dataesr/scientific-paragraphs-categorization huggingface dataset:
• Wavelength: The radar operates at wavelengths on the order of several centimeters, being sensitive to larger canopy components with variations in moisture changing the dielectric constant of plants. The UAVSAR operates at 23. 8 cm penetrating the forest canopy. The LVIS LIDAR operates at 1064 nm and is reflected by all intercepted surfaces including leaves and branches. Each instrument has its own limitations  [10]. LIDAR datasets are generally sparse, requiring spatial interpolation to generate spatially explicit maps of forest structure and carbon stocks  [11]. This interpolation can be supported by model relationships between forest structure, environmental variables and ancillary remote sensing observations  [3, 12]  (e. g. , MODIS, Landsat), to provide a realistic model of forest structure.
Repeat-pass interferometric acquisitions by the BIOMASS mission will allow for PolInSAR observations. Another mission, Tandem-L, proposes an innovative interferometric and polarimetric radar mission at L-band for global measurement of forest biomass. We first introduce the PolInSAR observations and approach in Section 2 and describe the LIDAR feature extraction in Section 3. In Section 4, the machine learning algorithms are discussed. The remote sensing data sets collected over the Réserve Faunique des Laurentides (from here Laurentides) are introduced and the results presented in Section 5. We conclude in Section 6.
The relationship between the value of one neuron H and the ones that belong to the previous layer can be expressed as: h 1 = f (i 1. w 1 + i 2. w 2 + b 1 ) with f an activation function such as softmax, sigmoid or Rectified Linear Unit (ReLU). The goal of the training step is to find the value of the weights w i and bias b i that make the model fit the training set. In order to approximate a solution to this complex problem, a cost function (error function) is defined, which is minimized through a back-propagation algorithm. When the solution for weights has converged, the values are fixed and used for testing. Figure  13  gives a schematic representation of a Multi Layer Perceptron where the input dataset is a 7-dimensional vector defined from the PolInSAR coherence region, and the outputs are the LIDAR parameters (the number of outputs depends on the target dimension).
Random Forests A Random Forest classifier is an ensemble learning method that fits a number of decision tree classifiers trained on various subsets of the full training set. A decision tree is a simple representation for classifying samples that is formed of nodes, branches and leaves as shown in an example in Figure  14. Each node tests a condition on one or several attributes. The binary decision leads to two new branches. The successive modes (i. e. , decisions) lead to leaves where class labels are assigned. The training of a decision tree is done recursively as branches are split until a accuracy of the prediction does not increase significantly anymore. Although very simple to understand and interpret, the decision tree is limited by its overall accuracy. When the number of nodes is not limited, the classifier tends to overfit the training dataset.
The Data Set We use fully polarimetric airborne L-band radar data from the UAVSAR system. Interferometry is acquired in repeat-pass mode. The selected test site is located around geographic coordinates 47. 251 • N, -71. 354 • E. The UAVSAR slant range images has a resolution of about 0. 6 m in azimuth and 1. 6 m in range. The LIDAR waveforms were collected with about 1 point every 20 m and height estimates are interpolated to fit the radar image as shown in Figure  15. To evaluate the performance of the method, a site with LIDAR and radar data, and field measurements. The site exhibits homogeneously distributed heights. without lakes and clear cuts. The selected area is highlighted in the Figure  15.
Summary and Conclusions This paper was intended to predict vegetation parameters from PolInSAR, by learning the inference relation between L-band POLINSAR data and LIDAR-derived labels describing these parameters. As such, this method could be used to map forest at large scale once PolInSAR data becomes available. Seven parameters describing the observed coherence region were proposed to characterize the PolInSAR information. We have been focusing on three canopy descriptors: canopy height and cover, and CHP class. We used perceptrons to associate the seven PolInSAR features to canopy height and cover; then we used random forests to associate our PolInSAR features to a vertical distribution class. Our choice of parameters was further supported by the comparison with models trained with more classical parameters, that led to less effective results.
