SPACE TECHNOLOGIES AND DEVOPS: INTEGRATING POSITIONING AND TIMING SYSTEMS FOR RELIABLE SOFTWARE DEPLOYMENT IN AEROSPACE APPLICATIONS

Authors

  • Prudhvi Singirikonda Independent Researcher
  • Praneeth Kumar Lenkala Independent Researcher

DOI:

https://doi.org/10.36676/jrps.v15.i2.1544

Keywords:

DevOps, Aerospace, PNT Technologies, GNSSt, Timing Precision, Software Deployment

Abstract

The specific topic of investigation in this paper is the integration of PNT technologies into the DevOps paradigm for the aerospace domain with an emphasis on improved robustness and accuracy of software releases. The paper's goal is to show how PNT systems, especially GNSS and machine learning applied to time-series data, can solve several critical issues faced in software deployment in aerospace, whether using simulation reports and real-world case study examples, areas and ways in which the PNT-enabled DevOps pipeline may be employed may be highlighted. The fact that the timing and positioning investigations improve the accuracy of the deployment also reduces synchronization errors, as evidenced by the research. Through the preceding technologies, aerospace organizations will be able to elevate the reliability of software installations, hence positively contributing to the operational and safety aspects of aerospace and space missions. This integration indicates a significant advancement in what must be done to handle DevOps processes for the critical requirements of aerospace applications.

References

Adochiei, F. C., Ciucu, R., Adochiei, I. R., Argatu, F. C., Larco, C. M., & Casian, M. (2019). using DEVOPS principles and time series machine learn-ing. http://www.tafpublications.com/gip_content/paper/Jater-5.1.2.pdf

Mallreddy, S. R., & Vasa, Y. (2023). Predictive Maintenance In Cloud Computing And Devops: Ml Models For Anticipating And Preventing System Failures. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 10(1), 213-219. DOI: https://doi.org/10.53555/nveo.v10i1.5751

Mallreddy, S. R., & Vasa, Y. (2023). Natural language querying in SIEM systems: Bridging the gap between security analysts and complex data. NATURAL LANGUAGE QUERYING IN SIEM SYSTEMS: BRIDGING THE GAP BETWEEN SECURITY ANALYSTS AND COMPLEX DATA, 10(1), 205–212. https://doi.org/10.53555/nveo.v10i1.5750 DOI: https://doi.org/10.53555/nveo.v10i1.5750

Vasa, Y. (2024). Optimizing Photometric Light Curve Analysis: Evaluating scipy’s minimize function for eclipse mapping of cataclysmic variables. Journal of Electrical Systems, 20(7s), 2557–2566. https://doi.org/10.52783/jes.4079 DOI: https://doi.org/10.52783/jes.4079

Vasa, Y., Mallreddy, S. R., & Jami, V. S. (2022). AUTOMATED MACHINE LEARNING FRAMEWORK USING LARGE LANGUAGE MODELS FOR FINANCIAL SECURITY IN CLOUD OBSERVABILITY. International Journal of Research and Analytical Reviews , 9(3), 183–190.

Vasa, Y., Singirikonda, P., & Mallreddy, S. R. (2023). AI Advancements in Finance: How Machine Learning is Revolutionizing Cyber Defense. International Journal of Innovative Research in Science, Engineering and Technology, 12(6), 9051–9060.

Vasa, Y., & Singirikonda, P. (2022). Proactive Cyber Threat Hunting With AI: Predictive And Preventive Strategies. International Journal of Computer Science and Mechatronics, 8(3), 30–36.

Vasa, Y., Mallreddy, S. R., & Jaini, S. (2023). AI And Deep Learning Synergy: Enhancing Real-Time Observability And Fraud Detection In Cloud Environments, 6(4), 36–42. https://doi.org/ 10.13140/RG.2.2.12176.83206

Katikireddi, P. M., Singirikonda, P., & Vasa, Y. (2021). Revolutionizing DEVOPS with Quantum Computing: Accelerating CI/CD pipelines through Advanced Computational Techniques. Innovative Research Thoughts, 7(2), 97–103. https://doi.org/10.36676/irt.v7.i2.1482 DOI: https://doi.org/10.36676/irt.v7.i2.1482

Vasa, Y., Cheemakurthi, S. K. M., & Kilaru, N. B. (2022). Deep Learning Models For Fraud Detection In Modernized Banking Systems Cloud Computing Paradigm. International Journal of Advances in Engineering and Management, 4(6), 2774–2783. https://doi.org/10.35629/5252-040627742783

Vasa, Y., Kilaru, N. B., & Gunnam, V. (2023). Automated Threat Hunting In Finance Next Gen Strategies For Unrivaled Cyber Defense. International Journal of Advances in Engineering and Management, 5(11). https://doi.org/10.35629/5252-0511461470

Vasa, Y., & Mallreddy, S. R. (2022). Biotechnological Approaches To Software Health: Applying Bioinformatics And Machine Learning To Predict And Mitigate System Failures. Natural Volatiles & Essential Oils, 9(1), 13645–13652. https://doi.org/https://doi.org/10.53555/nveo.v9i2.5764

Mallreddy, S. R., & Vasa, Y. (2022). Autonomous Systems In Software Engineering: Reducing Human Error In Continuous Deployment Through Robotics And AI. NVEO - Natural Volatiles & Essential Oils, 9(1), 13653–13660. https://doi.org/https://doi.org/10.53555/nveo.v11i01.5765

