None-Line-of-Sight (NLOS) signals denote Global Navigation Satellite System (GNSS) signals received indirectly from satellites and could result in unacceptable positioning errors. To meet the high mission-critical transportation and logistics demand, NLOS signals received in the built environment should be detected, corrected, and excluded. This paper proposes a cost-effective NLOS impact mitigation approach using only GNSS receivers. By exploiting more signal Quality Indicators (QIs), such as the standard deviation of pseudorange, Carrier-to-Noise Ratio (C/N₀), elevation and azimuth angle, this paper compares machine-learning-based classification algorithms to detect and exclude NLOS signals in the pre-processing step. The probability of the presence of NLOS is predicted using regression algorithms. With a pre-defined threshold, the signals can be classified as Line-of-Sight (LOS) or NLOS. The probability of the occurrence of NLOS is also used for signal subset selection and specification of a novel weighting scheme. The novel weighting scheme consists of both C/N₀and elevation angle and NLOS probability. Experimental results show that the best LOS/NLOS classification algorithm is the random forest. The best QI set for NLOS classification is the first three QIs mentioned above and the difference of azimuth angle. The classification accuracy obtained from this proposed algorithm can reach 93.430%, with 2.810% false positives. The proposed signal classifier and weighting scheme improved the positioning accuracy by 69.000% and 40.700% in the horizontal direction, 79.361% and 75.322% in the vertical direction, and 75.963% and 67.824% in the 3D direction.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

Airports, as Critical National Infrastructure (CNI), operate as tightly coupled socio-technical systems exposed to multifaceted threats, including cyber, physical, social, environmental, and Chemical, Biological and Radiological (CBR) threats. This study presents a structured review of the synthesis of conceptual frameworks, airport structural configurations, sensor networks, and multi-domain threat landscapes, as well as airport security and resilience analysis, while comparatively examining risk assessment approaches. The review shows that existing approaches are effective for threat identification and prioritisation but remain predominantly static, with limitations in scalability, data dependency, and real-time applicability. To address these limitations, Threat-Vulnerability-Risk Assessment (TVRA) is adopted as a structured, reusable approach to support metric allocation, redundancy design, and emergency capability development. It further serves as a bridge between traditional risk assessment and resilience-oriented system design by enabling the transformation of static risk scores into scenario-based inputs, thereby supporting stress-testing and lifecycle-based resilience planning across the prepare, act, and recover phases. However, its inherently static structure limits its ability to capture temporal dynamics and cascading interdependencies, highlighting the need to integrate it with dynamic modelling approaches.

As a key provider of Positioning, Navigation, and Timing (PNT) information, the characteristics of Global Navigation Satellite System (GNSS) signals, including types, Quality Indicators (QIs), and measurements, should be understood. This study employs temporally correlated deep learning models to classify GNSS signals as Line-of-Sight (LOS) or non-LOS using four QIs: the elevation angle, Carrier to Noise Ratio (C/N₀), code measurement’s standard deviation, and difference in azimuth angle. Autocorrelation analysis confirmed that these QIs exhibit significant temporal dependencies. The Bidirectional LSTM (Bi-LSTM) model, with four hidden layers, 64 units, and a sequence length of 18, achieved the best performance: 94.17% classification accuracy and a 2.61% False Positive (FP) rate. Positioning based on classified LOS signals significantly improved accuracy, reducing the mean errors in the horizontal, vertical, and 3D domain by 36.6%, 81.4%, and 59.6%, respectively, and reducing the Standard Deviation (STDEV) by 46.3%, 33.5%, and 45.5%, respectively. Moreover, the non-LOS probability output enables flexible signal selection and mitigates the issue of insufficient signal availability. These results highlight the effectiveness of temporally correlated models in GNSS signal classification and positioning performance.