dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). Guidance for the determination and expression of measurement uncertainty is widely available, e. g. from ISO (2008). • The output from the risk characterisation should be in the form of an overall assessment of the safety of the nanomaterial in its intended use, together with the description of the parameters under which the assessment is valid and the uncertainties associated with the assessment. • At every stage where information is assessed, a weight of evidence process should be applied to decide whether an adequate risk assessment can be undertaken. Risk assessment can be performed at any stage where the totality of the available information is adequate, and no further testing is necessary. • Where the data have been derived from appropriately conducted studies using validated methods and considering nanospecific issues, there should not be a reason for the use of any higher uncertainty factors for a nanomaterial than those used for a conventional material.
E. 1. Feed Additives Feed additives are substances, microorganisms or preparations other than feed materials and premixtures that are intentionally added to feed or water to perform one or more functions 32 mentioned in Article 5. According to Article 8 of Regulation (EC) No 1831/2003, EFSA shall undertake an assessment to determine whether the feed additive is safe (for the target animals, consumer, user and the environment) and efficacious, when the proposed conditions of use are followed. To allow EFSA to perform an assessment of a feed additive, its condition of use should be specified (at which dose range it is used and for which target species) and the additive and active substance should be characterised (including details on the impurities and manufacturing process); data on stability (shelf-life, stability in premixtures and feedstuffs) and homogeneity are also assessed.
S U P E R S E D E D to nanomaterials present in animal food products (indirect exposure, carry-over) should be assessed. To this end, data should be provided on nanomaterial residues in tissues/products from target animals receiving the nanomaterial feed additive under the conditions of the use requested (see FEEDAP guidance on the safety for the consumer) (EFSA FEEDAP Panel, 2012b). Assessment of carry-over for consumer safety is particularly relevant when there is a concern that the nanomaterial is persistent and bioaccumulative. If the same nanomaterial is also intended to be used as a food additive, there needs to be also an assessment of the nanomaterial for food use. In such cases, carry-over of the feed additive can also be supported by the food use evaluation (e. g. by the safe intake level).
S U P E R S E D E D Risks to users/workers should be assessed in a series of studies using the additive in all forms of the final product for which the application has been submitted. Any other available toxicological data should be used to assess the potential systemic toxicity of the additive. The requirements to assess the effect on the respiratory system, skin and eye irritation and skin sensitisation potential indicated in the FEEDAP guidance (EFSA FEEDAP Panel, 2011), should be followed.
(EU) No 528/2012). An example of an approved nanomaterial for use in biocidal products in Europe is that of synthetic amorphous silica, which is approved under the Biocides Regulation (EU) No 528/2012 38 as an active substance for use in biocidal products used for product type 18, insecticides, acaricides and products to control other arthropods, as defined in the Annex to the relevant Regulation. 39 The approval covers synthetic amorphous silicon dioxide (CAS No 112926-00-8) as a nanomaterial in the form of stable aggregated particles of size > 1 lm, with primary particles in the nanosize scale (< 25 nm). It is also noteworthy that there is a searchable database available for active substances approved in the EU under Regulation (EC) No 1107/2009 for use in plant protection products 40 ), which confirms that currently there are no nanomaterial-based pesticides.
However, there is no such database for formulations, and it is not clear if there are any nanomaterials already approved in the EU to be used as a pesticide formulation. The currently available information on R&D relating to active substances in the form of nanopesticides indicates that they are most likely to fall under one of following types of formulations where the active substance is: a) in the form of a nanoparticle as such or surface modified, or is contained in a nanoparticle carrier -such as porous nanosilica; b) in the form of nanosized droplets in an emulsion, or in solid lipid particles; c) nanoencapsulated in a natural or synthetic (usually degradable) polymer shell. From a risk assessment perspective, but deviating from the current legal requirements, 41 the Scientific Committee notes that safety considerations for a nanopesticide used in agriculture will be necessary in the future.
