dataesr/scientific-paragraphs-categorization huggingface dataset:
Hardware Identification and Installation Hardware installation is required for the overall upgrading of FURBOT for autonomous navigation and freight handling in an urban environment. Among the major hardware changes which are requested are the integration of new sensors, connecting and interfacing the installed sensors with MC and establishing the connection with Apollo Auto. Apart from new sensor requirements, their integration with MC is also essential. The cable/connectors requirements also need to be identified. In short, the transmission of sensor output data to MC whether wired or wireless needs to be identified.
Conclusions We highlight and address several issues for FURBOT to complete its transition from a drive-by-wire electric vehicle to a fully autonomous one. The research focus is to address the issues concerning licensing and autonomy of the vehicle. For licensing, the weight difference is enormous for the vehicle to qualify for the category of L7E-CU. The vehicle is overweight for this category, and shedding some weight will not solve the issue. However, qualifying the vehicle for the N1 category seems very promising. The only hurdle which needs to be addressed is the incorporation of a steering wheel instead of joystick, which is viable. Second, for crash testing, as the vehicle is a prototype and very expensive, it is not possible to do crash testing. However, to resolve this issue, it is possible to do simulated crash testing in structural analysis software.
Structural analysis in software for crash testing is now becoming increasingly common  [56]. Thus, after considering all the factors, it is decided for FURBOT to gain the license in compliance with the N1 category by installing a steering wheel and doing structural analysis for crash testing. Further conclusive results of our study showed the relevant hardware changes FUR-BOT must go through to change from a drive-by-wire vehicle to an autonomous one. For that, incorporating new sensors is of high importance. The identification of the right 3D LiDAR with the presence of single plane LiDARs and Radars will be sufficient for the vehicle to perform autonomous navigation. The identification and finalization of software architecture are also of prime importance, and Apollo architecture serves as an ideal platform.
We identify and highlight foreseeable issues that will help us in our path forward. Additionally, we critically analyze how we need to show autonomy, highlighting our strategy and constraints. Few issues which are envisioned during the implementation phase of the whole hardware/software upgrade are that we stripped the top 3D LiDAR from 128 L (as recommended by Apollo  [40] ) to 32 L. This means lower resolution with fewer data points for the perception module to detect shapes on the road. However, this was countered with front and back single plane LiDAR/radar for better perception. According to the manual of Apollo Auto, the backward motion is not developed for autonomous vehicles, which is a very crucial problem for parking for freight vehicle in the constrained urban environment. To resolve this issue, a new module has to be designed and incorporated into the system, which is cumbersome to do for off-the-shelf software.
In practice, evaluating the performance of an identification method on a real system proves to be a difficult task since the latter can never be perfectly modeled. This is a direct consequence of the wide variety of complex physical phenomena-such as backlash or nonlinear friction-that have a significant influence on the system behavior while being relatively challenging to model. On the contrary, the identification of a simulated system allows a rigorous quantification of an algorithm's performance since the characteristics and parameters of the simulated model are well known and can therefore be used as a reference. The simulation also makes it possible to highlight the influence of specific physical phenomena-e. g. , joint friction or measurement noise-on the estimates' quality. Therefore, our first contribution is to propose a dedicated framework, in the form of an open-source Matlab toolbox, which makes it possible to formally evaluate the performance of multiple identification algorithms on the same robot model and under the same conditions or assumptions.
Parameter Identification Using an Adaline Neural Network (AdaNN) The Adaline (ADAptive LInear NEuron) uses stochastic gradient learning to converge to the IDIM-OLS estimate. The estimator has the following dynamic equation θAdaNN = -η∇(e e) ( 49 ) where e is the error defined in Equation  (18). The parameter estimate βk AdaNN at epoch t k can hence be expressed recursively as βk AdaNN = βk-1 AdaNN + ηY k τ(t k ) -Y k βk-1 AdaNN ( 50 ) where Y k = Y β ( q(t k ), q(t k ), q(t k )) is the observation matrix of the real system at epoch t k , τ(t k ) is the corresponding torque, and η ∈ R is referred to as the learning rate. A single "neuron" thus allows full model identification (c. f. Figure  5a ). As several passes over the hole dataset may be necessary to achieve proper convergence, the sequence of observations Y k and τ(t k ) must be randomly reshuffled to avoid cycles  [86].
