Search Results (35)

This paper presents an innovative concept for the adaptive transformation of decommissioned coal mine shafts into advanced reduced-gravity research facilities, addressing both post-mining land management and continuous advancements in microgravity research. The proposed solution leverages existing underground infrastructure to create an exceptionally long drop tower, approximately 900 m, surpassing the operational capabilities of all current global facilities. The facility employs electromagnetic propulsion and braking systems compatible with maglev technology, enabling extended microgravity durations and the precise simulation of multiple planetary gravity environments. Comprehensive numerical simulations, taking into account realistic mining shaft geometries, aerodynamic resistance, and mechanical vibration isolation, demonstrate that the system achieves free-fall periods of at least 10 s, which will be longer in the case of a capsule drop for research in reduced-gravity conditions (controlled deceleration of the capsule during the drop). The six-point suspension system effectively isolates experimental payloads from vibrations generated during descent. Beyond technological innovation, the facility exemplifies multidimensional sustainability by integrating scientific advancement with regional economic revitalization, employment generation for mining communities, industrial heritage preservation, and alignment with European Green Deal objectives. This globally unique research center would provide unprecedented opportunities for materials science, space biology, and industrial experimentation, while demonstrating innovative repurposing of post-mining assets. Full article

This paper describes a compact low-cost telemetry system featuring ready-made sensors and an acquisition unit based on the ESP32, which makes use of the LoRa/Wi-Fi wireless standard for communication, and autonomous fallback logging to guarantee data recovery during communication loss. Ensuring safe atmospheric re-entry requires reliable onboard monitoring of capsule conditions during descent. The system is intended for sub-orbital, low-cost educational capsules and experimental atmospheric descent missions rather than full orbital re-entry at hypersonic speeds, where the environmental loads and communication constraints differ significantly. The novelty of this work is the development of a fully self-contained telemetry system that ensures continuous monitoring and fallback logging without external infrastructure, bridging the gap in compact solutions for CubeSat-scale capsules. In contrast to existing approaches built around UAVs or radar, the proposed design is entirely self-contained, lightweight, and tailored to CubeSat-class and academic missions, where costs and infrastructure are limited. Ground test validation consisted of vertical drop tests, wind tunnel runs, and hardware-in-the-loop simulations. In addition, high-temperature thermal cycling tests were performed to assess system reliability under rapid temperature transitions between −20 °C and +110 °C, confirming stable operation and data integrity under thermal stress. Results showed over 95% real-time packet success with full data recovery in blackout events, while acceleration profiling confirmed resilience to peak decelerations of ~9 g. To complement telemetry, the TeleCapsNet dataset was introduced, facilitating a CNN recognition of descent states via 87% mean Average Precision, and an F1-score of 0.82, which attests to feasibility under constrained computational power. The novelty of this work is twofold: having reliable dual-path telemetry in real-time with full post-mission recovery and producing a scalable platform that explicitly addresses the lack of compact, infrastructure-independent proposals found in the existing literature. Results show an independent and cost-effective system for small re-entry capsule experimenters with reliable data integrity (without external infrastructure). Future work will explore AI systems deployment as a means to prolong the onboard autonomy, as well as to broaden the applicability of the presented approach into academic and low-resource re- entry investigations. Full article

In commercial aviation, accurate estimation of the remaining track miles (RTM) during descent is essential for energy-efficient trajectory management. Currently, pilots often rely on heuristics and experience due to the lack of consistent RTM information, which can result in suboptimal decisions. This study investigates the accuracy of RTM estimations made by commercial pilots through a structured survey involving scenario-based assessments across seven European airports. Results show a consistent underestimation bias, with a root mean square error (RMSE) of 9.69 NM. To quantify the potential of data-driven alternatives, a machine learning model based on gradient boosting was developed using ADS-B surveillance and weather data. The model achieved significantly lower prediction errors, with an RMSE of 5.43 NM, particularly outperforming pilots in early descent segments. Feature importance analysis revealed that spatial and trajectory-related variables were key to accurate predictions. The findings suggest that integrating predictive models into flight management systems or pilot decision support tools could improve descent planning and operational efficiency. This study provides an empirical comparison between human and AI-based RTM estimations, highlighting the potential for machine learning to complement pilot expertise in future air traffic operations. Full article

