Abstract: Einstein's principle of the invariance of the speed of light in a vacuum is a core of modern physics, but the ISO 13690:2008 standard is only applicable to the visible light band, and traditional measurements have not verified the universality of vacuum permittivity and permeability across all bands. This study combs the limitations of historical precision measurements of the speed of light, derives the wavelength dependence of and in the interstellar medium based on quantum electrodynamics (QED) and Maxwell's equations, and proposes a micro-scale measurement scheme of "three-stage path splitting + two-dimensional compensation". Taking the 2026 Jupiter occultation as the carrier, a multi-band synchronous observation experiment is designed to predict the multi-band arrival sequence and time difference, providing theoretical and technical support for the refinement of light speed measurement and the expansion of the applicable boundary of relativity.This study provides a feasible precision metrology framework for high-accuracy light speed measurement in complex media, with potential engineering applications in astronomical observation and electromagnetic parameter calibration.

Abstract: Just energy transitions in the Global South unfold under conditions of institutional fragmentation, fiscal constraints, and high socio-ecological turbulence, making governance capacity a critical bottleneck for effective decarbonization and climate justice. This study proposes the Cybernetic Environmental Hub (CEH) framework, which extends the Viable System Model (VSM) to sustainability governance by integrating AIoT-enabled environmental monitoring, Early Warning Systems, decentralized data governance, and justice-centered institutional design. Methodologically, the research adopts a hybrid conceptual–empirical approach combining theoretical development with participatory territorial diagnostics. Empirical validation is illustrated through a case study in the Caribbean Mining Corridor, where socio-ecological challenges were collected through participatory innovation workshops, thematically coded, and mapped onto the five VSM subsystems to identify systemic “variety gaps.” The analysis demonstrates that fragmented operational initiatives coexist with weak meta-systemic coordination, limiting adaptive capacity in energy transition processes. The CEH architecture addresses these deficiencies by embedding AIoT sensing, federated learning, blockchain-based coordination, and Early Warning Systems within recursive governance structures. Additionally, the study introduces a Territorial Governance Maturity Model (H1–H3) to diagnose systemic learning capacities and transition readiness across technological, institutional, data governance, and justice dimensions. The findings suggest that cybernetic environmental hubs can function as socio-technical infrastructures enabling coordinated, adaptive, and justice-centered energy transitions in the Global South.

Abstract: To address the demanding requirements for high gain, wide bandwidth, and stable circularly polarized (CP) radiation in Wireless Local Area Network (WLAN) applications, this paper proposes and implements a broadband circularly polarized array antenna operating in the 2.4 GHz ISM band. The design employs a coplanar waveguide (CPW)-fed broadband CP monopole antenna as the radiating element. A sequential rotation (SR) technique is utilized to form a four-element array. Furthermore,​ a windmill-shaped defected ground structure (DGS) is innovatively introduced to further extend the bandwidth. The antenna is fabricated on a low-cost FR4 substrate with overall dimensions of 126 mm × 126 mm × 1 mm. Simulation and measurement results show that the array antenna achieves a -10 dB impedance bandwidth of 1.22–2.78 GHz (87.1% relative bandwidth) and a 3-dB axial ratio (AR) bandwidth of 1.85–2.66 GHz (35.0% relative bandwidth), completely covering the target band. At the center frequency of 2.2 GHz, the antenna exhibits left-hand circular polarization (LHCP) radiation, with a measured peak gain of 8.2 dBi and a cross-polarization isolation better than 15 dB. To verify its performance advantages in practical systems, the designed antenna was integrated into a ZigBee wireless communication system for data transmission testing. The results indicate that, in a complex multipath environment, the system employing the proposed antenna achieves a significantly lower packet loss rate (approximately 3.0%) compared to using a traditional linear-polarized whip antenna (19.0%), effectively optimizing the wireless link quality. The designed antenna features wide bandwidth, high gain, and strong anti-interference capability, making it suitable for WLAN, Internet of Things (IoT), and other wireless communication systems.

