dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials A screen printer from AUREL SPA (Modigliana, Italy) was used for screen printing. Before their functionalization, electrodes were stored protected from dust and kept at a constant temperature of 20 • C. Functionalized ones were kept in a TBS solution including KCl (2 mM) and used rapidly. NI-USB 6009 data acquisition card (DAQ) and TL081 and TL084 operational amplifiers (AO) used for the conception of our homemade electronic system were purchased from National Instruments France (Colombes, France) and RS Components SAS (Beauvais, France), respectively. Two commercial electrochemical sensor platforms (Mini PSAT 910 from MetrOhm, Villebon-sur-Yvette, France; reference KS510 from Kanichi, Warrington, UK) were used for comparison.
Methods AFM measurements were carried out with a Bioscope II setup (Veeco-Bruker, Santa Barbara, CA, USA) equipped with an IX71 Olympus inverted optical microscope coupled to a Nanoscope V controller. Images were obtained in tapping mode, using silicon cantilevers with a spring constant around 40 N/m and NCLV tips from Bruker, at a resolution of 512 × 512 pixels and with a scan rate between 0. 5 and 1. 0 Hz. Data presented below are typical from measurements obtained with 12 samples (6 samples before cleaning and 6 samples after treatment). It is noteworthy that before fritting, imaging was impossible since the pastes were viscous with a thick texture. Raman spectra were obtained with a DXR setup from Thermo Scientific (Illkirch-Graffenstaden, France), running with the Omnic 8. 2 software.
Design and Printing Process of Electrodes The first step of SPE design was to define the most convenient geometry for further amperometric measurements. We used the Inkscape software (version 0. 92. 1) using scalable vector graphics (SVG) image coding. As shown in Figure  1a , the working electrode has the geometry of a microdisk, similar to a previous system developed in the same context  [52] , characterized by a radius r (0 ≤ r ≤ R 1 ) and a thickness z. Thus, it can diffuse current only in both (Or) and (Oz) directions, depending on the ohmic drop of the whole sensor and the response time of the electron transfer between all the electrodes  [53, 54]. The diffusion equation with time t of this geometry is as follows: ∂c ∂t = D ∂c ∂r 2 + 1 r ∂c ∂r + ∂ 2 c ∂z 2 (1) where C is the concentration (mol•cm -3 ) of the electroactive species in contact with the electrode, r (cm) is the distance from the center O of the disk, and D is the diffusion coefficient (cm 2 •s -1 ) of the electroactive species.
The first step of SPE design was to define the most convenient geometry for further amperometric measurements. We used the Inkscape software (version 0. 92. 1) using scalable vector graphics (SVG) image coding. As shown in Figure  1a , the working electrode has the geometry of a microdisk, similar to a previous system developed in the same context [52], characterized by a radius r (0   ) and a thickness z. Thus, it can diffuse current only in both (Or) and (Oz) directions, depending on the ohmic drop of the whole sensor and the response time of the electron transfer between all the electrodes  [53, 54]. The diffusion equation with time t of this geometry is as follows: 𝜕𝑐 𝜕𝑡 𝐷 𝜕𝑐 𝜕𝑟 1 𝑟 𝜕𝑐 𝜕𝑟 𝜕 𝑐 𝜕𝑧 ( 1 ) where C is the concentration (mol•cm -3 ) of the electroactive species in contact with the electrode, r (cm) is the distance from the center O of the disk, and D is the diffusion coefficient (cm 2 •s -1 ) of the electroactive species.
• The software controls the generation and the acquisition of the I/O signals. It is developed using the graphical language LabVIEW, also used for the signal treatment, by integrating and applying numerical filters and amplifiers. The diagrams in Figure  4  illustrate the different steps followed for the potential application in CV and CA measurements. Simplified and numerical equations of Nernst are integrated in LabVIEW for this purpose. The process is described below in the case of CV measurements. To ensure the generation of a potential alternating between given negative and positive values, we used the differential mode where the output potential E applied on the electrodes is defined as follows:   The CV program contains a "for loop" block, necessary to control the workflow statement and to execute the number of cycles required for measurements.
