{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:09:51Z","timestamp":1775146191095,"version":"3.50.1"},"reference-count":67,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T00:00:00Z","timestamp":1618358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000844","name":"European Space Agency","doi-asserted-by":"publisher","award":["4000129792\/20\/NL"],"award-info":[{"award-number":["4000129792\/20\/NL"]}],"id":[{"id":"10.13039\/501100000844","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Horizon 2020","award":["761349"],"award-info":[{"award-number":["761349"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, research in the space community has shown a growing interest in Artificial Intelligence (AI), mostly driven by systems miniaturization and commercial competition. In particular, the application of Deep Learning (DL) techniques on board Earth Observation (EO) satellites might lead to numerous advantages in terms of mitigation of downlink bandwidth constraints, costs, and increment of the satellite autonomy. In this framework, the CloudScout project, funded by the European Space Agency (ESA), represents the first time in-orbit demonstration of a Convolutional Neural Network (CNN) applied to hyperspectral images for cloud detection. The first instance of this use case has been done with an INTEL Myriad 2 VPU on board a CubeSat optimized for low cost, size, and power efficiency. Nevertheless, this solution introduces multiple drawbacks due to its design not specifically being for the space environment, thus limiting its applicability to short-lifetime Low Earth Orbit (LEO) applications. The current work provides a benchmark between the Myriad 2 and our custom hardware accelerator designed for Field Programmable Gate Arrays (FPGAs). The metrics used for comparison include inference time, power consumption, space qualification, and components. The obtained results show that the FPGA-based solution is characterized by a reduced inference time, and a higher possibility of customization, but at the cost of greater power consumption and a longer Time to Market. As a conclusion, the proposed approach might extend the potential market of DL-based solutions to long-term LEO or interplanetary exploration missions through deployment on space-qualified FPGAs, with a limited cost in energy efficiency.<\/jats:p>","DOI":"10.3390\/rs13081518","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T23:35:12Z","timestamp":1618443312000},"page":"1518","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["An FPGA-Based Hardware Accelerator for CNNs Inference on Board Satellites: Benchmarking with Myriad 2-Based Solution for the CloudScout Case Study"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4889-0968","authenticated-orcid":false,"given":"Emilio","family":"Rapuano","sequence":"first","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9311-6392","authenticated-orcid":false,"given":"Gabriele","family":"Meoni","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8551-717X","authenticated-orcid":false,"given":"Tommaso","family":"Pacini","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0123-7977","authenticated-orcid":false,"given":"Gianmarco","family":"Dinelli","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7624-1415","authenticated-orcid":false,"given":"Gianluca","family":"Furano","sequence":"additional","affiliation":[{"name":"Data Systems Division of European Space Research and Technology Centre, European Space Agency, Keplerlaan 1, 2201 AZ Noordwijk, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3306-5698","authenticated-orcid":false,"given":"Gianluca","family":"Giuffrida","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5426-4974","authenticated-orcid":false,"given":"Luca","family":"Fanucci","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Pisa, via G.Caruso 16, 56122 Pisa, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Furano, G., Tavoularis, A., and Rovatti, M. (2020, January 19\u201321). AI in space: Applications examples and challenges. Proceedings of the 2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Frascati, Italy.","DOI":"10.1109\/DFT50435.2020.9250908"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MAES.2020.3008468","article-title":"Towards the Use of Artificial Intelligence on the Edge in Space Systems: Challenges and Opportunities","volume":"35","author":"Furano","year":"2020","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Giuffrida, G., Diana, L., de Gioia, F., Benelli, G., Meoni, G., Donati, M., and Fanucci, L. (2020). CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images. Remote. Sens., 12.","DOI":"10.3390\/rs12142205"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kothari, V., Liberis, E., and Lane, N.D. (2020, January 3\u20134). The Final Frontier: Deep Learning in Space. Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications, Austin, TX, USA.","DOI":"10.1145\/3376897.3377864"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Denby, B., and Lucia, B. (2020, January 16\u201320). Orbital Edge Computing: Nanosatellite Constellations as a New Class of Computer System. Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems, Lausanne, Switzerland.","DOI":"10.1145\/3373376.3378473"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.3934\/mbe.2019167","article-title":"A full convolutional network based on DenseNet for remote sensing scene classification","volume":"16","author":"Zhang","year":"2019","journal-title":"Math. Biosci. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1109\/LGRS.2018.2886534","article-title":"Semisupervised scene classification for remote sensing images: A method based on convolutional neural networks and ensemble learning","volume":"16","author":"Dai","year":"2019","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dong, C., Loy, C.C., He, K., and Tang, X. (2014). Learning a deep convolutional network for image super-resolution. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10593-2_13"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zheng, Y.Y., Kong, J.L., Jin, X.B., Wang, X.Y., Su, T.L., and Zuo, M. (2019). CropDeep: The crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors, 19.","DOI":"10.3390\/s19051058"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/s12145-019-00380-5","article-title":"Change detection techniques for remote sensing applications: A survey","volume":"12","author":"Asokan","year":"2019","journal-title":"Earth Sci. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Meoni, G., Valverde, A., Magistrati, G., and Fanucci, L. (2019, January 11\u201313). Estimating the downlink data-rate of a CCSDS file delivery protocol IP core. Proceedings of the International Conference on Applications in Electronics Pervading Industry, Environment and Society, Pisa, Italy.","DOI":"10.1007\/978-3-030-37277-4_60"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Blacker, P., Bridges, C., and Hadfield, S. (2019, January 22\u201324). Rapid Prototyping of Deep Learning Models on Radiation Hardened CPUs. Proceedings of the 2019 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), Colchester, UK.","DOI":"10.1109\/AHS.2019.000-4"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dinelli, G., Meoni, G., Rapuano, E., and Fanucci, L. (2020, January 10\u201321). Advantages and limitations of fully on-chip CNN FPGA-based hardware accelerator. Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Virtual.","DOI":"10.1109\/ISCAS45731.2020.9180867"},{"key":"ref_14","unstructured":"Cappellone, D., Di Mascio, S., Furano, G., Menicucci, A., and Ottavi, M. (2020, January 19\u201321). On-Board Satellite Telemetry Forecasting with RNN on RISC-V Based Multicore Processor. Proceedings of the 2020 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFT), Frascati, Italy."},{"key":"ref_15","unstructured":"(2021, April 02). Intel\u00ae Movidius\u2122 Myriad\u2122 2 Vision Processing Unit Specifications. Available online: https:\/\/ark.intel.com\/content\/www\/us\/en\/ark\/products\/122461\/intel-movidius-myriad-2-vision-processing-unit-4gb.html."},{"key":"ref_16","unstructured":"Coral.ai (2021, April 02). Coral Dev Board Datasheet. Available online: https:\/\/coral.ai\/docs\/dev-board\/datasheet\/#system-components."},{"key":"ref_17","unstructured":"Nvidia (2021, April 02). NVIDIA Jetson Nano System-on-Module Datasheet. Available online: https:\/\/www.realtimes.cn\/Uploads\/download\/JetsonNano_DataSheet.pdf."},{"key":"ref_18","first-page":"178","article-title":"High-performance embedded computing in space: Evaluation of platforms for vision-based navigation","volume":"15","author":"Lentaris","year":"2018","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_19","unstructured":"Microsemi (2021, April 02). Microsemi RTG4 Datasheet. Available online: https:\/\/www.microsemi.com\/product-directory\/rad-tolerant-fpgas\/3576-rtg4#documents."},{"key":"ref_20","unstructured":"Xilinx (2021, April 02). Xilinx Kintex Usage for Space Application. Available online: https:\/\/indico.esa.int\/event\/232\/contributions\/2161\/attachments\/1811\/2111\/2018-04-09_Xilinx_Space_Products_SEFUW.pdf."},{"key":"ref_21","unstructured":"Esposito, M. (2019). CloudScout: In orbit demonstration of machine learning applied on hyperspectral and multispectral thermal imaging. European Workshop on On-Board Data Processing (OBDP2019), European Space Agency."},{"key":"ref_22","unstructured":"(2021, April 02). Cosine Measurement Systems Website. Available online: https:\/\/www.cosine.nl\/."},{"key":"ref_23","unstructured":"(2021, April 13). TETRAMAX H2020 European Project Website. Available online: https:\/\/www.tetramax.eu."