Engineering Subsystems Analysis of Adaptive Small Satellites

The current point-based satellite electronic subsystem engineering design process is insufficient to address the dynamic operations and post-mission reuse of small satellites. Also, space systems and missions require an adaptive architecture(s) that can withstand the radiation-prone flight environment and respond to in-situ environmental changes using onboard resources while maintaining optimal performance. This enormous conceptual design variables space/task of highly adaptive small satellite (HASS) system can be too large to explore, study, analyse and qualify. This research involved a parametric electronic subsystem engineering design process and methodology development for the production of sustainable capability-based small satellites. Consequently, an adaptive multifunctional architecture with five levels of in-orbit spacecraft customisations that eliminate subsystem boundaries at the system level is presented. Additive manufacturing methods are favoured to fabricate the proposed adaptive multifunctional monolithic structures. The initial system engineering analyses reveal that the HASS system has mass-, cost- and power-savings over the conventional small satellite implementation. An adaptive small satellite link performance improvement satisfying a less than 2 dB link margin loss for a 0.1 dB in-band noise figure ripple has been established. Moreover, a power budget model for HASSs that ensures a reliable solar array design and eliminates undue equipment oversizing has been developed. An adaptive broadband beamformer that can improve the satellite link margin has been designed. Also, an estimating relationship has been developed and practically validated for the operational times analysis of small satellite subsystems. The reported novel findings promise to enable capability-based, adaptive, cost-effective, reliable, multifunctional, broadband and optimal-performing space systems with recourse to post-mission re-applications

Industry-linked Projects and Research-informed-and-enriched Curriculum for Sustainable Student Employability Metrics

This paper presents inclusive industry-linked projects and research-informed-and-enriched curriculum for sustainable improved student educational metrics. A research project questionnaire was designed for data gathering besides the use of the researcher’s historical engineering curriculum teaching and research project supervision data. Monte Carlo simulations, t-test analysis and probability density functions modelling were implemented to obtain future-predicting reliable outcomes. The results of the analyses show that the student engagement improved by over 50 %; 100 % of the students who experienced industry-linked pedagogy with formative assessment feedback and feedforward achieved a distinction grade. 100 % of taught and supervised students gained employment into the industry; and/or embarked on further education (MSc and PhD)

Black Identity Formation in Repression: A Study of Selected Poems of Maya Angelou

This work focuses on the struggle for Black identity formation by African-Americas which has always been a continuous task, despite their constant state of repression. This research, aims at revealing the excruciating ordeals of African-Americans in the hands of not just racist Whites but in the hands of black males as well. This research goes further to show how the black female character is struggling to assert herself from patriarchal subjugation. This research uses both primary and secondary sources of data to carry out a detailed exploration of the text under study. For the primary sources, a study of selected poems of Maya Angelou will be used. While the secondary sources consist studies that other researchers have made, concerning this research. This research adopts two theoretical criticisms the Feminist literary theory and the Marxist literary criticism. These theories have been able to promote the status of subjugated black females, by portraying them in positive light, making them conscious of their capabilities and rights and that they are not inferior to the males. It goes further to strive for class equality, so that African-Americas like European-Americans can enjoy equal rights and privileges. This study reveals that though African-Americas females suffer doubly as a result of their race and gender, their resilience at forging for themselves a sense of self amidst oppression remains intact. This paper concludes with a look into how Maya weaves imagery and symbols into ebonics to forge a unique and belligerent linguistic culture for African-AmericansKeywords: Black Identity, Formation, Repression, Ebonics, Belligerence

8-12 GHz pHEMT MMIC Low-Noise Amplifier for 5G and Fiber-Integrated Satellite Applications

