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2025年12月23日PARLIAMENT QUESTION: INDIAN SPACE PROGRAMME’S VISION AND CHALLENGES
2025年12月23日commercial satellite Latest Updates
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commercial satellite – As the National Oceanic and Atmospheric Administration (NOAA) prepares to launch a new generation of satellites tasked with weather forecasting and climate monitoring, the agency is seeking the help of researchers to ensure that accurate measurements can be obtained despite increasing radio frequency interference from wireless technologies. A large team of researchers involving Mustafa Aksoy, an associate professor in the University at Albany’s College of Nanotechnology, Science and Engineering, was recently awarded a two-year, $1.1 million grant to develop RF interference detection and mitigation strategies for future NOAA satellites. Other collaborators on the project include NASA’s Jet Propulsion Laboratory, NASA’s Goddard Space Flight Center, Ohio State University, and Noctua Technologies.
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Since joining the University at Albany’s Department of Electrical and Computer Engineering in 2017 and leading the Microwave Remote Sensing Laboratory, Axoy is leading the development of machine learning algorithms for NOAA. These algorithms are able to detect and remove radio frequency interference from their satellite measurements to improve the accuracy of weather forecasting and climate monitoring.
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“When you think about major weather events like hurricanes, droughts, floods, and climate change, you can’t understand them without reliable atmospheric measurements,” Aksoy noted. “Right now, one of the biggest threats to reliable measurements is radio frequency interference. Even the slightest interference can bias measurements and lead to completely different estimates, which can cost billions of dollars.”
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Weather and climate modelers rely on the radio spectrum to acquire and transmit highly sensitive observations of the Earth’s atmosphere, oceans, and surface. Satellites and other wireless communication technologies transmit this data via radio waves. Radio waves are the longest-wavelength, lowest-frequency invisible portion of the electromagnetic spectrum, ranging from 3 kHz to 300 GHz. Since the resources of this part of the spectrum are limited, only a limited number of users can be accommodated on a given frequency at a given time. At the same time, radio waves are the basis for wireless communications, which have experienced exponential growth in recent decades, resulting in an increasing number of users competing for limited radio frequencies.commercial satellite
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This has led to an increase in RF interference. RFI occurs when unwanted radio signals interfere with the normal operation of other technologies using that spectrum. These interfering signals can come from cell phones, transmission lines, GPS and vehicle radar, as well as from natural sources such as lightning, solar flares and auroras. Such interference can cause service interruptions and may reduce or even block the ability of weather satellites and radars to collect data.
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“The sources of interference are very numerous and vary from country to country and region to region, as different countries may issue different licenses for use on different frequencies to different users,” explains Aksoy.
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Governments and regulators responsible for managing the radio spectrum try to prevent RF interference by licensing spectrum users and hours of use, but unwanted signals continue to appear as the spectrum becomes more crowded and complex. Commercial users who can profit from their technology are also crowding out spectrum resources for scientific use, which does not generate immediate profits but is critical to providing accurate and life-saving weather forecasts and climate tracking.
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Axoy was commissioned by NOAA to develop advanced strategies for detecting and mitigating RF interference using sophisticated machine learning algorithms. “We are applying machine learning algorithms because we realized that the traditional algorithms used in previous studies were not efficient enough in this very crowded radio spectrum,” he said. “The spectrum is becoming more crowded and complex, so traditional algorithms are no longer sufficient.”commercial satellite
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Axoy’s work in this area dates back to his days as a doctoral student at Ohio State University, when he was involved in research aimed at assisting NASA satellites in accurately assessing the amount of moisture in Earth’s soil, a key measurement for understanding the planet’s climate. Under this new program, NASA’s Jet Propulsion Laboratory is developing atmospheric models capable of simulating the radiative state of the atmosphere without interference. Accordingly, Axoy is using these disturbance-free models to train algorithms that can detect and correct for anomalies as they occur.
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“Thus, our algorithms will know what radiation should look like in the absence of interference. When there is interference, the data will deviate from what is expected and the algorithm will detect it as an anomaly,” he explains.
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The first phase of the study will be funded through February 2027. The next phase could involve hardware development, Axoy said. “We’re currently building the algorithms,” he said, “but our next step is hardware development so that we can eventually integrate these algorithms directly into future satellites.”
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The study highlights the ongoing challenge of protecting critical scientific data streams in an era of exploding wireless technology. As society’s reliance on accurate weather forecasting and climate science grows, ensuring that satellites are able to “hear” Earth’s signals in an increasingly noisy electromagnetic environment has become a mission-critical technology. The work of the University at Albany team represents a cutting-edge effort to address this challenge, and the results are expected to directly improve the quality of data from future NOAA satellites, providing a stronger foundation for disaster warning, resource management and climate policymaking on a global scale.