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.


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.


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.


The dataset provides data to develop wireless sensing applications -- namely activity recognition, people identification and people counting -- leveraging Wi-Fi devices. Human movements cause modifications to the multi-path propagation of Wi-Fi signals. Such modifications reflect on the channel frequency response and, in turn, wireless sensing can be performed by analyzing the channel state information (CSI) of the Wi-Fi channel when the person/people move within the propagation environment.


Future mobile communication systems include millimeter wave (mmWave) frequency bands and high mobility scenarios. To learn how wave propagation and scattering effects change from classical sub 6 GHz to mmWave frequencies, measurements in both bands have to be conducted. We perform wireless channel measurements at 2.55 GHz and 25.5 GHz center frequency at velocites of 40 km/h and 100 km/h. To ensure a fair comparison between these two frequency bands, we perform repeatable measurements in a controlled environment.


Synthetic Digitally Modulated Signal Datasets for Automatic Modulation Classification contain CSPB.ML.2018 and CSPB.ML.2022, two high-quality communication signal datasets with eight modulation types: BPSK, QPSK, 8-PSK, pi/4-DQPSK, MSK, 16-QAM, 64-QAM, and 256-QAM. There are 14,000 signals of each modulation type in each dataset for a total of 112,000 signals per dataset. The two datasets are useful for signal processing testing, neural network (NN) training, initial NN testing, and out-of-distribution NN testing as signal generation parameters differ between the two datasets.


This dataset is related to a method for molecular communication in fluids described on "Fluorescent nanoparticles for reliable communication among implantable medical devices," Carbon, vol. 190, pp. 262-275, Apr. 2022, by Federico Calì, Luca Fichera, Giuseppe Trusso Sfrazzetto, Giuseppe Nicotra, Gianfranco Sfuncia, Elena Bruno, Luca Lanzanò, Ignazio Barbagallo, Giovanni Li-Destri, Nunzio Tuccitto; doi: 10.1016/J.CARBON.2022.01.016. 


The integration of communication and artificial intelligence has become a development trend, one of the applications is semantic communication, but the current research lacks the support of comprehensive datasets. To solve this problem, we built a new image and video dataset, named SCO dataset, for the researches on semantic communication and computing. First, we introduce the peculiarities of the dataset, which contains 5100 images and 138 video clips. Secondly, we we give the data generation and processing methods of the dataset, including images and videos.


The dataset is intended to cover core issues pertaining to the area of a traffic optimization via RET motors inside the antenna on the mobile base station system (BSS). The principle of RET operation was already known to scientists; however, the use of a machine learning and big data provides the possibility of creation an autonomous system, which control RET system.

Last Updated On: 
Sun, 10/23/2022 - 08:50

The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.