The analytical method used for measurement of size distribution is CIPAC method MT 187, which is based on ISO 13320 Also, as discussed before, unless a valid justification can be provided, each formulation should be assessed for any change(s) in the properties and behaviour of the nanopesticide. This is because different formulations may alter the degree of particle dispersion, agglomeration and aggregation. Thus, data on physicochemical parameters, including particle size distribution, will be required both for an active substance(s) and the formulation(s) intended for use. Any significant changes in the physicochemical properties of a nanopesticide, either as such or in a formulation, would make it difficult to justify the use of toxicological data on conventional equivalents in risk assessment.
Inhalation exposure/toxicity Regulation (EU) No 284/2013 setting out the data requirements for PPPs (formulations) requires acute inhalation toxicity studies. For this purpose, head/nose exposure shall be used, unless body exposure can be justified. The studies are carried out following the OECD Guidelines Tests 403  (OECD, 200b). In regard to a nanopesticide (especially nanoformulations), it is important to note that particle size and the mode of application (e. g. dusting or spraying) will determine the extent of the exposure in terms of whether the particles/droplets can be inhaled and which part of the respiratory tract they can reach. Guidance on nanotechnologies in the food and feed chain www. efsa. europa. eu/efsajournal 92 EFSA Journal 2018;16  (7) :5327.
• Where studies indicate dermal absorption of a nanopesticide, further investigations should be carried out to ascertain whether the absorbed substance(s) are in nanoform, or a degraded or dissolved form. Where the absorption of a nanopesticide cannot be ruled out either by experimental data or on the basis of information on dissolution/degradation, a default value of 100% absorption in nanoform should be applied in risk assessment. • Regulation (EU) No 284/2013 setting out the data requirements for PPPs (formulations)   requires acute inhalation toxicity studies. For this purpose, head/nose exposure should be assessed, unless body exposure can be justified. The studies should be carried out following OECD Guidelines Test 403. Guidance on nanotechnologies in the food and feed chain www. efsa.
38 Regulation (EU) No 528/2012 of the European Parliament and of the Council of 22 May 2012 concerning the making available on the market and use of biocidal products Text with EEA relevance, OJ L 167, 27. 6. 2012, p. 1-123. 39 Commission Implementing Regulation (EU) No 408/2014 of 23 April 2014 approving synthetic amorphous silicon dioxide as an existing active substance for use in biocidal products for product-type 18, Official Journal of the European Union, L 121, 24 April 2014. 40 http://ec. europa. eu/food/plant/pesticides/eu-pesticides-database/public/?event=homepage&language=EN 41 In the current legal framework the co-formulants used in the pesticide products are regulated under REACH and Pesticides Regulation. The European Commission is currently working on a Regulation setting out rules for the implementation of article 27 of the Regulation (EC) No 1107/2009 for the identification of non-acceptable co-formulants.
More than 21 hours of data were dedicated to observations using different triggers, usually for synchronising with laser shots, LED flashes and tests of the internal LI trigger of EUSO experiments. The LI trigger is tuned to future in-orbit EUSO observa- ground histogram consists of measurements of pixels with varying efficiencies, and hence varying count distributions. In this way, the overall distribution deviates from Poissonian for high values. The number of counts on a single pixel, which is sensitive to single photons, is well described by the Poisson distribution, as can be seen on Fig. 6 , right. This sample pixel has an average number of counts per frame on 100,000 frames equal to 1. 28. In the simplest case, the counts in this pixel would have to exceed 3er threshold of about 5. 8 counts to detect a signal.
4. U. Flat fielding It is a common practice in scientific imaging to equalise pixel sensitivities during data processing. This is especially important for a photomultiplier based photon counter, for which pixels can vary significantly in their efficiencies. For this purpose, flat fielding is often employed. This involves illuminating the telescope with a uniform, Lambertian light source. To achieve this for EUSO-TA, a large reflective flat screen illuminated by a diffused LED was used. The 2. 44 m x 2. 44 m flat screen was covered with Tyvek and placed 6. 5 m in front of the EUSO-TA front lens. The LED was placed in the centre of the front lens, emitting light of wavelength 375 nm through a 3. 2° neutral density filter. The non-uniformity of the detector illumination coming from this setup is small compared to the uncertainties from other sources, mainly due to the fact that the detector was focused at infinity  [15].
