Physical-layer authentication for LoRaWAN sensor nodes increasingly relies on SRAM Physical Unclonable Functions (PUFs) or Radio Frequency (RF) fingerprinting, yet prior work has not reported the joint failure trajectories of both modalities under identical, directly measured stress on the same physical device. This paper reports a device-level finding (referred to here as D4) obtained with a calibrated DS18B20 temperature probe (+/- 0.5 degrees C) bonded adjacent to the SRAM, so that every authentication outcome is paired with a measured rather than estimated temperature. Using a stressor-decoupling protocol on Device C (STM32 B-L072Z-LRWAN1 + Microchip 23K640 SRAM), we show that no single stressor drives the PUF past its 15% authentication threshold: at a fixed 50 cm range the genuine fractional Hamming distance (FHD) rises smoothly and monotonically from 3.0% to only 10.9% across 27.4-56.1 degrees C (0.374%/degrees C), with a False Reject Rate of 0% throughout. Distance alone (+2.2 pp) and elevated temperature alone (+7.0 pp) each leave the PUF below threshold. PUF authentication fails (Genuine Accept Rate 100% -> 0%, FHD 17.7%) only under the compound condition (50 degrees C + 100 cm), where the observed degradation exceeds the sum of the individual stressors, indicating a super-additive thermal-distance interaction. Throughout the same sweep the Carrier Frequency Offset (CFO) fingerprint drifts only-14.9 Hz/degrees C (Spearman = -0.52), matching an AT-cut crystal model and remaining within-device stable (158 Hz) against thousands-of-Hz inter-device separation (ANOVA = 706), so RF discriminability is preserved precisely where the PUF fails. The compound failure reproduces across both MCU platforms (Feather M0 and STM32), while a device sharing the SRAM2 chip with the failing STM32 node but hosted on a Feather remains robust (FHD 7.6%, GAR 100%), indicating platform-dependent rather than chip-intrinsic behaviour. These findings motivate, as a design implication, adaptive hybrid authentication in which RF-only fallback is invoked when PUF reliability degrades. The observations are drawn from a small device set and require validation on a larger population.
In recent years, the extensive use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has raised significant privacy and security concerns, motivating substantial research into Privacy-Preserving Techniques (PPTs). Although many studies have explored this area, existing surveys remain limited in scope, often focusing on specific techniques and rarely offering a clear overview and critical discussion of practical simulation tools. This paper presents a Systematic Literature Review (SLR) of 84 studies to address these gaps. It offers a structured analysis of major PPTs, including Federated Learning (FL), cryptographic methods, blockchain-based approaches, hardware-based solutions, and hybrid approaches. For each technique, the paper examines its core principles and the security and privacy challenges it aims to mitigate, alongside a comparative assessment of strengths and limitations in addressing issues such as communication interception, data leakage, scalability, and resource constraints. In addition, commonly used datasets and simulation tools for evaluating these techniques are reviewed, offering insights into their practical performance. Key challenges are identified, including high computational and energy demands, communication and scalability bottlenecks, data quality limitations, privacy trade-offs, the complexity of managing heterogeneous UAV systems, and the lack of real-world deployment studies across diverse operational scenarios. Finally, clear research directions are outlined, emphasizing the need for lightweight and asynchronous protocols, hybrid privacy-preserving architectures, holistic security frameworks, and, most importantly, improved real-world testing.
The inability of graphite to store Na+ ions at low potentials necessitates the use of disordered materials as anodes in Na-ion batteries, wherein complex degradative pathways limit cycle life. This study delineates the influence of synthetically induced material disorder in facilitating stable, high-rate sodium ion storage in a series of anatase titania analogs. The degree of disorder in the titanium oxyhydroxide (TiO x (OH) y -T anneal) materials, synthesized via a simple, solution-phase sol-gel protocol employing a Ti(III) precursor, is systematically varied by tuning the postsynthesis annealing temperature (T anneal). The variable disorder in TiO x (OH) y -T anneal stems from the distribution of residual Ti(III), N-dopants, and O-vacancies in these materials as a function of T anneal, as mapped by XPS, bulk-sensitive resonance inelastic X-ray scattering (RIXS), and EPR spectroscopy. In terms of electrochemical performance, we observe a unique, nonmonotonic dependence of Na+ storage capacity on the synthetically induced degree of disorder in TiO x (OH) y -T anneal, instead of a simple inverse size dependence, as normally seen in the case of fully crystalline anatase materials. Further in contrast to crystalline anatase, the disorder in TiO x (OH) y -T anneal opens up a bulk mode of Na+ storage instead of a purely surface confined or capacitive mode, as determined by scan rate-dependent cyclic voltammetry. TiO x (OH) y -400, with an intermediary amount of disorder in the prepared series of materials, shows the highest specific capacity (similar to 188 mAh/g) and cycling stability for Na+ storage at elevated rates. These results are in agreement with our first-principles simulated data exhibiting a higher number of possible sites that Na+ can occupy at an intermediate degree of disorder in a TiO x (OH) y structure.
Modern supply chains are increasingly expected to meet ambitious sustainability targets, yet they often suffer from limited visibility into upstream relationships, environmental risks, and ethical sourcing practices. This paper presents an artificial intelligence (AI)-based approach for supporting sustainability-oriented decision-making in supply chains through knowledge graph completion and link prediction. We construct a multi-relational supply chain knowledge graph that captures heterogeneous entities and relationships, including suppliers, products, certifications, and locations, and apply graph neural networks to infer missing links and sustainability-related attributes. By enabling reasoning over incomplete and sparse data, the proposed approach supports feasibility-oriented decisions, such as identifying alternative supplier relationships and assessing sustainability alignment across multi-tier networks. Building on recent advances in knowledge graph reasoning and heterogeneous graph learning, the framework integrates relational structure with inductive learning to provide interpretable recommendations under uncertainty. The approach is evaluated on two real-world supply chain datasets, demonstrating its applicability in complex, data-sparse settings. The results indicate that graph-based AI can provide a practical foundation for transparent and sustainability-aware supply chain decision support.
