Search

This brief note introduces a study that aims at quantifying the gain for sea ice observations which is achieved when combining L- and C-band SAR imagery for the retrieval of ice conditions and iceberg occurrences. An L-band SAR mission is currently under discussion at the European Space Agency (ESA) as one of the six Copernicus High Priority Candidate Missions (HPCMs). Since the emphasis of the study is on operational monitoring and ice charting, it involves contributions from different ice services that are members of the International Ice Charting Group (IICWG). Preliminary results confirm findings of earlier investigations on the use of L-band for sea ice monitoring and extend them with first systematic investigations of iceberg detection at L-band.

Estonia is known for its large floodplains that are annually covered by water. A methodology based on Sentinel-1 and −2 imagery was developed for operational monitoring of floodplains in Estonia. Filed campaign for validation/calibration of flood extent estimation methodology was performed using UAV in March 2019 over the Soomaa floodplain. Sensitivity analysis of Sentine1-1 data indicated greatest backscattering ( $\sigma^{0}$ ) difference between land and water for polarizations VH (IW mode) and HV (EW mode) at incidence angle 39-47°. For water mapping, incidence angel normalization was applied using linear relationship between backscattering ( $\sigma^{0}$ ) and local incidence angle established from our dataset. Water was mapped from Sentinel-2 data using MNDWI index. Flooded area mapped from Sentinel-l was compared with corresponding flooded area mapped from Sentinel-2. The kappa coefficient between these maps was 0.86 indicating good coincidence. Established methodology was tested to map floods at Keila test site for springs in 2018 and 2019. Difference in flood extent and duration at Keila test site in 2018 and 2019 was 6.8 km 2 and 9 days.

The article analyzes the diagnostics of a high-voltage power transformer by means of an acoustic camera. It is one of the basic diagnostics and monitoring of remote measurement and analysis of acoustic emission. The high-voltage power transformer generates acoustic emission, thanks to which we can analyze the operating and fault states of the device. The article shows the use of the acoustic camera for operational monitoring and diagnostics of its life through noise from the source of the transformer winding and the ferromagnetic core.

Forests, one of the most valuable and necessary resources and protect earth’s ecological balance, are a natural habitat to animals and forest products are vital in our lives in many direct and indirect ways. But wildfires can cause critical damage to grounds and many other resources like properties, human life, wildlife in superabundant amounts. Wildfires burn acres of land and destroy everything in their paths in mere minutes. Wildfire destroys homes, animals, trees and plants, wildlife as well as vegetation. The effects of wildfires are numerous and wide-ranging. it causes a hugely significant impact on the economy, environment, heritage and social fabric of rural areas. Naturally caused wildfires can be predicted using factors[3] like temperature, humidity, soil moisture, pressure and many more. In this paper, the prediction of forest fires by machine learning using some operational monitoring over a region and encountering changes in climate using different sensors are advocated. The Wildfire Prediction System (WiPreSy) monitors and records changes in climatic parameters and predicts the intensity of forest fire based on real-time data, thus avoiding the massive loss due to forest fires.

This paper describes the theoretical approach followed to perform an indirect load mass assessment during yarding activities. The goal of this methodology is the mass assessment of the loaded biomass during the inhaul phase in cable logging operations.The theoretical approach proposed is based on the development and the use of mathematical model which, thanks to the measurement of the skyline deflection, slope and tensions permit to estimate the hauled mass. Indeed, as reported in literature, these parameters, together with the mass of the load, are strongly related each other. For this experiment an in-scale yarding line has been developed in order to control and monitor all these parameters. The precision and accuracy of the obtained results related to the mass estimations have been then compared with similar results obtained through manual surveys using tools commonly used by the forestry community.In conclusion the proposed methodology has shown to be reliable for an indirect mass assessment of loads during yarding operations. Indeed, if the line is adequately installed this monitoring system ensures better accuracy if compared with the common tools employed for timber assessment.

Diagnosing a railway track is a complicated task, both from the point of view of measuring equipment and organization. An important factor here is to ensure the safety of rail traffic while minimizing the cost of diagnostics (including time expenditures). One of the most promising is the approach based on the use of integrable track monitoring systems containing compact, relatively inexpensive sensors operating on different physical principles. This paper is dedicated to the peculiarities of the functioning, processing, presentation and transmission of information in organizing this approach, which is analyzed using the example of the InerTrack system developed with the participation of the authors.

