dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated signal and its parameters Taking the considered setup (n = 3 and d = 5) in this section, we end up with 14 parameters for the simulated signal. So, with such large number of degrees of freedom, we decided to choose the set of parameters such that the simulated signal will resemble as much as possible real signals, and without a priori knowledge on what parameter values are appropriate, we decided to tweak the model parameters and choose the ones that gave a simulated signal "resembling" (similar waveform) typical real current waveforms from our dataset. The noiseless signal model is The polynomial degree is chosen relatively small (we found that n = 3 is sufficient to characterize the tested signals, and hence, this value will be used in the remainder of the paper). The previous transient signal is then corrupted by an additive white Gaussian noise of zero mean and variance σ 2 (varied such that the signal-to-noise ratio s(t k ) = e(t k )s s (t k ) = e p T t k + 1 d i=1 a i cos 2πf i t k + φ i , where p = p 0 , p 1 ,.
For A3, we relied on our dataset of real life signals to get our ad hoc choice of the "effective" polynomial degree n = 3 and "effective" number of harmonics d = 5 that have been experimentally shown to be suitable for a good modeling of the considered transient signals. As an example, Fig. 10  shows different plots comparing the real signal x(t) to its estimate x(t) for different values of the polynomial degree n (1, 3, 5 and 7). We note the improvement of the root-mean-square error (RMSE) between n = 1 and n = 3, hence a better estimation using n = 3, and a slight improvement of the RMSE between n = 3, n = 5, and n = 7. We consider n = 3 to be a good trade-off between model complexity and the estimation performance (less than 10% of relative RMSE difference, e. g. , between n = 3 and n = 5, we have a relative RMSE difference of 0.
Estimation with TCE on a real signal of the COOLL dataset The real signal is taken from a turn-on tran- sient dataset we built especially for transients analysis. The dataset is called Controlled On/Off Loads Library (COOLL)  [32]  and is freely available on the internet (https://coolldataset. github. io/). Since the measurement system  [33]  (Fig. 11 ) used to collect the dataset's signals allows the control over the turn-on/off, we know exactly the turn-on/off time instants and assumptions A1 and A2 hold then true. Moreover, we consider the signal starting from its maximum in order to verify A3. The COOLL dataset signals (Table  1 ) are sampled at F s = 100 kHz  3. The dataset consists of turn-on transient current and voltage signals of 12 different electrical appliances and each appliance has 20 signal examples.
Here, we propose to compare it to the average maximum value of the steady state amplitude (around 0. 2 A). We get a relative RMSE of 3. 6%. Note that we got an average relative RMSE of around 8% for the whole dataset, which is acceptable considering the variability of real signals. ⎤ ⎥ ⎥ ⎥ ⎥ ⎦ , RMSE = 1 N N-1 n=0 |x(t n ) -s( θ , t n )| 2 = 7. 1 × 10 -3 A, Figure  14  allows to get a visual feel for the estimation quality. Figure  14a  and b  show a good fit between the reconstructed signal and original zone.
Classification of cOOLL dataset's appliances using the model parameters Here, we propose to classify the appliances of the COOLL dataset using the model parameters. We use the classical supervised k-nearest neighbors algorithm (k-NN)  [34, Chap. 13] , which proceeds by taking the test example (here the vector of parameters representing the test signal) and classifying it according to a majority vote of the k-nearest examples (of the training dataset). We used the Euclidean distance as a distance metric. We assess the result using K-fold cross-validation with K = 10. This validation works by first partitioning the dataset to K equal partitions (in our case each partition contains 84 example), then take one partition for testing and keep the other nine partitions for training, and we assess the performance using for example the classification accuracy (CA).
Conclusion We proposed in this paper a new mathematical representation suitable for modeling turn-on transient current signals and proposed an algorithm for the model parameter estimation. The efficiency of the algorithm is assessed  theoretically via benchmarking its estimation error variances with respect to the CRB derived in Section 3 of this paper. Later on, the proposed parametric model is validated using real data from the COOLL dataset that we developed specifically for this research work. A "good" fitting between the proposed signal model and the real-life signals has been observed with, in particular, an average relative mean-square-error of about 8%. Note also that our experimental tests showed the need for estimating the fundamental frequency due to its deviation from the nominal value (i.
