dataesr/scientific-paragraphs-categorization huggingface dataset:
This made the correction of the aliased velocities difficult. Thus, although not critical, using the Doppler velocity-Doppler power product as input data is recommended as it can enhance the models' generalization ability, especially for challenging data. For subsequent experiments and results, we trained all DL methods with this input combination. 2) PDNet outperformed its original counterpart: We conducted a study to determine the optimal number of iterations for updating the primal and dual variables in both OriPDNet and PDNet, given the sensitivity of this type of method to this parameter. The results are shown in Table  III. From this table, we can see that OriPDNet reached a plateau after 10 iterations, and beyond 20 iterations, it became unstable during training and failed to produce results.
96, a recall of 0. 91, and a precision of 0. 89. Therefore, we conclude that nnU-Net is the best DL approach currently available for dealiasing tasks in echocardiography. Additionally, it is interesting to note that BATFormer showed a clear improvement when we increased the amount of synthetic data, indicating that this type of approach requires a larger dataset to improve its performance for the dealiasing task. Finally, it is worth noting that PDNet achieved promising results with 233 times fewer parameters compared to nnU-Net, highlighting the potential of incorporating analytical context into the DL framework to regularize the solution space. We also provide a visual inspection of the performance of the various methods on aliased images with different degrees of difficulty in Fig. 4. We can see that the DL methods performed similarly well on the easy and moderate cases (first two columns), but nnU-Net produced the closest results to the reference on the more challenging case (third column).
Although the DeAN technique largely solved the problem, it still fails in some situations, as shown in our study. Therefore, we turned to DL and conducted an in-depth analysis and comparison of three architectures, including PDNet, which utilizes an unfolding framework. In addition, we illustrated the potential benefits of incorporating Doppler power information, since low power generally indicates poor blood Doppler signal. To better balance the aliased and non-aliased input data during training, we resorted to data augmentation by generating synthetic aliasing.
A. Comparison of the DL Methods Our study found that the three DL methods we tested (PDNet, nnU-Net, and BATFormer) outperformed the non-DL DeAN method for color Doppler dealiasing. Notably, we observed that nnU-Net had the best performance, suggesting that the 2D U-Net architecture used in nnU-Net may be particularly well-suited for this task due to its ability to effectively capture spatial features. For example, in a challenging case where the aliased and non-aliased regions had similar hues (Fig. 4 , third column), nnU-Net was able to unwrap correctly while other DL methods failed or were less successful. Similar structures corrected by an expert were part of the training dataset, which implies that nnU-Net probably learned the flow patterns and leveraged this knowledge to achieve successful outcomes.
PDNet also performed well, requiring > 200 times fewer parameters than nnU-Net, highlighting the potential for simpler DL models to achieve competitive results in color Doppler processing. Further exploration of this type of unfolding approach, including more complex modeling of the forward operator, is needed. Although the third input strategy (velocity-power multiplication) contained less information than the second (velocitypower concatenation), it performed slightly better in the difficult fold (last row of Table  II ). The multiplication strategy largely suppressed velocity discontinuities in noisy regions, making the training task easier. In contrast, the concatenation of Doppler power and velocity allowed the model to learn the best strategy for combining these two inputs, which could be beneficial in larger datasets.
April 2023; revised 5 June 2023; accepted 20 June 2023. This work was supported in part by the MEGA Doctoral School (ED 162); in part by the French National Research Agency (ANR) through the "4D-iVFM" Project under Grant ANR-21-CE19-0034-01; in part by LABEX PRIMES, Université de Lyon, under Grant ANR-11-LABX-0063; in part by LABEX CELYA within the program "Investissements d'Avenir" operated by the French ANR under Grant ANR-10-LABX-0060; and in part by GENCI-IDRIS (HPC resources) under Grant 2022-[AD010313603]. (Corresponding author: Hang Jung Ling. ).
