dataesr/scientific-paragraphs-categorization huggingface dataset:
Use Case Definition The theoretical analysis of the DCP standard suggests that it is a good candidate to achieve standardized and flexible tool coupling solutions for ADS co-simulations. To investigate this in practice, a use case is designed incorporating a DCP compliant interface between a co-simulation master and an external co-simulation slave from the ADS domain. Fig. 1  shows an overview of the realized use case including the features of DCP and the selected tools linked to the raised requirements. A 3D vehicle and environment simulator with runtime rendering is chosen to be integrated as a DCP slave for this work as it represents one of the most prominent cases of tool-or platform-bound models required in ADS simulation, and there is a large diversity in available simulators. Based on RQ1, RQ2, RQ4, RQ6, and RQ7 the tool xMOD by FEV Software and Testing Solutions GmbH was selected as a co-simulation master.
For the role of the vehicle and environment simulation as well as 3D rendering software that is going to be transformed into a DCP slave, the open-source tool CARLA  [29]  is chosen. CARLA is tailored to the evaluation of automated driving functions. It simulates the road, infrastructure as well as vehicles and their motion which can be controlled manually by a SUT or by traffic behavior models. The scenery and traffic scenario are also rendered based on Unreal Engine  [30]. The virtual environment and objects can be captured by several simulated sensors  [9]. Bound to the Unreal Engine, CARLA's simulation models are incompatible with standardized model export and rely on tool or platform coupling solutions to be integrated with other models or software, which makes it a good fit for tool coupling solutions (RQ1).
Software Implementation The software implementation of the tool coupling consists of two main components, a communication interface in xMOD functioning as a DCP master and a client to control CARLA's simulation server and transform it into a DCP slave (see Fig. 2 ). DCP compliance of the client has been achieved by creating a reusable DCP wrapper as described in Sect. 4. 1. Subsequently, the original client which controls the server of the simulator is embedded into this wrapper. The communication interface that enables the master to act as a DCP master has been embedded into the structure of a proprietary C-code template for co-simulation slaves in xMOD. The main functionalities of the DCP master are analyzing an XML file to gather all necessary information on the DCP slave and providing communication with the DCP slave, as described in Sect.
DCP Wrapper The server of the vehicle and environment simulator CARLA can be controlled by a client via an API (see Fig. 2 ). This client is realized as a DCP slave. To make the solution tool independent, a DCP wrapper is implemented. The DCP wrapper provides the state chart structure of the slave and the communication in the form of protocol data units (PDU) as defined in the DCP standard. Each state is implemented as a class in Python. A super-state is realized via inheritance of functions and parameters. Each class in the DCP wrapper has the same main structure. It consists of three main functions. The first function, "init", is called after entering a state and sends a notification PDU to communicate the successful entering of the new state. Afterward the main function, "run", defines the state-dependent behavior.
Client Integration into DCP Wrapper To use the environment simulation, a client has to be implemented and connected to the simulator's server. The server runs the simulation itself and does all necessary calculations. The client controls the server and its settings. Also, it provides an interface to provide data for the calculations and receive data from the server. In order to read and set values in the simulation, a Python API is provided by the tool vendor to connect to the server during runtime. To simulate the vehicle, environment, and SUT, the client has to set up the simulation via the Python API accordingly to the use case. To follow the DCP standard, the client is embedded into the DCP wrapper. Thus, the phases of the simulator (Initialization, Running, Stopping) are mapped to specific DCP states.
This start condition depends on the parameters received by the master in the state "CONFIGURATION". The configuration parameters can be divided into two groups. The first group consists of DCP specific parameters such as resolution or sample time, which is defined as the fixed time step length used for the iterative calculation inside the DCP slave, transport protocol parameters and communication type. The second group is used to parametrize the DCP slave's functionality from the master and e. g. includes the sample time, number of ego vehicles, traffic participants, and map choice in the CARLA simulator. The execution time of the vehicle and environment simulation is controlled by the client via the Python API by triggering each calculation sample individually during runtime (CARLA synchronous mode).
