dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Jeff Freymueller  and an anonymous reviewer for their very useful comments. Reflection images resulting from basic processing of most legacy profiles are available in the GeoMapApp database (https://www. geomapapp. org/) and the  US Geological Survey (USGS)  data portal (https: //walrus. wr. usgs. gov/namss/search/). Data from the 1994 R/V Maurice Ewing survey EW9409 [McGeary et al. , 2018] and from MGL1110 (ALEUT cruise) are available from the Marine Geoscience Data System (MGDS) data portal (https://www. marine-geo. org/ tools/new_search/search_map. php).
g. Aki, 1967) : M 0 ∼ f -3 c (6) Analysis of spectra has been applied to many real seismic datasets and the cubic moment -corner frequency relation has been observed for large and moderate (e. g. Allmann & Shearer, 2009; Ross & Ben-Zion, 2016)  to small (with M W < 3) (e. g. Abercrombie, 1995; Prieto et al. , 2004)  earthquakes, and for the mine seismicity (events of magnitudes -4 < M W < 0) (e. g. Kwiatek et al. , 2011). The spectral method of determination of source parameters has been also applied to slow earthquakes (e. g. Bostock et al. , 2015; Farge et al. , 2020; Supino et al. , 2020). 3 Data.
Acknowledgments This study was supported by the  European Research Council  under the  European Union   Horizon 2020 research and innovation program  (Grant Agreement  787399-SEISMAZE ) ple estimates of source dimension, dynamic stress drop, and radiated seismic energy. Bulletin of the  Seismological Society of America , 70 (1), 1-27. doi: 10. 1785/BSSA0700010001 Bolton,  D. C. ,  Marone ,  C. ,  Saffer , D. , & Trugman, D. T. (2023). Foreshock prop--24-manuscript submitted to JGR: Solid Earth.
CMEMS-LSCE-FFNN model As most of the cruises took place during austral summer and data are not available each year, we completed the observations with the results from an ensemble of feed-forward neural network model (CMEMS-LSCE-FFNN or FFNN for simplicity here,  Chau et al. , 2022). The FFNN model allows mapping at global scale monthly surface fCO 2 given SOCAT gridded datasets and ancillary variables. The reconstructed fCO 2 is then used to derive monthly surface pH fields as well as air-sea CO 2 fluxes. This data product enables to investigate the trends for different seasons and to derive estimates of annual air-sea CO 2 fluxes to interpret the change in CO 2 uptake, if any. For a full description of the model, access to the data and a statistical evaluation of fCO 2 reconstructions please refer to  Chau et al.
Calculations of carbonate properties Based on the data available for each cruise (fCO 2 , or A T and C T ) or from the FFNN model (fCO 2 and A T ), other carbonate system properties (pH, [H + ], [CO 3 2-] and Ω) were calculated using the CO2sys program  (version CO2sys_v2. 5, Orr et al. , 2018)  developed by Lewis and Wallace (1998) and adapted by  Pierrot et al. (2006)  with K1 and K2 dissociation constants from  Lueker et al. (2000)  as recommended  (Dickson et al. , 2007; Orr et al. , 2015; Wanninkhof et al. , 2015). The total boron concentration was calculated according to Uppström  (1974)  and KSO 4 from  Dickson (1990). To calculate the properties with the underway surface fCO 2 dataset, we used the A T /S relationship based on A T -C T data from OISO cruises in 1998-2019 in the South Indian sector as described by  Leseurre et al.
(2022) : The use of other A T /S relationships (e. g. Millero et al. , 1998; Jabaud-Jan et al. , 2004; Lee et al. , 2006; Carter et al. , 2018)  would change slightly the A T concentrations but neither the A T trend nor the interpretation of the C T , pH or Ω trends. However, as salinity is an important predictor in the calculation of A T , C T or pH from fCO 2 data, we have assessed the original underway salinity data and found biases for few cruises in 1992, 1993 and 1995 (Table  S1b ). For these cruises or when salinity was not measured we used the salinity from the World Ocean Atlas, WOA  (Antonov et al. , 2006)  in the SOCAT data-sets  (Pfeil et al. , 2013, identified " WOA" in Table  S1b ). Monthly fCO 2 and A T data extracted from the CMEMS-LSCE-FFNN datasets at the station location (50.
