dataesr/scientific-paragraphs-categorization huggingface dataset:
The maximum value from all output neurons (last block in Figure  25 ) identifies the related gas type. As example, in the next reported case, we have developed a specific multi-layer perceptron neural network for the Air probe with 51 neurons as input layer, 8 neurons as hidden layer and 8 neurons as output layer. Extracted features as input are the single computed gas concentration MOX resistance value in DC mode and 50 values of MOX resistances coming from micro hot plate temperature modulation by internal current level driving circuitry. MOX resistance values from DC component and its 1st harmonic represent the input dataset for the MLP. In each graph in Figure  26,  x-axis shows the current level in LSB, while y axis represents the MOX resistance value filtered from DC component and its 1st harmonic called f(R).
Target Gas Gas Concentrations [ppm] Dataset Number  Preliminary results are really promising since the classification error has been less than 5% for all gasses. Some other attempts of selectivity enhancement are reported in  [13] [14] [15] 35]. On the Back-End-Server data analytics side, data mining algorithms were produced for the plant model that generated the consolidation of a prediction model for main nutrients like nitrogen. Based on available agricultural data, several initial conceptual plant models have been created. One of them reflects the plant needs in terms of nitrogen. A visualization of such model for corn can be seen in Figure  27. Similar models describe other plant characteristics, for example leaf area, number of leaves, etc. Some other attempts of selectivity enhancement are reported in  [13] [14] [15] 35].
Conclusions As in most industries, digital technologies represent a lever for innovation also in the agriculture industry. Parameter monitoring doubled by data analytics that can lead to recommendations for farming actions, may increase productivity and even protect the environment with the help of gaseous emissions monitoring. The most efficient way to evaluate the in situ parameters is via sensing with dedicated elements. This paper is presenting a multi-sensor IoT node that intends to measure soil nutrients and gaseous emissions just above the soil, the integration methods and communication path towards an analytics platform that provides advices to the farmer about the fertilization strategy. The paper is presenting the multi-sensor node architecture at hardware and software level, consisting on three main elements: a Soil Probe, a Gas Probe and a Smart Data Logger.
The section describing the node components presents also relevant physical and mechanical sensor design, that has to take in consideration the challenging set of constraints of in-field deployment including power and communication limitations. Details of the custom integration module, part of the Smart Data Logger, are presented, including embedded system design and the firmware architecture. The paper results related to the multi-sensor node are focused on the presentation of the gaseous sensing element, that measures gas concentration and is able to identify the gas type based on GHT25S sensor and data processing by AI algorithms. We have detailed the multi-layer perceptron (MLP) neural network used for the innovative gas fingerprinting algorithm with gas classification errors of less than 5%.
We have also emphasized the methods for testing and validation during different phases of the project, so we have considered relevant to present the developed test tool named PCTool that emulates most of the data running through the Smart Data Logger. Furthermore, we have detailed the architecture and functionality of the Back-End Server in charge of data storage and analytics, a typical computing element where relevant crop information can be modeled using machine learning algorithms. The paper illustrates the way the conceptual plant model was modeled in software including the milestones in the evolution of the crop (exemplified for corn). The paper presented an integrative graphical representation of the prediction for nitrogen consumption in relation to the corn evolution milestones. These models are decision triggers, as the Back-End Server has also the role of providing real-time visualization and prescriptions via the "SARMENTI Farm Advisor" web application and the mobile implementation presented in the paper.
We have emphasized the importance of the farmer feedback to our "Farm Advisor" decisions that is a relevant input/evaluation of the reliability and correctness of our automated recommendations. In order to assess the actual reliability of the sensing values, a Social Trust Index is used at the confluence between the two feedback loops of the system: the user feedback loop in which the farmer provides feedback, and the data science loop in which the data analyst and the farmer analyze the learning algorithms and make adjustments and improvements. To the best of our knowledge, there are no other complete systems that provide an end-to-end eAgriculture implementation, from real-time data acquisition based on sensors up to prediction and prescription of farming actions based on a complex series of parameters that include the biological model of the crop and environmental considerations.
Kulikova et al. (2016). Faults drawn from HimaTibet database of  Taylor & Yin (2009)  and  Styron et al. (2010). MF, Muji Fault; PFT, Pamir Frontal Thrust; SKF, Sarez-Karakul Fault; AV, Alai Valley; AM, Alai Mountains (Range). GPS vectors and 1σ uncertainty ellipses relative to stable Eurasia from  Ischuk et al. (2013). distribution during the 7 December 2015 event and map in detail the previously unreported surface ruptures of this event. Our observations reveal comprehensively the extent of discrete surface ruptures versus blind faulting, and further mapping of the system shows the relationship of coseismic slip with other ruptures along strike as well as with regional bedrock geology. Documentation of sequential ruptures along a fault system can be used to address questions of earthquake nucleation, terminatio and triggering; structural and topographic development; and strain accumulation and moment release.
