dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations Parameters Many network simulators exist, such as Network Simulator 2 and 3, OMNeT++ (Objective Modular Network Testbed in C++), Cooja, TOSSIM (Tiny OS SIMulator), etc. We chose to use the Cooja simulator (Contiki 3. 0, Adam Dunkels, Sweden). It is a emulator for Contiki systems. It is compatible with real hardware and the simulation code can be used on real-life nodes. In addition, Contiki comes with standard RPL implementation on which we added all the different enhancements including RRD+. We made some modifications on the default parameters of Cooja in order to make it more realistic, especially when it comes to emulating signal propagation. There are four propagation models in Cooja  [16]. One of them is Multi-path Ray-tracer Medium (MRM) which takes reflections and refractions into account to simulate real environment.
The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.
• The electron density profile n e is prescribed, but can be time-varying and provided by a real-time density profile reconstruction code such as  [30, 31]. • A boundary condition for  (1)  is obtained by prescribing the total plasma current, which is seen as an actuator input in u k. For (2), the edge electron temperature is prescribed. Inside RAPTOR, (1) and (  2 ) are written as a nonlinear state space model with the following state update equation and its corresponding output equation at time instant t k : f (x k+1 , x k , u k ) = 0, (3) y k = h(x k ). ( 4 ) The state vector x k contains the coefficients that are used to parameterize the T e and ψ profiles and the vector u k contains the actuator inputs, in this case the total plasma current and EC actuator powers. The state update equation gives the state at the next time instant x k+1 based on the present state x k and actuator inputs u k.
The vertical position of the magnetic axis is feedback controlled, whereas only feedforward shape control is used. The actuator deposition profiles and the achievable plasma profiles are illustrated in Figure  2  for shot #54423 during the flat-top phase. The normalized volume integrated power density profile retrieved from TORAY-GA (a) shows a central and narrow deposition profile. On TCV, an accurate evaluation of the EC deposition width would require a Fokker-Planck code to be used, since radial diffusion of fast electrons broadens the effective width predicted by TORAY-GA  [34]. RAPTOR assumes a distribution of EC power prescribed as input to the code. This can be taken from a separate ray-tracing (or Fokker-Planck) code, or manually specified. Here, the EC deposition width in RAPTOR is larger (∼ 0.
Controller development and implementation environment As mentioned in the introduction, it is important to prepare controllers thoroughly before testing them in experiments. In this section we present the set of controller development and implementation tools that facilitates the efficient preparation of profile controllers in simulations and implementation in experiments. These tools are all prepared in the MATLAB Simulink block-programming language, which greatly facilitates re-using components in different stages of the development and implementation with minimal changes. First a closed-loop simulation tool is prepared, separate from the TCV control system software environment that allows to largely prepare the profile controller on a local computer. Next we present the controller development and implementation tools inside the TCV control system environment that allows to develop the controller in more comprehensive closed-loop simulations and finally test it in experiments.
Controller development and implementation in TCV control system software environment Once the controller has been tested and verified in the environment described in Section 4. 1, it is ready to be tested in the more complete TCV control system software environment that includes other real-time algorithms. The digital TCV control system is distributed over several computational nodes linked via shared reflective memory and accommodates hundreds of diagnostic inputs and actuator outputs  [36]. Details on the recent upgrades to and applications of the TCV control system can be found in  [37]. The control system is entirely programmable by MATLAB Simulink, allowing rapid algorithm development and implementation using code generation  [38]. The developed profile controller development and implementation environment within the TCV control system allows three crucial applications: 1.
RAPDENS-observer Using the same EKF approach as in RAPTOR-observer, RAPDENSobserver reconstructs the density profile from far-infrared (FIR) measurements in combination with model-based predictions from a density evolution model  [30, 31]. Automatic detection and correction for diagnostic artefacts like fringe jumps in FIR channels is included. LIUQE A 2D Grad-Shafranov equilibrium reconstruction code that uses magnetic measurements  [21]. It supplies equilibrium geometry information to the RAPTOR-observer, the RAPDENS-observer and the profile controller. At present the RAPTOR q-profile estimate is not fed back to LIUQE. More details on recent advances of these state reconstructions algorithms will be provided in separate publications.