Manuscript received 14 14 April 2023; revised 5 June 2023; accepted 20 June 2023. This work was supported in part by the MEGA Doctoral School (ED 162); in part by the French National Research Agency (ANR) through the "4D-iVFM" Project under Grant ANR-21-CE19-0034-01; in part by LABEX PRIMES, Université de Lyon, under Grant ANR-11-LABX-0063; in part by LABEX CELYA within the program "Investissements d'Avenir" operated by the French ANR under Grant ANR-10-LABX-0060; and in part by GENCI-IDRIS (HPC resources) under Grant 2022-[AD010313603]. (Corresponding author: Hang Jung Ling. ).
Data sets In this study, we used mosaic data of 100 m/pixel Wide range Angle Camera (WAC) and data of Lunar Orbiter Laser Altimeter (LOLA) from the LRO mission. In this study, LROC WAC and LOLA topographic mosaic images were obtained for the analysis. These mosaic images were used to identify positioning and mapping of LWRs, and the topographic data was utilized to determine the morphological properties of LWRs. LRO is the first spacecraft in NASA's Lunar Precursor Robotic Program launched on 18 June 2009  (Tran et al. , 2010; Byrne et al. , 2015). LROC was created with two measuring goals in mind: determining polar illumination and assessing landing sites in meters  (Robinson et al. , 2010). The three imaging subsystems on board LROC include a multispectral WAC, two narrow-angle cameras, and a wide-angle camera.
In this study, LWRs were mapped using 100 m/pixel mosaic pictures compiled from more than 15,000 WAC images taken between 2009 and 2020. (https://astrogeology. usgs. gov/search/map/Moon/LRO/LROC_WAC/Lunar_LROC_WAC_ GLD100_79s79n_118m_v1_1). The Integrated Software for Imagers and Spectrometers (ISIS) was used to convert the format. All these images were mosaic and extracted study area in ARC GIS 10. 8. The high-resolution topography of the lunar surface may be accurately calculated using LOLA  (Chin et al. , 2007). The frequency of LOLA is 28 Hz, and its accuracy is 10 cm. A model of lunar orientation is used to identify the location of each laser point on the lunar surface after range calibration  (Smith et al. , 2010). One of the gridded data record products has a resolution of 1,024 pixels per degree, or about 30 m in diameter per degree of latitude at the equator, and is produced by further processing of the raw data: (http://geo.
Automated detection of lunar ridges Slope can be used to automatically extract ridges because it reflects the gradient fluctuations of the LWRs and some ridges have low contrast, making them difficult to extract from photographs. This is a preliminary investigation on the use of DEM data for automated ridge detection. Generate a slope map, handle phase symmetry, perform a morphological operation, and filter out any background noise using the used systematic technique. First, the slope map is generated and converted to grayscale image for post processing. Slope quantifies the steepest possible rate of variation in pixel values relative to their neighbors (Figure  3 ). The sharpest slope away from a pixel is determined by finding the largest elevation difference between it and its nearest eight neighbors.
8-33. 9 km), height (140-360 m), and elevation offset (28-240 m) for various locations. Robinson et al. (2010)  used Lunar Topographic Orthophoto maps to calculate the height of 12 wrinkle ridge segments and discovered that the average height was around 253 m. A study employing LROC WAC mosaicked images and LOLA data. Walsh et al. (2013)  assessed the height of 150 LWRs in the Mare Oceanus Procellarum, Mare Crisium, Mare Serenitatis, and Mare Frigoris as well as the Mare Imbrium, and displayed that the height of LWRs ranges from 33 to 590 m, with an average height of 187 m. According to our findings, the mean elevation offset, height and width are 50. 29 m, 0. 48 km, and 3. 98 km, respectively (Table  1 ). These values are similar to those reported previously, even though previous studies relied on different data sets  (Plescia and Golombek, 1986; Mbge and Reidel, 2001; Montési and Zuber, 2003)  or only a subset of the same data sets.
