This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.

This study aims to evaluate the performance of phase‐locked loop (PLL) under the presence of distinct scintillation patterns in the signal. Scintillation is very common in low‐latitude regions due to the ionospheric dynamics. Under strong scintillation scenario the occurrence of deep fading events is usually registered and may cause severe degradation in the communication. The investigation conducted in this study uses the amplitude scintillation index S₄, the decorrelation time, and the bandwidthas main parameters. The study evaluates 54 different combinations of these parameters. The results indicate that in order to minimize the occurrence of cycle slips in the output phase of the PLL, the most appropriate tracking loop bandwidthdepends on the values of both S₄and, which characterize the scintillation fading pattern. Simulations showed that as the S₄index increases, the automatic increment in the PLL bandwidth may not be the best choice as the performance depends on the temporal characteristics of the scintillating signal. The analysis showed that, among the configurations tested, the equivalent noise bandwidth of 10 Hz achieved the best performance overall. The investigation also showed that choosing the wrong parameter regarding the PLL bandwidth may increase the probability of cycle slip by up to 10 times during fading events.

The field of data compression has evolved over the last decades. In this way, several techniques to reduce the amount of acquired data from the sensor required to be transmitted have been developed. Those techniques are usually classified by lossless or lossy, where, for the lossless techniques, all acquired data is recovered, while the lossy techniques introduce errors to these data. Each of these techniques presents advantages and drawbacks, being the analyst responsible for choosing the appropriate technique for a specific application. This work presents a comparative study using lossy audio formats to be applied on a launch vehicle on-board acoustic data. The Opus format achieved a higher compression rate in comparison with standard compression techniques by saving up to 254 times the required amount of data to be transmitted through a telemetry link on launcher vehicle, and the lowest discrepancy from original data measured by the mean square error metric.

Sounding rockets provide unique opportunities for microgravity and near-space research. Still, telemetry bandwidth limitations pose a significant challenge to handling the large volume of dynamic vibration data generated during flight. This study proposes a novel hybrid discrete cosine transform (DCT)-modal analysis-based compression method tailored explicitly for the telemetry subsystem of sounding rockets, addressing the scientific question of whether incorporating mechanical properties, specifically modal analysis, can enhance compression efficiency while preserving signal integrity. Modal analysis, a reliable method for designing mechanical structures in aerospace applications, forms the foundation of this approach. The proposed method combines the DCT with modal analysis, exploiting the intrinsic structural dynamics of the sounding rocket to achieve superior data compression. This hybrid approach was rigorously tested using shaker-table experiments simulating flight conditions, which are particularly challenging due to the highly dynamic nature of rocket vibration signals. Results demonstrate a significant improvement in compression ratios compared to traditional DCT methods applied to independent channels. Specifically, the hybrid method achieved compression ratios up to 100% higher (e.g. 8.54:1 vs 4.2:1) while maintaining comparable peak signal-to-noise ratios above 53 dB, indicating minimal loss of signal fidelity. Furthermore, analysis in the frequency domain confirmed the preservation of critical vibrational modes with minimal distortion, demonstrating the method’s ability to filter out irrelevant data while retaining essential structural dynamic information. This represents an original measurement methodology in aerospace telemetry by adopting a physics-informed compression technique that embeds structural dynamic responses from the frequency spectrum. This research establishes a foundation for next-generation data acquisition systems in spaceborne telemetry, with potential applications extending to other bandwidth-constrained fields. The findings confirm that combining modal analysis with DCT offers a powerful solution for data reduction in telemetry, paving the way for broader applications in space and other domains where bandwidth is a critical constraint.

Transmission medium is always perturbed by noise with a random nature which can be characterized by taking a sequence of noise samples and, after analyzing the sequence, attributing a probabilistic model to represent the randomness of the noise. If thermal noise (receiver generated) is the only noise impairing the transmission (our only focus is digital transmission) the memoryless stationary discrete-time Gaussian process is the best model to probabilistically represent the noise. The mathematical representation of the transmission medium in such a situation yields the well known Gaussian Channel. As Information Theory points out, for a fixed noise power, the Gaussian channel is the worst channel to send information through. If thermal noise is not the only noise impairing the transmission (as in sonar communication and power line communication) finding the probabilistic model other than the single-parameter Gaussian process, which best match the noise can much improve the communication system design. The Bernoulli-Gaussian process, a three parameters model, is a common considered option. Finding the three parameters of the Bernoulli-Gaussian model (from known noise samples) is a formidable task that can be made simpler by considering the (original) results presented in the current paper. The Bernoulli-Gaussian model can be characterized, analytically, by using the noise power and two additional quantities: the expectation of the absolute value of the noise process plus the expected value of the third power of the absolute value. In practice the parameters would be calculated using estimates of the mentioned expected values. The communication system design can be much improved if a well fit Bernoulli-Gaussian stochastic process is selected to model the noise. This is an alternative to model the communication using power lines which is often modeled as Middleton Class-A. The rate harvested when modeling the medium as a Bernoulli-Gaussian channel, it is shown, is increased when compared to modeling the medium with the easily obtained Gaussian channel.

This letter was motivated by a practical problem in the optical image compression scope captured by remote-sensing satellites with energy restriction. A large number of embedded CPU architectures have a multicore capability. On the other hand, the most used algorithms for image compression were developed to perform the compression task using a single core. Therefore, this letter proposes a scheme to allow the most used compressor algorithms to run in parallel to perform the compression task more efficiently (Consultative Committee for Space Data Systems (CCSDS), Set Partitioning in Hierarchical Trees (SPIHT), and JPEG2000). In addition, the most used image compression algorithms were developed without considering the energy expenditure as an issue. Thus, this letter will use the algorithm's energy expenditure as one of the evaluation criteria, whose analysis can be extended to other systems that operate using batteries. Considering that the processing time and energy expenditure depend on the platform and may have different magnitudes, this letter will use two processing platforms for the analysis. The results will be analyzed in terms of rate-distortion-energy with the help of an external simple energy measurement tool. To avoid working only with the well-known rate-distortion curves of the image compressors mentioned previously, the scheme proposed herein allows using more than one algorithm to perform the image compression. The results show several gains in the processing time, especially for the SPIHT algorithm, which uses only 5% of the original processing time and 12.8% of energy expenditure (for a compression rate of 1.5 b per pixel), for the platforms evaluated using this scheme.