Abstract Automatic trading systems cope with the needs of put out emotional biases from the trading operation of public assets. These systems place orders based on a price model that forecasts the future price of an asset. Those systems, developed by edge funds and institutional investors, are not available to the public, and extensive research in this field is worth the effort. In this research, we developed a short-term price model based on a neural network and used it to forecast the near-future price direction. More in depth, we introduced the feature extraction process and parametric labeling strategy to build an ML ready dataset that includes more than 400 cryptocurrencies. The model is then validated by building a trading strategy on the two most capitalized cryptos at the time of writing: Bitcoin and Ethereum. The validation uses a trading simulation that spans six years of historical data for Bitcoin and Ethereum, including both retrospective (backtest) and prospective (forward test) evaluations. The results demonstrate that the neural network-based model exhibits a very good generalization to patterns found in historical data, enabling predictions in future data within the trading simulation. In addition, a comprehensive analysis of the importance of features was conducted to enhance the interpretability and performance of the model. Finally, we test our model in a simulated trading session; it shows that, with a simple buy-only strategy plus a stop loss, the trading system limits the draw dawn during bear markets.
Smart contracts are tools with self-execution capabilities that provide enhanced security compared to traditional contracts; however, their immutability makes post-deployment fault correction extremely complex, highlighting the need for a verification layer prior to this stage. Although formalisms such as Contract Language (CL) enable logical analyses, they prove limited in attributing responsibilities within complex multilateral scenarios. This work presents a proof of concept using the Relativized Contract Language (RCL) and the RECALL tool for the specification and verification of a purchase and sale contract involving multiple agents. The study demonstrates the tool's capability to detect normative conflicts during the modeling phase. After correcting logical inconsistencies, the contract was translated into Solidity and functionally validated within the Remix IDE environment, confirming that prior formal verification is fundamental to ensuring the reliability and security of the final code.
Traditional paper-based document management has long posed challenges related to security, authenticity, and efficiency. Despite advances in digitalization, official documents remain vulnerable to forgery, loss, and unauthorized access. This thesis proposes a decentralized infrastructure for digital notarization, signing, and sharing of documents using blockchain technology. The research addresses key issues of transparency, immutability, and feasibility by defining system requirements, evaluating existing solutions, and proposing a novel architecture based on distributed systems. By combining cryptographic techniques with decentralized storage, this research contributes to the development of a more secure and efficient framework for managing official documents. The findings highlight the potential of blockchain-based digital notarization to streamline bureaucratic processes, mitigate security risks, and enhance user trust in digital document management.
Awid Vaziry, Sandro Rodriguez Garzon, Christoph Wronka, Axel Küpper
Public blockchains provide no native mechanism to verify the legal identity behind a deployed smart contract, which blocks institutional adoption and compliance with EU regulations such as MiCA and AMLR. We present KYC Seal, the first protocol that extends the EU eIDAS trust infrastructure to Ethereum smart contracts by cryptographically binding them to Qualified Electronic Seals issued by Qualified Trust Service Providers (QTSPs). The protocol realizes the full eIDAS trust chain, from the European Commission's List of Trusted Lists through Member-State trusted lists and QTSP-signed X.509 certificates down to the individual smart contract, natively on-chain. An on-chain parser extracts identity fields directly from the QTSP-signed certificate bytes at registration. Both cryptographic verifications, the QTSP issuance signature and the certificate holder's seal signature, are performed once at registration and cached as on-chain state, reducing per-interaction seal verification to a pure state check. A new P-256 elliptic-curve precompile in Ethereum (deployed December 2025) makes these one-time cryptographic steps economical, enabling trustless on-chain verification of eIDAS identities without oracles or runtime intermediaries. A reference implementation, a formal security analysis, and a gas evaluation are the subject of forthcoming work.
