dataesr/scientific-paragraphs-categorization huggingface dataset:
The second one is to reuse this modelling in a broader decision-making framework by rescue teams  (Apvrille et al. , 2017; Tanzi et al. , 2016). The processed data will be produced by a variety of sensors and deployed in networks for real-time collection  (Liu, 2000; Berger, 2002; Shukla, 2016). The data produced are of various types: distance data obtained by two ultrasonic sensors (flight time), distance data obtained by optical laser sensors (LIDAR), position and attitude data produced by inertial systems (accelerometers, magnetometers, gyroscopes, etc. ), odometer data, environmental data such as temperature, pressure, etc. This data will be combined to detect different elements of the environment. The usage of autonomous flying systems (e. g. , Drones), or autonomous rolling systems(e.
At the origin of the mission such knowledge is relatively weak. In this paper, we have proposed a way to increase it even before arriving at the intervention site relying on simulation. We have defined a set of steps that allow to validate the behaviour of an autonomous system (movement, data acquisition, etc. ) in a hostile environment as well as we define and validate the adaptation of the configuration of the payload sensors for the target mission. We have finally determined through simulations the quantitative parameters which characterize a LI-DAR, mounted on the rover ArcTurius which we used as a reference in this paper, in order to keep the energy consumption low while keeping the sensor adapted to the mission. Our future work aims to enhance the existing instruments to make them adapted with respect to disaster management.
Acknowledgements. We thank the  German URSI  for allowing us to present this work during  Kleinheubacher Berichte 2019 , Miltenberg, Germany, 23-25 September 2019 and for the publication of this paper in Advances in Radio Science journal. Review statement. This paper was edited by  Madhu Chandra  and reviewed by  Emre Colak  and  Madhu Chandra.
Similar to other visionbased localization and navigation methods, this process is prone to drift errors while estimating positions and attitudes of drones due to non-perfect sensory reading  [12] [13]. These drift errors would eventually build up, resulting in degrading flight and mapping accuracy. IMU data readings at a 1 kHz rate and image capturing at 30 images per seconds (30 Hz) would be ideal to keep drift errors as small as possible. However, the constrained computation and limited communication capabilities of drones would not allow such high sampling rates since the preprocessing and fusion of the sensor data in general and the image processing tasks in particular are computationally intensive. To address issues related to highly intensive computation tasks with low latency requirements, many previous research studies have resorted to computation offloading to powerful surrogate devices such as mobile edge computing (MEC).
The lack of such information can be explained by the poor quality of GNSS signal or its absence altogether (in indoor environment) or due to problems related to precision (in a city district with a very dense building constellation). In such a context, the drones are required to perform a process named visual inertial odometry (VIO) in order to achieve their mission. Similar to other vision-based localization and navigation methods, this process is prone to drift errors in estimating positions and attitudes of drones in case of non-perfect sensor data; these drift errors will result in degrading flight and mapping accuracy. IMU data readings at a 1 kHz rate and image capturing at 30 images per seconds (30 Hz) would be ideal to keep drift errors as small as possible. However, the constrained computation and communication capabilities of small drones would not be able to handle such high sampling rates since the processing and fusion of the sensor data in general and the image processing tasks in particular are highly compute intensive.
First, task T1 involves (i) IMU data reading, (ii) attitude and acceleration deduction, and (iii) IMU data preintegration. T3 repeats the same instructions as in T1 but adds at the end of each cycle a "new pose estimation" routine. Data transferred in C1 encompasses: (i) the latest key frame image and (ii) up-to-date IMU data. The data sent in C1 will serve as input for T3, where the following operations are executed: (i) feature detection, (ii) feature tracking, and (iii) pose estimation and optimization. C2 will incorporate data about the newly estimated pose. Finally, the drone would.
