Pukhov Institute for Modelling in Energy Engineering

The paper investigates the issues of secure functioning of a two-level blockchain with a complex mixed consensus protocol — Proof-of-Work in the main blockchain (mainchain) and Proof-of-Stake in the secondary (sidechain). The principle of building such a blockchain is based on the Proof-of-Proof protocol, where a stable blockchain (mainchain) is used to ensure the stability of the sidechain, by referring the mainchain blocks to the sidechain blocks using special transactions. Such a structure allows for faster block generation in the sidechain and, accordingly, faster processing of transactions without reducing stability and without increasing the block size. In turn, such a two-level blockchain is of the greatest interest for the creation of a cascade system of state registers, which will be guaranteed to be protected against the substitution and forgery of documents. The main results of the work areexplicit analytical expressions for estimates of the probability of double spend attack on such a two-level blockchain, under the condition of an adversary in the sidechain and in the mainchain. The expressions obtained allow finding the number of confirmation blocks in the sidechain, which guarantees security against the attack with a probability no less than a preset value. Keywords: blockchain, mainchain, sidechain, cryptocurrencies, mining, Proof-of-Proof consensus protocol, double spend attack.

A method of parametric analysis of cross-correlation functions for determining the coordinates of leaks in pipelines is presented.The method is based on calculating several crosscorrelation functions of signals from sensors located at different points of the object, automatically decomposing these functions into dozens of frequency bands, and calculating the parameters of the functions that are important for diagnostics for each band and sensor position.

Power system resilience, defined as its readiness to fulfill society’s electricity needs, is analyzed by examining differing approaches to its assessment in peaceful and wartime conditions. It is noted that periodic massive attacks on power systems lead to their gradual degradation and eventual collapse. The circumstances influencing the scale of power system bombings during 20th-century wars are examined, and the current vulnerability of power systems in Eastern European countries is highlighted. The consequences of systematic missile and drone attacks on Ukraine’s power system in 2024 are assessed, and the destroyed facilities are categorized by the extent of their damage. Restoration efforts are characterized by the duration of repair works. The characteristics of power facility destruction and the duration of repair work are incorporated into an equation modeling the dynamics of generating units available for use in the current period. To assess the resilience of Ukraine’s power system, a cluster model for load modes of power facilities was applied. In this model, similar power facilities were grouped into clusters, and instead of traditional binary variables, integer variables were used to describe discrete states of startup, loading, and shutdown. Using the proposed cluster model enabled the reproduction of hourly load modes of the main types of generating equipment in Ukraine’s power system throughout 2024 and the evaluation of its resilience on a monthly basis. The presented modeling results demonstrate the adequacy of the cluster model of Ukraine’s power system when compared to actual data obtained from open publications. Key challenges in assessing the resilience of power systems functioning under systematic large-scale attacks are outlined, including anticipated attack strategies—their periodicity, scale, and targeting—dynamics of air defense system effectiveness in protecting power facilities, scheduled commissioning of protective structures with varying levels of protection, dynamics of the effectiveness of backup mechanisms, and the sufficiency of resources for repair work. The proposed cluster model is capable of addressing these pressing challenges.

Artificial Intelligence (AI) holds significant promise for advancing peace, digital resilience, and sustainable development in countries experiencing conflict, crisis, or undergoing political transition. Grounded in the Sustainable Development Goals (SDGs), this chapter presents a framework for prioritizing SDGs across the phases of crisis (emergency response, stabilization, and long-term development) and demonstrates how AI and digital technologies can operationalize sustainable development even in the most adverse conditions. By shifting from a peace-time ideal to a crisis-informed implementation model, we can transform the SDGs from aspirational blueprints into tools for resilience, recovery, and transformation. We argue that effective AI-enabled resilience begins with robust foundational infrastructure and services, upon which increasingly sophisticated digital security and decision-support layers must be built. Beyond technical measures, we emphasize the need for strategic cross-sector alignment, inclusive governance, and international cooperation coupled with a culture of cybersecurity awareness and preparedness.

