dataesr/scientific-paragraphs-categorization huggingface dataset:
This study considers aerosol products at 10 km spatial resolution, which is the native resolution of MODIS DB and DT products and seems to be the best compromise for comparing PARASOL/GRASP, MODIS DT, DB and MAIAC results. Also, 10 km is utilized by the aerosol community and other datasets (e. g. , ESA CCI products mentioned earlier). This was also a reason for the generation of PARA-SOL/GRASP Level-3 products. Thus, we adopted PARA-SOL/GRASP Level-3 daily 0. 1 • gridded aerosol products, MODIS/AQUA Level-2 daily DT and DB 10 km products, and the 1 km MODIS MAIAC aggregated to 0. 1 • (MA-IAC_0. 1) and 0. 01 • (MAIAC_0. 01) resolution for the intercomparisons. MAIAC_0. 01 essentially represents the single 1 km pixel retrieval. The PARASOL/Operational L2 daily aerosol product is directly used for validation, which is at 18.
5 km × 18. 5 km spatial resolution. The strategies to select PARASOL/GRASP retrieval products with the highest quality are presented in Table  2. The land pixel is defined only if 100 % of the 0. 1 • by 0. 1 • grid box has been identified as land, so an ocean pixel must contain 0 % land. Also, to guarantee proper coast elimination, the first pixel bordering ocean and land is removed (see Fig. 1 ). We selected the more reliable retrievals using "residual relative" (mean root square of relative error in fitting the measurements by the algorithm) for PARA-SOL/GRASP products. We adopted the same threshold for GRASP/Optimized and GRASP/HP (0. 05 over land and 0. 1 over ocean). These thresholds are suggested for general users. For the GRASP/Models product we did not use any filter because a stricter quality assurance filter has been applied in GRASP/Models product generation from L1 to L2 and L3 than for other GRASP datasets.
1. The minimal number of accepted satellite data pixels within the window is 1 over land and 41 over ocean; otherwise, the data were excluded from comparison. The PARASOL/Operational product is treated a bit differently over ocean due to its relatively coarse resolution (∼ 18. 5 km), with a similar land-like 3 × 3 window centered over the AERONET station. The AERONET direct-sun AOD, AE, AODF and AODC data were averaged within ±30 min of the MODIS/AQUA and PARASOL overpass time, while AERONET SSA and AAOD (which have a lower sampling frequency) are averaged within ±180 min. In addition, AERONET station elevations greater than 3600 m above mean sea level and satellite 3 × 3 or 9 × 9 datasets with AOD standard deviation greater than 0. 05 between window pixels were excluded.
334) than GRASP/Optimized (0. 356) and GRASP/Models (0. 415). The statistics of separated land and ocean AE validation are presented in Table  4. Over ocean, the correlation coefficients are significantly higher (R > 0. 93) than over land for all three datasets. Overall, the AE correlation statistical metrics are the best for GRASP/HP over both land and ocean. The GRASP/Models product has the smallest BIAS over land, which is counterpoised by overestimation of low and underestimation of high AE values due to assumed size distributions in the aerosol model-based approach. Both GRASP/Optimized and GRASP/HP capture AE well when large particles are dominant (AE < 1. 0), while the products tend to slightly underestimate AE when small particles are dominant (AE > 1. 0).
Single-scattering albedo Figure  6  shows the validation of PARASOL/GRASP SSA (670 nm) with AERONET L2 inversion products. The SSA products in the AERONET L2 database provide the values only for moderate-and high-AOD cases (AOD at 440 nm ≥ 0. 4) to assure the highest quality of the inversion products  (Dubovik et al. , 2000 (Dubovik et al. , , 2002b)). Following the same strategy, PARASOL/GRASP L2 and L3 products of SSA for low-AOD cases are also filtered out (land: AOD 443 nm < 0. 3; ocean: AOD 443 nm < 0. 02). The threshold for filtering SSA over ocean is very low because using higher values would eliminate a significant fraction of the retrievals. This low-AOD filtering is done over L2 products, and then L3 SSA is generated from filtered L2 products. The validation shows convincing correlation of all SSA PARAhttps://doi.