Postprocessing scripts The scripts to postprocess the observations and model results in order to calculate the model evaluation measures or to create the figures are provided. These scripts are written in R and python. General postprocessing of the different simulations including unit conversions, averaging over selected time periods, extraction of grid points nearest to the station location: • ONLINE_PLOT_HongKong_HW2009_CLASSICAL. R • ONLINE_PLOT_HongKong_HW2009_Final. R (for the NEW coupling approach) • ONLINE_PLOT_HongKong_HW2009_SURFFLUX. R • ONLINE_PLOT_HongKong_HW2018_CLASSICAL. R • ONLINE_PLOT_HongKong_HW2018_Final. R (.
Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically-consistent deep neural network architectures is an open issue. In the spirit of physics-informed NNs, PDE-NetGen package provides new means to automatically translate physical equations, given as PDEs, into neural network architectures. PDE-NetGen combines symbolic calculus and a neural network generator. The later exploits NN-based implementations of PDE solvers using Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to generate physics-informed NN architectures. They provide computationally-efficient yet compact representations to address a variety of issues, including among others adjoint derivation, model calibration, forecasting, data assimilation as well as uncertainty quantification.
g. PDE-Net  (Long et al. , 2017 (Long et al. , , 2018)). These previous works emphasize the connection between the underlying physics and the NN architectures. Designing or learning a NN representation for a given physical process remains a difficult issue. If the learning fails, it may be unclear to know how to improve the architecture of the neural network. Besides, it seems irrelevant to run computationally-1 https://doi. org/10. 5194/gmd-2020-35 Preprint. Discussion started: 2 March 2020 c Author(s) 2020. CC BY 4. 0 License. expensive numerical experiments on large-scale dataset to learn well-represented processes. The advection in fluid dynamics may be a typical example of such processes, which do not require complex non-linear data-driven representations. Overall, one would expect to accelerate and make more robust the learning process by combining, within the same NN architecture, the known physical equations with the unknown physics.
Note that code generator based on symbolic computation receives new interests to facilitate the design of numerical experiments see e. g. Louboutin et al. (2019). As an illustration, we consider in this paper the application of PDE-NetGen to the identification of closure terms. The paper is organized as follows. In the next section, we detail the proposed neural network generator, with an illustration of the workflow on a diffusion equation. In section 3, we present the numerical integration of the neural network implementation of the diffusion equation then an application to the data-driven identification of the closure of Burgers equation. Conclusion and perspective are given in section 4.
Workflow of the NN representation generator We now present the workflow for the NN generator given a symbolic PDE using the heterogeneous 2D diffusion equation as a testbed: ∂ t u = ∇ • (κ∇u) , (3) where κ(x, y) is a field of 2 × 2 tensors ((x, y) are the spatial coordinates) and whose python implementation is detailed in Fig. 1. Starting from a list of coupled evolution equations given as a PDE, a first preprocessing of the system determines the prognostic functions, the constant functions, the exogenous functions and the constants. The exogenous functions are the functions which depends on time and space, but whose evolution is not described by the system of evolution equations. For instance, a forcing term in a dynamics is an exogenous function. For the diffusion equation Eq. (  3 ), the dynamics is represented in sympy using Function, Symbol and Derivative classes.
The dynamics is defined as an equation using the Eq class of PDE-NetGen, which inherits from the Eq class of sympy with additional facilities (see the implementation in Fig. 1  for additional details). The core of the NN generator is given by the NNModelBuilder class. This class first preprocesses the system of evolution equations and translates the system into a python NN model. Other boundaries could also be implemented and might be investigated in future developments. All partial derivatives with respect to spatial coordinates are detected and then replaced by an intermediate variable in the system of evolution equations. The resulting system is assumed to be algebraic, which means that it only contains addition, subtraction, multiplication and exponentiation (with at most a real). For each evolution equation, the abstract syntax tree is translated into a sequence of layers which can be automatically converted into NN layers in a given NN framework.
For the current version of PDE-NetGen, we consider Keras  (Chollet, 2018). An example of the implementation in Keras is shown in At the end, a python code is rendered from templates by using the jinja2 package. The reason why templates are used is to facilitate the saving of the code in python modules and the modification of the code by the experimenter. Runtime computation of the class could be considered, but this is not implemented in the current version of PDE-NetGen. For the diffusion equation (3) detailed in the previous section. Then, we tackle a situation where a part of the physics remains unknown, showing the benefit of merging the known physics in the learning of the unknown processes.
