dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, between June 1997 and February 2001, the Two Micron All Sky Survey (2MASS) collected 25. 4 TB of raw imaging data covering 99. 998% of the celestial sphere in the near-infrared J (1. 25 µm), H (1. 65 µm), and Ks (2. 16 µm) bandpasses  [495]. As of 2019, the IRSA alone provides access to more than 1 petabyte of data consisting of roughly 1 trillion astronomical measurements, which span wavelengths from 1 µm to 10 mm and include all-sky coverage in 24 bands. The IRSA dataset will soon exceed 100 times the data size of the Library of Congress. The Sloan Digital Sky Survey (SDSS) telescope produces 200 GB of data every night. The new LSST captures 6-GB images at 3 GB/s with its 3. 2 billionpixel camera and will generate about 15 TB of raw image data every night. The Cassini mission collected over 600 GB of scientific data from 2004 to 2017  [496].
Astrophotonics is a relatively young field that leverages novel photonic components and integration for astronomical instrumentation. Integrated photonic technology is an extension of integrated electronics technology from processing electrons to photons. The integrated photonic circuits provide reduced size, weight, and power that are critical for compact instrumentation, especially for space-based systems  [501]. Astrophotonic solutions are already becoming an integral part of existing instruments. Examples include photonic lanterns, complex Bragg gratings, spectrographs, frequency combs, and interferometry on a chip  [502]. Astrophotonics also enables the next generation of large telescopes, such as the Extremely Large Telescope (39 m). Finally, despite the advances in instruments and data processing hardware, the sheer volume of data from space and extraterrestrial plasmas, which is essentially infinite, requires intelligent data reduction strategies.
Scientists routinely use standard methods, such as the ME Stokes inversion to deduce the three components of vector magnetic fields, Doppler shifts, and other plasma parameters. However, such inversion attempts do not always produce physically meaningful results, especially when Stokes profiles are complicated. Furthermore, the ME inversion for large datasets can be quite time-consuming. A Stokes profile can be modeled as waves, and a CNN is suited for capturing spatial information of the waves  [514]. Fig. 25  (left) presents some results obtained from the CNN model. The Stokes inversion appears to be quite successful: the ML method is ten times faster than the ME technique with much-reduced noise  [514]. Another example of information extraction using ML is SolarUnet  [515]  that identifies and tracks solar magnetic flux elements or features.
The method above demonstrates the feasibility of creating vector magnetograms, which are extremely important for SWx research, from historic data. The second example is related to the resolution improvement of historic data. The new observations can achieve a spatial resolution of around 100 km, while historic data had a resolution of no better than 1000 km. There is a need to improve the resolution of existing data to disclose the dynamic physics of solar active regions. Such a study has been  demonstrated by using the Hinode-HMI/SDO data pairs with a CNN in  [527] , as shown in Fig. 26. Hinode's resolution is five times better than that of SDO. Future work can be extended to improve the resolution even further using observations from large-aperture telescopes. [Haimin Wang and Jason T. L.
According to the publication  [51] , Fig. 27  illustrates the Mean Absolute Error (MAE) of three distinct neural network types, i. e. , MLP, CNN, and DFT. These neural networks employ different activation functions and undergo training using varying sample sizes (n sample ) of training data. These studies reveal that achieving optimal network performance requires a minimum size for a training dataset. It is also noteworthy that MLP necessitates a minimum number of neurons in its hidden layers, equivalent to the degrees of freedom in Fourier space, despite the input data being fed into the configuration space. Among the three models examined, DFT exhibits superior performance for clean data, possibly attributed to the presence of a simple Fourier expression for the Hammett-Perkins closure. However, it proves to be the least robust when confronted with input noise.
F. Magnetic Reconnection The amount of observational data and simulated data relative to space plasma physics is growing exponentially: as more computational power becomes available, on the ground or in situ in space, more data are being generated. This evergrowing data availability is now met with a growing use of ML tools able to consider amounts of data that even a large team of human researchers could not process. Generally speaking, ML for data processing can be subdivided into two types. First, supervised ML tools are designed to replace tedious well-known steps of processing with automatic tools. The typical scenario is that of taking a large but manageable set of cases and processing them the human way labeling each dataset according to our understanding of it. The machine can then learn from this dataset and replace the humans for the task.
The archetypal case is that of image recognition, one of the greatest successes in ML. Since 2015, tests have shown ML tools surpass humans in the accuracy of image recognition  [546]. In terms of speed, there is obviously no contest. The same techniques can be applied to analyze scientific data transferring human skills to the machine. However, this approach presumes that we already know how to analyze the data, and we simply want to transfer this knowledge to a machine. The second approach is that of unsupervised ML. In this case, different methods of ML are applied in ways where the machine learns on its own how to treat the data. The central idea is to deploy or design a method where the machine arrives at a reduced description of a complex dataset, and then, the human scientist investigates the reduced description attempting to make sense of it in light of our understanding of nature.
