The OGS–Northeastern Italy Seismic and Deformation Network: Current Status and Outlook

Abstract In this article, we describe the infrastructure developed and managed by the Italian National Institute of Oceanography and Applied Geophysics – OGS for the seismological and geodetic monitoring of northeastern Italy. The infrastructure was constituted in response to the ML 6.4 Friuli destructive earthquake of 1976, with the main mandate of supporting civil protection emergency activities. The OGS monitoring infrastructure is presently composed of a seismometric and a strong-motion network, complemented by a number of Global Navigation Satellite Systems stations, each delivering observational data in real time, which are collected and processed by the headquarters of the Center for Seismological Research of OGS in Udine. The OGS networks operate in close cooperation with Italian and international networks from neighboring countries, within the framework of the agreements for real-time data exchange, to obtain improved rapid earthquake location and magnitude estimations. Information regarding seismic events is released to the public through a dedicated web portal and, since 2013, through social media. Aside from the standard monitoring activities (>30,000 events have been recorded since 1976), the OGS has progressively increased the number of services to the public and to the Civil Protection of the Friuli Venezia Giulia and Veneto regions. The high availability of good quality data has resulted in the enhancement of scientific products, including advanced seismological studies of the area, spanning broadly from seismic source characterization to engineering seismology. In the future, the OGS networks are expected to further contribute to the development of seismological research and monitoring infrastructures of the Central European region.

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STORM<sup>3</sup>: a new flood forecast management and monitoring system in accordance with the recent Italian national directive

Advances in Geosciences ◽

10.5194/adgeo-2-309-2005 ◽

2005 ◽

Vol 2 ◽

pp. 309-311 ◽

Cited By ~ 1

Author(s):

A. Burastero ◽

F. Pintus ◽

L. Rossi ◽

C. Versace

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Exchange ◽

Information Quality ◽

Risk Estimation ◽

Civil Protection ◽

Time Data ◽

Innovative Management ◽

Technical Issues ◽

Forecasting And Warning

Abstract. The effectiveness of alert systems for civil protection purposes, defined as the ability to minimize the level of risk in a region subjected to an imminent flood event, strongly depends on availability and exploitability of information. It also depends on technical expertise and the ability to easily manage the civil protection actions through the organization into standardized procedures. Hydro-geologic and hydraulic risk estimation, based on the combination of different technical issues (in this case meteorological, hydro-geological, hydraulic matters), but also socio-economic ones, requires the integration between quasi-static and time-varying information within the same operative platform. Beside the real-time data exchange, a Decision Support System must provide tools which enable knowledge sharing among the civil protection centres. Moreover, due to the amount and heterogeneity of information, quality procedures become necessary to handle all forecasting and monitoring routines within operative centres, according to the latest national directive. In Italy procedures on the civil protection matter have been condensed into the Prime Minister's Directive (27 February 2004. STORM3, an innovative management and monitoring System for real-time flood forecasting and warning, takes in the Directive, supporting the operator step by step within the different phases of civil protection activities.

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OGS improvements in the year 2011 in running the Northeastern Italy Seismic Network

Advances in Geosciences ◽

10.5194/adgeo-34-5-2013 ◽

2013 ◽

Vol 34 ◽

pp. 5-8 ◽

Cited By ~ 3

Author(s):

P. L. Bragato ◽

D. Pesaresi ◽

A. Saraò ◽

P. Di Bartolomeo ◽

G. Durì

Keyword(s):

Real Time ◽

Data Exchange ◽

Broad Band ◽

Main Tool ◽

Seismic Network ◽

Time Data ◽

Seismic Stations ◽

Short Period ◽

Real Time Data ◽

Northeastern Italy

Abstract. The Centro di Ricerche Sismologiche (CRS, Seismological Research Center) of the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS (Italian National Institute for Oceanography and Experimental Geophysics) in Udine (Italy) after the strong earthquake of magnitude Mw = 6.4 occurred in 1976 in the Italian Friuli-Venezia Giulia region, started to operate the Northeastern Italy Seismic Network: it currently consists of 12 very sensitive broad band and 21 simpler short period seismic stations, all telemetered to and acquired in real time at the OGS-CRS data centre in Udine. Real time data exchange agreements in place with other Italian, Slovenian, Austrian and Swiss seismological institutes lead to a total number of 93 seismic stations acquired in real time, which makes the OGS the reference institute for seismic monitoring of Northeastern Italy, as shown in Fig. 1 (Bragato et al., 2011; Saraò et al., 2010). Since 2002 OGS-CRS is using the Antelope software suite as the main tool for collecting, analyzing, archiving and exchanging seismic data, initially in the framework of the EU Interreg IIIA project "Trans-national seismological networks in the South-Eastern Alps" (Bragato et al., 2010; Pesaresi et al., 2008). SeisComP is also used as a real time data exchange server tool. In order to improve the seismological monitoring of the Northeastern Italy area, at OGS-CRS we tuned existing programs and created ad hoc ones like: a customized web server named PickServer to manually relocate earthquakes, a script for automatic moment tensor determination, scripts for web publishing of earthquake parametric data, waveforms, state of health parameters and shaking maps, noise characterization by means of automatic spectra analysis, and last but not least scripts for email/SMS/fax alerting. A new OGS-CRS real time seismological website (http://rts.crs.inogs.it/) has also been operative since several years.

