dataesr/scientific-paragraphs-categorization huggingface dataset:
org/ package=CSTools (last access: 18 July 2022). It is being developed at BSC-CNS for a GitLab repository https://earth. bsc. es/gitlab/ external/cstools/ (last access: 22 July 2022) and shared via Zenodo (https://doi. org/10. 5281/zenodo. 5549474; Pérez-Zanón et al. ,  2021a). The code to reproduce the use cases and plots shown in this work is shared on the three sites, and we recommend finding it in the GitLab repository https://earth. bsc. es/gitlab/external/cstools/ -/tree/master/inst/doc (last access: 24 July 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-6115-2022-supplement.
(2021) requested to reduce the dimensionality of the Classification CST_RegimesAssign* dataset. Matches patterns with a set of reference maps based on the minimum Euclidian distance or the (i. e. , clusters from CST_WeatherRegimes) Yes Cortesi et al. (2019), Torralba et al. (2021) highest spatial correlation. CST_CategoricalEnsCombination Converts a multi-model ensemble forecast into Yes a categorical forecast by giving the probabil- ity for each category. The following different methods are available to combine the different ensemble forecasting models into probabilistic categorical forecasts: pool, for ensemble pooling where all ensemble DelSole et al. (2013) members of all forecast systems are weighted equally, comb, for a model combination where each DelSole et al. (2013) forecast system is weighted equally, and mmw, for model weighting.
To date, more than 6000 stereo pairs have been acquired by HiRISE and, of these, more than 800 DTMs and 2700 orthoimages have been produced and made available to the public via the Planetary Data System. The intended audiences of this paper are producers, as well as users, of HiRISE DTMs and orthoimages. We discuss the factors that determine the effective resolution, as well as the quality, precision, and accuracy of HiRISE DTMs, and provide examples of their use in time series analyses of active surface processes on Mars.
Introduction The ability to image the surface of Mars repeatedly with the High Resolution Imaging Science Experiment (HiRISE), onboard the Mars Reconnaissance Orbiter (MRO)  [1] , opened a new era of discoveries about the planet's geology, seasonal processes, and surfaceatmosphere interaction. HiRISE observations, at ∼30 cm/px from MRO's nominal 300 km orbital altitude, have revealed in unprecedented detail that some areas of the surface of Mars are active landscapes  [2, 3]. High resolution digital terrain models (DTMs) generated from HiRISE stereo pairs provide one of the key data products used to support studies of active surface processes on Mars. The ability of MRO to point repeatedly at a target allows the creation of a time series of images, orthorectified to a corresponding DTM, enabling pixel-scale evaluation of surface changes in three dimensions.
The high spatial resolution of HiRISE, combined with the duration of the MRO mission, has allowed for details of the surface at decimeter scale to be observed over many Mars seasons and years (8 Mars years, or 15 Earth years, as of this writing). Examples of key observations made using HiRISE data include migrating dunes and sand ripples  [4] [5] [6] [7] [8] , polar avalanches  [9, 10]  and block falls  [11] , gully activity (e. g. ,  [12] [13] [14] [15] ), the evolution of fresh impact craters  [16, 17] , expanding carbon dioxide ice pits  [18, 19] , new spider-like channel networks, known as araneiforms  [20] , and recurring slope lineae (RSL)  [21, 22]. The goal of this work is to provide readers with an understanding of the processes used to generate HiRISE DTMs and orthoimages that are publicly available via the Planetary Data System (PDS).
Pixel scale also depends on the binning mode used. HiRISE can bin CCD pixels on-chip, for various modes such as bin1 (no binning); bin2, corresponding to ∼60 cm/px; bin4, corresponding to ∼120 cm/px; and other binning modes that are rarely or never used. The most common imaging modes incorporate bin1 and bin2  [33]. Color CCDs (BG and IR) are usually binned to increase SNR to better than 100:1. In the downlink processing pipelines, the image data from binned CCDs are then enlarged by the appropriate factor to match that of the lowest binning mode, typically that used for the RED CCDs. Binning and the number of TDI lines (128, 64, 32, or 8) are two of the parameters adjusted during planning for each HiRISE image to avoid saturation and to optimize SNR, spatial resolution, spatial coverage, and data volume.
