Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Sugarcane vegetation on path-loss between CC2650 and CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)".

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy Rice vegetation on received signal strength between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy Rice crop monitoring from the period 01/07/2020 to 03/11/2020.

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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Millet vegetation on path-loss between CC2538 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for millet crop monitoring from period 03/06/2020 to 04/10/2020.

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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.

Instructions: 

This repository contains the AURORA dataset, a multi sensor dataset for robotic ocean exploration.

It is accompanied by the report "AURORA, A multi sensor dataset for robotic ocean exploration", by Marco Bernardi, Brett Hosking, Chiara Petrioli, Brian J. Bett, Daniel Jones, Veerle Huvenne, Rachel Marlow, Maaten Furlong, Steve McPhail and Andrea Munafo.

Exemplar python code is provided at https://github.com/noc-mars/aurora.

 

The dataset provided in this repository includes data collected during cruise James Cook 125 (JC125) of the National Oceanography Centre, using the Autonomous Underwater Vehicle Autosub 6000. It is composed of two AUV missions: M86 and M86.

  • M86 contains a sample of multi-beam echosounder data in .all format. It also contains CTD and navigation data in .csv format.

  • M87 contains a sample of the camera and side-scan sonar data. The camera data contains 8 of 45320 images of the original dataset. The camera data are provided in .raw format (pixels are ordered in Bayer format). The size of each image is of size 2448x2048. The side-scan sonar folder contains a one ping sample of side-scan data provided in .xtf format.

  • The AUV navigation file is provided as part of the data available in each mission in .csv form.

 

The dataset is approximately 200GB in size. A smaller sample is provided at https://github.com/noc-mars/aurora_dataset_sample and contains a sample of about 200MB.

Each individual group of data (CTD, multibeam, side scan sonar, vertical camera) for each mission (M86, M87) is also available to be downloaded as a separate file. 

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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.

Instructions: 

The dataset is acquired from a capacitive sensor array composed of a set of sensor electrodes immersed in three different phases: air, oil, and water. It is composed of digital signals obtained from one electrode while it was immersed in the oil and water phases at different times. 

## Experimental setup

The experimental setup is composed of a capacitive sensor array that holds a set of sensing cells (electrodes) distributed vertically along the sensor body (PCB). The electrodes are excited sequentially and the voltage (digital) of each electrode is measured and recorded. The voltages of each electrode are converted to intensity values by the following equation:

intensity = ( |Measured Voltage - Base Voltage| / Base Voltage ) x 100

Where the Base Voltage is the voltage of the electrode recorded while the electrode is immersed in air. The intensity values are stored in the dataset instead of the raw voltage values.

## Experimental procedure 

The aim of the experiments is to get fixed-size intensity signals from one electrode (target electrode) when being immersed in water and oil; labeled as +1 (water) or -1 (oil). For this purpose, the following procedure was applied:

- The linear actuator was programmed to move the sensor up and down at a constant speed (20 mm / second).

- The actuator stops when reaching the upper and bottom positions for a fixed duration of time (60 seconds).

- At the upper position, the target electrode is immersed in oil; intensity signals are labeled -1 and sent to the PC.

- At the bottom position, the target electrode is immersed in water; intensity signals are labeled +1 and sent to the PC.

- The sampling rate is 100 msec; since each intensity signal contains 10 values, it takes 1 second to record one intensity signal 

## Environmental conditions

The experiments were perfomed under indoors laboratory conditions with room temperature of around 23 degree Celsius. 

## Dataset structure 

The signals included in the dataset are composed of intensity signals each with 10 consecutive values and a label in the last column. The label is +1 for a water-immersed electrode and -1 for an oil-immersed electrode.

## Application

The dataset should be used to train a classifier to differentiate between electrodes immersed in water and oil phases given a sample signal.

