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Circular antenna array (CAA) is one of the most widely used antenna array designs. This paper addresses the design challenges of the CAA with the non-uniform single ring, which is placed in an X-Y plane with the best sidelobe level (SLL) and improved first null beamwidth (FNBW). It has been solved using differential evolution, craziness-based particle swarm optimization (CRPSO), and novel particle swarm optimization (NPSO) techniques. An optimal combination of feeding current and inter-element spacing provides an array pattern with the best SLL and improved FNBW, as well as some other parameter calculations of the antenna array like maximum directivity, maximum effective aperture, total effective aperture, maximum beam area, total beam area, circumference, and radius of the CAAs using these techniques. There are six designs of CAAs with different antenna elements (i.e., 10-, 12-, 16-, 20-, 36-, and 64-elements) which have been taken into account. Simulations are done in MATLAB. Based on various simulation results, we can analyze the performance of SLL and FNBW with other parameters using NPSO and compare them with different techniques of CAAs, as shown in the numerical analysis and simulation result section.
For dissolving active oil droplets in an ambient liquid, it is generally assumed that the Marangoni effect results in repulsive interactions, while the buoyancy effects caused by the density difference between the droplets, diffusing product and the ambient fluid are usually neglected. However, it has been observed in recent experiments that active droplets can form clusters due to buoyancy-driven convection (Krüger et al., Eur. Phys. J. E, vol. 39, 2016, pp. 1–9). In this study we numerically analyse the buoyancy effect, in addition to the propulsion caused by Marangoni flow (with its strength characterized by the Péclet number $Pe$). The buoyancy effects have their origin in (i) the density difference between the droplet and the ambient liquid, which is characterized by the Galileo number $Ga$; and (ii) the density difference between the diffusing product (i.e. filled micelles) and the ambient liquid, which can be quantified by a solutal Rayleigh number $Ra$. We analyse how the attracting and repulsing behaviour of neighbouring droplets depends on the control parameters $Pe$, $Ga$ and $Ra$. We find that while the Marangoni effect leads to the well-known repulsion between the interacting droplets, the buoyancy effect of the reaction product leads to buoyancy-driven attraction. At sufficiently large $Ra$, even collisions between the droplets can take place. Our study on the effect of $Ga$ further shows that with increasing $Ga$, the collision becomes delayed. Moreover, we derive that the attracting velocity of the droplets, which is characterized by a Reynolds number $Re_d$, is proportional to $Ra^{1/4}/( \ell /R)$, where $\ell /R$ is the distance between the neighbouring droplets normalized by the droplet radius. Finally, we numerically obtain the repulsive velocity of the droplets, characterized by a Reynolds number $Re_{rep}$, which is proportional to $PeRa^{-0.38}$. The balance of attractive and repulsive effect leads to $Pe\sim Ra^{0.63}$, which agrees well with the transition curve between the regimes with and without collision.
The history of wind power is discussed, from pumping water that reclaimed land in the Netherlands in the 1600s to today’s megawatt-scale, grid-tied, electricity-generating behemoths. Installations in Denmark (Vindeby, Copenhagen), the US (West Texas, Wyoming, offshore Atlantic), Spain (100% wind in El Hierro), the UK (London Array, North Sea), and China (China’s Wind Base program is expected to reach 1 terrawatt of grid power by 2050) are examined as are novel horizontal-axis, vertical-axis, and vibrating turbine technologies. The number of onshore and offshore sites continues to increase the amount of grid-tied renewable energy year on year (now 10%). The problems of long-distance transmission, stranded power, and recycling are discussed.
The origin of the grid is explained, starting with Nikola Tesla and Westinghouse at Niagara Falls, high-voltage transformers, and central-power plant construction across the globe. Renewable-energy technologies are discussed, including hydroelectric dams, geothermal (Iceland, Italy, the US), and marine energy (Scotland, Canada). The advent of the modern prosumer who buys and sells power to a bi-directional grid, virtual power plants, and microgrids are examined as intermittent renewables require new means to manage distributed resources. The social consequences, reliability, and privacy issues of a growing smart grid are examined.
