Search Results (404)

This paper aims to define the optimal microgrid topology for rural electrification based on the lowest total cost by comparing LVAC and LVDC microgrids across three different scenarios. An LVAC radial topology is first designed using mixed-integer linear programming for phase balancing and the shortest path for connections, then implemented with a genetic algorithm to allocate and size solar home systems, forming an LVAC microgrid. Next, an LVDC topology is then derived from the LVAC structure and integrated with solar home systems under three scenarios: (1) using the same solar home system sizes, locations, and quantities as the LVAC microgrid; (2) using a genetic algorithm to re-determine solar home system sizes and locations, forming an LVDC microgrid; and (3) clustering the LVDC topology into nano-grids, each defined by genetic algorithm for solar home system sizing and placement and connected to the main feeder via bi-directional converters. Finally, all LVAC and LVDC scenarios are simulated over a 30-year planning horizon for analysis. A non-electrified village located in Cambodia has been selected for a case study to validate the proposed methods. The results have been obtained and provide a comparison of performance indicators (i.e., costs, energy production, losses, CO₂ emissions, and autonomous energy) among the microgrids (LVAC and LVDC). The LVAC microgrid produced lower total energy losses than the LVDC microgrid in all scenarios. However, when considering environmental impact, LVDC Scenario 2 is preferable. Based on the total cost results, the LVAC microgrid is considered more economical than the LVDC microgrid in each scenario in this study. Full article

This study presents a method to retrieve the electron density structure of the transient C-region using very-low-frequency (VLF) Earth–ionosphere waveguide propagation. Here, we demonstrate the identification of the C-region from amplitude variations of a mid-range VLF propagation path that is nearly perpendicular to the solar terminator. Previous investigations have primarily relied on phase measurements along long-distance paths with small terminator angles, whereas the present approach utilizes amplitude information under conditions where modal interference is significant. The Faraday International Reference Ionosphere (FIRI-2018) provides an effective semi-empirical model of the lower-ionospheric electron density; however, discrepancies between simulations and observations are often observed at sunrise. To resolve this issue, we introduce Gaussian perturbations to the electron density profile output by FIRI-2018 and optimize their parameters so that finite-difference time-domain (FDTD) simulations reproduce the observed VLF amplitude. The analysis is performed for the 22.2 kHz JJI transmitter signal received in Chofu, Japan over a mid-range propagation path, ∼900 km. The optimized electron density profile successfully reproduces the characteristic features of the C-region, including a temporary enhancement near 65 km altitude during sunrise. These results demonstrate that mid-range VLF amplitude analysis provides a quantitative tool for identifying transient lower- ionospheric structures. Full article

Fully maintenance-free smart collars for range cattle, sheep and deer must survive years of uncontrolled grazing under highly variable shade and motion conditions. This paper presents an ultra-low-power buck converter governed by a fast integral terminal sliding mode controller (FITSMC) with a fixed-time observer. A new reaching law retains the initial sliding manifold and a negative-power term maintains the constant switching gain to preserve robustness near the surface while attenuating chattering without widening the bandwidth. The fixed-time observer estimates the irradiance and load changes and provides a feed-forward correction, tightening the output regulation regardless of initial conditions. Load step tests with moderate resistance swings showed the proposed method recovers noticeably faster and exhibits slightly lower overshoot than a recent method based on a two-phase power reaching law, while visible inductor current spikes are also suppressed. Simulations under daily grazing profiles confirmed tight output regulation adequate for microwatt data logging and periodic long-range (LoRa) bursts. The sleep mode quiescent current remained in the 9 microamps range, eliminating the need for manual recharge across multi-season field deployments. By integrating robust power electronics with collar-grade solar harvesting, the circuit offers a truly maintenance-free energy path for untethered livestock wearables and supports sustainable precision agriculture. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

