dataesr/scientific-paragraphs-categorization huggingface dataset:
An exception to this rule are the data provided by JT-60U, with data provided during a power scan during the discharge, with power steps after the plasma reaches stationary conditions. Spanning >15 years of data, ~3300 entries of stationary discharges have been collected; most of the data (75%) are from discharges with carbon walls (AUG, DIII-D, JET-CFC and JT-60U) as summarised in table 2. Data from C-Mod with a shorter duration (<5 τ E ) are also available as well as I-mode data; these data can be used to confirm trends seen in the data. Examples of discharges submitted to the database are given in figures 2(a)-(c), for AUG, C-Mod, and JET-ILW. The parameter variation in the database is given in table 2 and in figure  3  below. The data cover a wide range of plasma conditions in plasma current (0.
6 MA to 4 MA), toroidal field (0. 6 T to 5. 75 T), line averaged electron density (1. 3 × 10 19 m -3 to 6. 0 × 10 20 m -3 ) as well as a wide range of plasma shaping parameters, κ = 1. 5 to 2. 0 and δ x = -0. 15 to 0. 8, with κ the elongation at the plasma boundary and δ x the average triangularity at the plasma boundary. Normalised parameters achieved are H 98y2 = 0. 55-1. 55, β N = 0. 55-3. 8 and f GW = 0. 15-1. 15, containing a much larger operating space than foreseen for ITER. Most of the data lie in the range I p = 1-2 MA and only JET provides data up to 4 MA. C-Mod has data at the highest toroidal field values with H-mode entries up to B o = 5. 7 T, close to the ITER toroidal field value for Q ~ 10 operation. Table  2. Overview of the entries in the database. For ITER, P α ~ 100 MW.
CFC denotes a first wall made from Carbon Fibre Composite tiles, W-Wall implies a first wall full covered by tungsten tiles in ASDEX Upgrade, the ILW in the JET stand for ITER-Like Wall composed of beryllium first wall tiles and tungsten (coated) divertor tiles. DIII-D uses first wall tiles made from fine-grain graphite (carbon). In Alcator C-Mod the discharge categories are divided in H-mode and I-mode discharges. Note: Colour coding used for the different devices in some of the overview plots given in this paper. Most discharges lie in the range q 95 = 3-3. 3, with only few data entries for q 95 < 3, mainly from JET. AUG results with the carbon wall and DIII-D provide entries to the database that are well distributed around H 98y2 ~ 1 as do data from JET-CFC. For the all metal devices: (1) C-Mod entries for H-modes have H 98y2 below 1, (2) also most of the JET-ILW wall data have H 98y2 values below 1 and (3) AUG-W confinement values are on average just below H 98y2 = 1.
There is a significant difference for devices in the values for the line averaged density normalised to the Greenwald density (f GW ). DIII-D and JT-60U have all data entries for f GW < 0. 6. There is a significant split in f GW values between I-mode and H-mode operation in C-Mod, reflecting the lower particle confinement for I-mode discharges. Both AUG and JET have a large range for f GW , however data for all tungsten walls in AUG and beryllium-tungsten walls in JET are typically at f GW ~ 0. 8. The variation of the discharge parameters in the database provides an opportunity to explore parameter dependencies for H-mode operation at q 95 ~ 3 and where possible draw conclusions for ITER baseline operation. When data from carbon wall devices are plotted together in this paper, the data from carbon wall devices are given using grey symbols and experiments with metal walls are displayed in red.
Dimensionless parameter space and confinement trends As described in section 3, the assembled dataset at q 95 = 2. 7-3. 3 show variation in the energy confinement normalised to the standard H-mode confinement scaling, indicating possible deficiencies in the scaling coefficients used, or influence of additional physics variables absent from the scaling used for projections to ITER. Dimensionless parameters for the database are shown in figure  4. The definitions used are  [26] : ρ * ∼ ρ * = » W th /n e V/(aB 0 ) (2) where ρ * is a proxy for the normalised gyro-radius, ν C ∼ νC = n 3 e V 2 /W 2 th » R 5 geo /a 3 (3) where νC is a proxy for the normalised collision frequency, and β th ∼ βth = W th /(VB 2 0 ) (4) for the ratio of thermal kinetic to magnetic pressure (where, together with parameters defined earlier, V is the plasma volume (in m -3 ) and W th the thermal stored energy (in MJ).
