Zeitschriftenaufsätze

Development of a comprehensive channel propagation model for high-fidelity design and deployment of wireless communication networks necessitates an exhaustive measurement campaign in a variety of operating environments and with different configuration settings. As the campaign is time-consuming and expensive, the effort is typically shared by multiple organizations, inevitably with their own channel-sounder architectures and processing methods. Without proper benchmarking, it cannot be discerned whether observed differences in the measurements are actually due to the varying environments or to discrepancies between the channel sounders themselves. The simplest approach for benchmarking is to transport participant channel sounders to a common environment, collect data, and compare results. Because this is rarely feasible, this paper proposes an alternative methodology - which is both practical and reliable - based on a mathematical system model to represent the channel sounder. The model parameters correspond to the hardware features specific to each system, characterized through precision, in situ calibration to ensure accurate representation; to ensure fair comparison, the model is applied to a ground-truth channel response that is identical for all systems. Five worldwide organizations participated in the cross-validation of their systems through the proposed methodology. Channel sounder descriptions, calibration procedures, and processing methods are provided for each organization as well as results and comparisons for 20 ground-truth channel responses.

The next-generation smart grid for the storage and delivery of renewable energy urgently needs to develop a low-cost and rechargeable energy storage technology beyond lithium-ion batteries (LIBs). Owing to the abundance of potassium (K) resources and the similar electrochemical performance to that of LIBs, potassium-ion batteries (PIBs) have been attracted considerable interest in recent years, and significant progress has been achieved concerning the discovery of high-performance electrode materials for PIBs. This review especially summarizes the latest research progress regarding anode materials for PIBs, including carbon materials, organic materials, alloys, metal-based compounds, and other new types of compounds. The reversible K-ion storage principle and the electrochemical performance (i.e., capacity, potential, rate capability, and cyclability) of these developed anode materials are summarized. Furthermore, the challenges and the corresponding effective strategies to enhance the battery performance of the anode materials are highlighted. Finally, prospects of the future development of high-performance anode materials for PIBs are discussed.

Priority data have been obtained on the effects of repeated systemic administrations of water-dispersible single-walled carbon nanotubes (SWCNTs) to spontaneously hypertensive rats with respect to constitutive NO-synthase (cNOS). As is known, NO is an inhibitory transmitter in the cardiovascular system. It was found that the systemic (i.p., subcutaneous, and i.m.) introductions of SWCNTs during two weeks resulted in considerable elevations of the NO2- level (a marker of NO bioavailability) in the blood of experimental hypertensive animals. Thus, SWCNTs may be used in the future for antihypertensive therapy as a novel agent capable of activating cNOS and, thus, increasing the NO production in central and peripheral elements of the cardiovascular system.

The overall performance of complex industrial systems using control and optimization strategies is often hampered by the lack of available measurements of key quality variables. The development of a soft sensor is a solution that allows for the necessary process information to be made available. Due to the nonlinear characteristics of industrial processes, a new soft sensor development approach that incorporates infrequent, variable time delayed measurements is proposed. Based on the fast-sampled measurement and the nonlinear, first principles model, the frequent and delay-free key performance indicator (KPI) estimation is realized using the square-root unscented Kalman filter (SR-UKF). Then, a modified Kalman Filter (MKF) algorithm is proposed to deal with those infrequent, but accurate measurements, whose values are variably delayed in time. These two types of estimates are fused and optimized to give an optimal, reliable KPI estimate based on the distributed state fusion UKF filter algorithm. The performance and effectiveness of the proposed approach are demonstrated by an experimental application for alumina concentration monitoring in the aluminum electrolysis industry.