Tina and al. were introduced a process for the probabilistic handling of data renewable energy sources as a technique of estimating the electric power produced by each resource of the energy conversion systems  [15]. However, these methods have a restricted effect power and they are statistically intensive. Various commercial software packages and simulation tools are employed in evaluating the performance of hybrid power systems, as HOMER, RETScreen, Hybrid2, TRNSYS, RAPSIM, INSEL. One of the most Software is (HOMER) the hybrid optimization model for electric renewable, which has been produced by the National Renewable Energy Laboratory  [16, 17, 18]. Beck and al. In  [19] , used the simulated by HOMER software to achieve the optimal renewable system resources configuration (wind turbines, photovoltaic panels, converters, and batteries).
Anand and al. are intended an optimization and simulation technique of an independent hybrid energy system contain a Biomass gasifier generator set, Fuel cell and PV with battery storage system, this model produces to fulfill electric load requirement of energy center in MANIT Bhopal (India), which have been performed using HOMER pro software  [20]. This program is based on linear equations so that does not represent exactly the renewable energy source properties. Many researchers have used different optimization techniques for the hybrid renewable system to reduce the total cost of energy production and improve the efficiency of the hybrid system. As an example, several of these researches harnessed the Evolutionary Algorithm (EA) to considers multi-objective in the optimization issue of a hybrid system  [21, 22].
In addition, this algorithm has been harnessed to achieve multiple objective functions. The standalone hybrid renewable energy system depends on solar panels, tidal turbines ,wind turbines and with battery bank in order to fulfill an electric load located in the far west of France in Brittany, the results showed the good ability of the PSO developer to solve hybrid system optimization problems such as optimal size, optimal the total cost of energy (COE) and optimal energy management with superior speed and accuracy taking into consideration the several constraints as the State Of Charge (SOC) of the energy storage system, high reliability, and planning expansion for the future development. A variety of different scenarios have been studied to meet the electric load demand. PSO algorithm program was developed using MATLAB software and the total net present cost (TNPSC) is proposed as an objective function.
Simulation results and discussion This research focuses on the economic optimization of hybrid renewable systems using Particle Swarm Optimization (PSO), with an objective function based on minimizing the total net present cost (TNPC) and the cost of energy production (COE) of the hybrid renewable system consist of PV panels/wind turbine/tidal turbine/battery, taking into account many of the constraints, like the meet of the electrical demand with high reliability, the State Of Charge (SOC) of the battery, the optimal sizing of the system and planning expansion for the future development. The PSO algorithm program was developed using MATLAB environment. All variables and data concerned the hybrid system and renewable energy sources at its location are inserted, as the solar energy, the wind energy, the temperature, the depth state of charge of the battery, the tidal energy, sizes batteries, the type and size of wind and tidal turbines available, all prices details as the total cost, maintenance and operating costs, the coordinates of the location, the life of project, the nearness of the national electric grid, the numbers and prices of all components of hybrid power system generation if they already exist before the creation of the project and etc.
ACKNOWLEDGMENTS We thank  V. Sotnikov  and  M. Zeng  for many useful discussions. We thank  C. Duhr  for the use of his Mathematica package PolyLogTools. The work of S. A. is supported by the  Fonds de la Recherche Scientifique-FNRS ,  Belgium. The work of J. D. and F. F. C. is supported by the  Alexander von Humboldt Foundation , in the framework of the Sofja Kovalevskaja Award 2014, endowed by the  German Federal Ministry of Education and Research. The work of B. P. is supported by the  French Agence Nationale pour la Recherche , under grant  ANR-17-CE31-0001-01. The authors acknowledge support by the state of  Baden-Württemberg  through bwHPC. S. A. and B. P. thank the  Galileo Galilei Institute for Theoretical Physics  for the hospitality and the  INFN  for partial support during the final stages of this work.