The chains sampled from the posterior probability density (5) with the regularizing minimum norm prior, with S norm = 400 siemens and λ = 1. Only single period data were inverted at T = 3840 s. The first two panels, Figs 3(a) and (b), show relatively good convergence of the parameter subchains. The cell (N12, E24) in Fig. 3a  is located over a high-conductivity Carpathian anomaly and is well surrounded by the experimental data sites (see Fig. 4  for the cell position). The conductacnce histograms are sharp at that cell, giving a mean conductance and lower and upper bounds of the 95 per cent (equal tailed) credible interval of 2047, 650 and 8000 siemens, respectively, for the Gibbs sampler. The respective values from the SCAM sampler are 1634, 528 and 6133 siemens. The autocorrelation plots of the log-conductance in this cell, shown in the bottom subgraphs in Fig.
Data fit For an individual log-conductance model S from the Markov chain, its fitness to the experimental data is measured by the deviance parameter defined by D (S) = -2 log Prob T obs | S, S = M i=1 T i (S) -T obs i δT obs i 2 , ( 10 ) which has a form of a standard χ 2 -misfit. A Bayesian measure of model fit is defined as the posterior expectation of the deviance, and is estimated as a mean deviance of models from the converged section of the Markov chain, D = K -1 K i = 1 D(S i ), where K is the number of models considered from the chain. Partial deviances evaluated for individual data items or for selected data subsets can be also monitored. For the model from Fig. 5 , column 2 (model with a minimum norm prior, S n = 400 S, λ = 1. 0, 4000 iteration steps of the Gibbs V. Červ et al.
mindat. org -the database run by the Hudson Institute of Mineralogy, or in Du ďa & Rejl 1997, Fig. 9a ), lends support to this hypothesis. Further to the north, a strong anomalous conductivity feature in the NE part of the Bohemian Massif may represent the main source of relatively large, SW pointing induction arrows observed over the whole area to the west of the EBMA. The anomaly is located in SW Poland, and cannot be sufficiently constrained spatially due to lacking induction data in the N of that region. Electromagnetic induction measurements have been avoiding this area, mainly because of a a high level of cultural noise due to heavy industry in the Silesia region. The nearest long period induction arrows are available only at latitudes greater than 51 • N, more than 100 km to the N of the Czech-Polish border, and they are generally shorter than about 0.
The conductance model calculated using the MCMC method provides a plausible geological interpretation of the induction arrows recorded at the eastern margin of the Bohemian Massif and its transition to the West Carpathians. Above the Carpathian conductivity anomaly, the model fits in detail the recorded data features. The anomaly observed above the eastern slopes of the Bohemian Massif suggests an alternative interpretation explained by the NW-SE to W-E trending conductance distribution, which could reflect the fault systems trend of the area (Odra, Sudetic and Elbe fault zones), modulated by structures of the SW-NE tectonic style at the contact zone between the Bohemian Massif and West Carpathians. Nevertheless, a detailed identification of the tectonic structures causative for the induction anomalies in the studied region is, hardly feasible by relying only on the long-period geomagnetic responses.
Travelers interact with a large number and variety of products and services during their journeys. The quality of a travel experience depends on a whole urban mobility system considered in space and time. This paper outlines the relevant concepts to be considered in designing urban mobility. The goal is to provide a language and insights for the early stages of a design process. A literature review sheds light on the complexity of urban mobility from technical, socio-technical, and user experience (UX) perspectives. Observations of experiences in urban areas provide data for describing and understanding travel experience patterns and issues. The paper proposes a conceptual model to describe and analyze different facets of traveler experience, and categorizes problems that travelers face when they interact with an urban mobility system.
Observations Observations are relevant for generating data on human behavior in some contexts  (Sanoff, 2016, p. 77-89). Interaction of travelers with UMSs and their environment was accordingly captured by this method. First, self-observations were recorded in the form of diaries and photos where different travel episodes, UMSs, and activities are related. This provided a dynamic picture of travel flow over time and uncovered the hidden micro-interactions of traveler experience concerning not only physical artefacts, but also interactions with fellow travelers, activities during the trip, planning the activity to be done after the journey, etc. More localized observations in four different transport hubs in the Paris area provided data on subsystems forming a multimodal physical space, identifying artefacts, crowd phenomena, travelers' actions and reactions, and situations a traveler may face in his/her travel routine in such places.
Nature of travel problems The solution generation phase is conditioned by the distinction between essential and accidental situations  (Gorman 2005) : the predicates related to a component's nature (essential attributes) need a new design when those related to their accidental attributes can be solved by a change in how the UMS works. For example, if it is only one booking line agent who asks indiscreet questions (accidental), then correcting his/her behavior would solve the problem. Conversely, if it is the fact of asking travelers personal questions that is problematic, then the whole staff will be affected, and the process and databases will need a structural change.
