dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018) , able to operate in nearly real-time  (McNutt et al. , 2015; Boué et al. , 2016). However, the deployment of VSR systems in observatories suffers from three major drawbacks: (1) Designing costs: supervised VSR systems need to be trained using previously labeled catalogs of events gathered in a database (DB) to characterize the volcano-seismic classes. This so-called training stage requires considerable human resources and time  (Langer et al. , 2019) , not always affordable for modest observatories. Unsupervised VSR (U. VSR) does not need this training step, but achieves a lower recognition accuracy being relegated mainly to data mining purposes. (2) Lack of robustness: observed seismicity patterns and event types, thus, the seismic features and volcano-seismic catalogs on which supervised VSR is based, vary according to the current state of the volcano  (Carniel, 2014) , to the environmental noise  (Lecocq et al.
VSR paradigms: it can recognize events from a local volcano without any previous information about it, achieving an acceptable efficiency without implementing the whole system from scratch. It relies on prebuilt, universal VSR models describing universal databases of labeled events recorded on several volcanoes. Hence, our proposal provides a volcano-portable, operational and robust VSR platform, developed under the EU-funded VULCAN. ears project, which includes tools to build local VSR models or alternatively, to use prebuilt VI. VSR universal ones. VULCAN. ears deployed VI. VSR systems in several observatories, partnering with 18 international institutions to create the VSR-ALUE collaboration group. As a result, data from 21 volcanoes have been gathered and currently VSR-ALUE continues the support and development of the volcanoindependent approach and application to real-case scenarios.
Automatic Volcano-Seismic Recognition (VSR) A classic supervised VSR operation is divided in two stages shown in Figure  1 : 1) the training step, including the data preparation, (3) Recognition: eval DB events will be automatically detected and classified, outputting the recog labels. (4) Evaluation of the system is measured comparing those recog labels with the manual eval DB labels. description and classes characterization and, 2) the system evaluation, encompassing the automatic recognition of volcano-seismic events and measuring the performance. The system design is structured in: (1) Data preparation consisting of: a. DB building: expert technicians manually detect and classify seismic events to prepare a labeled catalog which is split into the train DB and eval DB databases to train and evaluate the system, respectively.
Their related train DB labels and eval DB labels detail the duration and type of the events appearing in each database. b. Waveform description consists of extracting relevant information from the data to be learnt by the system. To perform real-time VSR the continuous waveform is parametrized as a sequence of signal segments, each one described by a feature vector, resulting in a sequence O {o t } {o 1 , o 2 ,. , o t } of observable vectors. An adequate scheme description of the data increases the robustness, exportability and recognition scores  (Álvarez et al. , 2012; Soto et al. , 2018). Hybrid features describing waveform, geophysical and spectral information combined with their contextual, time derivatives components, provide an optimal scheme according to  Cortés et al. (2016)  and  Maggi et al.
VI. VSR Underlying Technology Given the similarities between speech and seismic events  (Ohrnberger, 2001) , former VI. VSR was inspired by the classic speech recognition area which successfully accomplishes speaker-independent tasks gathering multispeaker databases modeled with HMMs  (Rabiner and Schafer, 2007). HMMs characterize structured patterns, as volcano-seismic events, modeling not only the pattern waveforms, but also the relationship among patterns given by the temporal distribution of the HMM states. Each state represents a pattern observed in the {o t } sequence of feature vectors, such as P, S and superficial phase arrivals (Figure  1 ). HMMs are suitable for one-step continuous VSR in real-time, outperforming classic seismic detection algorithms even in noisy scenarios  (Beyreuther and Wassermann, 2008).
