Standard risk assessment approaches are sometimes time-consuming and subjective. In order to overcome these challenges an innovative method will be presented in this article by mixing sentiment analysis and machine learning (ML). The suggested technique improves the effectiveness, precision, and scope of risk insights when it comes to the detection of feelings in logs via the use of automated data collection. The research examines several different ML classifiers and makes use of a deep learning model that has been pre-trained to evaluate risks in logs that are multi-linguistic. This proves the adaptability and scalability of our technique when used in a multilanguage setting. This combination of sentiment analysis and ML are a significant advancement in comparison to traditional approaches since it enables real-time processing and delivers important insights into the management of organizational risks.

The orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplexing (OFDM) in a high-mobility environment with high time-frequency dispersive channels. Since radio frequency (RF) impairments have a direct impact on waveform behavior, this paper investigates the experimental implementation of RF-impairments that affect OTFS waveform performance and compares them to the OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, and then an experimental framework is established for investigating the impact of RF-impairments, including non-linearity, carrier frequency offset (CFO), I/Q imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-defined radio (SDR) at carrier frequencies of $2.4$ GHz and 5 GHz based on the Keysight EXG X-Series devices. The comparison of the performances of OFDM and OTFS in the presence of RF-impairments reveals that OTFS significantly outperforms OFDM.

Enhancing security level against potential internal eavesdroppers in Non-Orthogonal Multiple Access (NOMA) has been reported as a serious challenge. In this letter, a novel secure NOMA scheme based on physical-layer security concepts is proposed. The proposed scheme exploits the random and independent channel characteristics to induce a different phase shift in each user's symbol. Based on the assumption that the instantaneous channel phase between a user and the base station is available only at both ends, other users will not be able to decode the right symbol. Analytical and simulation average bit error rate performance results demonstrate the ability of the proposed scheme to degrade the performance at the eavesdropper without affecting the performance at other legitimate users and guarantee data confidentiality of each user.

The combination of CR-NOMA has enormous potential interest to improve spectral efficiency and increase system capacity in future wireless communication networks. Hence, according to the broadcast nature of the CR-NOMA wireless channel, handling the confidentiality of information is one of the most important requirements of the network. In this paper, PLS against internal Eve is investigated by the CR-NOMA under the limitations of joint delay and confidentiality rate. First, initial power allocation for SU based on the PU interference threshold is constructed. Then, a multi-objective problem based on different SUs demands is formulated based on Stackelberg game modeling. After that, the SE is studied and explored. At the final stage, an overall algorithm has been proposed whereby SE strategies are frequently updated to find the best parameters for the proposed system model. The simulation results show the superiority of the proposed scheme in achieving a higher average utility function, spectral efficiency, fairness index, up to 44%, 90%, and 54.7%, respectively, and reliability compared to the conventional scheme.

Joint radar communication (JRC) effectively integrates radar capabilities and data transmission that are used in vehicle-to-everything (V2X) applications.Within the JRC, the transmission suffers from a highly dispersive, time-varying channel due to the mobility environment, while the transmission requires a secure method with low latency and time complexity. The confidential information signal must be resilient to radar user eavesdropping while enabling the radar user to get the correct channel radar estimation image. The main contribution of this paper is to provide security for the JRC system used in V2X application networks by employing Artificial Noise (AN) Physical Layer Security (PLS) with low time complexity constraints, which integrates with the Orthogonal Time Frequency Space (OTFS) waveform that offers robustness against time-varying channel degradation considering the 3GPP TS 36 Extended Vehicular A channel model (EVA). Moreover, the practical implementation considers imperfect channel state information and involves a powerful eavesdropper who can estimate legitimate user channels with a certain amount of error. The secrecy capacity, average bit-error rate (BER), time complexity, and execution time results present the performance of the proposed AN scheme and its ability to protect legitimate users’ information in a promising manner while providing the radar user with sufficient quality of service (QoS), where the proposed approach provides a secrecy capacity level up to 7 nats/symbol with an execution time that is 42 times faster than the Advanced Encryption Standard (AES) and 6.4 thousand times better than Rivest-Shamir-Adleman (RSA) cryptographic algorithms.

Optical Wireless Sensor Networks (OWSNs) have emerged as a promising solution for energy-efficient and secure data collection in free-space optical (FSO) environments. A key challenge in such networks is minimizing the decision error rate (DER) during decision aggregation at the central entity (CE). Building on earlier Time-Difference-of-Arrival (TDoA) reporting methods, this paper introduces an Angle-of-Arrival (AoA) framework for decision gathering. In the proposed scheme, sensor nodes equipped with Corner Cube Retro-reflectors (CCRs) passively communicate their local decisions, while the CE identifies such decisions based on AoA estimation. A closed-form expression for the DER is derived, incorporating false-alarm and missed-detection probabilities, and is validated through Monte Carlo simulations. Comparative evaluation against TDoA, Single Wavelength Parallel (SWP), and Multiple Wavelength Series (MWS) schemes shows that the AoA-based approach achieves consistently lower DERs, particularly in high-SNR regimes and larger node counts, closely approaching the theoretical lower bound. These results highlight AoA as a practical and scalable alternative to conventional decision-gathering methods in OWSNs.