dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on the work on WEST scaling laws published in  [2, 61] , in our reduced D L-mode database in LSN, at Ip = 0. 5 MA and Ptot > 2 MW, the confinement time, τMHD, is estimated using WMHD normalized to Ptot, the total power. The database being at a unique Ip, τMHD is normalized only to the power degradation reported for WEST: P -0. 73  [2, 61]  with P = Ptot. The impact of the line-averaged density, n, traditionally used in global multi-machine scaling laws, is explored in this reduced database, before exploring other forms of the density. In figure  4 , on the cold branch, where Te(0) < 2 keV, the confinement time is mostly insensitive to n ¯: τMHD/P -0. 73 n0. 08 while on the hot branch, for Te(0) > 2 keV, it even degrades with larger density: τMHD/P -0. 73 ∝ n-0. 14.
2. Impact of the density at the separatrix on the confinement time Concerning the impact of the density at the separatrix, similarly to H-mode databases  [9, 10, 13] , in our L-mode database, a degradation of the confinement with higher nsep is reported. Note that the Greenwald density, at 0. 5 MA, ranges between 7 and 10 10 19 m -3 , hence, nsep normalized to the Green-wald density is below 50%. The density at the separatrix is measured by reflectometry in the outboard midplane  [56] , remapped on the magnetic equilibrium reconstructed by NICE constrained by eight polarimetry Faraday angles  [48, 49]  and averaged over the plateaus. In figure  5 , the confinement times normalized to, on the left, the H98 scaling law and on the right to the power degradation, P -0. 73 , are plotted against nsep.
The impact of nsep on the confinement time normalized to the H98y,2 scaling law in our L-mode database is very similar to the one reported in H-mode on AUG  [10]. The fact that H98y,2 values in the WEST L-mode database are similar to those in the AUG H-mode database reported in  [10]  is due to the unfavorable aspect ratio dependence of the H98y,2 scaling law, while the L96 scaling law has a weak A dependence confirmed by WEST L-mode data  [2]. This leads, at A = 5-6, to similar confinement time prediction using the ITER96-L or the H98y,2 scaling laws  [1, 63]. The issue, when using the H98y,2 scaling law for normalizing the confinement time, is that τMHD is normalized to n ¯0. 4 , which is not what we observe in our database, see figure  4. Since n and nsep are correlated, see figure  1  below, keeping the unrealistic n0.
3. Relation between density peaking and confinement time We now compare the impact of n, nsep and the global density peaking n/ n on the normalized confinement to its power degradation τMHD/P -0. 73 , in figures 6(a)-(c), respectively. The correlation coefficient r is computed in each case across the whole database including all pulses, >2 keV and <2 keV. The clearest correlation of τMHD/P -0. 73 is against the global density peaking, r = 0. 70, while we report r = 0. 53 with nsep and a yet weaker correlation with n, r = 0. 39. Therefore, a more peaked density profile correlates both with larger Te(0) (figure  3 (g)) and with better confinement time (figure  6(c) ).
Role of the collisionality at the separatrix on confinement As proposed in the 1990s by  [18]  and  [15, 17, 64] , resistive modes are likely at play near the separatrix of confined Lmode plasmas. These proposed theories were compared to experimental SOL data in L-and H-modes, see for example  [19]. It actively continues today by comparing experimental trends to state-of-the-art edge simulations using gyrokinetic codes  [20] [21] [22] [23]  and fluid nonlinear simulations  [24] [25] [26] [27] [28] [29] [30]. Following the path of Scott and Rogers-Drake-Zeiler, fluid-based turbulent models are capturing qualitatively and quantitatively the density limit and/or the L-H transition  [27, 28, 30, 31]. Hence, throughout the experimental database, one can expect to find some correlation between a normalized parameter characterizing the drive of resistive modes (typically scaling with the separatrix collisionality) and the overall plasma performances.
