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Castles situated in rural areas occupy a distinctive position within the European heritage landscape, serving economic, residential and symbolic functions. While the great urban royal residences have benefited from constant attention, conservation and valorisation in Central and Eastern Europe, castles in rural areas have often been subjected to systematic neglect. The objective of this study is to analyse three castles (Bulci, Căpâlnaș and Petriș) in the Lower Mureș Valley in Romania, with a view to observing how these symbols of a fragmented past have been marked by historical ruptures, regime changes and marginalisation policies, and the current potential of these castles to be transformed from derelict spaces into spaces of local importance. Should our research contribute to the study of the transformation of derelict spaces of historical castles into invigorating spaces, from a methodological point of view the following three steps were taken: (1) an assessment of the state of conservation and the factors that led to the degradation of these noble domains was carried out; (2) research was conducted on the perception of stakeholders regarding how castles can become a generator of local development; and (3) an analysis of the development proposals around these castles from stakeholders was performed. The study is based on 35 semi-structured interviews conducted with stakeholders and residents of the castle communities analysed, and on a thematic content analysis of these interviews. The findings of the research suggest a state of conservation that is insecure, and the perceptions of stakeholders indicate a necessity for institutional intervention and public–private partnerships. There is also a conviction that attracting large-scale investors is essential for the revitalisation of these monuments. The responses indicate a genuine concern for the future of the castles. The proposals for the development of the castles are oriented towards their utilisation in cultural tourist circuits. Full article

Rapid urbanization in developing countries has widened the gap between urban and rural development, due to inefficient land markets and weak institutional systems in rural areas. China’s innovative “Urban–rural Construction Land Linkage” policy was designed to address this imbalance by encouraging rural land consolidation and creating a transferable development rights mechanism. While this approach has shown potential in improving the utilization efficiency of existing construction land and continuously supplying urban development space, concerns remain about its actual benefits to villagers and rural development, with some arguing it disrupts traditional livelihoods and favors government interests over rural needs. To respond to this debate, this study investigates two core questions: first, does China’s transferable land development rights (TDR) program genuinely improve rural welfare as intended; second, why does the theoretically preferred self-organized governance model sometimes fail in practice? To address these research questions, this paper develops a new analytical framework combining the IAD framework of Ostrom with the hierarchical institutional framework of Williamson to examine three implementation approaches in China’s TDR implementation: government-dominated, market-invested, and self-organized models. Based on case studies, surveys, and interviews across multiple regions, this study reveals distinct strengths and weaknesses in each approach in improving villagers’ lives. Government-dominated projects demonstrate strong resource mobilization but limited community participation. Market-based models show efficiency gains but often compromise equity. While self-organized initiatives promise greater local empowerment, they frequently face practical challenges including limited management capacity and institutional barriers. Furthermore, this study identifies the preconditional institutional environment necessary for successful self-organized implementation, including clear land property rights, financial support, and technical assistance. These findings advance global understanding of how to combine efficiency with fair outcomes for all stakeholders in land governance, which is particularly relevant for developing countries seeking to manage urban expansion while protecting rural interests. Full article

