dataesr/scientific-paragraphs-categorization huggingface dataset:
9 Linezolid 2 48 92 2 33 94. 2 16 34 96 0 63 100 2 98 99 Tetracycline 22 28 92 - - - 43 7 98 46 17 76. 2 17 82 96. 9 Vancomycin 40 10 100 53 3 96. 4 16 34 98 0 63 100 - - - For the S. aureus DK dataset only, R, resistant and S, susceptible interpretations according to EUCAST clinical breakpoints were available. DK, Denmark; DE, Germany; BE, Belgium.
The University of Valladolid (UVa, Spain) has managed a calibration center of the AErosol RObotic NETwork (AERONET) since 2006. The CAELIS software tool, developed by UVa, was created to manage the data generated by AERONET photometers for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Ångström exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CAELIS. The AOD and cloud screening algorithms implemented in CAELIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CAELIS and AERONET are far below the expected uncertainty of the instrument, ranging in mean differences between -1.
3 × 10 -4 at 870 nm and 6. 2 × 10 -4 at 380 nm. The standard deviations of the differences range from 2. 8 × 10 -4 at 675 nm to 8. 1 × 10 -4 at 340 nm. The AOD and AE at nighttime calculated by CAELIS from Moon observations are also presented, showing good continuity between day and nighttime for different locations, aerosol loads and Moon phase angles. Regarding cloud screening, around 99. 9 % of the observations classified as cloud-free by CAELIS are also assumed cloudfree by AERONET; this percentage is similar for the cases considered cloud-contaminated by both databases. The obtained results point out the capability of CAELIS as a process-ing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.
Hence, in order to help in the management of the AERONET/ACTRIS calibration facility at Valladolid, a software tool called CAELIS was developed  (Fuertes et al. , 2018). It provides tools for monitoring the instruments, processing the data in real time and offering the scientific community a new tool to work with the data. For this purpose, CAELIS contains a database and a web interface to visualize raw data and metadata, provides processing of sky radiances, and supports the monitoring of the instrument performance. This tool is capable of detecting several technical problems with the network instruments through an automatic warning system based on the CAELIS metadata and products, which allows a quick response to detect and solve operation problems. In this framework, the calculation of the AOD is important because several checks can be applied to the data to ensure the reliability of the measurements.
, 2019) , including the cloud screening, which is necessary because AOD can only be derived when the Sun or Moon is not obstructed by clouds. We present the general framework for the AOD calculation (Sect. 2), and then the daytime (solar) and nighttime (lunar) algorithms are described in detail (Sects. 3 and 4). The latter includes a novel correction  (Román et al. , 2020)  that considerably improves the quality of the lunar retrievals. The cloud screening is described in Sect. 5, and finally the algorithm results are compared with the AERONET database (Sect. 6).
General framework for AOD calculation The calculation of aerosol optical depth in CAELIS is intended to provide this parameter for a number of instruments, i. e. , the photometers within the AERONET network that are calibrated at the University of Valladolid and routinely provide measurements to the CAELIS system. They constitute an operational network with about 40 active sites that deliver data in near-real time. Therefore, the algorithm needs to be robust and work in a large variety of circumstances, such any site location, different instrument types, incomplete ancillary information and with defective input data. The algorithm to calculate the aerosol optical depth is composed of two main parts. In the first part the algorithm searches in the database for all the meta-information about the photometer at the specific date and time, such as calibration coefficients, location and filters.