The Cherenkov Telescope Array (CTA) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. Employing more than 100 (north and south sites) Imaging Atmospheric Cherenkov Telescopes in the northern and southern hemispheres, it was designed to reach unprecedented sensitivity and energy resolution. Understanding and correcting for systematic biases on the absolute energy scale and instrument response functions will be a crucial issue for the performance of CTA. The Montpellier group and the Spanish/Italian/Slovenian collaboration are currently building two Raman LIDAR prototypes for the online atmospheric calibration along the line-of-sight of the CTA. Requirements for such a solution include the ability to characterize aerosol extinction at two wavelengths to distances up to 30 km with an accuracy better than 5%, within exposure time scales of about a minute, steering capabilities and close interaction with the CTA array control and data acquisition system as well as other auxiliary instruments.
Simulation The manipulator is made of 11 identical mechanisms that is the number of vertebrae in the green woodpecker. The dimensions were chosen to have a manipulator with the same proportions than the woodpecker neck. Simulations are performed with Matlab Simulink and Simscape Multibody. The use of the dynamic model in the control law allows obtaining an accurate tracking with a full-actuated robot. In this case, the forces applied have similar shapes for each mechanism, which is promising for under-actuation. Simple under-actuation strategies also succeed at tracking the movement with a good precision, and the neck musculature of the woodpeckers will be used to create related under-actuation strategies.
The same firing sequence was used to form Ca 2 CeCr 2 TiO 9 as was used to form SrLaCrSnO 6 except that an additional final firing at 1450 °C for 48 h was required. The polycrystalline sample of SrLaCrSnO 6 produced was brown in colour whereas Ca 2 CeCr 2 TiO 9 was a fine black powder. The progress of the syntheses was monitored using X-ray powder diffraction (XRD) data collected in our laboratory on a PAN'alytical Empyrean diffractometer operating with Cu K α1 radiation over an angular range of 5 ≤ 2θ/°≤125 at room temperature. Further powder diffraction data were collected using a Si-calibrated wavelength of λ = 0. 8259 Å on the instrument I11 at the RAL Diamond Light Source  [7]. In the latter case, the samples were loaded into a 0. 3 mm diameter borosilicate glass capillary and data were collected at room temperature using the Mythen position-sensitive detector (PSD) over an angular range of 2 ≤ 2θ/°≤ 92.
The data were analysed using the Rietveld method  [8] , as implemented in the GSAS program suite  [9] , in order to determine the unit cell parameters. A cylindrical absorption correction for each sample was estimated using the Argonne X-ray Absorption calculator  [10]. A pseudo-Voigt function  [11]  was employed to model the peak shapes and the background was modelled using a 20-term shifted Chebyshev function. Neutron powder diffraction (NPD) data were collected on SrLaCrSnO 6 using the instrument D2b at the ILL, Grenoble. NPD data were collected at 3. 5 K, 60 K, 110 K, 200 K and at room temperature over an angular range 0 ≤ 2θ/°≤150 in steps of Δ2θ = 0. 05°using a wavelength of 1. 594 Å. The sample was loaded into a 6 mm diameter vanadium can and mounted in a displex refrigerator for all measurements below room temperature.
Each dataset was fully analysed using the Rietveld method. A pseudo-Voigt function was employed to model the peak shapes and the background was modelled using a 12-term shifted Chebyshev function. Neutron powder diffraction (NPD) data were collected on Ca 2 CeCr 2 TiO 9 on the WISH time-of-flight diffractometer  [12]  at the ISIS spallation source at 1. 6 K, 60 K, 110 K, 200 K and at room temperature. The sample was loaded into a 6 mm diameter cylindrical vanadium sample can and mounted in an Oxford Instruments cryostat when data were to be collected below room temperature. The data collected on banks 2-9 were refined simultaneously using the Rietveld method. A pseudo-Voigt function was employed to model the peak shapes and the background was modelled using a 12-term shifted Chebyshev function for banks 2/9 and 3/8 and a 20-term shifted Chebyshev function for banks 4/7 and 5/6.
