PYCO is the native token of the Lindblad Protocol, emerging as a direct consequence of a network that measures and rewards physical coherence. This paper describes the mechanism by which PYCO is generated, distributed, and consumed within the Spectral Ledger, and establishes the economic properties that result from anchoring token issuance in physical hardware validation. Every PYCO in existence was produced by a physical node running the Lindblad Cryptography Protocol (LCP) stack on real hardware. As of June 2026, over 1,512,000 PYCO have been mined across 35,842+ epochs by physical hardware nodes deployed on mainnet on Arbitrum One.
Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.
Mario Mihetec, Goran Stunjek, Goran Krajačić, Gordana Mikulčić Krnjaja
ABSTRACT Suburban areas with dispersed buildings and low heat flux densities present distinct challenges for the decarbonization of heating systems. While district heating is often promoted in dense urban cores, its economic viability in suburban zones remains questionable due to high network costs and thermal losses. This study investigates whether decentralized, household level solutions combining high-temperature air source heat pumps with photovoltaics can outperform centralized district heating in such contexts. Using a case study of four peripheral settlements in Croatia, the research employs a dual-scale techno-economic optimization framework: a mixed-integer linear programming model for district heating and a prosumer-level model for individual heat pump-photovoltaic–battery systems. Three building renovation scenarios (no, partial, and full renovation) are evaluated alongside a mixed-financing scheme involving grants, household equity, and energy service company participation. Results show that decentralized heat pump-photovoltaic–battery systems under full renovation deliver the highest energy savings (75% reduction in household energy costs), the greatest carbon dioxide reduction (2,207 tonnes annually), with a net present value of 1.49 million EUR and an internal rate of return of 7.21%. When external costs of air pollution and carbon are internalized, the economic net present value rises to 68.39 million EUR. The results suggest that, under the assumptions and boundary conditions defined in this study, decentralized renewable heating systems are both technically viable and economically favorable compared to district heating in low-heat-density suburban contexts. This work provides a replicable decision-support framework for policymakers and planners seeking to accelerate the clean heating transition in dispersed residential areas.
This paper presents a comparative study between two leading blockchain platforms—Hyperledger Fabric and Ethereum—with emphasis on their architectural design, performance characteristics, and security mechanisms in the context of enterprise applications. The study aims to identify key differences between permissioned and public blockchain models, focusing on scalability, consensus efficiency, and data confidentiality. A controlled experimental environment was developed using Docker-based deployments for both platforms, and performance was evaluated through Hyperledger Caliper using standardized workloads. Metrics such as transactions per second (TPS), latency, resource consumption, and failure rates were analyzed under varying network sizes. The results indicate that Hyperledger Fabric achieves significantly higher throughput (≈900 TPS) and lower latency (<200 ms) compared to Ethereum (≈25 TPS, ≈1 s latency), due to its deterministic Raft consensus and modular architecture. Ethereum, however, demonstrates superior decentralization and transparency suitable for public and decentralized applications. The findings highlight that both platforms are complementary: Hyperledger Fabric is optimized for controlled, high-performance enterprise use, while Ethereum excels in open, trustless environments.
We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.
Thomas Bakaysa, Ahmet Kurt, Abdul-Salem Beibitkhan, J E Hernández Leon · 9 authors
Bitcoin's Lightning Network (LN) can be exploited as a covert, low-cost command-and-control (C&C) channel for botnets, as demonstrated by the LNBot and D-LNBot designs. However, both remain proof-of-concept prototypes evaluated only through simulation, leaving key questions about real-world topology formation, propagation complexity, and resilience to takedowns unanswered. We present LNTest, the first reusable testbed for LN-based botnets, built from Core Lightning nodes containerized with Docker over a shared Bitcoin Core regtest chain. LNTest supports three overlay topology modes (a deterministic chain, autonomous peer discovery, and user-supplied graphs), enabling controlled experiments across different botnet structures. Using LNTest, we report three main findings. First, D-LNBot's autonomous formation protocol does not produce the uniform chain from its design; instead, it creates a clustered chain in which cliques are linked by bridge nodes whose removal fragments the network. Second, command propagation scales linearly with botnet size ($Θ(n)$), not the $O(m \log n)$ previously claimed, and gains nothing from higher neighbor connectivity. Third, the overlay topology determines the effectiveness of takedown strategies: uniform-degree chains resist targeted removal but fragment under random failure, scale-free topologies show the opposite pattern, and the autonomous clustered chain is fragile under both, making it the most vulnerable of the three. LNTest is released as open source, with a script that reproduces all our experiments, to support reproducible research on LN-based botnet defenses.