In the second part, the raw measurement data and the meta-information are used to calculate the AOD. Figure  1  shows the process followed to generate the AOD, and as can be observed, it requires a lot of ancillary information. All that information is being stored in the CAELIS database. Each photometer on any particular date and time is linked in one deployment ("installation") with all that ancil- lary information. In this installation the information related to the beginning and ending dates when the photometer was deployed at one site, as well as the coordinates of the site, is stored. Once the photometer location is known, the next step is to know the specific instrument configuration. In this step all the information related to the instrument type 1  , which could change from one deployment to another, is needed in addition to information about the interference filters of the photometer, e.
g. , the central and nominal wavelength of the spectral channels and the specific gaseous and water vapor absorption coefficients for those wavelengths. Then, the algorithm reads the photometer calibration (extraterrestrial signal at mean Earth-Sun distance) and the temperature correction coefficients. This information is provided by the Valladolid calibration facility to the AERONET database and is therefore identical to that used in the AERONET version 3 products. The calibration is then adjusted to the Earth-Sun distance for each observation date and time. The ancillary information needed for the processing is the local pressure and the column of absorbing gaseous species taken into account: ozone, nitrogen dioxide, carbon dioxide and methane. A detailed description of how this information is obtained by CAELIS for the specific date, time and location is provided in Sect.
At this point the first main part of the algorithm flow is concluded. A series of flags have been filled in relation to the obtained meta-information, and the algorithm enters the second part, in which the raw data on direct irradiance are processed. The workflow of the computation is shown in Fig. 2. As already mentioned, CAELIS stores all the data generated by the photometers that are calibrated at our facility. Therefore, the AOD algorithm only needs to get the raw data from the correct table of the database and use them to calculate the AOD for each measurement. This procedure must be repeated a number of times, which depends on the instrument type. In digital and triple Cimel photometers equipped with 10 spectral channels, a total of 30 measurements are collected in each AOD observation.
CAELIS database structure for AOD CAELIS is composed of a relational database, a processing module and a web interface  (Fuertes et al. , 2018). As indicated above, in this database we can find all the information required to compute the AOD. Thanks to the deployment records (installations) in the database, we can link, for a specific date, all the physical and logical information about each particular instrument and how it is (or was) configured. That means we can access the calibration coefficients of each spectral channel, the raw data and the filter specifications. All this information is stored in different tables of the database. Similarly, after running the AOD algorithm, all the information generated will be stored in different tables of the database. Specifically, two tables are designed to store all the AOD information.
One table stores the information that is common for all the spectral channels, including date and time, site, solar zenith angle, Earth-Sun distance, pressure, and algorithm version. All the information stored in the com-mon table is used to calculate the AOD for each channel. It also stores the derived Ångström exponent and PWV values. The second table stores the specific information for each spectral channel. That means we can find in this table the exact central wavelength of the filter, the calibration coefficient and the temperature correction of the channel, the specific absorption coefficient for gases, and the calculated values for the various components (total, Rayleigh, gaseous absorption and aerosol optical depth). According to the CAELIS database structure  (Fuertes et al. , 2018) , the AOD is a level 1 product ("direct product"); therefore, some redundant information is included in these tables in order to facilitate data extraction by users.
Computing Thanks to the processing chain of CAELIS, the near-real-time provision of AOD can be achieved. Every raw data file that is received in CAELIS activates a set of triggers. First of all, the AOD algorithm runs between the first and last measurement included in the data file. Once a first version has been calculated, the system checks whether the AOD has been generated using a pressure value obtained by meteorological analysis (Sect. 2. 3. 1) or if only standard pressure was available. If pressure from a meteorological analysis was not yet in the database, the system creates a new task (12 h later) to reprocess the data until analysis pressure data are available. Each file received by the system activates the task to calculate the cloud screening. This task runs the cloud screening algorithm for the full day, between 00:00 and 23:59 local time, even if the file does not cover the entire day.
A flag indicates if the current AOD value is calculated with a standard pressure or using GDAS pressure. This strategy to obtain pressure for all network sites is similar to the one followed by AERONET using NCEP/NCAR reanalysis data  (Giles et al. , 2019). Figure  3a  presents the scatter plot between the pressure from CAELIS and AERONET; the range of pressure values spans from 660 hPa at the Teide site (3570 m a. s. l. ) up to 1030 hPa at sea level sites. More than 180 000 pressure values used for AOD observations are compared in this plot, showing a high correlation between the two databases. The differences between local pressure calculated by CAELIS and AERONET are in general below 2 hPa, as shown in Fig. 3b , where we observe a mean difference of 0. 07 hPa and standard deviation of about 1.
