dataesr/scientific-paragraphs-categorization huggingface dataset:
We study the structure interacting through moving load using Modelica language. The beam structure carried one or more subsystems which are modeled as simple degree of freedom systems. The excessive vibration of the subsystem is controlled using a dynamic vibration absorber. The paper is organized as follows: the governing equations of a flexible beam and a connected subsystem will be derived in Section 2. Section 3 presents the methodology for modeling and control design of such multibody system. Numerical examples of the proposed methodology appear in Section 4. Section 5 concludes the paper.
Modelica implementation The motivation of using Modelica is the object-oriented language characteristic for modeling complex, large, and heterogeneous physical systems. It is designed to support library development. For handling complexity, reuse is a key issue. Algebraic, differential and discrete equations are used in Modelica for modeling the physical phenomena. A flexible multibody system has been developed in Modelica using the Dymola simulation environment (see Fig. 4 ). The system model includes elements developed specifically for this application such as the Beam component and the Excitation source component as well as parts that belong to Dymola/Modelica library. The system is then comprised of a flexible beam, an excitation source, a subsystem, a vibration absorber and sensors. The interaction between the different components is described using connectors.
The beam element is developed in-house based on the analytical model formulation presented in Section 2. The Beam model is parameterized such that the user has the ability to easily enter the beam characteristics. Its parameters include the Young's modulus E, the beam length l and the mass density r, etc. It has been implemented with two point connectors. Excitation source object represent a vibration source moving along the flexible beam with constant magnitude and velocity. The user has the ability to modify its parameters. The beam is mechanically connected to the excitation source. We have developed a new connector in order to connect the excitation source to the flexible beam. The code of the new connector is: The Beam component contains a Flange type connector (as it is called in Modelica).
Validation In order to check the accuracy of the present model, some results obtained by Modelica/Dymola are compared with those available in the literature. The parameters used in the simulation are shown in Table  1. Table  2  lists the first three natural frequencies along with analytical results and those obtained by the finite element method used Matlab code  [27]. In  [27] , authors used fifty identical finite elements in order to study the dynamic behavior of a beam carrying a moving load. The beam is made of steel and square in cross section. The accelerating mass is modelled as moving finite element to include gravitation force and inertial effects of mass. The natural frequencies obtained by the two models are closely the same. Results are then in good accordance. For verification of the proposed model, an example of a bridge crane system is considered in this paper.
Conclusion In this paper, a new predesign approach to study the flexibility influence of a multibody system on its dynamic behavior is presented. To obtain quick resolutions, analytical method is approached to study the system response. Analytical models are developed using the object oriented modeling approach with Modelica language. The developed models use standard connectors to the compatibility with Modelica Standard library components. Our approach illustrated through an example of a system. In fact, the dynamic model of elastic beam under the action of a moving load and carrying attached subsystems is established based on mechatronic system approach. A dynamic vibration absorber is proposed to attenuate the excessive vibration amplitude of the subsystem. A variety of numerical results are shown to draw important conclusions for the purpose of system design.
We presented an innovative hydrogeophysical approach that allows numerical modeling of water flow in a variably saturated media. In our model, we approximated the subsurface by horizontally stratified porous media. The model output was a time varying water content profile. Then, we compared the water content provided by the model with the water content measurements carried out using the time-lapse Magnetic Resonance Sounding (MRS) method. Each MRS sounding provided a water content profile in the unsaturated zone down to twenty meters. The time shift between the profiles corresponded to the time lapse between individual MRS soundings. We minimized the discrepancy between the observed and the modeled MRS signals by varying hydraulic parameters of soil layers in the water flow model. For measuring and processing MRS data, we used NUMIS MRS instrument and SAMOVAR software.
, 2012; Legchenko et al. , 2014). In this paper, we presented results of a methodological study carried out with the goal to investigate the possibility of combining time-lapse MRS measurements with a water flow modeling in variably saturated media. For developing the unsaturated water flow model, we used MRS time-lapse measurements of the water content in the unsaturated zone. MRS averages the water content over the volume investigated with the MRS loop thus defining the scale of the water flow modeling. We build a model using available software based on the Richards equation (HYDRUS-1D). This model aimed to reproduce time varying water content observed with the MRS. Because of the subsurface in the investigated area is heterogeneous the hydraulic parameters of the synthetic layers in our model may not correspond to the hydraulic parameters of the real subsurface and did not represent the target for the hydraulic modeling.
