Search Results (392)

Surface seismic data often suffer from limited bandwidth and uneven illumination, which degrade PSDM (prestack depth migration) in deep and structurally complex settings. VSP (vertical seismic profiling), particularly DAS-VSP, provides a higher signal-to-noise ratio and richer high-frequency content near the wellbore but has a limited lateral imaging aperture. To exploit the complementary strengths of these two observation systems, we propose an OVT domain (offset vector tile) joint Kirchhoff prestack depth migration workflow that integrates surface seismic and VSP data within a unified depth domain framework. The workflow includes wavelet (amplitude–phase) matching, consistent datuming, joint well–surface tomographic velocity model building using both surface CIG (common image gather) residual moveout and VSP first-arrival constraints, efficient travel time table construction based on 3D eikonal solvers, OVT domain joint migration, azimuth-dependent CIG depth correction for anisotropy, and ray-based illumination compensation for amplitude balancing. Synthetic tests demonstrate that the proposed method improves reflector continuity and increases the effective bandwidth of the joint image relative to surface-only PSDM. A field application in the northwest Sichuan Basin further shows that the joint imaging better matches well synthetics in the target interval, increasing the correlation coefficient from 0.753 (surface-only) and 0.738 (VSP-only) to 0.787 (joint) while reducing inter-azimuth CIG depth residuals to within 3 m after anisotropy correction. These results indicate that OVT domain joint imaging can enhance thin-bed resolution and near-well structural delineation, providing a practical multi-source data fusion solution for high-fidelity depth imaging in complex reservoirs. Full article

This study systematically compared the effects of dietary supplementation with glutamine (Gln) and its precursors, including glutamic acid (GA) and α-ketoglutarate (AKG), on growth performance, serum antioxidant and immune parameters, and multi-region gastrointestinal microbiota in suckling lambs. Forty healthy suckling Hu lambs with similar body weight (7.37 ± 1.18 kg) and age (7 ± 0.8 d) were selected and randomly allocated into four groups (n = 10 per group): a control group (CON, without additive), and three treatment groups (GA, AKG, and Gln), each receiving 2 g per animal per day of the corresponding additive. The experimental period lasted for 42 d. All three additives showed a tendency to increase the final body weight (p = 0.056) and significantly increased the average daily gain (ADG) of lambs (p < 0.05). GA supplementation increased the dry matter intake throughout the entire trial (p < 0.05), whereas the addition of AKG and Gln increased the dry matter intake only during the later period (d 21–42) (p < 0.05). The feed-to-gain ratios did not differ among all groups (p > 0.05). Compared with the CON group, all three treatment groups showed elevated serum activities of catalase, glutathione peroxidase, and total antioxidant capacity, as well as increased IgA and IgG contents (p < 0.05). In addition, malondialdehyde concentration was decreased in all three treatment groups (p < 0.05). Moreover, GA supplementation reduced the ruminal alpha diversity while increasing the abundance of butyrate-producing bacteria (Ruminococcaceae UCG-014) (p < 0.05). All three interventions consistently decreased the abundance of the intestinal pathogen Escherichia-Shigella in the ileum (p < 0.05). Correlation analyses showed that ruminal Treponema 2 abundance was negatively correlated with ADG, whereas jejunal Methylobacterium and ileal [Eubacterium] coprostanoligenes group were positively correlated with final body weight or ADG. In conclusion, glutamine and its precursors play an important role in modulating gastrointestinal bacterial diversity and composition, enhancing antioxidant and immune functions, and improving the growth performance of suckling lambs. Full article

Arthrinium-like fungi in the family Apiosporaceae are taxonomically complex and still require a thorough characterization despite recent phylogenetic reassessments. This study aimed to investigate the diversity and taxonomic position of endophytic Arthrinium-like fungi associated with Itea japonica and I. riparia in Thailand. Two fungal strains discovered from healthy stems of these hosts were characterized by integrative approaches including morphology, multi-locus phylogenetic analyses based on ITS, LSU, TEF1-α, TUB2 sequence data, and nucleotide base–pair comparisons. One isolate from I. japonica is introduced as Nigrospora iteae sp. nov. supported by distinct morphological traits, a well-resolved phylogenetic placement, and significant nucleotide difference from its closest relatives. The second isolate was identified as Apiospora vietnamensis and is reported herein as a new host record for I. riparia based on morphological congruence, a close phylogenetic relationship, and TUB2 nucleotide similarity with the type strain. In addition, a new species, Apiospora fici, originally described from dead leaves of Ficus septica in Taiwan, is reclassified based on updated phylogenetic analyses to clarify its taxonomic placement within Apiosporaceae. Furthermore, Nigrospora wurfbainiae nom. nov. is proposed as a replacement name for the later homonym N. guangdongensis. A summary of important morphological characteristics, host relationships, current distribution, and biological activities of Nigrospora species is provided. This study emphasizes the previously unrecognized fungal diversity within Itea hosts and offers new insights into species diversity and phylogenetic relationships within the Apiosporaceae. Full article

