dataesr/scientific-paragraphs-categorization huggingface dataset:
Force Sensor Calibration Although the interaction force is accurate after tool dynamic identification, the robot does not know the placement of the force sensor, especially for the orientation on its z axis. In this way, the x and y axes were not aligned with the robot coordination frame. Hence a calibration procedure was required to transfer the interaction force into the robot coordinates. As mentioned above, after identifying tool dynamic component by MNN, we performed the force sensor calibration by collecting more data using hands to touch the force sensor and then applying the SVD to solve the transformation matrix. It should be noticed that the influences from the gravity of the force sensor have been eliminated with the tool gravity identification using neural networks together. For the force on the robot end-effector, we used the software package provided by the KUKA Fri interface  [40] , and we assumed that there were no disturbances from the robot dynamic effects on it.
Conclusions and Future Work This paper presents a novel model-free based tool dynamic identification using MNN and force sensor calibration for accurate force sensing. Firstly, tool gravity force was identified by CF and MNN methods. The results showed that a model-free based tool dynamic identification using MNN is more accurate than a model-based identification using CF. Afterwards, force sensor calibration was implemented using SVD. Results showed that the calibrated force was able to represent the force from physical interaction. Furthermore, the first CF-MNN model (M1) is proven as the best method for reconstructing the training dataset, while the first FF-MNN model (M2) is the best one to predict force on the testing dataset. It is observed that MNN approximation is more accurate than CF in the estimation of the gravity force, thus enhancing the accuracy of the tool dynamic identification and calibration for force sensing.
The comparison results of MSE, RMSE and correlation coefficient ρ among M1-M8 on the testing dataset. 10 -3 MSE channel x RMSE channel x 0. 998 channel x 8 0. 09 0. 9978 rad 7 0. 085 0. 9976 0. 08 0. 9974 6 0. 9972 0. 014 MSE channel y 0. 12 RMSE channel y channel y 0. 994 0. 012 0. 11 rad 0. 993 0. 01 0. 1 0. 992 0. 008 0. 09 0. 026 MSE channel z 0. 16 RMSE channel z 0. 99 channel z 0. 024 rad 0. 022 0. 15 0. 98 0. 02 0. 14 0. 018 0. 13 0. 97 0. 045 MSE overall 0. 36 RMSE overall 0. 992 rad 0. 04 0. 34 0. 99 0. 035 M1 M2 M3 M4 M5 M6 M7 M8 0. 32 M1 M2 M3 M4 M5 M6 M7 M8 0. 986 0. 988 M1 M2 M3 M4 M5 M6 M7 M8 overall Figure 11.
Due to the finite feasible uncertainty range represented by ∆p m1 and ∆p m2 in the designed LPV controller, much more severe flight velocity faults would not be considered in following contents to prevent dramatic values of ∆p 1 and ∆p 2. The above nonlethal flight velocity fault might be caused by incorrect installation and positioning of the sensor  [41]. The pitch rate fault might be induced by gyro damage, which results in evident measurement bias. The LPV controller gain K(p) and PIO gain L(p) for (  21 ) and (  26 ) are synthesized based on the LMI minimization problem  (39)  with Theorem 1. The results are calculated using MATLAB LMI Toolbox with minimum τ = 1. 751×10 3. For these results, the parameters in Theorem 1 are set as follows: F m is regarded as a solvable variable in the proposed theorems with respect to ϵ and F m , and the calculated value ∆p m1 = ∆p m2 = 0.
Compared with some other nonlinear control methods  [5, 11] , the LPV method in this paper is much easier to analyze the closed-loop stability with an observer-based control structure. Moreover, the computing time for the virtual and applied control inputs in the above simulation is approximately 0. 0094 s, where the active-set algorithm is used to solve the nonlinear programming problem (44) with MATLAB installed on a laptop (operating system: Windows 7, CPU: Intel Core i5-5200U). This computing time makes proposed method attractive for future practical applications.
Geometry and boundary conditions The axial geometry of the membrane module allows the reduction to a two dimensions problem. Two different meshes are superimposed to respectively describe the gas and the liquid phases (Figure  3 ). The model simulates the outlet compositions and flowrates from input parameters (inlet composition and flowrates, operating pressures and temperatures). The model was implemented under the open source CFD framework OpenFOAM. The same mesh was used for both the liquid and the gas phases: the convergence was achieved using a 9000 square cells mesh. Mass imbalance was verified to be below 10 -4. The solver tolerance was set to 10 -9 for all the numerical fields. The runtime for a single simulation is about 20 minutes on a single thread (Intel Core i7-4910 MQ Haswell at 2.
