Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

The distributed generation, along with the deregulation of the Smart Grid, have created a great concern on Power Quality (PQ), as it has a direct impact on utilities and customers, as well as effects on the sinusoidal signal of the power line. The a priori unknown features of the distributed energy resources (DER) introduce non-linear behaviours in loads associated to a variety of PQ disturbances.

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Data Analysis

This folder consists of data obtained from the experiments and the materials we use for analysis. Here we provide the following documents:

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Three well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, and Slammer, occurred in May 2017, May 2005, and January 2003, respectively.
The Route Views BGP update messages are publicly available from the University of Oregon Route Views Project and contain:
WannaCrypt, Moscow blackout, and Slammer: http://www.routeviews.org/routeviews/.

Instructions: 

Raw data from the "route collector route-views2" are organized in folders labeled by the year and month of the collection date.
Complete datasets for WannaCrypt, Moscow blackout, and Slammer are available from the Route Views route collector route-views2 site:
University of Oregon Route Views Project: http://www.routeviews.org/routeviews/
Collectors: http://www.routeviews.org/routeviews/index.php/collectors/
Route Views Collector Map: http://www.routeviews.org/routeviews/index.php/map/
University of Oregon Route Views Archive Project: http://archive.routeviews.org/
MRT format RIBs and UPDATEs (quagga bgpd, from route-views2.oregon-ix.net): http://archive.routeviews.org/bgpdata/
Tools: http://www.routeviews.org/routeviews/index.php/tools/
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.

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The data include:

  • Demographic data of the participants including: gender, group of participation and number of years in the company.
  • Results of the use of Ethool including: expended time and subjective evaluation of if using a Likert of 5 points. Two different files are available corresponding to each iteration (prototype 1 and prototype 2).
  • Results of the SUS questionnaire for both iterations (prototype 1 and prototype 2).
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This directory contains the Verilog sources of the TL multiplier proposed in the paper entitled ” A two-stage operand trimming approximate logarithmic multiplier”, submitted to TCAS-1.

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We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.

Instructions: 

DATASET WEBSITE: https://fivegophers.umn.edu/www20/

## OVERVIEW

5Gophers 1.0 is a dataset collected when the world's very first commercial 5G services were made available to consumers. It should serve as a baseline to evaluate the 5G's performance evolution over time. Results using this dataset is presented in our measurement paper - "A First Look at Commercial 5G Performance on Smartphones".

This dataset is being made available to the research community.

## FILES and FOLDER STRUCTURE

All the files are in CSV format with headers that should hopefully be self-explainatory.

5Gophers-v1.0
├── All-Carriers
│   ├── 01-Throughput
│   ├── 02-Round-Trip-Times
│   └── 03-User-Mobility
└── mmWave-only
├── 03-UE-Panel (LoS Tests)
├── 04-Ping-Traces (Latency Tests)
├── 05-UE-Panel (NLoS Tests)
├── 06-UE-Panel (Orientation Tests)
├── 07-UE-Panel (Distance Tests)
├── 08-Web-Page-Load-Tests
├── 09-HTTPS-CDN-vs-NonCDN (Download Test)
└── 10-HTTP-vs-HTTPS (Download Test)

## CITING THE DATASET

```
@inproceedings{10.1145/3366423.3380169,
author = {Narayanan, Arvind and Ramadan, Eman and Carpenter, Jason and Liu, Qingxu and Liu, Yu and Qian, Feng and Zhang, Zhi-Li},
title = {A First Look at Commercial 5G Performance on Smartphones},
year = {2020},
isbn = {9781450370233},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3366423.3380169},
doi = {10.1145/3366423.3380169},
booktitle = {Proceedings of The Web Conference 2020},
pages = {894–905},
numpages = {12},
location = {Taipei, Taiwan},
series = {WWW ’20}
}
```

## QUESTIONS?

Please feel free to contact the FiveGophers team for information about the data (fivegophers@umn.edu, naray111@umn.edu)

## LICENSE

5Gophers 1.0 dataset is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.

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Please find the ZIP files attached 

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This is the dataset of the experiment results of the ReuseTracker tool.

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Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.

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