The fifth-generation (5G) radio access technology promises to revolutionise integrated earth-space communications applications for ubiquitous, seamless and broadband services. The assigned sub-6 GHz and millimetre-wave 5G frequencies require the sensitivity of the receiver front-end subsystem(s) to detect and amplify the desired signal at a noise floor of less than -90 dBm for a cost-effective infrastructure deployment. This paper presents a broadband monolithic microwave integrated circuit (MMIC) low-noise amplifier (LNA) design based on a 0.15 µm gate length Indium Gallium Arsenide (InGaAs) pseudomorphic high electron mobility transistor (pHEMT) technology for 5G and fiber-integrated satellite communications applications. The designed three-stage 8-12 GHz LNA implements a common-source topology. The MMIC LNA subsystem performance demonstrates an industry-leading in-band gain response of 40 dB; a noise figure of 1.0 dB; and a power dissipation of 43 mW. For a constant bandwidth receiver, the sensitivity changes by approximately 1.5 dB over the operating satellite signal frequency. Similarly, for a variable bandwidth receiver, the sensitivity changes by approximately 1.5 dB over the channel bandwidth. Moreover, the sensitivity margin of the designed LNA is 40 dB and this holds a great promise for real-time radio access component-level reconfiguration applications

Regulated-element Frost Beamformer for Vehicular Multimedia Sound Enhancement and Noise Reduction Applications

A key requirement of an adaptive sensor array involves the ability to deterministically adjust the directional response of the array to reduce noise and reverberations, null interferences and enhance the gain and recognition of the desired signal. This paper presents a low-carbon adaptive broadband beamforming algorithm called the regulated-element Frost beamformer. It enhances the desired signal based on the noise conditions of the individual omnidirectional sensors deployed in a complex dynamic environment that is prone to steering errors. The investigation of this algorithm was carried out in an interference-dominant, noisy automobile environment characterised by diffuse noise conditions. An embedded system measurement of real-time signals was carried out using omnidirectional acoustic sensors mounted in a model convertible F-Type car driven at speed limits of 20 to 50 mph. The simulation results indicate an array gain enhancement of 2 dB higher than the conventional Frost beamformer and it requires less sensors and filter taps for real-time reconfigurable implementations. The experimental results reveal that the average array gain of the regulated-element beamformer is 2.9 dB higher than the conventional Frost beamformer response. The minimum floor array gain of the regulated-element beamformer is 5 dB, representing 70 % noise reduction than the conventional adaptive beamformers

Internet of Things Public Key infrastructure using Reconfigurable Hardware Root of Trust

The expanding massive smart internet of things (IoTs) sensors with emerging use cases have created legitimate security requirements concerns for device manufacturers. Within decentralised and distributed systems of smart IoTs devices, heterogeneous mix of wireless protocols and/or standards are deployed. These technologies enable short-range (proximity and wireless personal area network); short/medium-range (wireless local area network); medium-range (wireless neighbourhood area network); and long-range (wireless wide area network) connectivities. The 5G/6G convergence ecosystem will span edge, gateway and enterprise IoTs nodes with unique security requirements. The public key infrastructure (PKI) is currently the industry’s holy grail for building secure IoTs devices. However, the current design solutions lack post-manufacturing multi-radio dynamic key reconfiguration and integrated reconfigurable hardware solutions. PKI must be embedded into the hardware design and simplified for third-party developers and manufacturers to implement and deploy. In this paper, we propose dynamic key configuration protocol (DKCP) and reconfigurable hardware root of trust (RHRoT) for 5G/6G satellite-cellular convergence network IoTs PKI implementations. This hybrid hardware-application protocol security solution provides a three-tier authentication that can be optionally implemented depending on the threat level within the IoTs device environment. Hence, DKCP only; DKCP and RHRoT; and RHRoT only tiers can be implemented to achieve IoTs PKI-based authentication, encryption and integrity for devices at scale by device manufacturers with little or no cryptography knowledge. The proposed adaptive IoTs PKI model promises scalable ubiquitous, seamless, cost-effective, secure, simple and security solution to stay ahead of existing and emerging threats and regulations

A 3.2-3.8 GHz Low-Noise Amplifier for 5G/6G Satellite-Cellular Convergence Applications