N U M E R I C A L E X A M P L E S In the following section, we analyse the performance of the reduced parametrization implemented in PAST in comparison to the joint inversion strategy employed in AST  (Tavakoli et al. 2017). We benchmark the method on the well-known Marmousi model  (Bourgeois et al. 1991)  with further validation through time-domain FWI. We follow by a real application using high-end industrial BroadSeis data acquired in the Carnarvon Basin, north-west of Australia. The validation in the latter will be done through a TTI Kirchhoff migration.
Real TTI case study We proceed with a real data application as validity assessment of the proposed approach. The concerned region is in the vicinity of the Carnarvon Basin, north-west of Australia. The area is well known for its hydrocarbon potential, especially the gas reservoir found at 3 to 4 km depth across the basin. The data at hand is part of a previous exploration campaign. The 2-D profile is 57 km long. A total of 2479 shots were recorded by 648 receivers forming a 8250 m streamer. During the acquisition the streamer was submerged in a curvy manner at a depth up to 57. 5 m; such settings induce a notch diversity that is well exploited for acquiring state-of-the-art data  (Soubaras & Whiting 2011). The BroadSeis technology alleviates the low signal to noise ratio at low frequencies and higher frequencies accessibility.
We compare in Fig. 13  four vertical logs that are extracted from the legacy model, the AST model and the PAST model every 10 km. The velocity logs are mostly in agreement until 4 km depth where our retrieved model seem to have lower velocities. With further assessment, we will show that there is no underestimation of wave speeds in PAST. Another discrepancy between the three models is related to the low-velocity layer at 2. 5 km depth and 10 km distance (Fig. 13a ); the low-velocity zone reconstructed by AST is less sharp, which probably results from Velocity (km. s -1 ) Velocity (km. s -1 ) Velocity (km. s -1 ) Velocity (km. s -1 ) Figure  13. Real data application. Vertical velocity logs extracted from the starting model (black lines), the legacy reference model (green lines), the AST model (blue lines) and the PAST model (red lines).
Simulated sequences The simulated sequences were named CINE and CINE NOISE for T-MRI sequences, and TAG and TAG FADING NOISE for C-MRI sequences. The parameters of the simulated sequences were: image size 160 × 160 pixels with pixel resolution 1 × 1 mm, contraction/expansion ratio 30%, rotation 20 degrees, frame rate 20 frames per second. The total number of frames is equal to 21, in which the first seven frames are part of the systolic phase and the next 14 frames are part of the diastolic phase of the cardiac cycle. (1) Alessandrini's monogenic-signal-based method (MA)  (Alessandrini et al. , 2013) , which was shown to outperform the SinMod method  (Arts et al. , 2010) , and another monogenic-signal-based method  (Zang et al. , 2007)  (Code courtesy of the authors); (2) and Wang's analytical-phase-based method (Wang)  (Wang et al.
Preliminary results obtained on clinical data The method has been assessed on 5 clinical cases, including one healthy case and four pathological cases. All the clin- E ical image data are from a study protocol approved by the Ethics Committee of our institution (IRB 123406519). All subjects gave written informed consent before inclusion (clinicaltrials. gov: NCT01208727). In this section, we present the results from one healthy volunteer and two patients. The results of two additional patients are presented in a complementary document (see Sec.
Acknowledgments This study was conducted within the framework of the  LABEX PRIMES  ( ANR-11-LABX-0063 ) and  LABEX CELYA  ( ANR-10-LABX-0060 ) projects of the  University of Lyon , within the  "Investissements d'Avenir"  ( ANR-11-IDEX-0007 ) program operated by the  French National Research Agency (ANR).
Some nodes may collect data and information and then organize it following certain criteria or filters to forward it to other nodes. Finally, some nodes make decisions based on the information they receive. The SRTI model adheres to such a structure considering three type of nodes: terminals, brokers, and processors. There are different kinds of nodes in the system, and some of them should implement different services at each layer. The terminals are represented by the sensors and actuators present in the system. Clearly, these are part of the sensing layer and provide simple services to brokers (Figure  2 ). A terminal can be linked to more than a broker, and such a link is loosely coupled. In case of actuators linked to more than a broker, every change in the device's status is propagated to all involved brokers, following a change-propagation mechanism similar to the one proposed to the model-view-controller pattern  [43].