Carbon credit systems have emerged as a policy tool to incentivize emission reductions and support the transition to clean energy. Reliable carbon-credit certification depends on mechanisms that connect actual, measured renewable-energy production to verifiable emission-reduction records. Although blockchain and IoT technologies have been applied to emission monitoring and trading, existing work offers limited support for certification processes, particularly for small and medium-scale renewable installations. This paper introduces a blockchain-based carbon-credit certification architecture, demonstrated through a 100 kWp photovoltaic case study, that integrates real-time IoT data collection, edge-level aggregation, and secure on-chain storage on a permissioned blockchain with smart contracts. Unlike approaches focused on trading mechanisms, the proposed system aligns with European legislation and voluntary carbon-market standards, clarifying the practical requirements and constraints that apply to photovoltaic operators. The resulting architecture provides a structured pathway for generating verifiable carbon-credit records and supporting third-party verification.
The application of the standard static Geometric Brownian Motion (GBM) model for cryptocurrency risk management resulted in a systemic failure, evidenced by a 80.67% chance of loss in the 5% value-at-risk benchmark. This study addresses a critical literature gap by comparatively testing three conditional volatility models the EWMA/IGARCH baseline, an IGARCH model augmented with explicit mean reversion (IGARCH + MR), and a modified EGARCH-style asymmetric shock model within a correlated Monte Carlo VaR framework. Crucially, the analysis is applied specifically to high-beta altcoins (XRP, SOL, ADA), an asset class largely neglected by mainstream GARCH literature. Our results demonstrate that imposing stationarity (IGARCH + MR) drastically underestimates downside risk (5 percent value-at-risk reduced by 50%), while the asymmetric model (Model 3) leads to severe over-penalization. The EWMA/IGARCH baseline, characterized by infinite volatility persistence (alpha + beta = 1), provided the only robust conditional volatility estimate. This finding constitutes a formal rejection of the conventional financial hypotheses of volatility mean reversion and the asymmetric leverage effect in the altcoin asset class, establishing that non-stationary frameworks are a prerequisite for regulatory-grade risk modeling in this domain.
Overview This document presents a novel structural observation regarding the fundamental relationship between additive and multiplicative representations in number theory. The work introduces three topologically derived constants (ω₁, ω₂, ω₃) measured independently from prime number topology, which together sum exactly to 1. Using these constants, the framework predicts the first non-trivial zero of the Riemann zeta function (γ₁ = 14.134725...) with a relative error of only 0.0000043% – critically, without using γ₁ as an input parameter. The paper documents 11 independent methodological paths, all of which converge on the critical line σ = 1/2, providing a multi-faceted structural perspective on the Riemann Hypothesis. This is explicitly presented as an invitation to dialog and documentation of observed structural relationships, not a proof claim. The Three Adrian Constants The framework is built upon three fundamental constants derived from simplicial complex analysis of prime numbers: ω₁ = 0.560688544293288 (Champion frequency) – measured as E/(V+E+T) from Prime-to-Prime topology across 78,496 prime gaps ω₂ = 0.429261384222183 (Saturator frequency) – measured as T/(V+E+T) from (Prime-1)-to-(Prime-1) topology ω₃ = 0.010050071484529 (Slippage/Correction term) – computed as the residual 1 - ω₁ - ω₂ These constants emerge from counting vertices (V), edges (E), and triangles (T) in simplicial complexes constructed from prime numbers, with no prior knowledge of zeta zeros used in their derivation. Central Formula The first non-trivial zeta zero is predicted by: γ₁ = 8πω₁ + 10ω₂ω₃ - ω₁ω₃² Numerical verification: 8πω₁ = 14.091640093629991 10ω₂ω₃ = 0.043141075969808 ω₁ω₃² = 0.000056631750317 Predicted sum: 14.134724537849483 Known γ₁: 14.134725141734695 Relative error: 4.27 × 10⁻⁸ (0.0000043%) The 11 Independent Paths to σ = 1/2 Topological Path – Euler characteristic χ = V - E + T contains zeta frequencies; changes only at primes (100% verified) Spectral Path – Lomb-Scargle frequency analysis at π/2 spacing finds exactly the zeta zeros γ₁, γ₂, γ₃... Modulator Path – Structural modulator |Φ(s)| = 1 only at σ = 1/2 Interference Path – Pointer coherence |R| = 0.937 (93.8% dominance) ω₁ Measurement – Independent derivation from Prime-to-Prime topology (t-statistic = 177, p < 10⁻¹⁰⁰) ω₂ Measurement – Independent derivation from (Prime-1)-to-(Prime-1) topology No Circularity – γ₁ is predicted, not input; constants measured without spectral data Resonance Path – "Pluck model" shows primes must appear at π/2 to maintain resonance (median from 47,268 measurements) Holonomy Path – sign(H) correlates with sign(κ) in phase rotation analysis Gauss-Bonnet Path – Mean curvature ≈ 0, with 54.6% convex / 45.4% concave balance P = NP Connection – Structural compression 2ⁿ → O(n³) via projection onto (ω₁, ω₂, ω₃) The Springer Mechanism The framework includes a predictive model for prime-to-prime transitions, treating the gap between consecutive primes as a phase rotation in information space. The structure-invariant prediction formula uses: p_{k+1} ≈ p_k + (p_k/k) · (1 + Φ) where Φ describes structural resonance coupling at the stabilizer point π/2. Root Cause Analysis (5-Why Method) The paper applies systematic root cause analysis to the Riemann Hypothesis: W1: Why do all non-trivial zeros lie on σ = 1/2? → Only value where stable orthogonal interference forms W2: Why does orthogonal interference exist only there? → Fixed point of functional equation ζ(s) = χ(s)ζ(1-s) W3: Why does symmetry force zeros? → Complete balance of generative (ω₁) and resistive (ω₂) information streams W4: Why is π/2 the critical point? → Critical angle for total reflection; refractive index n = ω₂/ω₁ = 0.7656 W5: Why is this mechanism unavoidable? → Fundamental information slippage ω₃ at additive/multiplicative transition is a conservation law Three Independent Convergences (Delta Section) Bernoulli Duality – Continuum (6·B₂ = 1) parallels discrete (ω₁ + ω₂ + ω₃ = 1) normalization Holographic Projection – ω₃ vanishes as holonomy only at σ = 1/2 Phase-Neutral Closure – γ₁ emerges at phase-neutral point without being constructed Key Insights The compression term ε = 10ω₂ω₃ - ω₁ω₃² quantifies asymmetry between additive and multiplicative information At primes, additive derivative A' is orthogonal to multiplicative derivative P' (100% verified) σ = 1/2 functions as a structural horizon where information is globally conserved while local representations differ The critical line represents total reflection regime: zeros manifest as standing waves Verification The accompanying Python script ADRIAN_STRUCTURE_CLAY_VERIFICATION.py produces: TEST 1 (γ₁ Formula): PASSED (error 0.0000043%) TEST 2 (Significance): PASSED (p < 0.001, Monte Carlo) TEST 3 (Orthogonality): PASSED (100%) TEST 4 (χ Frequencies): PASSED (5/5) Acknowledged Limitations The coefficients 8π, 10, -1 are not derived from first principles Extension to γ₂, γ₃, ... requires further work This is observation, not proof Bilingual Content The document includes complete German translation (Das Adrian-Struktur-Framework) ensuring accessibility to German-speaking mathematical communities.
With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms.