Abstract: Detection of leaks in hydrogen (H2) infrastructure is required on a large scale to enable a safe widespread use of this clean energy source. Sensing solutions must be low-cost, use scalable fabrication methods and allow multiplexed detection, while providing reliable safety alarms as fast as possible. Optical methods can make this possible while avoiding the risk of ignition due to electronics at the point of detection. Metal hydride-based micro-mirror configurations benefit from a simple interrogation scheme, as long as the sensitive element can produce a large optical response. Magnesium thin films undergo a drastic variation of properties when hydrogenated, making them suitable for this application. In this work, a micro-mirror device using single-mode fibers capable of detecting the presence of H2 with a loading t10 and t90 of 1.2 and 3.0 seconds, respectively, is demonstrated. A complete interrogation unit was developed, presenting a solution suited for widespread deployment using industry-standard optical components and equipment.

Abstract: Although optical fibre-based surface plasmon resonance (SPR) sensors have revolutionized real-time, label-free biosensing, conventional designs suffer from limited multi-analyte detection capabilities. This study utilizes the novel Pi (π)-configured dual-SPR optical fibre sensor with two opposing side-polished surfaces, enabling plasmonic excitation for simultaneous multi-analyte detection. The proposed sensor leverages asymmetric metallic thin films such as Ag, Au, Cu and hybrid configurations (metal + TiO₂) to generate two distinct resonance peaks, significantly enhancing detection versatility. Numerical simulations using finite element method in COMSOL Multiphysics v6.3 demonstrate that the π-configuration achieves dual resonance dips at 982 nm and 1276 nm for Ag and Ag-TiO₂ films, 1040 nm and 1317 nm for Au and Au-TiO₂ films, and 977 nm and 1249 nm for Cu and Cu-TiO₂ films respectively for an analyte refractive index of 1.42. A peak spec-tral separation >125 nm was achieved for all the sensors for a refractive index range of 1.37 – 1.42, ensuring that the two dips are resolvable since the change in SPR wavelength is greater than or equal to the full width at half maximum, preserving dual-analyte capability and minimizing potential crosstalk. Results indicate that the π-configured dual-SPR sensor utilizing silver and silver-TiO2 sensing layers had the highest wavelength sensitivity of 12,600 nmRIU-1 and 20,000 nmRIU-1 respectively, slightly outperforming its gold and cop-per counterpart. The optimized metallic and hybrid nanostructured films ensures dual distinct peaks with high sensitivity, while maximizing refractive index resolution. This work presents the design of a π-configured SPR-based optical fibre sensor utilizing dielectric and multi-metallic thin films, thereby offering a breakthrough in multiplexed biosensing for applications in medical diagnostics, environmental monitoring, and chemical detection.

Abstract: Traditional reliability models for distribution grids often rely on static historical averages, overestimating the operational lifespan of power system assets by neglecting the dynamic interplay between electrical loading and microclimatic stressors. This paper addresses these limitations by introducing an innovative multi-physics methodology that shifts the analytical paradigm toward a Physics-of-Failure (PoF) approach. This methodology is operationalized through a novel simulation framework and a modular Python-based tool, integrating OpenDSS and Pandapower to perform high-fidelity reliability assessments. By calculating instantaneous failure rates and Mean Time Between Failures (MTBF) as functions of real-time environmental forcing—specifically temperature and humidity-induced stresses—the proposed system captures degradation dynamics that remain invisible to conventional models. The framework’s capabilities are demonstrated through a simulation on a rural distribution grid, which explicitly includes auxiliary digitalization components, such as Remote Terminal Units (RTUs), that are frequently overlooked in standard benchmarks. The results reveal that environmental forcing triggers a severe contraction in the MTBF of critical active assets, proving that asset seniority alone is an insufficient proxy for grid vulnerability. Furthermore, the integration of an advanced Reliability Dashboard enables Distribution System Operators (DSOs) to conduct sophisticated “What-If” analyses and quantify Expected Risk Costs (ERC) prior to physical deployment. Ultimately, this research provides a robust, innovation-driven decision-support system for the cost-effective hardening of smart grids, bridging the gap between theoretical power flow analysis and proactive, climate-resilient asset management.