⟨ ⟩ On WEST, the Thomson scattering diagnostic allow-ing simultaneous measurement of the upstream density and temperature will be available from 2023  [65]. In the present database, the density is measured by fast sweep reflectometry  [56]  around the separatrix. Concerning the temperature at the separatrix, the ECE signal is polluted by LHCD fast electrons for ρ > 0. 5, and even in the absence of LHCD fast electrons, the optical depth when approaching the separatrix makes this measurement inappropriate in the separatrix region. On the other hand, on WEST, the downstream Te and ne on the divertor target are very well diagnosed by two arrays of 29 Langmuir probes covering the inner and outer divertor targets  [66]. We have therefore applied the so called 'two-point model' on the parallel heat flux conservation to infer from the measured target Tt and parallel heat flux, q∥,t, the upstream temperature, Te,u = Tsep, as follows.
f cooling,in and fcooling,out are the fractions of the conducted power towards the inner and outer target, respectively, which are lost by Figure  7. WEST C4-C5 database, B = 3. 7 T, Ip = 0. 5 MA, LSN, D radiation before reaching the target. q ∥,e,t = γeTtj + /e is the only, Ptot> 2 MW. Histogram of the ratio 2ne,t T e,t. e,u e,u electron heat flux on target measured by the probes, using for the electron channel γe 5 as the sheath electron heat transmission coefficient. The inner and outer target quantities, Te,t,in and Te,t,out as well as q∥,e,t,in and q∥,e,t,out, are respectively momentum loss factor. f mom is expected to be around 1 for taken as the maximum measured over the inner (resp. outer) target array made of 17 (resp. 12) Langmuir probes The electron heat conductivity is κ∥0 2000/Zeff, with Zeff the effective charge; here, we assume Zeff = 1.
Surprisingly but very similarly to previously reported JET and CMod L-and H-modes database analysis in the conduction limited (high recycling) regime  [72]. This means that in the high recycling cases considered here, higher density at the separatrix correlates with larger heat fluxes on the target. The physics at play behind this correlation could be due to modified turbulent SOL transport and/or modified neutral recycled fluxes. A multi-machine database activity is being started including lower density cases to explore further such correlations. It is also an ideal challenge to be targeted by turbulent SOL-edge fluid codes, including neutral sources, such as SOLEDGE3X  [74]  and GRILLIX  [75]  for example. sep In appendix C, the H-mode plateaus obtained in LSN are identified in the plane (ne,sep, Te,sep) where the formula proposed for the L-H transition and L-mode density limit frontiers in AUG  [31]  are traced as well.
WEST data are compatible with these frontiers within the uncertainties on some quantities such as Zeff and the characteristic perpendicular scale. For more details, the reader is referred to appendix C. Using our two-point model estimate for Te,sep together with ne,sep measured by reflectometry, we can estimate the sep-factor of 5 variation of αt is hence seen also along the x axis in figure  9. This suggests that, in this L-mode database, the physics at play behind the confinement degradation with higher nsep is not related to enhanced resistively driven turbulence. In the following section, we will explore the role of neutrals on nsep and possibly on overall confinement.
This is due to the correlation, in our database, of larger Tsep with larger nsep. Hence as nsep increases, the normalized collisionality, embedded in αt, or in νSOL, does not increase as seen in figure  8 , where we can see that an identical value of νSOL can be obtained across the whole range of nsep. Nonetheless, at a given nsep, νSOL (or αt) has a scatter factor ∼5 on figure  8. This T T ∥ sep ∼ ∼ × × in most if not all tokamaks, controlled in feedback by the gas puff rate (and/or pellet fueling) to match as best as possible the required waveform. In our database, we find a weaker trend than the linear trend reported in the past nsep ∼ 0. 3n  [76] : n ¯ = 3. 5 × n 0. 3 with a large dispersion such that n = 4 10 19 m -3 can be associated with nsep between 1 and 4 10 19 m -3 , see figure  10.
The plateaus over which the central electron temperature Te(0) is below 2 keV are in blue, and in red where Te(0)> 2 keV. r is the correlation coefficient and p is testing the hypothesis of a correlation: if p < 0. 05 then the corresponding correlation is considered significant. in hotter plasmas  [51, 78]. We confirm the previously found scaling for the density peaking on the JET LHCD-heated Lmode database  [79]  such that ne(0)/< n ⩾ 1. 6li, see appendix F for more details. - ⟨ ⟩ ⟨ ⟩ ⟨ ⟩ The global density peaking was shown to correlate well with the volume-averaged effective collisionality νeff = 0. 2 ⟨n⟩R geo  [80]. We have therefore plotted n/⟨n⟩ over our database against νeff, see figure  14. We observe a correlation which is very similar to the previously reported correlation for RF-heated H-modes (equation (  7 ) of  [80] ) such that Over these plateaus, parameters correlating with optimized core performances are searched for.
