A Balkan Perspective: Balancing Growth Through Responsible Space Sustainability in Emerging Regions

Space exploration has profoundly impacted daily life, yet many developing regions, such as the Balkans, struggle to access its benefits due to corruption, brain drain, and systemic inefficiencies. This op-ed advocates for a sustainable and responsible approach to space development in emerging regions, emphasizing capacity building, space sustainability, and policy frameworks. By fostering education, technology transfer, and regional collaboration, Balkan nations can counter talent loss and contribute meaningfully to the global space sector. Additionally, integrating environmentalism into space activities and promoting green technologies can position the region as a leader in sustainable innovation. A strong policy framework, space diplomacy, and public engagement will be key to ensuring long-term success. Through these efforts, the Balkans can establish a responsible and forward-thinking space industry that balances growth with sustainability, setting a precedent for other developing regions

A CatBoost-Based Approach for Aerosol Optical Depth Estimation Using Multi-Spectral Sentinel-2 Data

Aerosol Optical Depth (AOD) is a critical parameter for understanding air quality, climate change, and public health impacts. This study introduces a novel approach for estimating AOD using multi-spectral Sentinel-2 data and advanced machine learning techniques. Leveraging hybrid feature engineering, including spectral ratios, wavelet decomposition, and texture analysis, we extracted features that capture the complex spatial and spectral characteristics of aerosols. A CatBoost Regressor was employed to model the relationship between these features and AOD values, achieving a mean Pearson correlation coefficient of 0.9640 ± 0.0460 across 200-fold cross-validation. The integration of wavelet-based features and Local Binary Patterns (LBP) proved particularly effective in improving AOD estimation accuracy. Spectral ratios involving visible and near-infrared bands, such as B3/B5 and B1/B8, were identified as highly predictive of aerosol scattering effects. The proposed methodology addresses limitations of traditional AOD estimation methods, such as spatial-resolution trade-offs and spectral underutilization, while demonstrating robust performance across diverse environments. The improved accuracy of AOD estimation has significant implications for environmental monitoring, climate modeling, and public health initiatives. Future work could focus on refining feature extraction techniques for challenging environments and incorporating additional datasets to further enhance model performance

Interplanetary CubeSat Networks: Challenges and Future Prospects in Deep Space Communication

The rapid development of miniaturized space technology has enabled CubeSats to extend their reach beyond low Earth orbit and be used for interplanetary missions. These small, low-cost spacecrafts hold new promises for distributed science observations, communication relay, and autonomous exploration. Establishing dependable communication networks for CubeSats in deep space is a significant challenge due to severe latency, limited power budgets, low bandwidth, and the lack of specialized interplanetary infrastructure. This review addresses the fundamental communication challenges of interplanetary CubeSats, including signal loss over large distances, Doppler shift, and frequency stability. It also speaks of current and future solutions such as Delay/Disruption Tolerant Networking (DTN), optical communications systems, and cooperative CubeSat swarm development. Through current mission analysis and projected architecture, this paper highlights the technological advances needed to enable scalable and fault-tolerant interplanetary CubeSat networks. The review is completed with a summary of future research directions and the urgent necessity of autonomous, adaptive communication systems to facilitate the next generation of deep space exploration

Multispectral Imaging and Astrophotographic Analysis of the Star-Forming Regions within the Orion Nebula (M42)

The Orion Nebula (M42), which is among the most beautiful and prominent star-forming areas in the evening sky, presents an interesting region to examine stellar life cycles and interstellar gas dynamics. In this study, we used an Astromaster 130EQ telescope with clear night sky conditions to observe the Orion Nebula and take high-resolution photographs of celestial beauty. Our main interest was in examining the dimensions and structural features of the nebula, such as its bright spots and dark spots, clouds of gas, and star-forming regions. After observing, we employed the DeepSkyStacker image processing software to improve the raw images by stacking several exposures, minimizing noise, and enhancing detailed features of the nebula. This enabled us to achieve a final, clear image that displays key features like the Trapezium Cluster and surrounding gas clouds. By this research, we hope to add to the knowledge of the physical characteristics of the Orion Nebula, specifically its gas content and star-forming activity. Our astrophotographic examination illustrates the capability of utilizing ground-based telescopes combined with sophisticated image processing methods to probe distant star-forming regions with unprecedented clarity