Comparisons of different datasets and the FFNN model To validate the properties calculated using the fCO 2 data for 1991-2021 or from the FFNN model over 1985-2020 we compared the calculated values (A T , C T , pH, [H + ], [CO 3 2-], Ω) with those from A T -C T data measured in the mixed-layer at stations occupied in 1985 and in 1993-2021. For this comparison, we averaged the continuous underway fCO 2 data selected in a box around the station location (50°S-51. 5°S/67. 5-69°E, yellow box in Figure  1 ). Results of the comparisons between various datasets are detailed in the Supplementary Material (Tables  S3  and S4 ). During the period 1993-2021, there are 22 stations with co-located fCO 2 data for different seasons (but mainly in summer). Since we found a close agreement between measured fCO 2 and the FFNN model (Table  S3 , Figure  S2 ), mismatches in all calculated carbonate system properties between the underway fCO 2 dataset and the FFNN model are small, falling within the range of the errors associated with the calculations  (Orr et al.
Acknowledgments: The  OISO program  was supported by the  French institutes INSU (Institut National des Sciences de l'Univers)  and  IPEV  ( Institut Polaire Paul-Emile Victor ),  OSU Ecce-Terra  (at  Sorbonne Université) , and the  French program SOERE/Great-Gases. We thank the  French oceanographic fleet ("Flotte océanographique française")  for financial and logistic support for the  OISO program  (https://campagnes. flotteoceanographique. fr/series/228/). We thank the captains and crew of  R. R. V. Marion Dufresne  and the staff at  IFREMER ,  GENAVIR and IPEV. We also thank  Jonathan Fin  and  Claude Mignon  for their help during the OISO cruises. The development of the neural network model benefited from funding by the  French INSU-GMMC  project " PPR-Green-Grog  (grant no  5-DS-PPR-GGREOG ), the  EU   H2020  project  AtlantOS  (grant no  633211 ), as well as through the  Copernicus Marine Environment Monitoring Service  (project  83-CMEMS-TAC-MOB ).
Previous research on real-time deterministic sea wave prediction has generally focused on evaluating the accuracy and efficiency of short-term wave fields within a specific prediction zone. However, for a real-time wave prediction system, it is necessary to provide a continuous description of the ocean wave surface based on short-term prediction segments. In this regard, we have developed algorithms for continuous wave prediction in directional wave fields based on the ''practical" prediction zone. The practical prediction zone refers to the time interval available for generating a continuous wave forecast by excluding the reconstructed waves from the prediction zone proposed by  Kim et al. (2023). We also introduce and discuss several important time factors, such as the update interval of the spatio-temporal wave dataset, the total computation time, and the length of the practical prediction zone.
Introduction A real-time short-term deterministic sea wave prediction, aiming to provide wave descriptions several tens of seconds before waves arrive at floating structures, is important for the safety and efficiency of surface vessel operations  (Grilli et al. 2011 , Nouguier et al. 2013 , Kusters et al. 2016 , Dannenberg et al. 2010). It can also improve ocean wave energy harvesting systems  (Li et al. 2012 , Previsic et al. 2021 ) and the design of marine structures, such as floating wind turbines  (Raach et al. 2014 , Ma et al. 2018 , Al et al. 2020). The present work was supported by the European H2020 FLOATECH and French ANR CREATIF projects. In this project, real-time wave forecasts are implemented for optimizing actions on structures, such as load mitigation on offshore floating wind turbines.
We herein develop algorithms for a continuous (in time) description of the ocean wave surface based on short-term wave prediction. Some operational systems for the floating structure in actual sea states require a continuous exchange of wave information  (Lee et al. , 2022). With constantly provided inputs (e. g. , spatio-temporal datasets), a series of short-term predictions can be generated. Here, short-term prediction refers to a limited wave description of the future over a specific domain in time and space. When using a set of short-term ocean waves to provide a continuous (long-term) wave prediction in time, we should optimize the prediction system by considering computational time, the presence of overlapping waves between short-term segments, and their corresponding accuracy. Nonetheless, to our knowledge, the influence of numerical modeling setups focusing on the final continuous prediction has not yet been studied.