• No active plasma shape feedback control is used. The plasma volume changes significantly in shot #54414 and also changes frequently from limiter to divertor shape. We expect that correctly modeling the delays and actuator dynamics will already remove the closed-loop instability. The last two expected origins of the closed-loop instability can be solved by adding an equation for the density in RAPTOR and including shape control in the experiments. This experiment shows the potential and limitations of using the plasma current I p in feedback in the present software configuration. It is shown that using the plasma current as feedback actuator allows to achieve a broader range of q-profiles. Applying the described remedies to remove the closed-loop instability should enable the controller to efficiently use the additional control freedom provided by I p.
Comparison of online and off-line profile reconstructions To investigate the sensitivity and accurateness of the reconstructed profiles by RAPTORobserver, we compare these to other (off-line) reconstructed profiles. For the shot #54402, we will now compare the real-time reconstructed q-profiles and plasma β from RAPTOR-observer with those obtained in: • Real-time reconstruction using LIUQE  [21]. See Section 4. 2 for more details. • Off-line reconstruction using LIUQE  [21]. The off-line version of LIUQE uses different basis functions than the real-time version, allows more iterations of the numerical scheme and includes measurements from the diamagnetic loop (DML). • Interpretative simulations using the ASTRA transport code  [35]. In these simulations of the poloidal magnetic flux evolution, ASTRA uses the EC deposition profiles from TORAY-GA  [33] , the Thomson electron density and temperature profile measurements, a simplified q-profiles @ t=2000ms TCV#54402 Figure  14 : Comparison of reconstructed q-profiles (at the same four time steps as in Figure  12 ) from the RAPTORobserver to real-time reconstructions of LIUQE and interpretative ASTRA simulations.
Acknowledgements We acknowledge  Hugo van den Brand (DIFFER)  for the real-time implementation of the Quadratic Programming solver. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No. 633053. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This work is also supported in part by the  Netherlands Organisation for Scientific Research (NWO)  via the  Innovational Research Incentives Scheme  and by the  Swiss National Science Foundation. ITER is a Nuclear Facility INB-174. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization.
4 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-256 Manuscript under review for journal The Cryosphere Discussion started: 27 November 2017 c Author(s) 2017. CC BY 4. 0 License. We used meteorological data from the Alplager station located in the Ala Archa valley, situated at an elevation of 2145 m a. s. l. at a distance of about 10 km from the glacier (Fig. 1 ). There are several other meteorological stations in the valley, however, the Alplager station has the most complete and continuous series at high elevation covering the entire study period. Intense glacier monitoring started in 1958 and continued until 1994 when the monitoring programme was stopped  (Aizen, 1988). In summer 2010, mass balance measurements were re-initiated. Figure  2b  summarizes the monitoring network at Golubin Glacier as of 2016, including a mass balance measurement network, an AWS and two terrestrial cameras installed in 2013.
a -1. An AWS (Tien Shan (Kumtor) AWS) installed at a distance of approximately 10 km to the glacier records continuous meteorological data for the study period (Fig. 1 ). We used daily precipitation sums and mean daily air temperature for modelling. Since 2010, in situ mass balance has been obtained annually in late summer (Fig. 2c ). Winter measurements exist for May 2014  (Kronenberg et al. , 2016). A description of the meteorological input data and the mass balance measurement network, as well as a reconstruction of the mass balance series back to 2003 are provided in  Kronenberg et al. (2016).