SMARTCOMP 2018 IEEE International Conference on Smart Computing, Taormina, ITALIE, 18-/06/2018 - 20/06/2018; The ability to detect critical spots in transportation networks is fundamental to improve traffic operations and road-network resilience in smart cities. Real-Time monitoring of these networks, especially in very large metropolitan areas, is a compelling challenge due to the complexity of computing robustness metrics. This paper presents a framework for identifying vulnerabilities in very-large road networks. The framework adopts graph-based modeling of road networks and exploits big-data techniques and technologies for processing such large and complex graphs. First, we use the framework to prove the existence of a significant correlation between global efficiency and betweenness centrality. Then, we focus on an efficient algorithm, integrated in the framework, to rank the nodes according to this metric for finding potential vulnerabilities of a road network. To keep computation time under a 'quasi' real-Time threshold, a fast, requirement-driven, approximated strategy for computing betweenness centrality is adopted. The evaluation shows that the algorithm, integrated in the framework, exhibits a very good approximation for the most critical nodes, thus being well-suited for on-line operational monitoring.

In this paper, intrusion detection system based on touch traps is presented. Aim of the work — investigate and design a software product for monitoring intruders in various systems for further analysis and prevention of repeated attacks. The work is designed for intrusion detection system that includes procedures for analyzing network honeypots and procedure for systematization received data. The results may be used for: Improving intrusion detection system; Providing users of intrusion detection system tool for operational monitoring of network honeypots; Providing additional tools for systematization of input data. The efficiency of the above-mentioned procedures verified by computer experiment.

BIGDATA 2017 IEEE International Conference on Big Data, Boston, ETATS-UNIS, 11-/12/2017 - 14/12/2017; Betweenness centrality is a metric widely used in several domains (social, biological, transportation, computer) to identify critical nodes of networks. Its exact computation is very demanding, with an O(nm) time complexity for unweighted graphs (where n is the number of nodes and m is the number of edges). Such complexity becomes an obstacle to the adoption of betweenness centrality for continuous monitoring of critical nodes in very large networks. Several solutions have been proposed to reduce computation time, mainly via parallelism, approximation or incremental recalculation. In this paper, we propose an algorithm for computing approximated values of betweenness that allows for tuning its performance on the basis of a tolerable error. The algorithm aims at reducing the number of single-source shortest-paths explorations via a pivot-based technique that exploits topological properties of graphs and clustering. It is evaluated by identifying the vulnerabilities (critical nodes) of a real-world, very-large road network. The evaluation shows that the approximation error does not significantly affect the most critical nodes, thus making the algorithm well-suited for on-line operational monitoring of road networks.

Modern systems of traditional and alternative energy, transport systems include powerful uninterruptible power supplies (UPS), implemented on batteries of charged electrochemical current sources — accumulators. These modules ensure the functioning of the systems in case of accidents or technological failures in the operation of main power supplies. The ability to provide the given energy modes by these modules is largely determined by the state and electrical parameters of the individual accumulators. In this paper, methods and embedded tools for operational monitoring of the main parameters of accumulators without disconnecting them from the main working circuits are considered. Current charge, activation resistances and capacitances, internal resistance and others are understood under controlled parameters. It is proposed the using of improved models and methods of active electric impulse action based on small perturbing potentiostatic tests and regularizing mathematical processing with reduction of measurements for the identification ofparameters.

The article presents a new method for predicting the reliability of the automobile starter using the AgenaRisk program and experimental research. The program allows to forecast the health status of the starter and provide service intervals of its technical condition. Simulation studies for selected faults in the electrical and mechanical circuits of the starter allowed to carry out an analysis of the parameters defining the technical condition of the starter. The scientific aim of the article was to develop and validate a diagnostic method designed for the operational monitoring of the startup circuit of a car. The aim of the experiment was to forecast the state of reliability of elements of the starter system, with the possibility of using the OBD (On-Board Diagnosis).

In the present paper we demonstrate the capabilities of the Sentinel-1 SAR data for operational sea-ice and iceberg monitoring. Most of the examples are drawn from the Copernicus Marine Environmental Monitoring Service (CMEMS) production.

The ‘Land-Surface-Analysis’ SAF (LSA-SAF, http://landsaf.meteo.pt/), is dedicated to the operational monitoring of land surface. For this purpose, it develops algorithms to obtain near real time data products. The evapotranspiration (ET), cannot be observed directly by remote sensing and is assessed indirectly through modelling. The ET algorithm uses Meteosat Second Generation (MSG) derived data. It produces in near real time ET estimates at the SEVIRI instrument spatial resolution each 30 minutes. Validation already carried out attests the robustness of the proposed algorithm, notably over Europe. Nevertheless, research will be pursued during coming years, looking for additional validation sites and evolving towards an improved combination of remote sensed observations and models.

Patent intelligence is an effective approach that enterprises cluster innovation network realizes through quantitative analysis. The paper builds the operational monitoring methods of enterprises cluster innovation network based on patent information and synthetically applying network and patent map analysis methods, and illustrates operation characteristics and existing problems of cluster innovation network taking Harbin equipment manufacturing industrial clusters as an example, so as to offer scientific theoretical methods guidance and empirical reference for risks monitor and management of enterprises cluster innovation network from the angle of patent.