Luisa von Albedyll, Robert Ricker, Frank Kauker, Daniel Krogmann · 5 authors
Abstract. Arctic sea ice thickness has declined rapidly over recent decades, yet the relative roles of thermodynamic growth and dynamic redistribution in driving this change remain poorly constrained at basin scale. We quantify thermodynamic and dynamic contributions to sea-ice thickness change together with their uncertainties across the Arctic from 2002 to 2020 by combining satellite-derived thickness with sea-ice model simulations (Icepack) along trajectories. Separating dynamical thickening (30%), dynamical thinning (−24%), and lead-ice growth (10.2%) shows that dynamic processes contribute nearly as much to the average winter ice growth of 0.21 m per month as thermodynamic processes (35.8%). Regional, seasonal, and thickness-dependent variability is consistent with large-scale dynamic patterns and the ice-growth feedback. We quantify the effects of the overly smooth deformation forcing, which leads to an underestimation of large dynamic events and a substantial noise floor during dynamically quiet periods, and relate their magnitude to other sources of uncertainty. Analyzing the long-term trend from 2002–2020, we resolve a weak increase in median sea ice deformation (1.5% per year) and net dynamic thickness change (12% per year) within the limits of our study setup. Overall, our results suggest that increasing deformation in the Arctic enhances net dynamic thickness change and acts as a negative feedback in the pan-Arctic winter thickness budget.
Abstract Four independent fields—physics, biology, economics, and cultural evolution—have converged on the same mathematical machinery for describing persistence-conditioned dynamics. The convergence is not metaphorical but literal: the same fitness landscapes, selection operators, and transmission kernels appear independently. We synthesize these into the Replicator-Optimization Mechanism (ROM): a unified apparatus instantiable at any scale. Key Contributions Cross-field synthesis: Physics, biology, economics, and cultural evolution share identical formal structure Political application: ROM instantiated with friction from stake-voice mismatch as primitive, legitimacy as survival probability Machine-checked proofs: Core algebraic results verified in Lean 4 with Mathlib (28 theorems, zero sorry placeholders) Key results: Simplex preservation, survival monotonicity, moving equilibrium existence, impossibility of static equilibrium under varying friction Links arXiv: arXiv:2601.06363 Lean 4 proofs: github.com/studiofarzulla/lean-formalizations ASCRI: systems.ac/4/DAI-2503 Research Lab: Dissensus AI v3.0.0 (2026-07-11): Matches arXiv v3 (69pp). Keystone-legitimacy example corrected; a coarse-graining citation that could not be verified was removed from the bibliography; the Δ→σ step is now disclosed as an explicit worst-case identification; total-variation legitimacy remark added, aligning the measurement form with the level-form dynamics used in companion papers; Lean 4 formalization tree included in the arXiv source.