Aerosol absorption optical depth Aerosol absorption optical depth (AAOD) is related to SSA and total AOD as AAOD (λ) = AOD (λ) × [1 -SSA (λ)]. (5) In the current PARASOL/GRASP L3 dataset, the AAOD value of each grid box (0. 1 • or 1 • ) is calculated based on Eq. (  5 ) using average AOD (λ) and SSA (λ) of the grid box. Note that the PARASOL/GRASP L3 SSA (λ) values are aggregated based on moderate-and high-AOD cases (land: AOD 443 nm ≥ 0. 3; ocean: AOD 443 nm ≥ 0. 02), and again the very low threshold for filtering SSA over ocean was chosen in order to retain a sufficient number of SSA and AAOD retrievals. Choosing even slightly higher values would eliminate the majority of retrievals over ocean. Thus, the direct use of L3 climatology of AAOD may lead to overestimation of the global aerosol absorption, because the low-AOD cases are filtered.
Similarly, the AERONET L2 database provides AAOD products only for moderate-and high-AOD cases (AOD at 440 nm ≥ 0. 4) to assure their highest quality  (Dubovik et al. , 2000). The statistics of PARASOL/GRASP spectral AAOD (443, 670, 865 and 1020 nm) validation versus AERONET AAOD (440, 675, 870 and 1020 nm) are shown in Table  8. The correlation coefficients of AAOD are relatively low (0. 4-0. 55), which is certainly due to the low absolute value of AAOD, and most cases are less than 30 % of total AOD. GRASP/HP and GRASP/Models AAOD products show the RMSE equal to 0. 042-0. 018 from 443 to 1020 nm for Models and 0. 047-0. 025 for HP. The bias is lowest for GRASP/Models AAOD: 0. 00 at 440, 870 and 1020 nm and 0. 01 at 670. Thus,   PARASOL/GRASP AAOD provides rather useful information about global AAOD values; even the uncertainties are rather significant given the generally low magnitudes of AAOD.
03, GCOS fraction = 52. 2 %), GRASP/Optimized (bias = 0. 06, In order to obtain more information about the quality of the retrieval products over different land surfaces, the statistics of satellite validation against AERONET were also analyzed separately for different land covers. Table  10  shows the statistic metrics for land surfaces with different normalized difference vegetation index (NDVI) values. The statistics are presented for several categories: bare soil/desert surfaces (NDVI < 0. 2), mixture of bare soil and vegetated surfaces (0. 2 ≤ NDVI < 0. 4) and surfaces covered different types of vegetation (0. 4 ≤ NDVI < 0. 6 and NDVI ≥ 0. 6). The global NDVI dataset is adopted from GRASP/Models L3 annual mean products for the year 2008 (Fig. 10 ). The statistic metrics in Table  10  show that, in general, all products show better performance over surface type with 0.
1 • × 0. 1 • , close to the DT and DB product native resolution of 10 km, and we use MAIAC_0. 1 data that are of similar resolution. Only GRASP/HP and GRASP/Models products for PARASOL/GRASP are used in the consideration of this section since GRASP/Optimized shows rather similar results to GRASP/HP. Since the focus of this section is global pixel-to-pixel comparison of satellite aerosol products, we use all available data of the highest quality for each dataset (Table  2 ).
Third, as can be seen from Table  15  and Fig. 21 , GRASP/Models, GRASP/HP and MAIAC have wider coverage over land than DB and DT, because of the lack of retrievals over bright surfaces for DT and reduced number of retrievals over dark vegetation for DB (although some of this was improved in DB Collection 6. 1;  Sayer et al. , 2019). Specifically, for the year 2008, there are more than 64 million pairs of MAIAC-GRASP/Models AOD over land, which is much higher than the number of pairs obtained with other two AOD products. Thus, the collocation statistics for MA-IAC/GRASP, DT/GRASP and DB/GRASP as well as MA-IAC/DT and MAIAC/DB were based on different datasets. Fourth, the different representation of various natural conditions in the global statistics and statistics over AERONET can be non-identical and, therefore, the average performance indications can differ.
Data availability The PARASOL/GRASP Optimized, HP and Models products are publicly available on the official GRASP algorithm website (https://www. grasp-open. com/products, last access: 28 March 2020) and at the AERIS/ICARE Data and Services Center (http://www. icare. univ-lille. fr, last access: 16 October 2018). The dataset used in the current study is registered under http://doi. org/10. 5281/zenodo. 3887265 (Chen et al. , 2020).
