Abstract The accuracy of solar cell models is crucial for enhancing the performance of solar photovoltaic (PV) systems. However, existing solar cell models lack precise parameters, and the manufacturer's datasheet does not provide the required information for reliable modeling. Consequently, accurate parameter estimation becomes necessary. This paper presents a simple multi-objective optimization algorithm (Hybrid Particle Swarm Optimization and Rat Search Algorithm (PSORSA)) designed to estimate cell parameters based on this observation. Unlike other optimization algorithms addressing this issue, the proposed algorithm aims to overcome challenges related to local minima and premature convergence, which often lead to suboptimal results. The paper focuses on assessing the reliability of the proposed algorithm by comparing its performance with other well-known optimization algorithms. The proposed optimizing algorithm is tested on the CEC 2019 benchmark function. Experimental results (RMSE), including statistical analysis, validate the algorithm's effectiveness by comparing them with other algorithms. At the end, non-parametric test is performed to justify the outcomes, vouching for the better performance of the proposed algorithm. The findings demonstrate that the proposed algorithms are particularly well-suited for estimating solar PV models. With its simple structure and high accuracy, the proposed algorithm exhibits great potential for various applications in the field of solar energy. Moreover, its computational efficiency and ease of implementation further contribute to its practicality.

Fifth-generation (5G) cellular networks have led to the implementation of beyond 5G (B5G) networks, which are capable of incorporating autonomous services to swarm of unmanned aerial vehicles (UAVs). They provide capacity expansion strategies to address massive connectivity issues and guarantee ultra-high throughput and low latency, especially in extreme or emergency situations where network density, bandwidth, and traffic patterns fluctuate. On the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks, on the other hand, rely on new enabling technologies such as air interface and transmission technologies, as well as a unique network design, posing new challenges for the swarm of UAVs.Keeping these challenges in mind, this article focuses on the security and privacy, intelligence, and energy-efficiency issues faced by swarms of UAVs operating in 6G mobile network. In this state-of-the-art review, we integrated blockchain and AI/ML with UAV networks utilizing the 6G ecosystem. The key findings are then presented, and potential research challenges are identified. We conclude the review by shedding light on future research in this emerging field of research.

In this article, unmanned aerial vehicles (UAVs) are expected to play a key role in improving the safety and reliability of transportation systems, particularly where data traffic is nonhomogeneous and nonstationary. However, heterogeneous data sharing raises plenty of security and privacy concerns, which may keep UAVs out of future intelligent transportation systems (ITS). Some of the well-known security and privacy issues in the UAV-enabled ITS ecosystem include tracking UAVs and vehicle locations, unauthorized access to data, and message modification. Therefore, in this article, we contribute to the sum of knowledge by combining the hyperelliptic curve cryptography (HECC) techniques, digital signature, and hash function to present a privacy-preserving authentication scheme. The security features of the proposed scheme are assessed using formal security analysis methods, i.e., real-or- random (ROR) oracle model. To examine the performance of the proposed scheme, a comparison with other existing schemes has been carried out. The results reveal that the proposed scheme outperforms its counterpart schemes in terms of computation and communication costs.

In this paper, a printed low‐profile antenna with frequency and pattern reconfigurable functionality is designed in three modes. Each mode operates at different frequency bands and has several options available for pattern reconfiguration in these bands. The proposed antenna consists of eight pin‐diode switches (S1 to S8). The switches S1 and S2, installed in the radiating patch, are used for frequency reconfigurability to control the operating bands of the antenna. The rest of the six switches (S3, S4, S5, S6, S7, and S8), loaded in the stubs on the rear side of the antenna, are used for pattern reconfiguration to control the main lobe beam steering. When all switches are off, the proposed antenna operates in a wideband mode, covering the 3.82‐9.32 GHz frequency range. When S1 is on, the antenna resonates in the 3.5 GHz (3.09‐4.17 GHz) band. When both S1 and S2 are on, the resonant band of the antenna is shifted to 2.5 GHz band (2.40‐2.81 GHz). A very good impedance matching with a return loss of less than ‐10 dB is attained in these bands. The beam steering is done at each operating frequency by controlling the on and off states of the six pin‐diode switches (S3, S4, S5, S6, S7, and S8). Depending on the state of the switches, the antenna can direct the beam in seven distinct directions at 4.2 GHz, 4.5 GHz, and 5 GHz. The main beam of the radiation pattern is steered in five different directions at 5.5 GHz, 3.5 GHz, and 2.6 GHz operating bands for the given state of the mentioned switches. The proposed antenna supports several sub‐6 GHz 5G bands (2.6 GHz, 3.5 GHz, 4.2 GHz, 4.5 GHz, and 5 GHz) and can be used in handheld 5G devices.

This paper presents a new adaptation of a Gaussian echo model (GEM) to estimate the distances to multiple targets using acoustic signals. The proposed algorithm utilizes m-sequences and opens the door for applying other modulations and signal designs for acoustic estimation in a similar way. The proposed algorithm estimates the system impulse response and uses the GEM to limit the effect of noise before applying deconvolution to estimate the time of arrival (TOA) to multiple targets with high accuracy. The algorithm was experimentally evaluated for different scenarios with active (trans-mitters) and passive (reflectors) targets at proximity. In the case of closely spaced static passive targets, results show that 90% of the ranging errors are below 7 mm. When tracking two moving active targets approaching very close proximity, results show that 90% of the ranging errors are less than 10 mm.