This research addresses the critical vulnerabilities inherent in centralized identity management systems, which aresusceptible to single points of failure, data breaches, and profound privacy violations. To mitigate these risks, we propose and detailthe architectural design of a novel, decentralized identity framework that integrates blockchain technology with biometricauthentication and advanced cryptographic principles.The proposed methodology generates a unique, blockchain-based identity for each user by cryptographically hashing personal dataand biometric templates (fingerprint and facial recognition) using SHA-256. Identity verification for service providers is facilitatedby access tokens issued via smart contracts, which allow for authentication without direct access to sensitive biometric data. Thesystem enforces secure access by validating tokens against real-time biometric verification, with automatic revocation uponmismatch.The framework incorporates a Zero-Knowledge Proof (ZKP) mechanism to enable privacy-preserving verification, allowing usersto authenticate their identity while withholding the underlying data. Decentralized storage of hashed biometric templates is achievedthrough integration with the Internet Computer Protocol (ICP), thereby eliminating centralized points of failure. The system'sperformance is rigorously evaluated using key metrics, including the False Acceptance Rate (FAR), False Rejection Rate (FRR),token generation latency, and blockchain transaction throughput.This work's primary contribution is the development of a resilient, interoperable, and privacy-centric model for digital identity. Theresults demonstrate enhanced security and a reduced risk of identity theft, positioning this solution as a secure and scalablealternative to traditional centralized identity infrastructures.
Araz Zirar, Abdul Jabbar, Hannan Amoozad Mahdiraji
Smart contracts (SCs), appended to a blockchain, protect digital environments and their resources, processes, and structures, reducing mismatches between legal and actual rights and ownership. They enhance digital resilience by improving transparency, traceability, and trust in digital transactions. Utilising SCs requires businesses to adapt their models, revenue streams, and customer relationships. For small and medium-sized enterprises (SMEs), SCs present challenges, requiring proactive decision-making for their effective utilisation and the trade-offs involved. By employing the integrated multi-layer ISM-MICMAC-SWARA framework (Interpretive Structural Modelling, Cross-Impact Matrix Multiplication Applied to Classification, and Stepwise Weight Assessment Ratio Analysis), we explain the complex interrelationships among the challenges and propose mitigating risk management strategies. We identify technical limitations and human errors as key drivers, confidentiality and manipulation as linkage challenges, and fraud and hacking as dependence challenges. These findings highlight the interconnected nature of the challenges and their impact on SMEs, and we emphasise the need for targeted resilience strategies. Our research highlights the global dimension of SC adoption. When deploying SCs, SMEs must navigate international regulations, cross-border transactions, and cultural diversity. This global perspective informs smart contracts’ strategic, business, and organisational aspects. Our findings offer insights for academics, industry leaders, managers, and policymakers seeking to understand the potential and risks of adopting SCs in SMEs.
Currently housing finance transaction platforms face challenges of data protection and cybersecurity. Blockchain technology, with its decentralization, non-tampering and high transparency, has become an effective tool for securing transaction data. In this paper, a blockchain-based data protection scheme for housing finance transaction platform is designed, which combines the shared energy storage system and realizes the cyber security protection of the transaction platform by optimizing the PBFT consensus mechanism. Methodologically, distributed file storage technology (IPFS) and smart contracts are adopted to ensure data encryption, storage and transaction transparency. Experimental results show that the proposed scheme excels in smart contract execution time, with a maximum execution time of 0.8ms, and achieves a significant increase in TPS when the concurrent volume of transactions reaches 1,200, and the throughput of the dual-chain architecture is increased by 28% compared to the traditional single-chain architecture. In addition, the system with ITPBFT consensus mechanism reduces the communication overhead by 46.19% compared to the traditional PBFT, and the consensus delay is also significantly reduced, with an efficiency improvement of 53.61%. The study shows that the proposed optimization scheme can enhance the efficiency and reliability of data transactions while improving the security of the system.
