dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments Our view on the field has been shaped by discussions and collaborations with numerous colleagues whom we thank warmly. This work was further supported by supercomputing time at IDRIS/ GENCI  Orsay, under project  091393 , an  ERC   Consolidator grant  (project number  617196 ),  NSF  under grant No. NSF   PHY11-25915 , grants from  MOST  ( 2010CB923000 ,  2011CBA001000 ,  2011CBA00102 ,  2012CB821403  and  2013CB921703 ) and  NSFC  ( 11004232 ,  11034011/A0402 ,  11234014  and  11274362 ) from China, and by the  Cai Yuanpei program.
They can store a data bit in both volatile and non-volatile contexts. Furthermore, the cells are able to quickly and efficiently transfer a data bit from one context to another, thus supporting the "normally-off and instant-on" computing concept. The remainder of the paper is organized as follows: in Section 2, we analyze the evolution of the MTJ writing mechanisms. In Section 3, we introduce our hybrid cells that contain four-transistors and two-MTJs (4T-2M) and six-transistors and four-MTJs (6T-4M), explaining their structure and functionality. In Section 4, we report the measured performance of the cells in terms of required silicon area, robustness, leakage, read/write speeds and energy consumption. Finally, Section 5 is reserved for our conclusions.
Mature and commercialized STT-MTJs with in-plane magnetization have very fast MTJ switching speeds (up to 100 ps, according to  [17] ). However, with the writing currents of hundreds of micro amperes, this switching approach is still not efficient since it consumes a lot of energy and requires large driving transistors. Furthermore, it suffers from reliability issues including data thermal stability, erroneous write by read current and short retention times  [18]. High error rate of reading circuits is an additional obstacle. Emerging perpendicular STT-MTJ structures in which the magnetization direction is perpendicular to the film plane have proved to be the breakthrough technology that enables a significant reduction in the switching current required (several tens of microampers) as well as improvements in data thermal stability.
Provided that the cell is fully symmetrical (the transistors in both branches have equal on resistances since they have the same dimensions), the voltage drops on the Q and !Q nodes entirely depends on the MTJ resistances in the MRAM_2 context that are in the path of the current. Furthermore, if both the transistors and the MTJs are carefully sized, the voltages on the latch nodes Q and !Q can be adjusted to be one below and another above the meta-stable voltage (V meta ), depending on the non-volatile data bit stored in MRAM_2 context. As illustrated on the transfer curve in Fig. 4a ), non-volatile data bit "1" stored in MRAM_2 context (Rap/Rp configuration) will cause the voltage on the Q node to be greater than the meta-stable voltage (V Q > V meta ). The opposite will occur if MRAM_2 context stores non-volatile data bit "0" (Rp/Rap configuration, Fig.
CONCLUSION This paper presents two hybrid cells that are able to store and process one data bit both electrically and magnetically. The cells are based on 4T-and 6T-SRAM architectures and use recently emerging perpendicular STT-MTJ nanopillars as non-volatile storage elements. Measured performance of both hybrid cells implemented in 28 nm FD-SOI technology combined with 45 nm round STT-MTJs showed that the cells are ready to be used in "normally-off and instant-on" computing systems. The cells need less than 100 ps to restore a non-volatile data bit, spending not more than 4 fJ for the operation. The volatile data bit can be written for a time bellow 10 ps. Moreover, 6T-4M hybrid cell presented here has a few clear advantages compared to existing hybrid cells: two non-volatile data contexts and the ability to write a volatile data bit.
Acknowledgments P. Ghendrih  and  R. Sabot  are thanked for their active participation to fruitful working discussions at IRFM. Y. Camenen ,  A. Monnier  are thanked for useful, stimulating discussions at the  Aix-Marseille University. An interesting discussion with O. Sauter on TCV work on L mode edge is here acknowledged. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. Part of  L. Chôné 's work has been supported by the  French National Research Agency , Project No. ANR-2010-BLAN-940-01.
In the present study, we investigate the scaling properties of the topography of Mars. Planetary topographic fields are well known to roughly exhibit (mono)fractal behavior. Indeed, the fractal formalism reproduces much of the variability observed in topography. Still, a single fractal dimension is not enough to explain the huge variability and intermittency. Previous studies have claimed that fractal dimensions might be different from one region to another, excluding a general description at the planetary scale. In this article, we analyze the Martian topographic data with a multifractal formalism to study the scaling intermittency. In the multifractal paradigm, the apparent local variation of the fractal dimension is interpreted as a statistical property of multifractal fields. We analyze the topography measured with the Mars Orbiter Laser altimeter (MOLA) at 300 m horizontal resolution, 1 m vertical resolution.
