Experimental performance analysis of inland vessel remote operation: A case study on the Yangtze River

The U.S. North Pacific harbors some of the densest and most diverse cold-water coral and sponge communities globally, yet quantitative data for depths below 900 m in the Gulf of Alaska and Aleutian Islands remain scarce. Most records originate from <300 m, despite ∼80% of Alaska’s seafloor exceeding 200 m depth. Using Remotely Operated Vehicle video imagery from two NOAA Ocean Exploration expeditions in 2023 (Seascape Alaska 3 and 5), we conducted a quantitative, image-based assessment of deep-sea coral and sponge communities across 15 previously unvisited sites spanning 380-3,200 m depth. From 15,531 observations, we documented 164 distinct morphotypes - 90 Porifera and 74 Cnidaria - substantially extending known distributions for multiple taxa, including five sponge genera new to Alaska and the northernmost Pacific record of the coral genus Umbellapathes . Density and diversity peaked along the margins of the Oxygen Minimum Zone (∼500-1,800 m; O 2 ≤1.43 mg L -1 ). Additionally, eight high-density aggregations were identified using quantitative spatial criteria, reaching densities up to 20.68 individuals m -2 . Community composition was structured by a hierarchy of drivers, with oceanographic setting explaining most variation. These findings highlight the importance of oceanographic context in structuring deep-sea biodiversity and provide a baseline for future ecological, taxonomic, and conservation research in North Pacific deep-sea ecosystems. Understanding the distribution and drivers of cold-water coral and sponge communities is vital for anticipating ecosystem responses to future ocean scenarios and for designing effective management strategies to safeguard these vulnerable deep-sea habitats. • Quantitative assessment of deep-sea corals and sponges across 380-3,200 m in Alaska • Fifteen previously unvisited sites surveyed using NOAA Ocean Exploration data • Eight high-density VME habitats identified using quantitative spatial criteria • OMZ drives abundance peaks while oceanographic regimes structure communities

The rapid advancement and deployment of next-generation nuclear reactors, which incorporate machine learning, artificial intelligence decision making, autonomous control, and remote operations, introduces new cybersecurity challenges not encountered by traditional light water reactors. Although these advanced reactors offer improved efficiency, scalability, and cost reduction, they also introduce vulnerabilities due to the integration of complex, interconnected digital systems. As a result, the cybersecurity landscape for advanced reactors requires a more dynamic and resilient architecture. This research proposes a framework for a vulnerability discovery and management tool for advanced reactors. The tool autonomously maps reactor networks and stores critical cybersecurity information, including the software bill of materials and vulnerability assessments using Common Vulnerabilities and Exposures. An emulated network environment, constructed using Minimega, replicates the logical topology of advanced reactor communication systems. This environment is paired with a high fidelity, full scope generic pressurized water reactor (GPWR) simulator that enables a controlled study of how cyber asset failures or compromises impact plant operations. The resulting test environment supports comprehensive penetration testing and validation of the proposed vulnerability discovery tool. The tool provides insight into security weaknesses and enables the evaluation of network architectures during both predeployment design and postdeployment operational phases. Initial testing shows that the framework automates network mapping and successfully identifies software and configuration vulnerabilities

A wideband, standalone, frequency agile, and completely reconfigurable Low-Level Radio Frequency (LLRF) controller has been designed in a hybrid analog-digital electronics domain. While the LLRF algorithm is entirely digital, an analog front-end (AFE) is used to up/downconvert various signals and enhance the operational bandwidth of the controller. The output of this controller is used to drive the high-power RF system to generate a high voltage gradient across the gap in the resonating RF cavity in a particle accelerator. By adjusting the phase of the RF drive, the resonating cavity can time-bunch a continuous charged beam and enhance the kinetic energy of the beam particles. The strength of this controller lies in the fact that the same hardware is utilized irrespective of the type or operational mode of the resonant RF cavity. It can maintain the required RF cavity field gradient using multiple feedback control loops simultaneously in Generator-Driven Resonator (GDR) or Self-Excited Loop (SEL) modes, respectively, for Normal-Conducting (NCRF) and Superconducting (SCRF) types of RF cavities. This paper outlines a simple mathematical basis for the operation of this setup and further elaborates the construction of a universal instrument, including the design of a novel frequency tracking, feedback controls and advanced signal-processing algorithms. The design leverages a commercial System-on-Chip Field-Programmable Gate Array (SoC-FPGA) platform with integrated fast analog-to-digital and digital-to-analog converters, minimizing costs and simplifying development. An Experimental Physics and Industrial Control System Input/Output Controller (EPICS-IOC) server-client application with custom drivers enables remote operation and reconfiguration of the instrument. It can be configured for any RF frequency within the range 1–50 MHz, whereas the AFE hardware supports extended bandwidths up to 200 MHz. This universal architecture aims to solve the problem associated with maintenance and upkeep of different cavity-specific control hardware in multi-accelerator facilities. Hence, the presented system has successfully been tested with different RF structures ranging from 12.125 to 97 MHz in both GDR and SEL modes, providing overall stability better than 0.5° in phase and ≤ 1.2% in amplitude. Beam test results with 12.125 MHz Multi-Harmonic Buncher, the 18.2 MHz Table-Top Cyclotron and the 97 MHz SCRF cavities of Superconducting Linear Accelerator of IUAC, New Delhi have been discussed in detail.