We used the log-normal distribution model allowing faster computing when using HYDRUS-1 software  (Kosugi, 1996 ) ) 0 ( , ) 0 ( , 1 2 ) / ln( 2 1 0 ≥ <                  = - - = h h h h erfc S r s r e σ θ θ θ θ. (2) where e S is the effective water content, erfc is the complementary error function, 0 h is the median metric head, 0 > σ denotes the standard deviation of the log-transformed soil pore radius and characterizes the width of the pore-size distribution. The unsaturated hydraulic conductivity is  (Mualem, 1976 ) ) 0 ( , ) 0 ( , 2 2 ) / ln( 2 1 ) ( 2 0 ≥ <                    + =                 h h K h h erfc S K h K s e l s σ σ , ( 3 ) where s K is the saturated hydraulic conductivity and l is the pore connectivity parameter. Both the water content and the hydraulic conductivity are scale dependent parameters.
RESULTS Between 26 April 1999 and 15 March 2000 the Bureau de Recherches Géologiques et Minières (BRGM, France) performed a one-year monitoring of the water content in Villamblain test site (Fig. 3 ). We used these data to investigate the possibility of the unsaturated flow modeling using time-lapse MRS measurements. The data set comprised 34 MRS soundings at 18 different dates  (Boucher et al. , 2003). With a few exceptions, we carried out two soundings per date aiming to avoid errors due to unexpected technical problems. We used the NUMIS instrument with a 75×75 m 2 square loop and the maximum pulse moment of 12,500 A-ms. The Larmor frequency was 2011. 3 Hz. The ambient electromagnetic noise varied between 150 and 300 nV being larger in spring. For interpreting MRS measurements, we used SAMOVAR software developed by the authors.
We considered the local Earth's magnetic field (47,214 nT; 63°N), and the resistivity of the subsurface provided by the electrical resistivity tomography (ERT) measurements  (Jodry et al. , 2018). For the unsaturated water flow modeling, we used HYDRUS-1D software  (Šimůnek et al. , 2008a; 2008b). Fig. 3  shows the location map of the monitoring site. The Villamblain test site covers about 7 km 2 in the western part of the Loire River subcatchment. This area is a part of the Beauce aquifer (9,000 km 2 ) located south-west of Paris. The average altitude of the Beauce aquifer is 140 m and a regional hydraulic gradient of about 0. 1%. The mean annual recharge is 110 mm  (Schnebelen et al. , 1999). Developed agriculture with irrigation in summer is widespread in the area  (Desprer and Megnien, 1975; Bruand et al.
-1°29'30"E) provided the daily rainfall data. Figures  4a  and 4b  show the annual rainfall and the groundwater level (GWL) variations between 1995 and 2005. We calculated the effective rainfall (ERF) as a difference between the rainfall (RF) and the actual evapotranspiration (AET). We have no data for calculating AET, and we simplified the estimate using the Penman potential evapotranspiration (PET)    Fig. 6  shows the results of a single MRS sounding (performed 13/08/1999). Figures  6b  and 6c  compare the water content and the relaxation time profiles with the lithological log of the borehole 03622X0119/FP3-25 (Fig. 6a ). The distance between the borehole and the MRS loop located was 76 m. The resistivity profile was extracted from the ERT measurements. The MRS log (Fig. 6b ) showed a smooth increase of the water content from the surface to the GWL.
ACKNOWLEDGEMENTS The authors carried out this study in the laboratory  IGE -IRD (Grenoble)  with the support of the  BRGM  and the IRD research programs. We acknowledge financial support from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ) and the  French National Program (ANR)  " Investment for Future -Excellency Equipment " project  EQUIPEX CRITEX.
