{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T10:50:12Z","timestamp":1779360612252,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,11]],"date-time":"2023-06-11T00:00:00Z","timestamp":1686441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Michigan Technological University (MTU)","award":["W56HZV-22-C-0019"],"award-info":[{"award-number":["W56HZV-22-C-0019"]}]},{"name":"Michigan Technological University (MTU)","award":["W56HZV-19-2-0001"],"award-info":[{"award-number":["W56HZV-19-2-0001"]}]},{"name":"University of Michigan\u2019s Automotive Research Center (ARC)","award":["W56HZV-22-C-0019"],"award-info":[{"award-number":["W56HZV-22-C-0019"]}]},{"name":"University of Michigan\u2019s Automotive Research Center (ARC)","award":["W56HZV-19-2-0001"],"award-info":[{"award-number":["W56HZV-19-2-0001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Terrain traversability is critical for developing Go\/No-Go maps for ground vehicles, which significantly impact a mission\u2019s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning (multi-layer perceptron (MLP) and convolutional neural network (CNN)) are used to perform a comparative study to estimate the soil properties, such as the soil moisture and terrain strength, used to generate prediction maps of these terrain characteristics. This study found that deep learning outperformed machine learning. Specifically, a multi-layer perceptron performed the best for predicting the percent moisture content (R2\/RMSE = 0.97\/1.55) and the soil strength (in PSI), as measured by a cone penetrometer for the averaged 0\u20136\u201d (CP06) (R2\/RMSE = 0.95\/67) and 0\u201312\u201d depth (CP12) (R2\/RMSE = 0.92\/94). A Polaris MRZR vehicle was used to test the application of these prediction maps for mobility purposes, and correlations were observed between the CP06 and the rear wheel slip and the CP12 and the vehicle speed. Thus, this study demonstrates the potential of a more rapid, cost-efficient, and safer approach to predict terrain properties for mobility mapping using remote sensing data with machine and deep learning algorithms.<\/jats:p>","DOI":"10.3390\/s23125505","type":"journal-article","created":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T02:28:42Z","timestamp":1686536922000},"page":"5505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Terrain Characterization via Machine vs. Deep Learning Using Remote Sensing"],"prefix":"10.3390","volume":"23","author":[{"given":"Jordan","family":"Ewing","sequence":"first","affiliation":[{"name":"Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1024-3474","authenticated-orcid":false,"given":"Thomas","family":"Oommen","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6893-0306","authenticated-orcid":false,"given":"Jobin","family":"Thomas","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anush","family":"Kasaragod","sequence":"additional","affiliation":[{"name":"Department of Geological Engineering, Michigan Technological University, Houghton, MI 49931, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Dobson","sequence":"additional","affiliation":[{"name":"MTRI Inc., Ann Arbor, MI 48105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Colin","family":"Brooks","sequence":"additional","affiliation":[{"name":"MTRI Inc., Ann Arbor, MI 48105, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paramsothy","family":"Jayakumar","sequence":"additional","affiliation":[{"name":"U.S. Army DEVCOM Ground Vehicle Systems Center, Warren, MI 48092, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Cole","sequence":"additional","affiliation":[{"name":"U.S. Army DEVCOM Ground Vehicle Systems Center, Warren, MI 48092, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tulga","family":"Ersal","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,11]]},"reference":[{"key":"ref_1","unstructured":"McCullough, M., Jayakumar, P., Dasch, J., and Gorsich, D. (2016, January 2\u20134). Developing the Next Generation NATO Reference Mobility Model. Proceedings of the 2016 Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), Novi, MI, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.jterra.2019.12.002","article-title":"Efficient Generation of Accurate Mobility Maps Using Machine Learning Algorithms","volume":"88","author":"Mechergui","year":"2020","journal-title":"J. Terramechanics"},{"key":"ref_3","first-page":"1682","article-title":"Decision-Making for Autonomous Mobility Using Remotely Sensed Terrain Parameters in Off-Road Environments","volume":"3","author":"Pandey","year":"2021","journal-title":"SAE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1504\/IJVP.2020.109192","article-title":"Quantitative assessment of modelling and simulation tools for autonomous navigation of military vehicles over off-road terrains","volume":"6","author":"Cole","year":"2020","journal-title":"Int. J. Veh. Perform."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S0021-8634(05)80010-1","article-title":"A Review of Soil Strength Measurement Techniques for Prediction of Terrain Vehicle Performance","volume":"50","author":"Okello","year":"1991","journal-title":"J. Agric. Eng. Res."},{"key":"ref_6","unstructured":"Shoop, S.A. (1993). Terrain Characterization for Trafficability, US Army Corps of Engineers Cold Regions Research & Engineering Laboratory."