The transformation of the copyright institution in the context of the intensive development of digital technologies and the globalization of the information space is studied. The legal nature of objects created with the help of artificial intelligence systems is analyzed, and the challenges facing the traditional anthropocentric model of authorship are identified. The features of non-fungible token technology (NFT) as a tool for monetizing digital art are identified
Abstract: This paper explores the changing legal framework surrounding virtual property and digital land ownership in metaverse environments. While blockchain technology provides immutability and provenance through non-fungible tokens (NFT), the rights it provides are still merely technologically symbolic, not legally certain. Virtual land ownership is shown to be contingent, contractual and revocable - more like a licence of access rather than legally enforceable proprietary ownership. The Indian system recognizes virtual assets tax policy as property, however do not provide ownership protection, leaving clients with the paradox of economic recognition without legal ownership. This research proposes a sui generis legal framework- Lex Metaversi – that streamlines digital property regulation and deals with the tension that exists between ownership of code and unenforceable legal control. Keywords: Virtual Property Rights, Metaverse Law, Non-Fungible Tokens (NFTs), Lex Metaversi, Digital Asset Regulation
Traditional distributed consensus mechanisms rely on probabilistic assumptions, economic weighting (Proof-of-Stake), or arbitrary computational work (Proof-of-Work) to secure ledger state transitions. These models leave the application layer inherently vulnerable to Man-in-the-Middle (MITM) attacks, Maximal Extractable Value (MEV) extraction, and semantic exploits against critical infrastructure (SCADA/PLC). This manuscript introduces Proof-of-Rigidity (PoR), a deterministic state-validation framework that locks the consensus machine within a continuous 150-decimal-place geometric manifold ($G_{24}$ volume space). The paper formalizes three core components: The Brittle Acceptance Predicate: A Coq-verified mathematical boundary that enforces an absolute $10^{-80}$ validation tolerance, structurally denying unauthorized state mutations. Mantissa Tail Parity (The MEV Sieve): A mechanism utilizing Canonical Decimal Arithmetic ($\mathbb{D}_{150}$) to mathematically neutralize routing interception and front-running. Capability-Constrained Semantic Policies: A bipartite matrix that structurally subordinates LLM-based ontological analysis to strict cryptographic Role-Based Access Control (RBAC), preventing adversarial paraphrasing against industrial endpoints. By enforcing strict geometric determinism, PoR transforms network security from probabilistic difficulty into mathematical brittleness. Included in this deposit are the Coq formal verification proofs, a Python reference implementation of the Layer-1 substrate, and a computational benchmarking harness demonstrating throughput scalability. LEGAL, ETHICAL, AND SAFE HARBOR DISCLAIMER The mathematical models, formal Coq proofs, and Python reference implementations contained within this deposit are published strictly for academic research, cryptographic peer review, and educational purposes. The architectures described herein represent a theoretical substrate and an experimental prototype. They have not undergone formal, independent security auditing for production deployment. No Warranty (As-Is): The mathematical models and reference code are provided "AS IS", without warranty of any kind, express or implied. The continuous geometric bounds and mechanisms detailed herein are theoretical thresholds; physical hardware limitations, truncation errors, or implementation flaws may affect real-world execution. Limitation of Liability: Under no circumstances shall the author, contributors, or affiliated research entities be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, loss of use, data, stablecoin assets, or profits; business interruption; or industrial infrastructure failure) arising in any way out of the use, deployment, or misconfiguration of this protocol. Assumption of Risk: Any entity choosing to implement the $G_{24}$ volume space boundaries, the Topological Shatter mechanics, or any variant of the PoR consensus layer within a live environment does so entirely at their own risk, and is solely responsible for ensuring compliance with all applicable cybersecurity and financial regulations.
This chapter examines the emerging role of decentralized finance (DeFi) as a transformative catalyst in the digitalization of electric vehicle (EV) charging infrastructure within the energy and utilities sector. While global sustainability agendas envision seamless, affordable, and interoperable charging networks to support large-scale EV adoption, existing systems remain fragmented, capital-intensive, and institutionally constrained. Ideally, charging ecosystems should enable transparent financing, efficient energy exchange, and user-centric governance. In practice, however, high deployment costs, limited grid flexibility, regulatory inconsistencies, and restricted access to investment continue to impede this vision. Building on prior research on smart grids, blockchain-enabled energy markets, and sustainable mobility frameworks, this work critically evaluates how 2 DeFi-driven models extend beyond conventional centralized approaches. Existing studies emphasize technical optimization and policy mechanisms, yet they often overlook decentralized financial governance, peer-to-peer energy trading, and tokenized infrastructure funding. Addressing this gap, the study develops an integrated conceptual model linking DeFi principles, smart grid technologies, and EV charging ecosystems. Through analytical synthesis and selected case evidence, the paper demonstrates how trustless transactions, decentralized autonomous organizations, and micropayment mechanisms can enhance financial inclusivity, operational transparency, and system scalability. By situating EV charging infrastructure within a broader digital-financial transformation paradigm, this study advances theoretical understanding and offers strategic insights for policymakers, utilities, and technology providers seeking to accelerate sustainable and resilient mobility transitions.