Temperature correction The Cimel photometers are not stabilized in temperature during operation. In turn, the sensor head is equipped with a temperature sensor that allows correcting the measured signals with respect to a reference temperature of 25 • C. The correction is based on a laboratory characterization in a thermal chamber. Whenever a hardware element is changed in the photometer head (filter, detector, electronic card) a new thermal characterization is run for the instrument. The AERONET procedure for temperature characterization of the Cimel photometers is described in detail in  Giles et al. (2019). The information produced during these characterizations, i. e. , the temperature correction coefficients for each wavelength above 400 nm, is stored in the corresponding table of the CAELIS database.
4. For the O 3 climatology we use the multi-sensor reanalysis from  GOME-2, OMI and SCIAMACHY sensors between 1978 and 2008 (van der A et al. , 2010). An example, in this case the global values of O 3 for the month of May, can be seen in Fig. 5 , where higher ozone values are observed in the Northern Hemisphere, as expected in spring. The comparison between the climatology tables used in CAELIS and AERONET for NO 2 and O 3 is shown in Fig. 6  by means of frequency distributions of the differences. For NO 2 , the determination coefficient between CAELIS and AERONET is high (R 2 = 0. 978), and the mean of all differences (-0. 04 DU) highlights a small underestimation by the CAELIS database of AERONET climatology values, with a standard deviation around 0. 02 DU. In the case of O 3 , the scatter plot indicates very good correlation (R 2 = 0.
The voltage signal (V ) has a temperature correction following Eq. (  2 ). C 1 and C 2 are the coefficients for the thermal characterization that are stored in the database, and T is the temperature given by the sensor head during the measurement. V = V / 1 + C 1 (T -25) + C 2 (T -25) 2 (2) An absolute calibration (given by V 0 ) is required for AOD retrieval. In order to obtain the top-of-atmosphere instrument signal, the Langley plot method can be applied  (Shaw, 1983; Toledano et al. , 2018)  or the calibration can be transferred from a reference instrument by side-to-side comparison  (Holben et al. , 1998). This calibration is supposed to be constant over time except for the Earth-Sun distance Different air mass factors m are taken into account for the various species; the reason behind this is the different vertical distribution of the gases (O 3 is mainly stratospheric, CO 2 is uniformly mixed).
Hence, Eq. (  1 ) can be rewritten as V (λ) = V 0 (λ) • R -2 • e -[τ a (λ)•m a +τ R (λ)•m R +τ g (λ)•m g ] , (3) where the "a" subscript stands for aerosol, "R" for Rayleigh and "g" for gases. Finally, the aerosol optical depth (τ a ) can be directly calculated from Eq. (3) by τ a (λ) = - 1 m a • ln V (λ) V 0 (λ)R -2 -τ R (λ) • m R -τ g (λ) • m g. ( 4 ) The gaseous absorptions considered in the processing are O 3 , NO 2 , H 2 O, CO 2 and CH 4. The air mass for molecular (Rayleigh) scattering m R is taken from  Kasten and Young (1989) , whereas the Rayleigh optical depth is taken from the  Bodhaine et al. (1999)  formula and weighted with local pressure. The O 3 air mass is taken from  Komhyr et al. (1989). For aerosol and NO 2 , CAELIS uses m R and for water vapor (m w ) the formulation given by  Kasten (1965).