Conditioned Bulk Density Conditioned Bulk Density (CBD) values were measured using a Freeman FT4 powder rheology equipment whilst running flow properties test. This test used a 25ml split vessel and a metal blade rotating at both directions to create a pre-conditioned state by removing the excess of air trapped in the bulk. To measure CBD, the vessel is splitted to constrain the volume. Three measurements were performed for each material. to 65 o. Five measurements were performed for each -PEKK powder and LS PEKK parts. The data was processed using Jade 6 software.
1 (b) by the yellow band. Save trees and the air, please don't print! bowen@unistra. fr 5 ) is achieved when I P and I AP are of opposite sign at constant V. A non-zero current that depends on the MTJ's magnetic state is observed for V = 0. Despite the ~50meV expected thermal smearing for this 295K measurement, the +300% TMR peak has a FWHM of ~1. 3meV and statistically relevant conductance oscillations with a ~0. 25meV width are observed (using averaged dI/dV data; see Fig. S4  for full dataset and error bars). This spectral sharpness is a direct signature of the spin engine. Using data from Fig. S3a , the inset to panel (c) shows how the bias-dependent output power depends on the MTJ's magnetic state and can exceed 0. 1nW at RT. As a rare experimental event, we have observed how a MTJ can generate electrical power at room temperature (RT) thanks to our conceptual spin engine, according to analytical and ab-initio theories.
2c  for another such sweep with differing maximum applied bias V max and bias step (i. e. a differing effective bias sweep rate), as well as by additional datasets (see SI). This eliminates any junction instability/memristive explanation  34. 6) The numerically derived junction conductance dI/dV of the data of Fig. 2(c ), shown in Fig. 2(d) , reveals spintronically determined conductance jumps, and spectral features as low as 0. 25meV ---despite the 2k B T≈50 meV thermal smearing expected at 295K ---that are statistically beyond the error bar (see Fig. S4  of the SI) thanks to an excellent signal-to-noise ratio. This spectral sharpness is also witnessed through a 300% TMR peak with a full-width-half-max of ~1. 3meV (see Fig. 2(e )), which arises from a combination of local maxima(minima) in I P (I AP ) at V = -3.
Supplementary Information 1 Junction Statistics Although the experimental data of the main manuscript was acquired on only one MTJ, its unusual magnetotransport features are highly reproducible. We begin by reproducing in In each of the MTJ's P and AP magnetic state, we thus observe through seven separate 0 → + Vmax → -Vmax → +Vmax → -Vmax → 0 (i. e. for a total of 32 datapoints at a given V and MTJ magnetic state), the same unusual magnetotransport features. Note the high fidelity of each dataset despite the presence of multiple bias sweep in each panel.
Controlling experimental artifacts The MTJ was measured on the cold finger of an optically sealed cryostat. Since the sample remained in the dark during measurements, this ensures that no spurious photovoltage/photocurrent effects are taking place here. Measurements were performed using a Keithley 2636 Sourcemeter, in 4-point mode with the top electrode labelled as V+/I+. This latter point in principle eliminates possible thermovoltage artifacts along the leads. Separate testing using calibrated resistances revealed a maximum current offset of 500pA and a maximum voltage offset of 0. 1 mV. We observe that the current across the MTJ can reach ≈ -50nA at V=0 (AP magnetic state), while several bias voltage values leading to I=0 are observed in the MTJ's P magnetic state. No shifting in either V and/or I (presumably to correct for a V or I offset) can prevent the dataset from entering a 'forbidden' IV quadrant (with I and V of opposite sign).
The dataset's 3 I(H)'s and 17 I(V)'s (files #10-29) were acquired over 90 minutes. Within that time interval, the temperature measured ≈15cm away from the MTJ on the cryostat's Cu cold finger decreased monotonously from 295. 3K to 294. 46K, i. e. at a rate of ≈0. 01K/min. Thus, the two P/AP 0 → +10mV → -10mV → +10mV → -10mV → 0 I(V) (file #26-27) of Fig. 2(c ) were acquired sequentially with a starting temperature of 294. 61K and 294. 6K, which decreased by 0. 01K over the 93s required by each measurement. The I(H) measurements of Fig. 2 (a) (file #10, i. e. the first measurement reported here; T:295. 3K→295. 13K) and Fig. 2 (b) (file #29, i. e. the last measurement reported here; T:294. 53K →294. 46K) initiate and conclude the reported dataset. In particular, Fig. 2(c)  shows a quantitative agreement between the I(H) data of Fig.
