Paulo de Souza

High concentrations of manganese and sulfur in deposits on Murray Ridge, Endeavour Crater, Mars

Abstract Mars Reconnaissance Orbiter HiRISE images and Opportunity rover observations of the ~22 km wide Noachian age Endeavour Crater on Mars show that the rim and surrounding terrains were densely fractured during the impact crater-forming event. Fractures have also propagated upward into the overlying Burns formation sandstones. Opportunity’s observations show that the western crater rim segment, called Murray Ridge, is composed of impact breccias with basaltic compositions, as well as occasional fracture-filling calcium sulfate veins. Cook Haven, a gentle depression on Murray Ridge, and the site where Opportunity spent its sixth winter, exposes highly fractured, recessive outcrops that have relatively high concentrations of S and Cl, consistent with modest aqueous alteration. Opportunity’s rover wheels serendipitously excavated and overturned several small rocks from a Cook Haven fracture zone. Extensive measurement campaigns were conducted on two of them: Pinnacle Island and Stuart Island. These rocks have the highest concentrations of Mn and S measured to date by Opportunity and occur as a relatively bright sulfate-rich coating on basaltic rock, capped by a thin deposit of one or more dark Mn oxide phases intermixed with sulfate minerals. We infer from these unique Pinnacle Island and Stuart Island rock measurements that subsurface precipitation of sulfate-dominated coatings was followed by an interval of partial dissolution and reaction with one or more strong oxidants (e.g., O2) to produce the Mn oxide mineral(s) intermixed with sulfate-rich salt coatings. In contrast to arid regions on Earth, where Mn oxides are widely incorporated into coatings on surface rocks, our results demonstrate that on Mars the most likely place to deposit and preserve Mn oxides was in fracture zones where migrating fluids intersected surface oxidants, forming precipitates shielded from subsequent physical erosion.

A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

Online automated quality assessment is critical to determine a sensors fitness for purpose in real-time applications. A Dynamic Bayesian Network (DBN) framework is proposed to produce probabilistic quality assessments and represent the uncertainty of sequentially correlated sensor readings. This is a novel framework to represent the causes, quality state and observed effects of individual sensor errors without imposing any constraints upon the physical deployment or measured phenomenon. It represents the casual relationship between quality tests and combines them in a way to generate uncertainty estimates of samples. The DBN was implemented for a particular marine deployment of temperature and conductivity sensors in Hobart, Australia. The DBN was shown to offer a substantial average improvement (34%) in replicating the error bars that were generated by experts when compared to a fuzzy logic approach.

Automated Data Quality Assessment of Marine Sensors

The automated collection of data (e.g., through sensor networks) has led to a massive increase in the quantity of environmental and other data available. The sheer quantity of data and growing need for real-time ingestion of sensor data (e.g., alerts and forecasts from physical models) means that automated Quality Assurance/Quality Control (QA/QC) is necessary to ensure that the data collected is fit for purpose. Current automated QA/QC approaches provide assessments based upon hard classifications of the gathered data; often as a binary decision of good or bad data that fails to quantify our confidence in the data for use in different applications. We propose a novel framework for automated data quality assessments that uses Fuzzy Logic to provide a continuous scale of data quality. This continuous quality scale is then used to compute error bars upon the data, which quantify the data uncertainty and provide a more meaningful measure of the data’s fitness for purpose in a particular application compared with hard quality classifications. The design principles of the framework are presented and enable both data statistics and expert knowledge to be incorporated into the uncertainty assessment. We have implemented and tested the framework upon a real time platform of temperature and conductivity sensors that have been deployed to monitor the Derwent Estuary in Hobart, Australia. Results indicate that the error bars generated from the Fuzzy QA/QC implementation are in good agreement with the error bars manually encoded by a domain expert.

Relocatable, Automated Cost-Benefit Analysis for Marine Sensor Network Design

When designing sensor networks, we need to ensure they produce representative and relevant data, but this must be offset by the financial cost of placing sensors. We describe a novel automated method for generating and combining cost and benefit values to decide on the best sensor locations using information about the specific constraints available in most coastal locations. Costs in maintenance, negotiation, equipment, exposure and communication are estimated using hydrodynamic models and Electronic Navigation Charts. Benefits in maximum coverage and reducing overall error are also determined using model output. This method demonstrates equivalent accuracy at predicting the whole system to expert-chosen locations, whilst significantly reducing the estimated costs.

Low-cost marine monitoring: From sensors to information delivery

The Tasmanian Marine Analysis Network (Tas-MAN) project has initiatives to reduce costs at every level of a marine sensor network; including hardware, deployment, maintenance, data management, and information delivery.

MelissAR: Towards Augmented Visual Analytics of Honey Bee Behaviour

We present the design and current prototype implementation of MelissAR, an augmented reality system for visual analytics of honey bee behaviour in the field. The system is intended to support bee keepers and other relevant users to monitor honey bee populations and to make effective decisions based on their status. The implementation of MelissAR is based on informed design choices with regard to usability in the field, effective communication of relevant information, and robustness to varying outdoor conditions.

Spatiotemporal Interpolation for Environmental Modelling.

A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.

Optimisation in the Design of Environmental Sensor Networks with Robustness Consideration

This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some sensors fail.

CIRCE: Correcting Imprecise Readings and Compressing Excrescent points for querying common patterns in uncertain sensor streams

Continuous sensor stream data are often recorded as a series of discrete points in a database from which knowledge can be retrieved through queries. Two classes of uncertainties inevitably happen in sensor streams that we present as follows. The first is Uncertainty due to Discrete Sampling (DS Uncertainty); even if every discrete point is correct, the discrete sensor stream is uncertain - that is, it is not exactly like the continuous stream - since some critical points are missing due to the limited capabilities of the sensing equipment and the database server. The second is Uncertainty due to Sampling Error (SE Uncertainty); sensor readings for the same situation cannot be repeated exactly when we record them at different times or use different sensors since different sampling errors exist. These two uncertainties reduce the efficiency and accuracy of querying common patterns. However, already known algorithms generally only resolve SE Uncertainty. In this paper, we propose a novel method of Correcting Imprecise Readings and Compressing Excrescent (CIRCE) points. Particularly, to resolve DS Uncertainty, a novel CIRCE core algorithm is developed in the CIRCE method to correct the missing critical points while compressing the original sensor streams. The experimental study based on various sizes of sensor stream datasets validates that the CIRCE core algorithm is more efficient and more accurate than a counterpart algorithm to compress sensor streams. We also resolve the SE Uncertainty problem in the CIRCE method. The application for querying longest common route patterns validates the effectiveness of our CIRCE method.

Linking Australian Government Data for Sustainability Science - A Case Study

Sustainability science has been viewed as a new discipline which focuses on the complex interactions between nature and society. It demands intensive integration of data from different sources within different domains. Governments collect and generate huge amounts of scientific data and thus are in a unique position to support sustainability research. However, there are many challenges in discovering and re-using government data. In this chapter, first, we survey the sustainability related datasets published by the Australian government. We believe this is the critical first step to identifying the opportunities and issues and advancing the Australian Government 2.0 agenda. Second, we investigate the role of Linked Data in integrating a selection of Australian government datasets to generate sustainability science hypotheses and support the data analysis.We discuss the challenges based on our survey experience and present some recommendations for data publishing and analysis.