ALBERT

Description: Mas, J.F. et al. have submitted a paper [1] for publication, which aims to respond to a paper published by Gebhardt et al. [2]. Mas, J.F. et al. had received a consultancy in 2013 to assess the quality of the early prototype products partly described in Gebhardt et al. in 2014. This consultancy, although a formal non-disclosure agreement had not been demanded, was awarded under the mutual understanding that the data handed over to Mas et al. constitute the early development phase of the program. Therefore, Mas et al. had been asked to give an assessment on the quality of the prototypes to obtain a proof of concept for the proposed workflow of MAD-Mex. It was clear that this assessment would suffer from limited availability of high quality training and validation data available in 2013. Mas et al. finally did not execute the consultancy due to the limited vector processing capacities in their lab. In October 2014, we sent the latest products, version 4.2 of the MAD-Mex products, including the more than 200,000 validation points gathered from independent expert interpreters of all Mexican ecosystems. Mas et al. did not respond to this transfer or to our request to collaborate in the quality control and assessment of MAD-Mex.

Description: Environmental conditions, such as air temperature and solar radiation, have a complex relationship with the energy requirements for heating and cooling of residential buildings. In this work, a comparative analysis of the insulation methods most commonly applied to low income single-family houses in Mexico is presented, in order to find the most energy-efficient combinations of methods for the various climates in this country. A common kind of building, small houses built with hollow cinder block walls and concrete slab roofs, was analyzed considering three insulation scenarios: walls only, roof only and both. We used dynamic simulation to evaluate energy consumption under the climate conditions found in several Mexican cities. From the energy consumption data and the cost of electricity in Mexico, we calculated net annual energy costs, including both annual energy savings and the annualized cost of the initial investment in better insulation. Results of this analysis show that insulating both roof and walls is most effective in cities with cold winters; insulating just the roof is best for temperate climates; and insulating walls (combined with high-albedo roofs) is most effective for cities with year-long warm weather.

Description: SBA-15 is an interesting mesoporous silica material having highly ordered nanopores and a large surface area, which is widely employed as catalyst supports, absorbents, drug delivery materials, etc. Since it has a lack of functionality, heteroatoms and organic functional groups have been incorporated by direct or post-synthesis methods in order to modify their functionality. The aim of this article is to review the state-of-the-art related to the use of SBA-15-based mesoporous systems as supports for hydrodesulfurization (HDS) catalysts.

Description: For more than a decade, the European Centre for Medium-Range Weather Forecasts (ECMWF) has used in-situ observations of 2 m temperature and 2 m relative humidity to operationally constrain the temporal evolution of model soil moisture. These observations are not available everywhere and they are indirectly linked to the state of the surface, so under various circumstances, such as weak radiative forcing or strong advection, they cannot be used as a proxy for soil moisture reinitialization in numerical weather prediction. Recently, the ECMWF soil moisture analysis has been updated to be able to account for the information provided by microwave brightness temperatures from the Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA). This is the first time that ECMWF uses direct information of the soil emission from passive microwave data to globally adjust the estimation of soil moisture by a land-surface model. This paper presents a novel version of the ECMWF Extended Kalman Filter soil moisture analysis to account for remotely sensed passive microwave data. It also discusses the advantages of assimilating direct satellite radiances compared to current soil moisture products, with a view to an operational implementation. A simple assimilation case study at global scale highlights the potential benefits and obstacles of using this new type of information in a global coupled land-atmospheric model.

Description: In recent years, glycopolymers have particularly revolutionized the world of macromolecular chemistry and materials in general. Nevertheless, it has been in this century when scientists realize that these materials present great versatility in biosensing, biorecognition, and biomedicine among other areas. This article highlights most relevant glycopolymeric materials, considering that they are only a small example of the research done in this emerging field. The examples described here are selected on the base of novelty, innovation and implementation of glycopolymeric materials. In addition, the future perspectives of this topic will be commented on.

Description: The renewable energy industry is undergoing continuous improvement and development worldwide, wind energy being one of the most relevant renewable energies. This industry requires high levels of reliability, availability, maintainability and safety (RAMS) for wind turbines. The blades are critical components in wind turbines. The objective of this research work is focused on the fault detection and diagnosis (FDD) of the wind turbine blades. The FDD approach is composed of a robust condition monitoring system (CMS) and a novel signal processing method. CMS collects and analyses the data from different non-destructive tests based on acoustic emission. The acoustic emission signals are collected applying macro-fiber composite (MFC) sensors to detect and locate cracks on the surface of the blades. Three MFC sensors are set in a section of a wind turbine blade. The acoustic emission signals are generated by breaking a pencil lead in the blade surface. This method is used to simulate the acoustic emission due to a breakdown of the composite fibers. The breakdown generates a set of mechanical waves that are collected by the MFC sensors. A graphical method is employed to obtain a system of non-linear equations that will be used for locating the emission source. This work demonstrates that a fiber breakage in the wind turbine blade can be detected and located by using only three low cost sensors. It allows the detection of potential failures at an early stages, and it can also reduce corrective maintenance tasks and downtimes and increase the RAMS of the wind turbine.

