This study enhances infrared communication security in nuclear power plants' secondary systems, addressing the risk of mechanical and cyber failures. A novel random address generator, employing an innovative S-box, was developed to secure IoT sensor data transmissions to gateway nodes, mitigating eavesdropping, interference, and replay attacks. We introduced a structured IR communication protocol, generating unique, encrypted addresses to prevent unauthorized access. Key-dependent S-boxes, based on a compound chaotic map system, significantly improved encryption, increasing data transmission randomness and uniqueness. Entropy analysis and reduced duplicated addresses confirmed the effectiveness of our method, with the Hash-CCM algorithm showing the highest entropy and fewest duplicates. Integrating advanced cryptographic techniques into IR systems significantly enhances nuclear power plants' security, contributing to the protection of critical infrastructure from cyber threats and ensuring operational integrity.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

We propose new methods for calculating fast VLSI arithmetic algorithms for secure data encryption and decryption in the elliptic curve cryptosystem (ECC), and also verify the proof-of-concepts by numerical expressions and through the use of HDL (hardware description language). We have developed a fast finite field multiplier that utilizes a new concept, and a finite field divider with an improved internal structure, as well as a novel fast algorithm for calculating kP, which is the most time-consuming operation in the ECC data encryption scheme. The proposed multiplier features a higher throughput per cost ratio than any other existing Galois field (GF) multiplier that can be used in the large prime finite field. Furthermore, our improved divider shows better extensibility. The developed algorithm for point multiplication decreases the steps required for iteration by half compared to that of the traditional double-and-add algorithm. It also reduces the number of field multiplications by about 19% and that of field divisions by about 9%.

This study enhances infrared communication security in nuclear power plants' secondary systems, addressing the risk of mechanical and cyber failures. A novel random address generator, employing an innovative S-box, was developed to secure IoT sensor data transmissions to gateway nodes, mitigating eavesdropping, interference, and replay attacks. We introduced a structured IR communication protocol, generating unique, encrypted addresses to prevent unauthorized access. Key-dependent S-boxes, based on a compound chaotic map system, significantly improved encryption, increasing data transmission randomness and uniqueness. Entropy analysis and reduced duplicated addresses confirmed the effectiveness of our method, with the Hash-CCM algorithm showing the highest entropy and fewest duplicates. Integrating advanced cryptographic techniques into IR systems significantly enhances nuclear power plants' security, contributing to the protection of critical infrastructure from cyber threats and ensuring operational integrity.

This paper introduces the novel design and implementation of a low-power wireless monitoring system designed for nuclear power plants, aiming to enhance safety and operational efficiency. By utilizing advanced signal-processing techniques and energy-efficient technologies, the system supports real-time, continuous monitoring without the need for frequent battery replacements. This addresses the high costs and risks associated with traditional wired monitoring methods. The system focuses on acoustic and ultrasonic analysis, capturing sound using microphones and processing these signals through heterodyne frequency conversion for effective signal management, accommodating low-power consumption through down-conversion. Integrated with edge computing, the system processes data locally at the sensor level, optimizing response times to anomalies and reducing network load. Practical implementation shows significant reductions in maintenance overheads and environmental impact, thereby enhancing the reliability and safety of nuclear power plant operations. The study also sets the groundwork for future integration of sophisticated machine learning algorithms to advance predictive maintenance capabilities in nuclear energy management.

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments.

We propose new methods for calculating fast VLSI arithmetic algorithms for secure data encryption and decryption in the elliptic curve cryptosystem (ECC), and also verify the proof-of-concepts by numerical expressions and through the use of HDL (hardware description language). We have developed a fast finite field multiplier that utilizes a new concept, and a finite field divider with an improved internal structure, as well as a novel fast algorithm for calculating kP, which is the most time-consuming operation in the ECC data encryption scheme. The proposed multiplier features a higher throughput per cost ratio than any other existing Galois field (GF) multiplier that can be used in the large prime finite field. Furthermore, our improved divider shows better extensibility. The developed algorithm for point multiplication decreases the steps required for iteration by half compared to that of the traditional double-and-add algorithm. It also reduces the number of field multiplications by about 19% and that of field divisions by about 9%.

A segment of Bacillus subtilis chromosomal DNA homologous to the Escherichia coli spc ribosomal protein operon was isolated using cloned E. coli rplE (L5) DNA as a hybridization probe. DNA sequence analysis of the B. subtilis cloned DNA indicated a high degree of conservation of spc operon ribosomal protein genes between B. subtilis and E. coli. This fragment contains DNA homologous to the promoter-proximal region of the spc operon, including coding sequences for ribosomal proteins L14, L24, L5, S14, and part of S8; the organization of B. subtilis genes in this region is identical to that found in E. coli. A region homologous to the E. coli L16, L29 and S17 genes, the last genes of the S10 operon, was located upstream from the gene for L14, the first gene in the spc operon. Although the ribosomal protein coding sequences showed 40-60% amino acid identity with E. coli sequences, we failed to find sequences which would form a structure resembling the E. coli target site for the S8 translational repressor, located near the beginning of the L5 coding region in E. coli, in this region or elsewhere in the B. subtilis spc DNA.