By core performances we mean maximized core electron temperature at a given ratio of power to density and maximized energy content normalized to the power degradation τMHD/P -0. 73 at a given (IP, BT). By definition, a database analysis cannot unveil the causality/ies. We can only report correlations and phrase the subsequent causality questions. We have explored the correlations between core, separatrix and SOL quantities: the central electron temperature in n r ⟨n⟩ = -0. 08 log(νeff) + 1. 18. We find a correlation coefficient the core measured by ECE, Te(0); the global energy confinement quality τMHD/P -0. 73 ; the global density peaking meas-= 0. 6. If we do the same exercise with respect to nsep, figure 12(b), the correlation coefficient is identical, 0. 6. Hence the question of the respective roles of core turbulent transport impacted by collisionality versus a potential impact of the density profile build-up around the separatrix on the plasma core is raised by the similar correlations reported here.
Conclusions and perspectives L-mode, high recycling plasmas are an unavoidable phase of all plasma scenarios, during which performance optimization is mandatory to minimize the flux consumption, to ease Hmode access, to avoid MHD onset, etc. The WEST L-mode database is analyzed from the SOL to the core. Quantities are averaged on identified plateaus at the intersection of stable power and plasma current lasting more than 0. 3 s. The plasmas considered are in LSN and in D, at Ip = 0. 5 MA with more than 2 MW of total power. Eight hundred plateaus populate the database, coming from pulses of the C4 and C5 campaigns (2019-2021) of WEST phase 1. Note that this database is automatically populated and made avail-able to all WEST team members. ⟨ ⟩ ured by interferometry, n/ n ; the separatrix density measured by reflectometry in the midplane mapped on a magnetic reconstruction constrained by magnetics and polarimetry, nsep, as well as similar quantities at 0.
Rather we report that nsep correlates with larger Tsep inferred from the two-point model (figure  8 ), hence, with larger parallel heat flux at the target. We also report that nsep correlates with larger D neutral recycled fluxes on These correlations pose the question of the causality chain between recycled neutrals at the targets, heat flux conducted to the target plates, density at the separatrix, density peaking around the separatrix and global peaking across the whole plasma, internal inductance, core temperature and overall energy confinement quality. To summarize, WEST L-mode high recycling plasma database analysis allows formulating the following questions: What is the causality chain between low neutral recycling/low parallel heat flux on the divertor plates, low density at the separatrix, high density peaking at the separatrix, high global density peaking, higher central temperature and better L-mode core energy confinement? What are the consequences in terms of performance extrapolation towards larger machines (ITER and DEMO) where the neutral recycling and penetration through the separatrix will be different due to the smaller ratio of the ionization length to the divertor leg/wall clearance, weaker pumping efficiency with respect to the vacuum vessel volume, etc? To answer these questions, the foreseen future work around the WEST plasma database is threefold: (a) challenge the universality of the trends reported here in other tokamak L-mode databases such as the AUG L-mode database  [31]  and other tokamaks; (b) explore the observed causality chain by integrating neutral penetration models with L-mode edge validated turbulent transport models across a significant subset of plateaus of the database using TGLF-sat2  [81]  in the High Fidelity Pulse Simulator  [82] ; (c) in the event of successful integrated modeling, challenge the modeling suit on other divertor regimes and in other tokamaks, explore the consequences on future machine scenarios in ITER and DEMO.
Overall the range of Tt covered in our We hence do not see a correlation between the core confinement and the temperature on the target, see figure  17 , unlike on JET  [7]. This is not inconsistent with  [7]  where the trend    3 ) of  [31] ). The blue line indicates the L-H-L transition (equation (  8 ) of  [31] ). The impact of L⊥ and Zeff are tested: full versus dashed and dot-dashed lines. The H-mode phases are highlighted in red and magenta. of the confinement with respect to Te,t,out is mostly reported in H-modes and for Te,t,out ⩽ 10 eV. Concerning the correlation between nsep and the maximum density measured by Langmuir probes along the outer divertor target, we do not find the expected trend from the two-point model nsep n 1/3 over the database. We rather observe a larger scatter of achieved nsep at a given nt as reported in figure  18.