Supersonic Aerodynamic Analysis of a High Performance Reconnaissance Aircraft via CFD

Modern advancements in aerospace engineering have accelerated the development of high-performance, stealthy reconnaissance aircraft. This research delves into the aerodynamic performance of such an aircraft operating at supersonic speeds. Through advanced computational tools, we explore the complex interplay of aerodynamic forces acting on this type of aircraft during critical reconnaissance missions. A key aspect of this research is the analysis of a digital model resembling a modern, high-speed reconnaissance aircraft designed for minimal radar signature. This model will be subjected to rigorous aerodynamic analysis using established computational techniques. By examining the flow characteristics around the aircraft at Mach 3, the study aims to gain valuable insights that can contribute to the optimization of future stealthy reconnaissance vehicles. Understanding the challenges associated with maintaining stability, maneuverability, and fuel efficiency at such extreme velocities is paramount for successful reconnaissance operations. This research provides a crucial step towards achieving this goal, ultimately contributing to the development of more advanced and effective high-speed, stealthy reconnaissance aircraft

Unsupervised Classification of Binary SMBH Candidates in Gaia DR3: A Machine Learning Approach to Astrometric Jitter and Cluster-Based Candidate Identification

We present an unsupervised machine learning analysis of astrometric variability in Gaia DR3 quasars, aimed at identifying indirect signatures of unresolved binary supermassive black holes (SMBHBs). Using a filtered sample of ∼10,000 high-quality quasars, we extract key features including RUWE, astrometric excess noise, parallax, color index, and G-band magnitude. These features are normalized and reduced using Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) to uncover low-dimensional structure. We apply both K-Means and DBSCAN clustering algorithms to the projected feature space. The K-Means algorithm identifies three distinct populations, with one cluster exhibiting statistically higher excess noise and intermediate RUWE values, suggestive of potential centroid jitter induced by binary SMBH orbital motion. The clustering results are further validated using silhouette scores and consistent spatial separability in t-SNE projections. A catalog of candidate high-jitter quasars is compiled from the most deviant cluster, comprising over 3500 sources. These candidates are promising targets for future multi-wavelength follow-up using VLBI, variability surveys, and higher-precision Gaia astrometry. Our work demonstrates that unsupervised learning techniques offer a powerful, scalable alternative to classical threshold-based methods for probing the hidden binary SMBH population at cosmological distances. This study represents one of the first applications of machine learning to stochastic astrometric variability in extragalactic sources and provides a reproducible framework for future discovery in Gaia DR4 and LSST-era datasets

Aerodynamic Panel Shape Optimization for CubeSats to Reduce Chaotic Motion in Lower Earth Orbit

CubeSats are increasingly employed in various low-Earth orbit (LEO) missions. However, their stability is often compromised by chaotic motion induced by aerodynamic disturbances and the deployment of appendages, such as solar panels or fins. Addressing these challenges is critical to ensuring mission reliability and extending operational lifetimes. This study explores the aerodynamic performance and stability implications of deployable fin geometries for CubeSats, where two configurations of square-shaped and elliptical fins are chosen for analysis. Using computational fluid dynamics (CFD) simulations under identical boundary conditions, velocity fields, flow structures, and turbulence intensity around the CubeSat have been examined. The results reveal that elliptical fins produce smoother flow patterns with reduced velocity gradients, minimizing turbulence and enhancing stability. In contrast, square fins exhibit higher turbulence intensity, which could promote chaotic motion. By establishing the aerodynamic advantages of elliptical fin designs, this work not only provides actionable insights for stabilizing CubeSats in LEO but also offers a framework for optimizing fin geometries to mitigate chaotic behavior. These findings lay the foundation for future advancements in CubeSat design, enabling improved aerodynamic performance and stability in dynamic orbital environments