Need of a new approach The previous observation has given birth to few recent contributions presented below. In  (Koo et al. , 2018) , the authors argue that traditional methods of safety study fail to take into account the versatility of "Plug-and-Produce" systems, confirming our previous assumptions  (Sallez et al, 2017). The paper identifies challenges for implementing a software solution for assessing the safety of Plug-and-Produce systems. The authors propose some challenges to face high changing dynamics of reconfigurable systems and safety concerns resulting from interactions between reconfigurable components. They argue that the risk assessments and the safety analysis, which are done manually, need to be stepwise automated. (Koch, 2019)  points out equally that safety can be a bottleneck on the way to changeable manufacturing systems.
ON-GOING WORKS Our team aims the deployment of the proposal according two ways: A software tool is currently under development to support the "off-line" design of the "safety" bubble. This software is based on the use of the multi-agent Netlogo platform  (Wilensky 1999). This environment was chosen because it easily supports the chosen modelling method and provides an intuitive, well-documented programming language with an elegant graphical interface. In NetLogo, each agent operates on a grid of patches (i. e. , a cellular world), and each agent can read and modify some of the patch attributes in its vicinity. This agent-environment interaction is well appropriate to support the simulation of safety areas. As depicted on Figure  4  and in accordance with the proposed methodology, this software tool is able to automate the building of the safety bubble.
In recent years, deep learning based approaches have been proven to offer efficient solutions to problems that are characterized with complex uncertain inner relationships between inputs and outputs. It has been widely utilized in multiple areas in the context of intelligent 5G network: such as mobile data analytics  [19] , network traffic control  [20] , resource management  [21] , MEC task offloading  [22]  and network optimization  [23]. However, to the best of our knowledge, no previous work has studied the MEC planning problem with uncertain resource demands by learning based approaches. The uncertainty comes from the facts that user demands are stochastic variables with arbitrary pattern. In this paper, we aim to address the problem of MEC edge servers planning with uncertain resource demands by the means of a learning-based approach.
We utilize this framework to practically solve this problem by accelerating the searching in the solution space. We also discuss the relevance of applying this mechanism in practical usages with sampled arbitrary BS demands data to highlight the pragmatism of our approach. To evaluate the performance of the learning-based framework on planning MEC servers with realistic BS demands, we conducted intensive simulations: a set of small network scale simulation with lognormal BS demands, and a set of real-data driven large network scale simulation with historical BS demands. The results show the effectiveness of our solution on planning edge servers with arbitrary BS demands by comparing to several baseline algorithms. The remainder of this paper is structured as follows. The related research works are discussed in Section II.
E. PRACTICAL RELEVANCE This learning-based framework is general in solving a series of practical problems with uncertain input parameters. In practice, historical BS demands data can be utilized in the place of the samples generated in the stochastic simulation part to estimate the effective expectation of demands-weighted distance C(x, y, z|V ), which essentially solve problems with real settings and arbitrary demands distributions. In Section V, we conducted two sets of simulations: a small scale simulation with samples generated through stochastic simulation method; and a large scale realtrace-driven simulation in which real BS demands data are utilized as samples. Moreover, multiple modules in the framework can be replaced by mechanisms with similar functionalities. For instance,  (1)  the GA in the framework can be replaced by other improved meta-heuristic algorithms to further improve the problem solving efficiency; (2) stochastic simulation can be substituted by other methods to estimate the value of the uncertain function; (3) the simplex algorithm can be replaced by other optimization algorithms as long as it can be used to solve the corresponding subproblems; and (4) the BP network Algorithm 2 Improved Genetic Algorithm Based on Neural Network Input: The potential feasible region R; The trained neural network in Section IV-C.
V. EVALUATION We implemented the proposed learning-based framework and designed experiments to evaluate the performance of the framework on planning MEC servers. We first show the performance of our framework in a small scale network with generated simulation data, then we test the effectiveness of our framework based on a real data set from Shanghai Telecom  [35]. We conduct the simulation on a PC equipped with Intel Core i3, 4 core processor, and 12 GB RAM and the software simulation environment of the proposed framework is based on ECLIPSE, MATLAB and IBM CPLEX. Stochastic simulation is implemented by java language, and IBM CPLEX is used in the third step of stochastic simulation to realize the simplex algorithm. GA and neural network is implemented with the MATLAB mathematical toolbox as it can reduce programming difficulty.