Genealogy is a powerful analytical framework for understanding the cultural evolution of tribes, communities, and societies. This article demonstrates that the recurrent reliance on genealogical structures is a common feature of human societies, serving as a fundamental mechanism for cultural evolution through time, space, and culture. Based on comparative analysis of indigenous tribal societies (e.g., Aboriginal Australian kinship, Polynesian chiefly genealogies), agrarian civilizations (e.g., European feudal lineages, Chinese patriliny), and modern nation-states (e.g., nationalist mythmaking, DNA-based ancestry movements), this study reveals consistent patterns in genealogical functions. Drawing on an interdisciplinary perspective from anthropology, sociology, history, and evolutionary biology, it is argued that genealogical systems are not passive records of descent but dynamic forces of cultural continuity and adaptation. The evidence shows that, despite vast sociocultural differences, genealogy widely operates as a dual-purpose instrument. It preserves cultural memory and legitimizes political authority while simultaneously facilitating social adaptation and innovation in response to new challenges. The paper also critiques contemporary trends like commercial genetic genealogy, highlighting its potential for reconnecting diasporic communities alongside its risks of biological essentialism. Ultimately, the work establishes that the persistent and patterned reliance on genealogy from oral traditions to genetic data offers a critical lens for understanding the deep structures of cultural continuity and transformation in human societies. It further underscores the importance of genealogy in cultural evolution, historical persistence, societal transformation, and the construction of belonging in an increasingly globalized world. Full article

This paper addresses the challenge of segmenting consumer behavior in contexts characterized by both numerical regularities and semantic variability. Traditional models, such as RFM-based segmentation, capture the transactional dimension but neglect the implicit meanings expressed through product descriptions, reviews, and linguistic diversity. To overcome this gap, we propose a hybrid clustering algorithm that integrates numerical and semantic distances within a unified scalar framework. The central element is a scalar objective function that combines Euclidean distance in the RFM space with cosine dissimilarity in the semantic embedding space. A continuous parameter λ regulates the relative influence of each component, allowing the model to adapt granularity and balance interpretability across heterogeneous data. Optimization is performed through a dual strategy: gradient descent ensures convergence in the numerical subspace, while genetic operators enable a broader exploration of semantic structures. This combination supports both computational stability and semantic coherence. The method is validated on a large-scale multilingual dataset of transactional records, covering five culturally distinct markets. Results indicate systematic improvements over classical approaches, with higher Silhouette scores, lower Davies–Bouldin values, and stronger intra-cluster semantic consistency. Beyond numerical performance, the proposed framework produces intelligible and culturally adaptable clusters, confirming its relevance for personalized decision-making. The contribution lies in advancing a scalarized formulation and hybrid optimization strategy with wide applicability in scenarios where numerical and textual signals must be analyzed jointly. Full article

Indirect detection phase control algorithms, such as the dithering algorithm and the stochastic parallel gradient descent algorithm (SPGD), have simple system structures and are applicable to filled-aperture coherent combinations with high efficiency. The performances of these algorithms are limited when applied to a coherent combination of pulsed fiber lasers with a low repetition rate (≤5 kHz). Firstly, due to the overlap of the phase noise frequency and repetition rate, conventional algorithms cannot effectively distinguish the phase noise from the pulse fluctuation, and directly applying filtering will result in the phase information being filtered out. Secondly, if the pulse fluctuation is ignored and only the continuous part of the phase information is utilized, it relies on the presetting of conditions to separate the pulse from the continuous part and loses the phase information of the pulse part. In this article, we propose a waveform self-referencing algorithm (WSRA) based on a two-channel near-infrared laser coherent combination system to overcome the above challenges. Firstly, by modelling a self-referencing waveform, a nonlinear scaling operation is performed on the combined signal to generate a pseudo-continuous signal, which removes the intrinsic pulse fluctuation while preserving the phase noise information. Secondly, the phase control signal is calculated based on the pseudo-continuous signals after parallel perturbation. Finally, the phase noise is corrected by optimization. The results show that our method effectively suppresses the waveform fluctuation at a 5 kHz repetition rate, the light intensity reaches an ideal value (0.9939 $I_{\max }$), and the root-mean-square (RMS) phase error is only 0.0130 $\lambda$. This method does not require the presetting of pulse detection thresholds or conditions, and solves the challenge of coherent combination at a low repetition rate, with adaptability to different pulse waveforms. Full article