For boosting the system robustness and portability, VI. VSR has pushed beyond the state-of-the-art these innovative concepts: • Universal databases: gathering data from different types of volcanoes increases the number and variety of patterns found in events of the same seismic class, improving the completeness and robustness of the given class model  (Cortés et al. , 2009a ). • Standardization of the seismic waveform based on unsupervised, data-driven decomposition and posterior selective reconstruction of a signal. Standardized events are less noisy and, hence, easier to recognize  (Cortés et al. , 2019b). • Efficient data description: the extended Discriminative Feature Selection algorithm extracts the most relevant information of a seismogram when selecting the most efficient components of a feature vector to describe it  (Álvarez et al.
VSR platform and its utilization steps. Basically, it encloses the usual (1)-(  4 ) VSR stages detailed in Section 2. 1 to deploy an improved VSR system but fed with labeled events from several volcanoes composing the universal joint DB. An extra 5) autoconfiguration stage optimizes the system to maximize its efficiency by performing iterative train-evaluation tests to select the best data description scheme and modeling setups. The (1)-(  5 ) stages are guided by suitable pyVERSO scripts accomplished to obtain robust VI. VSR models. The automatic recognition of events embedded in the seismic records of the untagged VS data can be carried out in two different manners: • Offline cataloging via geoStudio: data stored in files are loaded into a graphical interface and their events are recognized by VSR models selected from a prebuilt set.
Then, the tagged data can be plotted for inspection and the resulting catalog is stored for further analysis. • Online monitoring via liveVSR to perform a continuous, real-time monitoring of an active volcano. The liveVSR script is able to connect to any available FDSN data server plotting the recognized events and generating volcano-seismic catalogs. Several instances of liveVSR can be run concurrently receiving data from several stations or volcanoes. The resulting labeled catalogs are the input to posterior 6) VI. VSR applications.
Building VI. VSR Models With pyVERSO pyVERSO is a collection of Python scripts designed to perform VSR tasks from the command line. It has libraries to prepare and describe data, including many time-domain, cepstral and hybrid parametrization schemes and advanced feature selection routines. It is highly configurable and easy to use. Taking as input a labeled database and a configuration file it can implement serial or parallel architectures based on HMMs, Gaussian Mixture Models and Conditional Random Fields. pyVERSO main aim is to build own, local VSR systems of an active volcano. Once the system is optimized, its models can be exported to be used on online monitoring via liveVSR or offline analysis by geoStudio. pyVERSO is highly integrated within the Python scientific ecosystem and, currently, relies on the HMM Toolkit  (Young et al.
(2) Select VSR models suitable to your data among a built-in set of models trained with 10 databases from 10 different volcanoes respectively, six joint databases mixing two VS data recorded at several volcanoes compose the joint DB feeding the VI. VSR system. Usual (1), (  2 ), (  3 ) and (4) stages for building the VI. VSR model set are guided by (5) pyVERSO in order to optimize the recognition results. (6) VI. VSR applications: events in untagged VS data from a volcano can be detected and classified in an online monitoring loop by the liveVSR script, or analyzed offline by geoStudio providing automatically labeled VS catalogs. volcanoes and two universal volcano-independent solutions with data of several volcanoes (Figure  3B  2). Custom models created by pyVERSO can also be selected. (3) Launch the labeling task and examine the results plotting the event distribution of the generated catalog (Figure  3B  3) and the files automatically labeled (Figure  3B 4 ).
52 h of Mexican Colima and Popocatépetl events were combined to deploy a volcanoindependent system for classifying Arenal events detected by a broad-band sensor in Costa Rica. It was able to discriminate 46  TSs, 46 TRs, 53 pulsant tremors, 46 explosions, 26 REGs, 50  VTs and 285 noises, increasing the precision from 50% to 71% after auto-selecting the best hybrid features to describe the seismograms  (Cortés et al. , 2019b). Precisely, Figure  4  presents an example of the accuracy improvements when the training database is enlarged with events of a new volcano: Joint VI. VSR models (Figure  4B ) trained with Colima and Popocatépetl databases perform better recognizing Arenal events than Single VI. VSR models (Figure  4C ) built only with Colima.
