ESTABLISHING CLEAR-SKY LEVELS ON GEOSTATIONARY SATELLITE LINKS

Links between a geostationary satellite and a terminal on the ground are subject to changes in signal level with varying timescales. Assuming an extremely stable earth terminal, short-term variations, on the order of seconds in duration, are typically due to tropospheric scintillation; longer-term variations, on the order of many minutes, may be due to satellite instabilities (e.g. non-precise station-keeping and antenna pointing) and attenuation due to particulates in the atmosphere, the most important of which is rain; even longer-term variations, with periodicities on the order of days, seasons, and years, are due both to variations in the length (and constituents), along the path through the atmosphere, and the cyclical motion of the Earth-moon system and planets orbiting the sun. This research does not address tropospheric scintillation, even though it can be thought of as a clear-air phenomenon, and rain attenuation: both are well modelled in most climate regions [1]. The focus is to address the other phenomena that can change the clear-sky level on a geostationary satellite-to-ground link. Previous experiments in Papua New Guinea which used co-located radiometer and satellite beacon receivers, detected diurnal and seasonal variations in the received satellite beacon signal level during clear sky that suggested the atmosphere played a role in the signal variation. This research found additional evidence in several time series satellite propagation data collected from sites in North America and Brazil. These propagation databases contain time series data of the received signal level from geostationary satellite beacons together with inferred attenuation from co-located radiometer directed along the same path. Some databases also contain meteorological time series data collected at or near the receiver. Upon investigation, the time data plot of the received beacon signal level and inferred attenuation from the radiometer contained concomitant seasonal as well as diurnal variations. Results from three different spectral estimation techniques showed that the diurnal variation detected in the beacon and radiometer were both of a solar day periodicity. This showed that solar heating effects are driving the diurnal variation observed, and that they are not satellite induced. There is also a general trend of decreasing solar tidal effect as the climate becomes less warm and humid. The only site that did not show evidence of a solar day variation was in Fairbanks, Alaska from the ACTS database. This site is both the coldest, and has the lowest elevation angle. Other possible causes of variations in received beacon level such as front end instabilities, as well as a possible noise contribution from the antenna side lobes, were found to be untenable. In some warmer and more humid sites, a sidereal and anti-sidereal peak was also detected to the right and left of the solar peak, respectively. These side peaks are found mainly in cosmic ray research. They are generated when a daily solar variation is amplitude modulated by a seasonal variation of one cycle/year, producing sidebands at the sidereal and anti-sidereal frequencies, as shown in Fig.1. The seasonal variation detected in both the ACTS and Texas database were closely estimated using the gaseous attenuation model provided in the ITU recommendation P676-9. This calculation uses meteorological values such as averages of pressure, temperature and humidity together with the earth station's elevation angle with respect to the satellite, to calculate attenuation along the path in clear sky. From the result of the simulation we were able to identify as well as quantify the sources of the mean clear sky variation. The seasonal variation was highest in Alaska, with more than a 3dB difference between the maximum detected in summer and minimum detected during winter, as shown in Fig.2 [2]. For a Ku-band VSAT system that is designed to operate with a 7 dB margin, a change of 3 dB in just clear sky conditions is significant. Factors that contribute to the existence of atmospheric tides, and procedures that can help limit these effects in measuring clear-sky mean levels were also proposed

Deep Learning Based for Cryptocurrency Assistive System

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognized as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency investments, many Alternative Coins (Altcoins) resurfaced to mimic the fiat currency. There are several methods to forecast cryptocurrency prices that have been widely used in forecasting fiat and stock prices. Artificial Intelligence (AI) ,Machine Learning(ML) and Deep Learning(DL) provide a different perspective on how investors can estimate crypto price trend and movement. In this paper, as cryptocurrency price is time-dependent, Recurrent Neural Network (RNN) is presented due to RNN’s nature, which is well suited for Time Series Analysis (TSA). The topology of the proposed RNN model consists of three stages which are model groundwork, model development, and testing and optimization. The RNN architecture is extended to three different models specifically Long Short-Term Memory (LSTM), Gat-ed Recurrent Unit (GRU), and Bi-Directional Long Short-Term Memory (LSTM). There are a few hyperparameters that affect the accuracy of the deep learning model in predicting cryptocurrency prices. Hyperparameter tuning set the basis for optimizing the model to improve the accuracy of cryptocurrency prediction. Next, the models were tested with data from different coins listed in the cryptocurrency market. Then, the model was experiment-ed with different input features to figure out how accurate and robust these models in predicting the cryptocurrency price. GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Executional Time, scoring 2.2201, 0.8076, and 200s using the intraday trading strategy as input features

Deep Learning Based for Cryptocurrency Assistive System

Cryptocurrency is branded as a digital currency, an alternative exchange currency system with significant ramifications for the economies of rising nations and the global economy. In recent years, cryptocurrency has infiltrated almost all financial operations; hence, cryptocurrency trading is frequently recognized as one of the most popular and promising means of profitable investment. Lately, with the exponential growth of cryptocurrency investments, many Alternative Coins (Altcoins) resurfaced to mimic the fiat currency. There are several methods to forecast cryptocurrency prices that have been widely used in forecasting fiat and stock prices. Artificial Intelligence (AI) ,Machine Learning(ML) and Deep Learning(DL) provide a different perspective on how investors can estimate crypto price trend and movement. In this paper, as cryptocurrency price is time-dependent, Recurrent Neural Network (RNN) is presented due to RNN’s nature, which is well suited for Time Series Analysis (TSA). The topology of the proposed RNN model consists of three stages which are model groundwork, model development, and testing and optimization. The RNN architecture is extended to three different models specifically Long Short-Term Memory (LSTM), Gat-ed Recurrent Unit (GRU), and Bi-Directional Long Short-Term Memory (LSTM). There are a few hyperparameters that affect the accuracy of the deep learning model in predicting cryptocurrency prices. Hyperparameter tuning set the basis for optimizing the model to improve the accuracy of cryptocurrency prediction. Next, the models were tested with data from different coins listed in the cryptocurrency market. Then, the model was experiment-ed with different input features to figure out how accurate and robust these models in predicting the cryptocurrency price. GRU has the best accuracy in forecasting the cryptocurrency prices based on the values of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Executional Time, scoring 2.2201, 0.8076, and 200s using the intraday trading strategy as input features

