A battery used to smooth solar generation may have less reserve available than its rated power suggests because firming, recovery and reserve share the same stored energy and converter. We examine this interaction using telemetry from a solar plant in a small island developing state, publicly reported as 122 MWp and 92 MWac, over 25 November 2025 to 1 February 2026. A one-minute model propagates state of charge continuously, and each ten-minute reserve activation is assessed separately from its chronological starting state. In the original 56-date analysis, the largest observed ten-minute decline was 74.117 MW. For a 55 MW/15 MWh analytical battery under an idealised zero-latency sampled-information benchmark, the reserve award deliverable in at least 99
Abstract Unmanned aerial vehicle (UAV) swarm systems are increasingly deployed in time-critical operations where multiple agents must coordinate autonomously under dynamic and often adversarial conditions. A persistent challenge in such deployments is the simultaneous presence of Byzantine faults, wherein compromised nodes inject malicious data into the swarm, and constrained communication bandwidth, which limits the fidelity of inter-agent information exchange. To address these dual challenges, a Zero-Trust Graph-Attentive Intelligence (ZT-GAT) framework is introduced. The proposed model employs an eight-head, 256-dimensional Graph Attention Network (GAT) backbone with a multi-signal zero-trust authentication layer fusing behavioral scoring, cryptographic attestation, and graph neighborhood analysis at a composite threshold of 0.75. Experiments were conducted on the UAVSwarm dataset augmented with a dedicated Byzantine and communication overlay spanning spoofing, replay, Sybil identity fabrication, and gradient poisoning attacks at node corruption levels of 5%, 10%, 20%, and 33%. In the model-level comparison, the proposed framework achieved a Byzantine F1-score of 96.8%, a trust classification accuracy of 97.9%, a consensus latency of 134.6 ms, and a packet delivery ratio (PDR) of 93.7%, with the evaluation covering swarm sizes reaching 50 agents, outperforming the strongest baseline by 6.1 percentage points in F1 at a computational cost of 1.91 GFLOPs per forward decision cycle. Separately, the unweighted mean Byzantine F1-score across the 12 attack–corruption conditions was 92.83%. These findings confirm that graph-attentive zero-trust integration provides a scalable and communication-aware foundation for Byzantine-resilient UAV swarm coordination.
The profusion, flexibility, and recurrently declining installation costs of solar photovoltaic (PV) technology have made it a protuberant renewable energy source. However, environmental unpredictability, component deterioration, power-electronic failures, and aging effects continue to have a major impact on the long-term performance and reliability of PV systems. There are some drawbacks to the conventional maintenance techniques employed in PV installations, such as routine manual inspections and corrective actions. In order to improve the performance and operational efficiency of solar PV systems, this work presents a prognostic maintenance framework driven by machine learning techniques and enabled by IoT device. A low cost hardware prototype of real time solar data acquisition and monitoring system is developed using LoRa module which collects data from the solar station and in the transmitter station the data is pushed to cloud and displayed in dashboard. Different machine learning models linear regression, decision tree, random forest and XGBoost has been implemented for predictive maintenance of solar panels. A hybrid machine learning model based on Bayesian optimization of XGBoost is proposed and from the comparative assessment of the machine learning models it is validated that the proposed hybrid model showed superior performance in terms of highest accuracy and lowest computation time.
Multiplexers (MUXs) and demultiplexers (DEMUXs) are essential data-routing circuits used in memory addressing, processor datapaths, communication interfaces, and reconfigurable systems. However, CMOS scaling increases leakage power, while conventional MTCMOS techniques introduce delay, series-resistance, and control overhead. This work proposes a topology-aware high-threshold-voltage (HVT) MTCMOS power-gating architecture for cascaded transmission-gate (TG) based 4:1 MUX and 1:4 DEMUX circuits in 90 nm CMOS. The architecture uses a select-controlled HVT PMOS header at a topology-dependent TG stage and a block-level HVT header, requiring only two additional transistors without a dedicated sleep network. The MUX reduces power by 40.0
Abstract Bilayer perovskite solar cells (PSCs) represent an emerging strategy for enhancing photovoltaic (PV) performance. In this work, we present a lead-free bilayer PSC architecture utilizing Cs₂TiI₆ as the primary absorber and La₂NiMnO₆ as the secondary absorber, chosen for their favourable band alignment and environmental stability. Their complementary absorption profiles efficiently harvest different portions of the solar spectrum maximizing the photon utilization. This design also avoids the use of Sn²⁺-based materials, thereby preventing common issues related to oxidation-induced degradation. WS₂ is employed as the electron transport layer (ETL), while PEDOT: PSS serves as the hole transport layer (HTL). Starting from a single-layer Cs₂TiI₆ solar cell that achieved a power conversion efficiency (PCE) of 24.3%, this bilayer architecture offered enhanced device performance, resulting in a PCE of 28.5%, with a fill factor (FF) of 75.5%, a short-circuit current density (JSC) of 29.98 mA/cm2 and an open-circuit voltage (VOC) of 1.26 V under standard AM 1.5G illumination at 300 K. This architecture thus offers a blueprint for scalable, environmentally favourable bilayer photovoltaics outperforming single-absorber PSCs.