In this regard, we investigate the impact of temporal quantities, such as the length of the prediction zone in time and the computation time, on the accuracy of continuous wave prediction. Depending on the magnitude of these factors, the long-term https://doi. org/10. 1016/j. oceaneng. 2023  Methods for deterministic wave prediction have been developed using wave datasets obtained by LIDAR (Light Detection and Ranging) cameras (e. g. ,  Grilli et al. 2011 , Nouguier et al. 2013 , Kabel et al. 2019 , Desmars et al. 2020 ). An alternative to the LIDAR technique uses X-band radars (or microwave radars) to record observations of the sea surface elevation (e. g. ,  Hilmer and Thornhill 2015 , Kusters et al. 2016 , Naaijen et al. 2018 , Klein et al. 2020 , Zhang et al. 2022a ). Both LIDAR and X-band radar measurements, obtained by a structuremounted instrument with the grazing incidence angles of the beams, have inherent physical limitations, such as uneven spatial sampling.
Moreover, a wave shadowing phenomenon is more likely to occur due to the spatial gaps behind the illuminated wavefronts. The insufficiency of data over the shadowed regions may be compensated by using spatiotemporal datasets (e. g. ,  Grilli et al. 2011 , Nouguier et al. 2013 , Kabel et al. 2019 , Desmars et al. 2020). Nonlinear phase-resolved models can capture detailed nonlinear surface wave dynamics. As the sea states become more severe in terms of wave steepness or when the time horizon of the wave prediction is extended, nonlinear wave mechanics, such as bound waves and nonlinear phase velocity, need to be addressed. In this context,  Nouguier et al. (2009)  proposed the Choppy Wave Model (CWM) based on the Lagrangian description. Later,  Guérin et al. (2019)  developed the Improved Choppy Wave Model (ICWM) by redefining the location of reference particles, resulting in a modified third-order nonlinear dispersion relation that is entirely consistent with that of the Eulerian system.
Methods This section describes how to achieve a continuous wave forecast system based on wave measurements around the target point. The process begins with short-term wave predictions, which consist of two steps: (1) data assimilation (or reconstruction; 'nowcast') that uses wave datasets to initialize the wave models; (2) wave propagation (or prediction; 'forecast') that simulates ocean wave surfaces over the spatio-temporal domain where the short-term wave prediction is valid. The continuous wave prediction system in real-time is constructed by combining the short-term ocean surfaces appropriately.
e. , 𝑡 max 𝑖 = (𝑖 -1)𝜏 + T), which yields 𝜏 + 𝑇 𝑡𝑜𝑡𝑎𝑙 ≤ T 𝜏 ≤ T 2 ; if 𝑇 𝑡𝑜𝑡𝑎𝑙 = 𝜏. ( 11 ) The actual computation proceeds by means of the discrete data. For the discrete data, the number of points in the short-term forecast used to construct the continuous prediction is ⌈ 𝜏 𝛥𝑡 ⌉ (where ⌈ ⌉ denotes the least integer function, and 𝛥𝑡 is the time step of wave observations). Unlike the aforementioned case with 𝑇 𝑡𝑜𝑡𝑎𝑙 ≤ 𝜏, in most multidirectional simulations by nonlinear models, the computation time is longer than the time interval between datasets (i. e. , 𝑇 𝑡𝑜𝑡𝑎𝑙 > 𝜏, see Fig. 3 ). These simulations can be further divided into two groups depending on whether multiple processing units or a single processing unit is used. In a computing system with multiple units, two or more processing units are involved in the computations, and thus those different processing units can complete individual computations simultaneously.
Setup description Within the framework of the FLOATECH project, we obtained experimental data in the hydrodynamic and ocean engineering tank, which is 30 m wide, 50 m long, and 5 m deep, at École Centrale de Nantes (ECN)  (Bonnefoy et al. , 2023). The main purpose of these experiments is to examine the wave model performance for multidirectional and unidirectional nonlinear wave fields toward the location of a floating wind turbine. The absence of the floating structure in the experiments to obtain the measured wave field should be considered as an idealized configuration. In a real (operational) condition, the diffracted and radiated waves from the structure will be measured simultaneously with the incident wave field. This source of uncertainty is not addressed in the present work. However, those waves will propagate away from the structure, and the prediction methodology can solve for different directional components.
Repeatability errors of the multidirectional cases have been assessed using reference wave gauges. It is evaluated on the significant wave height with a 3% variation, or 0. 24 m at full scale  (Kim et al. , 2023). Although this measurement variation induced by repeated measurements is likely to increase the prediction error for multidirectional sea states,  Kim et al. (2023)  achieved comparable model accuracy in both multidirectional and unidirectional cases. The latter did not involve any repeatability. The assimilation procedure is performed based on discrete wave gauges rather than continuous data collection in the spatial domain. An additional downstream wave gauge WG21 (or target location) was installed about 0. 15𝐿 𝑝 away from the end of the measurement area. The temporal extent of wave data used in the present study is limited by 𝑡 𝑏 -𝑡 𝑎 ≈ 506𝑇 𝑝 with 𝑡 𝑎 ∕𝑇 𝑝 ≈ 158 to ensure that all the wave components generated by the wavemaker had reached the entire domain of the tank.