With the evolution of 6G technology toward global coverage and multidimensional integration, OTFS modulation has become a research focus due to its advantages in high-mobility scenarios. However, existing OTFS signal detection algorithms face challenges such as pilot contamination, Doppler spread degradation, and diverse interference in complex environments. This paper proposes the SPARSE-OTFS-Net algorithm, which establishes a comprehensive signal detection solution by innovatively integrating sparse random pilot design, compressive sensing-based frequency offset estimation with closed-loop cancellation, and joint denoising techniques combining an autoencoder, residual learning, and multi-scale feature fusion. The algorithm employs deep learning to dynamically generate non-uniform pilot distributions, reducing pilot contamination by 60%. Through orthogonal matching pursuit algorithms, it achieves super-resolution frequency offset estimation with tracking errors controlled within 20 Hz, effectively addressing Doppler spread degradation. The multi-stage denoising mechanism of deep neural networks suppresses various interferences while preserving time-frequency domain signal sparsity. Simulation results demonstrate: Under large frequency offset, multipath, and low SNR conditions, multi-kernel convolution technology achieves significant computational complexity reduction while exhibiting outstanding performance in tracking error and weak multipath detection. In 1000 km/h high-speed mobility scenarios, Doppler error estimation accuracy reaches ±25 Hz (approaching the Cramér-Rao bound), with BER performance of 5.0 × 10⁻⁶ (7× improvement over single-Gaussian CNN’s 3.5 × 10⁻⁵). In 1024-user interference scenarios with BER = 10⁻⁵ requirements, SNR demand decreases from 11.4 dB to 9.2 dB (2.2 dB reduction), while maintaining EVM at 6.5% under 1024-user concurrency (compared to 16.5% for conventional MMSE), effectively increasing concurrent user capacity in 6G ultra-massive connectivity scenarios. These results validate the superior performance of SPARSE-OTFS-Net in 6G ultra-massive connectivity applications and provide critical technical support for realizing integrated space–air–ground networks. Full article

Deterministic chaos in optically injected semiconductor lasers (OILs) has attracted significant attention due to its relevance in secure communications, entropy generation, and photonic applications. However, existing studies often rely on low-resolution parameter sweeps or include noise contributions that obscure the intrinsic nonlinear dynamics. To address this gap, we investigate a noise-free OIL model and construct high-resolution chaos maps across the injection strength and frequency detuning parameter space. Chaos is characterized using two complementary approaches for computing the largest Lyapunov exponent: the Rosenstein time-series method and the exact variational method. This dual approach provides reliable and reproducible detection of deterministic chaotic regimes and reveals a rich attractor landscape with alternating bands of periodicity, quasi-periodicity, and chaos. The novelty of this work lies in combining high-resolution mapping with rigorous chaos indicators, enabling fine-grained identification of dynamical transitions. The results not only deepen the fundamental understanding of nonlinear laser dynamics but also provide actionable guidelines for exploiting or avoiding chaos in photonic devices, with potential applications in random chaos-based communications, number generation, and optical security systems. Full article

As cities densify and lifestyles become increasingly individualized, older adults face heightened risks of isolation and reduced wellbeing. Yet in historic Chinese neighborhoods, everyday socio-cultural practices—square dancing, Mahjong, community gardening and street markets—continue to foster social cohesion and spatial familiarity. This study employs Christopher Alexander’s pattern-language framework to examine how these practices are spatially embedded across six traditional neighborhoods in Chengdu. Drawing on systematic field observation, photographic surveys and typological mapping, it identifies recurring spatial configurations that support older adults’ participation and cultural continuity. While many canonical patterns remain relevant, the analysis shows how several require contextual reinterpretation to reflect Chinese collectivism, threshold sociability and informal public-space use. Synthesizing these insights, the paper develops a pattern-based design toolkit for culturally sensitive urban regeneration, contributing to age-friendly planning grounded in lived spatial practices. Although centered on six historic neighborhoods in Chengdu, the findings are intended primarily for Chinese heritage-led regeneration and—where comparable high-density morphologies, edge conditions and management regimes exist—are cautiously transferable to heritage districts elsewhere. Full article

As society ages, there is a growing concern about the comfort and health of elderly people. Although populations around the world, including Slovenia, are rapidly aging, evidence that increasing longevity is being accompanied by an extended period of good health is scarce. An increasing number of older adults live with chronic diseases, functional limitations, or frailty. In 2025, Slovenia launched the project Systemic Approach to Frailty with a Focus on Pre-Frailty for Healthy and Hight-Quality Ageing, within the European Cohesion Policy Programme 2021–2027, aiming to address frailty through multidimensional and community-based interventions. In addition to presenting the project framework, this paper provides an analytical preliminary review of existing literature, critically reflecting on research gaps in the field. The main aim of this paper is to explore the possibilities for creating healthy living environments that support the prevention and management of frailty. The project’s core innovation lies in the integration of public health principles into urban planning and design through a structured, community-based approach and the use of the Living Environmental Assessment (OBO) Tool. This tool enables urban planners, municipalities, and local communities to collaboratively evaluate and co-design living environments (e.g., optimizing walkability, green space access, barrier-free design, and social amenities) to build resilience and independence among older adults. Designing inclusive, accessible, and health-promoting environments can help to prevent frailty and improve well-being across all age groups. Full article