Abstract: In the field of security screening imaging, millimeter-wave technology offers high imaging resolution and low radiation energy. However, it faces challenges such as difficulty in imaging non-cooperative moving targets, as well as bulky equipment and high costs. This paper proposes a high-resolution imaging method based on MIMO millimeter-wave radar. Firstly, the array model and slant range model are established, and a two-dimensional resolution scheme in range and height is constructed using a one-dimensional MIMO linear array and wideband signals. Then, the algorithm flow for MIMO millimeter-wave radar imaging is designed, and a range-domain super-resolution algorithm is introduced. This paper compensates for the phase coupling introduced by the transmitting array and target motion, and successfully achieves two-dimensional imaging of non-cooperative targets based on the back-projection principle. Subsequently, the influence of array errors on the imaging results is analyzed. This method compensates for the phase coupling introduced by the transmit array and target motion, and provides theoretical analysis of array arrangement errors. E Finally, the experimental results of the MIMO radar are analyzed. The final measured processing results show that the system can clearly reveal met-al objects through cloth occlusion, and super-resolution processing yields sharper con-tours in the imaging of metal plates. Simulation analysis of imaging with array errors indicates that among the azimuth–elevation–range array position errors, the range array position error has a relatively significant impact.

Abstract: Real-time imaging processing of microwave interferometric radiometer (MIR) has great potential in various application field, such as onboard data processing, onboard information fusion and alternative visual application. The primary challenge lies in the computational complexity of the entire processing chain, including both visibility function pre-processing and brightness temperature (TB) reconstruction. In this study, the real-time estimation of the normalized threshold level is identified as the key step for enabling re-al-time imaging of three-level quantized MIR system. Three algorithms - Acklam's algorithm (AKA), polynomial fitting algorithm (PFA), and Taylor expansion algorithm (TEA) - are proposed and evaluated. The PFA shows balanced performance in terms of estimation accuracy and computation efficiency. Leveraging the proposed algorithms, this paper further establishes an onboard real-time processing framework for three-level quantization MIRs, enabling real-time TB imaging and RFI localization. A real-time imaging experiment has been carried out with a 15-element, 50GHz one-dimensional MIR system, which successfully realizes real-time imaging of fast-moving vehicles on the expressway with greatly reduced computational latency (an imaging time of 570.9μs for 159 baselines). A further flight experiment employing an L-band system verifies the feasibility of onboard RFI localization and achieves a localization accuracy within 2.1% of the spatial resolution.

Abstract: This paper presents a numerical investigation of dispersion-managed dense wavelength division multiplexing (DWDM) transmission systems incorporating a non-midway optical phase conjugator (OPC) under randomly distributed residual dispersion per span (RDPS). Unlike conventional studies assuming ideal symmetric configurations, this work considers more realistic scenarios with asymmetric OPC placement and random dispersion distribution. To ensure the reliability of the analysis, simulations were performed for 100 different random RDPS patterns. A 960 Gb/s DWDM system consisting of 24 channels operating at 40 Gb/s was modeled using the nonlinear Schrödinger equation solved by the split-step Fourier method. To analyze the impact of OPC location, two asymmetric configurations, 23–27 and 27–23, were compared. System performance was evaluated using eye-opening penalty (EOP) and timing jitter (TJ). The results show that OPC location has a significant impact on compensation efficiency, with the 27–23 configuration providing overall better performance than the 23–27 configuration. Although randomly distributed RDPS does not always outperform uniform or deterministic dispersion maps, certain random patterns achieve comparable or even superior compensation performance. Further analysis reveals that high-performing random dispersion maps tend to resemble a half-cycle sinusoidal profile, characterized by positive accumulated dispersion before the OPC and negative accumulated dispersion after the OPC. These findings indicate that partial structural regularity within random dispersion plays a key role in enhancing OPC-based compensation. This study provides practical design guidelines for dispersion-managed optical transmission systems under realistic constraints and suggests that guiding random dispersion distributions toward favorable structures can improve system robustness and flexibility.

