Mutule, Anna · Zalostiba, Diana · Baltputnis, Kārlis · et al.

1 files · 435 KB · xlsxdeclared

This dataset provides aggregated hourly electrical load and generation profiles for the Kurzeme region of Latvia for the year 2025. The Kurzeme region is a western region of Latvia located along the Baltic Sea coast, characterised by a mixed urban, rural and coastal energy demand profile and increasing integration of renewable energy generation. The dataset is based on measurements from 17 transformer substations and contains only summed regional values; individual substation-level time series are not included. The published time series includes active and reactive power components for both generation and load. The dataset contains 8760 hourly records covering the period from 1 January 2025 to 31 December 2025. The following variables are included: date, time, active generation, reactive generation, active load, and reactive load. The aggregation covers transformer substations with a total installed transformer capacity of 553.9 MVA. The total installed generation capacity connected to the analysed substations is approximately 189.9 MW. This includes 108.6 MW of solar photovoltaic generation, 46.7 MW of wind generation, 25.3 MW of cogeneration, 3.8 MW of hydropower, and 5.4 MW of battery energy storage systems. The dataset can be used for regional energy system analysis, distribution-level load and generation profile assessment, energy community and flexibility studies, scenario development. Variables Date - calendar date of the hourly record. Time - hour of the day. Active generation, kW - aggregated active power generation. Reactive generation, kVAr - aggregated reactive power generation. Active load, kW - aggregated active power load. Reactive load, kVAr - aggregated reactive power load. Spatial coverage Kurzeme region, Latvia. Temporal coverage 2025-01-01 to 2025-12-31. Temporal resolution Hourly.

Ni, Xin · Liang, Qianyong

3 files · 8.7 MB · xlsxdeclared

Data for article " Global Ocean Methane Seepage Flux Based on Submarine Observation of South China Sea Combine with Machine Learning " Marine methane seepage represents a critical component of the global carbon-water cycle, yet existing studies face persistent challenges: scarcity of long-term continuous monitoring data, difficulty in capturing the complex interactions of multiple environmental factors, and limited scalability from local observations to global estimates. This research addresses these gaps through three major innovations. First, we pioneer the application of machine learning with SHAP analysis in marine methane seepage prediction, achieving exceptional model accuracy (R² > 0.95) while ensuring model transparency and interpretability . Second, our study systematically identifies key environmental sensitivity factors controlling seepage dynamics from seafloor sedimentary layers to the seawater interface, revealing that pressure dominates high-intensity seepage events (61% contribution) with distinct tidal periodicity . Third, we provide robust quantitative estimates of methane flux at both regional (South China Sea: 0.964 ± 0.482 Tg CH₄/year) and global scales (11.465±0.382 ~ 49.613±1.394 Tg CH₄/year), global ocean methane seepage flux is significantly underestimated (beforehand 6-12 Tg CH₄/year).

Spiekermann, Lukas · Sewani, Hitesh · Lochmann, Sebastian · et al.

8 files · 686 KB · xlsx, zipdeclared

This dataset provides life cycle inventories for the case studies of the manuscript. The two case studies (CO2-based methanol (CCU) and bioethanol (Humbird2011)) are discussed in the manuscript. The ALCHEMIA software package is provided as .whl files (see below). Additionally, this README and a LICENSE file are provided.

Senthil Kumar, P

2 files · 216 KB · xlsx, zipdeclared

This paper presents Quantum Entanglement-Assisted Adaptive Firewall Intelligence (QEAFIS) , a novel three-tier cybersecurity framework that integrates quantum entanglement and artificial intelligence to enhance threat detection in next-generation hybrid classical-quantum networks. The proposed system employs Entanglement-Guided Inter-Packet Correlation, Behavioral Fingerprint Memory, and Dynamic Quantum Threshold Adaptation to identify advanced persistent, polymorphic, and zero-day attacks while continuously adapting to evolving cyber threats. Experimental evaluation on benchmark datasets and real-world deployment demonstrates superior performance, achieving 98.4% unknown threat detection accuracy , significantly reducing false positives and response latency compared to existing classical and quantum-based approaches, thereby providing a scalable, intelligent, and sustainable solution for next-generation cybersecurity.

Senthil Kumar, P

3 files · 161 KB · xlsx, zipdeclared

This study presents FedCau-GNN , a novel federated continual causal graph attention network for intelligent chronic heart failure (CHF) detection that addresses key limitations of existing federated learning approaches, including static model training, catastrophic forgetting, limited adaptability to new hospitals, and reliance on correlational explanations. The proposed framework models multimodal cardiac data as evolving causal-temporal graphs, integrates Elastic Weight Consolidation with privacy-preserving generative replay for continual learning, produces counterfactual causal explanations, and employs federated meta-initialization to enable rapid adaptation with limited local data. Experimental results demonstrate sustained diagnostic performance under long-term distributional drift (AUC > 0.94), significantly improved causal explanation consistency (0.88), enhanced resistance to membership inference attacks, and low-latency inference (<200 ms) while maintaining GDPR- and HIPAA-compliant privacy. These findings indicate that continual, causally informed federated learning offers a robust, scalable, and privacy-preserving solution for real-world CHF diagnosis across heterogeneous healthcare institutions.

Suhri, Andi Gita Maulidyah Indraswari

1 files · 66 KB · xlsxdeclared

This dataset contains seed germination, seed physiological quality, vegetative growth, generative performance, productivity, and correlation data for chilli pepper ( Capsicum frutescens L.) under four pollination and seed-source treatments: Tetragonula -mediated pollination, self-pollination, open/random pollination, and commercial seeds. The data support a study evaluating whether Tetragonula -mediated pollination improves seed vigour and subsequent agronomic performance in chilli pepper.