The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is ${10}^{-3}$, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity. Full article

This study simulates an open-cavity random distributed Raman amplifier for optimal performance across a 5 THz S-band spectrum (196.2–201.1 THz; 1490.76–1527.99 nm), evaluating its capacity via a 50-channel WDM grid with 100 GHz spacing. The primary Raman pump wavelength was tuned from 1318 to 1344 nm to identify the optimal point. A Fiber Bragg Grating (FBG), placed at the end of a 60 km single-mode fiber and upshifted 88 nm from the pump, enhances efficiency by transferring energy to the amplified signal, minimizing power variation. Results yield < 2 dB gain ripple across channels using raw Raman amplification without flattening filters with minor degradation from residual channels, confirming the DRA design’s viability for high-density S-band optical communication expansion. Full article

Karen Tei Yamashita’s Tropic of Orange (1997) and Paul Beatty’s The White Boy Shuffle (1996) evoke the act of listening to music as a way to dismantle stereotypical representations of urban resistance and to paint a diverse picture of how communities throughout Los Angeles were impacted by unrest in 1992. From Yamashita’s Buzzworm, a character always tuned into the radio, to Beatty’s Nicholas Scoby, the protagonist’s best friend who is on a mission to listen to every jazz song ever made, these writers render secondary characters who are most concerned with the consumption of music and the act of listening as a form of culture sharing. In fact, these characters utilize portable devices, particularly the Walkman, to bring personal music and media consumption into public spaces. In this paper, I argue that characters like Buzzworm and Scoby facilitate the creation of specific sonic textures that allow authors to break down artificial barriers of racial representation in the aftermath of urban unrest. These writers highlight the act of listening in order to limn the cross-cultural impact that the 1992 unrest had throughout the Southern California region. Full article

Green infrastructure (GI) and nature-based solutions (NBSs) in land-use planning and landscape architecture represent a holistic, interdisciplinary response to environmental and societal challenges. Although integrated into Slovak legislation since 2019, their formal implementation has progressed rather slowly, creating a gap that has been increasingly addressed by civic initiatives (CIs) of diverse types and legal forms. This study examines approaches and methods of CIs in Slovakia implementing GI and NBSs, while focusing on their legal forms, activities, spatial delimitations, and their impact on green space development and governance. Analysis of seventeen case studies shows that many CIs operate at multiple levels—local, national, and international—often delivering professional, conceptually grounded solutions. The most frequent NBS activities involve creating or enhancing parks, green public spaces, and community gardens, as well as restoring natural and semi-natural areas through nature-based management. Knowledge creation and awareness-raising are central strategies, including environmental education centres, citizen science, public campaigns, and informal learning platforms. The transformation of derelict areas into multifunctional public spaces emerges as a notable practice, combining ecological regeneration with cultural and social uses. The findings highlight the growing role of civic actors in advancing inclusive, participatory, and knowledge-based environmental management and call for stronger institutional support to integrate their contributions into formal administrative frameworks. Full article

This study introduces a novel space feasibility assessment process and evaluates its effectiveness through a comparative analysis with a conventional manual process. The proposed method is designed to enhance spatial comprehension and integrate building performance analysis, thereby supporting budgetary considerations during the early design phase. By providing a more intuitive and interactive environment, the system enables stakeholders—such as building owners—to communicate their spatial requirements to architects and professionals more clearly and efficiently. To validate the effectiveness of the proposed approach, participants completed two distinct scenarios: (1) a manual space feasibility assessment, and (2) a system-supported space feasibility assessment utilizing the proposed method. Participant performance was measured in terms of speed and accuracy in each scenario. Additionally, a user satisfaction survey was conducted to evaluate the usability of the system’s functionality. The experimental results provide an empirical basis for comparing the proposed process with the manual approach. Findings demonstrate that the proposed process enables more efficient and accurate space feasibility assessments, thereby validating its effectiveness as a user-centered decision-support tool during early-stage architectural planning. Full article