Materials and Methods To analyze the actuation behavior at high strain and high voltage in order to obtain the actuation in terms of relative force, we have employed the state-of-the-art Dresden Smart Rubber Analyzer (DSRA) device. The DSRA was developed at Leibniz-Institut für Polymerforschung Dresden e. V. (IPF), Dresden, Germany. It is identical to a tensile machine; however, it uses a resistive sensor that records the resistance produced due to corresponding change in the geometry in the event of mechanical deformation (Figure  1b, c ). The mechanical system was designed for tensile forces of up to 200 kN with the maximum pull out distance of 400 mm. An internally developed software Smart-Rubber. vxe together with the development tool Agilent VEE (Version 9. 3, Agilent Technologies, Santa Clara, CA, USA) were used for this device.
(2013) , and all other forcings (aerosols, and non-CO2 greenhouse gases) are combined into a single term F ext (t) using global mean RCP values from  Meinshausen et al. (2011b). Uncertainty in the amplitude of non-CO 2 forcings is simply represented simply by an uncertainty factor f r , which is also optimized in the course of the MCMC calibration (Table  A1 ). The thermal response is calculated by expressing the derivative of the forcing timeseries F (t) as a series of step functions and using the CO2 quadrupling response T p from equation A1 to calculate the integrated thermal response. T (t) = t 0 dF dt (t ′ ) F 4xCO2 T p (t -t ′ )dt ′. (A6) This is again performed in a computationally efficient manner using MATLAB's 'filter' function.
σ T is defined as for the abrupt-CO2 case as the standard deviation ofHadCRUT 1850HadCRUT  -1950 values. values. For σ C , we lack an unforced standard deviation estimate -so a normalization constant of σ C = 0. 3ppm was chosen empirically to produce a ±1 ppmv range in 2016 observed concentrations in the posterior distribution. Shallow and Deep Ocean heat is taken as the 0-300m and 300m+ heat content respectively in Zanna et al. (2019) , with σ H and σ D taken as 1850-1950 standard deviations from the same dataset. In the 'C, T constraint' case, optimization is conducted using -E T and -E C as log likelihoods in the MCMC optimizer, with parameter boundaries as listed in TableA1. The 'C, T, Heat constraint' case uses the sum of -E T , -E C , E D and -E H cost functions. The 'C,T, paleo' case is implemented using the likely value and upper bound on Earth System Sensitivity from Goodman and Weare (2010)  fit the median and 90th percentile of a gamma distribution for equilibrium.
MCMC optimization of even a simple model of this form requires 10 7 or more calculations, so a very rapid model is required for computational tractability. This study employs a fast pulse-response model to represent the response of surface global mean surface temperatures to emissions. The model is implemented as a digital filter in MATLAB (see attached code) -allowing efficient computation and enabling Markov-Chain Monte Carlo parameter estimation for the physical parameters. The carbon scheme is a simple pulse dissipation model, with four atmospheric carbon pools R i (where i = 0. 3, following Myhre et al. (  2013 )) with dissipation timescales τ i as detailed in Table  A1. Each unit pulse of emissions is allocated to each of the four pools with a fraction a i : for which the solution for a unit emissions pulse δ(t) can be written: A generic emissions time-series E(t) can then be expressed as a sum of discrete pulses, allowing the corresponding carbon pools C i (t) to be expressed as a sum of pulse-responses R i (t) Author contributions.
2. Calculation of the expected damage crater diameter and ejected mass due to micrometeoroid impact. 1 3. Calculation, with the Peterson model,  4  of the BSDF that results from the impact craters. Calculation of the corresponding cleanliness level and slope due to contamination by ejected mass. 4. Optical software (FRED) calculation of the scattered light. In this paper, we apply this method to the case of the LISA (Laser Interferometer Space Antenna) telescope and consider the sun-orbiting LISA trajectory, 50 Mkm away from the Earth, in the micrometeoroid flux estimates. In the following, each step will be described, and the results will be presented.
