of water vapour transmittance could also affect their value. Once the coefficients a and b are known, the calibration constant V 0 is calculated by another modified Langley method, from observations taken at a high mountain site  (Reagan et al. , 1986; Bruegge et al. , 1992; Halthore et al. , 1997). This modified method, different from the one used in the ESR/PREDE-POM procedure, determines V 0 as the intercept of the straight line obtained by fitting y vs. the power term m b in Eqs. (3) and (4). In this case, as demonstrated in  Campanelli et al. (2014) , the stability of the W time pattern is required in order to avoid calibration errors. Finally using the retrieved calibration parameters a, b and V 0 the precipitable water vapour content, W AER , can be calculated from Eq.
Estimation of calibration constants W GPS obtained by the three GPS receivers in Rome, Aosta and Valencia was used as an independent dataset for calculat-ing the calibration constants of the co-located ESR/PREDE-POM sun-sky radiometers. The cloud screening of radiometers measurements was performed by selecting those measurements whose root mean square deviation (RMSD) between measured and reconstructed diffuse sky irradiance at all the wavelengths, used for aerosol study, and all angles is lower than 20 %. For the Rome site, an additional procedure was applied consisting in intercomparing the selected measurements with those provided by a co-located Multi-Filter Rotating Shadowband Radiometer (MFRSR), whose cloud screening is performed following the methodology by  Alexandrov et al. (2004).
In order to limit the influence of largest uncertainties at very high solar zenith angles, we selected the data with m < 8. The closest W GPS retrievals within 30 min, 15 min before and after the sun-sky radiometer measurements, were selected. Then the simultaneous [W GPS , V ] dataset was divided in two parts by picking every other day among the available days: one part [W GPS1 , V 1 ] was used for the application of the methodology and then the estimation of the calibration constants, while the other part of GPS estimations (W GPS2 ) was used to validate the W P retrievals. The two GPS datasets were found to be equally populated and with similar frequency distributions, and the statistical independence between the W GPS2 data used for the validation and W P was ensured. Because a and b parameters are supposed to depend on the total amount of water vapour, the entire yearly independent W GPS1 dataset was divided in three classes: [0-10] mm,  [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]  mm and  nm; an insufficient number of points were found with water vapour larger than 40 mm for the three sites.
Nevertheless, its total un-certainty (estimated as the standard deviation of the assumed values in each class divided by their average) resulted to be about 4, 8 and 14 % for Rome, Valencia and Aosta, respectively. The uncertainty affecting the retrieval of W P ( W P %) was estimated (as in Eq. 7) by calculating the percentage RMSD between W P and the W GPS1 dataset, used for calibrating the sun-sky radiometer: RMSD = < (W P -W GPS1 ) 2 > W P % = RMSD < W GPS1 > • 100. ( 7 ) One must bear in mind that this uncertainty is a statistical measure but not the total uncertainty of W retrieval which should include instrumental uncertainties, errors introduced at different steps of the method and their spread, and any other systematic errors. The estimated uncertainties (Table  2 ) values are comparable with that of AERONET retrievals  (Pérez-Ramírez et al.
4e ). This behaviour could be caused by the atmospheric stability; in the late morning, especially in summer and autumn when the insolation is higher, valley-mountain flows develop, mixing the humid air of the lower levels with the dried air above. Then, winds aloft could remove part of this humidity by advection, decreasing the water content of the air column. The other seasons conversely show more homogeneous W distribution during the day. Low W content associated with winds from W is due to the foehn. When wind comes from this side, air masses passed the Alps and arrived drier over Aosta. W P for each site was then validated against W GPS2 (the part of the GPS dataset not used for the calibration) to com- pare measurements within 1 min of difference (if more than one measurement of W GPS2 was found, their average was performed).
RMSD and bias, defined in Eq. (  8 ), as well as squared correlation coefficient R 2 , slope and intercept of the fitting straight line, were used for the statistical analysis, whose results are shown in Table  3  and Fig. 5. RMSD = < (W GPS2 -W P ) 2 > %RMSD = < (W GPS2 -W P ) 2 > < W P > •100 Bias =< (W GPS2 -W P ) > %Bias =< W GPS2 -W P W P • 100 > (8) The comparison between W P and W GPS2 for Rome and Valencia (Table  3 ) when all W classes are analysed shows high R 2 , varying from 0. 98 and 0. 96; RMSD assumes values from 1. 35 mm (6. 43 %) and 1. 67 mm (8. 09 %), and the bias is within -0. 01 mm (0. 34 %) and -0. 20 mm (-0. 05 %), which is within the estimated error W P. Investigating separately the three classes (divided using the thresholds on the W GPS2 dataset), the greatest difference was found for the first class in terms of % RMSD (9.
In this work the GPS dataset was divided in two parts by picking every other day from the available days: one part was used for the calibration of the sun-sky radiometer and the other part for the validation. In principle 1 entire year of W independent measurements are required for building the calibration table, but a smaller dataset can also be used, provided that it is taken over a large range of solar zenith angle and it covers a wide range of W. The obtained W P values were characterized by an uncertainty W P below 10 % for Rome and Valencia and of 20 % for Aosta. The yearly time pattern of W P for each site was then validated against the part of the GPS dataset not used for the calibration and against an AERONET sun photometer co-located in Valencia. In the former case for Rome and Valencia the agreement was found to be within the uncertainty W P when all the classes together are analysed, whereas for Aosta a % RMSD of about 14 % was found.
The present methodology can be easily applied to other kind of sun photometers or radiometers measuring the solar direct radiation at 940 nm wavelength, as PFR or MFRSR, provided that Ångström, exponent and aerosol optical depth at 940 nm are available. The calibration table containing a and b values for each W class can be used until the instrument is moved to another location or is submitted to maintenance. In these cases all the calibration parameters must be recalculated. The problem in the application of this methodology is, however, the availability of an independent, simultaneous W dataset to be used for calibrating the sun-sky radiometer or any other similar sun photometer. In  Campanelli et al. (2014)  the possibility of using a rough estimation of the needed W dataset was tested, using surface observations of moisture parameters that are much more common than W values estimated by other equipment.
campanelli@isac. cnr. it) or Victor Estellés (vestelle@uv. es). Regarding GNSS raw data for the two stations M0SE and VALE, they are freely downloadable from the EUREF EPN websites M0SE (http://www. epncb. oma. be/_networkdata/siteinfo4onestation. php? station=M0SE00ITA) and VALE (http://www. epncb. oma. be/ _networkdata/siteinfo4onestation. php?station=VALE00ESP). As for AOST, 30 s raw data are downloadable freely after registering at the NETGEO portal: http://www. netgeo. it/ (only in Italian) or by sending a request to NETGEO network operators. As for the IWV results, they can be requested from Eugenio Realini (eugenio. realini@g-red. eu) or Alessandra Mascitelli (alessandra. mascitelli@artov. isac. cnr. it) Competing interests. The authors declare that they have no conflict of interest.
Acknowledgements. We thank  ARPA Valle D'Aosta and ARPA Lazio  for providing ground measurements observations needed for the elaboration of GPS measurements. We also thank  Marco Cacciani  of the  University of Rome, La Sapienza, Department of Physics, G24 laboratory , for hosting the  PREDE-POM sun-sky radiometer of Rome. The  Spanish Ministry of Economy and Competitiveness  and the  Valencia Autonomous Government  are acknowledged for projects CGL2015-64785-R,  CGL2015-70432-R  and  PROM-ETEUII/2014/058 , which allowed the Burjassot-Valencia site measurements. Edited by: Stelios Kazadzis Reviewed by: three anonymous referees.