Here βth (given in % in this paper) is a proxy for the plasma β, being the ratio of plasma pressure to the magnetic pressure and is distinct from β N which describes the ideal MHD stability limit of a free-boundary plasma in the absence of a wall. In figure  4 , the data at q 95 ~ 3 are plotted. The dimensionless parameter definitions in equations (  2 )-(  4 ) are not identical to the standard definitions as given in table  3  in  [27]. The expression used for normalised gyroradius, beta and collision frequency are good proxies for the average values of ρ * , ν C and β th. These definitions are particularly suited for analyses using 0D database vales for W th , n e , V, a, B 0 and R geo. Since the safety factor is held ~fixed in our database, it is not possible to distinguish between the scaling of heat transport with different normalizations of the collision frequency, such as the collision frequency nor malized to the thermal transit time (ν C ) or the collision frequency normalized to the bounce frequency (ν * ).
The quantity ν * is only important theoretically in that it distinguishes where trapped particles play a role. The quantity ν C however, maintains orthogonality of the dimensionless parameters used. Hence, our results will be given in terms of ν C , as in other published work from the IOS-TG, e. g. [26]. Concerning ρ * and νC , the data from each device show that they are anti-correlated (negative slope in figure  4 (a)), while the overall cross-machine dataset shows a positive correlation, certainly for the minimum νC versus maximum ρ *. The correlation between βth and νC is very weak as shown in figure  4(b) , while ρ * increases with βth for each device, in-line with the definitions given in equations (  2 ) and (  4 ). Compared to the deuterium plasmas in the database, the ITER values for ρ * are significantly outside the existing dataset (>2 times smaller in ITER).
A generalised expression of the normalised confinement with the main dimensionless parameters has the following power-law form: Bτ th ∝ ρ * αρ β α β th ν αυ C q αq 95 ε αε κ ακ. (6) A power-law form for the dependence of Bτ th on the dimensionless parameters is not required by dimensional analysis but is convenient in comparisons with the traditional power-law scaling applied to the engineering variables such as used in equation  (1). Having collected stationary H-mode data for a restricted range of the dimensionless variable q 95 = 2. 7-3. 3, the amount of data collected is such that trends of the nor malized thermal energy confinement time can be studied, while restricting the other dimensionless parameters to a narrow range of the experimentally observed variation, 'slicing' the database in dimensionless parameter space.
This is first done for the dependence of Bτ th on ρ * , restricting range of β N = 1. 5 to 2. 1 from a full range of β N of 0. 5-3. 8 in the dataset and is equivalent to restricting βth from 0. 7% to  trend is clear in figure  5 (a) of increasing Bτ th at lower ρ * , but this is mainly a result from the definition of ρ * as given in equation (  3 ), with an inverse B dependence. However, from figure  5 (a) it is difficult to determine if the dependence is gyro-Bohm, like as for drift wave driven transport  [31] , with Bτ th ∝ ρ * -3 , or more Bohm-like  [32, 33] , with Bτ th ∝ ρ * -2. Trend lines for Bτ th ∝ ρ * -2. 7 are also plotted in figure  5 , to show comparison of experimental results with the ρ * dependence of the IPB98(y,2) scaling in dimensionless form (equation (  6 )). In figure  5 (b), the data shown maintains the restriction on β N = 1.
5 to 2. 1 but also restricts νC to 1 × 10 5 to 6 × 10 5 (low νC values in the database, 118 data points). In figure  5 (c), the data are plotted for νC to 1 × 10 6 to 2 × 10 6 (medium νC values in the database, 501 data points). In figure  5 (d), the data are plotted for νC to 5 × 10 6 to 1. 5 × 10 7 (high νC values in the database, 86 data points). The data (slope) show a tendency to more Bohm-like scaling at higher collision frequencies, while at the lowest νC values a scaling more consistent with Bτ th ∝ ρ * -2. 5 to-3 (close to Gyro-Bohm) is found. Next, the dependence of the plasma collision frequency is studied, restricting β N and ρ *. In figure  6 , the dependence of Bτ th on νC is plotted for a restricted range of β N = 1. 5-2. 1 In figure  6 (a), the full range of ρ * = 0. 019-0.
128 is plotted giving, again, 1383 data points. From the 'cloud' of points given in figure  6 (a), it would be difficult to determine the dependence of Bτ th on collision frequency. Figure  6(b)  shows the data maintaining the restriction on β N = 1. 5-2. 1, but also restricting ρ * to 0. 04 ± 0. 004 (low ρ * values in the database, 215 data points, all from JET). The data at the beta values of ITER and low ρ * show an increase of Bτ th at lower νC values, hence trend lines with dependence of Bτ th ∝ ν-0. 2 C are also plotted in figure  6. In figure  6 (c), the data are plotted for ρ * to 0. 075 ± 0. 0075 (medium ρ * values in the database, 386 data points, mainly from AUG and DIII-D). In figure  6 (d), the data are plotted for ρ * to 0. 1 to 0. 15 (high ρ * values in the database, 76 data points).