Chapter 11 contains a detailed description of each of the world's most popular analog and digital cellular telephone standards, as well as the most popular digital cordless telephone standards. Treatment includes the first analog mobile telephone standards that were implemented in the United States and Europe, and all of the second-generation (2G) cellphone standards deployed around the world. The most popular digital cordless telephone standards are also presented, as it is useful to see how the concepts taught in all of the earlier chapters of this textbook were implemented in very successful, large-scale commmercial deployments. The evolution of the cellular industry is clearly seen by studying the various standardspresented in this chapter, allowing the reader to understand the design decisions and approaches that are adapted to increase the capacity and reliability of wireless communications. Standards covered in this chapter include AMPS, NAMPS, ETACS, USDC, PDC, GSM,Qualcomm's CDMA IS-95, IS-54, IS-136, DECT, CT2, PACS, PHS, and wireless television.
INTRODUCTIONAn energy transition is underway in Southeast Asia. This process is dependent on an uninterrupted supply of the minerals and metals that are essential to produce low-carbon technologies. These raw materials are termed ‘critical minerals’ (CMs), owing to three broad features: their necessity as inputs in low-carbon technology, the lack of viable substitutes, and significant supply constraints. The demand for CMs such as lithium, nickel, cobalt, rare earth elements (REEs), copper, and silicon3 is expected to increase exponentially in the coming decades. To meet the global net zero target by 2050, mineral inputs will need to increase sixfold by 2040, compared to current levels. According to scenarios developed by the International Energy Agency (IEA), the demand for minerals used in electric vehicles (EVs) will increase thirty times compared to current levels, while mineral requirements for low-carbon energy generation will triple by 2040.
The development of CMs is impeded by several supply constraints. Currently, a handful of countries dominate the CMs market, with China playing an outsized role in both the upstream and downstream parts of the supply chain. For example, China currently extracts 65 per cent and processes 85 per cent of the world's REEs. The largest amount of copper, nickel and cobalt are extracted in Chile, Indonesia, and the Democratic Republic of Congo (DRC), respectively. Yet, as shown in Figure 1, China dominates the processing of all three minerals, as well as alumina and lithium.
There is growing academic and policy consensus on the need to develop sustainable and reliable supply chains for CMs, which is of great relevance to Southeast Asia. On the one hand, the region can become a major supplier of critical minerals, due to the existence of substantial deposits of bauxite, nickel, tin, REEs, cobalt, manganese and graphite. On the other hand, Southeast Asia is likely to become a significant consumer of critical minerals, owing to the region’s growing solar photovoltaic (PV) and electric vehicle industries. Malaysia and Vietnam are the world’s second and third-largest solar PV manufacturers and accounted for one-fifth of global shipments in 2020.9 Thailand has become the region’s leading producer of EVs, while the Philippines and Indonesia have undertaken initial steps to develop integrated battery and EV supply chains.10
The history of nuclear power is examined through the work of a number of pioneering physicists, chemists, and engineers, including Marie Curie in Paris (radiation), Ernest Rutherford, James Chadwick, and John Cockcroft in Cambridge (model of the nucleus), and Enrico Fermi in Rome, New York, and Chicago (the first nuclear reactor CP-1). Albert Einstein and Leo Szilard’s cautionary letter to Franklin Roosevelt, the Manhattan Project at Los Alamos that oversaw the making of the first nuclear bomb, US Admiral Hyman Rickover’s nuclear fleet, and the transition to electricity-generating fission power by the US, UK, and Soviet Union is explored.
The ‘70s growth of “too cheap to meter” nuclear power is shown to be expensive, dangerous, and incapable of treating its own waste. Examples of the failure of the nuclear industry are given, in particular, accidents at Mayak, Cumbria, Three Mile Island, Chernobyl, and Fukushima, as well as numerous deep geologic repositories. The state-of-the-art of nuclear power, so-called small modular reactors, and the current slate of existing and under-development power plants are discussed. The history, design specifications, and potential for success of nuclear fusion is included with examples from JET, ITER, NIF, and others.
The Muscle Analyzer System (MAS) project wants to create a standalone microwave device that can assess the muscle quality, called the MAS device. To achieve that an algorithm that can derive the properties of skin, fat and muscle from the measurements is needed. This paper presents a machine learning algorithm that aims to do precisely that. The algorithm relies on first predicting the skin using the data from the MAS device, then predicting the fat again using the data from the MAS but also the predicted skin value and lastly the muscle is predicted using the microwave data together with the skin and fat predictions. Data have been collected in phantom experiments, materials that mimick the dielectric properties of human tissues. The algorithm is trained to predict the properties of said phantoms. The results show that the prediction for skin thickness works well, the fat thickness prediction is okay but the muscle prediction struggles. This is partly due to the error from the skin and fat layers are propagated to the muscle layer and partly because the muscle layer is farthest away from the sensor, which makes getting information from that layer harder.