The scattering angles for the different detector banks are as follows: banks 2/9, 58°; banks 3/8, 90°; banks 4/7, 122°and banks 5/6, 153°. DC magnetometry data were collected on both compounds using a Quantum Design SQUID magnetometer. The measurements were taken on warming the samples through the temperature range 2 ≤ T/ K ≤ 300 in a field of 100 Oe, firstly after cooling from room temperature to 2 K in zero field (ZFC) and subsequently after cooling in the measuring field (FC). The magnetisation per formula unit (f. u. ) of the samples was also measured as a function of applied field at 5, 60, 110 and 200 K for SrLaCrSnO 6 and at 5, 100 and 200 K for Ca 2 CeCr 2 TiO 9 over a field range of -50 ≤ H/kOe ≤ 50.
Results Our XRD data showed that both SrLaCrSnO 6 and Ca 2 CeCr 2 TiO 9 crystallise in the space group Pbnm. The 4e site is occupied by a disordered arrangement of either 1:1 Sr/La or 2:1 Ca/Ce, and the 4d site by a disordered arrangement of either a 1:1 Cr/Sn or 2:1 Cr/Ti. Two crystallographically-distinct oxide ions occupy 4e and 8 f sites. The Rietveld fits to the data collected on I11 are shown in Fig. 1 ; R wp = 0. 0162 for SrLaCrSnO 6 and R wp = 0. 0365 for Ca 2 CeCr 2 TiO 9. SrLaCrSnO 6 was found to be contaminated with 0. 44(2) wt% La 2 Sn 2 O 7 and Ca 2 CeCr 2 TiO 9 with 3. 77(3) wt% CaTiO 3 and 0. 1(1) wt% CeO 2. The temperature dependence of the molar magnetic susceptibility, χ(T), of SrLaCrSnO 6 is shown in Fig. 2. There is a slight divergence of the ZFC and FC curves below 200 K, which is most noticeable in the plot of the inverse molar susceptibility against temperature shown in the inset.
938 O 9 , corresponding to an impurity concentration of 2. 64 wt%, which was used subsequently during the analysis of the diffraction data collected at lower temperatures. Note that for simplicity we shall continue to use the ideal formula in our text. The fits to the diffraction patterns recorded from both compounds at room temperature are shown in Fig. 6. The corresponding structural parameters and selected bond lengths and angles are listed in Table 1-6. No cation ordering was detected in either compound when test refinements were carried out in the monoclinic space group P2 1 /n. The NPD pattern collected from SrLaCrSnO 6 at 200 K could be accounted for using the same structural model that was used in the analysis of the data collected at room temperature. On cooling to 110 K a small amount of diffuse scattering at the position of the (101)/(011) reflection is visible, see Fig.
Our neutron diffraction data indicate that, in contrast to Ca 2 CrSbO 6 and LaSr 2 Cr 2 SbO 9 , respectively, SrLaCrSnO 6 and Ca 2 CeCr 2 TiO 9 both adopt a G-type antiferromagnetic ground state with no net magnetisation in the absence of an applied field. In the case of Ca 2 CeCr 2 TiO 9 this is clearly attributable to the absence of the B-site cation ordering that is directly responsible for the ferrimagnetism of LaSr 2 Cr 2 SbO 9. The ferromagnetism of Ca 2 CrSbO 6 has been attributed to the high tilt angle of the CrO 6 octahedra therein. A similar level of tilting is present in SrLaCrSnO 6 but the absence of cation ordering leads to the presence of strong nearest-neighbour antiferromagnetic Cr-O-Cr superexchange interactions that are absent from the cation-ordered antimonate and so, once again, the change in magnetic behaviour can be traced to the absence of cation ordering.
Neutron diffraction data collected at 1. 7 K revealed a G z antiferromagnetic spin arrangement at that temperature. Mӧssbauer spectroscopy showed that magnetic hyperfine interactions were present in SrLaFeSnO 6 below 215 K, leading Attfield et al. to postulate that short-range magnetic interactions start to develop below 250 K and that long-range antiferromagnetic ordering occurs at 38 K. Gibb expanded on this model by envisaging clusters of antiferromagnetically coupled spins gradually increasing in size and linking together as the temperature is lowered until long-range order occurs along a magnetic 'backbone'  [16]. Simulations showed that while 87% of Fe atoms in a fully disordered structure are expected to be biconnected to the magnetic backbone of the lattice, only 63% are connected by more than two nearestneighbour pathways.