An especially convenient way to identify a possible reconnection site is the local measure of the so-called Lorentz indicator based on computing the speed of a frame transformation that eliminates the local magnetic field  [551]. An example of this indicator is shown in Fig. 28  where many 3-D reconnection sites are identified in a turbulent region. This accumulated expertise provides a great opportunity for creating human-labeled datasets to use as training for a supervised ML tool. However, the intrinsic complexity even for a human to decide what is and is not reconnection gives unsupervised ML tools the opportunity for new discoveries. In the following, we explore some methods recently published to identify reconnection with ML. Identifying Reconnection From Velocity Distributions: VDFs, f (v 1 , v 2 , v 3 ), are provided as 3-D datasets by instruments that measure the count of collected particles in situ.
Kinetic simulation can provide a synthetic version of the same information. This information can be in different forms: energy angles, 3-D velocity bins, or polynomial expansions (e. g. , Hermite and spherical harmonics). The first aspect of this type of data is the overwhelming size. As an example, Fig. 28. Indicator defined in  [551]  identifies many reconnection sites visible in the picture as ghostly yellow-green areas. A group of electron flowlines is shown passing one of these reconnection sites and encountering also others. The flow lines are colored by the intensity of the local electric field that transfers energy between the magnetic field and the electrons, accelerating them and creating a turbulent flow. a typical modern particle in cell simulation produces TB of distribution data for each time step.
At each time step, there are millions or billions of such distributions to analyze. A recent mission, magnetospheric multiscale (MMS) produces in burst mode one distribution every 30 ms (though not all can be transferred to the ground due to limited telemetry). A survey of the literature shows that these distributions are rarely used in their full 3-D complexity, and usually, only very few 2-D reductions of specific instants are studied. The choice is guided by analyzing other quantities that suggest what distribution to study. There is no systematic analysis of all data taken; it is humanly impossible, but not impossible for ML. Supervised ML can be trained to recognize features like the crescent using a human-labeled dataset, an application of the widely used image recognition software. However, shapes in VDF are more in the imagination of the viewer than an objective feature.
Identifying Reconnection in Spatial Data: The quintessential example is the 2-D image: in the case of reconnection, this is a 2-D view of the EM field component or of a plasma moment. It is obvious that this type of data can benefit from the methods developed for image processing. CNNs  [557]  can be trained using expert-labeled images. Hu et al. [558]  report the example of a dataset of 2000 cases labeled using the expert community via zooniverse. org. The project can be accessed via http://aida-space. eu/reconnection where a tutorial on how to identify reconnection sites is provided. The project is public and unbiased experts helped with labeling. Once the labeled dataset is available, the CNN can be trained to recognize reconnection. Unsupervised ML can detect reconnection based on spatial information by using the clustering of pixels in the spatial data.
Reconnection is identified using the physics properties of the resulting classes. Sisti et al. [559]  use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN)  [560]  and k-means  [555]  to identify current layers with a sufficiently large aspect ratio to flag reconnection. In principle, 3-D datasets and 1-D fly-through datasets can be treated using similar methods, and future research will likely investigate this possibility. [Giovanni Lapenta].
D. Data Management in Manufacturing The semiconductor device technology is now far below the 10-nm critical dimension in manufacturing with its sights set on 2 nm. Successful device scaling, historically driven by lithographic patterning, is now driven by plasma etch. The tight process control afforded by modern plasma sources has enabled scaling. Moving past 3 nm requires even tighter levels of control. Tighter control translates to atomistic control. The "smart manufacturing" initiative is a means to meet this end. Its purpose is to enable adaptability in plasma process tools  [595]  facilitating reliable and accurate APC systems delivering nanometer-scale precision. APC in the form of run-to-run control enables continuous process tuning. Process output parameters are monitored by metrology tools potentially including VM models.
While AI/ML algorithms are attractive options for implementing high-accuracy VM models, there are some disadvantages. They require a large number of training datasets and lack the inference capability needed to link the predicted variations to their root causes. Plasma diagnostics paired with appropriate sensor technologies can reduce the advanced data processing load while maintaining or even improving model accuracy. This is done through direct extraction of variables that should correlate with target metrics via theory or model  [591] ,  [600] ,  [601] ,  [602] , a methodology termed "data quality improvement. " Data quality improvement relies heavily on the selection of appropriate in situ sensors, which, in turn, requires specific plasma domain knowledge. Fig. 32  shows how plasma domain knowledge is incorporated to improve data quality for building VM or classification models.