A parallel method is proposed for solving mixed-integer linear programming (MILP) problems that arise in the optimization of generation capacity structure in power systems. The core of the method is the decomposition of a high-dimensional MILP problem into a master problem, which iteratively searches for the optimal generation capacity structure, and an associated set of MILP subproblems aimed at finding optimal load modes of generating capacities under various operating conditions. At each iteration, the master problem is solved using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), while the associated set of MILP subproblems is solved using the SCIP solver. Since the MILP subproblems are independent and of significantly lower dimensionality, they are solved in parallel with SCIP and require relatively modest computational resources. Computational experiments show that the proposed method finds the global optimum in 97% of cases and achieves a speedup of up to 7.84× when using 64 threads, with memory consumption growing nearly linearly. The method scales well and is suitable for deployment in high-performance computing environments, providing a flexible and sufficiently accurate tool for strategic planning of power systems focused on renewable energy sources.

Constantly growing complexity of modern computer systems has become an undeniable trend affecting diverse aspects of engineering process of named systems. Among the pivotal prompting factors, there is the comprehensiveness increase of corresponding software component. Along with that, there is still a human factor impacting system safety and resilience. Named factor has been successively addressed these days with formal methods and related instruments, e.g., formalisms, frameworks, etc. Current paper introduces the approach providing the mechanism for flexible, transparent and unambiguous variation of formal specification abstraction level, thus fostering the diminishment of a state space explosion effect existing during the process of automated formal verification through model checking. Approach is grounded on operation with the formalism of the Temporal Logic of Actions, corresponding model checker and related instruments.

An Application Programming Interface (API) is a formally defined interface that enables controlled interaction between software components, and is a key pillar of modern microservice-based architectures. However, asynchronous API changes often lead to breaking compatibility and introduce systemic instability across dependent services. Prior research has explored various strategies to manage such evolution, including contract-based testing, semantic versioning, and continuous deployment safeguards. Nevertheless, a comprehensive orchestration mechanism that formalizes dependency propagation and automates compatibility enforcement remains lacking. In this study, we propose a Compatibility-Driven Version Orchestrator, integrating semantic versioning, contract testing, and CI triggers into a unified framework. We empirically validate the approach on a Kubernetes-based environment, demonstrating the improved resilience of microservice systems to breaking changes. This contribution advances the theoretical modeling of cascading failures in microservices, while providing developers and DevOps teams with a practical toolset to improve service stability in dynamic, distributed environments.

Стійкість інфраструктури кібербезпеки є критично важливою для захисту активів підприємства, оскільки відключення електроенергії та подібні масштабні джерела невизначеності становлять суттєву й часто недооцінену загрозу. Такі збої можуть порушити функціонування ключових механізмів моніторингу та логування, що призводить до втрати критичних даних і значного простою систем. Особливий вплив відключень позначається на динамічних компонентах систем, таких як пристрої,  динамічною конфігурацією мережевого стеку, конфігурації в оперативній пам’яті, які вразливі через свою волатильну природу. Мета дослідження: У статті розглядаються різнорідні наслідки відключень електроенергії в інформаційних системах для динамічних компонентів, з акцентом на те, як ці збої впливають на ширші аспекти роботи системи безпеки, зокрема на здатність реагувати на кіберінциденти. Методи: Шляхом оцінки динамічних властивостей і формалізації низки ключових характеристик динамічних компонентів, автори прагнуть покращити маркування даних та планування реагування на інциденти, підвищуючи ефективність оцінки динамізму системи та його впливу на робочі процеси інфраструктури й здатність до кіберреагування. Результати: Запропоновано аналіз наслідків відключень електроенергії для динамічних об'єктів і окреслено підходи до підвищення стійкості систем та мінімізації ризиків у сфері кібербезпеки, пов’язаних із втратою видимості динамічних компонентів. Висновки: Динамічні компоненти інфраструктури є вразливою ланкою архітектури захисту в умовах обмеженої видимості. Розглянуті в дослідженні аналітичні моделі є корисними для виявлення потенційного впливу динамічних компонентів на видимість системи. Наразі існує потреба в розробці прикладної моделі, яка буде придатною для практичного маркування даних і планування реагування на інциденти.