dataesr/scientific-paragraphs-categorization huggingface dataset:
Machine tool specification for kinematics and dynamics To virtually model a milling machine, we compute kinematics (e. g. , velocity and position) and dynamics (e. g. , force and power) corresponding to the events and movements of the machine tool. A STEP-NC program specifies a sequence of machining operations, and is used to create an NC program in the ISO 6983 (G-Code) format  [10]. Meanwhile, an MTConnect document generates continuous snap shots of a machine tool's actions and events using time as reference. Thus, for every instruction of the NC program, we need to compute the corresponding metrics of the machine tool. Computing these metrics requires equations that are derived from physical model-based analysis of machine tool metrics. We make a calculation of power, which indicates the amount of energy consumed per unit-time.
Architecture of the virtual machine model Figure  2  illustrates the process flow (including involved tools and generated outputs for each step) followed by the virtual milling machine model to generate MTConnect data. The virtual milling machine model takes, as an input, a STEP-NC file. The model parses and interprets this file using a toolkit for Parts 10, 11, and 111 (related to milling process data and tools) that is written in C++ and referred to as ISO 14649 Toolkit in the picture. This toolkit has been developed at the National Institute of Standards and Technology (NIST) for programing with ISO 14649, Parts 10 and 11, and is being applied to study different ISO 10303  [13]  application-protocol file characteristics and their interpretation  [14]. Using this toolkit, we can generate the sequence of G-Code instructions.
Combination of the virtual machine model into an agent-based model using a simulated environment To run our virtual machine model in a simulation environment, we integrate this virtual machine model in an agent-based model. The software environment called AnyLogic  [15]  allows us to extend the states and the transitions of a state chart using Java code. While implementing the state chart in an agent-based model, we can call virtual machine model functions by importing a Java ARchive (JAR) file that contains the needed functions. We first implement the state chart introduced in section 2. 4 in the agent-based model. We extend this state chart by implementing additional Java code to initialize the parameters needed for the virtual machine model functions. During the batchSetup state, we get the time needed by the machine to set up the batch as well as the power consumed during this step by reading the machine specification described using XML.
Production potential with available water resources The potential agricultural production with available renewable water resources (WA) was calculated at the national level using six combinations of water availability, water productivity and water reserves for environmental flow requirements (table A. 1, equation (A. 1)). Using the AQUASTAT database (FAO 2015b), we subtracted the total water withdrawal as sum of municipal, agricultural and industrial withdrawal-(TWW) from the total actual renewable water resources of each country (TARWR). TARWR includes internal and external (coming from other countries) surface and groundwater resources. In some scenarios we additionally subtracted the amount of water reserved for environmental requirements (EFR) and the water that is difficult to access due to high variability of precipitations (S).
Yield gap closure on used and spare land Potential calorie production due to productivity increases on cultivated areas (YG) was calculated by the difference between potential and actual yields, divided by the number of crops grown in a country, and multiplied by the harvested area (see equation (A. 3) and section A. 1. 3). Potential productivity increases on unused areas (YG_E) were calculated by multiplying spare land (see section 2. 2) by the difference between potential and actual yields (section A. 1. 4 and equation (A. 4)). Actual yields and harvested area were taken from FAOSTAT (2015), potential yields from  Mueller et al (2011). Mueller et al (2011)  estimated potential yields by using data from other regions with similar precipitation and growing degree day characteristics (i. e.
Biophysical redundancy We quantified the overall biophysical redundancy in three scenarios (R low , R middle , R high ) that combine different scenarios of the scaled values of LA, WA, YG and YG_E (see tables 1 and 2). All three scenarios follow the calculation scheme of equation (1): ( ) ( ) = + + R nInd min LA, WA YG YG_E , 1 x where nInd is the number of terms in the nominator of the equation for which there is data in the different datasets and scenarios used. For countries with a full set of data nInd is equal to 3. The minimum values of LA and WA were taken under the assumption that water and land are The R low scenario is designed to represent very constrained redundancy for all countries: managed grasslands are considered as used and thus not available, protected areas and areas worth of protection are assumed to be not available for agricultural production.