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.jterra.2019.12.004","article-title":"Predicting terrain parameters for physics-based vehicle mobility models from cone index data","volume":"88","author":"Huang","year":"2020","journal-title":"J. Terramechanics"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Riese, F.M., Keller, S., and Hinz, S. (2019). Supervised and Semi-Supervised Self-Organizing Maps for Regression and Classification Focusing on Hyperspectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12010007"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.3390\/rs6043170","article-title":"Surface Soil Water Content Estimation from Thermal Remote Sensing based on the Temporal Variation of Land Surface Temperature","volume":"6","author":"Zhang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_10","first-page":"17","article-title":"Deformable soil with adaptive level of detail for tracked and wheeled vehicles","volume":"5","author":"Tasora","year":"2019","journal-title":"Int. J. Veh. Perform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.geoderma.2015.07.017","article-title":"Digital soil mapping: A brief history and some lessons","volume":"264","author":"Minasny","year":"2016","journal-title":"Geoderma"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"104149","DOI":"10.1016\/j.catena.2019.104149","article-title":"Predicting regional spatial distribution of soil texture in floodplains using remote sensing data: A case of southeastern Iran","volume":"182","author":"Shahriari","year":"2019","journal-title":"CATENA"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.5194\/hess-20-3765-2016","article-title":"Estimating spatially distributed soil texture using time series of thermal remote sensing\u2014A case study in central Europe","volume":"20","author":"Bernhardt","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Dyson, J., Mancini, A., Frontoni, E., and Zingaretti, P. (2019). Deep Learning for Soil and Crop Segmentation from Remotely Sensed Data. Remote Sens., 11.","DOI":"10.3390\/rs11161859"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2013.06.004","article-title":"Estimation of soil moisture using optical\/thermal infrared remote sensing in the Canadian Prairies","volume":"83","author":"Berg","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"299","DOI":"10.2113\/gseegeosci.23.4.299","article-title":"Thermal Remote Sensing For Moisture Content Monitoring of Mine Tailings: Laboratory Study","volume":"23","author":"Zwissler","year":"2017","journal-title":"Environ. Eng. Geosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"F2","DOI":"10.1029\/2009JF001378","article-title":"Determining soil moisture and sediment availability at White Sands Dune Field, New Mexico, from apparent thermal inertia data","volume":"115","author":"Scheidt","year":"2010","journal-title":"J. Geophys. Res. Earth Surf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.1016\/S1003-6326(14)63265-9","article-title":"Improved spatial resolution in soil moisture retrieval at arid mining area using apparent thermal inertia","volume":"24","author":"Lei","year":"2014","journal-title":"Trans. Nonferrous Met. Soc. China"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"083624","DOI":"10.1117\/1.JRS.8.083624","article-title":"Soil moisture derived using two apparent thermal inertia functions over Canterbury, New Zealand","volume":"8","author":"Sohrabinia","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.proeng.2016.11.066","article-title":"Prediction of Soil Moisture from Remote Sensing Data","volume":"162","author":"Taktikou","year":"2016","journal-title":"Procedia Eng."},{"key":"ref_21","first-page":"934","article-title":"The potential of multitemporal Aqua and Terra MODIS apparent thermal inertia as a soil moisture indicator","volume":"13","author":"Peters","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.3390\/rs5083729","article-title":"Remote Sensing of Soil Moisture in Vineyards Using Airborne and Ground-Based Thermal Inertia Data","volume":"5","author":"Soliman","year":"2013","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.07.003","article-title":"Spatial upscaling of in-situ soil moisture measurements based on MODIS-derived apparent thermal inertia","volume":"138","author":"Qin","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, D., and Zhou, G. (2016). Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review. Sensors, 16.","DOI":"10.3390\/s16081308"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1680\/jgele.17.00053","article-title":"Friction angles at sandy beaches from remote imagery","volume":"7","author":"Stark","year":"2017","journal-title":"G\u00e9otechnique Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ewing, J., Oommen, T., Jayakumar, P., and Alger, R. (2021). Characterizing Soil Stiffness Using Thermal Remote Sensing and Machine Learning. Remote Sens., 13.","DOI":"10.3390\/rs13122306"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1016\/j.asr.2016.07.017","article-title":"Lunar soil strength estimation based on Chang\u2019E-3 images","volume":"58","author":"Gao","year":"2016","journal-title":"Adv. Space Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106121","DOI":"10.1016\/j.agwat.2020.106121","article-title":"Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area","volume":"234","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.apm.2019.12.016","article-title":"Selecting appropriate machine learning methods for digital soil mapping","volume":"81","author":"Khaledian","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_32","unstructured":"Hughes, I., Verdugo, J.L., Carcamo, A., Mark, E., Larenas, J.M., and Jayakumar, P. (2022). Self-Supervised Mobility Assessment from Unsupervised Proprioceptive Feature Learning on Simulated Environment, U.S. Army Combat Capabilities Development Command Ground Vehicle Systems Center, US ARMY DEVCOM GVSC."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.biosystemseng.2017.06.025","article-title":"Terrain assessment for precision agriculture using vehicle dynamic modelling","volume":"162","author":"Reina","year":"2017","journal-title":"Biosyst. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8746","DOI":"10.1109\/TVT.2017.2707076","article-title":"Combined speed and steering control in high-speed autonomous ground vehicles for obstacle avoidance using model predictive control","volume":"66","author":"Liu","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.ast.2016.03.018","article-title":"In situ identification of shearing parameters for loose lunar soil using least squares support vector machine","volume":"53","author":"Xue","year":"2016","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11255","DOI":"10.1109\/TVT.2021.3114088","article-title":"Terrain Adaptive Trajectory Planning and Tracking on Deformable Terrains","volume":"70","author":"Dallas","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jterra.2017.01.001","article-title":"Thermal vision, moisture content, and vegetation in the context of off-road mobile robots","volume":"70","author":"Iagnemma","year":"2017","journal-title":"J. Terramechanics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1007\/s41324-019-00310-z","article-title":"Development of mapping techniques for off road trafficability to support military operation","volume":"28","author":"Pundir","year":"2020","journal-title":"Spat. Inf. Res."},{"key":"ref_39","unstructured":"MicaSense, Inc (2021). User Guide for MicaSense Sensors, MicaSense, Inc."},{"key":"ref_40","unstructured":"MicaSense, Inc (2020). MicaSense RedEdge-MX\u2122 and DLS 2 Integration Guide, MicaSense, Inc."},{"key":"ref_41","unstructured":"BaySpec, Inc (2018). OCI-F Ultra-Compact Hyperspectral Imager User Manual, BaySpec Inc."},{"key":"ref_42","unstructured":"FLIR (2016). FLIR Vue Pro and Vue Pro R User Guide, Teledyne FLIR LLC."},{"key":"ref_43","unstructured":"DJI (2021). Mavic 2 Enterprise Advanced User Manual, DJI."},{"key":"ref_44","unstructured":"Propellor (2021). AeroPoints Manual, Propellor."},{"key":"ref_45","unstructured":"Spectrum Technologies, Inc. (2009). FieldScout SC 900 Soil Compaction Meter User Manual, Spectrum Technologies, Inc."},{"key":"ref_46","unstructured":"Spectrum Technologies, Inc. (2017). FieldScout TDR 150 Soil Moisture Meter, Spectrum Technologies, Inc."},{"key":"ref_47","unstructured":"Trimble (2012). Trimble GeoXH6000 User Manual, Trimble Navigation Limited."},{"key":"ref_48","unstructured":"Trimble (2013). Trimble Geo 7X Handheld User Guide, Trimble Navigation Limited."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"769","DOI":"10.1080\/01621459.1975.10480301","article-title":"Directed Ridge Regression Techniques in Cases of Multicollinearity","volume":"70","author":"Guilkey","year":"1975","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/00031305.1975.10479105","article-title":"Ridge Regression in Practice","volume":"29","author":"Marquardt","year":"1975","journal-title":"Am. Stat."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge Regression: Biased Estimation for Nonorthogonal Problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_52","first-page":"407","article-title":"Least angle regression","volume":"32","author":"Tibshirani","year":"2004","journal-title":"Ann. Stat."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression Shrinkage and Selection Via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0003-2670(86)80028-9","article-title":"Partial least-squares regression: A tutorial","volume":"185","author":"Geladi","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A Tutorial On Support Vector Machines for Pattern Recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Cristianini, N. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press.","DOI":"10.1017\/CBO9780511801389"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3188","DOI":"10.1109\/TGRS.2010.2045764","article-title":"Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data","volume":"48","author":"Bovolo","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","article-title":"Artificial neural networks (the multilayer perceptron)\u2014A review of applications in the atmospheric sciences","volume":"32","author":"Gardner","year":"1998","journal-title":"Atmos. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.catena.2016.09.007","article-title":"Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS","volume":"149","author":"Pham","year":"2017","journal-title":"Catena"},{"key":"ref_61","first-page":"26","article-title":"Multilayer Perceptron: Architecture Optimization and Training","volume":"4","author":"Ramchoun","year":"2016","journal-title":"Int. J. Interact. Multimed. Artif. Intell."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ewing, J., Oommen, T., Jayakumar, P., and Alger, R. (2020). Utilizing Hyperspectral Remote Sensing for Soil Gradation. Remote Sens., 12.","DOI":"10.3390\/rs12203312"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"92","DOI":"10.2136\/sssaj2011.0122","article-title":"Thermal Inertia Modeling for Soil Surface Water Content Estimation: A Laboratory Experiment","volume":"76","author":"Minacapilli","year":"2012","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_67","first-page":"1","article-title":"Development of Evaluation Framework for the Unconfined Compressive Strength of Soils Based on the Fundamental Soil Parameters Using Gene Expression Programming and Deep Learning Methods","volume":"34","author":"Hanandeh","year":"2022","journal-title":"J. Mater. Civ. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5505\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:52:50Z","timestamp":1760125970000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5505"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,11]]},"references-count":67,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125505"],"URL":"https:\/\/doi.org\/10.3390\/s23125505","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,11]]}}}