Achieving precise docking, reliable locking and damage-free emergency unlocking under complex ocean current conditions remains a key challenge for deep-water multi-way quick connectors (MQCs). This study proposes a novel MQC prototype characterised by a tiered tolerance guidance mechanism, an innovative L-shaped spatial helical cam locking system, and a real-time visual attitude indicator. Using Ansys 2023 R2 and its tools, the safe operating limits were determined through explicit non-linear finite element collision analysis. The results demonstrate that, under a controlled docking speed of 10 mm/s, the hierarchical guidance mechanism successfully accommodated extreme initial misalignments (25 mm lateral offset, 5° horizontal rotation and 15° axial rotation), whilst keeping the peak collision stress within the elastic limit. Furthermore, the L-shaped locking guide was analysed using a fifth-order polynomial motion law and a macro-micro elastoplastic Hertzian contact mechanics model, effectively eliminating rigid-flexible impact forces. Under extreme separation loads of 10,000 psi, the maximum equivalent plastic strain at the base of the locking shaft was strictly controlled at 0.00926. This is well below the failure threshold of 0.0865 specified by ASME, providing a substantial safety margin and completely preventing local yielding. Crucially, the emergency release strategy based on precision locating pins was validated through full-scale prototype testing. Destructive tests conducted under simulated severe jamming conditions demonstrated clean, damage-free disengagement under shear torques ranging from 2100 Nm to 2200 Nm. This threshold ensures that accidental triggering will absolutely not occur during routine operations (1400 Nm) and establishes a safe underwater robotic (ROV) operating speed of ≤4 r/min. This study provides a robust theoretical framework and empirical data for the future design of yield-resistant subsea connectors and safe emergency recovery. Full article

Agroecosystems are perpetually subjected to environmental factors. Driven by a shifting global climate, soil moisture deficits represent an increasingly frequent threat to crop productivity. In farming, however, these abiotic stressors seldom occur in isolation, as fields are invariably compounded by biotic weed pressure. Consequently, investigating plant responses to such combinatorial, multi-faceted stress is paramount to evaluating the realistic efficacy of modern agrotechnical interventions. A 2-year, three-factor pot experiment was conducted at the Research and Education Station in Swojczyce, belonging to the Wrocław University of Environmental and Life Sciences. The aim of the study was to examine the response of winter wheat (Triticum aestivum L., cv. Agil) to drought stress during the period when cereal plants were at the 51–65 BBCH developmental stages. Additionally, in some pots with winter wheat, Apera spica-venti (L.) Beauv. was sown as a weed to evaluate the effects of biotic stress. To observe the mitigation of stressors, three different types of biostimulants were used—a silicon-based preparation and two seaweed-based preparations derived from Ecklonia maxima (Osbeck) Papenfuss and Ascophyllum nodosum (L.) Le Jolis, respectively, representing structural, morphological, and biochemical defense strategies. Drought stress significantly and negatively affected the length of the wheat main stem, lateral tillers, and lateral spikes, as well as the weight of the main wheat spike. The simultaneous occurrence of drought stress and A. spica-venti competition resulted in the greatest cumulative reduction in main spike weight. Furthermore, drought stress was associated with an increase in nitrogen/protein content and potassium content in wheat straw. The presence of A. spica-venti significantly reduced both the weight of the main wheat spike and the number of non-productive tillers. The limited effectiveness of biostimulants may be associated with the severity and timing of stress exposure during reproductive development. Full article