Discussion We have analyzed the evolution of biophysical redundancy at the country level, as captured by different metrics relating to spare land, available water resources and potential for productivity increases. To our knowledge, our study is the first to assess past trends in biophysical redundancy of countries and show that the world as a whole has decreased its biophysical redundancy over the last two decades. The intensity and the drivers of change in biophysical redundancy vary widely from country to country. 2  for the characteristics of each scenario. However, population growth appears to be connected to decreasing biophysical redundancy in a number of countries. Detailed monitoring and reporting of technological, economic and infrastructural developments would help to build up the databases needed to better understand the drivers of change in biophysical redundancy.
The high estimate is in good agreement with LPJmL simulations of water availability (48 292 km 3 , Fader et al 2013) and the value 55 375 km 3 from Gleick (2000). The lower estimate agrees very well with the value of Gerten et al (2013) for 'accessible blue water resources': 16 300 km 3 and is not far away from 12 500 km 3 , the estimate by  Rockström et al (2009b). Our results are the outcome of a flexible methodology that can be applied using other production data, different datasets of resources use and alternative values for caloric nutritional needs, including changes in the composition of diets. It is worthwhile noting that our use of a reference diet of a 3000 kcal cap -1 d -1 from crop products does not account for the fact that most human diets include animal products such as meat, milk, or egg, which are partly produced using feed, and require several calories of feed crops to be produced.
Acknowledgments This work was supported by the  National Socio-Environmental Synthesis Center (SESYNC)  under funding received from the  National Science Foundation   DBI-1052875. Marianela Fader  was supported by the  Labex OT-Med  ( n°ANR-11-LABX-0061 ) funded by the  French Government «Investissements d'Avenir» program  of the  French National Research Agency (ANR)  through the A *  MIDEX  project (n°ANR- 11-IDEX-0001-02 ), and the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under the project  LUC4C  (grant agreement number  603542 ). Jessica Gephart  was supported by the  National Science Foundation  (Grant Number:  DGE-00809128 ). David A Seekell is supported by the  Carl Tryggers Foundation for Scientific Research. Samir Suweis thanks University of Padova Physics  and  Astronomy Department Senior Grant   129/2013 Prot.
1634. Miina Porkka received funding from the Maa-ja vesitekniikan tuki ry. Matti Kummu  received funding from the  Academy of Finland  funded project  SCART  (grant no. 267463 ). Michael Puma gratefully acknowledges support from the  Interdisciplinary Global Change Research  under  NASA  cooperative agreement  NNX08AJ75A  supported by the  NASA   Climate and Earth Observing Program. Alessandro Tavoni  acknowledges support from the  Centre for Climate Change Economics and Policy , funded by the  ESRC , and the  Grantham Foundation for the Protection of the Environment. Zak Ratajczak  was supported by the National Science foundation (DBI # 1402033 ). The authors wish to thank  Dr Kees Klein Golgewijk  ( University of Utrecht  and  Netherlands Environmental Assessment Agency) for providing of the HYDE 3.
Numerical solution procedure The linear optimization problems (  8 ) and (  10 ) are solved using the LP solver Gurobi in Matlab, with the discretization parameters N=48 and K=3. Initial values for the concentrations M and the light intensity I(t) are taken from  Lacour et al. (2012). Moreover, the parameter effmax is estimated to be 0. 0115. ℎ -1. -1. -2. -1 in order to match the experimental observation of roughly a doubling time in 24h. Finally, carbon storage (A) is assumed to be only in the form of carbohydrates (CARB) here. A schematic representation of these assumptions is shown in Fig  2. A first case study is conducted by solving (8) without any constraint (2. 6) on. This model predicts an instantaneous consummation of CARB during the first time step (t=0. 11h), which leads to an instantaneous synthesis of B (Fig 3A  -B ).