0, then this point is flagged as stand_alone. -In the case that the standard deviation (σ ) of AOD at 500 nm of the cloud-free remaining points in the day is larger than 0. 015, then the observations that exceed mean ±3σ in AOD or AE are labeled 3-sigma. A final step is done to recover observations with high spectral dependence in the case that AOD (870 nm) is larger than 0. 5 and the Ångström exponent (675-1020 nm) is larger than 1. 2. This prevents the removal of very high aerosol loading cases (occasionally with high temporal variability) due to biomass burning smoke and urban pollution  (Giles et al. , 2019; Smirnov et al. , 2000). The label applied in this case is "restoration" and it is equivalent to cloud-free, although very few data in our subset fulfill this condition. All flags mentioned above result in the measurement point not being considered cloud-free and allow us to identify in the database the reason for the rejection.
Thus, we can query the database for cloud-free data at a certain site and during a certain period, but we can also analyze the cloud-screened data and discriminate for any specific check. The full scheme as it has been described is applied to solar AOD data. For lunar observations, we have maintained the same analysis and thresholds, except for the aureole radiance check that cannot be performed at night. Further testing is needed to possibly refine the cloud screening algorithm for nighttime.
Another important aspect of the subset is the variety of Cimel photometer types. We have covered all generations of Cimel instruments (analog, digital and triple) and multiple versions (see Table  1 ). This feature involved consider- able work to ensure a flexible enough algorithm and adequate database construction so that all data can be consistently processed. In turn, we expect this experience will be of help in the addition of new photometer types to CAELIS. Thus, we have analyzed a large amount of data to have statistical strength in the comparison and cover multiple situations. The data set is used for validation of the daytime (solar) algorithm. This data set will also be used for cloud screening comparison (Sect. 6. 4), in which a variety of climate conditions is also crucial.
Cloud screening validation In this section we have compared the cloud screening performance for daytime AOD data only. The AERONET level 1. 5 (cloud-screened) data are used for this analysis. The procedure is very straightforward: we analyze the data with a confusion matrix in order to determine which data assumed to be cloud-free by the AERONET cloud screening algorithm are also flagged as cloud-free by CAELIS and vice versa. The other two possibilities, i. e. , that one algorithm indicates cloud but not the other one, represent the discrepancy between the two procedures. The confusion matrix C is such that C i,j is equal to the number of observations known to be in group i but predicted to be in group j  (Pedregosa et al. , 2011). Thus, in binary classification, the count of true negatives is C 0,0 , false negatives is C 1,0 , true positives is C 1,1 and false positives is C 0,1.
Note that level 1. 0 (unscreened data) in AERONET does not include all the measurements attempted by the photometers because many observations with raw signals that are too low or too variable do not qualify for AOD level 1. 0 compu- tation  (Giles et al. , 2019). For those data that passed this first requirement, the AERONET cloud screening will select the cloud-free cases and include them in the level 1. 5 database 2. According to the flagging system of our cloud screening algorithm in CAELIS, we have compared the cloud-free or restoration flags with the AERONET level 1. 5 database for all the investigated sites and time periods (Table  1 ). The cloud screening comparator links all the photometer observations (full triplet) from CAELIS with their correspondents in AERONET and stores the output in two different arrays, one for CAELIS and another one for AERONET.
The value of 1 will be stored in each database if the observation is cloud-free and 0 if it is not cloud-free. With those two arrays the confusion matrix has been generated and it can be seen in Fig. 9 , where we indicate the number of observations and the corresponding relative numbers in percent. In the confusion matrix the first row represents all the values that are cloud-free in AERONET, and the second row is for the cloud-contaminated data in AERONET. In the same way, the first column represents the cloud-contaminated data in CAELIS, and the second column includes the cloud-free data in CAELIS. More than 250 000 observations have been analyzed here. The results are clearly satisfactory, with more than 99. 8 % agreement in the classification. The number of points outside the main diagonal of the confusion matrix is marginal.