The data at medium and highest ρ * values in the database show a less clear trend of achieving higher Bτ th values at lower collision frequency. For the highest ρ * values, no clear trend is observed, more consistent with Bτ th ∝ ν0 C. In figure  6 , the ITER simulation points are included in plots (b)-(d) to indicate their location relative to the x-axes and the data available, although the ITER simulations are at lower ρ * values than used in the data selection of the plots. This comparison shows in figure  6(d ) that there are only νC data matching ITER at the highest values of ρ * in the dataset. They would imply an extrapolation of these results to ITER in ρ * by an order of magnitude and three orders of magnitude in Bτ th for the IPB98(y,2) scaling expression in dimensionless form. Finally, the dependence of Bτ th on beta is studied as shown in figure  7 , where dependence of Bτ th with βth is given for a restricted range of ρ * in the range 0.
036-0. 044 (lowest values in the dataset, from JET and JT-60U). In figure  7 (a), Bτ th versus βth is plotted for the full range of νC = 1. 3 × 10 5 to 1. 4 × 10 8 giving 397 data points. Already over the full range of collision frequency these data show no degradation of Bτ th with βth. Trend lines of Bτ th ∝ β-0. 9 th are also plotted in figure  7 , clearly indicating that this trend (as given by the IPB98(y,2) scaling in dimensionless form) is not represented by the data. Restricting νC to 1 × 10 5 to 6 × 10 5 (low νC values in the database, 30 data The data maintain the trend that Bτ th shows no degradation with βth , if anything the dependence at the highest collision frequency becomes slightly positive with beta. In figure  7 (a), also data at the higher values for normalised gyro-radius in the range 0.
Comparisons with energy confinement scaling laws Without restricting the dimensionless parameter space, the data can be compared to scaling laws for energy confinement. Sufficient data are available (2504 entries, with data for dimensionless variables), so that the data from devices with a carbon wall (1686 entries from AUG, DIII-D, JET and JT-60U) can be plotted separately from the data proved from devices with a high-Z metal wall (818 entries from AUG, C-Mod and JET). The IPB98(y,2) scaling given in equation (  1 ) has dimensionless form (  [27] , equation (4. 6)): Bτ th,IPB98 ∝ ρ * -2. 69 β -0. 9 th ν 0. 08 C q -3. 0 95 ε 0. 73 κ 3. 29 R -0. 01 geo (7) here, the negligible R -0. 01 geo dependence depicts the residual non-conformance of the IPB98(y,2) scaling expression with the dimensionless constraint.
Using the IPB98(y,2) scaling in dimensionless form, the entries of the database presented in this paper are shown in figure  8. The data have an overall RMS error of 190% (combining all devices, the ITER simulation results are not included in these RMS analyses), indicating that the underlying transport physics processes are not well captured by the scaling expression. The carbon wall data have an RMS error of 200% and the data from high-Z wall devices have an RMS error of 170%, showing not much difference. The strong scaling with elongation of the IPB98(y,2) scaling in dimensionless form warrants the question if the plots shown in figures 5-7 should have restricted the range of κ. However, restricting κ to a range 1. 6-2. 0 (ITER value of 1. 8% ± 10%) only removes 5% of the data, including all the data from JT-60U, without changing the trends shown.
It is common practice to plot normalised confinement values (such as H 98y2 ) versus ρ * , νC or βth (or β N ) without restricting the domain of the other dimensionless param eters. In figure  9 , H 98y2 is plotted versus νC , showing that the maximum H 98y2 that can be obtained for a given collision frequency increases with decreasing collision frequency. Similar trends with collision frequency are observed in the ITPA-IOS TG database for advanced inductive discharges  [26]. It is remarkable that the maximum values for H 98y2 obtained as a function of collision frequency are similar for a range of experiments as shown in figure  9. No difference in normalised confinement is observed between carbon wall and metal wall discharges at high col lision frequency (>5 × 10 6 ) and the trends towards lower col lision frequency show a similar rate of increase in confinement enhancement, H 98y2.