Preparation of DCP Master The co-simulation master software xMOD is adapted to function as a DCP Master. To achieve the functionalities of a DCP Master, which are listed in Section 3. 2, the option of the software to include C-code into the co-simulation as a slave is used. As DCP does not provide a specific structure for the master, the C-code structure provided by xMOD is Fig. 4  Implemented DCP slave functionality mapped to co-simulation master functions (realized DCP states as defined in Ref. [15] ) used. The resulting architecture of the master interface is a series of states called in a specific sequence shown on the left-hand side of Fig. 4. During the "MdlInit" function all available data of the slave is stored in objects. This is done by an implementation of a new class that has objects and parameters for all possible data types of a DCP slave.
This concept provides the master with the data at any time after the initial analysis of the DCP slave description file. The master reads the XML file and extracts the information, where protocols and operating modes are supported by the slave and what kind of parameters, inputs, and outputs are available. This data is used to create sockets for communication with the slave. The master dynamically adapts to the number and type of inputs and outputs specified in the file and accordingly passes the data to other tools in the simulation platform. The messages sent to and received from the slave have to be in PDU format. To transform the data into PDUs and to extract it from PDUs, a function for each kind of PDU is implemented that writes the data into a buffer in accordance with DCP and returns a pointer to the message.
Demonstrator Scenario The co-simulation master and slave were executed on two different workstations to benefit from the performance increase with distributed platform support of DCP. The co-simulation master xMOD was executed on a workstation running Windows 10 as an operating system. The other workstation ran the DCP wrapper with the embedded CARLA client and the 3D vehicle and environment simulation on the CARLA server on a Linux operating system. These two workstations were connected via a local area network (LAN) to enable communication via TCP sockets. The SUT for the demonstrator was a cooperative adaptive cruise control (CACC) function. CACC regulates the velocity of a vehicle to keep the safety distance to the preceding vehicle based on sensor and vehicle-to-vehicle communication data.
The safety distance is defined as the time interval needed to close the gap to the preceding vehicle at the current velocity considering the reaction time  [3]. This model was programmed in MATLAB/Simulink, and multiple instances were imported into the co-simulation set-up as FMUs. The CACC models are used to control vehicles in the scenario simulation in CARLA. The output Fig. 6  Two exemplary test scenarios defined for DCP interoperability verification  [33]  variables from the vehicle and environment simulation processed by the DCP slave client are object-based, thus the world coordinates of each object are provided. The CACC model needs relative values such as a sensor would provide. Therefore an object-based sensor model is integrated into the co-simulation between the DCP communication interface and the CACC model in xMOD which results in the final model configuration (see Fig.
A detailed analysis to what extent the calculation speed is related to the interoperability interface has not yet been conducted as the speed is not only impacted by the interface, but also by the computation power of the machines, the complexity of the models, and the efficiency of the simulators. However, it was found that Python as a programming language used for the DCP slave and CARLA API slows down the overall simulation. This was proven by implementing an alternative client in C++ using CARLA's newer C++ API that showed a significant acceleration of the simulated time. The C++ implementation was not fully DCP compliant, but it provided proprietary communication for the presented test scenario using the same sample time and data exchanged at simulation runtime. In this specific scenario, the average absolute time to calculate a simulation step was reduced by approximately 30%, enabling faster-than-real-time simulation.
Critical Reflection This paper focuses on the application of the DCP standard in the development of automated driving functions with the example of a CACC controller. To prove the usability of the standard in this domain, a DCP interoperability interface was implemented and demonstrated in the use case of the CACC development. The scope is limited to the non-functional coupling of software tools for the realization of the use case. In particular, the DCP standard was implemented for the coupling of the co-simulation platform xMOD as a master with a 3D vehicle and environment simulation in CARLA as a slave. The non-functional compatibility and successful realization of a distributed co-simulation on two platforms were successfully demonstrated with the implemented toolchain. This illustrates how standardized, nonfunctional tool or platform coupling via DCP can be used effectively in co-simulation for virtual testing of ADS.