Plasma edge simulations are typically carried out with mean-field codes such as SOLPS-ITER  [2, 3] , SOLEDGE2D  [4] , UEDGE  [5] , and EDGE2D  [6]. Especially SOLPS-ITER has been extensively used for designing and assessing the ITER divertor  [7] [8] [9] , it is routinely used for analysis of present-day fusion devices  [10] [11] [12] , and is currently supporting the design of next-step fusion reactors like DEMO  [13]. SOLPS-ITER is a complex and computationally expensive multi-physics code. The plasma transport in the magnetic field is described by fluid equations based on the Braginskii equations  [14]. Interactions with neutral particles and solid walls, strongly affecting the plasma edge behavior, are accounted for by coupling to a neutral transport model, solving either fluid or kinetic equations  [15, 16]  and extensively relying on reaction rates and reflection databases.
The first option leads to a higher computational time, while the second option increases memory requirements (thus an estimate on the memory bound for adjoint mode was previously not given). The optimal choice is case-dependent, and a balanced strategy between the two approaches, known as checkpointing, provides the best results  [36]. From a user point of view, the advantage is that the backward sweep is fully available as a code and can be tailored to specific needs. The other group of AD tools, operator overloading tools such as CoDiPack and ADOL-C  [37, 38] , essentially consist of a library that is compiled together with the primal code. This library augments the primal variables' types with new types containing the sensitivities. At the same time, the library overloads the elementary operations to deal with the new variable type and propagate the sensitivities according to the chain rule.
AD for SOLPS-ITER In this section, we give the specific details of adjoint AD implementation in SOLPS-ITER using the TAPENADE  [33]  tool, which we employed in a previous work to retrieve tangent derivatives  [23]. We chose this source transformation tool to keep memory requirements low and for the flexibility it allows in fine-tuning the differentiated code. The optimal performance of AD-differentiated codes highly depends on user's choices and insights. Knowledge of the underlying strategies for solving the primal code is vital, and allows fine-tuning the differentiated code, instead of simply employing it as a black-box. Therefore, we first start presenting the plasma edge modeling approach of SOLPS-ITER, and then describe the AD strategies adopted.
The computation of J is done according to J = max s q ⊥ = max s [ e T e + q e + q i + i (T i + E ion + K i ) + n (T n + K n )] /A ⊥ , ( 18 ) where is the poloidal particle flux, q is the internal energy flux, T is the temperature, and K the particle kinetic energy. The subscripts e/i/n refer to electrons, ions, and neutrals respectively. Finally, E ion = 13. 6 eV is the D ionization energy, A ⊥ the perpendicular area of contact, and s is the curvilinear coordinate along the target. Note that in principle the "max" function is non-differentiable. In practice, TAPENADE computes the sensitivity of the r. h. s. of Eq. (  18 ) without the max statement, at the coordinate where the maximum of the primal is located. The relative sensitivity S of J with respect to an input parameter x is then evaluated as S = ∂J ∂x x J.
Adjoint AD is identified with solid lines in shades of red while tangent vector AD with dash-dotted lines in shades of blue. The theoretical tangent AD scaling is identified with a gray dashed line. He+Ar (N s = 24), He+N+Kr (N s = 50), and He+Ar+W (N s = 99) to the reference scenario, employing similar BCs, transport, and recycling coefficients as for D. Note that these multi-species cases are not representative of COMPASS operation, but are simply a way of obtaining scalings as a function of N s. Three code bases have been used to compute the scalings: • the primal, i. e. the standalone B2. 5 solver in the standard SOLPS-ITER code distribution; • the tangent AD code, obtained by differentiating the primal in vector mode with respect to 2, 4, 8, and 16 parameters; • the adjoint AD code, obtained by differentiating the primal with respect to the same 2 and 16 parameters as for tangent AD.
Memory requirement We analyze next the memory footprint, employing the Arm ® MAP profiler to assess memory requirements of AD code compared to primal. The resulting scalings are shown in Fig. 5 , where we display only one curve for adjoint AD, since mem- ory requirements for 2 and 16 parameters are essentially the same. This confirms that the main computational bottleneck is indeed the adjoint fixed point iterator. From Fig. 5  it is clear that adjoint AD memory requirements grow more rapidly compared to tangent vector AD, either when the grid is refined or species are added. In fact, primal and tangent AD roughly follow a linear scaling, at least for larger case sizes, as internal variables are defined on the grid itself and for each species. In adjoint AD, the standard checkpointing strategy of TAPENADE stores variables at each procedure call within the fixed-point iterator, thus causing a more-than-linear growth.