In this equation, A O0 and A O1 are the analog outputs of the NI-USB 6009 DAQ. (I/O) signals. Details concerning the card are provided below; The software controls the generation and the acquisition of the I/O signals. It is developed using the graphical language LabVIEW, also used for the signal treatment, by integrating and applying numerical filters and amplifiers. The diagrams in Figure4illustrate the different steps followed for the potential application in CV and CA measurements. Simplified and numerical equations of Nernst are integrated in Lab-VIEW for this purpose.
Acknowledgments: Authors thank  Ali Othmane  and  Hafedh Ben Ouada , both specialists of electrochemistry, from the  Laboratory of Biophysics (Faculty of Medicine of Monastir, Tunisia)  and the  Laboratory of Physics and Chemistry of Interfaces (Faculty of Sciences of Monastir, Tunisia) , respectively. They also thank  Rodolphe Boisgard  for helpful discussions and  Jean-Michel Rampnoux  for the interferometric measurements, both from  LOMA (University of Bordeaux, France ). They also thank  Eddie Maillard  ( Mechanical Department of LOMA ) for the manufacturing of mechanical pieces of the setup.
We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.
The model consists of the SFM for distant interactions and a new decision model for conflicting interactions. The model includes various observed pedestrian behaviors, as well as social group behaviors. • We calibrate the proposed model and evaluate its predictive ability by qualitative and quantitative comparisons of the simulated trajectories with ground truth trajectories. Several pedestrians-vehicle interaction scenarios are considered. The results are compared with the SFM and show that the proposed model better reproduces observed pedestrian behaviors. The analysis of results indicates that the model generates realtime and explainable trajectory predictions. • We evaluate the applicability and generalizability of the proposed model on a new dataset, consisting of data recorded by car sensors among pedestrians.
The results show that the model generalizes well to new real-world scenarios and that it could be used by an AV to predict the trajectories of surrounding pedestrians. The main contribution of this work (i. e. , the decision model) was previously published as a conference paper authored by the same researchers  (Prédhumeau et al. , 2021). In this extended article, a more in-depth state of the art is added. The proposed model is presented in a more complete way than before by including more details, as well as new illustrations and algorithms in the appendices. The model is calibrated in a more formal way and the calibration process is detailed. Moreover, the qualitative and quantitative evaluation on two datasets in the original paper is performed in this extended paper on the model after its formal calibration.
An evaluation of the generalizability and applicability of the model using a new dataset is added. Finally, the paper was completed with a discussion on what was learned and on research directions for future work. The paper is structured as follows. Section 2 presents the pedestrian behaviors observed when interacting with a vehicle in a shared space. Some pedestrian-vehicle interaction models are then presented, together with their limitations. In Section 3, we propose a new model for pedestrian reactions. This section first explains the general approach and core model concepts, then details the decision model. Sections 4 and 5 describe the implementation, the calibration of the model's parameters, and the experimental setup for evaluating the model's validity. Section 6 presents the model evaluation against ground truth trajectories, using several datasets.
(2017) ,  Palmeiro et al. (2018) , and  Rothenbucher et al. (2016). These three studies have all reported that pedestrians behaved with the AV as they would have done with a conventional vehicle. We can therefore reasonably assume that pedestrians' behavior with an AV will be very similar to their behavior with a conventional vehicle. Due to the limited data on pedestrian behavior with AVs, we also observed pedestrian behavior when encountering a conventional car in shared spaces. Madigan et al. (2019)  are among the only ones that have reported on the natural (i. e. in uncontrolled experiments) behaviors of pedestrians when they shared their space with an autonomous vehicle. They analyzed 22 hours of video, recorded in La Rochelle (France) and Trikala (Greece) during demonstrations of the CityMobil2 autonomous shuttle.