The plot sums 2 shooting sessions, altogether encompassing an energy range of 2-22 mj. Each point was calculated from a few dozens shots. Fitted line shows good linearity of the detector response in the range of 4-22 mj, while for lower energies only the brightest tracks were reconstructed, enforcing a low brightness cut on the data points. Fig. 20. All UHECR detected by TAFD in the EUSO-TA held of view during its operation with non-detected events and laser shots superimposed. The vertical axis shows the distance to the shower axis. Left: the horizontal axis shows the energy of the event as measured by TA. Right: the horizontal axis shows the events equivalent energy (explained in the text). The fit to the detected points, explained in the main text, suggests a conservative estimate of the EUSO-TA detection energy threshold.
e. in the detectable region, as expected. Two typical events are shown in Fig. 21. It can be seen that rebinning of the images significantly increases the visibility of the tracks -making EUSO-TA data more similar to those of groundbased UHECR telescopes, which have much larger pixel sizes. However, such a pixel size is not suited for space-based observations, to which EUSO-TA has been tuned. Simulations of the events made with the "Offline" package  [27]  are also presented here. The shower image can be reproduced to very fine detail, however, some overestimation of the signal in the simulation can be spotted on the residuals shown in Fig. 22  (left). Positive residuals, shown in the picture, align mainly in the shower area, while negative, which were excluded here for the sake of clarity, are randomly scattered over the whole field of view.
However, with the implementation of the external trigger, data is collected for each TAFD event. Therefore, an event is considered as detected if a linear trace is found in the EUSO-TA data and a corresponding event in TAFD results. Simulations studies carried out in 2011  [13] , prior to the installation of the telescope, predicted the detection of about 15 events in the total data acquisition time achieved to date. However, that analysis assumed more optimal EUSO-TA elevation angle of 30°, instead of the most often used 10° -15°. Additionally, the analysis assumed a lower background light level than what was measured. In 2016, further simulations were performed, using the updated detector parameters  [28] , resulting in 8 predicted events, consistent with the 9 UHECR observed to date. These first UHECR events registered with EUSO technology allowed for an important improvement and optimisation of the reconstruction and simulation software.
EUSO-TA future plans EUSO-TA provides an excellent opportunity to test technology for existing and future experiments within the EUSO framework, as it allows for stable field observations for extended time periods. However, the observation time could be significantly increased if  remote automatic operation of the instrument was implemented. This would allow for the collection of data continuously over the year in all possible observational periods. This task will require introduction of some additional mechanical and control devices, as well as a non-remote automatic working test period. The sensitivity of the experiment will be enhanced with an upgrade of the EC-ASIC boards to the new version incorporating SPACIROC3 ASIC, which has ~5 ns single pulse resolution instead of the current 30 ns.
This parameter is important for nearby showers, which cross a single pixel in short timescales. Higher single pulse resolution decreases a chance of several photons from such an event falling into a single time bin and therefore being lost. The tests with EUSO-SPB in Utah show about a factor of 2 higher signal for CLF tracks, which may be attributed mainly to the use of SPACIROC3. The efficiency change should be much higher for close tracks, where the time spent in a single pixel is much shorter. Data Data rebinned 2x2 Simulation The experiment will also be upgraded with advanced selftriggering capabilities. This will be achieved by replacing the current PDM data processing board with a new board based on system-on chip -Zynq XC7Z030 FPGA, recently developed by XIL-INX, which has more memory and resources.
It allows the implementation of data read-out on three timescales, similar to the readout designed for the Mini-EUSO mission. This /is timescale readout is self-triggered, dedicated to UHECR observations. Integrated packets forming 320 ms frames would be passed to second level trigger, dedicated mainly to atmospheric phenomena. Finally, ms scale frames integrated to 5. 24 s exposures would be stored as a "movie" for analysis of slow phenomena, such as strangelets and meteors, detectable with offline event search algorithms.
The advantages of one design over another in space-based systems depends strongly on the mission profile (rocket accommodation, satellite or space station, etc. ). A challenging future goal is keeping EUSO-TA up to date with developments of the other experiments in the EUSO framework. Two detectors -the already launched EUSO-SPB and the prepared Mini-EUSO -use a new version of EC-ASIC boards equipped with SPACIROC 3 ASICs, resulting in significantly higher dynamic range and signal to noise ratio. Mini-EUSO also replaces a PDM, CCB and Clock boards with the PDM data processing board allowing for the efficient and parallelised performance of more computationally demanding tasks. Introducing these elements to EUSO-TA is a necessary step to increase its performance and keep compatibility with other missions.