, 2006) , providing a satisfying fit of the structure function computed in different areas of the globe and allowing one to quantify multifractal behavior at the planetary scale through the measurement of three parameters: H (the conservation exponent or fluctuation exponent), α (the degree of multifractality) and C 1 (the codimension of the mean). In this study, we aim to measure the global value of H , α and C 1 in the case of Mars. After a careful choice in the definition of fluctuations computed from all the available data in the MOLA database, statistical moments are computed on a large range of scales in order to estimate the universal multifractal parameters.
Methodology This section first describes the Martian topography data set used in this study and the MOLA instrument. The second part contains some elements of the theory of universal multifractality. The third part contains the description of the Haar fluctuation tool we adapted in the case of the irregularly sampled Martian topography. Smith et al. , 2001 ) is a laser altimeter on board the Mars Global Surveyor (NASA) spacecraft following a quasi-polar and circular orbit. MOLA has recorded a huge amount of topographic data providing a well-detailed global mapping of the planet. With 20 measurements every 2 s, a vertical accuracy of 1 m, a surface spot size of 168 m and approximatively 300 m between two consecutive measurements, the MOLA database stored in PDS (Planetary Data System, http://pds-geosciences.
wustl. edu) constitutes an ideal data set to study the global properties of Mars' topography down to the kilometer scale. Topography is calculated using the gravity field from Goddard Mars Model 3 (mgm1025,  Lemoine et al. , 2001). The global areoid error through degree 60 × 60 for the mgm1025 model is 1. 8 m. The total number of individual topographic measurements used for the purpose of this study is close to 600 mil-lion. For methodological reasons, we have decided to limit the scope of our analysis to the along-track series, meaning that we exclude all kind of across-track fluctuations (measurements extracted from different orbits). This restriction might introduce a bias, a consequence of the single preferred direction (close to north-south) of MOLA's orbit. Hence, anisotropic statistical properties if they occur will not be detected by our method.
Haar fluctuations for irregular signals Mars is well known for presenting huge fluctuations of altitude on its surface despite its relatively small radius in comparison to Earth (R Mars = 3390 km). Indeed, Olympus Mons culminates at 22. 5 km above the adjacent lowlands. In this context, we have to define fluctuations in order to provide an accurate statistical study. It is well established that data analysis strongly relies on defining the fluctuations at a given scale and location. Of course, the chosen definition must adequately characterize the fluctuations and has to be picked among all the possible definitions depending on the context. As mentioned earlier, the simplest definition is the absolute difference that might not be accurate in some cases. For instance, in the case of a fractal field with H < 0 (a Gaussian white noise has H = -1/2), absolute differences will only characterize fluctuations with high wave numbers instead of the local fluctuations at a given scale.
A1 Irregularity The above definition for Haar fluctuations implicitly requires that along-track points are regularly spaced. However, the actual MOLA data sets exhibit different kinds of irregularities, mainly due to the occurrence of clouds of various lengths and random dysfunction of the instrument that might introduce a bias into our analysis. Figure  A1  provides typical examples of such irregularities in the MOLA along-track series and Fig. A2  shows the distribution of distance between two along-track points computed among the entire MOLA data set. It clearly indicates that most of the points are ∼ 300 m spaced, as expected, but a subsequent numbers of irregularities occur (one point missing, two points missing, etc. ). In order to take this into account, we define an adjustable quality criterion for the Haar fluctuations (see  Lovejoy, 2014) : for a given fluctuation at scale L, we compute the spherical length of the first half L 1 and the spherical length of the second half L 2.
This paper describes the trigger and offline reconstruction, identification and energy calibration algorithms for hadronic decays of tau leptons employed for the data collected from pp collisions in 2012 with the ATLAS detector at the LHC center-of-mass energy √ s = 8 TeV. The performance of these algorithms is measured in most cases with Z decays to tau leptons using the full 2012 dataset, corresponding to an integrated luminosity of 20. 3 fb -1. An uncertainty on the offline reconstructed tau energy scale of 2 -4%, depending on transverse energy and pseudorapidity, is achieved using two independent methods. The offline tau identification efficiency is measured with a precision of 2. 5% for hadronically decaying tau leptons with one associated track, and of 4% for the case of three associated tracks, inclusive in pseudorapidity and for a visible transverse energy greater than 20 GeV.
The missing transverse momentum (with magnitude E miss T ) is computed from the combination of all reconstructed physics objects and the remaining calorimeter energy deposits not included in these objects  [27]. The ATLAS trigger system  [19]  consists of three levels; the first level (L1) is hardware-based while the second (L2) and third (Event Filter, EF) levels are software-based. The combination of L2 and the EF are referred to as the high-level trigger (HLT). The L1 trigger identifies regions-of-interest (RoI) using information from the calorimeters and the muon spectrometer. The delay between a beam crossing and the trigger decision (latency) is approximately 2 µs at L1. The L2 system typically takes the RoIs produced by L1 as input and refines the quantities used for selection after taking into account the information from all subsystems.