Scaling laws with engineering variables have been used to compare results of experiments in existing devices and to predict the energy confinement of future devices. The most commonly used IPB98(y,2), as given in equation (  1 ) was derived using data from 11 devices with a large range in parameters (  [35] , table 2), such as I p (0. 14 MA to 5. 1 MA), P loss (0. 18 MW to 18 MW), R geo (1. 3 m to 2. 9 m), q 95 (1. 9 to 11. 1), n e (1. 1 × 10 19 m -3 to 43 × 10 19 m -3 ) and normalised beta (0. 5-3. 5). The main differences with the database presented here is the restriction for q 95 from 2. 7 to 3. 3, the data being provided only from five (ITER-like) devices and the larger range for P loss (0. 5 MW to 30 MW). Another difference is that the original database for IPB98(y,2) allowed points with (-0.
05 (dW th /dt)/P loss 0. 35), with no criteria on achieving stationarity for five confinement times as applied for the data at q 95 ~ 3, giving (-0. 05 (dW th /dt)/P loss 0. 15). The analyses of the original database, summarised in  [10] , insured that the scaling expression found (with an RMS   (ν c  ) 0. 08 (β th ) -0. 9 (q 95 ) -3. 0 (ε) 0. 73 (κ) 3. 29   ∝ (ρ*) -3. 0 (ν c ) -0. 14 (β th ) 0. 0 (q 95 ) -1. 84 (ε) -1. 1 (κ) 2. 29  ∝ (ρ*) -3. 0 (ν c ) -0. 14 (β th ) 0. 0 (q 95 ) -1. 84 (ε) -1. 1 (κ) 2. 29 ∝ (ρ*) -2. 69 (ν c ) 0. 08 (β th ) -0. 9 (q 95 ) -3. 0 (ε) 0. 73 (κ)  error of 15. 6%) was a valid representation of the data, and that correlations among the data were appropriately dealt with. In figure  10 , the computed τ th for the data at q 95 ~ 3 is compared with the IPB98(y,2) scaling prediction.
Plotted on a log-log scale, the data for each device seem to follow the scaling with a normalised RMS error for all the data of 28%, and an average value for H 98y2 of 0. 91. This RMS is substantially smaller compared to the RMS error obtained from the dimensionless scaling regressions (190%). This is due to the high exponents of the ρ * , q 95 and κ dependencies in the dimensionless scaling, amplifying any discrepancy with the scaling expression, or that additional dimensionless parameters need to be considered. However, the RMS value is larger than the RMS value (15. 6%) of the original dataset for the IPB98(y,2) scaling. As shown in table 4 at the end of this section, the RMS error of the data from devices with a carbon wall is 25%, with H 98y2 ~ 0. 97, while the RMS error of data from high-Z metal wall devices is 34% with H 98y2 ~ 0.
80. The original data set for IPB98(y,2) scaling contained mainly results from carbon wall devices. Moreover, the data for q 95 ~ 3 certainly lie at the extreme range for q 95 of the original data. It is possible that the single power law model assumed for the IPB98(y,2) scaling regression breaks down, or at least acquires some curvature, near q 95 = 3. Some of the variables used in IPB98(y,2) scaling expression have a very high correlation in the database at q 95 ~ 3 as shown in table 3; minor radius and R geo have Pearson correlation R = 0. 99, as the devices contributing to the database at q 95 ~ 3 have very similar aspect ratio (R geo /a). Other parameters, such as B 0 , κ a and n e , have no or a low correlation with τ th , in the database presented in this paper. Even though the fit parameters used for the IPB98(y,2) are justified for the data in the original database of the mid-1990s, for the data collected on the ITER baseline for the devices listed in table 2, the IPB98(y,2) scaling represents an 'overfit' (using too many variables) for the data at q 95 ~ 3.
Although not shown here, the residuals for the four main parameters I p , P loss , R geo and n e also show residual trends for the DS03 scaling. Reasonable fits to the data in the database can be obtained using only three fit parameters (I p , P loss , and major radius, R geo ). The scaling of the thermal energy confinement obtained is: τ 3_parameters (s) = 0. 16P -0. 77 loss I 1. 33 p R 1. 38 geo. (10) The use of I p , P loss , and R geo is justified as these parameters have the highest correlation with the observed thermal confinement time, while containing over 90% of the information in the scaling. The regression using I p , P loss , and R geo has an RMS error of 30% and is shown in figure  12(b ). This reduced parameter scaling law is based on data from all devices, with carbon walls and high-Z metal walls, including I-mode data from C-Mod and has no residual scaling with plasma density.
Plasma confinement trends with beta and plasma density Despite the shortcomings of the IPB98(y,2) scaling, it continues to be regarded as the standard confinement scaling expression. Hence, it will be used in the remainder of this paper to present the analyses of the database at q 95 ~ 3. The dimensionless analyses given in the previous sections, indicates that the scaling with beta for IPB98(y,2) is too pessimistic. The scaling with loss power (P -0. 69 loss ) is not in agreement with observations of no change in normalised confinement time with input power or beta in dedicated experiments in DIII-D  [27]  and JET  [36, 37]. The database  shows that the maximum achievable H 98y2 increases with β N (see figure  13 ). This trend with β N is also observed in the advanced inductive discharges  [26].