CVs were also collected in an argon-saturated electrolyte at corresponding sweep rate and rotation rates, serving as blank spectra. Data reported herein are ORR data resulting from the subtraction of the ORR CV from the blank CV, where the capacitive contribution was removed from the Faradaic current reported. ORR activity in this work are evaluated by comparing (i) potential at a background subtracted current density of -0. 1 mA/cm 2 , (ii) half-wave potential (E 1/2 ), (iii) specific kinetic current (i K ) at 0. 75 V, (iv) rate constant (k) for ORR at equilibrium. Calculation of i K ,kand the number of electrons transferred per molecule of oxygen (n) was made using the Koutechy-Levich relationship (Eq. 1). 1 i ¼ 1 i K þ 1 i lim ð1Þ where i is the measured current density, i K is kinetic current density defined by i K ¼ nFkC O 2 i K ¼ nFkC O 2 and i lim is the limiting current density which can be described by i lim ¼ 0:62nFD 2=3 C O 2 # À 1 6 Here, F is the Faraday's constant (96485 C/mole of electrons), k is the rate constant for ORR, CO 2 is the bulk concentration of oxygen (1.
2472 cm 2 ) surrounded by a Pt ring. The Pt-ring was held at a constant potential of 1. 2 V while scanning the disk and calculation of the selectivity was done using Eq. (2). A selectivity of four corresponds to a complete reduction of oxygen to water, whereas that of two is equivalent to 100 % formation of hydrogen peroxide. n ¼ 4I D I D þ I R N ð2Þ where n is the number of electrons transferred per oxygen molecule, I D is disk current, I R is the ring current and N is the collection efficiency (37 % as specified by the manufacturer). Accelerated durability tests (ADTs) were performed by first collecting initial activity data as mentioned above. The electrode was then cycled from 1. 2 V to 0 V to 1. 2 V vs. NHE at 100 mV/s in an oxygen saturated electrolyte. The electrode was held at 1. 2 V for 5 s after each cycle.
Activity data was collected after 1000 cycles. EASAvalues reported in this study were obtained by measuring electrical double layer capacitance (C dl ) between 1. 2 and 1 V vs. NHE CVs were collected in argon saturated electrolyte at various scan rates by scanning the potential across the non-Faradaic region. The current in this potential window is attributed entirely due to double-layer charging and is denoted as I capacitive. The double-layer charging current (I capacitive ) is proportional to the scan rate (m) as shown by Eq. (3). I capacitive ¼ C dl m ð3Þ Thus, the slope of I capacitive vs. m plot corresponds to C dl. EASA defined in Eq. 4 was then obtained using the estimated C dl values. For our calculations, we have used a specific capacitance (C s ) value of 20 lF/cm 2 based on literature  [36] [37] [38] [39] [40] [41].
X-ray Photoelectron Spectroscopy (XPS) XPS was used to analyze the composition of the surface species present on various catalysts. A Kratos Ultra Axis spectrometer was used with a monochromatic aluminum anode source operated at 12 kV and 10 mA. Binding energies were referenced to the standard C 1 s binding energy of 284. 5 eV. XPSpeak 4. 1 software package was used for curve fitting. Spectra baselines were determined using Shirley-type background fitting. Spectra were deconvoluted using Lorentzian-Gaussian combination peaks.
Superconducting Quantum Conducting Device (SQUID) Magnetometry Magnetization was measured as a function of field using a SQUIDmagnetometer (Quantum Design MPMS XL) at 300 K. The applied field was varied from -30000 Oe to ?30000 Oe and then back to -30000 Oe. A total of 100 data points were collected for each sample. The data were then fitted to the Langevin function (Eq. 5) and the values for saturation magnetization were determined. M M sat ¼ L M s VH k B T ð5Þ where M is the magnetization (emu/g), M sat is the saturation magnetization (emu/g), M s is the spontaneous magnetization (emu/cm 3 ), V is the volume of particle (cm 3 ), H is the applied field (Oe), K b is the Boltzmann Constant (cm 2 gs -2 K -1 ), T is the temperature (K) Lx ðÞ¼coth x ðÞÀ 1 x is the Langevin function for superparamagnetic particles.
Acknowledgments This material is based upon work supported by the  U. S. Department of Energy, Office of Science, Office of Basic Energy Sciences  under Award Number  DE-FG02-07ER15896. We would also like to thank  Ohio Coal Research Consortium  or their financial support under Subcontract No. OCRC-C-04. Portions of this work were performed at the  DuPont-Northwestern-Dow Collaborative Access Team (DND-CAT)  located at Sector 5 of the  Advanced Photon Source (APS). DND-CAT  is supported by  E. I. DuPont de Nemours & Co. , The  Dow Chemical Company and Northwestern University. Use of the APS, an  Office of Science User Facility  operated for the  U. S. Department of Energy (DOE) Office of Science by Argonne National Laboratory , was supported by the  U. S. DOE  under Contract No. DE-AC02-06CH11357.
