Climate Change/Environmental

The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks.

The dataset is composed of digital signals obtained from a capacitive sensor electrodes that are immersed in water or in oil. Each signal, stored in one row, is composed of 10 consecutive intensity values and a label in the last column. The label is +1 for a water-immersed sensor electrode and -1 for an oil-immersed sensor electrode. This dataset should be used to train a classifier to infer the type of material in which an electrode is immersed in (water or oil), given a sample signal composed of 10 consecutive values.

Radio-frequency noise mapping data collected from Downtown, Back Bay and North End neighborhoods within Boston, MA, USA in 2018 and 2019.

This dataset was created from all Landsat-8 images from South America in the year 2018. More than 31 thousand images were processed (15 TB of data), and approximately on half of them active fire pixels were found. The Landsat-8 sensor has 30 meters of spatial resolution (1 panchromatic band of 15m), 16 bits of radiometric resolution and 16 days of temporal resolution (revisit). The images in our dataset are in TIFF (geotiff) format with 10 bands (excluding the 15m panchromatic band).

The raw data are collected from the websites of EPD (Environmental Protection Department, Hong Kong) and HKO (Hong Kong Observatory). Marine water quality data is provided by EPD and climatological data is provided by HKO. The data is interpolated by SAS “proc expand” and aligned to the beginning of each month.

The raw data used to produce this dataset are extracted from the following URL.

 This data package is parepared by Dr. Jianguo Niu (IMSG at NOAA NESDIS/STAR) on

        March 18, 2020

 The purpose of this OMPS LFSO2 retrieval products package is in support the paper:

 "Evaluation and Improvement of the Near-real-time Linear Fit SO2 retrievals from Suomi NPP (S-NPP) Ozone Mapping & Profiler Suite"

This package includes LFSO2 V8TOS retrievals of:

        1. "logic swith on" (original set as described by th paper 01824) products