Optical pixel-tracking with Landsat-8 In the near-field of the fault rupture, high displacements make successive radar images lose coherence, and high InSAR fringerates (strong displacement gradients) cause phase unwrapping errors, masking the displacement field where it contains the greatest share of deformation. To image the horizontal displacement field in the near-field of the fault rupture, we perform pixeltracking on successive 15-m optical Landsat-8 Level 1 scenes to measure subpixel offsets using the COSI-Corr 'Coregistration of Optically Sensed Images and Correlation' software of  Leprince et al. (2007 Leprince et al. ( , 2008)) ;  Ayoub et al. (2009). COSI-Corr uses the shift in phase of the images' Fourier transforms to map out the spatial translation of coherent windows of correlative pixels between two raster images, outputting two grids of orthogonal (Eastward and Northward, respectively) displacement components.
is recommended for meaningful correlations  (Ayoub et al. 2009). From each raster we remove statistical outlier pixels using COSI-Corr's non-local means filter, then further mask pixels that exhibit a signal-to-noise ratio of <0. 98  (Leprince et al. 2007). Each displacement map derived in this manner shows a discontinuity of similar shape and magnitude across the preexisting trace of the Sarez-Karakul fault, attesting to a robust signal of slip from this earthquake (Figs  4  and S1 ). However, slight geographical misalignment of pixel strips resulting from errors in satellite line-of-sight corrections lead to long wavelength artifacts of varying orientation and order (Landsat 8 Data Users Handbook, Department of the Interior U. S. Geological Survey 2016). These errors may stem from minor miscalculations or rounding within the 10 coordinate transformations among the satellite's attitude and the internal orientations of its optical components and detectors, and so they vary randomly from pass to pass amounting to non-systematic noise in the apparent temporal evolution of the pixel-tracking displacement field.
5. the surface location of the fault as imaged in the Landsat-8 offsets. These four planes comprise the southern and northern reaches that have roughly the regional strike; a reach between the Muzkol range and the Kokuibel valley that has a more easterly strike; and the Officer range mountain front fault, which is set back eastward from the others by the bend and a left-step at the northern end of the Kokuibel valley (Fig. 2 ). To solve for the fault geometry, we downsampled the InSAR deformation fields to 3156 and 2898 points for the ascending and descending tracks respectively using a nested uniform sampling approach with 2. 5 and 5 km spacing in the medium and far-field respectively. We equally weight the two InSAR data sets as we only have a single interferogram in each of the ascending and descending look directions which are each differently sensitive to surface motion.
1992)  with Monte Carlo restarts to avoid local minima  (Wright et al. 1999)  to solve for the dip, rake and bottom depth of the four major fault segments. We fix the fault location, strike and lengths to the geomorphic fault trace and discontinuity in the Landsat-8 displacement field (Fig. 4 ). We fix the fault to the surface for the southern three segments and solve for the top depth of the fault for the most northern segment, where no sharp surface discontinuity is observed in either data set. We also solve for nuisance parameters in the two interferograms that take into account the arbitrary unwrapping point in the InSAR technique and also any long wavelength signals. The ascending data fit very well in the far-field, with some greater misfits (∼25 cm) on the northern side of the rupture in the centre, and also to the north in the Kokuibel valley.
2009; Gold et al. 2013; Lozos et al. 2013 Lozos et al. , 2015)). We define a 9-segment model that is the simplest fault trace that fits within the discontinuity observed in Landsat displacements (Figs  4  and 5 ). Dips and rakes are taken as those calculated from the uniform-slip inversion for the corresponding fault sectionsslightly west-dipping but nearly subvertical (78-88 • ) in all but the 'bent' section which requires a dip of 68 • NW (Table  2 ). We extend the northern and southernmost fault segments beyond the extent of the uniform slip model and mapped surface ruptures to allow for subsurface slip beneath Lake Sarez and along the continuation of the mapped Sarez-Karakul Fault towards Lake Karakul. We downsample the Landsat-8 data as for the InSAR using a nested uniform approach (Figs S7 and S8).
To determine the distribution of slip, we subdivide each of the nine fault segments into 1 km × 1 km patches and solve for the magnitude of slip on each subfault, as well as nuisance parameters (interferogram offset and planar long-wavelength tilt), in a linear inversion on each subfault following the method of  Funning et al. (2005). A Laplacian smoothing constraint is used to regularize the inverse problem, as well as a positivity constraint. The Laplacian smoothing is applied across the fault segments and has boundary condition tapers to zero slip at the northern, southern and deepest segments, but is unconstrained for the surface slip patches. We select a smoothing value that balances the roughness of the slip solution with a meaningful reduction in the root mean square misfit to the data compared to the noise (Fig.