Acknowledgements The authors acknowledge the financial support of the research programme of the  University Carlos III de Madrid , which made this study possible through a mobility grant. Moreover, this work was supported by the  French "Investments  for the future" programme, which is managed by the  National Agency for Research  under contract  ANR-10-LABX-22-01 ,  Labex SOLSTICE  and by the  Spanish government  under the project  ENE2012-34255.
The popularization of the concept across disciplines A bibliometric survey of the term 'Anthropocene' using Thomson Reuters' Web of Science database was conducted for the 16 years (2000-2015 inclusively) since the concept was first introduced by Crutzen and Stoermer in IGBP's Global Change Newsletter  (2000). The results (Fig. 1 ) indicate 1066 publications employing the term in the title, abstract, or text body, which have been cited some 8451 times. Both the number of published items and their citation rates have inflected sharply upward since 2010. For example, the number of items published roughly doubled twice between successive years, once in  2010-2011 (42-86 items respectively), and again in 2013-2014 (122-274). The average citation rate currently stands at 7. 9 citations per item, producing an h-index (where h equals the number of papers with h citations) of 40.
While disciplines grouped under the "earth and environmental sciences" contributed by far the most published items (64%), those under "humanities and social sciences" make up 24% of the contributions. A similar pattern is observed in the Scopus database. What can be learned from this analysis? First, the term Anthropocene has clearly attracted a great deal of attention and debate. It is clear from the analysis that the concept originated in Earth system science. Yet in recent years, a number of interdisciplinary and dedicated peer-reviewed journals have been established, including Anthropocene,The Anthropocene Review, Elementa: Science of the Anthropocene, and Earth's Future, all joining an existing array of dedicated scholarly arenas. Secondly, as a topic of inquiry the Anthropocene is maturing rapidly as evidenced by the h-index and citation rates.
In fields such as ecology and conservation science, it is an accepted term, no longer queried by editors and reviewers. Despite claims from some in the geology community that this merely reflects "pop culture"  (Autin and Holbrook, 2012) , the emerging consensus is that the adoption of a term which emerged in the Earth sciences  (Crutzen, 2002)  has been adopted by other scientific communities. Notably, as the above data illustrates, the humanities and social sciences are making growing use of the concept, comprising a quarter of the published work, including literature that is critical the concept's use and utility. Finally, the term Anthropocene has taken on multiple meanings and uses in different scholarly communities. We suggest that the term has become a 'boundary object'  (Star and Griesemer, 1989) , enabling communication between scholars spanning different branches of the physical, life, social sciences, and humanities, as well as the arts and designing.
Concluding remarks The global change research community, broadly defined, has made significant advancements in understanding different components of local, regional, and global environmental problems and possible solutions toward more sustainable and desirables futures. The last 40 years have seen the mainstreaming of interdisciplinary collaborations, the rise of sustainability science, coupled socialecological systems frameworks, improved modeling of Earth system processes, and a broader engagement of the social sciences and humanities in global change research. It has also seen the immediate dissemination of increasingly voluminous data and information, and a better understanding of the social and economic drivers of greenhouse gas emissions, and of other grand cycles like nitrogen. As we move forward, however, these advances need to be matched by a deeper understanding of the complex functioning of the nexus between regional and global political economic processes that underlie global change and pathways of development within the Anthropocene.
Acknowledgments We would like to thank the Secretariats and the  Science Committees of the International Geosphere Biosphere Programme (IGBP)  and the  International Human Dimensions Programme on Global Environmental Change (IHDP)  for their support during the conceptualization, organization and funding of the efforts that led to the preparation of this article and the whole special issue on 'The Anthropocene'. This paper arose from a workshop supported by Grant # GEO-124560  from the  US National Science Foundation  to IGBP and funding from  IHDP. We would also like to thank the following institutions for supporting individuals and meetings contributing the special issue: the  Institut d'etudes avances-Paris (IEA) , the  Institut de France-Académie des sciences ,  Future Earth ,  Indiana University-Bloomington ,  University of Maryland , and the  Natural Sciences and Engineering Research Council of Canada.