Trajectory Data Generation To avoid data rank deficiency of the observation matrix W, the different components of the model should be sufficiently excited during the experiments. This can be achieved by means of specific joint trajectories, referred to as exciting. The design of excitation trajectories has been widely investigated over the past two decades, see e. g. ,  [9, 17, 23, 38, 53, [99] [100] [101] [102]. From these works, it appears that the conditioning of the observation matrix W is a relevant quality indicator for the generated trajectory. In our case, joint trajectories are obtained by parametrization of finite Fourier series as presented in  [9, 23, 101]. The following cost criterion J t is minimized over the experiment time horizon using a standard nonlinear programming solver (in our case the "fmincon" and "ga" Matlab functions): J t = k 1 • cond(W W ) + k 2 σ min , (67) where cond(•) refers to the condition number of a matrix; σ min is the smallest singular value of W; and k 1 , k 2 ∈ R + are tuning gains defined by the user.
It should be emphasized that this method also allows the generated trajectories to be constrained to comply with the physical limitations of the considered robot, such as joint, velocity, torque or operational space limits. The generated trajectory is eventually stored in a dedicated data container. We provide multiple interpolation routines, based on the "interp1" Matlab function, so that relevant trajectory data can be obtained at any epoch, from a limited number of points, either generated by the optimizer or coming from a pre-existing file, created outside BIRDy. Please note that any third-party trajectory file containing a time series of joint values can be imported into the benchmark, provided its data are first stored in one of its predefined data containers.
The term "raw" here refers to the fact that the signals used for identification are not necessarily filtered. This makes it possible to test the robustness of the different identification algorithms to measurement or numerical differentiation noise. The parallel-decimation process in BIRDy is carried out with a zero-shift low-pass Tchebyshef filter, implemented using the decimate Matlab function. Similar to  [2, 6]  the cutoff frequency f dec is set to be approximately one order of magnitude higher than the joint bandwidths. The decimation ratio n d by which the input data are being sub-sampled is defined as n d = 0. 8 f /2 f dec where f is the sampling frequency. As additional band-pass filtering may be required by some algorithms (such as IDIM-OLS), the pre-processing pipeline of BIRDy also features an implementation of zero-shift Butterworth filter that can potentially be applied to the raw joint position signal q before time derivation.
For each method and each independent run, the identification process was repeated 25 times with different initial parameter vectors to investigate the sensitivity to initial conditions, eventually resulting in a total of M = 25 • b = 1300 runs for each identification method and on each robot. The 25 initial parameter vectors were obtained using the reference parameters' values (i. e. , previously used for collecting simulation data) distorted by a relative error of 15%, which suitably represents the tolerances obtained with computer-aided design (CAD) software. The validation experiments were performed on the real robots. In this case, the identification methods requiring an initial parameter estimate -namely IDIM-IV, DIDIM, CLOE, CLIE, ML, NKF, AdaNN, and HTRNN-were initialized using the filtered IDIM-OLS estimate.
ML Implementation As with the IDIM-LS methods, two different variations of the ML identification algorithm were evaluated, namely with and without data-filtering. In the proposed implementation of the ML identification method, the values of the diagonal variance matrix σ 2  k are set based on variance measurements of the error signal between the measured joint derivatives q, q, q and the simulation reference q, q, q. Please note that it is usually difficult to obtain the value of σ 2  k on a real system as the reference quantities q, q, q are not available. The resolution of the nonlinear optimization problem (  36 ) is carried out using the Levenberg-Marquardt algorithm through the lsqnonlin function from the Matlab Optimization Toolbox. The Jacobian matrices G k are computed numerically, with finite differences and a tolerance of 10 -7.