Data-Driven Models Another approach is to build a data-driven model, which learn pedestrian motion from realworld datasets. Ren et al. (2021)  proposed a data-driven model applicable to shared spaces with heterogeneous road users, such as pedestrians, bicycles, tricycles and cars. This model, named Heter-Sim, uses data-driven optimization by choosing a velocity from a real-world dataset that tends to minimize the energy function of the simulated entities. Using videos of crowds, road traffic and shared spaces, several scenarios were studied and the model was executed in real time for up to 5000 road users. However, individual trajectories were not empirically validated. Moreover, the simulated trajectories depended on the datasets used for the optimization, and the datasets for mixed pedestrian-car scenarios contained very few pedestrians and so the model cannot be used to study their interactions.
Models based on recurrent neural networks have recently met great success in designing human trajectory models, and outperformed expert models for trajectory prediction  (Alahi et al. , 2016). This approach was applied to mixed traffic prediction in shared spaces by  Ma et al. (2019) , and  Cheng and Sester (2018). By taking real-world data as input, the models learn either individual movements and interactions or collision risks from real trajectories. The models were evaluated on scenarios where vehicles, bicycles and pedestrians share the space. However, the model by  Ma et al. (2019)  focuses on a scenario with many more vehicles than pedestrians, and the model by  Cheng and Sester (2018)  does not run in real time. To conclude, data-driven models can accurately predict pedestrian trajectories, but require a large amount of data to learn robust representations.
In the absence of many pedestrian-vehicle interactions in the datasets, the data-driven models are currently unable to represent the variety of existing behaviors in shared spaces, and are not scalable to new scenarios. Finally, these models often do not enable real-time simulation, and are "black box". The underlying generation of trajectories is difficult to explain, which is an issue when predictions are used for AV decision-making. Our model does not need huge datasets to produce accurate results in varied interaction scenarios, and in real-time.
Implementation The model proposed in Section 3 has been implemented in C++ using Pedsim ros  (Okal et al. , 2014). Pedsim ros is an open source crowd simulator that was adapted to implement the SFM described by  Prédhumeau et al. (2019 Prédhumeau et al. ( , 2020)) , and integrates a set of software libraries and tools to develop robot applications. The decision model presented in Section 3. 3 has been translated into algorithms, which can be found in Appendix C. The main algorithm (Algorithm 2) is executed at each simulation time step, for each pedestrian agent p who is perceiving the AV. Three boolean variables are used to store the pedestrian's decision from time step to time step: isRunning, isStopped and isSteppingBack. These three variables are initialized to f alse when the agent is created at the start of the simulation.
Dataset Used for Calibration We used the CITR dataset  (Yang et al. , 2019)  as reference data for real pedestrian trajectories. The CITR dataset focuses on fundamental scenarios in controlled experiments. The dataset has been recorded in a parking lot at The Ohio State University, USA, and is presented in detail by  Yang et al. (2019). The CITR dataset contains 4 types of interaction scenarios between 8 pedestrians and one vehicle (a Golf Cart): a front interaction (vehicle arriving in front of pedestrians), a back interaction (vehicle coming from behind pedestrians), an unilateral interaction (vehicle approaching from the right side of pedestrians), and a bilateral interaction (vehicle approaching from the side of pedestrians, with 4 pedestrians facing 4 other pedestrians). The CITR dataset contains 4 instances of each type of scenario, resulting in 16 interaction scenarios between 8 pedestrians and a vehicle.
γ and the social force factor were calibrated by hand with a visual analysis of the magnitudes of vehicle influence and by comparing the vehicle's influence to ground truth. The perception zone of pedestrians is extended behind them since pedestrians perceive a vehicle from a greater distance than another pedestrian because of engine noise. Even for electric and hybrid vehicles, the use of acoustic warning systems is mandatory in many countries. For example, in the European Union, as of July 2019, any vehicle must emit a continuous sound level of at least 56 dBA if traveling at less than 20 km/h (Commission delegated regulation (EU), 2017). The value of 3. 3 m is approximated from the CITR dataset  (Yang et al. , 2019). The AV's margins are zones around the vehicle that pedestrians absolutely avoid.