Abstract: This paper examines an algorithm and evaluates the limit values of technical parameters for step-by-step management of the coverage of the forecast schedule for the aggregated generation of solar power plants (SPPs) in Ukraine, given the high share of renewable energy sources in the structure of the integrated power system of Ukraine. The relevance of the research is due to the growth in the installed capacity of SPPs, stricter requirements for forecasting accuracy, and the full financial responsibility of producers for imbalances in accordance with the current electricity market model. The problem is formulated as a special case of a hierarchically controlled quasi-dynamic power system, taking into account technological, energy and economic constraints. The objective function is defined as the minimisation of the total hourly measure of discrepancy between the forecast and actual volumes of electricity supplied, whilst ensuring power balance through energy storage systems and flexible generation. The numerical implementation was carried out using the "SOPS" software and information complex. The input data used were hourly indicators of the forecasted and actual generation of Ukraine’s solar power plants for 2021–2025, published by the state-owned enterprise "Guaranteed Buyer". Hourly, daily and monthly operating parameters for aggregated solar power generation in 2025 have been calculated. It is shown that, with an installed storage system capacity of 30,000 MWh and corresponding limitations on charge/discharge power, full coverage of the forecast schedule (IMB(t)=0) is ensured even on the day of maximum mismatch between forecast and actual generation. The required volumes of flexible generation and the operating parameters of the storage systems have been determined. The practical significance of the results lies in their potential use for operational planning of the operating modes of solar power plants, energy storage systems and flexible generation on a daily and hourly basis, as well as for justifying technical and economic decisions aimed at reducing imbalances. The results obtained confirm the effectiveness of the proposed step-by-step control algorithm and demonstrate the possibility of minimising imbalances through the rational coordination of solar power plants, energy storage systems and flexible generation capacities.

Abstract: This study proposes a three-layer gateway control framework for a behind-the-meter virtual power plant (VPP) comprising vehicle-to-grid (V2G) capable electric vehicle (EV) chargers, battery energy storage systems (BESS), and rooftop photovoltaic (PV) generation in multi-dwelling residential developments. In particular, the first layer performs day-ahead scheduling to determine the hourly grid import baseline and frequency regulation ancillary service capacity for the following day. In the second layer, real-time regulation dispatch is performed by following the dynamic regulation signal from the grid operator, wherein V2G-capable EVs are coordinated alongside BESS as active demand-side participants in frequency regulation ancillary services, enabling the aggregated behind-the-meter fleet to respond to regulation signals in real time. The third layer performs per-minute three-phase load balancing to maintain network power quality compliance across the multi-dwelling site. The overall goal is to coordinate distributed energy resources behind a single network connection point to simultaneously reduce peak demand, maximise renewable self-consumption, and provide demand-side frequency regulation as a dispatchable VPP asset.

Abstract: The growing use of electric vehicles in the residential building sector presents new challenges in the management of the charging infrastructure, especially in deciding how to best price the use of it to balance operator revenue and user satisfaction with grid stability. Traditional pricing methods like fixed pricing rates and time-of-use tariffs cannot accommodate the dynamic nature of charging demand, which fluctuates depending on temporal patterns and weather conditions as well as user behavior. This limitation means that the resources are not used optimally and the revenue opportunities are lost during periods of high demand. To overcome this issue, we propose a reinforcement learning framework for dynamic pricing for residential electric vehicle charging stations. The framework models the pricing problem as a Markov Decision Process and uses Proximal Policy Optimization to learn a policy for setting optimal prices of private and shared charging stations according to real-time conditions. The state representation is done using ten features such as temporal indicators, current loading on the grid, grid status, traffic volume, and weather data. A multi-objective reward function is an approach to balance four objectives - revenue maximization, station utilization, grid stability, and user satisfaction. The system is trained on actual charging data from a residential complex in Trondheim, Norway. 6878 charging sessions during a 13-month period are used for training. We compare the learned policy with three baseline technologies: fixed pricing, time-of-use pricing and rule-based pricing. Experimental results show that the proposed approach reaches an overall score of 0.569, which is 32.9% and 48.9% improvements in comparison to fixed pricing and time-of-use pricing, respectively. The learned policy is able to successfully adjust the prices based on different conditions and sustain a balanced performance for all the goals. The main contributions include a custom reinforcement learning environment for residential EV charging pricing, a multi-objective reward formulation, and empirical evidence that learned policies outperform traditional pricing approaches.

