SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements.

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea-land segmentation.

Accurate Detection and Localization of Water Pipe Leaks through Model-Based TDR Inversion.

The problem of water scarcity affects many areas of the world due to water mismanagement and overconsumption and, more recently, to climate change. Monitoring the integrity of distribution systems is, therefore, increasingly important to avoid the waste of clean water. This paper presents a new signal processing technique for enhancing the performance of the methodology of leak detection in water distribution pipes based on time domain reflectometry (TDR). The new technique is based on a particular kind of TDR inversion (spatial TDR) based on a "gray-box" lumped parameter model of the system. The model does not include, e.g., radiative phenomena, non-TEM (transverse electromagnetic) modes etc. but is capable of reproducing accurately the complicated reflectograms obtained by a TDR leak detection system assuming a proper profile of capacitance per unit length along the sensing element. Even more importantly, the model is identified using only the reflectograms taken by the system with very little prior information about the system components. The developed technique is able to estimate with good accuracy, from reflectograms with unclear or ambiguous interpretation, the position and the extension of a region where water is located. The measurement is obtained without prior electromagnetic characterization of the TDR system components and without the need of modeling or quantifying a number of electromagnetic effects typical of on-site measurements.

Detection and Characterization of Multiple Discontinuities in Cables with Time-Domain Reflectometry and Convolutional Neural Networks.

In this paper, a convolutional neural network for the detection and characterization of impedance discontinuity points in cables is presented. The neural network analyzes time-domain reflectometry signals and produces a set of estimated discontinuity points, each of them characterized by a class describing the type of discontinuity, a position, and a value quantifying the entity of the impedance discontinuity. The neural network was trained using a great number of simulated signals, obtained with a transmission line simulator. The transmission line model used in simulations was calibrated using data obtained from stepped-frequency waveform reflectometry measurements, following a novel procedure presented in the paper. After the training process, the neural network model was tested on both simulated signals and measured signals, and its detection and accuracy performances were assessed. In experimental tests, where the discontinuity points were capacitive faults, the proposed method was able to correctly identify 100% of the discontinuity points, and to estimate their position and entity with a root-mean-squared error of 13 cm and 14 pF, respectively.

Wireless Sensor Networks for Environmental Monitoring.

In this editorial, an overview of the content of the Special Issue on "Wireless Sensor Networks for Environmental Monitoring" is provided [...].

An Extensive Unified Thermo-Electric Module Characterization Method.

Thermo-Electric Modules (TEMs) are being increasingly used in power generation as a valid alternative to batteries, providing autonomy to sensor nodes or entire Wireless Sensor Networks, especially for energy harvesting applications. Often, manufacturers provide some essential parameters under determined conditions, like for example, maximum temperature difference between the surfaces of the TEM or for maximum heat absorption, but in many cases, a TEM-based system is operated under the best conditions only for a fraction of the time, thus, when dynamic working conditions occur, the performance estimation of TEMs is crucial to determine their actual efficiency. The focus of this work is on using a novel procedure to estimate the parameters of both the electrical and thermal equivalent model and investigate their relationship with the operating temperature and the temperature gradient. The novelty of the method consists in the use of a simple test configuration to stimulate the modules and simultaneously acquire electrical and thermal data to obtain all parameters in a single test. Two different current profiles are proposed as possible stimuli, which use depends on the available test instrumentation, and relative performance are compared both quantitatively and qualitatively, in terms of standard deviation and estimation uncertainty. Obtained results, besides agreeing with both technical literature and a further estimation method based on module specifications, also provides the designer a detailed description of the module behavior, useful to simulate its performance in different scenarios.