[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124004-en":3,"doc-seo-124004-105":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},124004,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting the antenna properties of helicon plasma thrusters using machine learning techniques - research paper","Designing helicon plasma thrusters depends critically on the radio-frequency antenna impedance used to transfer power into the plasma. Impedance can be obtained experimentally or via numerical tools, including Adamant, but Adamant requires long runtimes and substantial computing resources. This study trains machine-learning models on Adamant-generated data to support faster evaluations for small design changes. Six MATLAB model types are trained with nested k-fold cross-validation and Bayesian hyperparameter optimization, targeting under 5% point-to-point error. The artificial neural network yields the best overall accuracy, with a reported maximum error of 3.98% on the test set.","Malm etal. Journal of Electric Propulsion (2024) 3:6 Journal of Electric Propulsion  \n[https://doi.org/10.1007/s44205-023-00063-w](https://doi.org/10.1007/s44205-023-00063-w)  \nRESEARCH Open Access  \nPredicting the antenna properties of helicon   plasma thrusters using machine learning techniques  \nOscar Malm1†, Nabil Souhair 1,2†, Alessandro Rossi 1†, Mirko Magarotto3† and Fabrizio Ponti 1*†  \n\n| †Oscar Malm, Nabil Souhair, Alessandro Rossi, Mirko Magarotto and Fabrizio Ponti contributed equally to this work. |\n| --- |\n| *Correspondence: [fabrizio.ponti@unibo.it](fabrizio.ponti@unibo.it) |\n\n1 Alma Propulsion Lab, Alma Mater Studiorum-Università di Bologna, Via Fontanelle 40, Forlì 40121, FC, Italy  \n2 Lerma Laboratory Aerospace and Automotive Engineering School, International University of Rabat Sala al Jadida, Rabat 11100, Morocco  \n3 Department of Electric Engineering, University of Padova, Padova 35131, Italy  \nAbstract  \nWhen designing helicon plasma thrusters, one important characteristic is the impedance of the radio-frequency antenna that is used to deposit power into the plasma. This impedance can be characterized both experimentally and numerically. Recently, a numerical tool capable of predicting the antenna impedance, called Adamant, has been developed. However, Adamant takes a long time to run and has high computer resource demands. Therefore, this work has been done to evaluate whether machine learning models, trained on Adamant-generated data, can be used instead of Adamant for small design change evaluations and similar works. Six different machine learning models were implemented in MATLAB: decision trees, ensembles, support vector machines, Gaussian process regressions, generalized additive models and artificial neural networks. These were trained and evaluated using nested k-fold cross-validation with the hyperparameters selected using Bayesian optimization. The performance target was to have less than 5% error on a point-to-point basis. The artificial neural network performed the best when taking into account both maximum error magnitudes and generalization ability, with a maximum error of 3. 98% on the test set and with considerably better performance than the other models when tested on some practical examples. Future work should look into different solver algorithms for the artificial neural network to see if the results could be improved even further. To expand the model’s usefulness it might also be worth looking into implementing different antenna types that are of interest for helicon plasma thrusters.  \nKeywords: RF antennas, Power deposition, Machine learning, Matching networks, Helicon plasma thruster  \nIntroduction  \nElectrical propulsion systems for satellites have long been popular due to their high efficiencies, with specific impulses of up to 10 000 seconds for plasma-based systems [1]. One particular type of plasma thruster that recently has garnered a lot of attention is the helicon plasma thruster, which has been the subject of many recent studies worldwide, with research carried out at e.g., Canberra [2], Tohoku University [3], Stuttgart [4], Auckland University [5], Madrid university [6], and Padova University [7]. Research has also been carried out at the University of Bologna, e.g., [1, 7–10].  \n© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the","cbCaiqLkkSJMMhg0","https://ap.wps.com/l/cbCaiqLkkSJMMhg0","pdf",2944288,1,24,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Helicon plasma thruster basics\n## RF antenna power deposition and impedance matching","[{\"question\":\"Why is antenna impedance important for helicon plasma thrusters?\",\"answer\":\"The RF antenna impedance governs how effectively power is deposited into the plasma. Maximizing deposited power improves thruster performance, which requires appropriate impedance characterization and matching.\"},{\"question\":\"What data source and workflow does the study use to build machine-learning models?\",\"answer\":\"Machine-learning models are trained on Adamant-generated data. Training and evaluation use nested k-fold cross-validation, with hyperparameters selected through Bayesian optimization.\"},{\"question\":\"Which machine-learning model performs best in the results?\",\"answer\":\"The artificial neural network performs best, balancing maximum error magnitude and generalization. It reports a maximum error of 3.98% on the test set and outperforms other tested model types on practical examples.\"}]","Predicting the antenna properties of helicon plasma thrusters using machine learning techniques - research paper | PDF",1785819765,60,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-antenna-properties-of-helicon-plasma-thrusters-using-machine-learning-techniques-research-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-antenna-properties-of-helicon-plasma-thrusters-using-machine-learning-techniques-research-paper/124004/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is antenna impedance important for helicon plasma thrusters?","Question",{"text":75,"@type":76},"The RF antenna impedance governs how effectively power is deposited into the plasma. Maximizing deposited power improves thruster performance, which requires appropriate impedance characterization and matching.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data source and workflow does the study use to build machine-learning models?",{"text":80,"@type":76},"Machine-learning models are trained on Adamant-generated data. Training and evaluation use nested k-fold cross-validation, with hyperparameters selected through Bayesian optimization.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine-learning model performs best in the results?",{"text":84,"@type":76},"The artificial neural network performs best, balancing maximum error magnitude and generalization. It reports a maximum error of 3.98% on the test set and outperforms other tested model types on practical examples.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":29,"slug":108},5,"Comic","comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]