IDIM-IV Implementation The IDIM-IV method is only executed on the unfiltered data set. The instrument matrix Z i at iteration i is filled with the noise-free simulated data, obtained by integration of the closed-loop DDM using the current parameter estimate βi-1 IV (c. f. Equation (  31 )). The control structure and gains are the same as those used during the experiment data generation process. Integration is carried out using the Euler (RK1) at the rate f c = 5kHz of the data generation loop. The data pre-processing step only involves sampling and decimation, to fit the sampled dataset. To stop the sequence of linear LS problems solved by the IDIM-IV method, a set of three stop criteria are implemented: • ρ i+1 IV 2 -ρ i IV 2 / ρ i IV 2 < tol 1 where ρ i IV denotes the error at iteration i, defined as ρ i IV = y τ -W (i-1) βi IV ; • max j βi+1 IV (j) - βi IV (j) / βi IV (j) < tol 2 , ∀ βi IV ( j) = 0 with j the index and i the iteration number.
Relevant Details of the CLIE and CLOE Implementations The proposed implementation of CLIE and CLOE makes use of the Levenberg-Marquardt algorithm, and is implemented using the lsqnonlin function from the Matlab Optimization Toolbox. The algorithm stops once any of the following criteria is reached: • ρ i+1 2 -ρ i 2 / ρ i 2 < tol 1 where ρ i denotes the error at iteration i. In the case of CLIE one has, ρ i = ε i CLIE as defined in  (43)  while in the case of CLOE, we have ρ i = ε i CLOE as defined in (40). • max j ( βi+1 CLIE (j) - βi CLIE (j))/ βi CLIE (j) < tol 2 , (resp. max j ( βi+1 CLOE (j) - βi CLOE (j))/ βi CLOE (j) < tol 2 ), ∀ βi CLIE (j) = 0, (resp. βi CLOE (j) = 0) with j the parameter index and i the iteration number. • maximum number of iterations: i max = 10. where the values of the function tolerance tol 1 and step tolerance tol 2 are set according to the guidelines given in  [59].
In our case, we have tol 1 = 2. 5% and tol 2 = 2. 5%. Please note that although only the Levenberg-Marquardt approach is investigated in this paper, BIRDy actually contains multiple CLIE and CLOE implementations, based on genetic algorithm, particle swarm optimization and the Nelder-Mead nonlinear simplex methods. These methods could be easily implemented using respectively the ga, particleswarm and fminsearch function from the Matlab Optimization Toolbox.
Please note that when badly initialized or in the presence of modeling errors, iterative methods such as IDIM-IV, DIDIM, CLIE, CLOE, NKF or AdaNN usually fail to converge to the objective values as the internal DDM or IDM simulation process converge to an inconsistent state. In practice, a reasonable initialization value for the parameters can be obtained from a modern CAD software or from filtered IDIM-OLS. Although AdaNN and IDIM-OLS are asymptotically equivalent, one may note that the results in chapters 2. 1 and 22. 1 of the attached experiment reports suggest that AdaNN is less efficient than IDIM-OLS -statistically speaking -as the variance of the AdaNN estimate appears to be at least one order of magnitude higher than that of the IDIM-OLS estimate. Finally, although the NKF identification methods provide good results in the case of the TX40 robot with undecimated data (c.
In our case, these computations were distributed on 12 parallel threads, which was made possible thanks to the Matlab parallel computing toolbox. Unlike the other methods, the time taken by the NKF to find a solution scales linearly with the number of samples. In the case of sigma-point Kalman filters, the whole distribution of prior estimates must be propagated through the nonlinear DDM. In our case, considering the set of 52 base parameters and the 12-dimensional robot state vector, a total of 129 sigma-points must be propagated through the DDM at each filter iteration. To speed up the computations, we took the decision to distribute the burden on four different threads. Finally, the sample propagation loop was compiled into a mex file to further accelerate the execution speed. In this manner, the time ratio between an EKF execution and a UKF execution could be reduced to 4.
Multiple improvement of BIRDy are currently being investigated, with a focus on enhancing the scalability of the benchmark. We indeed noticed that the current symbolic model generation engine, based on the MuPad symbolic kernel could take considerable time to generate identification model of robots with more than 7-DOF (we so far tested the generation routine with up to 9 DOF). We moreover noticed a strong correlation of the performance of the symbolic toolbox with the version of Matlab. Several approaches are currently being explored to tackle this issue, including the use of other symbolic kernels such as that of Wolfram Mathematica or the open-source SymPy, also used in the OpenSYMORO+ toolbox from  [112, 113]. Besides these issues, future developments of BIRDy will mainly consist of generalizing the process of identificationmodel computation using the Unified Robot Description Format (URDF) rather than DH parameters, considering the use of modern highly efficient dynamic libraries such as the Rigid Body Dynamics Library (RBDL  [114] ) or the Kinematics and Dynamics Library (KDL  [115] ) for model numerical simulation and eventually the inclusion of additional sensor modalities in the process of robot identification, such as accelerometers, gyroscopes or force sensors.