3 , center panel). In this case, dust emission is enhanced in areas with high FoO. The purpose of a source map scaling is to compensate for unrepresented processes and surface properties, which affect dust emission. The S01-S11 and K14 schemes are not scaled with any preferential source map. The physics of these schemes are assumed to account for spatial variations in the emitted dust mass, and the retrieved FoO map is only used as a mask defining the areas from which dust emission is possible as described above. An additional special feature of MONARCH is its ability to tag dust originating from natural and anthropogenic (agricultural) sources. For this purpose, the MODIS FoObased map is linked with fractions of anthropogenic land use, following the approach described in  Ginoux et al. (2012) , but using an updated land-use data set  (Klein Goldewijk et al.
Additionally, monthly MODIS LAI data  (Myeni et al. , 2015)  are available for use in the dust https://doi. org/10. 5194/gmd-14-6403-2021 Geosci. Model Dev. , 14, 6403-6444, 2021 Soil texture class information in MONARCH is obtained from the hybrid STATSGO-FAO data set at a resolution of 30 arc seconds (0. 0083 • )  (Pérez et al. , 2011). Additional soil information, such as on soil mineral content, is currently being implemented  (Gonçalves Ageitos et al. , 2021b). To aggregate soil texture data to model resolution, MONARCH utilizes a predominance approach; i. e. , the predominant soil texture class in each grid cell is applied to the entire cell.
Radiation and optical properties The model's radiation scheme is RRTM-G  (Iacono et al. , 2000 (Iacono et al. , , 2008)). MONARCH allows multiple options for setting the dust microphysical properties. In the longwave (LW), we assume refractive indices from the Optical Properties of Aerosols and Clouds (OPAC) data set  (Hess et al. , 1998)  and spherical particle shape. In the short-wave (SW), we use mineralogy-based refractive indices and non-spherical shapes. The multi-component Maxwell Garnett theory  (Markel, 2016)  is used to calculate refractive indices of internal mixtures of eight minerals  (Gonçalves Ageitos et al. , 2021a) , whose size-resolved proportions are estimated based on the mineralogical atlas from  Claquin et al. (1999)  combined with the brittle fragmentation theory of  Kok (2011a).
FoO values are slightly overestimated in the Arabian Peninsula in JJA and SON, and eastern north Africa in MAM and JJA. The modeled seasonal all-sky co-located FoO for each individual model run is shown in Fig. B2. The FoO obtained from the modeled clear-sky DOD coarse is generally larger than that obtained from the all-sky colocated DOD coarse. Due to the normalization with the number of days in the season for calculation of the FoO, differences in the frequency and location of clouds in MONARCH and MODIS directly impact the resulting FoO. Over dust source regions, MONARCH produces considerably fewer cloud pixels in its clear-sky product and hence a larger number of valid data values than are in the MODIS observations (not shown). As a result, the clear-sky FoO is based on a larger number of valid (high-DOD coarse ) values and is therefore larger than the MODIS and MONARCH all-sky colocated FoOs.
Acknowledgements. BSC co-authors acknowledge  PRACE (eFRAGMENT2)  and  RES  ( AECT-2019-3-0001  and  AECT-2020-1-0018 ) for awarding access to MareNostrum at the  Barcelona Supercomputing Center  and for providing technical support. Martina Klose ,  Carlos Pérez García-Pando ,  Sara Basart ,  Paul Ginoux ,  Catherine Prigent , and  Ron L. Miller  appreciated the opportunity for exchange and discussion at the  Institut Pascal at Université Paris-Saclay  with support of the program " Investissements d'avenir"  ( ANR-11-IDEX-0003-01 ). The authors thank all the principal investigators and their staff for establishing and maintaining the AERONET sites, as well as the MODIS mission scientists and associated  NASA  personnel for the production of the AOD, SSA, and AE data used in this study. Financial support.
