Archive
6 postsPolarized, coherent fields with embedded extremely low-frequency (ELF) components
RF Safe argues that non-thermal RF-EMF effects on biology may be driven by extremely low-frequency (ELF) components embedded in real-world, modulated wireless signals rather than by the RF carrier alone. The post highlights Panagopoulos’ ion-forced-oscillation (IFO) model as a proposed mechanism in which ELF-related ion motion could perturb voltage-gated ion channel (VGIC) gating and cascade into oxidative stress and immune effects. It cites a mix of supportive and null findings and frames electromagnetic hypersensitivity (EHS) as a threshold/phenotype within the same proposed VGIC–mitochondria–ROS pathway.
Experience of Polish Physicians on Electromagnetic Hypersensitivity
This cross-sectional questionnaire study surveyed 355 Polish physicians about EMF health effects and electromagnetic hypersensitivity (EHS). Physicians reported limited knowledge and low familiarity with WHO guidance for managing people who believe they are hypersensitive to EMF, though most were willing to learn more. Many physicians reported encountering patients attributing symptoms to EMF, which the authors frame as highlighting a need for improved physician education and reliable public information.
Comparative Analysis of Beamforming Techniques and Beam Management in 5G Communication Systems
This engineering paper reviews and classifies beamforming techniques in 5G New Radio and examines beam management procedures at Layer 1 and Layer 2. It analyzes the spectral spectrogram of Synchronization Signal Blocks (SSBs) to illustrate how configuration parameters influence spectral occupancy and synchronization-related performance in different deployment scenarios, including FR2. The work is framed as technical optimization, with only a general note that such knowledge may inform safety considerations related to EMF exposure.
Machine Learning Approach for Ground-Level Estimation of Electromagnetic Radiation in the Near Field of 5G Base Stations
This paper presents a machine-learning method to estimate ground-level electromagnetic radiation (electric field strength) in the near field of 5G base stations, using multiple technical and environmental input parameters. The authors report experimental performance with a mean absolute percentage error of about 5.89% and suggest the approach can reduce costs compared with on-site measurements. The work is positioned as supporting exposure management and base-station placement, while noting the need for careful EMF management due to potential health-risk links.
The Effect of Proximity Sensor & Grip Sensor Use on Specific Absorption Rate (SAR) in Smartphones
This engineering study examined how smartphone proximity and grip sensors affect SAR during LTE and 5G NR operation in a 3D measurement environment. The abstract reports that enabling these sensors reduces SAR relative to being turned off, with reductions varying by sensor and frequency. The authors attribute the reduction to sensor-driven power management and transmission power adjustment.
A Decision Support System for Managing Health Symptoms of Living Near Mobile Phone Base Stations
This analytical study evaluated machine learning models (SVM and Random Forest) to predict health symptoms in adults living near mobile phone base stations. The SVM model reportedly achieved high predictive performance for headache, sleep disturbance, dizziness, vertigo, and fatigue, and outperformed Random Forest and prior models. The abstract concludes that proximity to base stations is connected with increased prevalence of several symptoms and emphasizes distance, age, and duration of residence as key predictors.