[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122470-en":3,"doc-seo-122470-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122470,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning","Dielectric properties of substrates are critical for designing and characterizing electromagnetic components such as antennas, filters, and sensors. Conventional dielectric probes are costly and fragile, motivating a prediction approach based on transmission coefficient (S12) measurements using two waveguides combined with machine learning. Data are generated via extensive 3D electromagnetic simulations in the X-band (8–12 GHz), then validated against measurements for known substrates FR4 and Rogers 5880.","Wiley  \nJournal of Engineering  \nVolume 2025, Article ID 9418810, 7 pages [https://doi.org/10.1155/je/9418810](https://doi.org/10.1155/je/9418810)  \nResearch Article  \nDielectric Substrate Prediction Through Transmission Measurements and Machine Learning  \nMuhammad Inam Abbasi , 1 Moses Francis, 1 Sher Dali Khan , 1  \nNoor Hafizah Sulaiman ,2 Muhammad Hashim Dahri ,3 Imran Mohd Ibrahim  1  \n,  \nand Zaid Ahmed Shamsan 4  \n1Faculty of Electronics and Computer Technology and Engineering (FTKEK), Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia  \n2Department of Electrical Engineering Technology, Universiti Tun Hussein Onn Malaysia (UTHM), Parit Raja, Malaysia 3Department of Electronic Engineering, Dawood University of Engineering and Technology, Karachi, Pakistan  \n4Department of Electrical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia  \nCorrespondence should be addressed to Muhammad Inam Abbasi; [inamabbasi@utem.edu.my](inamabbasi@utem.edu.my)  \nReceived 2 October 2024; Revised 25 July 2025; Accepted 25 September 2025  \nGuest Editor: Shikha Binwal  \nCopyright © 2025 Muhammad Inam Abbasi et al. Journal of Engineering published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDielectric properties of the substrates play an important role in the design and performance characterization of communication components such as antennas, filters, and sensors. Conventionally, dielectric probes are used to measure the properties of the substrate. However, the dielectric probes are very expensive and easily breakable instruments. In this work, a novel method of dielectric substrate prediction has been proposed using S12 measurements with two waveguides, along with the application of machine learning. Extensive data collection is done using multiple simulations of the proposed method in 3D electromagnetic software in the X-band frequency range (8–12GHz) . The measurements are then conducted by using two waveguides, and the data is compared with the simulation data set, where the decision is made based on the comparison of dielectric properties. For verification of the proposed method, dielectric substrates of FR4 and Rogers 5880 have been used, which demonstrated very close agreement between the measured properties and properties from the data sheet.  \nKeywords: dielectric properties; machine learning; substrate; waveguide; X-band measurements  \n1. Introduction  \nDielectric properties of materials, specifically the permittivity and permeability, are essential for designing various electromagnetic devices, including filters [1], sensors [2, 3], and antennas [4–7] . Measuring these properties accurately is crucial for material characterization, quality control, and research and development. Different techniques have been proposed by researchers for measuring the dielectric properties of such materials [8–10] . One of these techniques is the cavity resonator method, which utilizes a resonant cavity where the material under test is placed. The shift in resonant frequency and change in quality factor are measured to determine the dielectric properties. This method is highly  \naccurate and is suitable for lost materials. However, it can be used for only limited frequency ranges, and sample preparation is also complex [11] . The microstrip resonator method is quite like the cavity resonator method; it uses amicrostrip resonator to determine the dielectric properties of the solid substrates. A microstrip resonator is a microstrip transmission line structure where standing waves are established at the resonant frequency; thus, it is used for dielectric measurement. This method includes physically depositing the material under test to the microstrip resonator and the shifts in the resonant frequency and qual","cbCaitkkM6OKF3hj","https://ap.wps.com/l/cbCaitkkM6OKF3hj","pdf",1043580,1,7,"English","en",105,"# Introduction\n## Dielectric property measurement methods\n## Proposed S12 waveguide and machine learning approach","[{\"question\":\"Why are dielectric properties of substrates important in electromagnetic device design?\",\"answer\":\"They directly affect the performance and characterization of devices such as antennas, filters, and sensors, making accurate material parameter measurement essential.\"},{\"question\":\"What problem does the proposed method address compared with conventional dielectric probes?\",\"answer\":\"Conventional dielectric probes are expensive and easily breakable, so the work proposes an alternative prediction method using S12 transmission measurements.\"},{\"question\":\"How is the dielectric substrate prediction method validated?\",\"answer\":\"The method is validated by testing known substrates (FR4 and Rogers 5880) and comparing measured dielectric properties with values from the data sheets.\"}]","Dielectric Substrate Prediction Through Transmission Measurements and Machine Learning | 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are dielectric properties of substrates important in electromagnetic device design?","Question",{"text":75,"@type":76},"They directly affect the performance and characterization of devices such as antennas, filters, and sensors, making accurate material parameter measurement essential.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the proposed method address compared with conventional dielectric probes?",{"text":80,"@type":76},"Conventional dielectric probes are expensive and easily breakable, so the work proposes an alternative prediction method using S12 transmission measurements.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the dielectric substrate prediction method validated?",{"text":84,"@type":76},"The method is validated by testing known substrates (FR4 and Rogers 5880) and comparing measured dielectric properties with values from the data 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