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[4] Lucia B, Denby B, Manchester Z, Desai H, Ruppel E, Colin A. Computational nanosatellite constellations: Opportunities and challenges.
GetMobile: Mobile Computing and Communications, 2021, 25(1): 16–23. [doi: 10.1145/3471440.3471446]
[5] Xu MW, Fu Z, Ma X, Zhang L, Li YN, Qian F, Wang SG, Li K, Yang JY, Liu XZ. From cloud to edge: A first look at public edge
platforms. In: Proc. of the 21st ACM Internet Measurement Conf. ACM, 2021. 37–53. [doi: 10.1145/3487552.3487815]
[6] Bhattacherjee D, Kassing S, Licciardello M, Singla A. In-orbit computing: An outlandish thought experiment? In: Proc. of the 19th ACM
Workshop on Hot Topics in Networks. ACM, 2020. 197–204. [doi: 10.1145/3422604.3425937]
[7] Xing RL, Xu MW, Zhou A, Li Q, Zhang YR, Qian F, Wang SG. Deciphering the enigma of satellite computing with COTS devices:
Measurement and analysis. In: Proc. of the 30th Annual Int’l Conf. on Mobile Computing and Networking. Washington: ACM, 2024.
420–435. [doi: 10.1145/3636534.3649371]
[8] Kassem MM, Raman A, Perino D, Sastry N. A browser-side view of Starlink connectivity. In: Proc. of the 22nd ACM Internet
Measurement Conf. Nice: ACM, 2022. 151–158. [doi: 10.1145/3517745.3561457]
[9] Michel F, Trevisan M, Giordano D, Bonaventure O. A first look at Starlink performance. In: Proc. of the 22nd ACM Internet
Measurement Conf. Nice: ACM, 2022. 130–136. [doi: 10.1145/3517745.3561416]
[10] Ma S, Chou Y C, Zhao HY, Chen L, Ma XQ, Liu JC. Network characteristics of LEO satellite constellations: A Starlink-based
measurement from end users. In: Proc. of the 2023 IEEE Annual Joint Conf.: INFOCOM, IEEE Computer and Communications
Societies. New York: IEEE, 2023. 1–10. [doi: 10.1109/INFOCOM53939.2023.10228912]
[11] Ye B, Mo LH, Liu T, Sun YM, Liu J. Influence of orbital parameters on SEU rate of low-energy proton in nano-SRAM device.
Symmetry, 2020, 12(12): 2030. [doi: 10.3390/sym12122030]
[12] Wang SG, Li Q, Xu MW, Ma X, Zhou A, Sun QB. Tiansuan constellation: An open research platform. In: Proc. of the 2021 IEEE Int’l
Conf. on Edge Computing. Chicago: IEEE, 2021. 94–101. [doi: 10.1109/EDGE53862.2021.00022]
[13] Panagopoulos AD, Arapoglou PDM, Cottis PG. Satellite communications at Ku, Ka, and V bands: Propagation impairments and
mitigation techniques. IEEE Communications Surveys & Tutorials, 2004, 6(3): 2–14. [doi: 10.1109/COMST.2004.5342290]
[14] Jiujiu. Given its downlink rate exceeding 100 Mbps, is Starlink a viable alternative to 5G? 2022 (in Chinese). https://www.51cto.com/
article/704582.html
[15] Terrasanta G, Ziarko MW, Bergamasco N, Poot M, Poliak J. Simulating optical single event transients on silicon photonic waveguides for
satellite communication. IEEE Trans. on Nuclear Science, 2024, 71(2): 176–183. [doi: 10.1109/TNS.2024.3353489]
[16] Landauer DC, Lovelly TM. Performance evaluation of the radiation-tolerant NVIDIA Tegra K1 system-on-chip. In: Proc. of the 2023
IEEE Space Computing Conf. Pasadena: IEEE, 2023. 24–33. [doi: 10.1109/SCC57168.2023.00014]
[17] Li ZQ, Jing XY, Zhu XK, Zhang HY. Heterogeneous defect prediction through multiple kernel learning and ensemble learning. In: Proc.
of the 2017 IEEE Int’l Conf. on Software Maintenance and Evolution. Shanghai: IEEE, 2017. 91–102. [doi: 10.1109/ICSME.2017.19]
[18] El-Hajjar M, Hanzo L. A survey of digital television broadcast transmission techniques. IEEE Communications Surveys & Tutorials,
2013, 15(4): 1924–1949. [doi: 10.1109/SURV.2013.030713.00220]
[19] Wang F, Jiang DD, Wang ZH, Chen JG, Quek TQS. Seamless handover in LEO based non-terrestrial networks: Service continuity and
optimization. IEEE Trans. on Communications, 2023, 71(2): 1008–1023. [doi: 10.1109/TCOMM.2022.3229014]
[20] Zhang QY, Che XY, Chen YJ, Ma X, Xu MW, Dustdar S, Liu XZ, Wang SG. A comprehensive deep learning library benchmark and
optimal library selection. IEEE Trans. on Mobile Computing, 2024, 23(5): 5069–5082. [doi: 10.1109/TMC.2023.3301973]
[21] Huang CY, Ye P, Chen T, He T, Yue XY, Ouyang WL. EMR-merging: Tuning-free high-performance model merging. In: Proc. of the
38th Annual Conf. on Neural Int’l Processing Systems. Vancouver: NeurIPS, 2024. 122741–122769.
[22] Albashish D, Al-Sayyed R, Abdullah A, Ryalat MH, Almansour NA. Deep CNN model based on VGG16 for breast cancer classification.
In: Proc. of the 2021 Int’l Conf. on Information Technology. Amman: IEEE, 2021. 805–810. [doi: 10.1109/ICIT52682.2021.9491631]
[23] Wu JX, Leng C, Wang YH, Hu QH, Cheng J. Quantized convolutional neural networks for mobile devices. In: Proc. of the 2016 IEEE
Conf. on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016. 4820–4828. [doi: 10.1109/CVPR.2016.521]
[24] Jacob B, Kligys S, Chen B, Zhu ML, Tang M, Howard A, Adam H, Kalenichenko D. Quantization and training of neural networks for
efficient integer-arithmetic-only inference. In: Proc. of the 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. Salt Lake
City: IEEE, 2018. 2704–2713. [doi: 10.1109/CVPR.2018.00286]
[25] Bharati P, Pramanik A. Deep learning techniques—R-CNN to mask R-CNN: A survey. In: Das AK, Nayak J, Naik B, eds. Computational
Intelligence in Pattern Recognition. Singapore: Springer, 2020. 657–668. [doi: 10.1007/978-981-13-9042-5_56]
[26] Girshick R, Jeff D, Trevor D, Jitendra M. Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proc. of
the 2014 IEEE/CVF Conf. on Computer Vision and Pattern Recognition. 2014. 580–587.
[27] Yu W, Yang KY, Bai YL, Xiao TJ, Yao HX, Rui Y. Visualizing and comparing AlexNet and VGG using deconvolutional layers. In: