This DataPort contains supporting data of paper entitled Graph Representations for programmable photonic circuits for the Journal of Lightwave Technology. All unreachable nodes for the first graph are tested in the undirected graph with arbitrary negative weight. Unphysical paths are found on all 21 nodes. These same terminal nodes are validated in a directed graph with and without artificial nodes representing the same optical port overlapping each other.


Smart Grids (SG) are a novel paradigm introduced for optimizing the management of the power generation, transmission, distribution and consumption. A SG system can efficiently work only if all the components are connected through a communication network able to satisfy the SG applications requirements. Wireless communications are the most appropriate candidates for handling SG requirements due to their flexibility.


Network traffic analysis, i.e. the umbrella of procedures for distilling information from network traffic, represents the enabler for highly-valuable profiling information, other than being the workhorse for several key network management tasks. While it is currently being revolutionized in its nature by the rising share of traffic generated by mobile and hand-held devices, existing design solutions are mainly evaluated on private traffic traces, and only a few public datasets are available, thus clearly limiting repeatability and further advances on the topic.


MIRAGE-2019 is a human-generated dataset for mobile traffic analysis with associated ground-truth, having the goal of advancing the state-of-the-art in mobile app traffic analysis. MIRAGE-2019 takes into consideration the traffic generated by more than 280 experimenters using 40 mobile apps via 3 devices.

A sampled version of the dataset (one app per category) is readily downloadable, whereas the complete version is available on request.

APP LIST reports the details on the apps contained in the two versions of the dataset.

If you are using MIRAGE-2019 human-generated dataset for scientific papers, academic lectures, project reports, or technical documents, please help us increasing its impact by citing the following reference:

Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico and Antonio Pescapè,"MIRAGE: Mobile-app Traffic Capture and Ground-truth Creation",4th IEEE International Conference on Computing, Communications and Security (ICCCS 2019), October 2019, Rome (Italy).



The Underwater Acoustic & Navigation Lab in University of Haifa conducted a shallow water long-range underwater acoustic communication experiment across the shores of Northern Israel in March 2019. The experiment was designed to verified the adaptive modulation scheme for long-range underwater acoustic communicaiton proposed by the authors. To make the work reproducible, the authors freely share the estimated channel impulse responses from the sea experiment.


This file contains codes and dataset for generating the results in our paper titled " Cluster Based Energy Efficient Lifetime Maximization and Resource Allocation for M2M Networks".


Frequency spectrum waterfall image dataset


This dataset contains the full set of experimental waveforms that were used to produce the article "Non-Linear Phase Noise Mitigation over Systems using Constellation Shaping", published in the Journal of Lightwave Technology with DOI: 10.1109/JLT.2019.2917308.



The dataset contains two files: PartIII_NZDSF.tar and PartIV_GS.tar, corresponding to part III and part IV of the paper. Each file contains: * Transmitted one-sample-per-symbol sequence, that is loaded into the DAC. * Intradyne back-to-back results. * Propagation results over the recirculating loop. All data has been captured with a 50 GS/s Tektronix oscilloscope, and symbol rate is 16 GBaud. Back-to back results are quoted as a function of noise power, i.e. attenuation of the ASE source used for noise loading. Loop results are quoted as a function of the per-channel optical power and recirculation number. Details on the experiment are available on the paper.


The dataset is used in machine learning method of the "A distributed Front-end Edge node assessment model by using Fuzzy and a learning-to-rank method" paper


This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.



Test dataset