Currently, titanium dioxide films are widely used as the electron transport layer material in perovskite solar cells. An alternative to titanium dioxide for this role could be niobium pentoxide (Nb₂O₅), an n-type conducting semiconductor oxide. However, the application of Nb₂O₅ in perovskite solar cells is hindered by a lack of data on its electron transport properties, electrophysical parameters, and current–voltage characteristics. Amorphous niobium pentoxide films were obtained by magnetron sputtering. To study their electrical and capacitive properties, a structure of heavily doped n⁺-silicon (n⁺)/niobium oxide/aluminum was used. Based on the analysis of the I–V curves, it was concluded that for a sample at 25 °C, the electron mean free path is greater than the width of the Schottky barrier layer, allowing electrons to pass through this layer without collisions. At temperatures of 35 °C and higher, electrons experience numerous collisions within the Schottky barrier layer. The height of the Schottky barrier for the contact between niobium pentoxide and aluminum was determined. The obtained capacitance frequency plots were explained using the concepts of dipole-relaxation polarization in a dielectric, where electric dipoles can reorient in an external electric field. It has been shown that the use of magnetron sputtering to produce amorphous niobium pentoxide films leads to a reduction in the effective Schottky barrier height. This allows for high electron injection density at low voltages when using such an oxide semiconductor as an electron transport layer, thereby potentially increasing the efficiency of solar cells. Full article

Although Afghanistan’s contribution to global and regional greenhouse gas (GHG) emissions is minimal, it remains among the countries most vulnerable to the impacts of climate change. Rising temperatures and decreasing precipitation have significantly disrupted the country’s natural resources, including water supplies, agriculture, forests, rangelands, and ecosystems, threatening its agrarian economy and socio-economic stability. Simultaneously, Afghanistan has substantial untapped renewable energy potential, especially in hydropower, solar, wind, and biomass. This study analyzes historical (1970–2014) and projected (2015–2099) climate trends across Afghanistan by examining mean annual temperature and precipitation using the Mann–Kendall test and Sen’s Slope estimator. Results indicate a significant warming trend, with a 1.58 °C rise in temperature and a 36 mm decrease in annual precipitation over the past five decades. Future projections based on Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) suggest continued temperature increases, while precipitation trends vary geographically and over time, showing increases, decreases, or little change. The study also evaluates Afghanistan’s GHG emissions, which are negligible on regional and global scales. Despite its abundant renewable energy resources, the country still depends heavily on electricity imports from neighboring nations, leaving much of its domestic potential untapped. Harnessing these renewable resources can provide a practical path toward energy independence, zero-emission energy generation, and sustainable long-term development. This research emphasizes the urgent need for Afghanistan to strategically develop its renewable energy sector to boost climate resilience, enhance energy security, and promote sustainable economic growth. Full article

In spite of the preeminence of Nicolas Poussin as one of the great classicist painters in seventeenth century France, some of his earlier work has not received the attention it deserves. This article turns to his Realm of Flora (c. 1631) in order to study some salient aspects that have been neglected. First, Poussin followed what I call the “Botanical Renaissance.” This study foregrounds which elements he followed and which he transformed. In conjunction with this movement, this article highlights Poussin’s uses of Platonic philosophy through the works of Marsilio Ficino. The importance of Sol in his works is replicated here in the power of the solar rays to nourish nature. Thirdly, we consider the many metamorphoses in the work and their significance. Finally, we turn to the circle in the heavens with the planets, stars and twelve constellations and contrast it with the more elongated circle of the metamorphic figures on Earth in order to highlight the relation between zodiacal signs/stars and the flowers depicted. The circular constellations contrast with an elongated, even elliptical shape of the figures on Earth, perhaps to suggest the conflict, prevalent at the time, between the Copernican heliocentric and circular system with Kepler’s elliptical view of the path of the heavenly planets. Full article