Abstract: Achieving Great Britain’s 2050 net-zero target requires strategic integration of hydrogen into the national energy system. This study evaluates the system-wide impacts of hydrogen blending (0–100%) using a bi-level optimisation framework that combines long-term cooperative investment planning with short-term operational Optimal Power and Gas Flow (OPGF) simulation. The strategic layer models infrastructure investment decisions under a cooperative game-theoretic structure, where system value is allocated among electricity, hydrogen production, and storage technologies using the Shapley-value payoff mechanism. Simulation results indicate that hydrogen blending up to 20% maintains operational stability and positive economic performance, with manageable increases in operational cost. Emissions reductions are realised when blending is combined with carbon capture and storage (CCS) deployment or through higher CO₂ pricing. Full hydrogen conversion (100%) increases peak electricity supply requirements by approximately 30% relative to low-blending scenarios due to electrolysis-driven load expansion and conversion losses. Sensitivity analysis shows that carbon pricing significantly reduces system emissions while moderately increasing hydrogen marginal costs and affecting Net Present Value. Under coordinated infrastructure planning, Net Present Value increases with hydrogen penetration, with full hydrogen deployment delivering the highest long-term system value. The findings demonstrate that hydrogen blending represents a viable transitional pathway when supported by integrated infrastructure development, CCS deployment, and appropriate carbon pricing, enabling a phased progression towards Great Britain’s 2050 net-zero target.

Abstract: Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between the timing of electricity production and demand. Without appropriate storage solutions, surplus energy may remain unused. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models for them, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems have become widely used due to their availability, high response speed, significant energy density, and sufficient power capacity. However, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model was carried out using a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results showed that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. Although the proposed methodology was applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients will differ, but the methodology itself and the sequence of its application will remain the same.

Abstract: Load profile forecasting and aggregation are essential for power system planning and operation, yet traditional deterministic AI models often function as black boxes, neglecting the rigorous quantification of input uncertainties. This study proposes a Software-in-the-Loop (SIL) Digital Twin architecture that integrates the Guide to the Expression of Uncertainty in Measurement (GUM) directly into the computational pipeline. Utilizing a Long Short-Term Memory (LSTM) forecasting core and Monte Carlo simulations, the system propagates uncertainties originating from physical measurement noise and SCADA data imputation. To establish a traceable metrological baseline, initial validation is conducted using a highly controlled synthetic load profile at a 15-minute granularity. Our results reveal that degraded input data quality can account for up to 40 % of the total prediction variance during high-volatility periods, exposing the "false confidence" inherent in deterministic point predictions. By outputting a probabilistic mean enveloped by a 95% coverage uncertainty band, this Digital Twin framework establishes a human-mediated closed loop, empowering "human-on-the-loop" operators to execute risk-informed decisions and safeguard grid stability. Given the importance of effective uncertainty propagation for reliable power system operation, informed decision-making, and risk mitigation, this study aims to develop artificial intelligence and/or machine learning (AI/ML) based load profile forecasting and aggregation models. This initial investigation assesses the models’ potential as digital representations to assist operators, specifically considering how uncertainty propagation can be modeled and assessed within them.

Abstract: The aim of this study is to develop an AI‑integrated Electronic Document Management System that improves the way Standard Operating Procedures are accessed and used in manufacturing plants. The system focuses on fast retrieval of information and clear guidance for operators. Many SOPs contain unclear lines that point to other documents. These lines slow down the operator and create confusion during production. The operator must stop the task and search for the linked document. The operator may not know the correct version or location. The operator may also open the wrong file. These issues increase downtime and reduce efficiency. The AI‑integrated system removes this delay. The system reads the request from the operator. The system understands the meaning of the line. The system finds the correct document. The system shows the needed information at once. The operator continues the task without losing time. The system also supports new operators who may not know the document structure. The system guides them with simple answers. The system reduces the chance of mistakes and improves safety. The system also improves the quality of the document set. The system detects duplication. The system highlights mismatches. The system keeps one source of truth. The system reduces the risk of outdated content. The system creates a clean and consistent knowledge base. The system also supports audits and compliance checks. The result of this work is a faster and more reliable workflow. The system reduces downtime by 27 percent. The system improves decision making. The system supports continuous improvement in the plant. The system creates a strong link between knowledge and action.

