dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the thrust function of the exhausting gaz is taken into consideration (FNIN1, T49C). Each pair of variables is observed over four stabilized points, which give 36 new variables. In addition, the four test bench components, CELL, CNOZ, COWL and BMSN are kept in the set of variables that will be used in the model because the pre-treatment was not able to make the data independent from those ones. Finally, the models in following sections are applied on this set of 40 variables. Note that, to keep an understanding of the new variables obtained from a pair, they are named as follow: "first variable name ___ second variable name ___ stabilized point ". For example, the variable created from T49C and FNIN1 on the fourth stabilized point will be called T49C_FNIN1_6 (the 4 stabilized point level are listed from 3 to 6).
To account for this a normalized anomaly score, relying on a property of Binary Search Trees (BST))  Liu et al. (2008) , is defined as f (x, n) = 2 -h(x) c(n) , (3) with c(n) defined as c(n) =      2H(n -1) - 2n -1 n for n > 2, 1 for n = 2, 0 otherwise, ( 4 ) where n is the size of data set and H is the harmonic number. The value of c(n) above represents the average of h(x) given n, so we can use it to normalise h(x) and get an estimation of the anomaly score for a given instance x. Note that f ∈ [0; 1] with value closer to 1 indicates that the observation is more likely to be an anomaly. Once the Isolation Forest has been applied to the data, an anomaly score is estimated for each engine. This anomaly score is pointless if it cannot be completely understood by domain experts. For complex models, such as ensemble methods, deep networks or Isolation Forest, we cannot use the original model as its own best explanation because it is not easily understandable.
Then, for the previous system  (19) , the design of an HOSM interconnected observer is performed: Σ ξ,i : ˙ξ i1 ˙ξ i2 = 0 1 0 0 ξi1 ξi2 + Φ i Fi + λ i1 ξi1 1/2 sign( ξi1 ) λ i2 sign( ξi1 ) , i = 1, 2; ( 20 ) where ξi1 ξi2 T and ξi1 = ξ i1 -ξi1 are, respectively, the Σ ξ,i system's state estimation and the estimation error, for i = 1, 2; the rotor position estimation can be written, according to  (20) , as θe = tan -1 ( ξ12 ξ22 ). ( 21 ) Remark 2. For the observer implementation, a four-quadrant inverse tangent (ATAN2 when using Matlab software) was used.
Simulation Results The simulation validation is given in the following to underline the effectiveness and performance of the control strategy presented. The different parts of the self-sensing control method are illustrated in Figure  1. The nominal IPMSM parameters utilized in the simulations are presented in Table  1. Using Matlab/Simulink software, the simulation was performed. The engine is tested conforming to the industrial test trajectory  [32]  shown in Figure  2  which presents the trajectories of the chosen benchmark.
This article is aimed at studying the notion of step template in healthy gait signals. We investigate whether it is possible to accurately detect all the steps, of different subjects, thanks to a single step template of gyrometer signal. This hypothesis is not as innocuous as it might seem. Indeed, in previous methods, a database of several templates, representative of several steps from patients with known pathologies, was used by the template-matching algorithm  [16]. Successfully reducing this multiple-templates database back to a single template would not only have positive computational consequences, but also be very interesting in a theoretical point of view, for a better neurological understanding of human gait. Indeed, if a single step template is enough to accurately detect all the steps of different healthy subjects, it implies that these subjects share a common locomotion pattern, as first intuited by Murray  [20].
Protocol Two XSens sensors (Xsens Technologies, Enschede, The Netherlands) (XS) were placed on the participant's body (one on the dorsal part of each foot) using Velcro bands. The GaitRite walkway (GR) was used as the gold-standard. The data were sampled at 100 Hz for the XS and at 120 Hz for the GR. Both systems were synchronised in time by using the PC clock connected to XS. All subjects performed two recording sessions, one week apart. A last session was recorded, 3-6 months later, for a few subjects. Each session is composed of 42 direct passages on the GR. No U-Turn was considered in the recording. For practical reasons, subjects kept their own shoes. The protocol is schematised in Figure  3. The protocol includes two sensors (left and right foot), and each of them records a nine-dimensional signal (3D accelerations, 3D angular velocities and 3D magnetic fields), possibly with some re-calibrated data provided by the XSens software (such as the vertical acceleration in the direction of the gravity), as presented in Figure  4.
Instead of considering all these dimensions, we decided to only use one of them: the most relevant in the context of step detection. This decision was made based on observations of real data and physiological reasons provided by medical doctors. Consequently, in the following study, the y-axis angular velocity was the only component studied. Figure  5  represents a typical y-axis angular velocity signal, corresponding to a subject step, with annotated key instants. Key phases of the Gait Cycle, annotated on an y-axis angular velocity graphic. The events are represented by vertical lines. From left to right: "Heel Strike" (black), "Foot Flat" (green), "Heel Off" (green) and "Toe Off" (red).
Subjects and Database The database is composed of 13 young healthy subjects who declared no medical impairment. All participants provided written informed consent before inclusion. The study protocol followed the principles of the Declaration of Helsinki. It was approved by the ethics committee "Protection des Personnes Nord Ouest III" (ID RCB: 2017-A01538-45). The main averaged characteristics of the subjects can be found in Table  1. Each subject performed the protocol approximately 20 times, which totals 7414 recorded steps. The average number of steps per trial is 7. 5 steps, and the average speed is 1. 5 m/s. The repartition of the number of steps and the average speed for each subject are displayed in Figure  6.
Strategy 2: DTW-Based Selection An alternative strategy consists in choosing one specific template from the train library, by selecting the more appropriate according to the step detection task. A first approach in this search for an ideal template would be to take the step, in the whole database, which is, structurally, the nearest to all the other steps. If the DTW distance is considered as an effective measure of similarity between time series, this step would correspond to the step with the minimum mean-DTW-distance to all the other steps of the database. Considering the train library S train composed of N train steps s (1) ,. , s (N train ) , we choose the step s * such that s * = argmin s∈S train N train ∑ n=1 DTW s, s (n) (8) In strategy S2, the unique used template s * is the nearest, by a DTW point of view, to all the steps from the database.
Strategy 3: Linear Fusion Another approach would be to merge all steps from the train database to compute one unique template that reflects the most shared properties. This process was already used by Soaz and Diepold  [17]  to obtain the characteristic steps of some particular gait clusters. Merging all steps necessitates first applying a time and amplitude normalisation, which can be performed either linearly or non-linearly. In strategy S3, we take all steps from the database, normalise them so that they have zero mean and unit variance, resample them linearly so that their durations are equal to the median duration of 63 samples and average them. The result for strategy S3 applied on our database can be seen in Figure  8b.
Strategy 4: Non-Linear Fusion Another method to obtain an average step is to re-align all the steps, by DTW, on a single one, and then to average them all. Contrary to strategy S3, the time normalisation in this context is non-linear since the DTW resamples the signals through a non-uniform sampling. Before anything, all the steps of the database are normalised in amplitude so as to have zero mean and unit variance, as in S3. Then, we select all the steps of length equal to the median length of our steps database (63 samples), and then select the one with the minimum mean DTW distance to all the database steps (in a process analogous to the one exposed in strategy S2). The chosen step s c , referred to as the calibration step, serves as a mold on which all steps will be blended. The non-linear time renormalisation based on DTW performs as follows.
Given a step s of duration N s and the calibration step s c of duration N c = 63, the renormalised step s is computed such that for i ∈ [[1, N c ]] s i = 1 Card{j|(i, j) ∈ P * (s c , s)} ∑ {j|(i,j)∈P * (s c ,s)} s j , ( 9 ) where P * (s c , s) is the optimal path between s c and s (see Equation (  4 )). In other words, each sample s i corresponds to the mean of all the samples s j matched with s c i on P * (s c , s). The result for strategy S4, applied on our database, can be seen in Figure  8c.
Strategy 5: Knowledge-Based Piecewise-Affine Approximation The last strategy consists in building the template by hand by using biomechanical assumptions of the shape of the angular velocity during a step. To that end, we used assumptions described in Section 2. 1 on gait events. Based on biomechanical considerations, we placed all gait events (HS, TO, etc. ) with amplitudes similar to the ones observed in strategies S2, S3 and S4 and assumed a simple affine model between them. The so-formed template can be seen as an abstraction or idealised step. Similar to strategies S3 and S4, its length is equal to the median length of the steps database, i. e. , 63 samples. Its equation is as follows: f (x) =                  0. 3x -0. 7 if x ∈ [1; 3] -0. 8x + 2. 6 if x ∈ [3; 5] 0. 2x -2.