We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The model combines a spatio-temporal transformer encoder with three convolutional neural network (CNN)/recurrent neural network (RNN) branches (CNN → long short-term memory (LSTM), LSTM → gated recurrent unit (GRU), CNN → GRU) and a dense pathway for tabular meteorological and calendar features. A multitask output head simultaneously performs the regression of PV power and binary classification of extremes defined above the 95th percentile. We evaluate HST–MB–CREH on the publicly available Renewable Power Generation and Weather Conditions dataset with hourly resolutions from 2017 to 2022, using a 5-fold TimeSeriesSplit protocol to avoid temporal leakage and to cover multiple seasons. Compared with tree ensembles (RandomForest, XGBoost), recurrent baselines (Stacked GRU, LSTM), and advanced hybrid/transformer models (Hybrid Multi-Branch CNN–LSTM/GRU with Dense Path and Extreme-Event Head (HMB–CLED) and Spatio-Temporal Multitask Transformer with Extreme-Event Head (STM–EEH)), the proposed architecture achieves the best overall trade-off between accuracy and rare-event sensitivity, with normalized performance of RMSE_z = 0.2159 ± 0.0167, MAE_z = 0.1100 ± 0.0085, mean absolute percentage error (MAPE) = 9.17 ± 0.45%, R² = 0.9534 ± 0.0072, and AUC_ext = 0.9851 ± 0.0051 across folds. Knowledge extraction is supported via attention-based analysis and permutation feature importance, which highlight the dominant role of global horizontal irradiance, diurnal harmonics, and solar geometry features. The results indicate that hybrid spatio-temporal multitask architectures can substantially improve both the forecast accuracy and robustness to extremes, making HST–MB–CREH a promising building block for intelligent decision-support tools in smart grids with a high share of PV generation. Full article

Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m² and an $R^2$ value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article

Future lunar south pole missions face dual challenges of highly variable illumination and rugged terrain that directly constrain rover mobility and energy sustainability. To address these issues, this study proposes a dynamic illumination-constrained spatio-temporal A* (DIC3D-A*) path-planning algorithm that jointly optimizes terrain safety and illumination continuity in polar environments. Using high-resolution digital elevation model data from the Lunar Reconnaissance Orbiter Laser Altimeter, a 1300 m × 1300 m terrain model with 5 m/pixel spatial resolution was constructed. Hourly solar visibility for November–December 2026 was computed based on planetary ephemerides to generate a dynamic illumination dataset. The algorithm integrates slope, distance, and illumination into a unified heuristic cost function, performing a time-dependent search in a 3D spatiotemporal state space. Simulation results show that, compared with conventional A* algorithms considering only terrain or distance, the DIC3D-A* algorithm improves CSDV by 106.1% and 115.1%, respectively. Moreover, relative to illumination-based A* algorithms, it reduces the average terrain roughness index by 17.2%, while achieving shorter path length and faster computation than both the Rapidly-exploring Random Tree Star and Deep Q-Network baselines. These results demonstrate that dynamic illumination is the dominant environmental factor affecting lunar polar rover traversal and that DIC3D-A* provides an efficient, energy-aware framework for illumination-adaptive navigation in upcoming missions such as Chang’E-7. Full article

The continuous degradation of mangrove ecosystems, considered among the most vulnerable worldwide, reveals multiple threats driven by human activities and climate change. In the Peruvian context, particularly in the Tumbes Mangrove ecosystem, these pressures are intensified by the absence of integrated spatial and educational infrastructures capable of supporting conservation efforts while engaging local communities. In response, this research proposes a Sustainable Interpretation Center for Conservation and Environmental Education in Ecologically Sensitive Areas of the Tumbes Mangrove, Peru. The methodology includes climate data analysis, identification of local flora and fauna, and site topography characterization, supported by digital tools such as Google Earth, AutoCAD 2025, Revit 2025, and 3D Sun Path. The results are reflected in an architectural proposal that incorporates sustainable materials compatible with sensitive ecosystems, including eco-friendly structural solutions based on algarrobo timber, together with resilient strategies addressing climatic variability, such as lightweight structures, elevated platforms, and passive environmental solutions that minimize impact on the mangrove. Furthermore, the proposal integrates a photovoltaic energy system consisting of 12 solar panels with a unit capacity of 450 W, providing a total installed capacity of 5.4 kWp, complemented by a 48 V LiFePO₄ battery storage system designed to ensure energy autonomy during periods of low solar availability. In conclusion, the proposal adheres to principles of sustainability and energy efficiency and aligns with the Sustainable Development Goals (SDGs) 7, 8, 12, 14, and 15, reinforcing the use of clean energy, responsible tourism, sustainable resource management, and the conservation of marine and terrestrial ecosystems. Full article

