ALBERT

Description: Energies, Vol. 11, Pages 706: Institutional Change and Environment: Lessons from the European Emission Trading System Energies doi: 10.3390/en11040706 Authors: Yolanda Fernández Fernández María Fernández López David González Hernández Blanca Olmedillas Blanco After more than ten years of operation of EU-ETS trading, it is time to analyse the results and draw lessons from the experience. Economic research typically considers emission price as the main explanatory variables when measuring the effects of Emission Trading Systems. The novelty of this work is to analyse whether or not trade alone, as an institutional change, is effective in reducing greenhouse gases emissions. The objective of this paper is to analyse to what extent the EU-ETS as a “regulatory” instrument of the supply of allowances is responsible for the effectiveness of the carbon market as a basic tool in the reduction of emissions. The analysis also includes other overlapping policies aimed at fighting climate change, for example, the promotion of renewables. For the empirical analysis, an econometric model is estimated using panel data for the 28 European Union countries between 1990 and 2014. The econometric model include three dummy variables to measure the effectiveness of the three phases of the EU-ETS commerce in reducing emissions. Furthermore, we analyse how effective the phases are when renewables energies are included in the analysis. The results show that the EU-ETS is effective to reduce emissions and each phase has a greater impact on the reduction. Nevertheless, the system should be more flexible to adapt to the fluctuations in the demand for rights.

Description: Energies, Vol. 10, Pages 2132: Energy Efficiency and Network Performance: A Reality Check in SDN-Based 5G Systems Energies doi: 10.3390/en10122132 Authors: Adriana Fernández-Fernández Fernández Cristina Cervelló-Pastor Leonardo Ochoa-Aday The increasing power consumption and related environmental implications currently generated by large data networks have become a major concern over the last decade. Given the drastic traffic increase expected in 5G dense environments, the energy consumption problem becomes even more concerning and challenging. In this context, Software-Defined Networks (SDN), a key technology enabler for 5G systems, can be seen as an attractive solution. In these programmable networks, an energy-aware solution could be easily implemented leveraging the capabilities provided by control and data plane separation. This paper investigates the impact of energy-aware routing on network performance. To that end, we propose a novel energy-aware mechanism that reduces the number of active links in SDN with multiple controllers, considering in-band control traffic. The proposed strategy exploits knowledge of the network topology combined with traffic engineering techniques to reduce the overall power consumption. Therefore, two heuristic algorithms are designed: a static network configuration and a dynamic energy-aware routing. Significant values of switched-off links are reached in the simulations where real topologies and demands data are used. Moreover, the obtained results confirm that crucial network parameters such as control traffic delay, data path latency, link utilization and Ternary Content Addressable Memory (TCAM) occupation are affected by the performance-agnostic energy-aware model.

Description: REDD+ implementation requires robust, consistent, accurate and transparent national land cover historical data and monitoring systems. Satellite imagery is the only data source with enough periodicity to provide consistent land cover information in a cost-effective way. The main aim of this paper is the creation of an operational framework for monitoring land cover dynamics based on Landsat imagery and open-source software. The methodology integrates the entire land cover and land cover change mapping processes to produce a consistent series of Land Cover maps. The consistency of the time series is achieved through the application of a single trained machine learning algorithm to radiometrically normalized imagery using iteratively re-weighted multivariate alteration detection (IR-MAD) across all dates of the historical period. As a result, seven individual Land Cover maps of Costa Rica were produced from 1985/1986 to 2013/2014. Post-classification land cover change detection was performed to evaluate the land cover dynamics in Costa Rica. The validation of the land cover maps showed an overall accuracy of 87% for the 2013/2014 map, 93% for the 2000/2001 map and 89% for the 1985/1986 map. Land cover changes between forest and non-forest classes were validated for the period between 2001 and 2011, obtaining an overall accuracy of 86%. Forest age-classes were generated through a multi-temporal analysis of the maps. By linking deforestation dynamics with forest age, a more accurate discussion of the carbon emissions along the time series can be presented.

Description: The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.

Description: Intelligent Transportation Systems (ITSs) integrate information, sensor, control, and communication technologies to provide transport related services. Their users range from everyday commuters to policy makers and urban planners. Given the complexity of these systems and their environment, their study in real settings is frequently unfeasible. Simulations help to address this problem, but present their own issues: there can be unintended mistakes in the transition from models to code; their platforms frequently bias modeling; and it is difficult to compare works that use different models and tools. In order to overcome these problems, this paper proposes a framework for a model-driven development of these simulations. It is based on a specific modeling language that supports the integrated specification of the multiple facets of an ITS: people, their vehicles, and the external environment; and a network of sensors and actuators conveniently arranged and distributed that operates over them. The framework works with a model editor to generate specifications compliant with that language, and a code generator to produce code from them using platform specifications. There are also guidelines to help researchers in the application of this infrastructure. A case study on advanced management of traffic lights with cameras illustrates its use.

Description: This work summarizes the activities carried out by the SMOS (Soil Moisture and Ocean Salinity) Barcelona Expert Center (SMOS-BEC) team in conjunction with the CIALE/Universidad de Salamanca team, within the framework of the European Space Agency (ESA) CALIMAS project in preparation for the SMOS mission and during its first year of operation. Under these activities several studies were performed, ranging from Level 1 (calibration and image reconstruction) to Level 4 (land pixel disaggregation techniques, by means of data fusion with higher resolution data from optical/infrared sensors). Validation of SMOS salinity products by means of surface drifters developed ad-hoc, and soil moisture products over the REMEDHUS site (Zamora, Spain) are also presented. Results of other preparatory activities carried out to improve the performance of eventual SMOS follow-on missions are presented, including GNSS-R to infer the sea state correction needed for improved ocean salinity retrievals and land surface parameters. Results from CALIMAS show a satisfactory performance of the MIRAS instrument, the accuracy and efficiency of the algorithms implemented in the ground data processors, and explore the limits of spatial resolution of soil moisture products using data fusion, as well as the feasibility of GNSS-R techniques for sea state determination and soil moisture monitoring.