This work has been supported by BayFrance (Franco-Bavarian University cooperation center) under the grant number FK20_2019. Informed Consent Statement: Not applicable. Data Availability Statement: The complete benchmark code as well as the raw experiment data are made available to the reader at the following addresses (accessed on the 9 May 2021): • BIRDy source code: https://github. com/TUM-ICS/BIRDy • Experimental data: http://doi. org/10. 5281/zenodo. 4728085 • Experimental results: http://doi. org/10. 5281/zenodo. 4679467 Acknowledgments: The authors would like to thank Katharina Stadler as well as Sebastian Stenner for their valuable assistance during the experiments.
Funding: This work has been supported by BayFrance (Franco-Bavarian University cooperation center) under the grant number FK20_2019. Informed Consent Statement: Not applicable. Data Availability Statement: The complete benchmark code as well as the raw experiment data are made available to the reader at the following addresses (accessed on the 9 May 2021): • BIRDy source code: https://github. com/TUM-ICS/BIRDy • Experimental data: http://doi. org/10. 5281/zenodo. 4728085 • Experimental results: http://doi. org/10. 5281/zenodo. 4679467 Acknowledgments: The authors would like to thank Katharina Stadler as well as Sebastian Stenner for their valuable assistance during the experiments.
Database We used the database of the Center that coordinates breast cancer screening (CRCDC-OC) in the Hérault department, France. This database registers women with precise, verified, and consolidated information on the development or not of breast cancer during the follow-up of their screening. We enrolled women, aged 50 to 60 at inclusion, who experienced a 12 year breast cancer screening from 2006. We extracted a database of 33,858 women, including 805 women who developed a breast cancer in 3-12 years following inclusion. We excluded women who have been diagnosed with breast cancer during the first two year of follow-up to avoid including women who might have already developed a cancer but not yet diagnosed at inclusion. Data were recovered by a telephone survey. It was not possible to interview 33,000 women.
Data collection Data were collected by telephone by two trained investigators. An explanatory leaflet (Supplementary material 3) was sent to all participants prior to the phone call and informed consent was obtained from all individual participants prior to the telephone interview. Responses to the study questionnaire were collected between November 2018 and May 2019. The inclusion flowchart is shown on Figure  1. This survey enabled collecting 3077 questionnaires, including 597 questionnaires from women who developed a breast cancer. The Case Report Form was developed with the ACCESS software. The analysis database was provided by the CRCDC-OC while having anonymized the data beforehand. This study complies the MR003 CNIL's (National Commission for Informatics and Liberties) methodology (Declaration: 2200402v0).
Bootstrap As we performed a random sampling among women without breast cancer, in our database, the distribution between women who developed cancer and the others was no longer representative of the starting population. To remedy this, we performed bootstrap samples. We computed 100,000 bootstrap databases using stratified random sampling with replacement among women for whom we obtained answers to all the covariates of the final Cox model, in order to obtain a database close to the initial CRCDC database (i. e. 33,858 women, including 896 who developed a breast cancer within the 12 years of follow-up) In these databases, mean risk was identical to the one of the CRCDC database. Then, we estimated, for each woman, Λ 0 (t), and calculated F(12|Z) to obtain a risk score of developing a breast cancer during the follow-up period.
Threshold determination Pre-test prevalence of the disease was the percentage of breast cancer in the CRCDC-OC database i. e. 2. 6%. So, as described in Supplementary material 2, we looked for : • s 1 such as ∀s ≥ s 1 : LR + (s) ≥ 2. 054 • s 2 such as ∀s ≤ s 2 : LR -(s) ≤ 0. 493 We then plot LR+(s) and LR-(s) in Figure  2. This method gave a low threshold at 1. 9% and a high threshold at 4. 5%.