Discussion Several alternatives can be found to facilitate the integration and use of VSR systems at volcano observatories but most of them are designed for a determined volcano or focused on pattern discovery  (Carniel et al. , 2006; Langer et al. , 2009; Messina and Langer, 2011). Some graphical programs are useful to characterize events guiding the manual labelling of data  (Lesage, 2009). Bueno et al. (2020)  designed an interesting interface based on two-step VSR recognition able to cluster events in basic categories. VULCAN. ears framework offers a complete volcano-independent VSR solution to monitor volcanoes providing built-in models for geoStudio and liveVSR alongside the pyVERSO ecosystem to build customized VSR systems focused on low-level integration. The proposed approach is an ongoing project and the current universal models are only trained with events from three volcanoes.
A universal database gathering events of, at least, 10 volcanoes is in preparation in strict cooperation with our partners including catalogs from Copahue, Etna, Flegrei, Merapi, Piton de la Fournaise, San Miguel, Soufrière of Guadeloupe, Stromboli, Turrialba, Ubinas and others publicly available as Llaima and Cotopaxi. geoStudio only wraps few functionalities of its pyVERSO backend, having a huge potential to be accomplished. The innovative techniques specified in Section 2. 2. 1 and designed to achieve the volcano-independent milestone are functional and their baseline results encouraging, scoring an efficiency in the 70-80% interval in challenging scenarios as continuous recognition under noisy conditions in different stations and epochs, achieving a 76%, or volcano-independent classification and recognition reaching 71 and 65%, respectively.
Concluding Remarks and Future Work This work presents the Volcano-Independent Seismic Recognition as a solution to classic issues when implementing automatic Volcano-Seismic Recognition systems in volcano observatories. Current monitoring centers usually have limited resources to develop their own systems. They still detect and classify manually, which restrains their response in case of volcanic unrest. The authors have deployed a platform to develop portable recognition systems providing several tools to easily integrate and use the framework in observatories and to build applications for cataloging volcano-seismic events: pyVERSO to design recognition systems adapted to a given volcano, geoStudio to graphically detect and classify events in offline interactive operations, and liveVSR to continuously recognize in real-time events from remote or local data servers.
DATA AVAILABILITY STATEMENT Following the recommendations of the European Commission regarding Open Science in EU-funded programmes, several material related with VULCAN. ears project are available, free of charge, in the Zenodo repository, including conference talks and posters, datasets and software. Specifically, the software preview versions and manuals of geoStudio, liveVSR and pyVERSO documented with demo videos of geoStudio running the case studies presented in Section 3. VULCAN. ears main webpage can be found at ResearchGate.
ACKNOWLEDGMENTS We would like to thank every contribution to the universal databases and everybody involved in the labeling process. A special acknowledge goes to all the partners in the  VSR-ALUE group , and the many institutions developing the VI. VSR platform, including the  Instituto Andaluz de Geofísica  and the  Department de Teoría de la Señal ,  Telemática y Comunicaciones , both of the  University of Granada, Spain , whereas the former VI. VSR technology was developed. We also thank the reviewers, the guest associate editor  Luca de Siena  and the chief editor  Valerio Acocella  for their valuable comments, which significantly improved the manuscript.
Numerical experiments To quantify the efficiency of the proposed merit approach, we compare our solver with the direct search method MADS where the progressive barrier approach has been implemented  [5]  to handle both relaxable and unrelaxable constraints. The progressive barrier approach, proposed in MADS, enjoys similar convergence properties as for our algorithm, hence, a comparison between the two solvers is very meaningful. For MADS solver, we used the implementation given in the NOMAD package  [1, 3, 29] , version 3. 6. 1 (C++ version linked to Matlab via a mex interface), where we enabled the option DISABLE MODELS, meaning that no modeling is used in MADS. The models are disabled since our solvers are not using any modeling to speed up the convergence. The parameter choices of Algorithm 2 and Algorithm 3 followed those in  [16].