This research has been supported by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement no. 789630. Martina Klose  has received funding through the  Helmholtz Association's Initiative and Networking Fund  (grant agreement no. VH-NG-1533 ). Jeronimo Escribano  and  Matthew L. Dawson  have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreements no. 754433  ( Jeronimo Escribano ) and no. 747048  (Matthew Dawson). BSC co-authors acknowledge funding from the following: the  European Research Council  ( FRAGMENT  (grant no. 773051 )); the  AXA Research Fund ; the  Spanish Ministry of Science, Innovation and Universities  (grant no. CGL2017-88911-R ); the  EU   H2020  project  FORCES  (grant no.
821205 ); the  CMUG-CCI3-TECHPROP  contract, an activity carried out under a program of and funded by the  European Space Agency (ESA) ; and the  DustClim  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate  and funded by  FORMAS (SE) ,  DLR  (DE),  BMWFW  (AT), IFD (DK),  MINECO  (ES), and  ANR  (FR), with cofunding by the  European Union  (grant no. 690462 ). Ron L. Miller  is funded by the  NASA Modeling, Analysis and Prediction Program  ( NNG14HH42I ). Jasper F. Kok  acknowledges support from the  National Science Foundation (NSF)  under grant nos. 1552519  and  1856389  and the  Army Research Office  (grant no. W911NF-20-2-0150 ). Yue Huang  has received funding from the  Columbia University Earth Institute Postdoctoral Research Fellowship  and from  NASA  (grant no.
Upon initial loading, the unconsolidated granular samples were held at a constant effective load of 6. 64, 6. 77 and 10. 5 MPa for samples S, SBS and SB, respectively, for 60 min. After an initial compaction of 9 %-18 %, a reference scan was acquired of the starting aggregate. The loading parameters were maintained throughout the experiments and consistently monitored over a total duration between 1089 and 1932 h. Following  Macente (2017)  and  Macente et al. (2018) , the conditions for the first experimental suite (SBS) were chosen to be similar which allowed comparison of the data with each other. For the second experimental suite (SB and S), the effective load was increased in order to increase the strain rate according to the rate law for diffusion-controlled DPC  (Spiers et al. , 2004).
Data acquisition During the experiment, the samples were scanned ex situ on the X-ray microtomography instrument at the School of Geosciences, University of Edinburgh, in regular intervals (for acquisition parameters, see Table  A2  in the Appendix). To enable this, the oedometers were unloaded and temporarily removed from the loading frames. In total, 19 scans (SBS sample), 10 scans (SB sample) and 5 scans (S sample) were acquired over a total duration of 1619, 1932 and 1089 h, respectively. At the beginning, scans were collected in shorter time intervals to image the rapidly progressing deformation within the first 200 h of the experiment. As the compaction slowed down, the intervals between each scan were gradually extended to monitor chemical compaction processes. The time-resolved 3-D data series obtained in this way were then combined into three 4-D data sets capturing the dynamic evolution of the porosity in the different samples.
Data processing After each scan, Octopus ® software v. 8. 9  (Dierick et al. , 2004)  was used to reconstruct the µCT data from the radiographic projections. The resulting stack of 2-D images, which contains a virtual representation of half of the sample, covers a volume of 5 × 10 3 µm 3. Each time step comprises two scans, vertically translated to cover the entire sample including the sample base and the top piston. Reconstructed image stacks were merged using the image processing software Avizo ® v. 9. 2. The relative shortening of the sample was calculated using ε t = L 0 -L t L 0 , (1) with the relative vertical shortening ε t at the time t, the initial sample height L 0 and the sample height L t at the time t. The height of each sample was determined from the merged µCT scans. The µCT data were prepared for analysis by pre-processing in ImageJ  (Schindelin et al.
NaCl redistribution measurements In order to quantify the amount of NaCl migrating within the sample, we used two different methods to isolate a pure NaCl signal from the rest of the sample. The first method is similar to the measurement of the 2-D porosity. For each slice along the loading axis of a microtomography scan, we discretely segmented the NaCl in the grey-scale image using Trainable Weka Segmentation for the pure NaCl sample and the Deep Learning Segmentation of Dragonfly software for both biotite-bearing samples. From the segmented data, we measured the relative area of NaCl in the two-dimensional image and calculated its proportion relative to the cross section of the sample. The results were plotted as relative NaCl content on the abscissa and the number of slices along the loading axis on the ordinate (Fig.