Conclusions Most previous studies on real-time deterministic sea wave prediction have primarily focused on short-term prediction, especially in time. However, in the final stage of a real-time wave prediction system, it is essential to continuously describe the ocean surface using short-term predictions. Therefore, we develop algorithms for continuous wave prediction in time, especially for directional sea states, involving a novel study on a couple of significant time components considered in an operational scenario for real-time wave prediction. These include the update interval of the wave datasets 𝜏 and the total calculation time 𝑇 𝑡𝑜𝑡𝑎𝑙. Also, the length of the practical prediction zone T is given by both the distance from the observation zone to the point of interest and the maximum group velocity of the prescribed wave spectrum.
Acknowledgments This work was done within the framework of the European  H2020 FLOATECH  project, Grant agreement ID:  101007142. The authors acknowledge the support of the  French Agence Nationale de la Recherche (ANR) , under grant  ANR-20-CE05-0039  (project  CREATIF ). The authors also thank the experimental team at  LHEEA/Centrale Nantes-CNRS  who contributed to the experimental campaign and the corresponding database.
, 2013 , Danelian et al. , 1996 , Ferriere and Stais, 1995 , Ferriere et al. , 2012 , 2015 , 2016 , Kukoc et al. , 2015 , Mercier et al. , 1975 , Robertson, 2012 , Saccani et al. , 2008]. Metamorphic data also document the subduction and obduction history in the Hellenides. High-temperature amphibolite-facies units resting on top of lower temperature (greenschist-facies) lenses are present below the ophiolitic nappes in the Hellenides  Brunn [1956] ,  Spray et al. [1984]. In the Albanides, the thick metamorphic sole associated with the Mirdita ophiolites gave thermobarometric constraints: 800-860 °C and 1 GPa  [Dimo-Lahitte et al. , 2001] , but the main amphibolite units show temperatures ranges from 624 ± 9°to 796 ± 50 °C with pressures always lower than 0. 7 GPa  [Gaggero et al. , 2009]  (Figure  9B ).
Acknowledgements This review paper is dedicated to the late  Professor Jean Aubouin  who animated and led a large team of young geologists exploring the Hellenides and the Dinarides in the 60s and 70s, revealing the first order structures and describing the evolution of an emblematic mountain belt and subduction zone. The recent evolutions of tectonic and geodynamic studies in convergence zones would not have been possible without the patient field work and innumerable observations of these pioneers. We warmly thank the guest-editor  Michel FAURE , and reviewers  Stephan SCHMID  and  Konstantinos SOUKIS  who provided constructive comments that greatly helped to improve the paper.
Acknowledgements We thank the  ECCO ,  OSNAP ,  SNAP-DRAGON , and  TICTOC research groups  for the helpful discussions. The numerical simulations used in this study were conducted on the ARCHER2 supercomputer. We express our appreciation to the group that perpetually maintains the ECCO state estimate. We also thank  Ralf Giering  from  FastOpt  for the algorithmic differentiation software TAF. The color map vik (Crameri 2018) is applied in this study to provide accessibility to readers with color-vision deficiencies (Crameri et al. , 2020). YK and MJM were funded by  NERC   TICTOC  grant  NE/P019064/1  and  NERC BLT  grant  NE/S001433/1. HM was funded by the  French National Center for Scientific Research (CNRS). DPM was supported by  SNAP-DRAGON  grant  NE/T013494/1  and  TICTOC  grant  NE/P019218/1.
Data availability All authors contributed to the conception, development, design, and writing of this study. The MITgcm code is available at https://github. com/MITgcm/. The ECCO state estimate model configuration is accessible at https://github. com/gaelforget/ECCOv4. ECCO's initial and boundary conditions are made available at https://web. corral. tacc. utexas. edu/OceanProjects/ECCO/ECCOv4. TAF is a proprietary algorithmic differentiation software provided by FastOpt. The scripts used to analyze data are available from the corresponding author upon a reasonable request.
