Communications
In the digital era of the Industrial Internet of Things (IIoT), the conventional Critical Infrastructures (CIs) are transformed into smart environments with multiple benefits, such as pervasive control, self-monitoring and self-healing. However, this evolution is characterised by several cyberthreats due to the necessary presence of insecure technologies. DNP3 is an industrial communication protocol which is widely adopted in the CIs of the US. In particular, DNP3 allows the remote communication between Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA).
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Aiming the analytical modeling of Age of Information (AoI) and Peak-AoI, uploaded codes construct and solve analytical models for Non-preemptive Bufferless, Probabilistic Generate-at-will (GAW) and Random Arrival with Single Buffer (RA-SB) servers using the theory of absorbing Markov Chains. In particular, they output per-source PAoI/AoI distributions in a setting with general number of sources where the sources may have different (i) general phase-type service time distributions, (ii) packet error probabilities and (iii) arrival rates.
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Recently, unmanned aerial vehicles (UAVs) have been receiving significant attention due to the wide range of potential application areas. To support UAV use cases with beyond visual line of sight (BVLOS) and autonomous flights, cellular networks can provide connectivity points to UAVs and provide remote control and payload communications. However, there are limited datasets to study the coverage of cellular technologies for UAV flights at different altitudes.
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A synthetic laser reliability dataset generated using generative adversarial networks (GANs) is provided. The data includes normalized current measurements estimated at the following times: 2, 20, 40, 60, 80, 100, 150, 500, 1000, and 1500 hours. The data can be used to train machine learning models to solve different predictive maintenance tasks such as prediction of performance degradation, remainng useful prediction, and so on.
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A monitoring data, which includes several OTDR traces incorporating various types of fiber events (e.g. reflective, non-reflective, merged events) induced along an optical fiber link, is provided. Different fiber faults such as fiber cut, and fiber bend are modeled using optical components such as connectors and variable optical attenuators (VOAs). The data can be used to train machine learning models for solving fiber fault diagnosis problems.
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This data is the Federal Communication Commission (FCC) F(50,50) signal strength variation curves for the Very High Frequency (VHF) Channel 7-13 and the Ultra High Frequency (UHF) Channel 14-69. The signal strength for both curves is in dBuV/m for an Effective Radiated Power (ERP) per dipole of 1 kW. All data are based on a 9 m mobile antenna height measurement for 30 m to 600 m antenna heights within a transmitter-receiver separation ranging from 1.5 km to 100 km.
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The monitored data is obtained using the optical time domain reflectometry (OTDR) principle, which is commonly used for troubleshooting fiber optic cables or links. The data set contains raw OTDR traces that include one or two reflective events caused by the placement of one or two reflectors and/or an open physical contact (PC) at the end of the monitored optical fiber link.
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Evolving from the well-known ray-tracing dataset DeepMIMO, the DeepVerse 6G dataset additionally provides multi-modal sensing data generated from various emulators. These emulators provide the wireless, radar, LiDAR, vision and position data. With a parametric generator, the DeepVerse dataset can be customized by the user for various communication and sensing applications.
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Beam management is a challenging task for millimeter wave (mmWave) and sub-terahertz communication systems, especially in scenarios with highly-mobile users. Leveraging external sensing modalities such as vision, LiDAR, radar, position, or a combination of them, to address this beam management challenge has recently attracted increasing interest from both academia and industry. This is mainly motivated by the dependency of the beam direction decision on the user location and the geometry of the surrounding environment---information that can be acquired from the sensory data.
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This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor).
iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network.
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