Additional offline tau calibration corrections and systematic uncertainties The systematic uncertainties on the tau energy scale are evaluated with two complementary methods. The deconvolution method gives access to uncertainties on both the absolute TES (differences between reconstructed and true visible energy) and the modelling (differences between data and simulation) and is based on dedicated measurements (such as test beam data and low-luminosity runs) and simulation. The in-situ method only tests the modelling and uses collision data with typical 2012 LHC run conditions. Both methods are also able to provide small additional data-driven corrections albeit only inclusively in E T and |η| due to the limited statistical power of the dataset. They thus depend on the first calibration step explained in the previous section to remove any significant TES dependencies on kinematics or pile-up.
A TES shift of 1% is observed with no significant dependence on E T or |η| and a trend towards slightly higher values for 3-track τ had-vis candidates. The shift is dominantly due to E/p response differences between data and simulation. The in-situ method is based on the fact that the distribution of the reconstructed visible mass m vis in Z → τ τ events where one tau decays hadronically and the other to a muon plus neutrinos can be used to measure a TES shift between data and simulation and its uncertainty. Here, m vis is defined as the invariant mass of the τ had-vis and the muon. The muon momentum scale is measured independently with high precision. The TES shift α is determined by introducing an energy shift E T → (1 + α)E T for τ had-vis objects and finding the value α for which the m vis peak position in data and simulation agrees.
A fifth-order polynomial fit is used to estimate the m vis peak position as simulation studies show that this gives both the highest sensitivity and robustness. For small values of α, the m vis peak position depends linearly on E T. The results are based on collision data recorded by the ATLAS detector in 2012 using a muon trigger threshold of 24 GeV. The event selection is similar to the one used by the Z → τ τ tag-and-probe studies described in Sect. 4. 1 with the following differences: the τ had-vis candidates are required to have E T > 20 GeV and to satisfy medium tau identification criteria. No selection requirement is applied to m vis , and a looser cos ∆φ > -0. 5 requirement is made. Additionally, a pseudorapidity difference between the τ had-vis and the muon smaller than 1. 5 as well as E τ T,vis -E µ T > -15 GeV is required.
Summary and conclusions The algorithms developed in the ATLAS experiment at the LHC for tau identification and tau energy calibration are described, along with their optimization and the associated procedures to mitigate the effects of pile-up. These algorithms were employed in the dataset corresponding to 20. 3 fb -1 of √ s = 8 TeV pp collisions. The performance of the tau algorithms have helped to fulfil a variety of physics searches and measurements with hadronically decaying tau leptons, an important part of the ATLAS physics program. The performance of trigger and offline tau identification and calibration is measured, in most cases using Z → τ τ tag-andprobe measurements. The uncertainties on the offline tau identification efficiency measurement are dependent on the working point and are about (2-3)% for τ had-vis with one associated track, and (4-5)% for the case of three associated tracks, inclusive in η and for a visible transverse momentum greater than 20 GeV.
A precision of (2-8)% for the tau trigger identification efficiency is measured for hadronic tau decays selected by offline algorithms, depending on the transverse energy. Stability of all algorithms with respect to the pile-up conditions is observed. The reconstructed tau energy scale is measured with a precision of about (2-4)% depending on transverse energy and pseudorapidity, using either a method based on estimating and deconvolving the response uncertainties of the hadronic tau decay products or a direct measurement of the Z → τ τ visible mass using collision data recorded in 2012. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN and the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA) and in the Tier-2 facilities worldwide.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina;  Yer-PhI , Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN;  CONI-CYT, Chile ;  CAS ,  MOST  and  NSFC , China; COLCIEN-CIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  RGC, Hong Kong SAR, China ;  ISF, MIN-ERVA, GIF, I-CORE  and  Benoziyo Center , Israel; INFN, Italy;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Ro-.
Finally, to search for wave-particle interactions at the highest cadences, we employ a direct high speed interface between the SWEAP and FIELDS  (Bale et al. 2015 , this issue) instrument suites. Particle counts are relayed to FIELDS through the interface, and a wave-particle correlator calculates correlations and phase delays between particles and electromagnetic fields. Our simulations of instrument performance show that the cadence strategy outlined above permits SWEAP to identify the heating and dissipation mechanisms in the solar wind. We have calculated various theoretical signatures of dissipation and fed the results into SWEAP instrument performance simulations to demonstrate that the SWEAP data products are sufficient to resolve the dominant mechanisms. The solar wind heating mechanism in Sect.