Conclusions The CAELIS software tool was primarily designed to assist in the management of the calibration facility for Cimel photometers at the University of Valladolid, associated with AERONET. It provides access to metadata information to users and intends to facilitate the daily operation of the photometers on-site, with the final aim of improving data quality. CAELIS already provides to users processing of sky radiances and a set of flags to monitor the instrument performance in real time. The AOD product now complements this tool. Moreover, the AOD is needed for exploiting remote sensing data with the application of inversion algorithms, like GRASP  (Dubovik et al. , 2014; Torres et al. , 2017). These are the reasons behind the development of an operational aerosol optical depth product and the necessary cloud screening algorithm.
The cloud screening schemes in CAELIS and AERONET agree in the identification of cloud-free and cloudcontaminated scenes in more than 99. 8 % of the more than 250 000 investigated cases. For future investigations, we will need to include a site with predominant biomass burning aerosol since this aerosol type was found to be insufficiently represented in the subset of data used to compare the cloud screening algorithms. This paper has shown the capability of CAELIS to provide AOD values and products with a similar accuracy as AERONET. The architecture of CAELIS is such that it can be applied to other instrument types or networks. The next planned step is to be able to assimilate and process photometers other than the Cimel. In this sense, we will be able to apply a common processing to data originating from different photometer types, each one with its own spectral channels and measurement sequence, for example.
Note that AOD retrieval and cloud screening algorithms differ for the different existing networks (AERONET, GAW-PFR, SKYNET). The modular approach has proven to be successful in adding several choices to the data processing or assimilating a variety of ancillary data. This will also help incorporate into the system any future improvements such as new gas absorption coefficients and the extraterrestrial spectral irradiance of the Sun and Moon. The flagging of data allows extracting in a powerful way a subset of data according to the desired criteria. Some of the steps in the cloud screening procedure are actually quality control flags. However, a full quality control of the AOD product is not implemented yet in CAELIS and will need to be developed. This approach is especially important for a robust operation of the algorithm and possible near-real-time applications.
Acknowledgements. The authors gratefully thank the AERONET and PHOTONS teams for the collaboration and support. The authors thank the  Spanish Ministry of Science, Innovation and Universities  for support through the  ePOLAAR project  ( RTI2018-097864-B-I00 ). Financial support. This research has been supported by the  Spanish Ministry of Science and Innovation  (grant no. RTI2018-097864-B-I00 ) and the  European Union 's  Horizon 2020 research and innovation program  (grant no. 871115 ). Review statement. This paper was edited by  Ciro Apollonio  and reviewed by three anonymous referees.
Application of Harmony Search algorithm in the e-waste collection on demand The method of mobile waste collection on-demand was presented in Fig. 1. A resident who would like to dispose of waste equipment request a collection from a WEEE collection company within specified time windows. After registering all requests for a certain day in a database it is necessary to perform optimization of routes. When the optimization is finished a driver of a collecting vehicle receives a sequence of the collection points. HS was proposed by  Geem (2000)  and assumes the storage of HMS sorted solutions (called harmonies, which are described with pitches, representing decision variables) in HM (harmony memory) structure. The procedure is based on iterative development of subsequent solutions and comparison of their values of objective function with the value of objective function of the worst harmony located in HM -if the created result is better, then it will replace the stored solution.
Transformative advances in molecular technologies, such as massively parallel reporter assays (MPRAs) and CRISPR screens, can efficiently characterize the effects of genetic and genomic variation on cellular phenotypes. Analysis approaches to date have focused on identifying individual genomic regions or genetic variants that perturb a phenotype of interest. In this work, we develop a wholistic framework (MPRAudit) to determine the global contribution of sequence to phenotypic variation across subsets of the entire experiment, opening the door to myriad novel analyses. For example, MPRAudit can reliably estimate the upper limit of predictive performance, the fraction of variation attributed to specific biological categories, and the total contribution of experimental noise. We demonstrate through simulation and application to several types of real MPRA data sets how MPRAudit can lead to an improved understanding of experimental quality, molecular biology, and guide future research.