dataesr/scientific-paragraphs-categorization huggingface dataset:
End-effector X-Y-Z velocities were derived using the three-point central difference method. Tangential velocity was calculated at each point in time as the square root of the sum of the three squared velocities. For each trial, the start and the end of the movement were determined by searching backward and forward, respectively, from the moment at which peak tangential velocity was reached. The start of the movement was identified as the first data point (working backwards from the moment of peak velocity) where the tangential velocity fell below a threshold of 5 cm/s, and the position in the forward direction and the position in the sideward direction was within the boundary of 1. 5 cm from the center of the start point. The end of the movement was identified as the first data point where the vertical position of the fingertip was at table level.
Movement time was determined by the time period between the start and the end of the movement. Peak velocity was defined as the maximum value in the tangential velocity profile. The symmetry index of velocity was defined as the time from movement onset to moment of peak velocity divided by total movement time where 0. 5 denotes a symmetric velocity profile. The moment the fingertip crossed the obstacle was identified as the first data point where the position of the fingertip in the forward direction passed the obstacle location at 10 cm in front of the start position. End-point precision was calculated as the within-participant SD per condition (pretest, practice, posttest) of the position of the fingertip in the forward and sideward directions at the end of the movement.
Statistical analysis SPSS software (IBM, Armonk, New York) was used to perform the statistical analyses. Five types of analysis were performed. First, to examine the influence of obstacle height on the maximum height of the fingertip during the pointing movement we performed a linear regression with obstacle height as dependent variable and maximum height of the fingertip as independent variable on the trials of the practice phase. Second, to examine whether practice with multiple obstacles gave rise to an increase in the range of joint angles, we compared the range of joint angles in the pretest and during practice of the experimental group. A repeated-measures ANOVA on the range of joint angles was performed with Condition (pretest and practice) and Instant (at obstacle and end of movement) as within-participant factors.
CE-SSCP electrophoresis was performed in an ABI Prism 3130 genetic analyzer (Applied Biosystems) in 50 cm capillary tubes filled with 10% glycerol, conformation analysis polymer and corresponding buffer (Applied Biosystems). Samples were eluted at 12 kV and 32 °C for 30 min, as described elsewhere  44. CE-SSCP profiles were aligned with an internal standard (ROX) to consider the inter-sample electrophoretic variability. CE-SSCP profiles were normalized using the StatFingerprints library 45 in R software version 2. 9. 2 (R. Development Core Team 2010).
Forward Model: Generation and Emission of Nonlinear Features The fundamental concept of the proposed sensor system is based on the detection of nonlinear waves leaking from the defect sample into the ambient air (i. e. , NACE). The sensor elements will detect the NACE signals and use them to identify the defect location. In order to demonstrate this new principle, we first need to model the concept of NACE. This is done by implementing a two-step 2D numerical model in the finite element-based commercially available software package COMSOL Multiphysics R. The first step of the model contains a time domain model for the calculation of nonlinear Lamb wave propagation in an aluminum sample with a horizontally-oriented near-surface crack. In the second step, NACE radiation patterns are calculated by bridging the results of the time domain solution to a spectral solution of air.
Conclusions In the current paper, a numerical study was performed in order to test the capability of an air-coupled ultrasonic sensor array to localize near-surface cracks in plate-like samples. Two models were developed: a forward model, used to simulate the generation and emission of nonlinearities, and an inverse model that uses localization algorithms to find the exact location of a defect. The forward model is implemented in the finite element-based software package COMSOL Multiphysics R and consists of two parts. The first part is a 2D time domain model that allows the simulation of (nonlinear) Lamb wave propagation in cracked samples and takes into account the clapping and frictional behavior of the crack, resulting in the generation of nonlinear features, such as the presence of harmonics.
Acknowledgments: The research leading to these results has gratefully received funding from the  European Union   Seventh Framework Program  ( FP7/2007-2013 ) under Grant Agreement n • 314768 (ALAMSA), from the  Research Council KUL ( C24/15/021  and  C16/15/059-nD ) and from the  Research Foundation Flanders (FWO)  through Grants  G. 0830. 14N  and  G. 0881. 14N.
Human activity recognition using smart home sensors is one of the bases of ubiquitous computing in smart environments and a topic undergoing intense research in the field of ambient assisted living. The increasingly large amount of data sets calls for machine learning methods. In this paper, we introduce a deep learning model that learns to classify human activities without using any prior knowledge. For this purpose, a Long Short Term Memory (LSTM) Recurrent Neural Network was applied to three real world smart home datasets. The results of these experiments show that the proposed approach outperforms the existing ones in terms of accuracy and performance.