2 (a) (file #10; blue crosses in Fig. 2(c ) ) and the I(V) data of Fig. 2(c ) (file# 26-27). This shows that the minor drift in temperature has no impact on the spintronic response reported here. We have carefully considered possible memristance/ageing effects here. First the 0 → +Vmax → -Vmax → +Vmax → -Vmax → 0 IV measurement protocol can reveal whether the junction's state is evolving due to its electrical history. As seen in Figs. S2-S3 , the data are highly reproducible if our Keithley 2636 remains within a fixed current range. We do not witness any opening of the IV loop associated with a memristance effect. We also observe quantitative agreement between IV files 11-12 and 26-27, despite having applied up to 100mV and performed multiple bias sweept between these initial and final datasets (see Fig.
S  2). We therefore do not witness any device ageing within this dataset. Referring to the dI/dV(V) graphs of Fig. 2d , despite the 295K acquisition temperature and the absence of a lock-in technique, which resulted in a numerical derivation of dI/dV from I(V) data, we witness spectroscopic features with an energy resolution well below 26meV expected from thermal smearing of the MTJ electrodes' Fermi level. As shown in Fig. S 4 , the spectral width of these features is as low as 0. 25meV and is well beyond the errors bars. We present in Fig. S5  a series of dI/dV data calculated from the raw IV data of files #18-23. Comparing these data, one concludes that, aside from very minor variations, spectrally sharp features are reproducible for a given MTJ magnetic state, and depend on the MTJ's magnetic state, i.
3meV and statistically relevant conductance oscillations with a ~0. 25meV width are observed (using averaged dI/dV data; see Fig. S4for full dataset and error bars). This spectral sharpness is a direct signature of the spin engine. Using data from Fig. S3a, the inset to panel (c) shows how the bias-dependent output power depends on the MTJ's magnetic state and can exceed 0. 1nW at RT.
Acknowledgements We are grateful to  J. Koski ,  G. Weick ,  K. McKenna  and  J. Blumberger  for useful discussions, and to  Y. Henry  for carefully reading our manuscript. Devices were synthesized at the STNano technological platform. We acknowledge financial support from the  Institut Carnot MICA  (project ' Spinterface '), from the  ANR  ( ANR-09-JCJC-0137 ,  ANR-14-CE26-0009-01 ), the  Labex NIE " Symmix " ( ANR-11-LABX-0058 NIE ) and  Vetenskapsrådet. This work was performed using HPC resources from the  Strasbourg Mesocenter  and from the  GENCI-CINES  Grant  2016-gem1100.
52 MPa, a temperature of the detonation products of 4533 K, and a mass velocity behind the detonation front of 1300 m s -1  [18]. The coatings were deposited on stainless steel substrates at a stand-off distance of 200 mm. Before spraying, sandblasting of the substrates was carried out for better adhesion of the coatings. Sandblasting was carried out by abrasive particles 300-600 µm in size. The temperatures and velocities of the particles at the exit of the barrel were calculated using LIH software (LIH SB RAS, Novosibirsk. Russia) developed based on the analysis presented in  [22]. The X-ray diffraction (XRD) phase analysis of the feedstock powder and detonation coatings was performed using a Bruker D8 ADVANCE diffractometer (Bruker AXS, Karlsruhe, Germany). The content of the amorphous phase in the coatings was determined by the Rietveld method using TOPAS 4.
Energy-dispersive spectroscopy (EDS) was conducted using a NORAN Spectral System 7 (Thermo Fisher Scientific Inc. , Waltham, MA, USA). The fine structure of the coatings was observed by transmission electron microscopy (TEM) using an EM-002B microscope (TOPCON, Tokyo, Japan) working at 200 kV. Dimpling and ion milling was used to prepare samples for TEM studies. The porosity of the coatings was measured using OLYMPUS Stream Image Analysis software Stream Essentials 1. 9. 1 (Tokyo, Japan). The optical images of the samples were obtained using an OLYMPUS GX-51 Optical Microscope (Tokyo, Japan). Vickers microhardness of the coatings was measured on the polished cross-sections using a DuraScan 50 hardness tester (EMCO-TEST, Kuchl, Austria) at a load of 100 g. The average values of microhardness were determined from 10 measurements.
Numerical Method To analyse the stress, pressure and velocity field in a matrixfracture system, an adequate physical model of the involved processes and their coupling is required. The Multiphysics Object Oriented Simulation Environment (MOOSE) in combination with GOLEM (Cacace and Jacquey, 2017), a MOOSE-based application for modelling coupled Thermal-Hydraulic-Mechanical (THM) processes in fractured and faulted geothermal reservoirs were used. To represent the rock matrix, we considered a poroelastic behaviour as implemented in  Cacace and Jacquey (2017). For the fluid phase, the incompressible Navier-Stokes flow is implemented  (Peterson et al. , 2018). The Navier-Stokes module solves for the dynamic pressure and the velocity field inside the fracture opening only. For the rock matrix, the primary variables solved for are pore pressure and displacement.