Abstract: Large diameter axial ventilation fans are widely used in poultry houses to regulate ai flow, temperature, and air quality. However, conventional induction motors driving these fans typically operate at fixed speed and suffer efficiency degradation under low speed, high torque conditions due to slip induced rotor copper losses. This study presents an experimental investigation of a manufacturing constrained conversion of a commercial induction motor platform into a direct drive surface permanent magnet synchronous motor (PMSM). Instead of developing a completely new motor design, the proposed approach reuses the existing stator lamination, housing structure, and winding production process while redesigning the rotor electromagnetic structure to incorporate surface-mounted permanent magnets. Experimental testing was conducted using a dynamo meter based measurement system to evaluate the performance of both the commercial induction motor and the converted PMSM prototype. The results show that the commercial induction motor exhibits significant efficiency degradation at high torque due to increased slip, whereas the PMSM eliminates slip dependent rotor copper losses and maintains efficiencies above 88% within the typical ventilation operating range of 650-750 rpm. The study further relates airflow demand to rotational speed using fan affinity laws, highlighting the cubic relationship between speed and input power and demonstrating the energy-saving potential of variable speed PMSM drives. The proposed conversion framework therefore provides a practical pathway for improving the energy efficiency of agricultural ventilation systems while maintaining compatibility with existing motor manufacturing infrastructure.

Abstract: This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multi-frequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. The paper develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain dictio-nary matching problem using normalized complex correlation and QR-domain process-ing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating char-acteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic frequency dither produces distinctive composite wave-forms with strong hypothesis separability. ROC simulations demonstrate reliable detec-tion at physical SNR levels below those typical of classical single-pulse matched-filter detection, while EIRP-based analysis indicates feasible detection for targets ranging from large aircraft to small drones and pedestrians. These results support the feasibil-ity of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition.

Abstract: The Low Earth Orbit (LEO)-based Positioning, Navigation, and Timing (PNT) System has been proposed as an attractive global navigation system because of its strong transmission power and convergence of Precise Point Positioning (PPP). However, constructing a PN family satisfies the constellation size of the LEO-PNT System while maintaining a correlation performance is a major challenge. In this paper, we propose the extended Weil family, constructed by concatenating two Weil sequences based on the Goldbach conjecture. Among 157 prime pairs capable of generating balanced 10,230-chip codes, the prime pair (p=10091,q=139) yields the largest number of candidate codes satisfying the auto-correlation function (ACF) threshold of BeiDou Navigation Satellite System (BDS) B1 Civil (B1C) family. Using this pair, the total of 254 codes satisfy the BDS B1C correlation thresholds, approximately twice the size of the B1C family. We conducted additional experiments with relaxed cross-correlation function (CCF) thresholds to achieve the target family size of 588, providing two codes for each of the 294 satellites. As a result, a total of 608 codes satisfy the ACF threshold of the BDS B1C family and the CCF threshold of the Global Positioning System (GPS) L1 Civil (L1C) Family, which is sufficient to support the LEO-PNT System.

Abstract: The long-term performance of photovoltaic (PV) modules significantly affects the reliability and economic viability of solar energy systems, as various environmental and operational factors can gradually degrade module efficiency and reduce energy output. This study investigates the long-term performance degradation analysis of 40 outdoor photovoltaic (PV) modules exposed for four years on a five-level building in Mirpur, Dhaka, Bangladesh. Electrical parameters, including voltage, current, power, and fill factor, were measured using a PROVA 1011 PV analyzer under IEC60904-1 standard test conditions were analyzed to evaluate the extent of long-term degradation of PV modules. The image-based analysis identified degradation factors such as dust accumulation, soiling, hotspots, discoloration, microcracks, delamination, and corrosion. All test data were normalized to standard conditions (1000 W/m², 25°C) for consistency. The measured average maximum power output was 9.85 W, with an average fill factor of 0.713 and a standard deviation of 0.939 for the 40 photovoltaic modules with a rated capacity of 10 W each. The dataset provides valuable insights for researchers and industry professionals to assess long-term PV performance, optimize maintenance strategies, and support solar energy deployment in tropical environments. Additionally, it can aid policymakers in developing regulatory frameworks for improving solar infrastructure resilience.