This chapter examines the convergence of immersive technologies, decentralized finance (DeFi), and digital transformation in reshaping the operational and financial foundations of the renewable energy sector, with particular emphasis on wind turbine systems. In an ideal sustainable energy ecosystem, advanced digital tools, transparent financing mechanisms, and intelligent infrastructure operate in harmony to optimize performance, ensure safety, and accelerate investment in green energy. Such a system is expected to integrate virtual and augmented reality for skill development and maintenance, Internet of Things (IoT)-driven analytics for real-time monitoring, and decentralized platforms for inclusive project financing. However, despite rapid technological progress, contemporary renewable energy systems remain constrained by fragmented digital adoption, centralized funding structures, regulatory uncertainty, and limited technological accessibility. Existing studies on smart grids, digital twins, and immersive training platforms highlight efficiency gains in turbine design and maintenance, while blockchain-based research emphasizes DeFi’s potential in peer-to-peer energy financing. Yet, these research streams largely evolve in isolation, offering limited insight into their systemic integration. Moreover, prior work rarely addresses how immersive technologies and decentralized finance jointly influence operational resilience and financial sustainability. Addressing this gap, the chapter develops an integrative conceptual framework grounded in digital ecosystem theory and decentralized governance models. Through critical synthesis and analytical evaluation, it demonstrates how coordinated deployment of virtual reality/augmented reality, IoT analytics, and DeFi platforms can enhance performance optimization, democratize investment, and strengthen trust in renewable energy systems. The findings provide strategic guidance for policymakers, utilities, and investors seeking to advance scalable and sustainable energy transitions.
يتناول هذا البحث مسألة حماية الهوية الثقافية للأسرة في ظل التحولات التي أفرزتها البيئة الرقمية اللامركزية في عصر الشابكة اللامركزية (Web3). ولم يعد أثر التطور الرقمي مقتصراً على الجوانب التقنية البحتة، بل امتد إلى المجالات القيمية والتربوية التي تمارس الأسرة من خلالها وظيفتها في التنشئة الاجتماعية، ونقل الموروثات، وترسيخ المرجعيات الثقافية بين الأجيال. ويهدف البحث إلى بيان طبيعة الأثر الذي تمارسه هذه البيئة الرقمية في إعادة تشكيل المجال الثقافي داخل الأسرة، مع تحليل الإشكالات القانونية التي يثيرها هذا التحول، ولا سيما ما يتصل بمدى كفاية التشريعات التقليدية لمواكبة هذه التغيرات. وتعتمد الدراسة على المنهج الوصفي التحليلي، مع الاستفادة من لمحات مقارنة محدودة، لتقويم فعالية الأطر القانونية القائمة. وقد خلصت إلى أن الأدوات القانونية التقليدية لم تعد كافية، بمفردها، لضمان حماية الهوية الثقافية للأسرة، وأن المرحلة الراهنة تقتضي مقاربة قانونية متوازنة تقوم على الوقاية المسبقة، وتقييم المخاطر، والتصميم الآمن للمنصات الرقمية، مع توزيع واضح للمسؤوليات بين مختلف الفاعلين في الفضاء الرقمي، ومراعاة المصلحة الفضلى للطفل، وتحقيق التوازن بين الانفتاح الرقمي، والحفاظ على البعد الثقافي للأسرة، بما يعزز قدرتها على صون قيمها، وتماسكها في وجه التحولات المتسارعة. This study examines the protection of the family’s cultural identity in light of the transformations produced by the decentralized digital environment in the era of Web3. Digital influence is no longer confined to purely technical aspects; rather, it now extends to the value-based and educational spheres through which the family carries out its role in socialization, transmitting heritage, and consolidating cultural references across generations. The study aims to clarify the nature of this environment’s impact on reshaping the family’s cultural sphere while analyzing the legal challenges arising from this transformation, particularly those related to the adequacy of traditional legal frameworks. It adopts a descriptive-analytical approach, supported by limited comparative insights, to assess the effectiveness of existing legal frameworks. The study concludes that traditional legal tools are no longer sufficient on their own to effectively protect the family’s cultural identity. Instead, the current stage requires a balanced legal approach grounded in prevention, risk assessment, secure-by-design principles, clear allocation of responsibilities among digital actors, consideration of the child’s best interests, and preservation of the family’s cultural dimension.