This paper highlights the eco-efficiency of a sustainable digital solution to support decision-making in resolving the problem of sudden production drops and associated energy waste in industrial power plants, especially those operating with a steam turbomachine. The solution involves a multi-interface digital dashboard that generates insightful visual reports and gives proactive alerting to the decision-makers about potential underperformances to ensure resource optimization. For the studied use case, it involves the development of three interfaces of the dashboard, so as to perform the sustainable monitoring of a thermoelectric power plant based on the Rankine–Hirn cycle as follows: the first interface is about real-time monitoring of thirty-two key physical parameters equipped with a notification system. The second interface displays the historical trends of all the plant variables, in order to help in detecting incipient abnormal deviations before they impact environmental efficiency. Lastly, the third platform covers a predictive model using the XGBoost algorithmic method to forecast the future behavior of the electrical power as the target variable of the power plant. The XGBoost method was selected after a comparative assessment which also included the algorithms of Random Forest Regressor (RFR) and Gated Recurrent Unit (GRU). As a final step, this solution was later tested in a simulation environment built under the “Node-Red” platform, through an industrial decision scenario. The concrete findings validate the framework’s sustainability metrics, demonstrating the ability of the solution to help in preserving, for each production cycle of two years, up to 7.6 GWh of electrical energy that would otherwise be wasted, which translates into a potential cost-saving exceeding 633,247.9 USD, as well as an ecological impact by preventing the emission of 4628 tons of CO₂. Full article

The Mugan Syncline in northeastern Yunnan represents a significant relay area for shale gas exploration in China. However, due to the combined effects of tectonic superimposition and sedimentary heterogeneity, systematic investigations into the intervals hosting high-quality shales and the coupling relationships among microfacies, reservoir quality, and gas-bearing properties remain insufficient. The core objective of this study is to establish a high-resolution microfacies framework and to quantitatively elucidate the multi-parameter coupling mechanisms by which microfacies control organic matter enrichment, pore development, and gas storage capacity in this structurally complex, basin-margin setting. By integrating core observations, thin-section petrography, scanning electron microscopy (SEM), whole-rock X-ray diffraction (XRD), total organic carbon (TOC) analysis, trace-element geochemistry, and well-logging data, we establish a stratigraphic subdivision and cross-well correlation framework for the Wufeng (WF) Formation and the Long11 submember. Furthermore, a lithofacies (microfacies) identification scheme based on a “TOC + siliceous (quartz + feldspar)–carbonate–clay” ternary classification is applied. The results reveal the following: (1) Based on the locally developed erosional contact at the boundary between the Longmaxi (LMX) Formation and the underlying Guanyinqiao Formation, the WF Formation in the study area can be subdivided into two submembers, whereas the Long11 submember comprises four sublayers. The thicknesses of the Long11-1 through Long11-3 sublayers range from 21.42 to 25.47 m, exhibiting a subtle northward-thickening trend. In contrast, the Long11-4 sublayer displays a relatively uniform thickness and high stratigraphic continuity of shale deposition. (2) Based on TOC content and ternary mineral composition, the shales are classified into four lithofacies associations and sixteen lithofacies subtypes. The main favorable microfacies assemblages are identified as high-carbon siliceous/calcareous shale (C-1), high-carbon calcareous/siliceous mixed shale (M-1), carbon-rich argillaceous siliceous shale (S-3), and high-carbon siliceous/argillaceous mixed shale (M-2). (3) High-quality shales (TOC > 2%) are predominantly developed in the upper member of the WF Formation and in the Long11-1 through Long11-4 sublayers. Their lateral distribution is markedly controlled by variations in paleotopography and terrigenous sediment supply. (4) The microfacies exert a synergistic control on shale gas enrichment. Carbon-rich argillaceous siliceous and siliceous-rich microfacies generally correspond to higher TOC contents and better-developed organic-matter pores. Siliceous-rich and mixed microfacies exert a positive influence on pore preservation and rock brittleness. The gas-bearing properties are influenced not only by TOC content but also by pore structure, mineral composition, and tectonic preservation conditions. The findings of this study provide a scientific basis for the prediction of shale gas sweet spots and the optimization of target intervals in the Mugan Syncline and other structurally and sedimentologically complex regions of northeastern Yunnan. Full article

Offshore reservoirs are commonly characterized by complex geology, constrained operations, and high per-well investment. Improving economic performance while maintaining deliverability is therefore a pressing need during field development. By combining multilateral wellbores with staged hydraulic fracturing, complex-structured wells can markedly enhance well–reservoir connectivity; however, for fishbone-type complex-structured wells, published studies still provide limited case-based discussion on how branch-junction loss, completion staging, fracture parameters, and the economic metric jointly affect the final design outcome. In this study, a semi-analytical productivity model for complex-structured fractured wells is developed, accounting for a multi-scale coupling among the reservoir, fractures, and the wellbore. The wellbore and fractures are discretized to quantify the contribution of each completion stage and fracture element to overall productivity. An economic metric with net present value (NPV) as the objective function is then introduced, and a genetic-algorithm-based joint optimization method is established for completion staging parameters and fracture-geometry parameters, enabling an automatic search for the key design variables. The model is validated against water–electric analogy experiments and a field case, demonstrating good predictive accuracy. Case studies show that, with a reasonable parameter configuration, complex-structured fractured wells can significantly increase the cumulative oil production and the NPV under a limited increase in cost; the optimized NPV is improved by approximately 20.2%, illustrating the potential interaction among completion staging, fracture parameters, and the NPV metric under the studied reservoir and economic conditions. Full article