Suite Electrical Interface and SWEM The SWEAP electronics module (SWEM) is the data and power interface between the SPC and SPAN instruments and the spacecraft. It provides intermediate data processing (moment calculations, mode switching, data compression and data storage) for the SWEAP data as well as centralized power conditioning. In general, the SWEM acts as the SWEAP suite central processing unit, housing command scripts for sensor initialization and configuration, high voltage control, and attenuator actuation and accumulating data into various packets for inclusion into the data stream to be transferred to the SPP solid state recorder (SSR). The SWEM houses most of the spacecraft interface electronics for SWEAP in a single box to reduce mass, to simplify harnessing and interfaces, and to reduce duplication of interface logic, power converters and other common services.
SWEM allows for a single interface to the spacecraft DPU, easing integration and test activities later in the project. The module is composed of 2 separate approximately 6U-VME sized boards (160 × 200 mm): the Data Controller Board (DCB), and a Low Voltage Power Supply Board (LVPS). The SWEM derives its design from THEMIS, HESSI, STEREO, and MAVEN, and makes extensive use of the technical capabilities demonstrated in the Instrument Data Processing Units (IDPUs) developed for these missions. The suite data interface and control design is derived from the MAVEN DCB board, modified slightly to incorporate the different SPP interfaces. The Coldfire processor implementation was qualified for use by MAVEN, and the design will be copied for SWEAP. The remaining circuitry needed on the DCB and LVPS are standard designs used extensively in previous missions.
The DCB FPGA also provides the SWEAP to SPP DPU interface, controlling the flow of raw data through the system. It receives commands from the SPP DPU processor and manages the SWEAP suite. Following the RBSP and MAVEN architecture, the DCB has memory on-board (rad-hard and SEE tolerant 10 × 8 GB Flash Memory that is the same as is currently flying on MAVEN) and takes formatted packetized data from the instruments directly making them available when requested for transmission to the ground without further processing. Support circuitry on this board includes address/data demultiplexing and an ADC for housekeeping data.
Flight Software The core of the SWEM DCB is a Coldfire processor implemented in an FPGA, as an IP-core. The Coldfire processor was used on MAVEN and builds on heritage code that has flown on many missions. This processor and the board electrical design was developed for UCB's Instrument package on RBSP, allowing SWEAP to take advantage of both hardware and software heritage. The SWEAP FSW architecture uses dedicated FPGAs for routine data and memory management, instrument interfaces, and other repetitive tasks in order to leave the processor free for specialized duties and less frequent, higher level functions. Instrument housekeeping and commanding is implemented via low speed serial data interfaces in a point-to-point architecture. Instrument science data is relayed over separate, higher speed serial lines to the DCB where data is directly fed into on-board memory, formatted as packets, and sent to the S/C mass memory for eventual downlink transmission.
Science Data and Operations The Suite is operated through the SWEM. The science data is taken along with calibration sequence run according to a command plan uploaded and then executed by the SWEM. Details of the suite operating modes and data products produced by the SWEM are described in Sect. 5. The SWEM has a number of independent operating configurations, which mainly affect instrument science data accumulation rates. Only two basic modes (Safe and Science Mode) affect power consumption and dissipation. In Safe Mode, only the core systems are powered on. Safe Mode is the power on condition, with only the core power and processing system powered on (the instrument sensors are not powered on). Science Mode is the normal operating state. In this mode, SWEAP is ready for full science data collection and is autonomously controlled using table driven mode configurations.
Science Operations, Data Processing & Data Products The SWEAP instrument suite operates autonomously during each solar encounter, with the SWEM controlling each of the SWEAP sensors, formatting data products, archiving high resolution data to internal storage in the SWEM, and transmitting summary data to the spacecraft for quick transmission to Earth following each encounter. As of PDR, the SWEAP Suite was allocated 20 Gbits of data volume per orbit to meet the Level 1 science goals described in Sect. 1. 1. This section describes the individual operating modes of the sensors, the SWEAP science operations plan, and the data products that will be produced and distributed to the public.
Software/Instrument Modes The sensor modes are determined by a command plan timeline that is uploaded to the SWEM from the SWEAP SOC. The sensors operate autonomously under direction of the SWEM. The available science modes are described for the SPC and the SPANs. SPC always operates at the same cadence, returning the four currents (4I) for a given E/q window (E) 128 times every 0. 874 seconds. The SWEM decides what data products to form with SPC by commanding the instrument to scan through different values of E. In Proton Tracking (PT) mode, SPC scans across the center of the proton VDF with 8E || /q (8E) windows, producing 8Ex4I measurements 16 times every 0. 874 seconds. These measurements are easily solved for V p , T p , and n p of the core of the proton distribution. With the addition of magnetic field data on the ground, variation in T p with field direction can be used to solve for the temperature anisotropy of protons at a lower cadence.