Methods In this section, we describe the methods used by the HiRISE team to create DTMs with an emphasis on the techniques specific to generating data to be used for change detection. We use the term "digital terrain model" (DTM) rather than "digital surface model" (DSM), and we do not make a distinction in this paper between DTM and "digital elevation model" (DEM). A HiRISE DTM describes elevation in a raster image format, with each pixel assigned a 32-bit signed, floating point value representing elevation in meters relative to the areoid (Martian geoid), to be consistent with the global topography derived from the Mars Orbiter Laser Altimeter (MOLA)  [35]. HiRISE DTM pixels represent elevation values at evenly spaced "posts". The DTM pixel size is also referred to as the ground sample distance (GSD).
Creation of DTMs Using ISIS and SOCET Set Workflow The processing chain starts by preprocessing HiRISE images in ISIS to calibrate and convert them to the formats and generate metadata expected by SOCET Set v5. 6. 0  [23]. Photogrammetric processing occurs within SOCET Set through a combination of automated and interactive steps. The majority of interactive steps take place during the bundle adjustment stage, referred to in SOCET Set as Multi-Sensor Triangulation (MST). Following MST, the DTM is created in the terrain extraction stage, which is largely automated. The files are then exported from SOCET Set for post-processing in ISIS to convert back to planetary coordinates and map projections in standard formats. The final step is validation of the products and archiving with the PDS. Each of these stages is described briefly below with an emphasis on how the quality of the final product is affected (Figure  2 ).
The pixel values within the noproj mosaic are converted from 16-bit to 8-bit digital number (DN) values and exported from ISIS cubes to a raw image format. The pushbroom sensor model plugin developed by the USGS for SOCET Set is a rigorous physical model based on geometric optics. The camera position and attitude are modeled as continuous functions of time by being broken into a fixed part that is interpolated through an arbitrary number of values supplied when the image is imported, and an adjustable part that is represented by a low-order polynomial (order 2 for position, 3 for Euler angles). The generic model includes the possibility of radial optical distortion, but the pre-processing in ISIS corrects such distortions in the images, so this capability in SOCET is not used. For image import with initial position and pointing information, SOCET Set requires the image metadata in a text file format which is generated by socetlinescankeywords (i.
e. , "SOCET linescan keywords"). The keywords required by SOCET Set include the camera parameters, image information, and information about the spacecraft trajectory and pointing obtained from the Spacecraft Planet Instrument C-matrix Events (SPICE) kernels  [40]. Additional data are prepared for the project setup in SOCET Set, such as global reference topography, geographic coordinates, and approximate elevation values. Laser altimetry from MOLA is obtained over a region centered on the HiRISE stereo pair and padded by 0. 5 • in latitude and longitude. Crossover-corrected MOLA Precision Experiment Data Records (PEDRs)  [35]  are the primary dataset used to provide absolute spatial control. The MOLA global DTM, or Mission Experiment Gridded Data Record (MEGDR), which is interpolated from the crossover-corrected global PEDR data, is also extracted and clipped to the same region and exported as an ASCII (plain text) format file.
Bundle Adjustment Bundle adjustment, which is performed with the Multi-Sensor Triangulation (MST) tool in SOCET Set, is the process of controlling the images to each other and to a reference elevation dataset. The inputs are image-to-image feature correspondences ("tie points") and usually image-to-ground correspondences ("control points"). SOCET Set provides tools for measuring these inputs interactively via Interactive Point Measurement (IPM) and by automated matching (Automatic Point Measurement, APM). The outputs are revised estimates of camera position and pointing parameters that minimize mismatches at the points in a least-squares sense, as well as the ground coordinates of the tie points. The quality of the solution largely determines the absolute accuracy of the DTM, and influences how well the stereo matcher can extract elevation values from the stereo images, which affects internal precision.
