Hydrochemical and isotopic tracing of mixing dynamics and water quality evolution under pumping conditions in the mine shaft of the abandoned Frances Colliery, Scotland.

Since 1995, when pumps were withdrawn from deep mines in East Fife (Scotland), mine waters have been rebounding throughout the coalfield. Recently, it has become necessary to pump and treat these waters to prevent their uncontrolled emergence at the surface. However, even relatively shallow pumping to surface treatment lagoons of the initially chemically-stratified mine water from a shaft in the coastal Frances Colliery during two dynamic step-drawdown tests to establish the hydraulic characteristics of the system resulted in rapid breakdown of the stratification within 24 h and a poor pumped water quality with high dissolved Fe loading. Further, data are presented here of hydrochemical and isotopic sampling of the extended pump testing lasting up to several weeks. The use in particular of the environmental isotopes d18O, d2H, d34S, 3H, 13C and 14C alongside hydrochemical and hydraulic pump test data allowed characterisation of the Frances system dynamics, mixing patterns and water quality sources feeding into this mineshaft under continuously pumped conditions. The pumped water quality reflects three significant components of mixing: shallow freshwater, seawater, and leakage from the surface treatment lagoons. In spite of the early impact of recirculating lagoon waters on the hydrochemistries, the highest Fe loadings in the longer-term pumped waters are identified with a mixed freshwater–seawater component affected by pyrite oxidation/melanterite dissolution in the subsurface system.

Secure Switch-and-Stay Combining (SSSC) for Cognitive Relay Networks

In this paper, we study a two-phase underlay cognitive relay network, where there exists an eavesdropper who can overhear the message. The secure data transmission from the secondary source to secondary destination is assisted by two decode-and-forward (DF) relays. Although the traditional opportunistic relaying technique can choose one relay to provide the best secure performance, it needs to continuously have the channel state information (CSI) of both relays, and may result in a high relay switching rate. To overcome these limitations, a secure switch-and-stay combining (SSSC) protocol is proposed where only one out of the two relays is activated to assist the secure data transmission, and the secure relay switching occurs when the relay cannot support the secure communication any longer. This security switching is assisted by either instantaneous or statistical eavesdropping CSI. For these two cases, we study the system secure performance of SSSC protocol, by deriving the analytical secrecy outage probability as well as an asymptotic expression for the high main-to-eavesdropper ratio (MER) region. We show that SSSC can substantially reduce the system complexity while achieving or approaching the full diversity order of opportunistic relaying in the presence of the instantaneous or statistical eavesdropping CSI.

Foreword

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Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Clustering-Based Ensemble Method as an Alternative to Stacking

One of the most popular techniques of generating classifier ensembles is known as stacking which is based on a meta-learning approach. In this paper, we introduce an alternative method to stacking which is based on cluster analysis. Similar to stacking, instances from a validation set are initially classified by all base classifiers. The output of each classifier is subsequently considered as a new attribute of the instance. Following this, a validation set is divided into clusters according to the new attributes and a small subset of the original attributes of the instances. For each cluster, we find its centroid and calculate its class label. The collection of centroids is considered as a meta-classifier. Experimental results show that the new method outperformed all benchmark methods, namely Majority Voting, Stacking J48, Stacking LR, AdaBoost J48, and Random Forest, in 12 out of 22 data sets. The proposed method has two advantageous properties: it is very robust to relatively small training sets and it can be applied in semi-supervised learning problems. We provide a theoretical investigation regarding the proposed method. This demonstrates that for the method to be successful, the base classifiers applied in the ensemble should have greater than 50% accuracy levels.