A C K N O W L E D G E M E N T S This work was supported by the UK National Environmental Research Council (NERC)'s Looking Inside the Continents from Space (LiCS) grant (NE/K011006/1), the NERC-ERSC Earthquakes without Frontiers (EwF) consortium (EwF NE/J02001X/1 1) and the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET, GA/13/M/031, http://comet. nerc. ac. uk). SPOT-6/7 imagery was purchased from Airbus through Apollo Mapping; Sentinel-1 radar images were obtained from ESA and this work contains modified Copernicus Sentinel data 2015-2016; Landsat-8 scenes are freely available through the USGS Earth Explorer portal. JRE is supported by a Royal Society University Research fellowship (UF150282). Two anonymous reviews guided improvements to the presentation of content and figures in this manuscript.
Black line represents simple fault used for initial uniformslip inversion; grey line represents 9-segment fault used for final slip-varying inversion. To derive these interferograms we use Sentinel-1 Precise Orbit Ephemerides data files (available from https://qc. sentinel1. eo. esa. int/) to extract state vectors for the SAR images. We process the Single-Look-Complex (SLC) data in TOPS mode to combine bursts and use all three sub-swaths. To coregister the slave to the master image, we estimate the SLC offsets with intensity cross-correlation. We then refine the azimuth offset estimation with an enhanced spectral diversity method in the burst overlap regions to remove phase discontinuity errors at burst edges due to mis-alignment in the azimuth direction. To simulate the interferometric phase due to topography we use the SRTM 30 m digital elevation model.
Introduction Many environmental and Earth science Research Infrastructures (RIs) act as data hubs and publishers of scientific data and serve their user communities via an integrated data portal  [6]. The Euro-Argo RI  [8]  is a typical example of a long-established, distributed RI from the marine domain and is the European contribution to the Argo programme. Argo monitors the world's oceans measuring temperature, salinity, pressure, etc. via the deployment of robotic floats to create a roughly even network of data collecting nodes across the marine surface of the earth. These floats periodically send data back via satellite to data assembly centres, which provide integrated, cleaned data products to various regional centres, archives and research teams; all data is then made publicly available via a common portal within 24 h of acquisition.
Due to the maturity in data acquisition, Euro-Argo seeks improved publishing methods for accessing existing curated data collections, and thus, prototypes a subscription service for their data. In contrast, to merely providing collected data freely for download and requiring researchers to monitor the core Argo dataset for updates manually, researchers are instead allowed to subscribe to specific subsets of Argo data and have updates pushed to their cloud storage, thus streamlining data delivery and accelerating data science workflows involving those data. In this chapter, we will demonstrate how the Dynamic Real-time Infrastructure Planner developed in the project can be used for optimising virtual infrastructures for the EuroArgo research infrastructure to realise its data subscription service.
Data Subscription in RIs 2. 1 A Data Subscription Scenario in EuroArgo In the Euro-Argo data subscription scenario, investigators subscribe to customised views (e. g. specific regions, time durations, and observation parameters) on the Argo data using a data subscription service. Euro-Argo provides the infrastructure services needed for computing data products to match each subscription and then dispatches those products to their destinations; the subscription service can then distribute the tailored updates to investigators' private storage. A typical subscription task can be made up of a set of input parameters: 1. An area expressed as a bounding box (geospatial data are widespread in environmental and earth science). 2. A time range (typically investigators want the most recent data, but updates to past readings due to quality control or restoration of missing data may also be of interest).
Generalising the Service to Different RIs In addition to Euro-Argo, other RIs are now looking into the data subscription scenario as an approach to better serve their communities. RIs differ in several aspects, such as in their maturity in various data life cycle phases, in their internal diversity, and in collaboration between RIs. Projects such as ENVRIplus seek approaches to enable convergence through reference modelling  [7, 10] , helping the RIs to identify common processes and structures and to adopt best practices, and integrate to common infrastructural environment (eInfra) services. However, several of the problems encountered in traditional data curation and publishing still exist and can be summarised as follows: • accumulating, large, and complex datasets can only be disseminated with extensive effort • frequently changing datasets can only be monitored with extensive effort • unpublished, confidential data can only be disseminated to the designated audience Moreover, as discussed in Chapter 12, other encountered challenges include lack of accounting information, lack of data provenance information, and the complexity involved in using and integrating distributed systems  [5].
Architecture Design We applied a prototype of the data subscription service in the scenario depicted in Fig. 3. Currently, the resources from e-infrastructures such as EUDAT  [4]  and EGI FedCloud are used. Figure  3  shows the use-case scenario based on the use of EUDAT and EGI services. In this case, EUDAT provides services for data subscription, storage, and data transfer, while EGI FedCloud provides the services for the computing of data products for each subscription. The data subscription service scenario thus involves the following basic components: 1. A data selection community portal serving as the front-end; 2. The global data assembly centre of Euro-Argo  [8] , providing the source research dataset; 3. B2SAFE data repository  [2]  provided by EUDAT; 4. A deployment of DRIP  [1]  (deployed within EGI FedCloud); 5.