Acknowledgments This work was partially supported by  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS KAKENHI  Grant ( 22340063 ,  23340081 , and  24244042 ), by the  Italian Ministry of Foreign Affairs and International Cooperation , by the  Italian Space Agency  through the  ASI INFN  agreement no. 2017-8-H. O, by  NASA  award ll-APRA-0058 in the USA, by the  Deutsches Zentrum fur Luft-und Raumfahrt , by the  French space agency CNES , the  Helmholtz Alliance for Astroparticle Physics  funded by the  Initiative and Networking Fund of the Helmholtz Association (Germany) , by  Slovak Academy of Sciences  MVTS JEM-EUSO, by  National Science Centre in Poland  grant ( 2015/19/N/ST9/03708 ), by Mexican funding agencies  PAPIIT-UNAM ,  CONACyT  and the  Mexican Space Agency (AEM) , as well as  VEGA  grant agency project  2/0132/17 , and by  State Space Corporation ROSCOSMOS and Russian Foundation for Basic Research  (grant  16-29-13065 ).
This paper proposes a protocol to assess the space-time consistency of satellite precipitation estimates (SPEs)      according to various indicators including: (i) direct comparison of SPEs with 72 precipitation gauges; (ii) sensitivity of streamflow modelling to SPEs at the outlet of four basins; and (iii) the sensitivity of distributed snow models to SPEs using a MODIS snow product as reference in an unmonitored mountainous area. The protocol was applied successively to four different time windows (2000-2004, 2004-2008, 2008-2012 and 2000-2012)  to account for the space-time variability of the SPEs and to a large dataset composed of 12 SPEs (CMORPH-RAW, CMORPH-CRT, CMORPH-BLD, CHIRP, CHIRPS, GSMaP, MSWEP, PERSIANN, PERSIANN-CDR, TMPA-RT, TMPA-Adj and SM2Rain), an unprecedented comparison.
The aim of using different space-time scales and indicators was to evaluate whether the efficiency of SPEs varies with the method of assessment, time window and location. Results revealed very high discrepancies between SPEs compared to precipitation gauge observations. Some SPEs (CMORPH-RAW, CMORPH-CRT, GSMaP, PERSIANN, TMPA-RT and SM2Rain) are unable to estimate regional precipitation whereas the others (CHIRP, CHIRPS, CMORPH-BLD, MSWEP, PERSIANN-CDR and TMPA-Adj) produce a realistic representation despite recurrent spatial limitation over regions with contrasted emissivity, temperature and orography. In nine out of ten of the cases studied, streamflow was more realistically simulated by the hydrological model tested when SPEs were used as forcing precipitation data rather than precipitation derived from the available precipitation gauge networks.
These estimations are based on the attenuation of the electromagnetic signals between telecommunication antennas during precipitation events and their first results are promising as precipitation estimates were found to be very accurate (see e. g. Doumounia et al. , 2014). However, this technique faces the problem of private cellular phone company policy about sharing data and telecommunication antenna are mainly located in urban areas, which limits accurate precipitation estimates in space. On the other hand, several satellite precipitation estimates (SPEs) are now available, making possible to monitor Hydrol. Earth Syst. Sci. Discuss. , https://doi. org/10. 5194/hess-2018-316 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 13 September 2018 c Author(s) 2018.
Satellite precipitation estimates (SPEs): opportunities and limitations Several SPEs have become available in recent decades to monitor precipitation at global scale and on regular grids. The first generation of SPEs appeared with the Tropical Rainfall Measuring Mission (TRMM) launched in 1997 by NASA (National Aeronautics and Space Administration) and the Japan Aerospace Exploration Agency (JAXA). Over the last 18 years, the TRMM Multisatellite Precipitation Analysis (TMPA)  (Huffman and Bolvin, 2014) , the Climate prediction centre MORPHing (CMORPH)  (Joyce et al. , 2004) , the Precipitation Estimation from remotely Sensed Information using Artificial Neural Networks (PERSIANN)  (Sorooshian et al. , 2000)  and the Global Satellite Mapping Precipitation (GSMaP) (GSMaP, 2012) SPE datasets have been developed based on the TRMM mission to deliver precipitation estimates at the 0.