L⊥ = 4 cm is the density gradient length from reflectometry averaged over the C4-C5 LSN database analyzed here. Zeff = 2 is the mean resistive Zeff of the database. With these values, we find the full lines in figure  19 , in blue for the L-H transition and in red for the density limit. With Zeff = 2, the density limit is not consistent with our database: it predicts a too low density limit. By reducing Zeff down to 1, hence, the drive for resistive modes, the density limit frontier becomes coherent with our database. Reducing Zeff also reduces the predicted frontier between L-and Hmode. Reducing L⊥ as well as Zeff lowers further the L-H transition frontier and it becomes closer to the observed transitions. At this stage, the inconsistencies are difficult to discuss further as uncertainties on Tsep are large (assumed from the two-point model applied on Langmuir probe target data assuming no equipartition) as well as the uncertainties on the pressure gradient length, here approximated by the reflectometry density gradient length averaged over the database.
05 normalized to the W density slightly more outward, at normalized poloidal flux 0. 05-0. 2; (g) density peaking given by the ratio between the line avergaed density nbar to the volume averaged density nvol; (h) the density at the separatrix, nsep, obtained by reflectometry (the database is reduced to the plateaus on which the reflectometry was available) and using the magnetic equilibrium reconstruction by NICE constrained by polarimetry angles.
Acknowledgments The corresponding author would like to thank  Bart Lomanowski ,  Thomas Eich  and  Y. Li  as well as the EUROfusion TSVV11 team, in particular  Clemente Angioni , for stimulating discussions. This work has been carried out within the framework of the  EUROfusion Consortium  and has received funding from the European research and training program 2014-2018 and ORCID iDs C. Bourdelle https://orcid. org/ 0000-0002-4096-8978R. Dumont https://orcid. org/ 0000-0002-1030-138XJ. Gaspar https://orcid. org/ 0000-0003-0104-1616P. Maget https://orcid. org/ 0000-0002-8746-8102.
It is notably composed of convolutional layers only. The absence of densely connected layers enables a smaller number of parameters but implies that the local descriptors produced with this architecture cannot be rotation-invariant. The similarity of two descriptors is measured with the L 2 norm. For training the neural network, pairs of colocated raster patches are generated, relying on the rendering processes of the data modalities involved. We train the network using a triplet margin contrastive loss  [17, 19]  defined as follows: l(ν i , µ i , ν j , µ k ) = l Q (ν i , µ i , ν j ) + l R (ν i , µ i , µ k ) (7) with l Q (ν i , µ i , ν j ) = max 0, δ + ν i -µ i 2 -ν j -µ i 2 (8) and l R (ν i , µ i , µ k ) = max 0, δ + ν i -µ i 2 -ν i -µ k 2 (9) Here, δ stands for some margin (we set δ = 1).
OpenStreetMap OpenStreetMap is an open worldwide geographic database together with a rich ecosystem of tools to manipulate its content. We extracted from the OpenStreetMap global archive a few subsets corresponding to our regions of interest in Germany. The patch-rendering process associated with OpenStreetMap data was as follows. First, a color raster having the requested geometry was rendered using libosmscout, a software library that provides the functionality of drawing OpenStreetMap data. We customized the stylesheet that controlled the drawing appearance and behavior. We notably disabled zoom-conditional drawing, we removed labels and icons, we set all lines to be drawn as solid instead of dashes, and we removed line terminal markers. The rendered patch was finally converted to grayscale and normalized to zero mean and unit variance.
This amounted to 30 description models. Each description model was tested on its corresponding query remote sensing image and was trained on the other query remote sensing images of the same modality. We could thus perform a cross-validation for each combination of a query modality and a reference modality. The neural networks were trained using stochastic gradient descent. The learning rate was 10 -2 with a decay of 0. 95 every four epochs. We considered to hard-mine the negatives j and k but having observed no benefit, we discarded this strategy. The negatives were sampled from the entire dataset. Each batch referenced 128 distinct geographic locations. Given that a geographic location was associated with a positive and a negative patch from the reference and from the query modalities, each batch was accordingly associated with 4 × 128 = 512 descriptors.