Results on known test problems Our test set is the one used in  [23, 24, 27, 31]  and comprises 13 well-known test problems G1-G13 (see Table  1 ). These test problems, coded in Matlab, exhibit a diversity of features and the kind of difficulties that appear in constrained global optimization. In addition to such problems, we added three other engineering optimization problems  [23, 12] : PVD the pressure vessel design problem, TCS the tension-compression string problem, and WBD the welded beam design problem. Problems G2, G3, and G8 are maximization problems and were converted to minimization. Problems G3, G5, G11, and WBD contain equality constraints. When a constraint is of the form c e i (x) = 0, we use the following relaxed inequality constraint instead c i (x) = |c e i (x)| -10 -4 ≤ 0.
Satellite image time series (SITS) is a sequence of satellite images that record a given area at several consecutive times. The aim of such sequences is to use not only spatial information but also the temporal dimension of the data, which is used for multiple real-world applications, such as classification, segmentation, anomaly detection, and prediction. Several traditional machine learning algorithms have been developed and successfully applied to time series for predictions. However, these methods have limitations in some situations, thus deep learning (DL) techniques have been introduced to achieve the best performance. Reviews of machine learning and DL methods for time series prediction problems have been conducted in previous studies. However, to the best of our knowledge, none of these surveys have addressed the specific case of works using DL techniques and satellite images as datasets for predictions.
Therefore, this paper concentrates on the DL applications for SITS prediction, giving an overview of the main elements used to design and evaluate the predictive models, namely the architectures, data, optimization functions, and evaluation metrics. The reviewed DL-based models are divided into three categories, namely recurrent neural networkbased models, hybrid models, and feed-forward-based models (convolutional neural networks and multi-layer perceptron). The main characteristics of satellite images and the major existing applications in the field of SITS prediction are also presented in this article. These applications include weather forecasting, precipitation nowcasting, spatio-temporal analysis, and missing data reconstruction. Finally, current limitations and proposed workable solutions related to the use of DL for SITS prediction are also highlighted.
Introduction Numerous satellites orbit the Earth and provide users with a large quantity of data such as optical and radar images. According to the Union of Concerned Scientists Satellite Database, more than 4000 satellites were operational on 30 April 2021 and approximately 24% of them were dedicated to the observation of the Earth. This category of satellites produces images that are used for several purposes, such as climate and precipitation studies, land use and land cover monitoring, military reconnaissance, and agricultural and forestry applications  [1]. With the short revisiting time of satellites above an area, it will be soon possible to obtain free satellite images with excellent spatial resolution every day for any area in the world  [2]. In 2001, the European Union and the European Space Agency launched a program called Global Monitoring for Environment and Security (GMES) to equip Europe with the autonomous capacity for observing and monitoring the Earth.
In 2012, the GMES became the Copernicus program, which has been providing data from a constellation of six families of satellites called the "Sentinel" mission (Sentinel 1 to Sentinel 6) since 2014. Each of these missions has its own objective and ambition to provide free data accessible on the Internet. Another great project dedicated to Earth observation is the Landsat program, developed by the National Aeronautics and Space Administration (NASA) after the request of the United State Geological Survey (USGS). It is the first such program intended for civil purposes. The Landsat series consists of eight operational missions (Landsat 1 to Landsat 8). The Landsat 1 satellite was launched in 1972 and Landsat 8 was put into orbit in February 2013. Landsat 9 is the latest satellite in the Landsat series.
In the field of SITS analysis, applications may include weather forecasting  [11] , land use and land cover change detection, precision agriculture, deforestation monitoring, and others  [12]. Reviews of ML and DL methods for time series prediction problems have been conducted in previous studies. However, these reviews do not focus on the special case of the application of DL approaches for SITS prediction. In fact, some surveys concern the use of DL in general, without emphasizing the type of data used in the models. In other papers, although the reviews concern works using SITS as a dataset, the studied techniques are diverse, including classical ML methods. For instance, in  [1, [12] [13] [14] , the authors surveyed the use of ML and DL techniques for time series analysis and forecasting.