This paper applies physics-informed neural networks (PINNs) to laterally excited liquid sloshing in a two-dimensional rectangular tank, where near-resonant forcing (${\omega }_e/{\omega }_1=0.9$) produces a multi-frequency beating response with a period of approximately $10T_1$. Linearized potential flow theory governs the problem; the network learns the velocity potential $\phi (x,z,t)$ while the free-surface elevation $\eta$ is injected analytically. Two training obstacles specific to forced sloshing are analyzed. First, a zero-solution trap arises because the trivial solution $\widehat{\phi }=0$ satisfies all equations except the free-surface conditions, whose residuals are roughly ${10}^{-4}$ times smaller than the Laplace residual; characteristic-scale normalization combined with loss weighting (${\lambda }_D={\lambda }_K=100$) breaks this trap. Second, spectral bias prevents standard MLPs from resolving the three co-existing frequencies (${\omega }_1$, ${\omega }_e$, $\mathsf{\Delta }\omega$); a Fourier time embedding that augments the input from 3 to 9 dimensions overcomes this limitation. Two additional techniques further reduce errors: a hard-wall boundary condition enforced exactly via a $\cos (\pi x/B)$ spatial embedding, which eliminates wall collocation points; and a gradient-enhanced Laplace regularizer (${\parallel \nabla \left({\nabla }^2\widehat{\phi }\right)\parallel }^2$) that constrains velocity smoothness through third-order automatic differentiation. An ablation study shows that these four techniques progressively reduce the horizontal velocity error from ${\epsilon }_u=12.46\%$ to $0.84\%$. Results are validated against a viscous finite-difference benchmark. Over one beating cycle the errors are ${\epsilon }_{\eta }=0.15\%$, ${\epsilon }_u=0.84\%$, and ${\epsilon }_w=1.65\%$. A frequency parameter study across ${\omega }_e/{\omega }_1$ = 0.5–1.1 gives ${\epsilon }_{\eta }<0.25\%$ and ${\epsilon }_u<2.3\%$ for all near-resonance cases. For long-time simulation, a time-domain decomposition strategy with transfer learning partitions the domain into one-beat windows; extending to five beating cycles ($50T_1$) yields ${\epsilon }_u=3.43\%$ and ${\epsilon }_{\eta }=0.30\%$ with no monotonic error accumulation across windows. The methodology is then extended to a three-dimensional rectangular tank ($B\times W\times H$) with bi-directional lateral excitation. The 3-D formulation introduces the y-dimension into the Laplace equation (${\nabla }^2\phi ={\phi }_{xx}+{\phi }_{yy}+{\phi }_{zz}=0$), adds transverse wall boundary conditions ($\partial \phi /\partial y=0$) enforced exactly via a $\cos (\pi y/W)$ embedding, and extends the Fourier time embedding from 9 to 16 dimensions to accommodate six physical frequencies. The bi-directional excitation excites both $(m,0)$ and $(0,n)$ modal families, producing a genuinely three-dimensional beating response. Experimental results verify that the proposed methods can be well generalized to three-dimensional scenarios. Within a single beating cycle, the relative errors reach ${\epsilon }_{\eta }=0.24\%$, ${\epsilon }_u=1.31\%$, ${\epsilon }_v=1.78\%$ and ${\epsilon }_w=2.32\%$, with a total training time of 2499 s. By applying time domain decomposition to carry out two-cycle three-dimensional simulations, the model can steadily maintain satisfactory prediction precision across segmented time intervals, achieving overall errors of ${\epsilon }_{\eta }=0.30\%$ and ${\epsilon }_u=1.32\%$. Full article