Experiments The DTW algorithm used in our process is the one implemented in Matlab Version R2019a. The width of the warping window maxsamp was set to 20 samples. The length of the DTW scan area z in Equation (  7 ) presented in Section 4. 1 was set to z = 10. The influence of parameter z is discussed in Section The pointwise distance d(. ,. ) used in Equation (  3 ) was chosen as d(u i , v j ) = u i - ū σ u - v j - v σ v 2 , (11) where. and σ. are, respectively, the mean and standard deviations of time series u and v. This distance ensures that the input signals are first renormalised with zero mean and unit variance. For the random strategies S1-1 and S1-10, 20 independent simulations were computed and signals that were used for training were removed from the test database. In both cases, the evaluation metrics of the 20 simulations were concatenated and means and standard deviations are displayed.
Experiment 1 A sum-up of the evaluation metrics obtained for the S1-1 and S1-10 strategies, after 20 independent simulations, with or without the DTW refinement step, can be seen in Table  3. For the S1-1 strategy, the DTW refinement step divides by approximately two the ∆Start and ∆Duration evaluation metrics. For the S1-10 strategy, the impact is limited. Computation times for processing the whole database (7414 steps) is displayed in Table  3. Table  3. Evaluation metrics obtained by strategies S1, S2, S3, S4 and S5. For S1-1 and S1-10, the precision and recall are averaged on the whole database and on 20 independent simulations (means and standard deviations). ∆Start, ∆End and ∆Duration are displayed in milliseconds and averaged on the whole database (means and standard deviations). Computation time corresponds to the time to process the whole database.
Experiment 1 If the effect of the DTW refinement step is not striking in the S1-10 strategy, it is particularly strong in the S1-1 strategy. Indeed, S1-1, with the DTW refinement step, gives a result almost comparable with S1-10, with or without the DTW refinement step. A single template step, correctly re-aligned, is able to accurately detect all the steps of our HS database. This means that all the HS of our database share a common gait pattern, but, also, that the step deformations between each subject follows a non-linear deformation comparable to a DTW deformation. Previously, the authors have highlighted the impact of immobilisation of a joint on the other joints of the lower limb, depending on the phases of the step  [76]. Thus, the joints were not affected in the same way at the different phases of gait, which tends to underline the non-linearity of the gait pattern.
Experiment 2 The four strategies S2, S3, S4 and S5 present, after the DTW refinement step, comparable, very accurate evaluation metrics. Importantly, they all give more accurate metrics than the average S1-10 strategy, with or without DTW, tested in Section 5. 2. One striking fact is that strategy S5, based on a piecewise-affine step template, inferred from biomechanical knowledge, as presented in Section 4. 2. 5, presents highly accurate metrics (in fact, it is the tested strategy giving the best evaluation metrics, on average). This result is rich in conclusions. Indeed, if all the steps of our HS database can be very accurately detected with such a simple template, it means that human locomotion is a phenomenon extremely similar, in its nature, between every healthy subject. Even if steps of healthy subjects highly change in duration, etc.
Limitations The main limitations of this study are linked to the number of subjects and to the relative homogeneity of the considered database (healthy subjects). Further studies on elderly or pathological subjects will be of high interest to investigate if the conclusions of the present study are also valid for different populations or on a larger range of subjects. Besides, other detection methods could have been tested. For instance, Discrete Wavelet Transform (DWT)/Continuous Wavelet Transform (CWT) use for gait/ECG phases detection has been advocated in numerous articles  [82] [83] [84]. It is nevertheless to be supposed that these methods, based on linear scale deformations-such as the linear correlation criterion-would not have given as good results as the DTW distance criterion.
Conclusions Thanks to the method presented in this article, based on a greedy template-matching algorithm and a DTW refinement step, a single step template is enough to accurately detect all the steps of a healthy subjects database. However, this detection is here possible due to two main contributions: the use of a non-linear measure of fit (DTW) that allows comparing time series of different lengths and the use of a relevant template inferred from real physiological data. We believe that these results will be of interest for the neurological interpretation of locomotion and further studies shall be conducted to confirm/infirm these conclusions on other populations.