},{"key":"ref_24","unstructured":"Xilinx (2021, April 02). ZCU106 Evaluation Board User Guide. Available online: https:\/\/www.xilinx.com\/support\/documentation\/boards_and_kits\/zcu106\/ug1244-zcu106-eval-bd.pdf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/JSSC.2016.2616357","article-title":"Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks","volume":"52","author":"Chen","year":"2017","journal-title":"IEEE J. Solid State Circuits"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, X., Liang, Y., Li, X., Yu, C.H., Zhang, P., and Cong, J. (2018, January 5\u20138). TGPA: Tile-Grained Pipeline Architecture for Low Latency CNN Inference. Proceedings of the 2018 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD), San Diego, CA, USA.","DOI":"10.1145\/3240765.3240856"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1145\/2654822.2541967","article-title":"Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning","volume":"42","author":"Chen","year":"2014","journal-title":"ACM Sigarch Comput. Archit. News"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1109\/JETCAS.2020.3015294","article-title":"MEM-OPT: A Scheduling and Data Re-use System to Optimize On-chip Memory Usage for CNNs On-board FPGAs","volume":"10","author":"Dinelli","year":"2020","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Qiu, J., Wang, J., Yao, S., Guo, K., Li, B., Zhou, E., Yu, J., Tang, T., Xu, N., and Song, S. (2016, January 21\u201323). Going deeper with embedded fpga platform for convolutional neural network. Proceedings of the 2016 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2847263.2847265"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/TCAD.2017.2705069","article-title":"Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA","volume":"37","author":"Guo","year":"2018","journal-title":"IEEE Trans. Comput. Aided Des. Integr. Circuits Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, P., Sun, G., Guan, Y., Xiao, B., and Cong, J. (2015, January 22\u201324). Optimizing fpga-based accelerator design for deep convolutional neural networks. Proceedings of the 2015 ACM\/SIGDA International Symposium on Field-Programmable Gate Arrays, Monterey, CA, USA.","DOI":"10.1145\/2684746.2689060"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7218758","DOI":"10.1155\/2019\/7218758","article-title":"An FPGA-Based Hardware Accelerator for CNNs Using On-Chip Memories Only: Design and Benchmarking with Intel Movidius Neural Compute Stick","volume":"2019","author":"Dinelli","year":"2019","journal-title":"Int. J. Reconfig. Comput."},{"key":"ref_33","unstructured":"Li, H., Fan, X., Li, J., Cao, W., Zhou, X., and Wang, L. (September, January 29). A high performance FPGA-based accelerator for large-scale convolutional neural networks. Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1007\/s00521-018-3761-1","article-title":"A survey of FPGA-based accelerators for convolutional neural networks","volume":"32","author":"Mittal","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Venieris, S.I., and Bouganis, C. (2016, January 1\u20133). fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs. Proceedings of the 2016 IEEE 24th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), Washington, DC, USA.","DOI":"10.1109\/FCCM.2016.22"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Shen, Y., Ferdman, M., and Milder, P. (2017, January 24\u201328). Maximizing CNN accelerator efficiency through resource partitioning. Proceedings of the 2017 ACM\/IEEE 44th Annual International Symposium on Computer Architecture (ISCA), Toronto, ON, Canada.","DOI":"10.1145\/3079856.3080221"},{"key":"ref_38","unstructured":"Esposito, M. (2019, January 9\u201313). HyperScout-2: Highly Integration of Hyperspectral and Thermal Sensing for Breakthrough In-Space Applications. Proceedings of the ESA Earth Observation \u03d5-week 2019, ESRIN, Frascati (ROMA), Italy."},{"key":"ref_39","first-page":"111310C","article-title":"In-orbit demonstration of artificial intelligence applied to hyperspectral and thermal sensing from space. CubeSats and SmallSats for Remote Sensing III","volume":"11131","author":"Esposito","year":"2019","journal-title":"Int. Soc. Opt. Photonics"},{"key":"ref_40","unstructured":"Python (2021, April 02). Keras: The Python Deep Learning API. Available online: https:\/\/keras.io\/."},{"key":"ref_41","unstructured":"Sinergise Ltd (2021, April 13). Website. Available online: https:\/\/www.sinergise.com\/."},{"key":"ref_42","unstructured":"Intel (2021, April 02). Intel Movidius SDK. Available online: https:\/\/movidius.github.io\/ncsdk."},{"key":"ref_43","unstructured":"(2021, April 02). AMBA Advanced Extensible Interface 4 Specifications. Available online: https:\/\/www.arm.com\/products\/silicon-ip-system\/embedded-system-design\/amba-specifications."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Grady, B., Lian, R., Brett, J., and Anderson, J.H. (2017, January 5\u20138). FPGA-based CNN Inference Accelerator Synthesized from Multi-Threaded C Software. Proceedings of the 2017 30th IEEE International System-on-Chip Conference (SOCC), Munich, Germany.","DOI":"10.1109\/SOCC.2017.8226056"},{"key":"ref_45","unstructured":"(2021, April 13). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/."},{"key":"ref_46","unstructured":"Xilinx (2021, April 02). Accurate Design Power Measurement Made Easier. Available online: https:\/\/developer.xilinx.com\/en\/articles\/accurate-design-power-measurement.html."