Real-time tracking of high-speed targets in autonomous systems requires detection and decision-making pipelines that can operate within sub-millisecond time budgets. Single Photon Avalanche Diode (SPAD) sensors are well suited for this task, offering 10 kHz Time-of-Flight (ToF) measurements with picosecond timing precision. However, processing such high-frequency time-series data with conventional deep learning models introduces computational bottlenecks that are difficult to handle on resource-constrained embedded hardware. This paper presents an ultra-lightweight, dual-head architecture built on the MiniRocket transformation algorithm, where a single shared feature extractor simultaneously feeds two independent decision pathways: one for multi-class target classification and one for 3-parameter kinematic regression covering velocity, pitch, and yaw. As a single-pixel sensor, the device provides only 1D range information; lateral 3D spatial localization is outside the scope of this work. To the best of the authors’ knowledge, this is the first application of MiniRocket to continuous kinematic estimation from high-frequency sensor data. Since collecting labeled physical flight data at these speeds is largely infeasible, a physics-based ray-casting simulation was developed to generate a 55,440-sample dataset across four 3D CAD target models under varying speed (100–450 m/s), orientation, and noise conditions. The proposed architecture achieves 98.6% classification accuracy and a velocity Mean Absolute Error (MAE) of 0.26 m/s, with orientation estimation yielding a pitch MAE of 3.47° and a yaw MAE of 2.46°—values consistent across all five cross-validation folds, indicating that the orientation performance floor is governed by the sensor’s physical angular resolution rather than by model capacity. With approximately 27,000 trainable parameters, the system completes full dual-task inference in 0.56 ms on a 16-core CPU (1785 Frames Per Second-FPS), satisfying the 1 ms real-time constraint of a 10 kHz sensor without GPU acceleration. It should be noted that the single-pixel SPAD architecture provides only 1D range-along-beam information; full 3D spatial localization is physically not extractable from a single sensor and is not addressed in this study. Full article

In situ stress field inversion is a fundamental challenge in geothermal resource development, oil and gas exploration, and mine safety assessment. To address the non-uniqueness and limited accuracy of traditional single-data-source inversion approaches, this study proposes a two-dimensional in situ stress field inversion method constrained by multi-source data, based on integrated well-seismic fault identification. By incorporating dynamic and static mechanical parameters from well logs and employing both a combined spring model and an anisotropic model, a fault-constrained stress field inversion framework is established. Deep learning and optimization algorithms are utilized to integrate the vertical constraints from well logging data with the lateral continuity characteristics of seismic data, enabling high-resolution reconstruction of the in situ stress field. Taking the complex fault-developed geothermal field in the Xiong’an New Area of the Jizhong Depression, Bohai Bay Basin, as a case study, the proposed method demonstrates a marked reduction in inversion error and a substantial improvement in both fault localization accuracy and stress characterization reliability. Full article

Background: Single-person households have grown rapidly in the Republic of Korea and are consistently associated with higher rates of smoking and high-risk drinking. However, it remains unclear whether this vulnerability is structural and chronic or merely situational. This study examined whether the COVID-19 pandemic—a major societal disruption—altered the preexisting behavioral gap between single- and multi-person households. Methods: We used repeated cross-sectional data from the Korea Community Health Survey (KCHS) for 2019 (unweighted n = 229,099), 2020 (unweighted n = 229,269), and 2021 (unweighted n = 229,242), representing weighted populations of approximately 43.0, 43.5, and 43.6 million adults aged ≥19 years, respectively. We applied complex sample logistic regression models including an interaction term between survey year and household type, adjusting for sex, age, income, and education. Results: High-risk drinking significantly declined in both household types across all three time points following the COVID-19 pandemic, whereas smoking showed no significant overall change. Critically, the year x household type interaction was non-significant for high-risk drinking across all years; for smoking, a marginally significant interaction emerged only in 2021 (adjusted odds ratio [AOR] = 1.08, 95% CI: 1.01–1.16), suggesting a slight but limited divergence in the later pandemic period. Single-person households consistently showed higher odds of smoking (AOR = 1.65, 95% CI: 1.56–1.74) and high-risk drinking (AOR = 1.32, 95% CI: 1.25–1.39) across all three time points, relative to multi-person households and referenced to 2019 as the pre-pandemic baseline. Conclusions: The health behavioral vulnerability of single-person households is structural and persistent, underscoring the need for household-structure-sensitive public health strategies to promote sustainable well-being. Full article