The paper highlights need for R&D in the area of Intelligent Underwater Systems and Technologies (particularly use of ROV and AUV) for Strategic Marine Ecosystem Research Goals, Environmental Monitoring of Ocean Resources and their exploitation in Croatia. Also this approach support small and medium enterprises for adoption of new technologies and development of highly sophisticated products and services of high value added. Current situation, in regard to needs for technical support linked to obligations of proclamation of the ecological and fishing zone and establishment of coast guard, points to necessity of development in this area. The paper gives an overview of modern technology in this area and possible application in the Republic of Croatia. The research project “ Guidance and Control of Autonomous Moving System for Underwater Monitoring” is presented in the paper.

Network intrusion detection systems (NIDSs) critically rely on processing a great deal of state. Often much of this state resides solely in the volatile processor memory accessible to a single user-level process on a single machine. In this work, we highlight the power of independent state, i.e., internal fine-grained state that can be propagated from one instance of a NIDS to others running either concurrently or subsequently. Independent state provides us with a wealth of possible applications that hold promise for enhancing the capabilities of NIDSs. We discuss an implementation of independent state for the Bro NIDS and examine how we can then leverage independent state for distributed processing, load parallelization, selective preservation of state across restarts and crashes, dynamic reconfiguration, high level policy maintenance, and support for profiling and debugging. We have experimented with each of these applications in several large environments and are now working to integrate them into the sites' operational monitoring. A performance evaluation shows that our implementation is suitable for use even in large scale environments

Out of the requirements of the operational monitoring systems of large steam power plants, this paper suggests a scheme for developing the intelligent control systems in steam power plants by using modern artificial intelligence techniques. Together with some practical cases of application, the idea of the design of the system's functions and its constitution are introduced in this paper. And also, the characteristics and the prospects of the application of intelligent control system in steam power plants are analyzed.

GMES (Global Monitoring for Environment and Security) is a joint initiative of the European Commission and the European Space Agency, designed to establish a European capacity for the provision and use of operational information for Global Monitoring of Environment and Security. To support the development of the Ocean and Marine Applications component of GMES, the EC is funding the four-year MERSEA integrated project, which started in 2004. At the end of its first year, we present the scope of the project and some preliminary results.

Near-real time QuikSCAT image products are evaluated for ice mapping from the perspective of the Canadian Ice Services Operational Environment.

The unparalleled safety and integrity of current air traffic surveillance is founded on the practice of assuring independence between sources of data wherever possible and using validation techniques to cross check reported data. Beacon surveillance in particular may be considered a form of dependent surveillance and the validation procedures in place today suggest ways to extend this practice to Automatic Dependent Surveillance Broadcast (ADS-B) based surveillance. The integration of ADS-B into the Traffic Alert and Collision Avoidance System (TCAS) offers an instructive example of how one might implement operational monitoring of ADS-B data using existing surveillance assets.

A system to operationally monitor snow cover in the Swiss Alps using NOAA-AVHRR data is presented. The data are calibrated and normalised to allow the application of physically based classification thresholds. The results of the analysis of two time series of images dating from the years 1992 and 1996 are displayed. First experiences gathered during the real-time application in 1999 are reported.

This paper presents preliminary results of research to improve upon an existing operational forest change detection monitoring strategy in California. Comparisons were. made between Landsat 5 TM and Landsat 7 ETM scene normalization techniques (absolute versus, relative). Prior to normalization, scenes containing wildfire smoke plumes were successfully corrected using a space-varying haze equalization algorithm. Simple dark object subtraction provided improved performance over relative (pseudo-invariant feature) approaches. A decision tree classifier produced high change map overall accuracy (86%) for five categories of forest cover change.

The state of surface waters is affected by the quantity and quality of various suspended and dissolved substances. Loading from sources such as agriculture, fish farming, industry and municipalities can cause eutrophication and have other adverse effects on water quality. In Finland the quality of lake water is traditionally determined by collecting water samples and analyzing the samples in a laboratory and by making on-site measurements (e.g. Secchi depth, temperature). However, sampling is slow and expensive and does not include all lakes. Remote sensing instruments can cover large areas quickly and repeatedly and they are also relatively cheap to use once the retrieval methods have been developed into operational level. However, the revisit time for Landsat is 16 days so it is not suitable for operational monitoring especially in areas where cloud cover is frequent. Another interesting instrument is SeaWiFS. It produces daily data but only with 1000-m pixels. One of the latest spaceborne remote sensing instruments, the MODIS spectrometer, produces daily data with two 250-m channels. The objective of this paper is to assess the capabilities of MODIS for monitoring turbidity in Finnish lakes.