This work evaluates the performance of compression algorithms based on discrete cosine transform (DCT) when used to encode sounding rockets’ vibration signals. Different compression methods that use the three-step strategy (transform, quantization, and entropy encoder) are presented. The performances are evaluated using the relationship between the compression rate and distortion, the latter being measured using the peak signal-to-noise ratio (PSNR) and the difference observed in the power spectral density. The focus of the work is on vibration signals from rocket launches conducted by the Institute of Aeronautics and Space of the Brazilian Air Force. The obtained results present a great variation for the vibration data in the test set. Compressors operating with PSNR close to 50 dB can achieve very high compression rates (over 100) for low power signals. However, if the vibration signal strength increases significantly, the compression rate can be much lower. For the worst case in the test set (signal with the highest power), the work shows that the best proposed method needs a rate of 9.8 to achieve a PSNR of 48.2 dB. When compared to the performance of recently published DCT-based compression algorithms, the proposed technique shows significant improvements. Given a PSNR around 50 dB, for signal with moderate power, improvements above 100% are observed in compression rate.

The study of microgravity, a condition in which an object experiences near-zero weight, is a critical area of research with far-reaching implications for various scientific disciplines. Microgravity allows scientists to investigate fundamental physical phenomena influenced by Earth’s gravitational forces, opening up new possibilities in fields such as materials science, fluid dynamics, and biology. However, the complexity and cost of developing and conducting microgravity missions have historically limited the field to well-funded space agencies, universities with dedicated government funding, and large research institutions, creating a significant barrier to entry. This paper presents the MicroGravity Explorer Kit’s (MGX) design, a multifunctional platform for conducting microgravity experiments aboard suborbital rocket flights. The MGX aims to democratize access to microgravity research, making it accessible to high school students, undergraduates, and researchers. To ensure that the tool is versatile across different scenarios, the authors conducted a comprehensive literature review on microgravity experiments, and specific requirements for the MGX were established. The MGX is designed as an open-source platform that supports various experiments, reducing costs and accelerating development. The multipurpose experiment consists of a Jetson Nano computer with multiple sensors, such as inertial sensors, temperature and pressure, and two cameras with up to 4k resolution. The project also presents examples of codes for data acquisition and compression and the ability to process images and run machine learning algorithms to interpret results. The MGX seeks to promote greater participation and innovation in space sciences by simplifying the process and reducing barriers to entry. The design of a platform that can democratize access to space and research related to space sciences has the potential to lead to groundbreaking discoveries and advancements in materials science, fluid dynamics, and biology, with significant practical applications such as more efficient propulsion systems and novel materials with unique properties.

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.

Automated surface defect detection in hot-rolled steel is a prerequisite for real-time quality control, yet most deployed inspection systems operate in isolation from Industrial Internet of Things (IIoT) infrastructure. This paper reports a laboratory-scale proof of concept with two contributions. The first is a segmentation study on the Severstal dataset using a leakage-free, defect-stratified split of 1886 test images. Because a trivial all-background predictor already attains 96.66% pixel accuracy, performance is reported through Dice, IoU, precision, recall, and F1 with 95% confidence intervals. A compact from-scratch U-Net (0.49 M parameters) reaches a Dice of 0.416 at 38.6 ms per image, an ImageNet-pretrained DeepLabV3+ model reaches 0.677 at 46.3 ms and 37 times the parameters, and a classical Otsu baseline reaches 0.060, bracketing an explicit accuracy-versus-footprint design space rather than a single recommended model. The second contribution is architectural: a three-layer IIoT architecture whose messaging layer is empirically characterized on a Raspberry Pi broker over 158,500 messages. A factorial experiment isolates the transport configuration of the broker, rather than that of the publisher, as the determinant of end-to-end latency, yielding a seventeen-fold reduction. The layer sustains 1920 messages per second without loss, and a deliberate broker outage shows that MQTT delivery guarantees are semantic rather than temporal, motivating an application-level message-expiry policy. Embedded inference deployment is identified as the primary next step.

This work proposes and experimentally validates a structured development flow from rapid prototyping to printed circuit board (PCB) implementation for embedded systems operating in constrained environments. The approach is applied to a battery pack monitoring system for sounding rockets, and it is responsible for acquiring voltage, current, and temperature data and transmitting telemetry via an RS-422 interface at 38.4 kbps. The methodology starts with the integration of commercially available modules and evolves to a custom PCB while preserving the validated functional architecture. Experimental validation was conducted through bench and tri-axial vibration tests, demonstrating consistent system performance in sensing, processing, and telemetry, with no observed signal degradation or discontinuities. The results confirm that the proposed approach preserves system functionality and reduces development uncertainty, while providing a clear framework for design stages, validation procedures, and component selection. Furthermore, the findings support the use of low-cost commercial off-the-shelf (COTS) components in non-critical applications when appropriate validation strategies are applied, while emphasizing the need for additional qualification for critical use cases. The development flow is explicitly designed to support TRL progression, such that the evidence accumulated at each stage provides the traceability required for future qualification under critical classification.

Vibration data obtained from sounding rocket missions is critical for structural analysis, model verification, and refinement of the qualification environment. Because of their high sampling rates, vibration channels usually consume most of the telemetry bandwidth, causing strict constraints on instrumentation and telemetry. In aerospace applications, vibration data analysis primarily focuses on spectral and energy-related features rather than waveform details. This paper introduces an embedded vibration data compression system that specifically targets the high-bandwidth requirements of vibration channels in aerospace telemetry systems, enabling operation in a very low bit rate environment. The proposed system integrates spectral features with structural modeling concepts developed from modal analysis. Rather than transmitting detailed spectral information, the system leverages inter-sensor correlations to predict vibration responses, transmitting only spectral summaries and error components while intentionally omitting low-energy components. System performance is assessed using energy metrics in accordance with aerospace qualification standards, with special attention to maintaining the root-mean-square acceleration error within 5% for the frequency range considered, as recommended by NASA’s finite element modeling continuous improvement community. The new approach maintains a telemetry spectral efficiency of ρ < 0.2 bits s − 1 Hz − 1 , an improvement of more than an order of magnitude over traditional vibration telemetry systems, which operate at 12–15 bits s − 1 Hz − 1 . The entire compression method is developed on a microcontroller-supported embedded system that is compatible with avionics systems on board an aircraft and tested under real-time processing requirements, simulating flight scenarios. Although motivated by sounding rocket missions, the proposed framework is also applicable to other bandwidth-constrained vibration-based monitoring systems, including industrial condition monitoring applications such as rolling bearing fault detection and predictive maintenance, as well as edge-based sensing platforms.