Seabed discrimination data from SeaScan™

Raw data from SeaScan™ transects off Wide Bay (south Queensland) taken in August 2007 as part of a study of ecological factors influencing the distribution of spanner crabs (Ranina ranina). The dataset (comma-delimited ascii file) comprises the following fields: 1. record number 2. date-time (GMT) 3. date-time (AEST) 4. latitude (signed decimal degrees) 5. longitude (decimal degrees) 6. speed over ground (knots) 7. depth (m) 8. seabed roughness (v) 9. hardness (v) Indices of roughness and hardness (from the first and second echoes respectively) were obtained using a SeaScan™ 100 system (un-referenced) on board the Research Vessel Tom Marshall, with the ship’s Furuno FCV 1100 echo sounder and 1 kW, 50 kHz transducer. Generally vessel speed was kept below about 14 kt (typically ~12 kt), and the echo-sounder range set to 80 m. The data were filtered to remove errors due to data drop-out, straying beyond system depth limits (min. 10 m), or transducer interference.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.