In recent years, there has been a growing interest in deep learning techniques. Deep learning is a general term for neural network methods which are based on learning representations from raw data and contain more than one hidden layer. The network learns many layers of non-linear information processing for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Deep learning techniques have already outperformed other machine learning algorithms in applications such as computer vision  [6] , audio  [7]  and speech recognition  [8]. In this paper, we introduce a recurrent neural network model for human activity recognition. The classification of the human activities such as cooking, bathing, and sleeping is performed using the Long Short-Term Memory classifier (LSTM) on publicly available Benchmark datasets  [9].
An evaluation of the results has been performed by comparing with the standardized machine learning algorithms such as Naive Bayes, HMM, Hidden Semi-Markov Model (HSMM) and Conditional Random Fields (CRF). The paper is organized as follows. Section 2 presents an overview of activity recognition models and related work in machine learning techniques. Section 3 introduces Long Short-Term Memory (LSTM) recurrent neural networks. Section 4 describes the datasets that were used and explains the results in comparison to different well-known algorithms. Finally, Section 5 discusses the outcomes of the experiments and suggestions for future work.
Related work In previous research, activity recognition models have been classified into datadriven and knowledge-driven approaches. The data-driven approaches are capable of handling uncertainties and temporal information  [10]  but require large datasets for training and learning. Unfortunately, the availability of large real world datasets is a major challenge in the field of ambient assisted living. The knowledge-driven techniques are used in predictions and follow a descriptionbased approach to model the relationships between sensor data and activities. These approaches are easy to understand and use but they cannot handle uncertainty and temporal information  [11]. Various approaches have been explored for activity recognition, among them the majority of the techniques focuses on classification algorithms such as Naive Bayes (NB)  [12] , Decision Trees  [13] , HMM  [5]  , CRF  [14] , Nearest Neighbor (NN)  [15] , Support Vector Machines (SVM)  [16]  and different boosting techniques.
Dataset Publicly available and annotated sensor datasets have been used to evaluate the performance of the proposed approach  [9]. In this dataset, there are three houses with different settings to collect sensory data. The three different houses were all occupied by a single user named A, B, and C respectively. Each user recorded and annotated their daily activities. Different number of binary sensors were deployed in each house such as passive infrared (PIR) motion detectors to detect motion in a specific area, pressure sensors on couches and beds to identify the user's presence, reed switches on cupboards and doors to measure open or close status, and float sensors in the bathroom to measure toilet being flushed or not. The data were annotated using two approaches: (1) keeping a diary in which the activities were logged by hand and (2) with the use of a blue tooth headset along with a speech recognition software.
A total of three datasets were collected from the three different houses. Details about the datasets are shown in Table  1  where each column shows the details of the house with the information of the user living in it, the sensors placed in the house and the number of activity labels that were used. The data used in the experiments have different representation forms. The first form is raw sensor data, which are the data received directly from the sensor. The second form is last-fired sensor data which are the data received from the sensor that was fired last. The last firing sensor gives continuously 1 and changes to 0 when another sensor changes its state. For each house, we left one day out of the data to be used later for the testing phase and used the rest of the data for training. We repeated this for every day and for each house.
Electric Field Setup The simultaneous electric field application and optical fluorescence microscopy observations were performed by using the cell setup portrayed in Figure  1. The Teflon cell contains a glass sheet at the lower part allowing the visualization under an optical microscope and two 0. 09 mm diameter electrodes separated by 0. 5 mm located between the lower glass and the upper opening of the cell as indicated by the arrows in Figure  1. DIC objective) immersed in water. The three dimensional (3D) images were obtained using confocal laser scanning microscopy (CLSM) with a Nikon C1 scanner. The images were captured with the EZ-C1 software (Nikon, version 3. 50). The fluorescence of the fluorophore rhodamine-DOPE present in the GUVs membranes was detected in confocal mode after excitation at 543 nm (helium-neon laser, Melles-Griot).
The images were analyzed using the software Magneto developed at the Charles Sadron Institute. The whole set of data was acquired at 18 ºC. The fluorescence images of the GUVs under the application of electric field were obtained using an inverted microscope (TE2000, Nikon, Japan) equipped with a 40X objective lens because of the thickness of the glass sheet used to build the electric field cell. The 40X objective lens was previously used in phase contrast mode in order to locate the electrodes and vesicles and further switched to fluorescence mode for the application of electric fields. The microscope uses a mercury lamp as light source with excitation filter placed within the illumination path for the selection of the incident wavelength. The fluorescence images were collected using a digital camera (Diagnostic Instruments NDIAG 1800) coupled to the microscope and allowing the acquirement of images and videos.