3) GENETIC ALGORITHM SETTING The improved GA is deployed to search the solution space to obtain the best locations for MEC servers. As described earlier in Section IV-A, MATLAB GAOT toolbox is used to implement GA. Normalized geometric selection was chosen to select as it has a shorter compilation time. We choose arithmetic crossover as the crossover procedure because point crossover is too simplistic to work effectively and arithmetic crossover procedure is usually the ideal crossover option for use in projects. Non-uniformly distributed mutation operator was chosen as the mutation operator because it is considered to function well with multiple variables. To present the solution convergence, we show the change of fitness function with the number of generations in Figure  5 , and the BS demands and MEC servers setting are the same in large scale simulation that will be introduced later.
Mapping developmental changes in the afferent connectivity of PV and SST interneurons In order to understand the areal differences in connectivity of PV and SST cINs within infragranular cortex across development, we restricted our analysis to layers 5 and 6 of either primary sensory (S1 and V1) or associate (M2) areas (Extended Data Fig. 1 a ). To determine the breadth of presynaptic connectivity impinging upon these populations, we utilized a genetically modified form of CVS N2c (N2cRV) rabies8. This strain is less toxic and more comprehensively reports afferent connectivity than the B19 variant originally utilized 7. Rabies tracing is achieved by infecting starter cells with an AAV-helper that provides dual complementation allowing for rabies infection (TVA) and monosynaptic transport (G protein) (Fig.
By contrast, SST cINs already have strong long-range connectivity at early postnatal times (contralateral Fig. 3a ; Student t-test p<0. 01; ipsilateral, Extended Data Fig. 3c ). Similarly, transient connectivity to PV versus SST cINs was distinct in specific cortical regions. To investigate this, we quantified subplate neurons, and confirmed their subplate identity using CTGF + staining42. While subplate connectivity is transient on both PV and SST cINS (S1 dev vs adult PV p<0. 01; SST p<0. 0001), S1 SST cINs have considerably higher afferent connectivity than either PV cINs or SST cINs within M2 at P10. (Fig. 4cb ; p<0. 001). We and others have previously shown that in S1, PV cINs receive strong transient inputs from SST cINs 12,43 that control their maturation. Following N2cRV tracing of PV cINs, we stained for somatostatin and found that connectivity was higher during development onto M2 PV cINs (Fig.
Notably, PV cINs in S1 and V1 continue to acquire FO afferents throughout their maturation. Although at P0, SST cINs in sensory areas only receive FO afferents (Fig. 4c  P3 vs P7 p<0. 0001), as development progresses, they acquire ever greater proportions of HO afferents (P7 vs adult p<0. 001). This results in them having equal numbers of FO and HO afferents by adulthood (Fig. 5b  &c; Extended Data Fig. 5c, d ). Perhaps because the rabies labeling window for adult brains overlapped with the visual critical period (P30-P42), the variability was higher in V1. FO and HO neurons transmit different types of information. We therefore investigated whether the difference in input proportions onto SST cINs also reflects differences in TC input strength. To do so, we stereotaxically injected an AAV-expressing hChr2 in the thalamus and recorded SST cINs at both mature (P21) and postnatally (P7) time points.
Imaging and Data analysis (yannick's methods name+ manual counting) Each brain section containing labelled cells was acquired as a tiled image on a motorized Zeiss Axio Imager A1. Brains with no helper expression were used as control of good pseudotyping and specificity of the EnvA-N2cRV (data not shown). Starter cells (colocalization of GFP + helpers and tdTomato + N2cRV) were manually quantified on Adobe Photoshop software. Representative brains with less than 10 starter cells were discarded. The highest concentration of starter cells was in deeper layers for all brains (L5-6. Extended Data Fig. 1a ). RFP+ retrogradely labeled cells were registered for each region of the Allen Reference Brain atlas for adult brain and of the "Atlas of Developing Mouse Brain at P6" from George Paxinos 2006.
