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While there is a diverse selection of models available for studying the characteristics of sound absorption, the challenge lies in choosing the most suitable model for a given situation. This paper provides a comprehensive review of sound absorption characteristics prediction models, including a comparison of various models. Sound Absorption models can be categorized as empirical and micro-structural/semi-empirical. Empirical models use limited parameters, such as the flow resistivity of air to estimate the sound absorption coefficient (SAC) without taking into account the specific sound propagation mechanism occurring within the material. In contrast, Micro-structural/semi-empirical models analyze the pore geometry to ascertain the SAC characteristics. This paper discusses the measurement uncertainty in the evaluation of sound absorption characteristics of acoustical materials using the sound absorption facility and prediction errors associated with various analytical models for the prediction of sound absorption characteristics of various acoustical materials. The study shall be helpful for the selection of appropriate models and pave the way for the design and development of future prediction models.

Spoofing attempts are inevitable and must be detected and mitigated rather than eliminated. Adversaries can manipulate signal properties to induce spoofing. Identifying the key properties most affected by interference is crucial for early detection, ensuring the security of critical missions such as ground and aerial navigation. With this objective, this paper analyzes three key parameters of Navigation with Indian Constellation (NavIC) receiver, Pseudorange, Doppler, and azimuth-elevation for identifying spoofing occurrences before the Position, Velocity, and Time (PVT) solution is computed. This framework offers a proactive approach to detecting spoofing attempts and implementing countermeasures in advance. A small rectangular region with a perimeter of approximately 2.3 km within the Sardar Vallabhbhai National Institute of Technology (SVNIT) campus is selected to monitor discrepancies across all three parameters for NavIC and Global Position System (GPS) both. The results confirm that these parameters are effective early indicators of spoofing attacks, enabling the timely implementation of appropriate countermeasures to ensure secure and accurate navigation. This is especially crucial for NavIC-equipped UAVs, allowing them to detect spoofing a priory, reducing complexity, and saving time.

At the Istituto Nazionale di Ricerca Metrologica (INRiM), an approach for reliably calibrating ultra-high-value resistors with the dual source bridge (DSB) is proposed as part of the INRiM knowledge transfer task. This approach is particularly suitable for commercial DSBs, which can be used by electrical calibration laboratories for their activities for external clients. The approach is based on ratio measurements, metrological triangulation rule and measurement compatibility. The proposed reliable calibration value is the corrected weighted mean of three intermediate calibration values which must satisfy a strict triangulation rule and be compatible within small uncertainties. This approach helps to reduce systematic errors or to include them into the uncertainty of the corrected weighted mean. For this paper the method was applied to three high value resistors using a commercial DSB to obtain a reliable calibration value of a 1 PΩ resistor. This technique meets the requirements of the EN 17025 standard for risk assessment of calibration activities.

Virtual metrology (VM) involves using statistical models from production process data to predict measurements and quality attributes of products in modern manufacturing processes without considering the direct measurement. This approach provides real-time monitoring and maintains product quality. On the other hand, predictive maintenance focuses on forecasting equipment failures before they occur. Virtual metrology and predictive maintenance create a robust framework for enhancing manufacturing processes. The integrated approach of Virtual Metrology and Predictive Maintenance (PdM) supports a more responsive manufacturing environment, ultimately leading to better performance and competitiveness in the market. This review presented recent advancements in virtual metrology and predictive maintenance in the present industry. A detailed analysis was conducted based on specific search and selection criteria. Applications of VM and PdM across various industrial sectors are thoroughly discussed along with key advantages and challenges.

Navigation with Indian Constellation (NavIC), earlier known as the Indian Regional Navigation Satellite System (IRNSS), is an effort made by the Indian Space Research Organization to provide navigation services to the Indian subcontinent and its nearby regions. Since May 2023, NavIC has been providing the Standard Positioning Service (SPS) on three frequency bands, namely, L5 (centered at 1176.45 MHz), S (centered at 2492.028 MHz) and L1 (centered at 1575.42 MHz) through its newest satellite, NVS-01, launched on May 29, 2023. The SPS signal broadcast in the L1 Band of NVS-01, employs Synthesized Binary Offset Carrier (SBOC) modulation scheme. An important and vital subsystem of the NavIC ground segment is the network of IRNSS Range and Integrity Monitoring Stations (IRIMS). The fundamental measurement engine of an IRIMS station is the NavIC reference receiver. One of the dominant sources of noise for a reference receiver is signal multipath. Traditional multipath mitigation methods such as High Resolution Correlator perform well for Binary Phase Shift Keying signals. However, this method is not optimal for multipath mitigation of SBOC signals. A novel method for the mitigation of multipath effect has been investigated and has been developed for SBOC signals using the shaping of S-curve. This technique performs exceptionally well in the mitigation of multipath experienced by SBOC signals like the NavIC L1 signal. This paper details the novel method developed and the results obtained. The paper also provides a comparative performance analysis of the multipath mitigation in SBOC signals by the new method with that of other methods.