High-resolution satellite images and DEMs To compute geodetic mass balances for Abramov Glacier, high-resolution DEMs were used based on Pléiades data acquired in 2015, and on stereo images from 2003 and 2011 from Satellite Pour l'Observation de la Terre (SPOT) 5. For Glacier No. 354, DEMs from 2003 (QuickBird) and from 2012 (GeoEye) were available from  Kronenberg et al. (2016). In addition, a SPOT6 stereo-pair acquired in 2015 was used to produce an updated DEM. High-resolution images for Golubin Glacier are sparse and we had to rely on a SPOT7 tri-stereo scene from November 2014 and on Advanced Land Observing Satellite (ALOS) Prism scenes from 2006 (Table  1 ). For modelling purposes, the most complete and accurate DEM available of each glacier was used to represent the surface topography (Table  1 ).
In 2015, continuous coverage was obtained from Camera 1 but only a few images could be retrieved from Camera 2 (Fig. 2a ). A complete set of data was collected from both cameras for the first time in 2016. A similar setup has been installed for Glacier No. 354 in 2014, delivering continuous coverage since implementation (Fig. 2c ). The camera is located at an elevation of 4145 m a. s. l. Images from the two cameras installed at Golubin were not used here due to limited image quality. Figure  3  illustrates the number of camera and satellite images with satisfying quality that were used to obtain maps of snowlines for the three glaciers. Image availability for snowline  3 Methods.
Meteorological data For Abramov Glacier, the air temperature data measured at the AWS was adjusted to the elevation of the former glaciological station by applying a constant lapse rate of -6 • C km -1  (Suslov et al. , 1980). The ERA-interim Reanalysis dataset was adapted by applying mean monthly additive and multiplicative biases for air temperature and precipitation, respectively. The biases were calculated from long-term in situ measurements  (Barandun et al. , 2015). From the corrected monthly means, daily series were generated by superimposing day-to-day variability, observed at the meteorological station from 1969 to 1994. For Abramov, we generated air temperature series from 1995 to 2011 and precipitation series from 1995 to 2016. More detailed information on data preparation and their suitability is given in  Barandun et al.
No. 354: 25%)  were assumed to have experienced the same elevation change as the measured areas in the same altitude band and the median of the corresponding elevation bin was used for gap-filling. For elevation bins higher than 4300 m a. s. l. at Golubin (9% of total area) and for bins higher than 4500 m a. s. l. at Gl. No. 354 (8% of total area), obvious DEM errors dominated and not enough realistic values for median elevation-change calculation were available. There, the median of the uppermost elevation band with reliable data was used to fill in the gaps. 9 The Cryosphere Discuss. , https://doi. org/10. 5194/tc-2017-256 Manuscript under review for journal The Cryosphere Discussion started: 27 November 2017 c Author(s) 2017. CC BY 4. 0 License. To derive the geodetic mass balance ∆M geod , a density ρ ∆V of 850 kg m -3 was used for volume-to-mass conversion  (Huss, 2013) : ∆M geod = ∆V • ρ ∆V A • ∆t (1) where, A is the average glacier area and ∆t is the time in years between the corresponding image pairs.
The methodological steps are described in more detail in the following. A mass balance model with a spatial resolution of 20 m was driven with daily mean air temperature and precipitation sums measured at nearby meteorological stations or inferred from Reanalysis data (see Section 2. 1). We used a classical temperature index melt model (e. g. ,  Braithwaite, 1995; Hock, 2003). Melt M was calculated for each grid cell x, y and time step t based on a linear relation with positive daily mean air temperature T air (x, y, t) as M x,y,t =    DDF ice/snow • T air(x,y,t) T air > 0 • 0 T air ≤ 0 • (2) Daily air temperatures are extrapolated to each grid cell using a constant temperature lapse rate based on literature values (Table  2 ). Different degree-day factors DDF ice/snow were chosen for snow and ice surfaces.