Modern data processing workflows frequently encounter ragged data: collections with variable-length elements that arise naturally in domains like natural language processing, scientific measurements, and autonomous AI agents. Existing workflow engines lack native support for tracking the shapes and dependencies inherent to ragged data, forcing users to manage complex indexing and dependency bookkeeping manually. We present Operon, a Rust-based workflow engine that addresses these challenges through a novel formalism of named dimensions with explicit dependency relations. Operon provides a domain-specific language where users declare pipelines with dimension annotations that are statically verified for correctness, while the runtime system dynamically schedules tasks as data shapes are incrementally discovered during execution. We formalize the mathematical foundation for reasoning about partial shapes and prove that Operon's incremental construction algorithm guarantees deterministic and confluent execution in parallel settings. The system's explicit modeling of partially-known states enables robust persistence and recovery mechanisms, while its per-task multi-queue architecture achieves efficient parallelism across heterogeneous task types. Empirical evaluation demonstrates that Operon outperforms an existing workflow engine with 14.94x baseline overhead reduction while maintaining near-linear end-to-end output rates as workloads scale, making it particularly suitable for large-scale data generation pipelines in machine learning applications.

냉각 파이프 시스템에서 누설 감지는 안전성과 운영 무결성을 유지하는 데 중요하다. 기존의 누설 감지 방법으로는 음향 방출 센서와 분광법이 있으나, 복잡한 파이핑 시스템에서는 감도, 응답 시간, 그리고 정확한 누설 위치 파악에 한계가 있다. 본 연구에서는 반응로 냉각 파이프 주변의 단열층 내에 견고한 가이드 튜브를 설치하고, 개선한 주파수 중심 이동 계산법과 새로운 신호 대 잡음비 분석을 통한 진보된 감지 기준을 결합한 새로운 누설 감지 방법을 제안하여 저비용, 고효율의 탐지기술을 구현한다. 실험 평가는 이 시스템의 효율성을 보여주며, 현재 단열 배관 누출 탐지 기술에 새로운 가치를 입증한다. 향후 연구는 기계 학습 기술을 사용하여 주요 매개 변수들을 최적화하고, 임계 주파수 변화(Δf)와 무작위로 선택된 주파수 수(N)를 조정함으로써 다양한 환경에서 시스템의 정확도와 신뢰성을 더욱 향상시킬 것이다.

본 논문은 노후 파이프 감시를 위한 저전력 무선 모니터링 시스템의 새로운 디자인과 구현을 제안한다. 시스템은 안전성과 운영 효율성을 향상시키기 위해 고안되었으며, 고급 신호 처리 기술과 에너지 효율적인 기술을 활용하여 빈번한 배터리 교체가 필요 없는 실시간 지속적 모니터링을 지원한다. 헤테로다인 주파수 변환 기술을 활용하여 고주파 음향 신호를 저주파로 변환함으로써 저전력 설계를 실현하고, 엣지 컴퓨팅을 통합하여 현장에서의 실시간 데이터 처리 및 이상 감지를 가능하게 한다. 또한 6채널 음향 센서 모듈을 통해 38kHz 주변의 여러 주파수 대역을 동시에 모니터링하고, 라운드 로빈 방식의 신호 처리로 전력 소비를 최적화 한다. 측정한 소비전류는 2-3 mA으로 채널당 333-500 uA에 해당한다.

본 논문에서는 적외선 통신의 사실상 표준인 NEC 적외선 통신 프로토콜을 분석하고 16비트 주소 공간을 사용하도록 수정하여 목표 시스템에서 최대 216개의 센서 노드를 수용 가능하도록 한다. 간단하지만 효과적인자가 재구성 유사 난수 주소 생성기를 고안하여 추가적인 암호학적 복잡성을 제고하였다. 이 생성기는 두 S-box의 이전 결과를 반복적으로 공급하는데, 이 이전 결과는 매번 S-box의 입력 시드로 작용하며 주소 암호화가 수행될 때마다 그 값들을 스스로 재구성한다. 제안된 자가 재구성 유사 난수 주소 생성기는 두 개의 16비트 256항목 S-박스 테이블과 하나의 8비트 XOR 논리 연산 쌍만을 필요로 하여 구조가 간단하다. 제안한 주소 생성기의 결과는 상용 라이브러리 기능에서 지원하는 것보다 우수한 암호학적 복잡도 특성을 나타낸다. rag_e3 버전은 2000개의 샘플 중 23개의 중복 값만을 보여주어, 상용 소프트웨어의 난수성에 비해 24.5% 감소된 우수한 결과를 나타낸다.