1) Type 1 Domain Knowledge-What to Measure: Plasma variables such as the ion flux, neutral flux, and deposition rate defined by phenomenological surface reaction models or interpretation of postprocess profile formation using theoretical mechanisms  [603] ,  [604] ,  [605]. 2) Type 2 Domain Knowledge-How to Measure: Noninvasive in situ sensors to measure plasma variables that are derived from type 1 domain knowledge. Preprocessing measured data with the interpretive functions afforded by type 1 and 2 domain knowledge is key. Preprocessing not only involves the conversion of raw data into plasma variables but also is important for error removal  [606]. OES provides a good example of useful preprocessing. OES intensities most often vary during production runs due to varied transmittance through the view window caused by film deposits.
The intensity variation is independent of the plasma condition, hence registered as an error. One way to reduce error is to normalize the OES spectra by the OES intensity at the chosen wavelength  [607]. Both sensor and preprocessing method selection (i. e. , data quality) can be evaluated by benchmarking VM model performance with versus without the studied sensor data added to other default sensor datasets. The following example illustrates one such evaluation. TOX flat wafer etching rates were varied by installing various combinations of new and used chamber parts. OES and RF sensor data were collected during the etching of TOX wafers. VM models were constructed to predict TOX etching rates using exhaustive least-squares regression with preprocessed OES and RF sensor data. The number of terms in the VM models was limited to below 4 to enhance the sensitivity to data quality.
The impact of RF sensor data on the VM model performance was evaluated using CV scores calculated as an average of R2 values from each fold of the fivefold CV. Fig. 33  shows CV scores of all VM models generated from exhaustive least-squares regression with two datasets-OES only (OESonly) and OES with RF sensor data (OESwRF). del_RF represents models that include RF sensor data, i. e. , del_RF = OESwRF -OESonly. As can be seen, significantly improved high-performance VM models were generated with RF sensor data. The results illustrate overall data quality improvement by adding RF sensor data with preprocessing. Successful development and deployment of APC to meet the tight control limits demanded by sub-10-nm technology plasma processes require AI/ML to be augmented by improved data quality.
For instance, a well-defined dataset of experimental sputtering yields for different ion-solid combinations is publicly available  [616]  and has been successfully used  [613]. These are limited to integral information, however, eliminating the details of the flux and energy distributions emanating from the surface. Energy and angle-resolved data from MCSs (with BCA) provide a compromise between computational costs and physical fidelity  [54] ,  [615]. Accurate physical simulation data at the atomic level (e. g. , MD) are typically sparse and may require data augmentation because the computational cost to obtain large datasets imposes a significant challenge. 2) The process of defining independent features depends on the requirement of physical interpretability. Given a set of possibly correlated physical variables, a subset of descriptive physical parameters has been devised by hierarchical clustering and corresponding descriptor analysis for sputtering yield regression  [613].
Naturally, this affects all aspects of the topic, but the larger variance of biomedical experiments and the resulting data, and medical safety aspects accelerated the correspondent efforts. When surveying current publications on biomedical aspects of cold plasma, the use of bioinformatics tools has become the normal case  [675] ,  [676] ,  [677]. Currently, when proteomics (proteins) or lipidomics (lipids) data are a central piece of the paper, most journals desire the upload of these data into public repositories to ensure their long-term persistence and preservation. A number of dedicated databases have evolved, e. g. , the members of the ProteomXchange consortium http://www. proteomexchange. org/, such as MassIVE, PeptideAtlas, or PRIDE for proteomics data; Metabolomics Workbench https://www.
metabolomicsworkbench. org/ for small molecules, including lipids; or the Genome Sequence Archive https://ngdc. cncb. ac. cn/gsa/ for genomic information. The major benefit for any research community is the long-term conservation of the data independent of individual working groups, the possibility to share the data with colleagues to allow additional data analysis approaches, and the increase in reliability and reproducibility as defined by the FAIR Guiding Principles for scientific data management and stewardship that were introduced in 2016 https://www. go-fair. org/fairprinciples/  [674]. In the plasma science community with a special focus on plasma medicine, a dedicated repository INPTDAT has been established https://www. inptdat. de/, adhering to the FAIR principles, as discussed in Section VIII-E.
The concept was extended using artificial peptides, providing a more complex chemical environment and a greater variety of chemical structures to be attacked by the plasma-generated species  [677] ,  [686]. Again, this approach involved high-resolution mass spectrometry and the use of an advanced software solution to filter the raw data for relevant information on oxidative posttranslational protein modifications (oxPTMs, Byonic, ProteinMetrics, Palo Alto, CA, USA). As a result, the introduction of 17 different oxPTMs was determined along with four main targets: cysteine, methionine, tryptophane, and tyrosine. For example, in the two decapeptides, Ala-Asp-Gln-Gly-His-Leu-Lys-Ser-Trp-Tyr and Ala-Cys-Glu-Gly-Lyl-Ile-leu-Lys-Tyr-Val, the modification nitration (+44. 98 m/z, +N + 2O -H) is introduced in dependence on gas-phase composition (Ar ≫ Ar/O 2 ), plasma source (kINPen ≫ COST jet), and solvent system [H 2 O ≫ phosphate-buffered saline (PBS)].