Touch X The Touch X serves as a master device to control remote slave device (Figure  2a ). The Touch X with six degrees of freedom (DoF) was manufactured by 3D Systems, Inc. and it could capture the position (three DoFs) and its orientations (another three DoFs) with force feedback. The Touch X is widely applied in various areas in terms of simulation, virtual assembly, robot control, etc. (The kinematics information of the Touch X with respect to its forward/inverse kinematics and Jacobian matrix have been deeply studied  [40, 41] ). In this study, Touch X was controlled via the Matlab/Simulink  [42].
• Demonstrations and robot learning module. This module is the main part of the proposed frame. The data processing unit is used to process task trajectories from the remote Baxter robot. The collected data contains task trajectories and human stiffness. The collected task trajectories and collected stiffness are ready for the demonstration and learning process. DTW unit is used to align the demonstration data in a united time scale. The robot learning model is mainly used to obtain a generative model according to the demonstrated information related to the collected task trajectories and collected muscle stiffness. • Robot execution module. This module is mainly used to enable the remote Baxter robot to execute the task according to the learned task trajectories and learned muscle stiffness.
Data Constitution The proposed teleoperation system employed a heterogeneous master-slave structure, therefore we utilized motion of the slave as the data source for demonstrations. Owing to the workspace differences between the master and the slave, a workspace mapping between the master and the slave was carried out to ensure the accuracy of tracking. The collected demonstrated information contains the position of the slave and human muscle stiffness. In the process of demonstration, the demonstration data can be organized as follows: O = {P, ā}, (6) where P = {P x , P y , P z } and ā indicate the collected task trajectories and collected muscle stiffness, respectively.
Glycoside hydrolases (or glycosidases) are ubiquitous enzymes responsible for the hydrolytic breakdown of polysaccharides and glycoconjugates. 2  Along with lytic polysaccharide monooxygenases  3  and polysaccharide lyases,  4  glycosidases comprise one of the enzymatic cornerstones of biomass catabolism and are highly sought after as tools for industrial biocatalytic process development. The Carbohydrate Active EnZymes (CAZy) database (www. cazy. org) lists hundreds of thousands of putative glycosidase open reading frames (ORFs) identified by homology to known glycosidases. 5 Because of the slow pace of enzyme characterization, the vast majority of these sequences remain uncharacterized, making the identification of the right combination of enzymes for new biomass-degrading processes challenging.
The patterns of labeling produced by 4 and 9 were identical to those produced by equivalent Cy5-tagged ABPs 3 and 8 (Supplemental Figure  7 ). Pulldown of labeled enzymes onto streptavidin beads followed by on-bead tryptic digestion and LC-MS/MS analysis of the resulting peptides supported our hypothesized identification of the ∼130 kDa and ∼30 kDa bands. Each protein identified in the total secretome was quantified using spectral intensity-based relative quantification using nonconflicting peptides to assess its abundance in the pulldown, compared to its abundance in either the total secretome, a DMSO only no-probe pulldown control, or a competitorinhibited pulldown control following treatment by 2 or 5 (Figure  3 , Supplemental File 1). As expected, XlnC (NRRL3_08708; Genbank: AIC36735. 1) and XlnD (NRRL3_02451; Genbank: AIC36733.
1) were clearly abundant in the pulldown following treatment with 9, whereas XlnD, but not XlnC, was highly enriched following treatment with 4. Conversely, EglA (NRRL3_00819; Genbank: CAK46524. 1), a retaining endo-glucanase, and AxhA (NRRL3_08707; Genbank: AIC36734. 1), an inverting arabinofuranosidase, were both abundant in the secretome, but not pulled down by either 4 or 9. Although XlnD was the only βxylosidase found in the secretome, BglM (NRRL3_10133; Genbank: GAQ41973. 1), a low abundance GH3 β-glucosidase in total secretome, was also enriched by 4 and 9, suggesting some off-target reactivity between GH3 enzymes. Surprisingly, XlnB (NRRL3_01648; Genbank: CAK43456. 1), a GH11 βxylanase abundant in the total secretome digest, was not detected in the pulldown (discussed further below).
