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.
Five well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, occurred in May 2017, May 2005, January 2003, September 2001, and July 2001, respectively.
The Reseaux IP Europeens (RIPE) BGP update messages are publicly available from the Network Coordination Centre (NCC) and contain:
WannaCrypt, Moscow blackout, Slammer, Nimda, Code Red I, and regular data: https://www.ripe.net/analyse/.
Raw data from the "route collector rrc 04" are organized in folders labeled by the year and month of the collection date.
Complete datasets for WannaCrypt, Moscow blackout, Slammer, Nimda, and Code Red I are available from the RIPE route collector rrc 04 site:
RIPE NCC: https://www.ripe.net
Internet Measurements: https://www.ripe.net/analyse/internet-measurements
Routing Information Service (RIS): https://www.ripe.net/analyse/internet-measurements/routing-information-s...
RIS Raw Data: https://www.ripe.net/analyse/internet-measurements/routing-information-s...
The date of last modification and the size of the datasets are also included.
BGP update messages are originally collected in multi-threaded routing toolkit (MRT) format.
"Zebra-dump-parser" written in Perl is used to extract to ASCII the BGP updated messages.
The 37 BGP features were extracted using a C# tool to generate uploaded datasets (csv files).
Labels have been added based on the periods when data were collected.
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.
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).
These are OMNeT++ flooding simulation results of four-UAV FANET in three different topologies, as mentioned in "The Broadcast Storm Problem in FANETs and the Dynamic Neighborhood-Based Algorithm as a Countermeasure".
Please, check the paper for more information.
Raw results are packaged in the 7zip file and were generated using OMNeT++ 5.0. They are all UTF-8 encoded text files.
OMNeT++ raw results (with extension
.vec) are named as the following scheme:
T [#topology] - [#replication]
where #topology ranges from 1 to 3, and #replication ranges from 0 to 9. See the paper to understand those values.
CSV files contain list of sent and received messages.
If you want to check the integrity of the downloaded files, consider the following MD5 hashes:
2a78e9daaa2c6c925f88234d930d8535 *results-flooding-fixed-topologies (CSV files).zip
c5bd4ab10430cb6466220fb1f2777cb2 *results-flooding-fixed-topologies (raw files).7z
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