The increasing vulnerability of navigation signals to disruption, driven by advancing technologies, has heightened the need for alternative navigation solutions. This study introduces an innovative approach designed for contested environments, where navigation systems that rely on the Global Navigation Satellite System (GNSS) are vulnerable to interference mechanisms, such as jamming and spoofing. An open-source dataset was used, comprising measurements from magnetic field, current, and voltage sensors, barometric sensors, inertial navigation systems, radar, and other sources. The dataset includes tens of thousands of records with 96 features collected across six different flights. All features that depend on the Global Navigation Satellite System were excluded, and the remaining data were pre-processed using advanced data engineering techniques. State-of-the art artificial intelligence (AI) and machine learning (ML) methods were then applied to predict aircraft position without relying on GNSS features. Seven distinct machine learning models were developed using the multi-flight dataset to estimate positional error, quantified by Distance Root Mean Squared. The proposed AI-driven methods demonstrated location accuracies approaching those of GNSS-based systems, with optimised models achieving Distance Root Mean Squared errors below 10 metres across all flights. These findings validate that AI-supported algorithms can provide a reliable and effective navigation alternative under adverse conditions.
The reflection coefficient measurement of the RF signal generator output is clear when the signal generator output is turned off, as no interfering signal is present. However, measuring the reflection coefficient while the signal generator output is turned on creates complexity, as the generator’s output power can interfere with the reflected signal. A vector network analyzer (VNA) is the reference instrument for measuring the reflection coefficient, capturing both the magnitude and phase of scattering parameters. For measuring the active output of a signal generator, the signals created by the generator and the VNA must be isolated to prevent signal mixing and interference. This paper proposes a unique method to measure the output reflection coefficient of an RF signal generator when the output is on, using a VNA configured for one port reflection coefficient measurement. The method involves tuning the VNA receiver to a frequency slightly offset to the generator’s output. Simultaneously, selecting a narrow intermediate frequency bandwidth (IFBW) reduces the receiver’s noise floor and also eliminates out-of-band interference. As a result, the VNA and the generator operate in different frequency bands to avoid interferences between them, enabling accurate magnitude and phase measurements. To automate the process, a Windows-based software has been developed. This software automates the measurement sequence, controls generator power levels and VNA sweep parameters, captures both the magnitude and phase of the reflection coefficient, and records the result data. It also supports measurement at different output power levels, enabling characterization across a wide range of operating conditions.
Objective: Accurate assessment of biological effects in high-intensity focused ultrasound (HIFU) research is often limited by the lack of standardized calibration protocols for in vitro applications. This study established a metrology-guided calibration framework to evaluate the spatially confined thermal and biological effects of a Sonic Concepts H-102 HIFU transducer.Materials and Methods: Ultrasonic thermal doses were calculated using the Sapareto--Dewey model, and an automated 3D system was developed for precise temperature characterization. Biological effects of thermal doses (TD 60, TD 120, and TD 240 min) were investigated in HT-29 and HCT-116 colorectal cancer cell lines. Temperature elevation, cell viability (MTT assay), and apoptosis-related responses (TALI image cytometry and JC-1 assay) were compared between focal and adjacent wells.Results: The HIFU transducer achieved precise focal heating (50$degree$C - 52$degree$C) while avoiding significant thermal spread to adjacent regions (\>2 mm). HT-29 cells exhibited significant biological responses at focal sites with minimal effects in adjacent wells (p\<0.0001). HCT-116 cells demonstrated higher thermal sensitivity with less distinct focal confinement (p values ranged from \<0.001 to \>0.05), indicating that biological responses to indirect HIFU-related stress may vary according to intrinsic cell line characteristics.Conclusions: This metrology-guided platform provides a reliable, reproducible framework for standardized in vitro HIFU applications while revealing cell-line-specific differences in thermal sensitivity.
The advancement of Additive Manufacturing (AM) technologies, particularly Selective Laser Melting (SLM), has significantly influenced the development of orthopedic, dental, and spinal implants. This paper provides a comprehensive review of the role of SLM in enhancing the properties of implants, including biocompatibility, wear, fatigue and corrosion resistance. SLM offers significant advantages such as customization, design flexibility, and the ability to produce intricate geometries with precise porosity, which plays a critical role in osseointegration and bone regeneration. This paper examines various materials used in implant manufacturing, such as titanium alloys and Co-Cr-Mo alloys, and discusses their mechanical and tribological properties, emphasizing their suitability for use in load-bearing implants. Furthermore, the impact of surface texture, roughness, and porosity on the performance and longevity of implants is explored, highlighting how these factors influence mechanical properties, cell attachment, and overall integration with human tissue. In summary, SLM has emerged as a powerful method for producing orthopedic and dental implants with tailored mechanical properties, improved bioactivity, and enhanced biocompatibility, making it a promising tool for the future of implantable medical devices. In this study, 2D drawings and suggestions for further research have been provided to future researchers in various sections.
Nodular graphite (Ductile) cast iron is produced by a double treatment; Nodularization / Spheroidization (Mg and/or Rare Earth) treatment + Inoculation (FeSiX-alloy treatment, with X = Ca, Ba, Sr, Ce, La, Zr etc.). Each step has specific objectives. The spheroidization of the graphite depends on the thickness of the mould and thus cooling rate is an important factor. In this work, GGG70L alloy was cast into plates that had different cross-sections. The change in the modulus has been investigated by means of microstructure and mechanical properties. It turned out that as nodule size was decreased, tensile stress and elongation at fracture were decreased however hardness was increased. It was concluded that the section thickness was an important parameter that determines the microstructure and mechanical properties of spheroidal cast irons.