In this study, we evaluated the returns and return volatility of a Brazilian stablecoin linked to fertilizers during periods preceding its discontinuation. In light of the safe haven literature, we also tested the correlation between this stablecoin and a traditional cryptocurrency, Bitcoin, and modeled its behavior during periods of Bitcoin’s extreme returns. In terms of methodology, we employ GARCH-family models (including DCC-GARCH) to analyze daily data from 1 December 2022 to 16 January 2025. We also employ an analysis using Large Language Models (LLMs), evaluating the stablecoin time series considering the period of its discontinuation. The results indicated that as the discontinuation date approached, the stablecoin exhibited statistically significant lower returns and higher volatility. While the DCC-GARCH indicated no correlation between the assets, we found that the stablecoin’s returns exhibited a negative relationship with Bitcoin’s extreme returns, challenging its potential efficacy as a safe haven. This article offers practical contributions for digital asset investors, indicating that even physically backed stablecoins, designed for stability, are subject to significant volatility, idiosyncratic risks, and potential discontinuation.
Perpetual futures account for approximately 93% of cryptocurrency futures trading volume, yet funding rate dynamics across fragmented markets remain understudied. We construct a high-frequency panel dataset comprising 35.7 million one-minute observations across 26 cryptocurrency exchanges (11 centralized, 15 decentralized) spanning 749 symbols over eight consecutive days. Using time-series econometrics, correlation analysis, and Granger causality tests, we characterize funding rate dynamics, market integration, and information flow. We find evidence of a two-tiered market structure: centralized exchanges (CEX) dominate price discovery with 61% higher integration than decentralized exchanges (DEX), and all significant information flow runs CEX-to-DEX with zero reverse causality. While 17% of observations exhibit economically significant arbitrage spreads (≥20 basis points), only 40% of top opportunities generate positive returns after transaction costs and spread reversals. Delta-neutral portfolio simulations reveal that successful arbitrage requires both high spreads and sufficient duration before inevitable reversals, with forced exits occurring in 95% of opportunities. The findings show that cryptocurrency derivatives markets exhibit a persistent two-tiered structure in which centralized platforms dominate price discovery while transaction costs and spread reversal risks prevent arbitrage from eliminating large mispricings between platforms, resolving the apparent paradox of substantial price fragmentation coexisting with market efficiency.
Cryptocurrency is a novel exploration of a form of currency that proposes a decentralized electronic payment scheme based on blockchain technology and cryptographic theory. While blockchain has the security characteristics of being distributed and tamper-proof, increasing market demand has led to a rise in malicious transactions and attacks, thereby exposing cryptocurrency to vulnerabilities, privacy issues, and security threats. Particularly concerning are the emerging types of attacks and threats, which have made securing cryptocurrency increasingly urgent. This paper classifies existing cryptocurrency security threats and attacks into five fundamental categories based on the blockchain infrastructure, and it analyzes in detail the vulnerability principles exploited by each type of threat and attack. Furthermore, the paper examines the attackers’ logic and methods and provides insights that enable easy reproduction of the vulnerabilities. We also summarize and evaluate existing detection and defense solutions, offering important references for ensuring cryptocurrency security. Finally, the paper discusses the future development trends of cryptocurrency.
We develop a mathematical framework to optimize leveraged staking ("loopy") strategies in Decentralized Finance (DeFi), in which a staked asset is supplied as collateral, the underlying is borrowed and re-staked, and the loop can be repeated across multiple lending markets. Exploiting the fact that DeFi borrow rates are deterministic functions of pool utilization, we reduce the multi-market problem to a convex allocation over market exposures and obtain closed-form solutions under three interest-rate models: linear, kinked, and adaptive (Morpho's AdaptiveCurveIRM). The framework incorporates market-specific leverage limits, utilization-dependent borrowing costs, and transaction fees. Backtests on the Ethereum and Base blockchains using the largest Morpho wstETH/WETH markets (from January 1 to April 1, 2025) show that rebalanced leveraged positions can reach up to 6.2% APY versus 3.1% for unleveraged staking, with strong dependence on position size and rebalancing frequency. Our results provide a mathematical basis for transparent, automated DeFi portfolio optimization.