Digital volume correlation Digital volume correlation (DVC) was used to quantify deformation on the grain scale between pairs of consecutive 3-D µCT data sets using the Python packages SPAM  (Stamati et al. , 2020)  and TomoWarp2  (Tudisco et al. , 2017). While SPAM calculates the displacement field between two data sets, TomoWarp2 uses the latter as an input to calculate the strain field. In a first step, a grid of regularly spaced measurement points was defined in the reference image. Each of these points is the centre point of a cubic correlation window. A corresponding cube in the deformed image was transformed with a homogeneous deformation function in order to minimise a sum-of-squared-differences error function. The correlation parameters were individually tuned for each data series and are documented in Table  1.
Both samples show that, while the biotite content is indeed constant within the analysed subvolumes, the relative reduction of porosity is compensated by a relative increase in the NaCl content in these volumes. In the SBS sample, just over 10 % of additional salt was measured in the biotite-bearing layer, with a slightly lower increase in the SB sample. Mass balance analysis (Table  4 ) shows that no additional NaCl entered the samples; hence, pure redistribution of NaCl was observed in our experiments. The deviation of the mass calculated from binary data is for all three samples in the same order as the absolute error from image segmentation. The absolute values of deviatoric and volumetric strain were calculated from digital volume correlation for three different time steps. The top row shows the two microtomography scans which were compared to each other using digital volume correlation.
Acknowledgements. We would like to thank  Johannes Glodny  from  GFZ  for the provision of sieved biotite mineral separates. Alex Hart  and  Ivan Febbrari  are thanked for technical support. We also kindly thank the two anonymous reviewers for their helpful reviews. Financial support. This work was financially supported be the  School of Geosciences, The University of Edinburgh. Review statement. This paper was edited by  David Healy  and reviewed by two anonymous referees.
We investigate the impact of different covariance estimates on RNA-Seq data classification. Results: The performances of qtQDA classifier based on two different covariance matrix estimates are compared over two real RNA-Seq data sets, in terms of classification error rates. The results show that using local dependence function approach yields a better estimate of covariance matrix and increases the performance of qtQDA classifier. Conclusion: Incorporating the true/accurate covariance matrix into the classification model is an important and crucial step particularly for cancer prediction. The local covariance matrix estimate allows researchers to classify cancer patients based on gene expression profiles more accurately. R code for local dependence function is available at https://github. com/Necla/LocalDependence.
Since it is known that RNA-Seq data sets are composed of many genes which are highly correlated with a high dependence degree, we claim that new samples will have an individual impact on the estimation of the covariance matrix while classifying the new samples. For this purpose, in this study, we propose a new type of covariance matrix estimate, which is called local covariance matrix, that can be implemented in qtQDA classifier. Integrating this new local covariance matrix into the qtQDA classifier improves the performance of the classifier. In this study, since the local covariance is updated for each new sample observation with a newly proposed method, the classifier, qtQDA, becomes an adaptive algorithm and we call it Local-quantile transformed Quadratic Discriminant Analysis (L-qtQDA).
Parameter Estimation In order to apply the model in practice, there exist some parameters to be estimated in the classification model. Now, we explain how they are estimated. • Negative Binomial Parameters(mean and dispersion). Like qtQDA, we use estimateDisp function in the R package edgeR. This function estimates mean using maximum likelihood and calculates a matrix of likelihoods for each gene at a set of dispersion grid points. Then weighted likelihood empirical Bayes method is applied to obtain posterior dispersion estimates for each gene  [7, 8]. • Covariance Matrix. We apply quantile transformation to produce multivariate normally distributed vectors to be used in evaluating the posterior probabilities. To do so, we need to estimate the covariance matrix for each class. Unlike Kochan et al.