Description: Background subtraction is considered the first processing stage in video surveillance systems, and consists of determining objects in movement in a scene captured by a static camera. It is an intensive task with a high computational cost. This work proposes an embedded novel architecture on FPGA which is able to extract the background on resource-limited environments and offers low degradation (produced because of the hardware-friendly model modification). In addition, the original model is extended in order to detect shadows and improve the quality of the segmentation of the moving objects. We have analyzed the resource consumption and performance in Spartan3 Xilinx FPGAs and compared to others works available on the literature, showing that the current architecture is a good trade-off in terms of accuracy, performance and resources utilization. With less than a 65% of the resources utilization of a XC3SD3400 Spartan-3A low-cost family FPGA, the system achieves a frequency of 66.5 MHz reaching 32.8 fps with resolution 1,024 x 1,024 pixels, and an estimated power consumption of 5.76 W.

Description: Materials, Vol. 11, Pages 1276: Tool Wear Mechanism in Cutting of Stack CFRP/UNS A97075 Materials doi: 10.3390/ma11081276 Authors: Severo Raul Fernandez-Vidal Sergio Fernandez-Vidal Moises Batista Jorge Salguero The aeronautics industry’s competitiveness has led to the need to increase productivity with one shot drilling (OSD) systems capable of drilling stacks of dissimilar materials (fibre/metal laminates, FML) in order to reduce riveting times. Among the materials that constitute the current aeronautical models, composite materials and aluminium (Al) and titanium (Ti) alloys stand out. These one-pass machining techniques produce high-quality holes, especially when all the elements that have to be joined are made of the same material. This work has followed a conventional OSD strategy and the same cutting conditions applied to CFRP (carbo-fibre-reinforced polymer), Al and CFRP/Al stacked sheets to know the wear mechanisms produced. With this purpose, results were obtained by using current specific techniques, such as microstructural analysis, monitoring of the shear forces and analysis of macrogeometric deviations. It has been determined that when these drilling techniques are applied under the same cutting conditions to stacks of materials of a different nature, the results of the wear mechanisms acting on the tool differ from those obtained when machining each material separately. This article presents a comparison between the effects of tool wear during dry drilling of CFRP and UNS A97075 plates separately and when machined as stacks.

Description: Energies, Vol. 11, Pages 2053: Productive Efficiency of Energy-Aware Data Centers Energies doi: 10.3390/en11082053 Authors: Damián Fernández-Cerero Alejandro Fernández-Montes Francisco Velasco Information technologies must be made aware of the sustainability of cost reduction. Data centers may reach energy consumption levels comparable to many industrial facilities and small-sized towns. Therefore, innovative and transparent energy policies should be applied to improve energy consumption and deliver the best performance. This paper compares, analyzes and evaluates various energy efficiency policies, which shut down underutilized machines, on an extensive set of data-center environments. Data envelopment analysis (DEA) is then conducted for the detection of the best energy efficiency policy and data-center characterization for each case. This analysis evaluates energy consumption and performance indicators for natural DEA and constant returns to scale (CRS). We identify the best energy policies and scheduling strategies for high and low data-center demands and for medium-sized and large data-centers; moreover, this work enables data-center managers to detect inefficiencies and to implement further corrective actions.

Description: Remote Sensing, Vol. 10, Pages 733: Remote Sensing Applied to the Study of Fire Regime Attributes and Their Influence on Post-Fire Greenness Recovery in Pine Ecosystems Remote Sensing doi: 10.3390/rs10050733 Authors: Víctor Fernández-García Carmen Quintano Angela Taboada Elena Marcos Leonor Calvo Alfonso Fernández-Manso We aimed to analyze the relationship between fire regime attributes and the post-fire greenness recovery of fire-prone pine ecosystems over the short (2-year) and medium (5-year) term after a large wildfire, using both a single and a combined fire regime attribute approach. We characterized the spatial (fire size), temporal (number of fires, fire recurrence, and return interval), and magnitude (burn severity of the last fire) fire regime attributes throughout a 40-year period with a long-time series of Landsat imagery and ancillary data. The burn severity of the last fire was measured by the dNBR (difference of the Normalized Burn Ratio) spectral index, and classified according to the ground reference values of the CBI (Composite Burn Index). Post-fire greenness recovery was obtained through the difference of the NDVI (Normalized Difference Vegetation Index) between pre- and post-fire Landsat scenes. The relationship between fire regime attributes (single attributes: fire recurrence, fire return interval, and burn severity; combined attributes: fire recurrence-burn severity and fire return interval-burn severity) and post-fire greenness recovery was evaluated using linear models. The results indicated that all the single and combined attributes significantly affected greenness recovery. The single attribute approach showed that high recurrence, short return interval and low severity situations had the highest vegetation greenness recovery. The combined attribute approach allowed us to identify a wider variety of post-fire greenness recovery situations than the single attribute one. Over the short term, high recurrence as well as short return interval scenarios showed the best post-fire greenness recovery independently of burn severity, while over the medium term, high recurrence combined with low severity was the most recovered scenario. This novel combined attribute approach (temporal plus magnitude) could be of great value to forest managers in the development of post-fire restoration strategies to promote vegetation recovery in fire-prone pine ecosystems in the Mediterranean Basin under complex fire regime scenarios.