This chapter examines the transformative role of artificial intelligence (AI) and the Internet of Things (IoT) in strengthening decentralized finance (DeFi) frameworks for renewable energy management. In an ideal sustainable energy ecosystem, intelligent monitoring systems, transparent financing mechanisms, and decentralized governance structures operate cohesively to optimize resource utilization and promote inclusive economic growth. Such a system is expected to support real-time energy forecasting, automated funding processes, and participatory decision-making. However, existing renewable energy infrastructures remain constrained by centralized financial control, limited predictive capability, and insufficient integration of digital intelligence, thereby restricting scalability and community participation. Building on prior research in AI-driven energy analytics, blockchainbased financing, and distributed governance models, this chapter critically 298 evaluates how their convergence reshapes green energy ecosystems. While earlier studies highlight the technical efficiency of smart grids and the transparency of blockchain platforms, they often overlook the systemic integration of predictive analytics, decentralized lending, and collaborative governance through decentralized autonomous organizations. Addressing this gap, the present work proposes an integrative conceptual framework grounded in digital ecosystem theory and financial decentralization principles. Through analytical synthesis, the chapter demonstrates how IoT-enabled data streams, AI-based forecasting, and smart contracts enhance risk assessment, financing accuracy, and operational resilience. The findings underscore the importance of ethical governance, data security, and algorithmic transparency in sustaining public trust. Ultimately, this research positions AI- and IoT-enabled DeFi as a critical pathway toward equitable, resilient, and sustainable renewable energy economies.
Suresh Jaganathan, Venkatavara Prasad D, Aditya Krishna P, A Karthik
Health insurance claims processing and data storage pose challenges for security, efficiency, and transparency. Traditional distributed databases often rely on centralized management systems and enterprise-grade hardware, which can be costly and vulnerable. In contrast, Blockchain technology offers a decentralized approach to data management, ensuring transparency, security, and record immutability without requiring extensive hardware infrastructure. This paper examines the feasibility of leveraging blockchain, specifically the Internet Computer Blockchain (DFINITY), to automate health insurance claims processing and securely store insurance data. Additionally, a time-efficient algorithm is proposed to enhance querying and updating of insurance claims on the blockchain.
This chapter examines the growing convergence of peer-to-peer (P2P) energy trading and decentralized finance (DeFi) as a transformative force within contemporary energy markets. In an ideal decentralized energy ecosystem, producers and consumers engage directly through transparent, automated, and secure digital platforms that ensure fair pricing, efficient resource allocation, and inclusive financial participation. Such a system is expected to minimize intermediary dependence, enhance market responsiveness, and promote sustainable energy practices. However, existing energy infrastructures continue to rely heavily on centralized market mechanisms, regulatory rigidities, and limited financial accessibility, which constrain innovation and restrict equitable participation. Prior studies on blockchain-enabled energy trading and distributed energy resources emphasize operational efficiency and transaction transparency, while DeFi literature highlights liquidity enhancement and automated financial governance. Yet, these research streams frequently remain disconnected, offering limited insight into their integrated market 262 dynamics and socio-economic implications. Moreover, empirical evidence on regulatory adaptation, risk governance, and long-term scalability remains fragmented. Addressing these limitations, this chapter advances an integrative conceptual framework grounded in decentralized market theory and platform ecosystem models. Through analytical synthesis and selected case analyses, it demonstrates how smart contracts, decentralized exchanges, and liquidity mechanisms can enhance trust, resilience, and financial inclusion in P2P energy systems. The findings provide strategic guidance for policymakers, utilities, and innovators seeking to institutionalize decentralized, sustainable, and equitable energy marketplaces.