The proposed model with these default values has been evaluated in our previous work  (Prédhumeau et al. , 2021). In order to improve the reliability of the model, we calibrated the five parameters: margin danger , margin risk , φ, T T C danger considered and T T C imminent with an optimization algorithm. We searched for the best-fitting parameter values using pedestrian trajectories from the CITR dataset  (Yang et al. , 2019).
Method -Simulation Setup The CITR dataset  (Yang et al. , 2019)  was used as a baseline for the real pedestrian trajectories. We simulated the same 16 scenarios: 4 frontal, 4 rear, 4 lateral and 4 bilateral interactions with the AV. In order to reproduce the scenarios, we defined the coordinates of the start and end points of each of the 8 pedestrians similar to the coordinates in the dataset. The AV speed and trajectory were defined as observed in the CITR dataset. For each of the 486 parameter combinations tested, the simulation of each of the 16 scenarios with the proposed model is repeated 5 times in order to smooth out stochastic errors and obtain more robust results, giving a total of 38,880 simulations. For each simulation, we recorded the simulated trajectories for the pedestrians and the AV.
Method -Cross-Validation We used a 3-block cross-validation method in order to select the best-fitting parameter values so the model can generalize over different datasets. Our final goal is to simulate and reproduce human behavior in various situations of interaction with an AV in a shared space. The model should therefore be as general as possible and not over-fitted on a specific dataset. The method used for the cross-validation is illustrated in Figure  7. We split the dataset scenarios into a training set (the data known by the model), a test set (data unknown by the model used in the cross-validation loop) and a validation set (data unknown by the model). We randomly selected 12 scenarios for the training and test set, shown in orange in Figure  7  and 4 scenarios for the final validation set, shown in blue in Figure  7.
Datasets Used for the Evaluation In order to evaluate the realism of the simulated trajectories we compared them to real trajectories from three datasets. These datasets include videos and trajectories of pedestrians and vehicles interacting in several shared space scenarios, as shown in Figure  8. The DUT dataset  (Yang et al. , 2019)  consists of uncontrolled interactions in a crowded campus at Dalian University of Technology, China. Part of the dataset has been recorded in a large shared space, in which pedestrians and vehicles can move freely. The main interactions are lateral crossings between a pedestrian flow and a car. The CITR dataset  (Yang et al. , 2019)  has been previously presented in Section 4. It contains pedestrians-vehicle interactions in 4 fundamental scenarios. It has been recorded during controlled experiments in a parking lot at The Ohio State University, USA.
The Nantes dataset similarly focuses on fundamental scenarios in controlled experiments. The dataset has been recorded in a parking lot at the École Centrale de Nantes campus, France. The dataset is composed of 19 various interaction scenarios between several pedestrians and one vehicle (a Renault Fluence car): frontal, back, unilateral, bilateral and more mixed interactions are represented. While the DUT and CITR datasets have been recorded from a top view with a drone, the Nantes dataset has been recorded by on-board vehicle sensors (cameras, Velodyne, Inertial Movement Unit and GPS). This dataset therefore represents a use case for on-board trajectory prediction that is closer to reality, where the vehicle navigates among pedestrians and retrieves all pedestrian information from its sensors.
Evaluation Process We adopted an approach commonly applied by similar works  (Yang et al. , 2020; Anvari et al. , 2016; Zeng et al. , 2017). After a qualitative evaluation, we first evaluated the model on the dataset that was used for calibration, using only the 4 scenarios kept for the validation set, to indicate the performance that can be obtained after the model calibration. We then evaluated the model on a new dataset without prior recalibration in order to study the generalizability of the model to new situations. We first used the DUT dataset to perform a qualitative evaluation of the simulated pedestrian behavior. The scenario is a lateral interaction between a car and a pedestrian flow, as in the dataset. The simulation has been replicated 20 times in order to smooth out stochastic errors.