Clusters with few connections to the backbone are more susceptible to competing superexchange interactions and will be the first to decouple from the magnetic backbone as the temperature is raised. They are likely to carry a small net moment due to the incomplete cancellation of spins and they will therefore enhance the susceptibility and be strongly influenced by an external magnetic field, hence the observed hysteresis in the magnetic susceptibility. The ordered magnetic moment per Fe 3+ cation at 1. 5 K, 2. 94 µ B  [6] , is very low, suggesting that even at this temperature not all of the spins and clusters are linked to the magnetic backbone. Attfield et al. only collected neutron diffraction data on SrLaFeSnO 6 at room temperature and 1. 7 K but we have been able to collect data on SrLaCrSnO 6 over a greater range of temperatures and hence to follow the temperature evolution of the long-range magnetic ordering.
The refined ordered moment per Cr ion at 3. 5 K of 2. 24(3) µ B is only slightly smaller than expected given that the ordered moment per Cr 3+ ion in LaCrO 3 is 2. 63(3) µ B  [17]  and 2. 56(3) µ B in Ca 2 CrSbO 6  [5]. If we assume that only Cr 3+ ions that are at least biconnected to the magnetic backbone (~87%) contribute to the longrange magnetic order at this temperature then the moment per Cr 3+ ion becomes a more reasonable 2. 57 µ B. The rise in the susceptibility at the lowest temperatures measured can be attributed to the presence of the disconnected spins. The similarity of the magnetic susceptibility and NPD data collected on Ca 2 CeCr 2 TiO 9 to those from SrLaFeSnO 6 suggests that the magnetic cluster model can also be applied to this system. The ZFC and FC χ -1 (T) curves differ below 175 K, see Fig.
4 , and a magnetic Bragg peak is apparent in the NPD data collected at 110 K. The refined value of the ordered moment, 0. 89(1) µ B , is, however, very small. Below the increase in gradient of χ(T) at 80 K the intensity of the magnetic scattering increases but the ordered moment per Cr 3+ cation at 1. 6 K, 1. 974(5) µ B , is smaller than that found in SrLaCrSnO 6. This suggests that at base temperature cluster formation is extensive but that fewer spins are connected to the magnetic backbone in Ca 2 CeCr 2 TiO 9 than in SrLaCrSnO 6. This is consistent with the larger molar susceptibility at 2 K, the larger hysteresis in M(H) and the greater difference between the ZFC and FC χ(T) of Ca 2 CeCr 2 TiO 9 compared to SrLaCrSnO 6. However, the B sites in Ca 2 CeCr 2 TiO 9 contain a greater proportion of paramagnetic ions than in SrLaCrSnO 6 so more ions will be at least biconnected to the magnetic backbone of the lattice.
MATERIALS AND METHODS: We performed a retrospective analysis of the prospectively maintained Endovascular Treatment of Ischemic Stroke multicentric registry. Data from consecutive patients who benefited from thrombectomy with a first-line Sofia approach between January 2013 and April 2018 were studied. We excluded other first-line approaches (stent retriever or combined aspiration and stent retriever) and extracranial occlusions. Baseline characteristics, procedural data, and angiographic and clinical outcomes were analyzed.
Study Design and Population We performed a retrospective analysis of our prospectively maintained, multicentric Endovascular Treatment of Ischemic Stroke (ETIS) data base. ETIS is a prospective registry collecting data from patients benefiting from mechanical thrombectomy in 4 major French comprehensive stroke centers. Local ethics committees approved this study. Consecutive patients from January 2013 to April 2018, presenting with acute ischemic stroke due to large-vessel occlusion strokes of the anterior or posterior circulation treated by mechanical thrombectomy using first-line ADAPT with a Sofia catheter were analyzed. We excluded patients treated first-line by another DAC, stent retrievers combined with distal aspiration, or stent retrievers alone. Extracranial occlusions were also excluded.
Data Collection and Outcome Evaluation Age, sex, medical history, previous medication, stroke severity using the NIHSS score, stroke etiology, imaging technique, ASPECTS, IVT, and angiographic (arterial occlusion topography, degree of revascularization assessed by the modified TICI [mTICI] score) data were recorded. Clinical outcome was assessed by the mRS 3 months after stroke. Favorable outcome was defined by an mRS Յ2. Angiographic reperfusion was rated using the mTICI score. Successful revascularization was defined by mTICI Ն2b. Near-complete and excellent reperfusion was defined, respectively, by mTICI of 2c/3 and 3. The FPE was defined as an mTICI 3 after a single aspiration maneuver. Safety criteria were also recorded, including procedural complications (dissection, embolism in a new territory, and intracranial arterial perforation), intracranial hemorrhage, and mortality.