Abstract: The non-fungible token (NFT) marketplace has rapidly evolved into a transformative space, experiencing remarkable growth in recent years. NFTs serve as digital ownership certificates linked to unique assets such as art, collectibles, and digital media, exemplifying blockchain innovation. This paper employs an exploratory, systematic literature review of Scopus-indexed sources to examine the fraud-prone dimensions of the NFT ecosystem. Using the fraud triangle framework—pressure, opportunity, and rationalization—it investigates individual and organizational drivers of deceit. The study identifies major fraud types including rug pulls, wash trading, Ponzi schemes, whitelisting, and phishing, offering insights to guide policymakers and participants in mitigating NFT-related risks. Keywords: Non-Fungible Tokens, Blockchain, Digital Fraud Vulnerabilities, Three-Factor Fraud Framework, Risk Mitigation JEL Classification Number: G32, G18, K83, K24, O33
Abstract: As Web4 evolves into a symbiotic and intelligent ecosystem, current terminology fails to capture its fundamental nature, relying on abstract descriptions or generic Web3 derivatives. This paper introduces "Myceloom" as a critical linguistic artifact and conceptual framework for this emerging infrastructure. Through a digital archaeological analysis, the study synthesizes the etymological and functional characteristics of fungal networks (mycelium) and weaving technologies (loom) to describe a web defined by decentralized collaboration and active integration. By bridging biological intelligence and technological craft, the concept of Myceloom offers a precise vernacular for the symbiotic protocols, interfaces, and governance models that will define the collaboration between human and synthetic consciousness. Keywords: Myceloom, Web4, Symbiotic Web, Digital Archaeology, Sentientification, Linguistic Infrastructure, Synthetic Consciousness, Biomimetic Networks, Human-AI Symbiosis, Swarm Intelligence, Active Inference, Collaborative Architecture
Gian Sebastian Mier Bello, Alexander Martinez Mendez, Carlos J. Barrios H., Robinson Rivas · 5 authors
In most High Performance Computing (HPC) projects nowadays, there is a lot of data obtained from different sources, depending on the project's objectives. Some of that data is very huge in terms of size, so copying such data sometimes is an unrealistic goal. On the other hand, science requires data used for different purposes to remain unaltered, so different groups of researchers can reproduce results, discuss theories, and validate each other. In this paper, we present a novel approach to help research groups to validate data integrity on such distributed repositories using Blockchain. Originally developed for cryptographic currencies, Blockchain has demonstrated a versatile range of uses. Our proposal ensures 1) secure access to data management, 2) easy validation of data integrity, and 3) an easy way to add new records to the dataset with the same robust integrity policy. A prototype was developed and tested using a subset of a public dataset from a real scientific collaboration, the Latin American Giant Observatory (LAGO) Project.
Nicolas Nicolaou, Kishori M. Konwar, Moritz Grundei, Aleksandr Bezobchuk · 6 authors
This paper introduces OPTIMUM-DERAM, a highly consistent, scalable, secure, and decentralized shared memory solution. Traditional distributed shared memory implementations offer multi-object support by multi-threading a single object memory instance over the same set of data hosts. While theoretically sound, the amount of resources required made such solutions prohibitively expensive in practical systems. OPTIMUM-DERAM proposes a decentralized, reconfigurable, atomic read/write shared memory (DeRAM) that: (i) achieves improved performance and storage scalability by leveraging Random Linear Network Codes (RLNC); (ii) scales in the number of supported atomic objects by introducing a new object placement and discovery approach based on a consistent hashing ring; (iii) scales in the number of participants by allowing dynamic joins and departures leveraging a blockchain oracle to serve as a registry service; and (iv) is secure against malicious behavior by tolerating Byzantine failures. Experimental results over a globally distributed set of nodes, help us realize the performance and scalability gains of OPTIMUM-DERAM over previous distributed shared memory solutions (i.e., the ABD algorithm [3])
While many resource-constrained networks, such as Internet of Things (IoT) and Internet of Vehicles (IoV), are inherently distributed, the majority still rely on central servers for fast authentication and data sharing. Blockchain-based solutions offer decentralized alternatives but often struggle to meet the stringent latency requirements of real-time applications. Even with the rollout of 5G, network latency between servers and peers remains a significant challenge. To address this, we introduce SWORD, a novel offline-first authentication and data-sharing scheme designed specifically for resource-constrained networks. SWORD utilizes a proximity-based clustering approach to enable offline authentication and data sharing, ensuring low-latency, secure operations even in intermittently connected scenarios. Our experimental results show that SWORD outperforms traditional blockchain-based solutions while offering similar resource efficiency and authentication latency to central-server-based solutions. Additionally, we provide a comprehensive security analysis, demonstrating that SWORD is resilient against spoofing, impersonation, replay, and man-in-the-middle attacks.