The simulation was run both with the SFM only and with the proposed model for comparison. The SFM is used as a reference to show the potential benefits of the proposed solution. The SFM models pedestrian reactions with a repulsive force, while the proposed model combines a repulsive force for non-conflicting interactions and a decision model for conflicting interactions. This qualitative evaluation validates whether the proposed model can reproduce well the variety of pedestrian behaviors observed in the dataset (research question 1). We then used the CITR dataset to perform a quantitative evaluation of the model. For each of the 4 scenarios of the validation set (i. e. back1, front2, unilat1 and bilat3), we simulated pedestrians with the same initial conditions as in the data. In order to have the same rate as in the dataset we set the simulation time step to 0.
033s, i. e. 30 frames per second. Simulated agents were placed at positions corresponding to their initial positions in the dataset and given a destination point corresponding to a straight line trajectory from their initial position to the arrival point. The agents were assigned a preferred walking speed following the normal distribution generally observed in real pedestrians with µ = 1. 34 m/s and σ = 0. 26 m/s  (Bosina & Weidmann, 2017). The simulated AV moves in the simulation according to its ground truth trajectory. The model is partially stochastic, that is, the pedestrians' preferred speeds, body size, the small random force, and the decision to run or stop when hesitating, are random variables. Each of the simulation cases was repeated 20 times in order to obtain average results. This quantitative evaluation provides an indication of the model's accuracy for prediction, and a comparison of the accuracy of the model's predictions with the SFM predictions (research question 2).
We finally used the Nantes dataset to evaluate the generalizability and the applicability of the proposed model for an on-board prediction by an AV. For each of the 19 scenarios, we extracted a snapshot of the vehicle perceptions at the moment when it detects at least one pedestrian within 10m of it. The snapshot is reproduced in simulation by placing the simulated agents (pedestrians and AV) at their detected position at the snapshot time. The pedestrian agents were given a destination point corresponding to a straight line trajectory from their initial position to the arrival point, and were assigned a preferred walking speed following a normal distribution with µ = 1. 34 m/s and σ = 0. 26 m/s  (Bosina & Weidmann, 2017). The simulated AV moves in the simulation according to its ground truth trajectory.
Model Evaluation This section presents the results of the evaluation process and addresses each of the three research questions considered. 6. 1 Pedestrian Behavior Variety (Research Question 1) Table  7  shows the pedestrian behaviors identified in the DUT dataset that are reproduced by the SFM and by the proposed model. A video  (Prédhumeau et al. , 2021)   1  illustrates the simulated pedestrian behaviors in both models. The proposed model reproduces new observed behaviors, in addition to the behaviors that were already reproduced by the SFM. Pedestrians with the proposed model run to cross in front of the vehicle, stop to wait for the vehicle to pass, and pedestrian group members avoid the vehicle together without colliding with it. Moreover, the pedestrians' trajectories observed during a lateral interaction with a vehicle are more accurately reproduced with the proposed model than with the SFM.
Trajectory Predictions Accuracy (Research Question 2) The results presented in this paper are different from those obtained in our previous work  (Prédhumeau et al. , 2021)  because the models are calibrated more finely (see Section 4) and the results here only consider the 4 scenarios of the validation set. In order to evaluate the predictive ability of the proposed model, we used measures commonly used in the literature for trajectory prediction  (Alahi et al. , 2016; Bi et al. , 2019; Kabtoul et al. , 2020). In order to compare and aggregate prediction errors, the prediction time horizon is limited by the shortest interaction time in the dataset, which was 5s. For each measure, we computed the prediction error in each simulation for each pedestrian. For each scenario (front, back, unilateral and bilateral), we then computed the average and median error, the first and third quartiles, and the minimum and maximum values.