15 The names given to the BSDF models are the same as the ones used in Sec. 3. 2. 1 in the calculation of the DCD. In the Peterson model 4 the digs are considered to be circular, and the intensity of diffracted light from a dig (crater) is calculated using scalar diffraction theory in the Fraunhofer (far-field) geometric and diffraction contributions: BSDF (θ) = N D D 2 4 × 1 + π 2 D 2 4λ 2 1 + sin 2 (θ) l 2 D -3/2 , (4) where N D is the number of digs per unit area, D is dig (crater) diameter, λ = 1. 064 µm is optical wavelength, and the roll-off angle  4  is l D = ( 4 π 4 ) 1 3 λ D. After integration over the range of micrometeorite masses, the calculated BSDF for different models of DCD is given in Fig. 5. We further proceed with FRED optical software in which the "ABg model" of BSDF is embedded.
This model is widely used to describe scattering due to microroughness  10, 22  and involves only three parameters: A, B, and g coresponding, respectively to a proportionality factor, to a roll-of angle, and to a slope. To implement this Peterson model in the FRED software, we fit the resulting BRDF (see Fig. 5 ) with the ABg model plus a constant term: BSDF = A B + (sin(θ)) g + R π (5) The first term in Eq. 5 corresponds to the diffraction of light that passes around the crater, and the second term corresponds to Lambertian scattering of level R inside the crater. The fit of the Peterson BSDF by the ABg model  22  curve was previously used to simulate light scattering by digs in the METIS coronograph. 23 us now consider backscattering from the primary mirror of NASA's model of the LISA telescope.
BSDF due to ejected contamination To calculate the BSDF due to ejected contamination, we assume that the size distribution of particles can be described with the (IEST)CC1246 standard. 24 It describes the number of particles N p (per 0. 1 m 2 ) whose diameter is greater than or equal to D p by: N p (S, CL, D p ) = 10 |S|[log 2 10 (CL)-log 2 10 (Dp)] , (6) where S is the slope of particle size distribution, CL is the cleanliness level, D p is particle diameter in µm. This is a common way to describe the distribution of the contamination on an optical surface. The model is implemented in FRED optical software and is easy to use as it requires only a few parameters (λ, S, CL, etc. ) and relies on the properties of Mie scattering. Here we will calculate the cleanliness level using the definition of the CL parameter (the largest particle [in microns], which can be found on a surface of 0.
Scattering due to impact craters To calculate scattering due to impact craters in FRED software, we use two embedded BSDF models: Lambertian and ABg. The coefficients of these models were obtained from the fit of total Peterson's BSDF, as was described in section 3. To study the impact of micrometeoroid parameters on the final result, we consider a situation when the density of micrometeoroid follows the step distribution given in Fig. 2    the assumption that 100% of the ejecta contribute to the M1 contamination and should not be neglected in stray-light estimates caused by micrometeoroid impacts.
The modelling presented here should benefit from any future improvements in the experimental data, particularly when optical materials are used as targets for the hypervelocity impact experiments. The method is straightforward to apply, to modify,and it can be used for any space optical instrument with minor parameter changes. The code is available on GitHub. 26 The model can be used not only for reflective but for refractive optics as well. The final result of the scattered light calculation depends on the optical design of the telescope, in our case the beam expanding telescope that is required to transmit the emitted beam to the distant spacecraft.
Introduction Today, building IoT applications is more and more supported by tools as well as by standardized activities for networking, accessing, or controlling devices. Standards (e. g. , W3C Web of Things  [1] , OPC UA  [2] , or OneM2M  [3] ) allow the reliable development of integration and interaction mechanisms between IoT devices or platforms. No-/low-code tools enable the easy composition of devices and their functionalities to combine them on a higher level to IoT applications. For cloud and mobile environments, examples for such tools are "If This Then That" (http://ifttt. com) or Mendix (https://mendix. com). For the device-level, an example tool is Node-RED (http://nodered. org) that supports the IoT application development with a visual flow programming approach. Facilitating the IoT application development with such tools becomes a key enabler towards an IoT app economy  [4]  with novel business models.