Abstract A Non-Fungible Token (NFT) is a digital asset representing ownership or proof of authenticity of a unique digital item. NFTs are used for various purposes, including digital art, collectibles, virtual real estate, and tokenizing unique digital or physical items, and have introduced new dimensions to digital ownership and enabled individuals to tokenize unique digital assets using blockchain technology. Although NFTs offer exciting opportunities, they suffer from interoperability, high energy consumption, piracy, ownership control, and security issues. In this paper, an idea has been proposed in which any image, pdf file, or video file can be converted to an NFT and owned. We have used the ERC-721 standards, the PoS consensus protocol, and a smart contract to address the challenges. The proposed framework provides a step-by-step guide to create, list NFTs and maintain secure ownership where metadata are stored on IPFS, which generates a unique URL. This URL is then logged on the blockchain, saving time and costs. The created NFTs are interoperable among various applications and frameworks. Smart contract has been formally verified using Slither and tested against vulnerability using Smart Contract Weakness Classification (SWC) standards. Performance of the proposed system has been measured in terms of execution cost, latency, and throughput including statistical indicators like variance and confidence intervals. Minting cost has been compared with the similar network condition.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Erik Blasch, Beth Probert, Ruaridh A. Clark, Malcolm Macdonald
Given the importance of space communications to the world’s economy, it is important to secure, authenticate, and make available data for effective business and efficient social services. A current technology supporting these services is Distributed Ledger Technology (DLT), such as blockchain and Directed Acyclic Graphs, that provide security to transactions and is being developed for other forms of transparent, secure, and zero-trust data distribution. DLT relies on consensus protocols among different agents to assess the transactions in a network. The decentralized nature of the DLT affords robustness as there is no central or single point of failure. This paper reviews DLT techniques, especially those that are applicable to the space environment and addresses metrics of importance from proof of state, work, credit, energy, and time with a focus on Proof of Inter-Satellite Evaluation (PoISE). The paper builds upon the current themes and develops metrics for comparison including conformal prediction to determine space navigation sensor performance within a space domain awareness (SDA) scenario.
Trust in climate data remains a significant barrier to effective climate action. Skepticism about data manipulation and politicization reduces confidence and hinders evidence-based policy. Existing climate data systems lack transparent verification and accessible analytical tools, limiting accountability and stakeholder engagement. This study presents a reproducible framework that applies blockchain technology to provide transparent verification, analysis, and governance of climate data. The architecture includes three layers: a data ingestion layer that standardizes verified observations, a blockchain layer that ensures immutability and provenance through proof-of-stake consensus, and a statistical analysis layer that uses deterministic methods for anomaly detection and trend evaluation. The framework was tested using 8,403 hours of temperature data from the Manila, Philippines monitoring station during 2024. Analysis identified 33 temperature anomalies ranging from 36.9 to 38.0 °C that aligned with documented April–May 2024 heat waves, confirming the ability to detect genuine meteorological extremes. Estimated transaction latency was 1–2 seconds per observation, with on-chain storage requirements of about 138 kilobytes and off-chain storage requirements of 2.1 megabytes for a 90-day deployment. Estimated energy use for the same period was approximately 0.06 kilowatt-hours, representing a 97–99 percent reduction compared with proof-of-work systems. These findings demonstrate that the proposed framework can securely record, verify, and analyze climate data while consuming very little energy. By combining blockchain immutability with transparent statistical methods, this approach directly addresses the trust deficit in climate science and provides a foundation for verifiable, reproducible, and efficient climate information systems.
Federated Learning(FL) is predominantly deployed in enterprise environments, where limited transparency and restricted auditability hinder broader adoption. Existing FL systems often suffer from opaque aggregation processes, making it unclear which model updates are accepted or discarded. Current mitigation strategies typically rely on external validators introducing additional computational and communication overhead. In this paper, we propose a novel FL framework that leverages existing Web3 technologies to enhance transparency, trust and auditability throughout the training process. The framework adopts a hierarchical architecture in which delegated managers orchestrate the FL training process within their respective federations. To mitigate adversarial and poisoning attacks, a combination of novelty detection and consensus mechanisms were employed. Model updates are encoded and broad casted to all managers, who independently evaluate their validity and those model updates that are approved by the consensus are incorporated into the global model. Additionally, a reputation score based backup mechanism is employed to ensure model generation. Extensive experiments conducted under real world scenarios demonstrate the effectiveness, resilience of the proposed framework, highlighting its potential to enable transparent FL beyond traditional enterprise setting.