Exploring Exploring the Effects of Martian Seasons on Surface Features, Climate, and Atmospheric Behavior: A Comprehensive Review

Mars has specific orbital and axial characteristics that give rise to seasons unlike those encountered on Earth. This paper explores seasonal changes on the Red Planet and demonstrates how deep such changes are about features on its surface, climate, and atmospheric dynamics. It analyses temperature, pressure in the atmosphere, and dust activities as descriptors which show influences towards some phenomena such as polar ice caps, dust storms, and surface weathering. The paper further discusses the interaction between Martian seasons and the thin atmosphere of the planet-the seasonal sublimation of CO2 at the poles and its implications for the Martian climate, revealing insights into how these seasonal changes affect Mars' potential for future human exploration and habitability through a comprehensive examination of recent data from missions to Mars. Thus, this review gives a greater insight into the Martian environment and acts as an argument supporting further studies on seasonal dynamics to advise future missions and searches on other planets for life

Exploring the Formation, Dynamics, and Structure of Galaxy Clusters: A Comprehensive Analysis

This study explores the formation, structure, and dynamics of galaxy groups and clusters, highlighting their significance in the cosmic web. Galaxy clusters are massive, self-gravitating systems that display complex interactions influenced by dark matter and the intracluster medium (ICM). Recent advancements in X-ray and optical observations have significantly improved our understanding of the morphology and kinematics of these clusters, shedding light on the processes governing their evolution. The research employs the virial theorem to determine mass distributions and examines gravitational lensing effects to probe the mysterious nature of dark matter. Key challenges addressed include understanding how galaxy clusters form, analyzing the dynamics of the galaxies within them, and identifying the missing mass that holds these structures together. By combining observational data with theoretical models, this investigation aims to enhance our comprehension of the Universe's large-scale structure and the fundamental forces driving galaxy group dynamics

Review of Active and Passive Devices for Drag Reduction

Base drag accounts for up to 40% of the total aerodynamic drag experienced by aerodynamic bodies like projectiles, missiles, and rockets, significantly reducing their range and aerodynamic performance. This paper reviews 36 scientific research papers exploring active and passive methods of base drag reduction. It considers active methods, such as base bleed and external burning, and passive methods, which include boattailing, cavitation, and passive porosity to understand their effectiveness in base drag reduction. Active methods like base bleed and external burning work by injecting additional mass or fuel into the base flow, influencing the flow pattern in the recirculation region. Active methods have some downsides, including an increase in the overall weight of the projectile and a higher fuel consumption rate, which impacts the projectile’s performance and efficiency. On the other hand, passive methods aim to reduce drag through modifications in the shape or structure of the projectile itself. They work by enhancing the base pressure of the projectile, which, in turn, reduces the base drag. The review includes an analysis of three different flow regimes—transonic, supersonic, and subsonic. The papers reviewed modified aerodynamic bodies with different active and passive methods, evaluating their impacts on each of the flow regimes. In transonic flow, the base pressure distribution pattern is affected by phenomena like drag divergence. Besides boattailing and cavitation, the reviewed papers found methods like passive porosity effective in reducing base drag. However, the studies found a combination of passive porosity in boattailed geometry as most effective in drag reduction in the transonic flow regime. In supersonic flow, the papers reviewed studied passive methods like boattailing and cavitation for reducing drag, in which boattailing was found to be the most effective in drag reduction. In hypersonic flow, the reviewed research showed no significant link between cavitation and drag reduction in the hypersonic regime. While a fin configuration increased drag with higher Mach numbers and angles of attack, the use of a counter jet flow mechanism with an aerospike was found to significantly aid drag reduction