Finally, we efficiently obtained for the first time sensitivity maps in plasma edge codes, showing that a large amount of new sensitivity information is now available for the community through adjoint AD. Several plasma edge applications will benefit from adjoint AD sensitivity calculation now available in SOLPS-ITER. First and foremost, gradient-based optimizations with large sets of design variables are now feasible by replacing tangent AD gradient calculation with adjoint AD in the optimization framework recently deployed  [56]. This enables divertor design with high-fidelity codes in terms of optimal shape and magnetic field  [19, 20] , and model calibration based on experimental data of several unknown input parameters and their uncertainty in a Bayesian setting  [56]. Furthermore, also UQ efforts benefit from an efficient sensitivity analysis, starting from simple first order moment methods to more accurate sensitivityaccelerated Monte Carlo methods  [21].
Acknowledgements This work has been carried out within the framework of the  EUROfusion Consortium , funded by the  European Union  via the  Euratom Research and Training Programme  (Grant Agreement No  101052200 -EUROfusion ). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the  European Commission  can be held responsible for them. Stefano Carli  is a postdoctoral research fellow of the  Research Foundation Flanders (FWO)  under grant number  12E3623N. Parts of the work are supported by the  Research Foundation Flanders (FWO)  under project grant  G078316N  and  G085922N. The resources and services used in this work were provided by the  VSC (Flemish Supercomputer Center) , funded by the  Research Foundation Flanders (FWO)  and the  Flemish Government.
Data availability The results presented in this work have been obtained with SOLPS-ITER version ea8c1a68067, with B2. 5 version 01a98c0af71, and TAPENADE version 3. 16-v2-120-g686e29d1a. The data that support the findings of this study are available in an open source online repository  [49]. The scripts to reproduce the figures of this paper using such data are also available online  [60].
Best practices for the development of national or regional near real-time databases (with cloud storage/warehouse of data specific to the spatial scale at which the EWS is run, where possible) should be implemented. Data may be protected with a nondisclosure agreement if EWS output is open access, and long-term data should be broadly accessible via international data platforms such as the IOC-UNESCO Harmful Algae Information System (HAIS), 15 Harmful Algae Event Database (HAEDAT), 16 or Ocean Biodiversity Information System (OBIS). 17.
General schematic of an EWS for HABs Source: adapted from Bauer,  P. , et al. 2021. The digital revolution of Earth-system science. Nature Computational Science. 1(2): p. 104-113. • transect sampling, such as may be collected from a ship or ferry; and • synoptic sampling, such as from satellite. The type of data needed will depend on the HAB in question and the local environment. Data assimilation. The data must be brought into a reliable database that can support models and predictions. The challenge then becomes one of data assembly and assimilation for use in the EWS components. These several questions need to be answered. • Who is responsible for data delivery? • If water sample data, how does it get organized in order to be ingested into a database? • If an automated system like a mooring or pier-based instrument, who is responsible for maintenance? • Who is responsible for notifications when data flow stops? • What happens when it stops working? • How are the data sets, whether from water samples or instruments or satellites quality controlled? • Who maintains the data assimilation system? • What products are produced to help in inspecting and monitoring the data sets? • If one component of software or hardware is updated does it affect the operation of the EWS? The answers to these questions depend on the type of data and the local circumstances, but thinking through them carefully beforehand will help guide the setup of the system and avoid problems later on.
What are the failures and what are potential solutions? It may be that the failures are hard to solve. Perhaps a better computer system or monitoring technology is needed, or a key observation system is difficult to maintain. A failure could also be in the mode of communication and how a stakeholder is understanding the information. And it may take some significant resources to make those critical observations. Other failures may be more solvable, perhaps an adjustment of a model. Gaps and weakness will tend to fall together. For example, some areas cannot be forecasted reliably because we don't have the critical observations. Perhaps more frequent observations are needed. New research, technologies and datasets may be relevant to the problem. However, in all cases there needs to be some assessment of the potential impact of the change: "If we make this change, that problem will be addressed, which will have a benefit of this.