Research results for remote sensing applications using ML methods are reviewed in  [10, [15] [16] [17] [18]. The authors of  [19, 20]  reviewed studies that involve the use of SITS for data fusion or missing data reconstruction. More recently, in  [8] , authors reviewed the DL concepts, especially the convolutional neural networks. An overview of previous reviews related to the topic are presented in Table  1. [21]  Deep Learning for Time Series Forecasting: A Survey 2021 Yes No Yes The use of DL on satellite images has some specifications that are important to note in a particular study and thus save time for all scientists interested in the field. However, to the best of our knowledge, there is not yet a published study on a review of the specific use of DL for SITS prediction. Thus, the goal of this paper is to examine recent publications related to the application of DL techniques for SITS prediction.
Survey Methodology To conduct this review, we first selected research papers published by Elsivier and then those published by other publishers such as MDPI, IEEE, Springer, and others. Most of the selected documents were journal articles (more than 90%) and some conference papers as well as books were also included in the bibliographic database. As the studied field was quite specific, no restrictions were made on publication dates, the objective being to have a maximum of documents to analyze. The language used for the research and all the selected documents was English. First, we selected all documents retrieved from the keywords deep learning, artificial intelligence, satellite image time series, remote sensing, prediction, forecasting, nowcasting, and neural networks. Initially, approximately 400 documents were selected based on the keywords search.
• Papers that did not use DL to make predictions; • Works that used DL for predictions but not from SITS; and • Publications that focused on the use of DL and SITS but whose applications were not prediction, such as classification, segmentation, and others (in the cope of this study, prediction is related to forecasting or missing data reconstruction). Moreover, for the comprehension of additionally concepts related to the topic, other documentations were also reviewed as part of this survey. At the end of the literature search step, only 77 documents were retained and analyzed. The publication years of the collected documents cover the period from 2012 to 2021, distributed as shown in Figure  1.
The Recurrent Neural Network (RNN) These are generally used when input data are sequential, such as in time series, sound, or natural language processing. RNN architectures are suitable for designing predictive models  [12, [27] [28] [29]. Indeed, this network includes a feedback loop, which allows for consideration of the information of the previous layers for predictions. However, there are two main issues that may occur during the application of RNN architectures: the problem of vanishing and exploding gradients  [8, 24]. To overcome these limitations, two variants of RNN have been developed: the long short-term memory (LSTM)  [30, 31]  and, more recently, the gated recurrent unit (GRU)  [32, 33]. They are recurrent networks with memory units called "cell". The LSTM architecture has three gates (input, output, and forget gate), while the GRU has the reset and update gates.
The Generative Adversarial Network (GAN) This is a very famous architecture that has had impressive success since its creation  [34]. The GAN model consists of two opposing networks (generator and discriminator) which oppose each other in order to generate an artificial instance of data strongly resembling that of the real one  [34]. The generator aims to create new data received by the discriminator which determines whether they are true or generated due to a database made up of real data. Thus, the first network learns to create more and more realistic elements while the second learns to recognize real data in the best way.
Background on SITS 4. 1. Description of Satellite Images Remote sensing can be defined as all the techniques that allow for the acquiring of information about objects without direct contact with them. One of the most common types of remote sensing is remote sensing using satellites as a platform and producing information in the form of images. Today, more than 4000 satellites are orbiting around the Earth. Each satellite has its own sensor which gives specific data. They take pictures of the Earth at a given place and time. Thus, a satellite image (SI) or remote sensing image is an image representing a part of the Earth (or another planet) obtained from data recorded by a sensor installed on a satellite  [2]. A list of operational satellites, with their characteristics and main objectives, is available on the satellite database of the Union of Concerned Scientists: https://www.