Adjustments to enable comparison of different methods Geodetic surveys provide an estimate of the total mass change of a glacier ∆M geod , whereas snowline-derived mass balance series refer to the surface balance B a and do not account for internal and basal components of the mass balance  (Cogley et al. , 2011). For comparing the results, we adjusted the glaciological and snowline-derived surface mass balances with an estimate of the internal/basal mass balance of +0. 07 m w. e. a -1 for Abramov  (Barandun et al. , 2015) , +0. 08 m w. e. a -1 for Golubin  (Aizen et al. , 1997)  and +0. 04 m w. e. a -1 for No. 354  (Kronenberg et al. , 2016). Values are positive due to a significant amount of refreezing of meltwater in cold firn. To compare the results of the different methods, the time periods covered by the datasets also needed to be homogenized.
Comparison to glaciological and geodetic mass balances The glaciological and geodetic surveys delivered two extensive and independent datasets for validation of the snowline-derived mass balance series. Joint analysis of the data sets permitted robust conclusions to be drawn about the mass change and its temporal dynamics over the past two decades. The glaciological mass balance measurements showed a good agreement with the mass balance inferred using snowline observations for the same time periods (Table  6 ). Annual glaciological mass balances were reproduced with an RMSE of less than ±0. 26 m w. e. a -1 for all three glaciers using the snowline approach (Fig. 6 ). For Golubin and Glacier No. 354, the glaciological mass balances was slightly more negative than the snowline-derived balances (Table  6 ).
Acknowledgements. This study is supported by the  Swiss National Science Foundation (SNSF) , grant  200021_155903. Additional support by the  German Federal Foreign Office  in the frame of the  CAWa  project (http://www. cawa-project. net) and the support of the Federal Office of Meteorology and  Climatology MeteoSwiss  through the project  Capacity Building and Twinning for Climate Observing Systems  (CATCOS) Phase 1 & 2, Contract nos. 7F -08114. 1 , 7F -08114. 02. 01 , between the  Swiss Agency for Development and Cooperation (SDC ) and MeteoSwiss as well as the project  CICADA (Cryospheric Climate Services for improved Adaptation ), and contract no. 81049674.
Radium 228 ( 228 Ra), an almost conservative trace isotope of the ocean, supplied from the continental shelves and removed by a known radioactive decay (T 1/2 = 5. 75 yr), can be used as a proxy to constrain shelf fluxes of other trace elements, such as nutrients, iron, or rare earth elements. In this study, we perform inverse modeling of a global 228 Ra dataset (including GEOSECS, TTO and GEOTRACES programs, and, for the first time, data from the Arctic and around the Kerguelen islands) to compute the total 228 Ra fluxes toward the ocean, using the ocean circulation obtained from the NEMO 3. 6 model with a 2 • resolution. We optimized the inverse calculation (source regions, cost function) and find a global estimate of the 228 Ra fluxes of 8. 01 -8. 49 × 10 23 atoms yr -1 , lower and more precise than previous estimates.
Introduction Trace Elements and Isotopes (TEI) are low concentration components of the ocean, but they contain decisive information for our understanding of its dynamics. The international program GEOTRACES has been designed to improve our knowledge on the TEIs concentrations and the oceanic processes controlling their distribution, by means of observations, modeling and laboratory experiments. Since 2006, GEOTRACES cruises have been mapping the global distribution of tens of these TEIs. Some of them are studied because they constitute micronutrients for living organisms, like iron (Fe), or pollutants, like lead (Pb) and cadmium  (Cd). Others are proxies of ocean dynamics or of biogeochemical processes: For instance, neodymium (Nd) is a proxy of the exchanges between seabed and seawater  (Jeandel et al.
It also contains data from the Arctic, a basin absent from Kwon's study. The forward model is built on the Ocean General Circulation Model (OGCM) NEMO. Our main improvement is a careful analysis of sensitivity and errors, which reveals that the result depends on the model mathematical parameters, such as the cost function and the number of regional sources that are considered. We have performed several inversions and analyzed the residuals and uncertainties, to determine the most appropriate mathematical parameters and evaluate the precision of the flux estimates. This paper is organized as follows. In section 2, we describe the different aspects of the inversion technique, e. g. the global dataset, the forward model based on the NEMO OGCM, the different cost functions, the choice of the source regions related to SGDs, and the inverse method.