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Rezzak, N., Zhang, E.X., Alles, M.L., Schrimpf, R.D., and Hughes, H. (2010, January 11\u201314). Total-ionizing-dose radiation response of partially-depleted SOI devices. Proceedings of the 2010 IEEE International SOI Conference (SOI), San Diego, CA, USA.","DOI":"10.1109\/SOI.2010.5641057"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sterpone, L., Azimi, S., and Du, B. (2016, January 19\u201323). A selective mapper for the mitigation of SETs on rad-hard RTG4 flash-based FPGAs. Proceedings of the 2016 16th European Conference on Radiation and Its Effects on Components and Systems (RADECS), Bremen, Germany.","DOI":"10.1109\/RADECS.2016.8093152"},{"key":"ref_49","unstructured":"NanoXlore (2021, April 02). NanoXlore NG-Large Datasheet. Available online: https:\/\/www.nanoxplore.com\/uploads\/NanoXplore_NG-LARGE_Datasheet_v1.0.pdf."},{"key":"ref_50","unstructured":"ECSS (2021, April 02). Single Event Effects Test Method and Guidelines. Available online: https:\/\/escies.org\/webdocument\/showArticle?id=229."},{"key":"ref_51","unstructured":"Sinclair, D., and Dier, J. (, January August). Radiaton Effects and COTS Parts in SmallSats. Proceedings of the 27th Annual AIAA\/USU Conference on Small Satellites, Logan, UT, USA."},{"key":"ref_52","unstructured":"ESA (2021, April 02). Space Product Assurance\u2014Radiation Hardness Assurance, EEE Components for JUICE. Available online: https:\/\/sci.esa.int\/documents\/33960\/35865\/1567258918598-ESA-TEC-Q_2012-155_v1_RHA-requirements.pdf."},{"key":"ref_53","first-page":"15","article-title":"Space environment analysis: Experience and trends","volume":"392","author":"Daly","year":"1996","journal-title":"Environ. Model. Space Based Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1109\/TAES.2012.6129661","article-title":"A real time EDAC system for applications onboard earth observation small satellites","volume":"48","author":"Bentoutou","year":"2012","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1109\/TNS.2004.834955","article-title":"Selective triple modular redundancy (STMR) based single-event upset (SEU) tolerant synthesis for FPGAs","volume":"51","author":"Samudrala","year":"2004","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_57","unstructured":"ECSS (2008). SpaceWire Links Nodes Routers and Networks, ECSS. ECSS-E-ST-50-12C."},{"key":"ref_58","unstructured":"ECSS (2019). Space Engineering\u2014SpaceFibre\u2014Very High-Speed Serial Link, ECSS. ECSS-E-ST-50-11C."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.actaastro.2020.01.010","article-title":"A serial high-speed satellite communication CODEC: Design and implementation of a SpaceFibre interface","volume":"169","author":"Nannipieri","year":"2020","journal-title":"Acta Astronaut."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1088\/1742-6596\/898\/8\/082007","article-title":"RapidIO as a multi-purpose interconnect","volume":"898","author":"Baymani","year":"2017","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MAES.2018.170161","article-title":"Definition and performance evaluation of an Advanced Avionic TTEthernet Architecture for the support of Launcher Networks","volume":"33","author":"Eramo","year":"2018","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_62","unstructured":"Intel (2021, April 02). Intel OpenVino Toolkit. Available online: https:\/\/docs.openvinotoolkit.org\/latest\/index.html."},{"key":"ref_63","unstructured":"Silva, H., Sousa, J., Freitas, D., Faustino, S., Constantino, A., and Coutinho, M. (2009, January 10\u201311). RTEMS Improvement\u2013Space Qualification of RTEMS Executive. Proceedings of the 1st Simp\u00f3sio de Inform\u00e1tica-INFORUM, University of Lisbon, Lisbon, Portugal."},{"key":"ref_64","unstructured":"(2005). Environmental Conditions for Space Flight Hardware: A Survey, NASA Electronic Parts and Packaging (NEEP) Program."},{"key":"ref_65","first-page":"1440","article-title":"Structural vibration analysis of electronic equipment for satellite under launch environment. Key Engineering Materials","volume":"270","author":"Jung","year":"2004","journal-title":"Trans. Tech. Publ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/23.211366","article-title":"Characteristics of spontaneous electrical discharging of various insulators in space radiations","volume":"39","author":"Frederickson","year":"1992","journal-title":"IEEE Trans. Nucl. Sci."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.actaastro.2011.12.014","article-title":"A survey and assessment of the capabilities of Cubesats for Earth observation","volume":"74","author":"Selva","year":"2012","journal-title":"Acta Astronaut."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1518\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:48:06Z","timestamp":1760161686000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/8\/1518"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,14]]},"references-count":67,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["rs13081518"],"URL":"https:\/\/doi.org\/10.3390\/rs13081518","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,14]]}}}