Description: This paper focuses on issues arising from the need to automatically analyze disturbances in the future (smart) grid. Accurate time allocation of events and the sequences of events is an important part of such an analysis. The performance of a joint causal and anti-causal (CaC) segmentation method has been analyzed with a set of real measurement signals, using an alternative detection technique based on a cumulative sum (CUSUM) algorithm. The results show that the location in time of underlying transitions in the power system can be more accurately estimated by combining CaC segmentation methods.

Description: The objective of this work was to study Cr(VI) sorption/desorption on two by-products from the wood industry: pine sawdust and oak wood ash. The retention/release experiments were carried out using standard batch-type trials. In the sorption-phase experiments, pine sawdust showed 23% sorption when a concentration of 100 mg Cr(VI)L−1 was added, whereas sorption on oak wood ash was 17%. In the desorption-phase, chromium release was clearly higher from pine sawdust than from oak wood ash (98% and 66%, respectively). Sorption curves were well fitted to the Freundlich and Lineal models. In view of the results, both materials can be considered of very limited value to remove Cr from polluted soil and water, which can be of relevance regarding its appropriate use as biosorbents and recycled by-products.

Description: Many environmental incidents affect large areas, often in rough terrain constrained by natural obstacles, which makes intervention difficult. New technologies, such as unmanned aerial vehicles, may help address this issue due to their suitability to reach and easily cover large areas. Thus, unmanned aerial vehicles may be used to inspect the terrain and make a first assessment of the affected areas; however, nowadays they do not have the capability to act. On the other hand, ground vehicles rely on enough power to perform the intervention but exhibit more mobility constraints. This paper proposes a multi-robot sense-act system, composed of aerial and ground vehicles. This combination allows performing autonomous tasks in large outdoor areas by integrating both types of platforms in a fully automated manner. Aerial units are used to easily obtain relevant data from the environment and ground units use this information to carry out interventions more efficiently. This paper describes the platforms and sensors required by this multi-robot sense-act system as well as proposes a software system to automatically handle the workflow for any generic environmental task. The proposed system has proved to be suitable to reduce the amount of herbicide applied in agricultural treatments. Although herbicides are very polluting, they are massively deployed on complete agricultural fields to remove weeds. Nevertheless, the amount of herbicide required for treatment is radically reduced when it is accurately applied on patches by the proposed multi-robot system. Thus, the aerial units were employed to scout the crop and build an accurate weed distribution map which was subsequently used to plan the task of the ground units. The whole workflow was executed in a fully autonomous way, without human intervention except when required by Spanish law due to safety reasons.

Description: Physically-based radiative transfer models (RTMs) help understand the interactions of radiation with vegetation and atmosphere. However, advanced RTMs can be computationally burdensome, which makes them impractical in many real applications, especially when many state conditions and model couplings need to be studied. To overcome this problem, it is proposed to substitute RTMs through surrogate meta-models also named emulators. Emulators approximate the functioning of RTMs through statistical learning regression methods, and can open many new applications because of their computational efficiency and outstanding accuracy. Emulators allow fast global sensitivity analysis (GSA) studies on advanced, computationally expensive RTMs. As a proof-of-concept, three machine learning regression algorithms (MLRAs) were tested to function as emulators for the leaf RTM PROSPECT-4, the canopy RTM PROSAIL, and the computationally expensive atmospheric RTM MODTRAN5. Selected MLRAs were: kernel ridge regression (KRR), neural networks (NN) and Gaussian processes regression (GPR). For each RTM, 500 simulations were generated for training and validation. The majority of MLRAs were excellently validated to function as emulators with relative errors well below 0.2%. The emulators were then put into a GSA scheme and compared against GSA results as generated by original PROSPECT-4 and PROSAIL runs. NN and GPR emulators delivered identical GSA results, while processing speed compared to the original RTMs doubled for PROSPECT-4 and tripled for PROSAIL. Having the emulator-GSA concept successfully tested, for six MODTRAN5 atmospheric transfer functions (outputs), i.e., direct and diffuse at-surface solar irradiance ( E d i f , E d i r ), direct and diffuse upward transmittance ( T d i r , T d i f ), spherical albedo (S) and path radiance ( L 0 ), the most accurate MLRA’s were subsequently applied as emulator into the GSA scheme. The sensitivity analysis along the 400–2500 nm spectral range took no more than a few minutes on a contemporary computer—in comparison, the same analysis in the original MODTRAN5 would have taken over a month. Key atmospheric drivers were identified, which are on the one hand aerosol optical properties, i.e., aerosol optical thickness (AOT), Angstrom coefficient (AMS) and scattering asymmetry variable (G), mostly driving diffuse atmospheric components, E d i f and T d i f ; and those affected by atmospheric scattering, L 0 and S. On the other hand, as expected, AOT, AMS and columnar water vapor (CWV) in the absorption regions mostly drive E d i r and T d i r atmospheric functions. The presented emulation schemes showed very promising results in replacing costly RTMs, and we think they can contribute to the adoption of machine learning techniques in remote sensing and environmental applications.