Glacier outlines For comparison purposes, all analyses, including validation data estimates, were performed using glacier outlines from the Randolph Glacier Inventory (RGI) version 6. 0  (RGI Consortium, 2017). Therefore, elevation-change estimates were derived over the same fixed glacier area. Note that the RGI outlines (Fig. 1 ) typically date from before the target periods and, hence, do not capture glacier area changes over time. Consequently, the calculated specific elevation changes are potentially biased in regions with large area changes, i. e. in the Alps. Original sources of the glacier outline assimilated into the RGI database for the study glaciers are  Paul et al. (2011)  for Hintereis and Aletsch,  Andreassen et al. (2012)  for Vestisen,  Mölg et al. (2018)  for Baltoro and  Rivera et al.
ASTER DEMs The Terra satellite (EOS AM-1) was launched in December 1999 on a sun-synchronous orbit and is currently in a constellation exit with expected to decommission in ca. 2025, without being replaced. It carries several sensors including the ASTER system, which collects pairs of stereo images globally at a spatial resolution of 15 m in the near-infrared band, making its data the largest consistent multi-temporal dataset of stereo images available worldwide  (Girod et al. , 2017). The stereo pairs consist of a nadir-pointing image (Band 3N) and a backward-looking image (Band 3B) with an effective parallax angle of 30. 6°. The average elevation precision of ASTER DEMs is reported to be around ±5-10 m on flat terrain and ±15-20 m on steeper terrain  (Kääb, 2002; Hirano et al. , 2003; Toutin, 2008).
, 2001; Rignot et al. , 2001). In the inter-comparison experiment, the penetration bias was corrected only for the Baltoro study site by two groups, while others included it in the error budget (Sect. 3. 3. 7). The UST group captured penetration within their probabilistic framework as an elevation-dependent Gaussian probability distribution, as proposed by  (Agarwal et al. , 2017). Similarly, the GAC group applied an elevation-dependent C-band penetration model to the SRTM dataset based on the specific results for the East Karakoram region by  Kumar et al. (2019). The X-band radar penetration model was applied to the TanDEM-X tiles collected in January and February based on the C/X-band penetration differences calculated for the Karakoram region by  Lin et al. (2017). On a larger regional scale, such an assessment based on SRTM X/C-band data needs to consider also the differences in processing of the two data streams.
Temporal corrections Homogenization of the observation period and thus temporal correction of the glacier elevation change estimate may be required when comparing different datasets, such as for IPCC estimates. In this experiment, such a correction was required when the spaceborne observation dates differed from the airborne validation period (study sites Hintereis, Aletsch and Vestisen). The participants followed different strategies for temporal corrections: (a) no temporal correction (e. g. , FAU, UIO, USG), which assumes that the observed elevation change rate corresponds to change rate of the validation period, (b) linear scaling using the long-term trend (e. g. , BAW, UGA, UIO), based on the same assumption, (c) annual corrections using glaciological observations (e. g. , LEG, UZH, UIO), and (d) non-linear regressions of the experiment DEM time series (ETH, LMI), which aimed at correcting for the long-term trend as well as for annual and seasonal variabilities.
These features are more visible in the ASTER results using the DEM pair and mosaic approaches than in the time series approach. The TanDEM-X results show different signals in the large accumulation area, with several results indicating more negative elevation changes than those observed in the airborne dataset, although these were considered low confidence. For Vestisen, the provided ASTER DEMs suffer from large data voids and thus low spatial coverage on the glacier with data voids in the accumulation area over the entire time series (white area in Fig. A2 ). This resulted in corresponding data voids in the elevation-change maps or extensive interpolation (Fig. A6 ). Consequently, the workflow applied in other sites failed to yield satisfactory results -more than half of the optical outcomes were flagged as low confidence results.
(2021)  can currently be considered good practice for computing glacier elevation changes from spaceborne DEM differencing. As such, their DEM processing routines  (Girod et al. , 2017)  are well tailored to ASTER stereo images, their time series approach is able to derive a maximum of information from DEM stacks, and their post-processing workflow seems to optimally derive glacier-wide results and related uncertainties from off-glacier terrain. Their experiment runs, which were based on the published dataset, generally perform well in the present study, rank one of the closest to the validation data in the case of Vestisen (Fig. A12 ), and come with reasonable error bars, which overlap with validation data and ensemble means in all experiments. Baltoro Glacier and over the entire Northern Patagonian Icefield.