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.
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.
The dataset should be used to train a classifier to differentiate between electrodes immersed in water and oil phases given a sample signal.
Radio-frequency noise mapping data collected from Downtown, Back Bay and North End neighborhoods within Boston, MA, USA in 2018 and 2019.
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.
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 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:
Within each South American region zip files there are the corresponding zip files to each Landsat-8 WRS (Worldwide Reference System). For example:
Within each of these Landsat-8 WRS zip files there are all the corresponding 128x128 image patches for the year 2018.
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.
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)
E. coli (cfu/100mL)
Faecal Coliforms (cfu/100mL)
Total Phosphorus (mg/L)
Orthophosphate Phosphorus (mg/L)
Volatile Suspended Solids (mg/L)
Suspended Solids (mg/L)
Dissolved Oxygen Saturation (%)
Dissolved Oxygen (mg/L)
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)
Unionized Ammonia (mg/L)
Dew Point Temp
Wet Bulb Temp
Mean Relative Humidity
Mean Amount Cloud
Grass Minimun Temperature
Global Solar Radiation
Prevailing Wind Direction
The large variability of system and types of heating load is a feature of the commercial metering of thermal energy. Heating consumption depends on many factors, for example, wall and roof material, floors number, system (opened and closed) etc. The daily data from heating meters in the residential buildings are presented in this dataset for comparing the thermal characteristics. These data are supplemented by floors number, wall material and year of construction, as well as data on average daily outdoor temperatures.
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
This data are in NetCDF format. Which can be read by an IDL code "rd_v8tos_nc.pro". The usage example
The "data" is a structure, which included most of the parameters you needed.
The dataset is mainly used for leak detection and localization.
The target scene consists of a black card with six cocoa beans of three different fermentation levels (High, correct, and low fermentation), two beans for each class, whose false-color composite is shown in the provided Figure (a), ground-truth map is shown in Fig. (b), and Fig. (c) presents its representative spectral signatures. The spectral image was acquired by the AVT Stingray F-080B camera by acquiring one band each time from 350 - 950 nm. The acquired image has a spatial resolution of 1096x712 pixels and 300 spectral bands of 2 nm width.
This is an observation data for water quality monitoring.
Attempts to prevent invasion of marine biofouling on marine vessels are demanding. By developing a system to detect marine fouling on vessels in an early stage of fouling is a viable solution. However, there is a lack of database for fouling images for performing image processing and machine learning algorithm.