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

Instructions: 

Data consist of :
* distance, in meters, along the measurement path. This field is likely not useful for anyone other than the authors, but is included here for completeness.
* geographic location of the measurement, in decimal degrees, WGS84
* median external radio-frequency noise power, measured in a 1 MHz bandwidth about a center frequency of 142.0 MHz, in dBm
* peak external radio-frequency noise power, also measured in a 1 MHz bandwidth about a center frequency of 142.0 MHz, in dBm. Here, peak power is defined as the threshold where 99.99% of the data lie below this value.
* for North End and Back Bay datasets, the official zoning district containing the measurement location is included. Measurements in the Downtown data were all collected within Business and Mixed Use zoning districts, and thus are not listed.

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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).

Instructions: 

The images in our dataset are in georeferenced TIFF (geotiff) format with 10 bands. We cropped the original Landsat-8 scenes (with ~7,600 x 7,600 pixels) into image patches with 128 x 128 pixels by using a stride overlap of 64 pixels (vertical and horizontal). The masks are in binary format where True (1) represents fire and False (0) represents background and they were generated from the conditions set by Schroeder et al. (2016). We used the Schroeder conditions to process each patch, producing over 1 million patches with at least one fire pixel and the same amount of patches with no fire pixels, randomly selected from the original images.

The dataset is organized as follow. 

It is divided into South American regions for easy downloading. For each region of South America we have a zip file for images of active fire, its masks, and non-fire images. For example:

 - Uruguay-fire.zip

 - Uruguay-mask.zip

 - Uruguay-nonfire.zip

Within each South American region zip files there are the corresponding zip files to each Landsat-8 WRS (Worldwide Reference System). For example:

- Uruguay-fire.zip;

      - 222083.zip

      - 222084.zip

      - 223082.zip

      - 223083.zip

      - 223084.zip

      - 224082.zip

      - 224083.zip

      - 224084.zip

      - 225081.zip

      - 225082.zip

      - 225083.zip

      - 225084.zip

Within each of these Landsat-8 WRS zip files there are all the corresponding 128x128 image patches for the year 2018. 

 

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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.

Instructions: 

The marine water samples are taken from 76 stations which are located in 10 water control zones. The water quality can be measured in 3 different water depths, namely ‘Surface’, Middle’ and Bottom’.

The columns of the water quality data are named in the format of “Zone + Station + Water depth +Water quality Parameter”. For example, the column “Zone1_TM2_Bot_VSSolids” contains the Volatile Suspended Solids data (mg/L) of the bottom-level water at the TM2 station, which is located in Zone 1.

 

The list of water quality parameters and Climatological parameters are tabulated below.

 

Water quality parameters

-------------------------------------

5-day Biochemical Oxygen Demand (mg/L)

Silica (mg/L)

Chlorophyll-a μg/L

pH

Temperature (C)

E. coli (cfu/100mL)

Faecal Coliforms (cfu/100mL)

Total Phosphorus (mg/L)

Orthophosphate Phosphorus (mg/L)

Volatile Suspended Solids (mg/L)

Phaeo-pigments (μg/L)

Suspended Solids (mg/L)

Dissolved Oxygen Saturation (%)

Dissolved Oxygen (mg/L)

Salinity (psu)

Secchi Disc Depth (M)

Nitrite Nitrogen (mg/L)

Nitrate Nitrogen (mg/L)

Ammonia Nitrogen (mg/L)

Total Nitrogen (mg/L)

Total Kjeldahl Nitrogen (mg/L)

Total Inorganic Nitrogen (mg/L)

Turbidity (NTU)

Unionized Ammonia (mg/L)

 

 

Climatological Parameters

--------------------

Mean Temp

Max Temp

Min Temp

Pressure

Dew Point Temp

Wet Bulb Temp

Mean Relative Humidity

Mean Amount Cloud

Rainfall

Grass Minimun Temperature

Bright Sunshine

Global Solar Radiation

Evaporation

Prevailing Wind Direction

Wind Speed

Sea Temperature

Reduced Visibility

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