Change Detection Studies The capability to resolve meter-scale features in HiRISE images, along with repeated imaging over the same target, has enabled unambiguous observations of surface changes on Mars. HiRISE DTMs are the best and often only way to get accurate slope and height estimates at high resolution from existing Mars datasets. Many types of surface changes have been observed by comparing HiRISE RDRs acquired at different times (e. g. ,  [10, [60] [61] [62] [63]. Although a large number of sites can be qualitatively assessed using the RDRs, aligning images manually does not correct for differences in observation geometry, making visual comparisons challenging, especially on steep slopes. The RDRs are map projected onto a smoothed version of the MOLA global shape model  [36] , which can only provide topographic correction over the scale of features resolved by MOLA.
The ability to acquire images at a wider range of illumination angles due to TGO having a non-Sun-synchronous orbit, comparable spatial resolution, and overlapping bandpasses make CaSSIS a complementary experiment to HiRISE investigations of surface activity, including RSL  [149]  and the structure of polar layered deposits  [150]. HiRISE DTMs also provide a valuable reference dataset for the development of the CaSSIS DTM pipeline  [55, 151]. MRO and HiRISE data have been crucial for the science characterization and engineering assessment, mission planning, and visualization of potential exploration sites. Although landing site characterization with HiRISE data is described in detail in other places  [23, 53, [152] [153] [154] , it is worth mentioning here as many of the methods described in this paper have benefitted from the need to seamlessly merge multiple overlapping HiRISE DTMs with each other and with topographic data from other instruments, as well as from the methods of quality assessment developed for landing site characterization.
HiRISE images and DTMs have proven critical for assessing hazards such as boulders (e. g. ,  [23, 155] ) and steep slopes (e. g. ,  [153] ). HiRISE DTMs have also been used as reference datasets in the development and testing of innovative exploration technologies such as terrain relative navigation, which was successfully demonstrated by NASA's Mars 2020 Perseverance rover  [156] [157] [158]. Since becoming available in the PDS, HiRISE DTMs have been included in more than 800 refereed publications, with the number of papers generally increasing each year (Figure  18 ). Not all of the HiRISE DTMs used in these publications were derived using the methods described here. NASA Ames Research Center developed the Ames Stereo Pipeline (ASP) for use with stereo data from planetary as well as Earth-orbiting satellites  [159].
The methods described here have been developed and refined over many years through collaborative efforts across multiple institutions. The ISIS/SOCET Set workflow is not the only process available to create HiRISE DTMs, but it was used for all the DTMs that have been made available in the PDS archive. Although the resolution, quality, and accuracy of HiRISE DTMs are unprecedented in planetary datasets, the difficulty of the workflow described here presents a tradeoff between interactive and automated processes in terms of time and quality. This tradeoff has resulted in only a fraction of the thousands of acquired HiRISE stereo pairs being made into high quality DTMs so far, which has restricted studies of active processes to relatively few sites. Future development efforts by the HiRISE team will be focused on automating the orthorectification procedure as much as possible, particularly for sequences of monitoring images over existing stereo targets.
Acknowledgments: We are grateful to all the members of the  HiRISE team , the  MRO  project, the  USGS Astrogeology Science Center Planetary Photogrammetry lab , and in particular the many undergraduate students who have worked in the HiROC photogrammetry lab (listed as the Producer in each DTM's label and README file). This manuscript also benefitted from discussions with the Goddard Volcano Writing Group. The authors are grateful for the thoughtful reviews of this manuscript provided by  Kenneth Herkenhoff ,  David Mayer.