Kinetics of probe/inhibitor reactivity were determined by measuring the inhibition of enzymatic activity over time, since covalent labeling of a glycosidase nucleophile necessarily leads to enzyme inhibition. In the absence of recombinant A. niger xylosidase/xylanase XlnD and XlnC, we utilized two enzymes from closely related Aspergillus species: XlnD from A. nidulans (GH3; Genbank: CAA73902. 1; hereafter referred to as AnidXlnD), which shares 66% sequence identity with A. niger XlnD, and ASPAC-DRAFT_127619 from A. aculeatus (GH10; Genbank: XP_020051463. 1; catalytic domain only), which shares 89% We observed substantial variability in the rates of reactivity across all enzyme/inhibitor combinations tested (Table  1 , Supplemental Figure  8b ). β-Xylosidase AnidXlnD was inhibited by mono-xylo probes/inhibitors 1, 2, 3, and 4 with k inact /K I values of 0.
In contrast, biotin-tagged xylobiose aziridine 9 showed only a 2fold lower k inact /K I compared to 6, with poor k inact for 9 largely offset by a substantial decrease in K I. Taken together, our results show that the identity of the linker moiety can markedly affect cyclophellitol-derived ABP/inhibitor reactivity, at least in the case of retaining β-xylosidases and β-xylanases. Structural Basis of GH3 and GH10 ABP Labeling. To complement biochemical characterization of ABP reactivity from inhibition kinetics, we sought to obtain structural insights into ABP interactions with recombinant GH3 and GH10 enzymes via X-ray crystallography of reacted enzyme-ABP complexes. The three-dimensional structure of apo AnidXlnD (PDB accession code 6Q7I) was solved at 1. 50 Å resolution by molecular replacement using a GH3 from Hypocrea jecorina (Trichoderma reesei) as a search model (PDB: 5A7M).
14 Å resolution (PDB accession code 6Q7J). As expected, the ABP ligand observed within the AnidXlnD active site was not 8 itself, but mono-xylo aziridine 8a released by cleavage of the terminal xylose of 8. Reacted 8a was bound to the enzyme nucleophile Asp307 in a 4 C 1 chair conformation, with direct H-bonding interactions to Arg183, Lys222, His223, and Glu107. The C5 methylene of the xylose probe resided ∼4. 5 Å from Trp105, consistent with a C-H-π stacking interaction at this position (Figure  4b ). Although the N-linked fluorophore tail of 8a was observed to project out of the enzyme active site, only the first five carbon atoms of the linker were modeled due to increasing disorder along the alkyl chain. The crystal structure of unliganded GH10 ASPAC-DRAFT_127619 catalytic domain (PDB accession code 6Q8M) was solved at 1.
42 Å resolution using a Fusarium oxysporum GH10 (PDB: 3U7B) as a molecular replacement search model, with which ASPACDRAFT_127619 shares 51% sequence identity. ASPACDRAFT_127619 crystallized with two molecules in the ASU, each comprising a single (β/α) 8 barrel containing an extended central cleft capable of binding xylan oligosaccharide chains. Two Asn-linked GlcNAcs resulting from EndoH digest of N-glycans were observed on each protein monomer (Figure  4c ). We were unable to obtain a complex of ASPAC-DRAFT_127619 with 8, possibly due to the slow reactivity of 8 with ASPACDRAFT_127619 (as measured by inactivation kinetics; Table  1 ). However, cocrystallization of ASPACDRAFT_127619 with the substantially more reactive xylobiose epoxide 5 furnished a 1. 76 Å complex with a single molecule of 5 in each enzyme active site (PDB accession code 6Q8M).
Supporting the notion that the 2,5 B conformation is unfavored but not necessarily inaccessible for xylo-configured ABPs, we were able to obtain an in crystallo complex of 5 reacted with the A. niger GH11 enzyme XynA 41 (41% sequence identity with XlnB; PDB accession code: 6QE8), although extended ligand soaking times (>24 h) were required to achieve acceptable active site occupancy. The reacted warhead of 5 adopted a distorted 5 S 1 skew boat bound to the XynA catalytic nucleophile Glu106, supporting computational predictions proposing 5 S 1 as the covalent intermediate conformation utilized by GH11 enzymes  39  (Supplemental Figure  9c, d ; Note, herein we have numbered XynA residues according to the full protein sequence, including the cleaved 27 amino acid signal peptide. Glu106 in our structure corresponds to Glu79 in Vandermarliere et al.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acscentsci. 9b00221. Supplemental Figures 1-11, Supplemental Table  1 , synthetic protocols (PDF) Proteomics data sets (XLSX) Movies of GH3, GH10 and GH11 enzyme-ABP complexes (MPG1, MPG2, and MPG3).