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A Dynamic Mobility Traffic Model Based on Two Modes of Transport in Smart Cities

Smart Cities ◽

10.3390/smartcities4010016 ◽

2021 ◽

Vol 4 (1) ◽

pp. 253-270

Author(s):

Mohammed Bin Hariz ◽

Dhaou Said ◽

Hussein T. Mouftah

Keyword(s):

Data Exchange ◽

Smart Cities ◽

Time Data ◽

School Bus ◽

The Public ◽

Ride Sharing ◽

Dynamic Mobility ◽

Proposed Model ◽

Loading System ◽

Suitable Combination

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

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A Comprehensive Survey on Cyber-Physical Smart Grid Testbed Architectures: Requirements and Challenges

Electronics ◽

10.3390/electronics10091043 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1043

Author(s):

Abdallah A. Smadi ◽

Babatunde Tobi Ajao ◽

Brian K. Johnson ◽

Hangtian Lei ◽

Yacine Chakhchoukh ◽

...

Keyword(s):

Smart Grid ◽

Real Time ◽

Smart Grids ◽

Data Exchange ◽

Information Technologies ◽

New Technologies ◽

Rapid Development ◽

Environmental Benefits ◽

Future Research ◽

Time Data

The integration of improved control techniques with advanced information technologies enables the rapid development of smart grids. The necessity of having an efficient, reliable, and flexible communication infrastructure is achieved by enabling real-time data exchange between numerous intelligent and traditional electrical grid elements. The performance and efficiency of the power grid are enhanced with the incorporation of communication networks, intelligent automation, advanced sensors, and information technologies. Although smart grid technologies bring about valuable economic, social, and environmental benefits, testing the combination of heterogeneous and co-existing Cyber-Physical-Smart Grids (CP-SGs) with conventional technologies presents many challenges. The examination for both hardware and software components of the Smart Grid (SG) system is essential prior to the deployment in real-time systems. This can take place by developing a prototype to mimic the real operational circumstances with adequate configurations and precision. Therefore, it is essential to summarize state-of-the-art technologies of industrial control system testbeds and evaluate new technologies and vulnerabilities with the motivation of stimulating discoveries and designs. In this paper, a comprehensive review of the advancement of CP-SGs with their corresponding testbeds including diverse testing paradigms has been performed. In particular, we broadly discuss CP-SG testbed architectures along with the associated functions and main vulnerabilities. The testbed requirements, constraints, and applications are also discussed. Finally, the trends and future research directions are highlighted and specified.

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Experiment Control and Monitoring System for LOG-a-TEC Testbed

Sensors ◽

10.3390/s21196422 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6422

Author(s):

Grega Morano ◽

Andrej Hrovat ◽

Matevž Vučnik ◽

Janez Puhan ◽

Gordana Gardašević ◽

...

Keyword(s):

Real Time ◽

Monitoring System ◽

Data Exchange ◽

Interference Avoidance ◽

Sensor Management ◽

Time Data ◽

Experiment Control ◽

A Tec ◽

Control And Monitoring System ◽

And Control

The LOG-a-TEC testbed is a combined outdoor and indoor heterogeneous wireless testbed for experimentation with sensor networks and machine-type communications, which is included within the Fed4FIRE+ federation. It supports continuous deployment principles; however, it is missing an option to monitor and control the experiment in real-time, which is required for experiment execution under comparable conditions. The paper describes the implementation of the experiment control and monitoring system (EC and MS) as the upgrade of the LOG-a-TEC testbed. EC and MS is implemented within existing infrastructure management and built systems as a new service. The EC and MS is accessible as a new tab in sensor management system portal. It supports several commands, including start, stop and restart application, exit the experiment, flash or reset the target device, and displays the real-time status of the experiment application. When nodes apply Contiki-NG as their operating system, the Contiki-NG shell tool is accessible with the help of the newly developed tool, giving further experiment execution control capabilities to the user. By using the ZeroMQ concurrency framework as a message exchange system, information can be asynchronously sent to one or many devices at the same time, providing a real-time data exchange mechanism. The proposed upgrade does not disrupt any continuous deployment functionality and enables remote control and monitoring of the experiment. To evaluate the EC and MS functionality, two experiments were conducted: the first demonstrated the Bluetooth Low Energy (BLE) localization, while the second analysed interference avoidance in the 6TiSCH (IPv6 over the TSCH mode of IEEE 802.15.4e) wireless technology for the industrial Internet of Things (IIoT).