3. Recordings were performed using a Multiclamp 700B amplifier (Molecular Devices) and digitized using a Digidata 1550A and the Clampex 10 program suite (Molecular Devices). Voltage-clamp signals were filtered at 3 kHz and recorded with a sampling rate of 10 kHz. Recordings were performed at a holding potential of -70 mV. Cells were only accepted for analysis if the initial series resistance was less than 40 MΩ and did not change by more than 20% during the recording period. The series resistance was compensated at least ~50% in voltageclamp mode and no correction was made for the liquid junction potential. Under low magnification, the barrels could be readily identified allowing to target the deeper Layer 5 of the cortex and highpower magnification was used to guide the recording electrode onto visually identified neurons.
Whole-cell patch-clamp recordings were obtained from tdTomato-expressing SST cINs or adjacent non-fluorescent pyramidal-shaped neurons in L5. The following number of cells were recorded for each condition: P7: 10 SST, 4 pyramidal; P19-21: 14 SST, 6 pyramidal. To activate thalamic afferents expressing ChR2, blue light was transmitted from a collimated LED (Mightex) attached to the epifluorescence port of the upright microscope. 1 ms pulses of light were directed to the slice in the recording chamber via a mirror coupled to the 60x objective (1. 0 numerical aperture). Flashes were delivered every 5 s for a total of 15 trials. The LED output was driven by a transistor-transistor logic output from the Clampex software. Recordings were performed in the presence of 1 µm TTX and 1 mM 4-AP (Tocris).
Data analysis was performed off-line using the Clampfit module of pClamp (Molecular Devices) and Prism 8 (GraphPad). The amplitude of evoked synaptic currents was obtained by averaging the peak amplitude of individual waveforms over 15 trials per cell. EPSC amplitudes recorded from SST interneurons were then normalized for injection size by dividing the average EPSC by the evoked current amplitude from a putative L5b pyramidal neuron in the same animal.
Acknowledgements We thank  Marian Fernandez-Otero  for her support along the project. This work was supported by grants from the  National Institutes of Health (NIH) :  MH071679 ,  NS08297 ,  NS074972 ,  MH095147 , as well as support from the  Simons Foundation (SFARI)  (to G. F);  EMBO Long-Term fellowship, Early and advanced Swiss Foundation postdoctoral fellowships , a Hearst foundation grant (to GP).
Conditions and tools The optical configuration of the HiCAT testbed for this experiment and, in particular, the numerical parameters useful for the model implemented in the COFFEE algorithm, is explained in Section 3. 2. 1, and the main software steps of the experiment, from the images taken by the camera to the processed data, are detailed in Sections 3. 2. 2, 3. 2. 3, and 3. 2. 4.
Acknowledgements. This work was supported in part by the  National Aeronautics and Space Administration  under Grant  80NSSC19K0120  issued through the  Strategic Astrophysics Technology / Technology Demonstration for Exoplanet Missions Program  (SAT-TDEM; PI:  R. Soummer ). This work has also received support of  IRIS Origines et Conditions d'Apparition de la Vie (OCAV) of PSL Idex  under the program « Investissements d'Avenir » with the reference  ANR-10-IDEX-0001-02   PSL. The author would also like to thank the  LESIA laboratory (Laboratoire d'Etudes Spatiales et d'Instrumentation en Astrophysique) of the Observatoire de Paris , the  French Aerospace Lab (ONERA)  in the frame of the  VASCO Research Project  and.
Writing and spelling According to  Amadieu and Tricot (2006)  textual learning involves activities of comprehension, memorization and inference production. We have developed a word processer, Docyrus, with advanced customization functions, justified by the central place occupied by this type of software in the office suites and whose purposes and the interfaces on which they are based are plural and multi-forms. We have enhanced the ability of the learner to highly personalize Docyrus interface at any time of the activity. Word processors can be facilitating instruments for writing invention for purposes of creation, revision or even memorization. It places the student in a continuous interaction with the machine  (Bruillard, 2016)  with the purpose of modifying students writing skills  (Delbrassine, 2018).