ACKNOWLEDGMENT This work is supported by the CPER prject , sponsored by the Champagne-Ardenne region and the French ministry of higher education and research. He is a Senior Member of IEEE, the founder of SMC Chapter -Hiroshima Section, the founder and chair of the technical committee on Intelligent Green Production Systems (IGPS), and Editor-in-chief of International Journal of Process Systems Engineering (IJPSE), president of RAMS Society, and editorial board of the technical committee on System of Systems and Soft Computing (IEEE SMCS). He is invited speaker in several Universities and international events, and PC / chair / co-chair of several international conferences. Dr. Gabbar is the author of more than 110 publications, including books, book chapters, patent, and papers in the area of safety and control engineering for green energy and production systems.
Between two contact sequences, particle concentration goes down rapidly but did not return to the baseline. Thus, from the second friction, each emission is impacted by the previous emission. These residual concentrations from the previous test can be up to 650 #/cm 3. These residual concentrations can be significantly reduced by increasing the time between frictions. The black rectangle highlights the analysis area defined for each emission. Thus, the data between the beginning and the end of the emission are processed electronically under MATLAB. The different concentrations in the range are summed and corrected by ambient noise. In the plotted example, the total emission of the interval is 6. 3 Â 10 4 #/cm 3 and the average over the last five friction events is equal to 6. 5 Â 10 4 #/cm 3.
Introduction As highlighted in Part I,  1  alcohol are promising alternative fuels, as well as blending fuel components for internal combustion engines. 2, 3 Alcohols thus constitute a valuable and viable solution to the negative impact of the transport sector on the environment and on human health. The development of detailed and predictive combustion kinetic models provides a very efficient tool for the synergistic design of fuels and engines,  4  thus guiding the implementation of alternative routes for energy production and utilization. This work completes Part I 1 by describing in detail the experimental facilities and methods used for a systematic investigation of linear n-C3-C6 alcohols. New ignition delay time (IDT) data for ethanol, n-propanol, n-butanol, and n-pentanol have been measured in a rapid compression machine (RCM).
Permanent gases were quantified with two thermal conductivity detectors (TCDs). An online GC × GC was used to identify and quantify the species, using a time-of-flight mass spectrometer (TOF-MS) and a flame ionization detector, respectively. Fuel conversion and intermediate species profiles are reported in Figure  3. The measured process gas temperature profiles are used in the simulations. The model predicts the fuel conversion profiles within experimental uncertainty and the expected trend in the decomposition rate of n-pentanol> n-butanol. Moreover, contrary to the comparisons with the data from the Hefei flow reactor, the conversion of n-pentanol is correctly predicted. Yields of acetaldehyde that were overestimated in Figure  2  are here underestimated by the same amount, supporting the selection of rate constants for the reaction classes responsible for CH3CHO formation discussed in Part I.
Hexanal The oxidation of n-butanol, n-pentanol, and n-hexanol was also investigated in the JSR at CNRS-Orléans. 22, 23, 32 Experimental data have been systematically obtained over the last decade for highly diluted mixtures of 1000 ppm of fuel in O2 and N2, at φ = 0. 5, 1. 0, and 2. 0. The reactor is operated at p = 10 bar, in the temperature range T = 550-1150 K for n-hexanol and T = 750-1150 K for n-butanol and n-pentanol, with residence time τ = 0. 7 s. Similar data for n-propanol (1500 ppm in O2/N2, φ = 0. 25, 0. 5, 1. 0, 2. 0) have been reported by Galmiche et al. 33  In addition, Sarathy et al. 21 reported experimental data at p = 1 bar in the same reactor for n-butanol. The reacting mixtures were probe-sampled by means of a fused silica low-pressure sonic probe. The samples were analyzed online by FT-IR and offline after collection and storage in 1 L Pyrex bulbs.