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Real-time data exchange system based on compact PCI and DAM for heavy ion synchrotron control

High Power Laser and Particle Beams ◽

10.3788/hplpb20142611.115103 ◽

2014 ◽

Vol 26 (11) ◽

pp. 115103

Author(s):

李桂花 Li Guihua ◽

赵五元 Zhao Wuyuan ◽

马进忠 Ma Jinzhong ◽

汪荣荣 Wang Rongrong

Keyword(s):

Real Time ◽

Data Exchange ◽

Heavy Ion ◽

Time Data ◽

Exchange System ◽

Real Time Data

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Real-time data exchange of power supply digital adjustor of HIRFL-CSR

High Power Laser and Particle Beams ◽

10.3788/hplpb20122412.2897 ◽

2012 ◽

Vol 24 (12) ◽

pp. 2897-2900

Author(s):

曾贤强 Zeng Xianqiang ◽

敬岚 Jing Lan ◽

龙银东 Long Yindong ◽

姚泽恩 Yao Ze’en ◽

郭玉辉 Guo Yuhui

Keyword(s):

Real Time ◽

Power Supply ◽

Data Exchange ◽

Time Data ◽

Real Time Data

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iPOS–an integrated production operation solution for Longtom Field

The APPEA Journal ◽

10.1071/aj10018 ◽

2011 ◽

Vol 51 (1) ◽

pp. 259

Author(s):

Rajesh Trivedi ◽

Shripad Biniwale ◽

Adil Jabur

Keyword(s):

Data Acquisition ◽

Real Time ◽

Joint Venture ◽

Direct Access ◽

Quality Data ◽

Processing Plant ◽

Production Operation ◽

Time Data ◽

Integrated Production ◽

Real Time Data

With a vision of innovation, integrity and agility, Nexus Energy began first production of Longtom field in October 2009. The Longtom gas field is located in the Gippsland Basin, offshore Victoria where the produced gas is transported to Santos’ Patricia Baleen gas processing plant. All production data is acquired by Santos with the supervisory control and data acquisition (SCADA) system. The challenge for Nexus Energy was to monitor the field remotely in the absence of a data historian and to support the operational people proactively. Data acquisition from Santos, validation, and storage in a secured centralised repository were therefore key tasks. A system was needed that would not only track accurate production volumes to meet the daily contractual quantity (DCQ) production targets but that would also be aligned with Nexus’s vision for asset optimisation. We describe how real-time data is acquired, validated, and stored automatically in the absence of a data historian for Longtom field, and how the deployed system provides a framework for an integrated Production Operation System (iPOS). The solution uses an integrated methodology that allows effective monitoring of real-time data trends to anticipate and prevent potential well and equipment problems, thus assisting in meeting DCQ targets and providing effective analysis techniques for decision making. Based on full workflow automation, the system is deployed for acquisition, allocation, reporting and analysis. This has increased accuracy, accountability and timely availability of quality data, which has helped Nexus improve productivity. The comprehensive reporting tool provides access to operational and production reports via email for managers, output reports in various formats for joint venture partners, and nontechnical users without direct access to the core application. A powerful surveillance tool, integrated with the operational database, provides alarms and notifications on operation issues, which helps engineers make proactive operational decisions. The framework allows a streamlined data flow for dynamic updates of well and simulation models, improving process integration and reducing the runtime cycle.

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Progressive System: A Deep-Learning Framework for Real-Time Data in Industrial Production

Processes ◽

10.3390/pr8060649 ◽

2020 ◽

Vol 8 (6) ◽

pp. 649

Author(s):

Yifeng Liu ◽

Wei Zhang ◽

Wenhao Du

Keyword(s):

Deep Learning ◽

Real Time ◽

Large Scale ◽

Quality Data ◽

Time Data ◽

High Quality ◽

Real Time System ◽

High Quality Data ◽

Learning Framework ◽

Data Accumulation

Deep learning based on a large number of high-quality data plays an important role in many industries. However, deep learning is hard to directly embed in the real-time system, because the data accumulation of the system depends on real-time acquisitions. However, the analysis tasks of such systems need to be carried out in real time, which makes it impossible to complete the analysis tasks by accumulating data for a long time. In order to solve the problems of high-quality data accumulation, high timeliness of the data analysis, and difficulty in embedding deep-learning algorithms directly in real-time systems, this paper proposes a new progressive deep-learning framework and conducts experiments on image recognition. The experimental results show that the proposed framework is effective and performs well and can reach a conclusion similar to the deep-learning framework based on large-scale data.

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