EPR spectroscopy was employed to determine the nature of the trapped electron species (detailed analyses in the Supporting Information). The light-generated "blue" NCN-CN x shows a strong, uncharacteristic and nearly symmetric Gaussian line near the free electron g value at X-band frequency of approximately 9. 6 GHz (data not shown). The intensity of the EPR signal begins to plateau after 1 h of irradiation (Figure  S5a ), then decays over 16 h after removing the light source (Figure  S5b ), thus demonstrating the longevity of this radical species. At a higher field of about 3. 361 T, anisotropy of the g tensor is resolved in a continuous-wave (CW) EPR spectrum, although the principal values are distributed by significant anisotropic strain (Figure  2 a, b ), indicating the presence of several types of radicals that differ slightly in their electronic structure.
Acknowledgements This work was supported by the  Deutsche Forschungsgemeinschaft  (project  LO1801/1-1 ) and an  ERC Starting Grant  (B. V. L. , grant number  639233 ), the  Max Planck Society , the cluster of excellence  Nanosystems Initiative Munich (NIM) , and the  Center for Nanoscience (CeNS). We acknowledge support by the  Christian Doppler Research Association (Austrian Federal Ministry of Science, Research and Economy, National Foundation for Research, Technology and Development)  and the  OMV Group  (H. K. , E. R. ). V. W. -h. L. gratefully acknowledges a postdoctoral scholarship from the  Max Planck Society. We would also like to thank  Ms Michaela Wieland  for the XPS analysis,  Dr. Igor Moudrakovski  for the solid-state NMR measurements,  Dr. Reinhard Kremer  for the preliminary EPR characterizations, and  Mr Benjamin Martindale  for fruitful discussions.
This study follows our previous work (see reference  26  ) on the measurement of water diffusion anisotropy in a Nafion® membrane under traction using the same technique. As pointed out by Klein et al. 26  , one of the main advantage of PFGNMR compared to conductivity measurements is that it is insensitive to interface effects and membrane thickness variations as it is a non-contact method. We present data on the evolution of both water content and water diffusion in the stressed membrane in the range [0-12MPa] and [15-98% RH]. The water diffusion properties were probed along the three orthogonal axes in order to detect stress induced anisotropic effects. The water content data were compared to the predictions of the model developed by Colinart et al. 25.
5 to 2 hours long successive humidity steps. The signal area was measured at equilibrium for each RH and converted to water content using a scaling factor corresponding to the water content measured at 50% RH on a microbalance at the same temperature : The membrane was then exposed again to 0% RH and the stress was applied to the dry membrane (the dew point of the dry air was below -60°C). The sorption isotherm of the compressed membrane was measured using the same protocol as for the unconstrained one. The real-time measurement of the RH during the NMR experiments allowed the determination ofthe mean value and standard deviation of each RH (reported as error bars in Figure  7 ). Figure  7  presents the experimental data fitted to the following polynomial function with the parameters given in Table  1.
These results are discussed in more details in the Discussion section of the paper. When the 11 MPa stress was applied, the water self-diffusion coefficient dropped significantly whatever the relative humidity. At 95% RH it decreased from 2. 37. 10 -10 m²/s to 1. 96. 10 -10 m²/s when the stress was set to 11. 1 MPa (17. 3% drop) while it decreased from 1. 11. 10 -10 m²/s to 0. 86. 10 -10 m²/s at 61% RH (23. 5% drop). The fit to the data at 11MPa, using Equation 2, gives =7. 92. 10 -11 m²/s. The coefficient was also measured in the two other orthogonal directions (x and z) to check the presence of any stress-induced diffusion anisotropy. The measurements were performed in four different humidity conditions as the applied stress was increased in the range 0 MPa < σ < 11. 1 MPa. As observed in Figure  10 , the diffusion coefficient decreased in the membrane-plane directions in the same manner and in the same proportions as in the through-plane direction showing that diffusion remained isotropic (within the experimental uncertainties of our measurements) when a moderate stress was applied in the normal direction of the membrane.