Reduction of the dimensions of extremely large datasets to make the data more tractable by computation is also another challenge, as discussed in Section VII-H for chemical reactions in plasmas. Although a large amount of data may be obtained from individual plasma processing tools and their processed material surfaces, what remains a challenge in plasma technologies is the shortage (or sometimes lack) of fundamental data on elementary processes that can be applied to any processing tools, such as chemical reaction rates of specific surface materials with specific incident gaseous species that characterize the plasma surface interaction. Of course, it is unrealistic to expect to obtain such data for all possible combinations of surfaces and gaseous species exhaustively. However, it is desirable to establish new techniques for HTS to obtain fundamental chemical reaction data associated with desired plasma processing efficiently.
A. Introduction In the study of any of the different plasmas discussed in this review, a common challenge is to obtain a thorough understanding of the physical and chemical properties of plasmas. In order to determine such properties, it is essential to assemble authoritative databases that allow the design, diagnostics, and monitoring of the plasma. The plasma community has been active in assembling such databases that include the following: 1) AM databases detailing both spectroscopic data (commonly used as plasma diagnostics to identify key plasma species) and collisional data characterizing electron, ion, and photon interactions with those AM species within the plasma and knowledge of both the cross sections and reaction rates for such collisions, both in the gas phase and on the surfaces of the plasma reactor; 2) material databases that provide data on the properties used in the design and operation of plasma systems with databases for fusion reactors being among the most extensive; 3) plasma chemistry databases that provide access to complete and validated data for plasma modeling with preassembled and validated chemistry sets; 4) low-temperature plasma databases that have been among the most common databases since these have been constructed to support specific industrial plasmas, such as those used in the semiconductor, lighting, and medical industries.
However, the compilation of such databases remains a major challenge, and the necessary coordinated infrastructure and funding to build and sustain them have often been lacking. This, in turn, challenges the broader scientific community to recognize that their fields also rely upon the compilation and access to relevant databases, and a united research community must then confront the funders of research (government and industrial), specifying that scientific and technological progress is based upon a strong fundamental bedrock; if this is neglected, then the scientific and technological advances that they require will not occur, and their investment will not be rewarded. This section reviews the current status of the different databases and gives indications as to present data deficits. Core to all databases is the criteria for data selection: whether the database then recommends datasets or leaves the user to select data is an important parameter.
B. Atomic and Molecular Databases AM processes are elementary processes in plasmas and important to understand the microscopic behavior of plasmas and radiative processes in plasmas. Radiative and collisional processes of atoms and molecules govern the energy balance of plasmas. It is also useful to use emissions from atoms and molecules for spectroscopic diagnostics, for example, to know impurity behavior in fusion plasmas and plasma properties, such as electron temperature and density. AM data, such as wavelengths and transition probabilities of emission lines or collision cross sections, are important fundamental data to describe AM processes. AM databases compile and store such important data since the 1970s and provide them for users in various research fields  [687]. In recent years, many databases are available through the internet, and there have been some attempts to provide such data more conveniently for users.
As a new attempt, databases are used to train ML methods, for example, to estimate a set of electron-impact cross sections from swarm transport data  [688]. There are two kinds of AM databases available: one has evaluated data and the other has original data. The former databases contain evaluated one value (or one dataset) for one process, e. g. , one wavelength for one specific transition and one set of ionization cross sections as a function of collision energy for a specific atom. Data evaluation is done by the organizers of the database in various ways. The accuracy of data is carefully examined experimentally, by checking the method of the study, or by comparing it with other data, and one value or one dataset is selected and stored in the database. The NIST Atomic Spectra Database  [689]  is this type of database for atomic wavelengths, transition probabilities, energy levels, and ionization potentials.
The atomic database in CHIANTI  [690]  for spectroscopic diagnostics for solar physics is also this type. The IAEA ALADDIN database contains evaluated data of cross sections and rate coefficients for electron collisions, photon collisions, and heavy particle collisions  [691] , but several datasets evaluated by different research groups are stored for one process. The second type of database contains many data for one process obtained by various theoretical or experimental studies. All data or dataset have their references on their origins, and users can track the data source. Users can compare several datasets for one process, such as ionization cross sections for a specific atom, and can evaluate and select data by themselves. The NIFS AM numerical database is this type of database for collision cross sections and rate coefficients for ionization, excitation, recombination, and charge exchange processes of atoms and molecules  [692].