It is also important to note that several works have been conducted to improve the energy efficiency of sensor networks by enhancing routing protocols, topology control and data aggregation, and the results are confirmed using simulation tools such as Ns-2, Omnet, MATLAB, etc. [7, 8]. However, building energy-efficient hardware has not yet gained much interest from the research community. Consequently, most of the commercialized sensor nodes have a high consumption level, energy still being an unresolved problem. In addition, the commercialized nodes do not give any possibility to improve the performances related to the physical and MAC layers, namely the registers of transmission and reception, the size of packets, and others. Thus, the first challenge that we tackle in this paper is to design a prototype of the sensor node that reduces the energy consumption while maintaining a high level of performance.
Finally, in order to evaluate the communication performances of the hardware, we measure the delivery of packets to the coordinator. We illustrate in Figure  13  the packet loss ratio according to hardware and topology. We first deduce that the packet loss ratio is reduced by our designed sensor nodes compared with the Memsic nodes. This is explained by the efficiency of data processing offered by the Arduino platform and by the reduction of the packet size, which leads to the reduction of the dropping of packets in the router nodes. We also note from Figure  13  that the MSCT topology reduces the packet loss ratio; this can be explained mainly by the minimal cost of transmissions and also because most of the parent nodes have a maximum of two children. In summary, we deduce from the above experiments that the developed system significantly improves the energy consumption and the network's lifetime.
Introduction LPG (Liquefied Petroleum Gas) is considered as a byproduct of the petrochemical industry, and has become since the 1930s, a powerful energy source  [1]. LPG domain has a long history of technological advances and major accidents. Indeed, LPG sector is considered among the most hazardous industrial sectors based on accidents occurrence statistics. Among these accidents: Feyzin in France (1966), San Juan Ixuatepec disaster in  Mexico (1984) , Izmit in Turkey  (2002). Latest accidents that touched LPG transportation are centralized on the ARIA database (Research and Information Analysis accidents) such as Bollene accident (2008), Morannes accident (2011), Bastia accident (2014), and Lamentin (2015). The aim is to check first compliance, conformity and authorization of the truck when it enters the site for loading / unloading, to verify the authorization to make the transfer, and then, to identify the driver and his training to ensure that the transfer operations are going to take place without incident in order to avoid leaks and problems leading to explosions.
AnyLogic Platform AnyLogic is a simulation platform that includes a graphical modelling language facilitating the models' construction. This platform allows the user performing custom simulation models through its large graphical editor, visual and animated representation (2D and 3D) of the different entities of simulation, and with Java code. Furthermore, it is the first and the only dynamic simulation tool that gathers and combines these threedifferent modelling-simulation approaches: System Dynamics, Discrete event, and Multi-Agent Systems (MAS). AnyLogic is the most suitable platform for modelling industrial problems especially the one of the supply chain  [3] [4] [5] [6] [7].
Torsional Mode Design To integrate the torsional resonator on the vibrating beam, the complete structure was simulated, including the facets due to chemical etching. Figure  2 , shows the results of etching facets predicted thanks to MicroCAD ® software, the finite element model used and the actual cell with its etching facets. The frequency shift of the torsional resonator with the dimensions of the etching facets was investigated and showed 0. 1% evolution of the frequency for 1 μm evolution which highlights that realization dispersions are very critical for such design. Thus eigen frequencies where thoroughly studied thanks to modal finite element analysis for both the flexural mode of the VBA and the torsional mode to isolate them from other potential parasitic modes.
Results In order to improve the performances of the thermal sensor the frequency of the torsional resonator was increased in the design "Candidate B" compared with former version "Candidate A" leading to a lower motional resistance Rm. This frequency increase is also aimed at reducing electrical coupling of the two modes enabling a more efficient signal filtering. The two versions characteristics are described in Table  1  and Candidate B is presented on Figure  3. The thermal transient behavior of both candidates was studied thanks to the finite element analysis software OOFELIE::Multiphysics ©. The finite element analysis includes the thermal transient behavior of the structure chained with static thermomechanical and a prestressed modal analysis. The transient evolution of the vibrating beam frequency is caused by the time delay between the temperature evolution outside and inside the packaging and the thermal gradient which appears in the quartz cell itself.
MATERIALS AND METHODS For this study, we summarized selected biomarker data from the literature, where the indigeneity of components was evident or highly likely (see the Supplementary Materials for approach and rationale), and performed laboratory analyses on sedimentary rock samples. We analyzed the molecular inventory of 158 Neoproterozoic rocks, of which 61 yielded trustworthy and reliable steroidal signatures that were deemed as unambiguously syngenetic (table  S1 , Supplementary Materials). Five studied formations did not yield a single sample with uncontaminated steroidal signatures (table  S2 ). Furthermore, 68 selectively chosen Neoproterozoic sterane values from the literature were used (table  S1 ).