An analysis of the medico-economic gain must be carried out taking into account, in particular, interval cancers, over-diagnosis and the stage of the cancers detected. This study proposes a new approach to personalize the organized screening by stratifying the risk within the population at "average" risk and currently considered as homogeneous. However, the results of this study remain to be confirmed in other contexts to validate whether the proposed strategy could be implemented. Six variables were included our the score of breast cancer risk, two of which (age and breast density) are already included in the databases of the different French CRCDCs. Only four more questions are required to be asked in order to establish the score. This information is easy and simple to collect at the time of mammogram.
Moreover, an internal validation on another database, for example of women aged 40 to 50 in the Hérault department, could be performed. A double external geographic validation could be carried out on the databases from other CDCRCs and a database from another country  33. We collected information of interest for the creation of our score at t 0 , the inclusion time. But, over time, the value of variables retained at t 0 may change and the risk at t 0 + x years may also change. It will therefore be necessary to regularly recalculate women risk of breast cancer in order to assign them to the appropriate group of risk. A re-evaluation of the score will also be necessary on regular time intervals to reappraise the coefficients estimated by the model in order to evaluate whether they would need to be adjusted.
Karl et al. (2009) , forests account for 55 % of the total emission, agricultural lands for 27 %, and grasslands, wetlands and shrubs for 18 %. These values are however based on few datasets of BVOC fluxes  (Keenan et al. , 2009) , especially for crops. This leads to uncertainties in the estimates of BVOC fluxes from this type of ecosystem. To date, only a few studies have investigated VOC fluxes from crops, most of them by chamber techniques  (Copeland et al. , 2012; Crespo et al. , 2013; Eller et al. , 2011; Konig et al. , 1995; Das et al. , 2003; Graus et al. , 2013; Gonzaga Gomez et al. , 2019; Wiss et al. , 2017). Flux measurements made at the canopy level on crops are still rare and have been rather focused on orchards and grasslands  (Karl et al. , 2001 (Karl et al. , , 2005;; Copeland et al.
Acquisition and pre-processing of PTR-Qi-TOF-MS data A data acquisition software was developed on Labview to synchronize the measurements of the PTR-Qi-TOF-MS with the sonic anemometer and the other fast-response instruments and to store the data. This software was based on the acquisition of a list of ion peaks integrated online by the TOFDaq software (TOFWERK, SW) using shared variables that are exchanged between the acquisition computer and the https://doi. org/10. PTR-Qi-TOF-MS computer via a local Ethernet connexion. The desired ion peak list was set up at the start of the experiment based on a list of known compounds tuned to the site. The acquired ion peak integral counts per seconds (cps) were synchronized at 20 Hz with the ultrasonic anemometer. Every 5 min, a mass calibration was performed by the TOFDaq software using masses m/z 21.
0221 (H 18 3 O + ) and m/z 59. 049 (acetone C 3 H 6 OH + ). When the calibration of these masses shifted from the real masses by more than 0. 005 m/z the 5 min record was discarded. Additionally, the quality of the peak integration was verified a posteriori by integrating several spectra with PTR-MS viewer (Ionicon, V3. 3). We found errors lower than 5 % for most of the compounds in the most emitted ions as shown by Gonzaga  Gomez et al. (2019). The synchronized data, stored every 5 min in binary files, were pre-processed with another Lab-view® program to compute the mixing ratios and fluxes.
In addition, the calibrations factors reported by  Koss et al. (2018)  were used to compute calibration factors for 145 more compounds (Tables  S2 ). They did indeed compute a calibration dataset for a similar, though not exactly identical, instrument as used in this study. However, they operated their PTR-TOF-MS instrument with an E / N of 120 Td, which is similar to our setup after the 29 June. The difference in S i (t 0 ) /S toluene (t 0 ) between  Koss et al. (2018)  and this study was below 10 % for acetaldehyde, acroleine, acetone and benzene and below 50 % for methanol, acetonitrile and crotonaldehyde but only < 84 % for ethanol, isoprene and monoterpenes. It should be noted that in all cases, S i (t 0 ) /S toluene (t 0 ) -1 has the same sign in both studies. Hence, the calibration factors adopted from that study were good for 4 of 11 compounds and fair for more than half of them.