We investigate numerically the propulsion characteristics of an oscillating foil undergoing coupled heave and pitch motion in a linearly density-stratified flow. A parameter space defined by the internal Froude number ($1 \le Fr \le 10$) and the maximum angle of attack ($5^\circ \le {\alpha _0} \le 30^\circ$) is considered in our study. The results demonstrate a significant enhancement in both thrust production and propulsive efficiency due to the stratification influence. Notably, the highest efficiency exceeding $80\,\%$ is achieved under moderate stratification conditions, surpassing the performance observed in a homogeneous fluid. We attribute this optimum performance to the proper match between the stratification effect and foil kinematics, which gives rise to intense vortex interactions and sufficient wave–mean flow interactions in the near wake of the oscillating foil. Consequently, the energy is transferred towards wake structures to form a high-intensity momentum jet in close proximity to the foil's trailing edge, indicating efficient propulsion. Furthermore, we find that the stratifications within the moderate-to-strong transitional regime display a reduced dependence of propulsive efficiency on the maximum angle of attack, primarily due to the delaying and alleviating effects on dynamic-stall events. Such a mechanism enables the oscillating foil to maintain a satisfactory performance by sufficiently high angles of attack without the penalty of stall events. Based on our findings, we propose that animals or artificial vehicles utilising oscillatory propulsion can benefit from the presence of density stratification in the surrounding fluid.
Chapter 7 provides fundamental treatment of channel equalization, antenna diversity combining methodologies, and error correction/control codes used in modern wireless communication systems. Both linear and non-linear equalizers are presented, along with the most popular feedback algorithms for equalizer training and practical operation, such as zero-forcing (ZF), least mean squares (LMS), and recursive least squares (RLS). These algorithms are applied to the Decision Feedback Equalizer (DFE) and the Maximum Likelihood Sequence Estimation (MLSE) equalizer structure.Antenna diversity combining methodsfor Rayleigh small-scale fading channels show the theoretical increase in signal-to-noise ratio (SNR) for a wide range of antenna combining and diversity methods used in today's wireless systems. The RAKE receiver for Code Division Multiple Access and spread spectrum is also studied. Finally, practical error control codes, such as block codes and convolutional codes, used in today's cellphone and wi-fi standards are presented and analyzed, along with interleaving methods and popular decoding algorithms such as the Viterbi algorithm. Trellis codes and turbo codes are presented, as well.
We have developed a parameter-free, two-phase, volume-averaged approach to predictively describe the spin-up flow of dilute, cluster-free ferrofluids excited by low-frequency rotating magnetic fields. Predictive validation of the model was performed through a thorough comparison with local velocity profile measurements, and it demonstrated its ability to capture the spin-up flow dynamics without the need for parameter tuning by carefully delineating the validity domain of the ferrofluid dilutedness conditions. To gain insight into the underlying flow mechanisms, we performed a systematic parametric analysis examining the effects of the induced magnetic field, the dipolar interactions between magnetic nanoparticles and the demagnetizing field. How these mechanisms shape the flow of dilute ferrofluids excited by low-frequency rotating fields in a standard spin-up flow geometry has been addressed using probabilistic nanoparticle orientational dynamics, combining Faxén's laws and the Smoluchowski equation to describe the transport of particle magnetic moments. Our findings revealed that the induced magnetic field is the primary driving force of ferrofluid spin-up flow. The dipole interactions and demagnetizing field, on the other hand, contribute only as secondary phenomena to the overall flow behaviour. Furthermore, we have discussed the potential extension of the two-phase approach, in particular with respect to the formation of chain-like aggregates under the influence of strong magnetic fields. Overall, our study provides valuable insights into the complex dynamics of ferrofluid flow and contributes to a comprehensive understanding of the key mechanisms governing the spin-up flow of dilute ferrofluids excited by low-frequency rotating magnetic fields.
Sensing is a key requirement for any but the simplest mobile behavior. In order for Robot to be able to warn the crew of Lost in Space that there is danger ahead, it must be able to sense and reason about its sensor responses. Sensing is a critical component of the fundamental tasks of pose estimation – determining where the robot is in its environment; pose maintenance – maintaining an ongoing estimate of the robot’s pose; and map construction – building a representation of the robot’s environment.