Solar thermal technology holds significant potential for widespread utilization in the drying of agricultural and industrial commodities, effectively addressing challenges related to storage and transportation. This research seeks to evaluate the thermo-electrical performance of a forced-air convection-based PVT air-collector for drying tomatoes. Simulations were conducted in Ghaziabad City, India, considering local atmospheric conditions viz. solar irradiance intensity, ambient temperature, and the relative humidity of four different days from 7th–10th August 2023. The proposed dryer system comprises a PVT air-collector designed to provide both thermal and enhanced electrical energy, alongside a drying chamber, heat recovery system, and DC fan. Results indicate average thermal, electrical, and overall efficiencies of 36.04%, 12.09%, and 48.83%, respectively, for the PVT air-collector based solar dryer. Over a 20-h drying period, tomato moisture content decreases from 78% to 8.5%. Moreover, as air velocity rises from 0.5 m/s to 2 m/s, the average moisture content decreases from 22.55% to 16.693%, indicating accelerated moisture elimination at higher air velocities. The innovative dryer demonstrates its potential for reducing energy consumption and subsequent CO₂ emissions in the food processing sector.

This study investigates the integration of distributed energy resources (DERs) to enhance the efficiency and cost-effectiveness of distribution networks (DNs) while considering both meteorological and metrological variability. The primary objective is to optimize the operation of battery energy storage systems (BESS) in coordination with demand response (DR) strategies, addressing uncertainties in wind power generation and the integration of shunt capacitors (SCs). A key aspect of this research is the precise measurement and calibration of wind speed data to ensure accurate stochastic modeling, which is critical for optimizing power system operations. The study employs metrological techniques to validate wind power predictions, ensuring the reliability of input data used for optimization. A multi-objective optimization framework is proposed, integrating time-of-use demand response mechanisms while maintaining voltage stability across all nodes. The stochastic nature of wind generation is modeled based on historical meteorological data, which is refined through metrological validation methods. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to derive the Pareto optimal front (POF), which is further evaluated using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to identify the most balanced solution. The methodology is tested on two systems: a standard 33-bus DN and a realistic 108-bus radial DN representing Indian conditions. Results indicate that the coordinated operation of DERs significantly reduces power losses and grid demand costs while maintaining operational performance under varying meteorological conditions. By incorporating metrological validation in wind power scenario modeling, this study ensures data accuracy and enhances the technical and economic performance of DNs. The findings highlight the importance of integrating demand response programs, stochastic wind generation models, and BESS with SCs, providing a robust strategy for sustainable and resilient power system operations.

Specifically, the process of creating the particles using a modified sol-gel method and synthesizing them in a uniform, non-dispersed form was the focus of the investigation. The synthesis of silica nanoparticles, which are particles with a diameter of less than 20 nanometers, was the subject of the study. In the course of the numerous condensation processes that take place in the silica solution, the processes of organizing particles through the magnetic field integration mechanism are powerful and possess a particular energy. It is dependent on the method that is used to modify the pH, the intensity of the magnetic field, and also the time that will affect the length of exposure of the distributed silica nanoparticles particularly. This is because it will be feasible to correctly control the size of these particles in the future. The outcome will depend on how these elements change. By using it, the size of the nanoparticles that have been prepared can either be decreased or raised, depending on the capacity of the material that has been prepared. Through the utilization of dynamic light scattering, an investigation into the relationship between the magnetic field and the size and distribution of silica particles is being taken. Because of the process that influences the introduction of particles into production, medical, military, or other applications depending on the materials that are utilized, this study will be important in the future for modifying the size of particles and their many applications.

This work presents an advanced metrology method for defect detection in rotating machine components using infrared thermography integrated modified artificial neural network (MANN) based image processing in a real-time manner. The proposed method utilizes the precise, accurate metrological features of infrared thermography, which are highly useful in accurate defect detection. Infrared thermography images are pre-processed by converting them to greyscale images, and then a median filter is used to remove the noise from the images. Thereafter, the proposed MANN method is used via MATLAB program for fault detection and finding the accurate location of the fault. The quantitative analysis is performed on synthetic star images using performance metrics like accuracy, Jaccard similarity index, Dice similarity index, Sensitivity, and Precision. The resulting yield of the proposed method is measured and compared with the yield of various benchmark edge-based segmentation methods from the literature, and it is found that the proposed MANN method is relatively accurate at 98.92% and 94.92% precise. The proposed method is validated using a real-time experimental setup with a FLIR ONE PRO LT iOS Pro-Grade android-fitted infrared thermography camera to capture the experimental setup images in running condition. The three different conditions of the rotating machine are identified as healthy state at 25 °C, defective state at 50 °C, and defect start to arise state at 45.5 °C. These three conditions in the experimental setup are further automated for easy identification of machine state and shown with the help of blinking of three different LED color lights, red color LED represents the defective condition, blue color LED represents the defect starting to arise condition and white color LED represents the healthy condition. The proposed method offers robust accuracy and reliability by aligning it to metrological principles, making it suitable for advanced industry 4.0 setups and industrial applications.

The length of calibration is related to the national economy and people’s livelihood. The traditional calibration of length value was sent the uncalibrated instrument to the laboratory, and the intrinsic error of the tested instrument was obtained in the laboratory. However, calibrating the instrument on the site produced additional errors. In this article, a remote calibration method of length measurement based on optical fiber information transmission followed by a proof of principle system was studied to reduce the additional error. The 1.0-level displacement guide rail was calibrated according to this method. The results showed that the relative error of guide rail displacement under different lengths was within 0.6%. The calibration results meet the actual standards of the guide rail. This optical method is a good candidate for remote calibration.