The rapid evolution of wireless communication systems towards 5G standards has imposed stringent requirements on the performance of radio frequency front-end components. Among these, the Low-Noise Amplifier (LNA) plays a pivotal role in determining the overall noise figure and sensitivity of the receiver chain. This paper presents a comprehensive design and analysis of a 3.2-3.8 GHz LNA tailored for 5G applications, employing a 0.3 mm gate length Gallium Arsenide (GaAs) pseudomorphic high electron transistor (pHEMT) technology process. The proposed LNA design focuses on achieving a low noise figure (NF), high gain, and robust linearity to accommodate the dense signal environment and wide bandwidth of 5G networks. The design leverages advanced matching networks and feedback topologies to enhance stability and reduce the noise contribution from the active devices. Simulation results predict a noise figure of 1.3-1.4 dB, a gain of 20-21 dB across the band of interest, and an input-referred third-order intercept point (IIP3) of 18 dBm. The LNA demonstrates excellent performance in a 5G testbed, showing a significant improvement in the signal-to-noise ratio and the potential to enhance 5G receiver sensitivity. The research substantiates the LNA’s viability for integration into 5G base stations and user equipment, underscoring its potential to contribute to the efficient and reliable operation of next-generation wireless networks. This LNA can be used for 5G New Release (NR) band of n77 and n78 (3.5-3.7 GHz

Adaptive Beamforming for mmWave 5G MIMO Antennas

The direction of arrival (DOA) estimation and beamforming are effective methods for spatial diversity realization. Various algorithm already exists for implementing these methods. This paper explore the performance of least mean square algorithm (LMS) beamforming algorithm. This adaptive beamforming algorithm investigates receiver signal processing method that continuously monitor, calculate and update the weights in a continuously changing electromagnetic environment. Several optimization algorithms are studied, and a comparison of the least mean-squared algorithm and the minimum variance distortionless response is investigated with varying parameters (i.e. number of antenna element, element spacing etc.) using analytical method and Matlab simulation. It would be demonstrated through simulation that LMS algorithm increases signal quality by elimination interfering signals and noise by nulling them, while sending maximum signal (beams) to the desired direction

Application of Machine Learning in Smart Factory Electromagnetic Radiation Exposure Levels Monitoring

Public and occupational exposure, both at home and at work, to electromagnetic (EM) fields of complex mix of electric and magnetic fields is not a new phenomenon. However, the environmental exposure to artificial EM radiations has been steadily increasing as growing electricity demand, domestic appliances, industrial equipment, telecommunications, broadcasting, ever-advancing wireless technologies and changes in social behaviour have created massive expanding known and unknown artificial sources. It is not disputed that EM fields above certain levels can trigger biological effects. The current debate is also centred on whether long-term low-level exposure can evoke biological responses and influence people's wellbeing. Experiments with healthy volunteers indicate that short-term exposure at the levels present in the environment or in the home do not cause any apparent detrimental effects. Exposures to higher levels that might be harmful are restricted by national and international guidelines. This paper seeks to address the uncertainties and resolve the debates on the impacts of short-term medium-term and long-term electromagnetic (EM) radiation on the public and occupational wellbeing, a robust guideline and system driven by artificial intelligence (AI). This AI/deep learning capabilities and high computational capacity would extend and combine diverse studies, methods, and statistical (data) association on cellular, animal, and epidemiology studies to attain the missing consistency and certainty about the true health and environmental effect of non-ionising EM radiations. Our scope is smart manufacturing environment with active always-on massive internet of things sensors with an extension to supply chain and smart cities use cases. Our proposed application of machine learning for monitoring EMF radiation in smart factory solution and operational system would tremendously improve safety, regulatory compliance and workforce confidence in the working environment. The visible, predictive and prescriptive functionalities of the AI-driven EMF monitoring system would proactively alert and position all stakeholders (workers, business leaders, the public, academia and regulatory bodies) in vintage situations necessary to forestall harms and damages to life, property and the environment