This study proposes a task planning approach for a distributed constellation dedicated to space target monitoring, grounded in an adaptive genetic algorithm. The approach is designed to address challenges such as the growing number of space targets and the complex constraints inherent in space target monitoring activities. After reviewing the research progress of distributed satellite task planning and adaptive genetic algorithms, a distributed task model featuring master-slave satellites was developed. This model integrates multi-constraint modeling and aims to optimize key performance indicators: task yield rate, task completion rate, resource utilization rate, and load balancing. To enhance the approach, the contract net algorithm is fused with the adaptive genetic algorithm: Firstly, in the tendering phase, centralized tendering is adopted to reduce communication overhead; Secondly, in the bidding phase, improved genetic mechanisms (e.g., dynamic reverse adjustment of crossover and mutation probabilities) and a dynamic population strategy are employed to generate task allocation schemes; Thirdly, in the bid evaluation and winning phase, differentiated strategies are applied to non-repetitive and repetitive tasks. Simulation validation shows that this approach can complete 80% of space target monitoring tasks, balance satellite loads effectively, and manage space target catalogs efficiently. Full article

The accurate prediction of the point-ahead angle (PAA) is crucial for applications of inter-satellite laser links (ISLLs), especially laser ranging and continuous communication. Herein, a real-time and high-precision point-ahead-angle algorithm is presented; the principle of the algorithm is mathematically characterized, and its performance is simulated and verified using typical on-orbit scenarios. The maximum PAAs of a typical geosynchronous equatorial orbit (GEO)–GEO link and low Earth orbit (LEO)–GEO link were simulated with this algorithm, and the results are consistent with those of typical calculation methods, proving the algorithm’s accuracy. The performance of the proposed algorithm was verified using a practical engineering application of ISLLs, where it was used to calculate the point-ahead angle during stable on-orbit communication. The Pearson correlations between the curves of azimuth, elevation, and total point-ahead angles, and the actual experimental data are 99.91%, 52.32%, and 98.01%, respectively. The corresponding average deviations are −5.8510 nrad, −1.0945 nrad, and −79.5403 nrad, respectively. The maximum calculation error is 5.2103%, and the calculation accuracy exceeds 94%. The above results show that the algorithm produces results that closely match actual on-orbit experimental data with high calculation accuracy, enabling the accurate prediction of the point-ahead angle and the improvement of ISLL stability. Additionally, with this method, the measurement error of the laser ranging is smaller than 50 μm, further enhancing the accuracy of precision measurements based on ISLLs. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article

In urban regeneration, co-production has become a significant approach for shaping public space in urban communities. While existing studies focus on the processes and stakeholders involved in co-production of community public space (CPS), few have examined the influence of structural factors. Based on the politics of scale, this study uses thematic analysis within an embedded case study of community centers in Shanghai, China, to analyze the impact of scalar restructuring on community co-production across three dimensions: material scale, organizational scale, and discursive scale. The study finds that local governments actively reshape public space through scalar restructuring, thereby transforming power relations among participants and promoting community co-production. In response to different community conditions and dilemmas, local governments adopt context-specific scalar restructuring strategies. When implementing scalar restructuring strategies such as downscaling, upscaling and scalar recompositing, three corresponding patterns of community co-production often emerge: bonded, procedural, and bridged. This paper contributes by providing a new perspective on the mechanism of community co-production, identifying novel patterns of community co-production and refining the scalar restructuring strategies. It moves beyond spatial limitations and captures the co-production of CPS through a broader lens of power dynamics. Full article