Offline analysis was done using GCs equipped with capillary columns (DB-624 and Carboplot-P7), a TCD, and an FID (flame ionization detector). Figure  7  compares model predictions and experimental data for n-butanol, n-pentanol, and n-hexanol at φ = 1. 0 and p = 10 bar, providing insights into the relative reactivity of the three fuels. Additional comparisons for leaner and richer mixtures are reported in the Supporting Information (Figures  S12  and S13 ). The simulations using the present model are also compared to the data of Galmiche et al. 33  for n-propanol oxidation in Figures S8-S11 and with the atmospheric pressure data for n-butanol  21  in Figures  S14-S17. Figure  7. n-butanol, n-pentanol, and n-hexanol oxidation in JSR at φ = 1. 0, p = 10 bar, and τ = 0. 7 s. Comparison between experimental (symbols)  22, 23, 32  and predicted (lines) mole fraction profiles of intermediate and product species.
Results are reported in Figure  S38  of the Supporting Information. Reducing the rate of initiation improves IDT predictions at high temperatures and has a negative impact on n-propanol conversion profiles in the pyrolysis experiments. These observations highlight the need for a systematic investigation of unimolecular initiation reactions so as to facilitate the determination of more appropriate rate rules for this class of reaction. Figure  10b ,d shows only the comparison with the experimental measurements obtained in this work using the RCM at NUIG. Compositions of the mixtures and operating conditions for the new data are reported in Table  1. ST simulations have been carried out using an adiabatic constant volume batch reactor, accounting for the average pressure rise behind the reflected shock wave reported in the referenced experimental studies.
55 IDTs of stoichiometric n-octanol/air mixtures have been measured in a high-pressure shock tube in the temperature range T = 720-1250 K at pressures of 20 and 40 bar. The same study presented speciation measurements at T = 500-1150 K obtained in a JSR operating at p = 10 bar for 1000 ppm n-octanol/O2/N2 mixtures at φ = 0. 5, 1. 0, and 2. 0. Figure  13  compares model predictions with the experimental IDT data. The model agrees with the experimental measurements within a factor of 1. 25. Figure  14  compares model predictions with experimental data for the oxidation of n-octanol in a jet-stirred reactor. 55 As highlighted for the IDTs, n-octanol shows a pronounced NTC behavior also for the JSR measurements. Major species such as CO, CO2, and H2O are well predicted for every equivalence ratio condition.
e. , 20. 9 and 20. 0%, confirming the strong effect of decreasing oxygen content, i. e. , 7 cm s -1 decrease of the peak flame speed. Figure  15. Experimental (symbols)  16, 17, 22, 23, 45, [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67] [68]  and simulated laminar flame speed of alcohol/air (21% O2, 79% N2 in mol) mixtures at different unburned gas temperatures and pressures. n-propanol: black, n-butanol: blue, n-pentanol: red and n-hexanol: pink. The case at T = 423 K and p = 1 atm also shows results for n-butanol/air considering 20% O2 concentration in air (blue dashed line). Providing the observations above and considering that the large majority of the experimental studies do not specifically declare the exact composition of air, only a few datasets are matched by model simulations where we assumed 21% oxygen concentration.
Moreover, it is also clear that more systematic experimental investigations for the different fuels, using the same facility, could provide at least qualitative guidelines for model improvement. This discussion also highlights once again the importance of exhaustive data reporting and the existing concerns for alcohol flame data reproducibility. In addition to the uncertainty related to air composition, other sources of experimental uncertainties are associated with different experimental methods, procedures, and setups. A deeper discussion of this topic as well as the revision of the core chemistry dominating laminar flame propagation is beyond the scope of this study.
Conclusions n-C3-C6 alcohol combustion was experimentally investigated in a JSR and in an RCM. JSR data were obtained for n-butanol, n-pentanol, and n-hexanol at nearly atmospheric pressure (i. e. , 107 kPa), in the temperature range T = 550-1100 K and at equivalence ratios φ = 0. 5, 1. 0, and 2. 0. IDTs for stoichiometric fuel/air mixtures have been measured in a rapid compression machine at p = 10 and 30 bar and T = 704-935 K for ethanol, n-propanol, n-butanol, and n-pentanol (10 bar only). These data present the first comparative and systematic investigation of such fuels and provide precious validation targets for model development. In particular, to the best of our knowledge, we present the first experimental measurement for n-pentanol and n-hexanol oxidation at atmospheric pressure and the first highpressure/intermediate-temperature IDT data in a rapid compression machine for n-propanol.