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Ranging errors are inevitable in all local positioning systems, including those based on Time-of-Flight (ToF) technique. Results of experiments show that the major cause for these errors is a signal degradation from multipath propagation. This effect is especially critical in case of Non-Light-of-Sight (NLOS) conditions. This paper describes causes that affects ranging errors for nanoLOC™-TOF-technology and presents estimations for the probability density functions of such errors under different NLOS conditions. The provided estimations allow the improvement of the accuracy of the localization through the subsequent mitigation of the ranging errors from the measurements. Additionally, it is proposed to increase the number of cases of NLOS-conditions for the improvement of the accuracy.
The combination of fossil-derived fuels with ethanol and methanol has acquired relevance and attention in several countries in recent years. This trend is strongly affected by market prices, constant geopolitical events, new sustainability policies, new laws and regulations, etc. Besides bio-fuels these materials also include different additives as anti-shock agents and as octane enhancer. Some of the chemical compounds in these additives may have harmful properties for both environment and public health (besides the inherent properties, like volatility). We present detailed Raman spectral information from toluene (C7H8) and ethanol (C2H6O) contained in samples of ElO gasoline-ethanol blends. The spectral information has been extracted by using a robust, high resolution Fourier-Transform Raman spectrometer (FT-Raman) prototype. This spectral information has been also compared with Raman spectra from pure additives and with standard Raman lines in order to validate its accuracy in frequency. The spectral information is presented in the range of 0 cm-1 to 3500 cm-1 with a resolution of 1.66cm-1. This allows resolving tight adjacent Raman lines like the ones observed around 1003cm-1 and 1030cm-1 (characteristic lines of toluene). The Raman spectra obtained show a reduced frequency deviation when compared to standard Raman spectra from different calibration materials. The FT-Raman spectrometer prototype used for the analysis consist basically of a Michelson interferometer and a self-designed photon counter cooled down on a Peltier element arrangement. The light coupling is achieved with conventional62.5/125μm multi-mode fibers. This FT-Raman setup is able to extract high resolution and frequency precise Raman spectra from the additives in the fuels analyzed. The proposed prototype has no additional complex hardware components or costly software modules. The mechanical and thermal disturbances affecting the FT-Raman system are mathematically compensated by accurately extracting the optical path information of the Michelson interferometer. This is accomplished by generating an additional interference pattern with a λ = 632.8 nm Helium-Neon laser (HeNe laser). It enables the FT-Raman system to perform reliable and clean spectral measurements from the materials under observation.
In the dual membrane fuel cell (DM-Cell), protons formed at the anode and oxygen ions formed at the cathode migrate through their respective dense electrolytes to react and form water in a porous composite layer called dual membrane (DM). The DM-Cell concept was experimentally proven (as detailed in Part I of this paper). To describe the electrochemical processes occurring in this novel fuel cell, a mathematical model has been developed which focuses on the DM as the characteristic feature of the DM-Cell. In the model, the porous composite DM is treated as a continuum medium characterized by effective macro-homogeneous properties. To simulate the polarization behavior of the DM-Cell, the potential distribution in the DM is related to the flux of protons and oxygen ions in the conducting phases by introducing kinetic and transport equations into charge balances. Since water pressure may affect the overall formation rate, water mass balances across the DM and transport equations are also considered. The satisfactory comparison with available experimental results suggests that the model provides sound indications on the effects of key design parameters and operating conditions on cell behavior and performance.
In this paper we present a model of the discharge of a lithium–oxygen battery with aqueous electrolyte. Lithium–oxygen batteries (Li–O2) have recently received great attention due to their large theoretical specific energy. Advantages of the aqueous design include the stability of the electrolyte, the long experience with gas diffusion electrodes (GDEs), and the solubility of the reaction product lithium hydroxide. However, competitive specific energies can only be obtained if the product is allowed to precipitate. Here we present a dynamic one-dimensional model of a Li–O2 battery including a GDE and precipitation of lithium hydroxide. The model is parameterized using experimental data from the literature. We demonstrate that GDEs remove power limitations due to slow oxygen transport in solutions and that lithium hydroxide tends to precipitate on the anode side. We discuss the system architecture to engineer where nucleation and growth predominantly occurs and to optimize for discharge capacity.
In the brain-cell microenvironment, diffusion plays an important role: apart from delivering glucose and oxygen from the vascular system to brain cells, it also moves informational substances between cells. The brain is an extremely complex structure of interwoven, intercommunicating cells, but recent theoretical and experimental works showed that the classical laws of diffusion, cast in the framework of porous media theory, can deliver an accurate quantitative description of the way molecules are transported through this tissue. The mathematical modeling and the numerical simulations are successfully applied in the investigation of diffusion processes in tissues, replacing the costly laboratory investigations. Nevertheless, modeling must rely on highly accurate information regarding the main parameters (tortuosity, volume fraction) which characterize the tissue, obtained by structural and functional imaging. The usual techniques to measure the diffusion mechanism in brain tissue are the radiotracer method, the real time iontophoretic method and integrative optical imaging using fluorescence microscopy. A promising technique for obtaining the values for characteristic parameters of the transport equation is the direct optical investigation using optical fibers. The analysis of these parameters also reveals how the local geometry of the brain changes with time or under pathological conditions. This paper presents a set of computations concerning the mass transport inside the brain tissue, for different types of cells. By measuring the time evolution of the concentration profile of an injected substance and using suitable fitting procedures, the main parameters characterizing the tissue can be determined. This type of analysis could be an important tool in understanding the functional mechanisms of effective drug delivery in complex structures such as the brain tissue. It also offers possibilities to realize optical imaging methods for in vitro and in vivo measurements using optical fibers. The model also may help in radiotracer biomarker models for the understanding of the mechanism of action of new chemical entities.