Alignment We aligned each crop of a query remote sensing image onto each reference modality. The description model used to align a crop was not trained on the geospatial product to which the crop belonged. We built one piece of alignment data (i. e. , one set of reference points and their respective local descriptors) for each combination of a query remote sensing image and a reference modality. Without parallelization, building one piece of alignment data comprising about 8 million points took a few hours. Building the alignment data was fully parallelizable though, so that the execution time could be reduced proportionally to the number of processing units. One piece of alignment data composed of 8 million points took about 4 Gio of storage. Note that each piece of alignment data was shared between several crops and was consequently larger than necessary for a single alignment.
Relation to Image Retrieval Image retrieval is the task of searching in a database an image that is most similar to a query image. The similarity between a query image and a database image is measured using global image descriptors. Pattern matching can be thought of as image retrieval. In our method, the global descriptor associated with an image is the concatenation of all the local descriptors, and the similarity metric of global descriptors is the squared L2 norm. With traditional NCC and MI methods, the global descriptor associated with an image is the image itself, and the similarity metric of global descriptors is the NCC or the MI. However, with typical image retrieval systems, the global descriptor is obtained by a more complex aggregation of local descriptors, typically via a bag of visual words (BOVW)  [37, 38]  or via a vector of locally aggregated descriptors (VLAD)  [39, 40].
We are not aware of an image retrieval system that has been experimentally demonstrated to work on such a challenging multimodal dataset as ours. There is little work on multimodal remote sensing image retrieval  [41, 42] , and remote sensing image retrieval systems are generally tested on databases of a few tens of thousands of images. In our method, the distance between two global descriptors is the average point-to-point dissimilarity of the two images, as measured by local descriptors. This property might be lost if the local descriptors were aggregated by a complex process. The matching performance would probably be reduced, and the behavior of the algorithm would be less intuitive. Even if the local descriptors were aggregated by a complex process, we could still not simultaneously improve execution time and memory use, since our local descriptors are shared among global descriptors so that a map of global descriptors and a map of local descriptors would occupy the same volume of memory (provided that the sampling step is the same and that the global and local descriptors have the same dimension).
Data Availability Statement: Restrictions apply to the availability of these data. Pléiades images were purchased through the company SkyWatch. Miranda images were purchased from Fraunhofer Institute For High Frequency Physics (https://www. fhr. fraunhofer. de/, accessed on 20 July 2023). OpenStreetMap data are available under the open database license. Map data are copyrighted by OpenStreetMap contributors and available from https://planet. osm. org/, accessed on 20 July 2023. The DOP images of the states of Rhineland-Palatinate and North Rhine-Westphalia are under a Germany Zero license. The DOP image of the state of Baden-Wuttemberg was purchased from https://www. lgl-bw. de/, accessed on 20 July 2023. KOMPSAT-5 images were purchased and are subject to a license from the Korean Aerospace Institute.
Results We compiled data from 31 study landscapes spanning from 0. 8-62. 6 degrees latitude (Fig. 2 ) and sampling sites with high historical disturbance (n = 16) and low historical disturbance (n = 15; see Methods and Supplementary Dataset 1). Intensive field surveys recorded 1564 populations of 1034 bird species, of which BirdLife International  [33]  treated 276 as "Forest-specialists" and 874 as "Forest-associated" (i. e. the same 276 species combined with a further 598 species with medium forest dependency). The remaining 160 species are not associated with forest (Supplementary Dataset 1). To focus our analyses on relevant populations, we only assigned fragmentation sensitivity to Forest-specialists (Restricted analyses) and Forest-associated species (Expanded analyses; see Methods).
Patterns of fragmentation sensitivity Based on patterns of abundance with respect to distance from forest edge, most (225/382; 58. 9%) "Forest-specialist" populations and many (583/1302; 44. 8%) "Forest-associated" populations were classified as "Forest-core" (i. e. BIOFRAG software assigned them a "Forest" habitat preference and a "Core" affinity, suggesting edgeintolerance; see Methods). We restricted classification as fragmentation sensitive to these two groups in our Restricted and Expanded analyses, respectively. In our Restricted analysis, we found that 14. 4% (n = 225) of all study populations (n = 1564) were fragmentation sensitive, increasing to 37. 3% (n = 583) in our Expanded analysis (see Methods). Despite being more prevalent in some clades (e. g. suboscine passerines) than others (Fig.