A data filtering application. This is the software that actually takes the input parameters and composes the requested data product from the raw source research dataset; 6. EGI FedCloud virtual resources, forming the fundamental infrastructure for data processing and transportation; 7. EUDAT data subscription service (which maintains and matches the subscriptions defined via the data selection community portal). Users interact with the subscription service via a portal, registering to receive updates for specific areas and time ranges for selected parameters such as temperature, salinity, and oxygen levels and optionally set a deadline for receiving the requested results. The global data assembly centre (GDAC) of Euro-Argo receives new datasets from regional centres and pushes them to the EUDAT B2SAFE data service.
Partitioning input parameters should provide faster execution due to increased speed-up. The workers perform the actual query on the dataset based on the partitioned input parameters provided by the master node. When new data is available to the GDAC, it pushes them to the B2SAFE service, triggering a notification to the subscription service, which consequently initiates actions on the new data. If the application is not deployed to FedCloud already, then DRIP provisions the necessary VMs and network so that the application may be deployed. Next, the deployment agent installs all the necessary dependencies along with the data filtering application including configurations to access on the Argo data. The subscription service signals to the application master node the availability of the input parameters to be processed, whereupon it partitions the input tasks into sub-tasks and distributes them to the workers.
Two basic optimisation strategies have been investigated for partitioning and scheduling subscription tasks in order to minimise resource usage while meeting all necessary deadlines. Input Partitioning. We investigated two types of input partitioning: linear and logarithmic. With linear partitioning, we simply divide the input range into equal parts for parallel processing. With logarithmic partitioning, we split the range into larger sections at the beginning of the range (accounting for the sparser data recorded early in the Euro-Argo dataset) and smaller sections towards the end (when observations become more detailed). Deadline-Aware Auto-Scaling. The user has the option to specify a deadline for obtaining the requested data. To ensure on-time data delivery, the application master calculates the 'importance' of each task based on its deadline and input parameters: Imp(task) = |P| • w p + ttd • w p + (tr • w d ) + (α • w a ) (1) In Eq.
Input Partitioning Before attempting to partition input, parameters and distribute them to worker nodes, we must first identify which parameter is responsible for the most computing time when generating the data products. To do this, we generated a set of tasks on a region of randomly selected raw data requiring computing of all parameters. We performed 550 tasks spanning the Mediterranean Sea while requesting data in a time window from 1999 to 2007 and covering more than 400 possible parameters in the data products. We executed these tasks on identical VMs and measured their execution time to determine the correlation between area, time range and the number of parameters with execution time. Additionally, we investigated the effect of the end date on execution time, e. g. whether execution time changes when processing three months of data leading up to 1999 rather than leading up to 2007 (indicating a general shift in the typical volume of data collected at different points in time).
Discussion The results presented here demonstrate that a linear partitioning strategy can provide non-linear variations in speed and efficiency. This can be attributed to an unequal load distribution, where some workers were assigned far smaller loads than others, despite the data being split 'evenly' across a certain dimension. In the case of the Euro-Argo dataset used for this experiment, this is because more recent data samples contain more data than older samples (due to improvements in data acquisition over time), which explains why the logarithmic partitioning performed better. However, the recorded speed-up can be improved further if the partitioning is calibrated based on the actual end date selected for a sample. Moreover, a more linear speed-up could be achieved if the partitioning was performed based on all, rather than just one, input dimensions.
Historical observations on the same or similar (for a given judgement of 'similarity') can also contribute to selecting the best partitioning strategy. One area that we have not investigated, but which has an impact on both the performance and requirements of the data subscription pipeline is the case where subscribers subscribe not just to one custom view on a single dataset (albeit a very rich one), but to a view that combines data from multiple datasets, possibly hosted by multiple RIs. In this scenario, there will be multiple distinct persistent identifiers for data objects or collections. The objects and collections are curated by another entity than the subscriber. For example, when the location of the data objects or collections changes, the subscription remains valid assuming the curator updates the property of the identifier.
Moreover, there will be multiple sources from which to retrieve the data required for processing, and it will be necessary to consider how to join as well as partition the data in a way that accounts for factors not in play here; for example, where different datasets are geographically dispersed and so workers may actually be deployed in different data centres to ensure performance. A further consideration emerges from a requirement for immutable semantics of persistent identifiers. When the investigator finds a data set and metadata describing the data through the discovery process, interpretation of using the corresponding identifier in a subscription, and inclusion of an action which perceives the structural semantics of the data, need to be considered.