Funding: S. S. S. was supported by the  National Science Foundation Graduate Research Fellowship  Grant  DGE-1746060 , and by the  HiRISE/MRO  project. M. C. was supported in part by  NASA   Mars Data Analysis Program  Grants  80NSSC21K0040 ,  80NSSC20K1066 , and by the  HiRISE/MRO  project. C. M. D. was funded by MDAP  80HQTR19T0087. The work by S. D. was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under  NASA  contract  80NM0018D0004. S. C. is supported for her HiRISE related work by the  French Space Agency, CNES.
used an artificial neural network with temperatur intensity, humidity, plant age, pH, and EC as inputs of the network. Then, the Deep neural networks consist of several deep layers (hidden layers), which means there are many layers between the input and output. The huge increase in both dataset size and the huge surge in computing power have led to the emergence of a new class of deep neural networks, Convolutional Neural Networks (CNNs), with huge potential in big data analysis. CNNs are very powerful in object recognition and image classification. CNNs are trained on the images to be analyzed, and during the training process, the network automatically recognizes the high-dimensional features of all the input images. Once the training process is completed, the trained networks are used to identify and classify the different images.
Feeding Decisions Apart from the loss of profits due to overfeeding, food waste accumulating from poor feeding strategies of aquaculture farms can harm the aquaponics environment. The integration of smart systems with the aquarium helps in evaluating the level of fish satiety, controlling the quantity of food, as well as making feeding decisions. Ubina et al. developed a smart system for assessing the feeding intensity of fish in aquaculture using convolutional neural networks, with an accuracy of 95%  [75]. Måløy et al. developed a deep video classification model to identify salmon feeding behavior or non-feeding. The proposed Dual-Stream Recurrent Network captures the Spatio-temporal behavior of salmon species with a prediction accuracy of 80%  [89]. Adegboye et al. evaluated feeding behavior predicated on Noda and Gleiss's research sample dataset used in prior research.
In this context, aquaponics parameter-monitoring systems were designed using IoT in combination with microcontrollers. The sensed parameter data are sent to a web-based platform to be stored and displayed on a graphical user interface (GUI) in real-time  [19, [98] [99] [100]. Recently, Elsokah and Sakah developed an iOS app that allows for real-time and continuous monitoring of an aquaponics ecosystem through data obtained directly from sensors and microcontrollers  [101]. These collaborations are heading towards information reliability and real-time mobility (through mobile applications, not only on the web). More recently, a remote monitoring system was designed using IoT combined with Convolutional Neural Networks (CNN) to monitor the greenhouse environment using an A6 GSM module to develop an android mobile application for notifying operators of any changes that occurred in the system by sending an alert in case of an anomaly  [32].
Remote Control Applications and Strategies Remote control refers to the ability to send certain signals to system operators to interact or change the state of a certain environmental parameter. The potential of these applications does not stop at mere monitoring, but also extends to control systems and actuators. Using remote control applications, operators can control pumps, artificial lights, fans, ventilation pumps, and other different actuators. Wang et al. developed an Intelligent Voice Control System (IVCS) combined with IoT to monitor and control aquaponic parameters  [102]. Many applications of remote control were found in the reviewed literature using various communication technologies and microcontrollers. To design an IoT-based monitoring and control system for aquaponics environmental parameters, a NodeMCU microcontroller with a Wi-Fi module was used to connect to the Internet.
The data are sent to the Blynk-IoT (a multi-language platform that enables remote control of different microcontrollers), and finally, the local server receives the measurements and sends them to the mobile phone. In these systems, the operators control the different actuators in real-time by sending a message to the receptor  [24, 25, 81]. A simple GSM Arduino-based monitoring and control system was developed to notify farmers when aquaponics parameter measurements are outside the specified ranges where the measurements were displayed on a GUI. This system enables operators to control various parameters in real-time  [31, 63]. An IoT-based monitored and controlled aquaponics system using a microcontroller (Raspberry Pi and Arduino) was also applied to monitor water quality parameters in aquaponics systems.
In the contribution of Wang et al. , Arduino, OpenWrt, and WRTnode were used to connect field monitoring and remote monitoring centers for collecting information and managing the aquaponics system. The information was collected and sent wirelessly to the management and control center for storage, processing, and transmission to a remote server. The data stored in the server are analyzed and decisions are made regarding the different actuators, such as artificial lights, water, and air pumps  [33]. Finally, the control systems found in the contributions listed in this paper aim to implement autonomous systems by using a variety of techniques that shift from traditional linear regression to complex prediction approaches such as convolutional neural networks (CNN). In Kumar et al. 's system, WSN (6LoWPAN PROTOCOL) was included to monitor and control the nitrate level, pH, and temperature  [56].