Mohandas K. Gandhi (popularly known as Mahatma Gandhi) has primarily been recognized for his work in developing the theory and practice of nonviolence (ahimsa) for the purpose of building a culture of sustainable peace. Although Gandhi’s writings do not explicitly engage such categories as negative and positive peace, peace and international relations, or pacifism and nonviolence, scholars in peace studies have nonetheless assessed his contributions to the evolution of the field. This article advances the study of peace by emphasizing the dynamic nature of nonviolence (ahimsa), which is inextricably connected to Gandhi’s vision of sarvodaya (uplift of all). It further argues that his approach to peacebuilding, grounded in the upholding of pluralism across civic life, offers a conceptual framework for disrupting hegemonic monolithic systems. Gandhi lived in a time when the concept of pluralism had not gained currency; however, his vision, rooted in the values of diversity and tolerance, can appropriately be understood under the now widely accepted concept of pluralism. Gandhi thus uniquely connected nonviolence, peace, pluralism, and sarvodaya. For him, peaceful co-existence mandates attention to diversity—an approach that can enrich contemporary conversations in a divided political, social, and religious landscape. As a political leader and social reformer, he promoted indigenous languages, diverse village industries, local economies, and multi-faith religious education. In his later life, he also advocated for inter-caste and interreligious marriages in order to mitigate communal tensions. Such attention to diversity offers a promising path toward realizing the goal of sustainable peace and sarvodaya in a contemporary landscape increasingly prone to monolithic systems. Sarvodaya inherently requires a commitment to pluralistic, dialogical, dialectical, and nonviolent engagement in all spheres of life. By emphasizing shared humanity and committing to diversity, Gandhi offers a social philosophy of respect for all life as well as uplift of all trades, languages, and belief systems grounded in the vision of welfare of all. His practical methods of engaging diverse actors, along with his radical efforts to disrupt autocratic, authoritative, and centralized systems, affirm that the objectives of sarvodaya and sustainable peace can be realized only through a radical pluralism. Full article

Rural settlements are significant carriers of rural production, living, and land use activities and are also significant subjects for researching regional socio-economic development and spatial structural changes. With regard to the unique topographical environment and transportation situation in the Qiongbei volcanic lava area, a settlement form with prominent topographical constraints and transportation orientation is created. This paper utilizes land use/land cover data from different periods, along with rural settlement expansion patch data, to quantitatively analyze the spatial patterns and expansion characteristics of rural settlements, as well as their influencing factors, from 1990 to 2025 using GIS spatial analysis, buffer gradient analysis (BGA), and multi-order adjacency index (MAI). The research results indicate the following: (1) The spatial pattern of rural settlement distribution in the study area is “peripheral agglomeration and core sparsity,” and the general expansion trend is “rapid in the early period and stable in the late period.” The settlement area expands from 37.21 km² in 1990 to 80.87 km² in 2025. (2) The evolutionary pattern of rural settlements in the study area changes from “core–peripheral extension” in the early period to a mixed “core stabilization and peripheral leapfrogging development” model in the later period. The new patches formed in the peripheral areas have obvious discrete features, such as varying land use patterns and differing population densities compared to the core areas. (3) The spatial correlation factors for rural settlement expansion in the study area exhibit stage differences and distinct spatial non-stationary characteristics. During the early period (1990–2008), with strict limitations imposed by the natural material environment, sunlight (interpretability of 0.367) and water systems (0.286) show significant spatial coherence, indicating the great adaptability of rural settlements to the material conditions of the landforms; during the later period (2008–2025), after the implementation of the rural revitalization strategy, the population density (0.135) and transport-related factors become the main spatial correlation factors. The GWR model also shows the percentage of positive and negative influences by influencing factors at each stage and their significant differences in space, proving that human activities break through in the limitations of natural topology in a discontinuous way. According to this research, “inefficient land use” should be understood in a dialectical manner in volcanic geomorphological areas, and spatial optimization should be achieved on the premise of respecting the physicality of volcanic landscapes and rural identity. The research conclusions have important guiding significance for the spatial resilience planning in tropical volcanic areas and traditional settlement culture preservation. Full article

This paper presents the Performance Prediction Tool developed within the SATERA project. The tool evaluates the performance of a space-based composite ADS-B and multilateration system for independent aircraft localization. It uses receivers deployed onboard a constellation of LEO satellites. Multilateration can be evaluated using time-based measurements, as well as additional measurements such as, frequency and angle of arrival of the received signals. The tool is based on the evaluation of the Cramér–Rao lower bound and it is implemented in MATLAB with a user-friendly graphical interface. The tool allows the user to define the satellite constellation, link budget, measurement types and errors, and to simulate the system performance over an aircraft trajectory or an area. Moreover, the outputs include DOP, number of visible satellites and system availability, which can be visualized and exported for further analysis. Full article