The time step is dt = 0. 002, and the dynamics is computed over 500 time steps so to integrate from t = 0 to To train the parameters (a, b, c) in Eq. (  7 ), we build a training dataset from an ensemble prediction method where each member is a numerical solution of the Burgers equation. The numerical code for the Burgers equation derives from PDE-NetGen applied on the symbolic dynamics Eq. (5). Using this numerical code, we generate a training dataset composed of 400 ensemble simulations of 501 time steps, where each each ensemble contains 400 members. For each ensemble forecast, we estimate the mean, variance V u and diffusion tensor ν u. Here, we focus on the development of the front where we expect the unknown term to be of key importance and keep for training purposes the last 100 time-steps of each ensemble forecast.
For the training only, the RK4 time-scheme is computed as the ResNet implementation given in Fig. 5 , so to provide the end-to-end NN implementation of the dynamics. The resulting dataset involves 40000 samples. To train the learnable parameters (a, b, c), we minimize the one-step ahead prediction loss for the diffusion tensor ν u. We use ADAM optimizer  (Kingma and Ba, 2014)  and a batch size of 32. Using an initial learning rate of 0. 1, the training converges within 3 epochs with a geometrical decay of the learning rate by a factor of 1/10 after each epoch. The coefficients resulting from the training over 10 runs are (a, b, c) = (0. 93, 0. 75, -1. 80) ± (5. 1 10 -5 , 3. 6 10 -4 , 2. 7 10 -4 ). In the general case, the choice of the terms to be introduced in the closure may be guided by known physical properties that need to be verified by the system.
Conclusions We have introduced a neural network generator PDE-NetGen, which provides new means to bridge physical priors given as symbolic PDEs and learning-based NN frameworks. This package derives and implements a finite difference version of a system of evolution equations, where the derivative operators are replaced by appropriate convolutional layers including the boundary conditions. The package has been developed in python using the symbolic mathematics library sympy and keras. We have illustrated the usefulness of PDE-NetGen through two applications: a neural-network implementation of a 2D heterogeneous diffusion equation and the uncertainty prediction in the Burgers equation. The later involves unknown closure terms, which are learned from data using the proposed neural-network framework.
Acknowledgements. The  UML  class diagram has been generated from  UMLlet Auer  et al. (2003). This work was supported by the  French national program LEFE/INSU (Étude du filtre de KAlman PAramétrique, KAPA). RF has been partially supported by  Labex Cominlabs  (grant SEACS),  CNES  (grant  OSTST-MANATEE ) and  ANR  through programs EUR Isblue, Melody and OceaniX. 12 https://doi. org/10. 5194/gmd-2020-35Preprint. Discussion started: 2 March 2020 c Author(s)  2020. CC BY 4. 0 License.
Results and Discussions As shown in Figure  13a , the proposed filter is fabricated on a Rogers Duroid 5880 (Rogers Corporation, Chandler, AZ, USA) substrate with a dielectric constant of 2. 2, thickness of 0. 508 mm and loss-tangent of 0. 0009. The frequency response of the prototyped dual-band BPF is measured by the Agilent N5230A (Agilent Technologies, Santa Clara, CA, USA) network analyzer. The proposed filter simulation was performed using the Advanced Design System 2011. 10 (ADS) software, and the EM simulated and experimental results are compared in Figure  13b. According to the measurement, the filter has two passbands with the center frequencies of f1 = 4. 42 GHz and f2 = 7. 2 GHz, and 3 dB bandwidths of 94 MHz and 83 MHz, respectively. Insertion and return losses at f1 and f2 are about 0.