Their network conceived a 10 m communications range and a transfer rate of 250 kbit/s. Moreover, in this system, the IBM Mote Runner (run-time platform) is used as a sensor network. In addition, to collect and store information from the set of sensors, a cloud data storage system was used. Then the time-series values of different variables were predicted with the help of a trend analysis. To predict the levels of pH and nitrates, linear regression was implemented to create an automated aquaponics system concerning these two parameters. itoring centers for collecting information and managing the aquaponics system. The information was collected and sent wirelessly to the management and control center for storage, processing, and transmission to a remote server. The data stored in the server are analyzed and decisions are made regarding the different actuators, such as artificial lights, water, and air pumps  [33].
Wang et al. designed a smart system to monitor and control aquaponics using wireless sensor network (WSN) technologies and an Arduino microcontroller with a Wi-Fi module. The data are stored on the WRT nodes and then transmitted via Wi-Fi to the OpenWrt server  [33]. Kumar et al. Used the 6LOWPAN protocol and WSN to design a monitored and controlled aquaponics system  [56]. To monitor the temperature and pH of the water in the aquaponics system, GSM technology was used to send an alert message to the operator if the values were outside the specified range  [107]. To collect and store data from the aquaponics system, Mamatha and Namratha used the ThingSpeak data logging platform  [108]. Sreelekshmi and Madhusoodanan monitored aquaponics using the ThingSpeak IoT platform combined with an Arduino Uno microcontroller and a transceiver (ESP8266-01 Wi-Fi)  [27].
To design an IoT-monitored and -controlled aquaponics system, Jacob used a Raspberry Pi microcontroller equipped with a Wi-Fi module. Cloud-based platforms integrating an IoT dashboard and Freeboard were used to collect, store, and control target parameters  [51]. The application of wireless technologies for transmitting data and integrating them with sensors is a promising field in the development and improvement of monitoring and control techniques. Table  6  lists current wireless communication technologies. aquaponics system, GSM technology was used to send an alert message to the operator if the values were outside the specified range  [107]. To collect and store data from the aquaponics system, Mamatha and Namratha used the ThingSpeak data logging platform  [108]. Sreelekshmi and Madhusoodanan monitored aquaponics using the ThingSpeak IoT platform combined with an Arduino Uno microcontroller and a transceiver (ESP8266-01 Wi-Fi)  [27].
94% success rate, pH sensor of 92. 35%, and T [62] emergency source sensor of 97. 91%. IoT Source node, sink, database server, and visualization in mobile application Level, T, pH, and TAN sensors Water level, T, pH, and TAN Water heater, coolant, fish feeder, and ammonia alarm The plant growth was improved [35] Smart monitoring and control system Fish feeder and water supplier T, water level, and moisture sensors T, water level, and moisture content Water pump, oxygen pump, fish feeder, and LED light The climate has the least or and less water consumption no interference in the aquaponics, cost-effective, Autonomous monitoring [63] DHT11, BH1750 light, IoT and deep learning Recirculating aquaculture system, actuators, and sensors soil moisture, HC-SR04 water level, and Air T, RH, soil moisture, light, water level, and pH Water pump, and lamps, Helped enhance the plant and fish growth.
Construction of a reference POD basis As explained in the previous sections, we first need to construct a reference POD basis which is rich enough to take into account the large features of scales in the case of turbulent and incompressible fluid flows. This reference POD basis is obtained in this semi-industrial case of the Preccinsta burner, in association with snapshots data generated from only one LES HF simulation of the Navier-Stokes equations with the reference geometrical configuration presented in Fig. 11. We illustrate that the UGPOD is robust with only one complete HF simulation, the baseline simulation, and one dissipative large scale reduced order basis in association. All the detailed informations concerning the dissipative reduced order basis construction can be found in  [2].