Optimal Design of a New Cascaded Multilevel Inverter Topology With Reduced Switch Count

Multilevel inverters (MLIs) are a great development for industrial and renewable energy applications due to their dominance over conventional two-level inverter with respect to size, rating of switches, filter requirement, and efficiency. A new single-phase cascaded MLI topology is suggested in this paper. The proposed MLI topology is designed with the aim of reducing the number of switches and the number of dc voltage sources with modularity while having a higher number of levels at the output. For the determination of the magnitude of dc voltage sources and a number of levels in the cascade connection, three different algorithms are proposed. The optimization of the proposed topology is aimed at achieving a higher number of levels while minimizing other parameters. A detailed comparison is made with other comparable MLI topologies to prove the superiority of the proposed structure. A selective harmonic elimination pulse width modulation technique is used to produce the pulses for the switches to achieve high-quality voltage at the output. Finally, the experimental results are provided for the basic unit with 11 levels and for cascading of two such units to achieve 71 levels at the output. © 2013 IEEE

A Systematic Literature Review (SLR) on Autonomous Path Planning of Unmanned Aerial Vehicles

UAVs have been contributing substantially to multi-disciplinary research and around 70% of the articles have been published in just about the last five years, with an exponential increase. Primarily, while exploring the literature from the scientific databases for various aspects within the autonomous UAV path planning, such as type and configuration of UAVs, the complexity of their environments or workspaces, choices of path generating algorithms, nature of solutions and efficacy of the generated paths, necessitates an increased number of search keywords as a prerequisite. However, the addition of more and more keywords might as well curtail some conducive and worthwhile search results in the same pursuit. This article presents a Systematic Literature Review (SLR) for 20 useful parameters, organized into six distinct categories that researchers and industry practitioners usually consider. In this work, Web of Science (WOS) was selected to search the primary studies based on three keywords: “Autonomous” + “Path Planning” + “UAV” and following the exclusion and inclusion criteria defined within the SLR methodology, 90 primary studies were considered. Through literature synthesis, a unique perspective to see through the literature is established in terms of characteristic research sectors for UAVs. Moreover, open research challenges from recent studies and state-of-the-art contributions to address them were highlighted. It was also discovered that the autonomy of UAVs and the extent of their mission complexities go hand-in-hand, and the benchmark to define a fully autonomous UAV is an arbitral goal yet to be achieved. To further this quest, the study cites two key models to measure a drone’s autonomy and offers a novel complexity matrix to measure the extent of a drone’s autonomy. Additionally, since preliminary-level researchers often look for technical means to assess their ideas, the technologies used in academic research are also tabulated with references

Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively

A New Single Phase Single Switched-Capacitor Based Nine-Level Boost Inverter Topology with Reduced Switch Count and Voltage Stress

Based on the concept of switched-capacitor based multilevel inverter topology, a new structure for a boost multilevel inverter topology has been recommended in this paper. The proposed topology uses 11 unidirectional switches with a single switched capacitor unit to synthesize nine-level output voltage waveform. Apart from the twice voltage gain, self-voltage balancing of capacitor voltage without any auxiliary method along with reduced voltage stress has been the main advantages of this topology. The merits of proposed topology have been analyzed through various comparison parameters including component counts, voltage stresses, cost and efficiency with a maximum value of 98.3%, together with the integration of switched capacitors into the topology following recent development. Phase disposition pulse width modulation (PD-PWM) technique and nearest level control PWM (NLC-PWM) have been used for the control of switches. Different simulation and hardware results with different operating conditions are included in the paper to demonstrate the performance of the proposed topology. - 2013 IEEE.This work was supported in part by the QU High Impact under Grant QUHI-CENG-19/20-2 from Qatar University, and in part by the Qatar National Library, Doha, Qatar.Scopu

Low Switching Frequency Based Asymmetrical Multilevel Inverter Topology with Reduced Switch Count

The inceptions of multilevel inverters (MLI) have caught the attention of researchers for medium and high power applications. However, there has always been a need for a topology with a lower number of device count for higher efficiency and reliability. A new single-phase MLI topology has been proposed in this paper to reduce the number of switches in the circuit and obtain higher voltage level at the output. The basic unit of the proposed topology produces 13 levels at the output with three dc voltage sources and eight switches. Three extentions of the basic unit have been proposed in this paper. A detailed analysis of the proposed topology has been carried out to show the superiority of the proposed converter with respect to the other existing MLI topologies. Power loss analysis has been done using PLECS software, resulting in a maximum efficiency of 98.5%. Nearest level control (NLC) pulse-width modulation technique has been used to produce gate pulses for the switches to achieve better output voltage waveform. The various simulation results have been performed in the PLECS software and a laboratory setup has been used to show the feasibility of the proposed MLI topology. - 2013 IEEE.This publication was made possible by QU High Impact Grant # [QUHI-CENG-19/20-2] from Qatar University. The statements made herein are solely the responsibility of the authors. The publication charges are funded by the Qatar National Library, Doha, Qatar.Scopu