Modeling the Publish-Subscribe Paradigm on the SRTI Model This section presents the implementation of the publish-subscribe interaction scheme, considering the structure of the IoT scenario and design decisions presented in the previous section. In order to do that, we specify the behavior of the processors (subscribers), terminals (publishers), and brokers (publisher/subscriber) when the SRTI model is used to support interactions in the IoT study scenario. The terminals are associated to the sensing layer in the IoT model. In this scenario, they are either producers of data (sensors) or consumers (actuators). Provided that most participants in the sensing layer are sensors, we associate terminals to publishers. As introduced in the previous section, the processors transform data coming from sensors into information that is available for the system components.
• Data completed DC is used to identify if enough data has been received for the processing. In this case, the variable is set or reset by the application handling the queue. The transitions associated to the WAIT are determined by the values of the Boolean variables. In the third transition, there is time (L = true) and the queue is empty; therefore, it remains in the WAIT state. The fourth transition corresponds to the case in which there is time and some data has been received. In this case, the data is unqueued and delivered to the application for its use. In the fifth transition, there is no more time and the state machine decides if it has to increment the failure count or to reset all the parameters for the next period. In this case, the DC is used to evaluate if the data received is enough for the application.
Extensions to MQTT Regarding the protocol specification, this work proposes three major extensions to make it suitable to allow soft real-time interactions in IoT scenarios. The first one is to add a topic registration related to the MQTT control packets, similar to MQTT-SN, which a variant of the protocol that introduces a way to register a topic in order to reduce packet sizes. The aim of the topic registration in SRTI is not to shrink the packets, but to inform the temporal data associated to the publisher and the periodicity of the topic. Therefore, its variable header should have the timestamp of the publisher, the period in which it will send the corresponding data of that topic, and the length of the topic along with its name. The packet identifier value could be 0 or 15, both currently unused.
The second extension implies modification of the headers of the PUBLISH and SUBSCRIBE MQTT Control Packets, adding in both a timestamp of the issuer. Moreover, in the case of SUBSCRIBE, also add the period and deadline in which it is expected to receive the data of that topic. The third extension considers adding a new return code in the SUBACK package, so that the broker can reject a subscription in case it cannot offer data with the temporary parameters required by the subscriber. The value of the return code could be any between the failure code 0x80 and the maximum available 0xFF.
Code and Example Repository MQTT has been implemented for personal computers with the Mosquitto  [48]  and Mosca  [8]  applications. Using the second one, we implemented the extensions to the broker that add the IRTA to such a component. In  [49] , the code of this implementation can be downloaded in order to install the Mosca extension. Moreover, this work also makes available an example that shows the way in which a subscriber requests a value that is obtained by processing two publications in the broker. In  [50] , a simple graphical interface is available to configure the Mosca application, allowing it to implement different IRTA functions that can be added by the system administrator.
The fifth generation (5G) for cell phones technology has not reached yet a standard for its deployment, but it will be available during the next year  [52]. In South America, at the moment, LoRa is the technology that is being adopted by most of the IoT application developers. We have implemented the MQTT extended protocol with real-time primitives on a LoRa server acting as gateway. The proposal can be used in different areas of IoT, but specially in those related to monitoring systems that require bounded response times and operate on batteries. The SRTI model incorporates the Intelligent Real-Time Agent (IRTA) to process published data and adapts it to the subscriptions demands when possible. Such a model was implemented through a software infrastructure named SRTI-IRTA, of which the performance was evaluated in three different IoT scenarios.