Predictive maintenance (PdM) often fails to progress beyond pilot projects because machine learning-based anomaly detection requires expert knowledge, extensive tuning, and labeled fault data. This paper presents an automated prototype that builds and evaluates multiple anomaly detection models with minimal manual configuration. The prototype automates feature creation, model training, hyperparameter search, and ensemble construction, while allowing domain experts to control how anomaly alerts are triggered and how detected events are reviewed. Developed in a multi-year photovoltaic (PV) solar farm case study, it targets operational anomalies such as sudden drops, underperformance periods, and abnormal drifts, using expert validation and synthetic benchmarks to shape and evaluate anomaly categories. Experiments on the real PV data, a synthetic PV benchmark, and a machine temperature dataset from the Numenta Anomaly Benchmark show that no single model performs best across datasets. Instead, diverse base models and both rule-based and stacked ensembles enable robust configurations tailored to different balances between missed faults and false alarms. Overall, the prototype offers a practical and accessible path toward PdM adoption by lowering technical barriers and providing a flexible anomaly detection approach that can be retrained and transferred across industrial time-series datasets. Full article

This article presents the design of a perovskite photovoltaic (PV)-based power management system, which uses a power converter (a four-stage bootstrap charge pump) to boost the output of the solar cell and supply selectable rectified power rails to CMOS image sensor circuit blocks. A perovskite photovoltaic, also known as a perovskite solar cell (PSC) was fabricated in the laboratory. The PSC has an open-circuit voltage of 1.14 V, short-circuit current of 1.24 mA, maximum power of 0.88 mW, and a current density of 20.68 mA/cm² at 62% fill factor. These measured forward scan parameters were closely reproduced with a solar cell simulation model. In a Cadence simulation that used 180 nm CMOS process, the power converter efficiently boosts the maximum output voltage of the PSC from 0.85 V to a rectified 3.7 V. Stage modulation and level shifting enable selectable output rails in the 1.2–3.3 V range to supply the image sensor circuit blocks. Keeping the output capacitance of the power converter much larger than the flying capacitance reduces the ripple voltage to approximately 73 µV, much smaller than the typical 1 mV in several other literatures. Through simulation, this work demonstrates the concept of directly using PSC (to be implemented on an outer ‘packaging’, not on a die) to supply CMOS image sensor power rails, in the same sense as in wearable devices and other consumer devices. This work highlights a path toward self-powered image sensors with improved conversion efficiency, compactness, and adaptability in low-light and variable operating environments. Full article

Tandem solar cells offer a promising route to surpass single-junction efficiency limits. The amorphous silicon (a-Si)/lead sulfide quantum dot (PbS QD) configuration is a strong candidate for broadband solar spectrum utilization. Planar devices with this material combination suffer from significant optical losses, making advanced light management essential. To address this, we propose a novel experimentally guided nanostructure design. Our proposed method utilizes nanostructures to increase the optical path length by diffracting light to off-normal directions and employs graded-index material stacks to suppress surface reflectance. This work establishes a clear design pathway and provides valuable insights into alternative light management strategies for the future commercialization of these tandem solar cells. Full article

This review surveys the past five years of research on energy-aware path optimization for both solar-powered and battery-only UAVs. First, the energy constraints of these two platforms are contrasted. Next, advanced computational frameworks—including model predictive control, deep reinforcement learning, and bio-inspired metaheuristics—are examined along with their hardware implementations. Recent studies show that hybrid methods combining neural networks with bio-inspired search can boost net energy efficiency by 15–25% while maintaining real-time feasibility on embedded GPUs or FPGAs. Among the remaining challenges are federated learning at the edge, multi-UAV coordination under partial observability, and field trials on ultra-long-endurance platforms like the Airbus Zephyr HAPS. Addressing these issues will accelerate the deployment of truly persistent and green aerial services. Full article