As input for a rule engine, these patterns can be automatically monitored and enforced through our approach. As compared to traditional SDN network setups, this enables low-code and dynamic management of the underlying network without having to manually manage the configuration overhead of the Software-defined Networking (SDN) Controller. We thus provide mechanisms to develop distributed IoT applications under consideration of QoS constraints and for the flexible enforcement of these constraints. For the basis of the tool to create IoT applications, we utilize Node-RED, as it gains more and more traction in the industry, while still being open, extendable, and reliant on a large open source community. We abstract from the network and device specific properties by using the semantically-enriched format of Thing Descriptions  [5] , which are discoverable via a Thing Directory.
com). The platform provides a simple interface to create and execute cloud-centralized orchestrations of IoT service choreographies, however, it lacks systematic engineering support  [32]. The Node-RED (http://nodered. org) tool focuses also on usability and therefore follows a visual programming approach. Its browser-based editor can be easily used to connect IoT devices, APIs, and online services through an interactive drag and drop and wiring to an application flow. A shortcoming of Node-RED is that developed applications cannot be executed in a decentralized way, i. e. , all program logic designed in a flow is executed locally on one machine-even when defining flows in separate sub-flows or on different tabs. It is possible to view multiple Node-RED instances at a central machine and connect them via communication protocol nodes, e.
Similarly, the display image node is labeled with 'piA', which means that it is running on Raspberry Pi A. We could already connect the multipart decoder node and the display image node to create a distributed flow between Raspberry Pis A and B. However, with DNR only, no further specifications for the underlying networking can be made. Hence, we developed the DirectCom node, which is representing the network connection (see Figure  4 ). The main functionality of the DirectCom node is to create a UDP link between the source node on the left and destination node on the right. Using only the DNR without this extension, all communication (even the video data between the two nodes) happens via an MQTT server running in the background of DNR. The DirectCom node is running instances on all involved machines of the cluster (here: Raspberry Pi A and B).
These types of IoT application activity implementers are grouped under the general concept of a placeholder, which is accessible through a set of interfaces. Based on the above, language constructs are used to define an orchestration pattern. A textual representation of the model in the form of an EBNF  [48]  grammar is used as input to the Eclipse ANTLR4  [49]  plugin for the creation of a lexer and parser. In this way, any input can be checked for compliance with the defined grammar. For the sake of brevity, only a sample for the definition of a Placeholder is presented in Listing 1.
We have implemented this transformation in a Python (http://python. org) script using the networkx library. Then we start the graph conversion process. In the first step, a sequence is created from two nodes, causing a Sequence node consisting of two placeholders to be created. Then, a merge is created from three nodes, causing a Merge node consisting three placeholders to be created. Finally, another sequence is created from the Merge and Sequence nodes. The graph consists of only one node, so the transformation is complete. The shortened output looks like this: Additionally, to allow the monitoring of network configurations, we add properties to the graph. These properties are either static facts about the devices (such as available link bandwidth or processing speed), or need to be proven by the Pattern Orchestration Engine (such as required bandwidth, or maximum latency).
Automated Processing of Network Configurations An important requirement for implementing the pattern-driven management and adaptation of IoT applications is to support the automated processing of the patterns developed using the language described in Section 4. 3. To achieve this, the SPDI patterns can be expressed as Drools  [50]  business production rules, and the associated rule engine, by applying and extending the Rete algorithm  [35]. The latter is an efficient pattern-matching algorithm known to scale well for large numbers of rules and data sets of facts, thus allowing for an efficient implementation of the pattern-based reasoning process. A Drools production rule has the following generic structure: rule name <attributes>* when <conditional element>* then <action>* end Thus, herein Drools are leveraged to encode the relation between properties in SPDI and QoS patterns in a way that allows the inference of the activity properties required of the activity placeholders present in the orchestration of said pattern in order for the orchestration to have the SPDI property guaranteed by the pattern.
The IoT application transformed into the pattern language is communicated to the Pattern Orchestrator and is fed to an ANTLR4 lexer, parser, and listener. These three programs manage to create a Drools fact, i. e. , an instance of the corresponding Java class of the IoT application model, for every orchestration activity, control flow operation, or property. During this procedure, the ANTLR4 lexer recognizes keywords and transforms them into tokens. The created tokens are used by the ANTLR4 parser for creating the logical structure, i. e. , the parse tree. The main functionality of the ANTLR4 listener is to become aware of the node additions in the parse tree. Whenever such an addition takes place, the listener takes information from the tokens that were used for the creation of instances of the corresponding Java classes.