25° grid scale. In 2014, the Global Precipitation Measurement (GPM) mission was launched to ensure TRMM continuity. The second generation of SPEs based on GPM missions included the Integrated Multi-SatellitE Retrievals for GPM (IMERG)  (Huffman et al. , 2017)  and a new GSMaP version product which deliver precipitation estimates at a finer grid scale (0. 1°) than the first generation of SPEs but estimates are limited to the period from 2014 to the present. At the same time, some SPEs took advantage of previous SPEs and missions to estimate precipitation over larger time window: long-term SPE generation. This is the case of PERSIANN-Climate Data Record (PERSIANN-CDR)  (Ashouri et al. , 2015) , Multi-Source Weighted-Ensemble Precipitation (MSWEP)  (Beck et al. , 2017)  and Climate Hazards Group InfraRed Precipitation (CHIRP) with Station data (CHIRPS)  (Funk et al.
State of the art evaluation of SPEs In the context described above, many studies have reported on the efficiency of SPEs over different regions. The most common way to evaluate SPE potential is to compare their estimates with precipitation gauge measurements, as reviewed by  Maggioni et al. (2016)  and  Sun et al. (2017). The comparison of gauge-based assessment studies confirmed the spatial variability of SPE efficiency in reproducing precipitation, so no single SPE can be said to be the most effective one at global scale. For example, when TMPA, CMORPH, and PERSIANN SPE datasets were compared, TMPA was found to be closer to the observed precipitation in India  (Prakash et al. , 2014) , the Guyana shield  (Ringard et al. , 2015) , Africa  (Serrat-Capdevila et al. , 2016) , Chile  (Zambrano-Bigiarini et al.
The efficiency of SPEs can be evaluated indirectly via their ability to generate reasonable discharge simulations at the outlet of the basin concerned. Compared to gauge-based assessment studies, fewer authors have reported on hydrological sensitivity to SPEs, as reviewed in  (Maggioni and Massari, 2018). For example, TMPA, CMORPH and PERSIANN datasets were compared as forcing data for hydrological modelling in Africa  (Casse et al. , 2015; Thiemig et al. , 2013; Tramblay et al. , 2016)  and South America  (Zubieta et al. , 2015). These studies provided complementary information to gauge-based assessments, offering an operational overview of SPEs for the management of water resources. However, due to the aggregation process at basin scale, the potential of SPEs over specific ungauged regions remains unclear.
Moreover, in these studies, the SPEs were not compared with gauge observations  (Thiemig et al. , 2013)  or provided only a brief comparison at basin  (Casse et al. , 2015; Tramblay et al. , 2016)  or gauge  (Zubieta et al. , 2015)  scale. Therefore it is difficult to conclude on the respective advantages and limitations of using gauges or streamflow data as indicators to assess the ability of SPEs to reproduce precipitation patterns as SPEs could rank differently depending on the indicator used. For example, considering CMORPH, PERSIANN and TMPA datasets over two African watersheds, TMPA showed the closest estimate in comparison with gauges for both basins  (Thiemig et al. , 2012)  while CMORPH and TMPA provided more accurate streamflow simulations depending on the basin considered  (Thiemig et al.
, 2013). Whatever the selected approach (based on gauges and/or hydrological modelling), the analysis is performed using a single time window which does not assess the temporal variability of SPEs due to the acquisition process and/or aging sensors. To date, only a few studies have been conducted to observe changes in the efficiency of SPEs over time. For example, CHIRPS precipitation estimates were analysed over two distinct time windows to assess potential changes in precipitation accuracy over Cyprus and Nepal from one period to another  (Katsanos et al. , 2016; Shrestha et al. , 2017). Similarly,  Bai et al. , 2018  analysed the accuracy of CHIRPS in mainland China separately for each year to assess its interannual variability. These studies highlighted temporal inconsistencies in CHIRPS estimates inherent to variations in the input data used for precipitation retrieval.