The unmanned aerial vehicle (UAV) network plays important roles in emergency communications. However, it is challenging to design reliable routing strategies that ensure low latency, energy efficiency, and security in the dynamic and attack-prone environments. To this end, we design a secure routing architecture integrating software-defined networking (SDN) for centralized control and blockchain for tamper-proof trust management. In particular, a novel security degree metric is introduced to quantify the UAV trustworthiness. Based on this architecture, we propose a beam search-proximal policy optimization (BSPPO) algorithm, where beam search (BS) pre-screens the high-security candidate paths, and proximal policy optimization (PPO) performs hop-by-hop routing decisions to support dynamic rerouting upon attack detections. Finally, extensive simulations under varying attack densities, packet sizes, and rerouting events demonstrate that BSPPO outperforms PPO, BS-Q learning, and BS-actor critic in terms of delay, energy consumption, and transmission success rate, showing the outstanding robustness and adaptability.
Federated real-time object detection using transformers in Intelligent Transportation Systems (ITS) faces three major challenges: (1) missing-class non-IID data heterogeneity from geographically diverse traffic environments, (2) latency constraints on edge hardware for high-capacity transformer models, and (3) privacy and security risks from untrusted client updates and centralized aggregation. We propose BlockSecRT-DETR, a BLOCKchain-SECured Real-Time Object DEtection TRansformer framework for ITS that provides a decentralized, token-efficient, and privacy-preserving federated training solution using RT-DETR transformer, incorporating a blockchain-secured update validation mechanism for trustworthy aggregation. In this framework, challenges (1) and (2) are jointly addressed through a unified client-side design that integrates RT-DETR training with a Token Engineering Module (TEM). TEM prunes low-utility tokens, reducing encoder complexity and latency on edge hardware, while aggregated updates mitigate non-IID data heterogeneity across clients. To address challenge (3), BlockSecRT-DETR incorporates a decentralized blockchain-secured update validation mechanism that enables tamper-proof, privacy-preserving, and trust-free authenticated model aggregation without relying on a central server. We evaluated the proposed framework under a missing-class Non-IID partition of the KITTI dataset and conducted a blockchain case study to quantify security overhead. TEM improves inference latency by 17.2% and reduces encoder FLOPs by 47.8%, while maintaining global detection accuracy (89.20% mAP@0.5). The blockchain integration adds 400 ms per round, and the ledger size remains under 12 KB due to metadata-only on-chain storage.
Large loads are expanding rapidly across North America, led by data centers, cryptocurrency mining, hydrogen production facilities, and heavy-duty charging stations. Each class presents distinct electrical characteristics, but data centers are drawing particular attention as AI deployment drives unprecedented capacity growth. Their scale, duty cycles, and converter-dominated interfaces introduce new challenges for transmission interconnections, especially regarding disturbance behavior, steady-state performance, and operational visibility. This paper reviews best practices for large-load interconnections across North America, synthesizing utility and system operator guidelines into a coherent set of technical requirements. The approach combines handbook and manual analysis with cross-utility comparisons and an outlook on European directions. The review highlights requirements on power quality, telemetry, commissioning tests, and protection coordination, while noting gaps in ride-through specifications, load-variation management, and post-disturbance recovery targets. Building on these findings, the paper proposes practical guidance for developers and utilities.