Unmanned Aerial Vehicles (UAVs) play a crucial role in the continuous monitoring and coordination of disaster relief operations, providing real-time information that aids in decision-making and resource allocation. However, optimizing the deployment of multi-UAV systems in disaster-stricken areas presents a significant challenge. This challenge arises due to conflicting objectives, such as maximizing coverage while minimizing energy consumption, critical to ensuring prolonged operational capability in dynamic and unpredictable environments. To address these challenges, this paper proposes a novel successive deployment method specifically designed for optimizing UAV placements in complex disaster relief scenarios. The overall optimization problem is decomposed into two NP-hard subproblems: the coverage problem and the Energy Consumption (EC) problem. To achieve maximum coverage of the affected area, we employ the Strong Weiszfeld (SW) algorithm to determine optimal UAV placement. Simultaneously, to minimize energy consumption while maintaining optimal coverage performance, we utilize the Steepest Descent with Goldstein (SDG) algorithm. This dual-algorithmic approach is tailored to balance the trade-offs between wide-area coverage and energy efficiency. We validate the effectiveness of the proposed SW + SDG method by comparing its performance against traditional deployment strategies across multiple scenarios. Experimental results demonstrate that our approach significantly reduces energy consumption while maintaining extensive coverage, and outperforms conventional algorithms. This not only ensures a more sustainable and long-lasting operational network but also enhances deployment efficiency and stability. These findings suggest that the SW + SDG algorithm is a robust and versatile solution for optimizing multi-UAV deployments in dynamic, resource-constrained environments, providing a balanced approach to coverage and energy efficiency. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

Effective earthquake early warning (EEW) is essential for disaster prevention in the built environment, enabling a rapid structural response, system shutdown, and occupant evacuation to mitigate damage and casualties. However, most current EEW systems lack rigorous reliability analyses of their predictive outcomes, limiting their effectiveness in real-world scenarios—especially for on-site warnings, where data are limited and time is critical. To address these challenges, we propose a Bayesian neural network (BNN) framework based on Stein variational gradient descent (SVGD). By performing Bayesian inference, we estimate the posterior distribution of the parameters, thus outputting a reliability analysis of the prediction results. In addition, we incorporate a continual learning mechanism based on elastic weight consolidation, allowing the system to adapt quickly without full retraining. Our experiments demonstrate that our SVGD-BNN model significantly outperforms traditional peak displacement (Pd)-based approaches. In a 3 s time window, the Pearson correlation coefficient R increases by 9.2% and the residual standard deviation $SD$ decreases by 24.4% compared to a variational inference (VI)-based BNN. Furthermore, the prediction variance generated by the model can effectively reflect the uncertainty of the prediction results. The continual learning strategy reduces the training time by 133–194 s, enhancing the system’s responsiveness. These features make the proposed framework a promising tool for real-time, reliable, and adaptive EEW—supporting disaster-resilient building design and operation. Full article

The Tomographic Synthetic Aperture Radar (TomoSAR) technique is widely used for monitoring urban infrastructures, as it enables the mapping of individual scatterers across additional dimensions such as height (3D), thermal dilation (4D), and deformation velocity (5D). Retrieving this information is crucial for building management and maintenance. Nevertheless, accurately extracting it from TomoSAR data poses several challenges, particularly the presence of outliers due to uneven and limited baseline distributions. One way to address these issues is through statistical detection approaches such as the Generalized Likelihood Ratio Test, which ensures a Constant False Alarm Rate. While effective, these methods face two primary limitations: high computational complexity and the off-grid problem caused by the discretization of the search space. To overcome these drawbacks, we propose an approach that combines a quick initialization process using Fast-Sup GLRT with local descent optimization. This method operates directly in the continuous domain, bypassing the limitations of grid-based search while significantly reducing computational costs. Experiments conducted on both simulated and real datasets acquired with the TerraSAR-X satellite over the Spanish city of Barcelona demonstrate the ability of the proposed approach to maintain computational efficiency while improving scatterer localization accuracy in the third and fourth dimensions. Full article

The Impact Monitor Project is a European initiative designed to develop an impact assessment toolbox and framework, targeting the European aviation sector. The proposed framework is not only aimed at the environment, economics, and operations but also the societal impacts of new technologies and aircraft configurations. The toolbox works by setting out the key steps in the impact assessment cycle and presenting guidance, tips, and best practices. Led by DLR, the consortium includes research institutions and universities that have contributed their expertise and tools to develop the collaborative assessment toolbox and framework. The project defines three use cases by considering three assessment levels: aircraft, airport, and air transport system. This article focuses on Use Case 2 on continuous descent operations (CDOs) at the aircraft and airport levels. It describes the workflow proposal, along with the tools involved. The collaborative approach showcases integrating these tools and using collaborative strategies enabled by CPACS (Common Parametric Aircraft Configuration Schema) and RCE (remote component environment). The list of tools includes Scheduler (DLR; flight schedule simulation), AirTOp (NLR; TMA simulation), Dynamo/Farm (UPC; trajectory simulation and assessment), LEAS-iT (NLR; emissions simulation), Tuna (NLR; noise simulation), AECCI (ONERA; emissions simulation), TRIPAC (NLR; third-party risk simulation), and SCBA (TML; social and economic impact assessment). Interactions with other use cases of the project will be demonstrated via new aircraft configurations stemming from the use case at the aircraft level of the project. The results demonstrate the workflow’s feasibility, the cooperation among the tools to obtain and refine the outcomes, as well as the analysis of the operational scenario of a generic airport, CAEPport, which has been extensively used in previous Clean Sky 2 projects. Full article