GC and MS Full-scan analyses were performed on a Trace GC Ultra (Thermo Scientific) coupled to an ALMSCO BenchTOF-dx mass spectrometer. The gas chromatograph was fitted with a VF-1 MS column  [40 m ; inner diameter (i. d. ), 0. 15 mm; film thickness, 0. 15 mm] and operated with a constant flow (1. 4 ml/min) of helium (99. 999 % pure, Westfalen AG) as a carrier gas. Samples (between 1 out of 50 and 1 out of 500 ml) were injected in splitless mode using a programmed temperature vaporizer injector (ramped from 60°to 315°C at 14. 5°/s). The GC oven was held at 60°C (2 min) before ramping at 4. 5°/min to a final temperature of 325°C, which was held for 10 min. Ionization was achieved at 70 eV (electron impact) and 250°C with a filament current of ca. 4 A. Data were measured from m/z 30 to 800 but only recorded from m/z 50 to 550 at ca.
Acknowledgments: We thank  B. Nettersheim  for the discussions and  P. Pringle  for the laboratory support. R. Summons  is thanked for providing two samples. Funding: This study was principally funded by the  Max Planck Society (Max Planck Research Group  grant to C. H. ); S. N. was supported by the  Deutsche Forschungsgemeinschaft  ( Na1172/2-1 ). D. M. was supported by the  Petroleum Branch of the Department of Primary Industries and Resources, South Australia. Author contributions: C. H. conceived and directed the study; Y. H. , A. P. , W. M. , C. S. , V. P. , G. J. M. V. , N. K. , A. L. , L. v. M. , M. N. , S. N. , M. M. , J. J. B. , A. J. M. J. , Q. T. , S. X. , D. M. , S. K. D. , J. J. A. , and P. S. performed experiments and provided materials and data; Y. H. and C. H. analyzed the data; and C.
The aim of our project is to develop a processing chain allowing to extract automatically the maximum of information from point clouds in order to integrate easily the result in BIM software. Three items were considered for the development of the approach. The first item is the automation. A 3D semi-automatic reconstruction is proposed and tasks which can be automated have to be identified. Secondly, in order to guaranty the generalisation of the approach, a transferability item is considered. It involves the definition of characteristics of studied buildings. The last but not least item is the geometric quality of the results. Quality criteria are intended to be integrated in the approach and quality indexes have to be proposed for the inspection of reconstruction results. In this article, a state of the art of the process which consists in the creation of as-built BIM from point clouds is first proposed (Section 2).
The Section 3 is dedicated to the full description of the developed approach which considers indoor point clouds as input. The project framework is presented and the two parts of the approach, namely the segmentation of building elements point clouds and the 3D reconstruction of walls and slabs of buildings, are detailed. The assessment of the developed approach is then considered in Section 4. Datasets and thresholds used for the assessment are presented and results of both parts of the approach are shown. Moreover, quality indexes are introduced for the inspection of results. Finally, some future works are proposed and new trends for as-built BIM creation are discussed (Section 5).
Software dedicated to point cloud processing such as Realworks (Trimble), CloudCompare (EDF R&D) or 3D Reshaper (Technodigit) propose tools to create geometric primitives or meshes directly from 3D data. Unfortunately such a modelling does not generate objects which can be integrated directly in a BIM software such as Revit (Autodesk), ArchiCAD (Graphisoft) or Tekla Structures (Trimble). A conversion step, which often involves several software, is necessary. Moreover, this conversion can cause problems of data interoperability. Thus, semi-automatic software (e. g. , EdgeWise, Trimble) or plugins integrated to BIM software (e. g. , Scan-to-BIM, ImaginIT and PointSense, FARO) were developed specifically for the scan-to-BIM process. In such software, structural elements are adjusted to point clouds from information given by the user.
Features of different types of surfaces and the contextual relationships between them are learned to perform the labelling. For non-parametric models, 3D object recognition algorithms may label object instances or recognized classes of objects. They are used whether in urban environments  [52] , industrial environments  [53]  or in indoor areas of buildings  [54]. The recognition is based on a database of objects which have to be recognized. Shape descriptors are computed for both objects of the database, and the scene containing these objects. Then, a matching step allows to identify objects in the scene. Recognition of object instances is usually performed when objects with known shape and which are repeated in a scene have to be identified. For example, Johnson et al. [55]  use this method in power plants for recognizing objects such as machinery, pipes and valves.
The output of the approach is a file in the IFC format (BIM format) where walls and slabs are described. The entire approach was carried out thanks to MATLAB (MathWorks) software. In the remainder of this section the different steps of the developed approach are detailed and illustrated based on indoor point clouds acquired in a public building namely a building of the National Institute of Applied Sciences (INSA) in Strasbourg (Figure  2 ). The ground floor, the first floor and a part of the second floor of the building which represent approximately 2500 m 2 were scanned. The acquisition and the post-processing of this dataset have already been presented in  [58].