Vasa, Y., Jaini, S., & Singirikonda, P. (2021). Design Scalable Data Pipelines For Ai Applications. NVEO - Natural Volatiles & Essential Oils, 8(1), 215–221. https://doi.org/https://doi.org/10.53555/nveo.v8i1.5772 DOI: https://doi.org/10.53555/nveo.v8i1.5772

Singirikonda, P., Jaini, S., & Vasa, Y. (2021). Develop Solutions To Detect And Mitigate Data Quality Issues In ML Models. NVEO - Natural Volatiles & Essential Oils, 8(4), 16968–16973. https://doi.org/https://doi.org/10.53555/nveo.v8i4.5771 DOI: https://doi.org/10.53555/nveo.v8i4.5771

Vasa, Y. (2021). Develop Explainable AI (XAI) Solutions For Data Engineers. NVEO - Natural Volatiles & Essential Oils, 8(3), 425–432. https://doi.org/https://doi.org/10.53555/nveo.v8i3.5769 DOI: https://doi.org/10.53555/nveo.v8i3.5769

Kamuni, N., Jindal, M., Soni, A., Mallreddy, S. R., & Macha, S. C. (2024, May). Exploring Jukebox: A Novel Audio Representation for Music Genre Identification in MIR. In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-6). IEEE. DOI: https://doi.org/10.1109/AIIoT58432.2024.10574541

Dodda, S., Kunchakuri, N., Kumar, A., & Mallreddy, S. R. (2024). Automated Text Recognition and Segmentation for Historic Map Vectorization: A Mask R-CNN and UNet Approach. Journal of Electrical Systems, 20(7s), 635-649. DOI: https://doi.org/10.52783/jes.3413

Chintala, S., Jindal, M., Mallreddy, S. R., & Soni, A. (2024). Enhancing Study Space Utilization at UCL: Leveraging IoT Data and Machine Learning. Journal of Electrical Systems, 20(6s), 2282-2291. DOI: https://doi.org/10.52783/jes.3179

Sukender Reddy Mallreddy. (2023). ENHANCING CLOUD DATA PRIVACY THROUGH FEDERATED LEARNING: A DECENTRALIZED APPROACH TO AI MODEL TRAINING. IJRDO -Journal of Computer Science Engineering, 9(8), 15-22. DOI: https://doi.org/10.53555/cse.v9i8.6131

Mallreddy, S.R., Nunnaguppala, L.S.C., & Padamati, J.R. (2022). Ensuring Data Privacy with CRM AI: Investigating Customer Data Handling and Privacy Regulations. ResMilitaris. Vol.12(6). 3789-3799

Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). Achieving PCI Compliance with CRM Systems. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535.

Jangampeta, S., Mallreddy, S.R., & Padamati, J.R. (2021). Anomaly Detection for Data Security in SIEM: Identifying Malicious Activity in Security Logs and User Sessions. 10(12), 295-298

Jangampeta, S., Mallreddy, S. R., & Padamati, J. R. (2021). Data Security: Safeguarding the Digital Lifeline in an Era of Growing Threats. International Journal for Innovative Engineering and Management Research, 10(4), 630-632.

Sukender Reddy Mallreddy(2020).Cloud Data Security: Identifying Challenges and Implementing Solutions.JournalforEducators,TeachersandTrainers,Vol.11(1).96 -102.

Nunnaguppala, L. S. C. , Sayyaparaju, K. K., & Padamati, J. R.. (2021). "Securing The Cloud: Automating Threat Detection with SIEM, Artificial Intelligence & Machine Learning", International Journal For Advanced Research In Science & Technology, Vol 11 No 3, 385-392

Padamati, J., Nunnaguppala, L., & Sayyaparaju, K. . (2021). "Evolving Beyond Patching: A Framework for Continuous Vulnerability Management", Journal for Educators, Teachers and Trainers, 12(2), 185-193.

Nunnaguppala, L. S. C. . (2021). "Leveraging AI In Cloud SIEM And SOAR: Real-World Applications For Enhancing SOC And IRT Effectiveness", International Journal for Innovative Engineering and Management Research,10(08), 376-393

Sayyaparaju, K. K., Nunnaguppala, L. S. C. , & Padamati, J. R.. (2021). "Building SecureAI/ML Pipelines: Cloud Data Engineeringfor Compliance and Vulnerability Management", International Journal for Innovative Engineering and Management Research,10(10), 330-340

Nunnagupala, L. S. C. ., Mallreddy, S. R., & Padamati, J. R. . (2022). "Achieving PCI Compliance with CRM Systems", Turkish Journal of Computer and Mathematics Education (TURCOMAT), 13(1), 529–535 DOI: https://doi.org/10.61841/turcomat.v13i1.14689

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Published

29-06-2024

How to Cite

Prudhvi Singirikonda, & Praneeth Kumar Lenkala. (2024). SPACE TECHNOLOGIES AND DEVOPS: INTEGRATING POSITIONING AND TIMING SYSTEMS FOR RELIABLE SOFTWARE DEPLOYMENT IN AEROSPACE APPLICATIONS. International Journal for Research Publication and Seminar, 15(2), 286–294. https://doi.org/10.36676/jrps.v15.i2.1544