Results and Discussions As shown in Figure  13a , the proposed filter is fabricated on a Rogers Duroid 5880 (Rogers Corporation, Chandler, AZ, USA) substrate with a dielectric constant of 2. 2, thickness of 0. 508 mm and loss-tangent of 0. 0009. The frequency response of the prototyped dual-band BPF is measured by the Agilent N5230A (Agilent Technologies, Santa Clara, CA, USA) network analyzer. The proposed filter simulation was performed using the Advanced Design System 2011. 10 (ADS) software, and the EM simulated and experimental results are compared in Figure  13b. According to the measurement, the filter has two passbands with the center frequencies of f 1 = 4. 42 GHz and f 2 = 7. 2 GHz, and 3 dB bandwidths of 94 MHz and 83 MHz, respectively. Insertion and return losses at f 1 and f 2 are about 0.
We further developed a set of test vectors based on log files of a vehicle enriched by different intrusions including simulations of stealthy intrusions, which only mask certain sensor values within specific CAN messages and are therefore difficult to detect. The effectiveness of the proposed methods is demonstrated by a comparative study of the behavior of the two detection models with respect to input structured in byte boundaries versus structured by the method from  [6]  and the optimized CFC algorithm. This approach is aimed to generate generic intrusion detection models, that do not rely on information only available to manufacturers. Therefore this kind of approach might enable third party intrusion detection systems for a wide variety of vehicles or assist in research for the classification of malicious messages in data for non-automotive areas.
Background and related work Despite the trend to use Automotive Ethernet in recent in-vehicle architectures, CAN bus is still in use and can be utilized to attack modern vehicles  [4]. The standardized automotive open system architecture (AUTOSAR) specified a module for secure onboard communication (SecOC) to check the authenticity of protocol data units. SecOC adds a message and freshness value to each frame. Due to the limited frame size of 8 byte in classical CAN, only a very small part of the MAC and freshness value can be used in cases where the payload does not use the whole frame. The MAC could also be sent in an extra frame, however, this is not accepable in many cases because of the impact on the busload. SecOC also has subtle problems with synchronisation on CAN  [7]. Thus, SecOC is of limited use on classical CAN bus  [8] , and in fact we have not found any SecOC artifacts in the log files which we used for our work.
As a result of this not yet adopted authentication of ECUs, it is possible to launch impersonation attacks, where one ECU -which is already controlled by an attacker -sends messages that utilize an ID of another ECU. Most current research approaches on the detection of such ECU impersonating attacks use some kind of physical fingerprinting by voltage or timing analysis with specific hardware  [9, 10]. Said approaches seek to mitigate the problem of CAN message authentication and thus are complementary to the work presented here. The detection of specification violations is another possibility to detect attacks. The work on specification based intrusion detection methods uses, for example, checks for formality, protocol and data range  [11] , a specific frequency sensor  [12] , a set of network based detection sensors  [13] , or state machines  [14].
Attack simulation for model training and testing Each CAN data frame contains its own ID, the length of data in bytes and 0 to 8 bytes of data. The data is not necessarily byte aligned and does not contain a fixed amount of distinct values. In case a data base CAN (DBC) file is available, a recorded log file can be split into the different sensor values occurring therein. The formats for all CAN bus values values are not standardized and differ from one vehicle to another, especially in between manufacturers. Since this information could assist intruders in finding exploits and gaining access to otherwise locked systems, DBC files and the accurate division of data are often kept secret. Due to this a multitude of intrusion detection approaches such as Chevalier et al. [5]  or Taylor et al.
[31]  utilize a byte-wise or bit-wise view on the data. This may lead to a distortion of the data and might make it harder to detect intrusions. Therefore, a reverse engineering approach for message interpretation is evaluated as a focus of this work. For our research we use an approximately 10 minutes long log file recorded from a Renault Zoe electric car. This log file was recorded in an urban environment with mixed sections of driving up to about 30km/h and standing still. All in all this log contains 1 million messages with 110 different IDs being used. The original data set contains no intrusions and was previously used in process mining  [22] , for machine learning methods using neural networks  [27]  and characteristic functions  [5]. In the following we will reference and use two specific IDs from the recorded log file as examples and targets for directed attacks.
Test vectors The direct modification of any data streams within a vehicle, either by integrating new intrusion detection tools or by inserting attack messages, may damage the vehicle and risk physical hazards, e. g. , high voltage, or airbag explosives, which may harm the life and limb of researchers  [33]. Thus, for our work we used log files taken from an electric car as described above and composed a set of synthetic logs with different inserted attack messages in order to test against different scenarios. Stationary In addition to the main log used in training a second log has been recorded on standing vehicle. This log file is 188. 3s long in total and contains 355,957 messages in total. There were no intrusions applied to this log file. Fuzzy A randomized denial of service intruder. Over the course of the log file a total of 32.
Field alignment evaluation Using the original and the Continuous Field Classification algorithm two alignment files were generated from a clean version of the recorded log. In Fig. 1a  and 1  b the splittings of the fields of the messages with ID 0x5d7, which is used in our Speed test vector, and ID 0x29a, which is used in our RPM_Front_Left test vector, are shown. For the field classification algorithm the original scoring method  [6]  and our Continuous Field Classification method are separately depicted. The last row of the tables shows for comparison the field separation retrieved from a DBC file which was extracted from the open source software project CanZE  [34]. The numbers displayed in each segment are the fields width in bits, whereas the upper row corresponds to a byte-alignment where every segment is 8 bits wide.
Parameters for the Adam optimizer were left as default in the Keras  [36]  implementation. A vector containing the messages payload split into eight fields is used as model input. In addition, we utilize the previous messages payload from the same ID formatted in the same way, as well as the messages identifier. For this portion of the training data the previous message of the same ID is chosen regardless of whether it is an intrusion or valid message. Additionally a set of all valid messages concatenated with the previous valid messages is used as training data. This is done to not only train on an intruded log but also on a clean log file. Other information from the message such as the Data Length Code (DLC) or the time stamp are disregarded for this test.
Acknowledgment This research work has been funded by the  German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research  and the Arts within their support of the  National Research Center for Applied Cybersecurity ATHENE  and by the  German Federal Ministry of Education and Research  in the context of the project  VITAF  ( ID 16KIS0835 ). This work was financially supported by  Government of Russian Federation  (Grant  08-08 ).
In the next step, we performed more detailed simulations to reveal the spectral detuning of the resonances by the refrac- tive index changes of the bottom dielectric n h and distance g between the gold NPs and NHA. These simulations represent the expected effect of the swelling and collapsing of the pNIPAAm-based hydrogel cushion. In general, an increase in swelling is assumed to be accompanied with a prolongation of distance g, a decrease in polymer volume content, and consequently a decrease in the refractive index n h. Fig. 5a  and b  show that the refractive index n h gradually increased from 1. 38 to 1. 46 and distance g varied between 50 and 100 nm, respectively. The increase in the substrate refractive index n h led to a redshift for all three resonances (Fig. 5a ), which is consistent with the experimental data presented in Fig.
Morphological characterization Atomic force microscopy (AFM) measurements of the patterned structures in air were performed in tapping mode using PPP-NCHR-50 tips (Nanosensors, Switzerland) and a PicoPlus instrument (Molecular Imaging, Agilent Technologies, USA). In addition, a scanning electron microscope (Zeiss Supra 40 VP (Carl Zeiss Microscopy GmbH, Germany) was used for imaging of the longitudinal and cross-section interfaces of the nanostructures at an electron high tension of EHT = 5 kV. The height, diameter and lateral spacing of the nanoscale features were determined using the Gwyddion free software (version 2. 47 from gwyddion. net).
Tracking of resonant wavelengths Polychromatic light emitted from a supercontinuum laser source (WhiteLaser Micro, Fianium, UK) was collimated and the beam was expanded and spectrally filtered by a long-pass filter. The beam was made incident at a normal angle on the structure mounted in the temperature-stabilized flow cell. The transmitted beam was collected by a GRIN lens to a multimode fiber and delivered to the input of a spectrometer (S2000, Ocean Optics, USA). The acquired transmission spectra were normalized to that measured for a reference flat gold film (thickness of 50 nm) and analyzed using the SPR UP software developed at the Institute of Photonics and Electronics, Czech Academy of Sciences.