Usually, such a system is capable of the simultaneous recording of a large amount of data. Depending on the type of ship, main engines and subsystems, SeaPerformer collects from a few hundred up to a couple thousand signals. Data collection is performed with the use of two general hardware interfaces: NMEA 0183 for the communication with navigation equipment such as ship GPS, log, echo-sounder, gyro-compass, weather station etc. and Modbus (RTU or TCP) for data collection from machinery e. g. , main and auxiliary engines, boilers, fuel system, alarm and monitoring system, loading computer etc. Information collected with the use of the mentioned interfaces are stored in two databases. The high frequency database collects signals with frequency ranging from 1 Hz up to 100 Hz as appropriate, with respect to the nature of the signal.
Due to the large amount of information, this database is kept onboard and can be transferred onshore only on request, usually when detailed signal examination is needed for the purpose of troubleshooting. Standard ship-to-shore data exchange is facilitated with used low frequency database created by 1-min averaging of high frequency database. This process, illustrated in Figure  2 , allows for significant reduction of data amount and allows for data exchange with the use of a contemporary ship satellite communication system. Data measured directly onboard are supplemented additionally by weather data with the use of ship weather service. Weather data are interpolated along ship track and integrated with data measured onboard. ranging from 1 Hz up to 100 Hz as appropriate, with respect to the nature of the signal.
Due to the large amount of information, this database is kept onboard and can be transferred onshore only on request, usually when detailed signal examination is needed for the purpose of troubleshooting. Standard ship-to-shore data exchange is facilitated with used low frequency database created by 1min averaging of high frequency database. This process, illustrated in Figure  2 , allows for significant reduction of data amount and allows for data exchange with the use of a contemporary ship satellite communication system. Data measured directly onboard are supplemented additionally by weather data with the use of ship weather service. Weather data are interpolated along ship track and integrated with data measured onboard. In order to secure communication against unauthorized access, data package is encrypted with use of Rivesta-Shamira-Adlemana (RSA) and Advanced Encryption Standard (AES) algorithms.
g. , hull fouling trend or monitoring of main engine specific fuel oil consumption (SFOC) trend as presented in Figure 3  [14]. In the monitored time period, an increased value of SFOC index was observed. In order to evaluate main engine performance, the system computes actual SFOC based on momentary fuel consumption and main engine load. Based on additional data collected by the system, actual SFOC is corrected to reference conditions  [12, 15, 16]. In parallel, theoretical SFOC in reference conditions is calculated based on data specific for the engine, usually based on results of factory acceptance tests. Finally, difference between actual and theoretical SFOC is plotted as the time trend allowing for detection of gradual engine wear or improper engine tuning); • Operational optimization-based on actual data and reference model  [11]  system recommends optimum parameters of operation (e.
g. , optimum trim with respect to ship draught and speed through the water). Sensors 2019, 19, x FOR PEER REVIEW 7 of 19 monitored time period, an increased value of SFOC index was observed. In order to evaluate main engine performance, the system computes actual SFOC based on momentary fuel consumption and main engine load. Based on additional data collected by the system, actual SFOC is corrected to reference conditions  [12, 15, 16]. In parallel, theoretical SFOC in reference conditions is calculated based on data specific for the engine, usually based on results of factory acceptance tests. Finally, difference between actual and theoretical SFOC is plotted as the time trend allowing for detection of gradual engine wear or improper engine tuning);  Operational optimization-based on actual data and reference model  [11]  system recommends optimum parameters of operation (e.
Measurement of the Operational Parameters of Main Engine Main engine can be monitored based on general output or detailed signals describing engine operation performance such as cylinder combustion pressure. The latter requires special equipment since very high resolution of signal recording is required. In order to reproduce dynamics of combustion process, angular resolution of 0. 1 degree of crankshaft position must be provided. For this purpose, FPGA processing unit in real time is employed. Data acquisition of following analog signals of engine operation are executed: Main engine general output may be monitored with use of shaft torque meter  [14]. A good illustration of such a kind of measurement is propulsion control assistance system ETNP-10 for optimization of ship operation according to operation area of the main engine, being a part of information provided by SeaPerformer system, is depicted in Figure 5  [14].