228 Ra dataset Since the late 1960's  (Moore, 1969; Kaufman et al. , 1973)  tens of oceanographic cruises have carried out measurements of 228 Ra (e. g. articles listed in Table  S1 ). The dataset used in this study includes, among others, observations from three international programs sampling trace elements. Data from the Indian Ocean cruise of  GEOSECS, in 1977 -1978 , are included. From 1981 to 1989, the TTO (Transient Tracers in the Oceans)  program produced a considerable number of 228 Ra measurements in the Atlantic, from 80 • N to 60 • S, at all depths, making of the Atlantic Ocean the best sampled ocean by far. Currently, new data from all oceans are being produced by GEOTRACES. 6059 data from all basins are used in total, of which 2789 are shallower than 10 m, 1107 are located between 10 and 200 m deep, 606 between 200 and 600 m deep, and 1557 deeper than 600 m.
Our data set comprises 1359 more measurements than in  Kwon et al. [2014] : Two GEOTRACES sections, GA03 (United States -Cape Verde -Portugal) and GP16 (Ecuador -French Polynesia), and data from the Arctic, the Southern Ocean, the North Pacific, the Mediterranean Sea and the Indonesian Seas have been added in the present study (See Table  S1 ). These supplementary observations extend the data coverage to regions north of 70 • N and around the Kerguelen islands. In a near future, other sections from the GEOTRACES program will complete the global covering and may help in studying deeper sources. For the purpose of our study, data have been averaged in each model grid cell (see section 2. 2 for more details on the model configuration), leading to the 3076 cell averages shown on Fig. 1. The density of the measurement is noticeably uneven.
5 • near the equator. The mesh is tripolar in order to overcome singularities, the North Pole being replaced by two inland poles in the Northern Hemisphere. It has 31 vertical levels, ranging from the surface to 6000 m deep, the upper layer covering the first 10 m. The simulation is forced by a seasonal climatological dataset, based on NCEP/NCAR reanalysis and satellite data. λ is the radioactive decay constant, 0. 12 yr -1 , given by the half-life of 228 Ra which is 5. 75 yr. Decay is the sole sink. It is known and does not depend on environmental parameters, leaving the source term as the only unknown to be determined by the inversion technique. S i is the source term specific to the i th region, representing riverine inputs, sedimentary diffusive fluxes and groundwater discharge fluxes of 228 Ra from region i.
Extensive regions are lacking observations, mostly in the Southern Hemisphere (Pacific, Indian, and mostly Southern Ocean, as well as western equatorial Pacific Ocean), and better coverage in basins where SGDs are known to be influential and to produce large horizontal gradients is also needed (such as in South Asia). But the main impediment to achieve precise estimates of global Ra SGD fluxes comes from the very poor knowledge of diffusive sedimentary fluxes: without a proper way to separate diffusion and SGD, inversions can compute the total bottom flux but are not able to precisely evaluate these two components. 6 Code and Data availability The source code of NEMO is available on the NEMO website. Studies providing data are listed in Table  S1  in the supplementary materials. The inversion code and data used in this study can be obtained directly by contacting the authors.
More detail on the processes implemented in the GGCMs can be found in the Supplement, Tables  S1-S4. Here we use data from simulations by these 14 GGCMs driven by the weather data set AgMERRA  (Ruane et al. , 2015) , for which all modeling groups have performed simulations and historical atmospheric carbon dioxide (CO 2 ) concentrations  (Thoning et al. , 1989). The AgMERRA data set spans the time frame of 1980-2010 and provides daily data on the most important meteorological driver variables and groups applied their own interpolation to sub-daily values if needed. If additional weather data were needed by individual modeling groups (such as long-wave radiation), these were supplemented from the Princeton Global Forcing data set (PGFv2)  (Sheffield et al. , 2006). We assume this to have little impact on simulation results, as all data sets are based on station data and/or re-analysis data, and as bias correction of re-analysis data is performed for each meteorological variable individually, there is no explicit dependency between individual variables (e.