However, for toxigenic species, although acquiring information on cell abundance is important, determination of toxin levels associated with these cells (that is, cellular toxicity or toxin quota) is essential, as cell counts maybe be decoupled from toxin levels. Since a bloom population can range from minimally toxic (or even non-toxic) to highly toxic (even over the course of a single event) depending on environmental factors as well as the presence and/or absence of toxigenic strains, determination of both cells and toxins provides the most complete and useful dataset to inform early warning systems. Similarly, toxin accumulation in vector species can be decoupled from the aforementioned measurements, so it is often important to measure toxin levels in vector species (for example, Abraham and Rambla-Alegre, 2017;  Grasso et al.
These approaches can be combined as well, depending on the needs of the program and the availability of data. For example, machine learning can be used to parameterize areas of uncertainty within mechanistic models. Whichever model or algorithm is developed, it is advised to use an open-source platform, such as R (www. r-project. org/) or Python (www. python. org/) programming languages and to maintain code in an open repository such as GitHub (https://github. com/). Use of these platforms supports reproducibility and accountability and is free to the developer. Additionally, packages and libraries for building predictive models are freely available (for example, the "forecasting" and "keras" packages in R, or the "scikit-learn" library in Python, as well as many others), and there are online communities available to help troubleshoot code.
The datasets on which predictions depend must be sustainable. The documentation, software, and training must be available so that the system is personnel independent. Stakeholder engagement must be maintained. There should be an expectation that updates and improvements will be needed. And finally, some mode of funding must be available long-term to support all of this. Accountability. It is unlikely that any EWS will get every prediction correct. Stakeholders will develop their own understanding of how reliable the system is over time. When a bad prediction is made, or when the system goes down for a period of time (for example, if a data stream becomes unavailable), it is useful to have planned procedures for troubleshooting a process for identifying where in the system something went wrong, and how it is being addressed, and how it might be minimised or mitigated against in the future Furthermore, communicating these steps to stakeholders, and listening to and incorporating feedback, will help maintain trust in the system.
What were the consequences of the early warning system for stakeholders? The EWS based on benthic sampling conducted by trained personnel (described in  Vila et al. , 2022)  proved to be an effective EWS for authorities to quickly make recommendations on the health risks associated to Ostreopsis blooms in Mediterranean beaches. The monitoring data protocol also contributes to the joint effort to build a time-series dataset for elucidation of climate change effects on Ostreopsis blooms.
The A. tamarense species complex appears to be the most widely dispersed within the genus, and occurs in many locations worldwide, covering all ocean basins and many regional seas  (Lilly et al. , 2007; Brosnahan et al. , 2020). Members of this species complex are absent from the equatorial tropics  (Anderson et al. , 2012, Alexandrium tamarense [cabi. org]  www. cabi. org/isc/datasheet/112110#todistributionDatabaseTab le). The distribution of toxic and non-toxic strains of the same species, or of closely related species, generally, do not overlap spatially or temporally  (Brosnahan et al. , 2010) , as is the case for A. minutum in Ireland, where toxic forms are found in the south, and non-toxic strains in the west  (Touzet et al. , 2008). Two known exceptions are the Shetland Islands in Scotland, United Kingdom  (Touzet et al.
1. 5 and 3. 3. 4. A statistical analysis was conducted on a global dataset extracted from the Harmful Algae Event Database (HAEDAT) and Ocean Biodiversity Information System (OBIS) for the period 1985 to 2018 to investigate global trends in the occurrence, toxicity and risk posed by harmful algal blooms to natural systems, human health, and coastal economies (Figure  4. 9). The results suggest that the intensity and frequency of HABs vary at regional and local scale and at species level, with increasing or decreasing trends and sudden occasional outbursts, but with no general uniform trend that can be discerned from that of increased observational efforts. Despite increased monitoring activities, impacts on human activities such as aquaculture, fishery, use of natural marine resources and tourism keep on posing economic activities at risk in many regions  (Hallegraeff et al.