85 with H 98y2 = 1 is at the top of the data range available in experiments with metal walls. In the early stages of the ITER design, the plasma shape was chosen with a triangularity δ ~ 0. 5, aimed at maximising the pedestal stored energy and to obtain H 98y2 ~ 1 at high plasma density with f GW ~ 0. 85. For H-mode discharges that obtain H 98y2 > 0. 95, the dataset for carbon wall and metal wall discharges shows indeed an overall increase of f GW attainable with average triangularity as shown in figure  15 , although only a few discharges for JET-ILW are available at the operating point of ITER. The data at f GW ~ 1 with the carbon wall at JET were obtained in the so-called mixed type I + II ELM  For AUG with the tungsten wall and JET with the ILW no data are available with gas fuelling rates below 1 × 10 21 electrons s -1 ; discharges with lower fuelling rates are not stable as gas fuelling is required to reduce the tungsten influxes and ensuring high enough ELM frequency to prevent tungsten accumulation  [19, 24].
C-Mod entries in the database are ELM free or have enhanced D-alpha activity (EDA) without discrete ELMs, requiring no gas dosing to obtain stable discharges with the molybdenum wall. The use of gas fuelling at for high-Z metal wall experiments at JET and AUG typically lowers the pedestal pressure, compared to carbon wall experiments, reducing global energy confinement times. Nevertheless, at fuelling rates in the range 5 × 10 21 /s to > 1 × 10 23 /s the plasma confinement is similar for H-mode operation with carbon walls and metal walls, especially for seeded plasmas in AUG  [39]. The database contains only a few discharges with nitrogen seeding from both AUG and JET, not enough to obtain trends compared to non-seeded H-mode plasma results. Inter-machine comparisons of electron fuelling rates in H-modes is complicated by the fact that devices have different pumping speeds and divertor geometries; at the moment it is not clear how these results would scale to ITER.
Hence, the reduction of confinement with gas puffing and the roll-over of confinement when approaching the Greenwald density limit would warrant the inclusion of the edge density or recycling flux as additional information to the database, rather than using the line averaged density n e alone. Kallenbach et al  [40]  showed that the electron density/ neutral hydrogen flux in the scrape-off layer is found to be a key parameter for particle and energy confinement as well as for the radiative losses.
Most of the data in the database are provided from experiments with dominant co-NBI, heating mainly the ions in the core of the plasma, and as a result do not represent the heating conditions in ITER with dominant electron heating. The data from C-Mod are with pure radio-frequency (RF) heating. The contribution of the RF heating can be determined using P RF / (P NB + P RF ). Although this does not reflect the split between ion and electron heating in the core, it does represent a measure for changing the heating scheme to more electron heating with low net torque applied to the plasma. The data with NBI heating alone obtain normalised confinement, H 98y2 , up to 1. 55 in the database. As shown in figure  17 , the data with significant RF contribution can obtain similar values, and certainly can achieve H 98y2 ~ 1 in a range of devices over a range of plasma conditions.
However, the total radiation plays an important role for H-mode dynamics, such as entry into stationary H-modes, as only the power convected through the separatrix contributes to the loss power that keeps the plasma in H-mode. The importance of bulk radiation can be assessed by comparing the radiation normalised to the total input power, i. e. radiation fraction f rad. In the ITER simulations, the total bulk radiation is the sum of bremsstrahlung (P brem ), the cyclotron radiation (P cycl ) and line radiation (P line ) and ranges from 40 MW to 48 MW in the two simulations provided. DIII-D and JET have data for the bulk radiation of H-mode discharges in the database. These values have been obtained by integrating the reconstructed radiation inside the plasma separatrix, which in some cases may give large uncertainties.
Another method to compare radiation values is to normalise to the particle density, as the core radiation is thought to scale with the particle density times the impurity density. The latter is often converted into a scaling with the square of the density, assuming a constant impurity content  [48]. In figure  19 , the radiation power, per plasma volume, is plotted versus n 2 e. A satisfactory scaling is found when the radiation is normalised with the entire volume as in the database are not necessarily dominated by edge line radiation with data from devices with both carbon and metal walls. As the discharges in the database are stationary, no strong outliers are observed due to high-Z impurity radiation for devices with metal walls as sometimes observed when entering or terminating H-mode  [49].