The purpose of this article is to investigate some tensorial norm inequalities for continuous functions of self-adjoint operators in Hilbert spaces. Our first approach is to develop a gradient descent inequality and some relational properties for continuous functions involving Huber convex functions, as well as several new bounds for Simpson type inequality that is twice differentiable using different types of generalized convex mappings. It is believed that this study will provide a valuable contribution towards developing a new perspective on functional inequalities by utilizing some other types of generalized mappings. Full article

Learning is a fundamental property of intelligent systems, observed across biological organisms and engineered systems. While modern intelligent systems typically rely on gradient descent for learning, the need for exact gradients and complex information flow makes its implementation in biological and neuromorphic systems challenging. This has motivated the exploration of alternative learning mechanisms that can operate locally and do not rely on exact gradients. In this work, we introduce a novel approach that leverages noise in the parameters of the system and global reinforcement signals. Using an Ornstein–Uhlenbeck process with adaptive dynamics, our method balances exploration and exploitation during learning, driven by deviations from error predictions, akin to reward prediction error. Operating in continuous time, Ornstein–Uhlenbeck adaptation (OUA) is proposed as a general mechanism for learning in dynamic, time-evolving environments. We validate our approach across a range of different tasks, including supervised learning and reinforcement learning in feedforward and recurrent systems. Additionally, we demonstrate that it can perform meta-learning, adjusting hyper-parameters autonomously. Our results indicate that OUA provides a promising alternative to traditional gradient-based methods, with potential applications in neuromorphic computing. It also hints at a possible mechanism for noise-driven learning in the brain, where stochastic neurotransmitter release may guide synaptic adjustments. Full article

To address the technical challenges of implementing Continuous Descent Operations (CDO) in high-traffic-density terminal control areas, we propose a cooperative low-carbon trajectory planning method for multiple arriving aircraft. Firstly, this study analyzes the CDO phases of aircraft in the terminal area, establishes a multi-phase optimal control model for the vertical profile, and introduces a novel vertical profile optimization method for CDO based on a genetic algorithm. Secondly, to tackle the challenges of CDO in busy terminal areas, a T-shaped arrival route structure is designed to provide alternative paths and to generate a set of four-dimensional (4D) alternative trajectories. A Mixed Integer Programming (MIP) model is constructed for the 4D trajectory planning of multiple aircraft, aiming to maximize the efficiency of arrival traffic flow while considering conflict constraints. The complex constrained MIP problem is transformed into an unconstrained problem using a penalty function method. Finally, experiments were conducted to evaluate the implementation of CDO in busy terminal areas. The results show that, compared to actual operations, the proposed optimization model significantly reduces the total aircraft operating time, fuel consumption, CO₂ emissions, SO₂ emissions, and NO_x emissions. Specifically, with the optimization objective of minimizing total cost, the proposed method reduces the total operation time by 22.4%; fuel consumption, CO₂ emissions, SO₂ emissions by 22.9%, and NO_x emissions by 23.7%. The method proposed in this paper not only produces efficient aircraft sequencing results, but also provides a feasible low-carbon trajectory for achieving optimal sequencing. Full article

In this manuscript, we propose an efficient algorithm for solving a class of nonlinear operator equations. The algorithm is an improved version of previously established method. The algorithm’s features are as follows: (i) the search direction is bounded and satisfies the sufficient descent condition; (ii) the global convergence is achieved when the operator is continuous and satisfies a condition weaker than pseudo-monotonicity. Moreover, by comparing it with previously established method the algorithm’s efficiency was shown. The comparison was based on the iteration number required for each algorithm to solve a particular problem and the time taken. Some benchmark test problems, which included monotone and pseudo-monotone problems, were considered for the experiments. Lastly, the algorithm was utilized to solve the logistic regression (prediction) model. Full article