The Intelli Spy Robot with Live Streaming and Location Surveillance is an advanced autonomous system designed to perform real-time environmental monitoring, hazard detection, and remote surveillance in areas that are inaccessible or dangerous for human personnel. The proposed system integrates an ESP-32 microcontroller with multiple sensing units including a fire sensor, gas sensor, and metal sensor, alongside an ESP-CAM module for high-definition live video streaming, and a GPS module for precise location tracking. The robot is powered by a dedicated rechargeable battery unit (RPS) ensuring uninterrupted field operation. Upon detection of hazardous conditions such as fire outbreaks, toxic gas leakage, or concealed metallic objects, the system instantly triggers audio alerts via a buzzer and displays critical data on an onboard LCD screen. All processed information is simultaneously transmitted to a centralized IoT platform, enabling remote operators to monitor, analyze, and respond to threats in real time from any geographic location. The system eliminates human risk in hostile environments by providing continuous, automated, and intelligent surveillance. It is designed to be deployable in military zones, disaster-affected areas, industrial sites, and border patrol regions. The integration of robotics, embedded intelligence, and cloud connectivity makes this system a comprehensive solution for modern surveillance challenges. This project represents a significant step forward in combining embedded systems, computer vision, wireless communication, and sensor fusion for autonomous field operations.

This project presents the design and development of a smart line follower robot using IoT system. The robot is capable of autonomously following a predefined path using infrared (IR) sensors, ensuring accurate navigation without human intervention. In addition to line-following functionality, the system enhances security by incorporating a theft detection mechanism. The theft detection module uses sensors such as motion sensors or IR interruption sensors to detect unauthorized access or disturbances. When a theft attempt is detected, a buzzer is activated to provide an immediate local alert. Simultaneously, the system sends realtime alert messages to the user via Telegram, ensuring instant notification regardless of location. To further enhance monitoring, the project includes a live video streaming feature, allowing users to visually monitor the environment in real-time through an IoT platform. This enables better surveillance and quick decision-making during security breaches. The system operates without traditional remote control, relying entirely on autonomous behaviors and IoTbased communication for monitoring and alerts. The main components used in this project include a microcontroller (such as Arduino or Node MCU/ESP8266), IR sensors for line detection, motion or proximity sensors for theft detection, a buzzer for alerts, a camera module for live streaming, motor drivers, and DC motors for robot movement. The integration of these components creates a cost-effective and efficient smart robotic system suitable for industrial automation and security applications. Overall, this project demonstrates the effective combination of robotics and IoT technologies to achieve autonomous navigation, realtime monitoring, and enhanced security without the need for manual remote operation.

Abstract The increasing integration of automation in warehouse operations has created a demand for intelligent material handling systems capable of improving efficiency and reducing human intervention. Conventional transportation methods depend largely on manual labor, leading to delays, increased operational costs, and susceptibility to errors. This paper presents the design and implementation of an Internet of Things (IoT)-based smart warehouse transport robot utilizing an ESP32 microcontroller and a Mecanum wheel drive system. The ESP32 serves as the central control unit, enabling wireless communication and real-time monitoring through a web-based interface. The Mecanum wheel configuration provides omnidirectional mobility, allowing the robot to navigate confined spaces with high precision. The system integrates motor drivers, sensors, and IoT protocols to achieve efficient remote operation and control. Experimental evaluation demonstrates that the proposed system improves maneuverability, reduces manual workload, and enhances operational efficiency in warehouse environments. The developed prototype offers a scalable and cost-effective solution for smart logistics and automated material transport. Keywords IoT, ESP32, Smart Warehouse, Mecanum Wheels, Mobile Robot, Automation, Material Handling.

Maritime Autonomous Surface Ships (MASS) introduce new safety challenges associated with complex cyber–physical systems, distributed control architectures, and remote supervisory operation. Traditional maritime risk assessment approaches primarily focus on component failures and historical accident data and may therefore be insufficient for capturing interaction-driven hazards arising in autonomous vessel systems. This study develops a parallel and architecturally synchronized risk assessment framework integrating System-Theoretic Process Analysis (STPA) and Fault Tree Analysis (FTA) for the safety assessment of MASS. Within the proposed framework, both analyses evolve concurrently within a shared system architecture, enabling explicit traceability between hazards, unsafe control actions, causal scenarios, failure events, and accident propagation pathways. The framework is demonstrated through a case study of a Degree of Autonomy 3 short-sea freight vessel operating in a high-density North Sea traffic environment. The integrated analysis identifies dominant accident pathways related to perception degradation, communication disturbance, authority coordination conflicts, maneuver execution deviations, and incorrect collision-risk assessment. The results illustrate how the framework supports structured safety assessment of MASS while preserving traceability between systemic control deficiencies and accident propagation mechanisms.

The rapid growth of electric vehicles (EVs) has increased so the need for efficient and reliable Battery Management Systems (BMS) to ensure safe and optimal battery operation. This paper presents the design and implementation of a smart BMS for a load-carrying electric vehicle powered by a 60 V, 60 Ah Lithium Iron Phosphate (LiFePO₄) battery. The system utilizes a 32-bit microcontroller integrated with a smart BMS to monitor key battery parameters such as voltage, current, and temperature. The proposed system incorporates features such as fault detection and alert mechanisms, adaptive on-board charging, and an LCD-based display for real-time monitoring. In addition, a multi-mode access system using NFC/Wi-Fi card, remote control, and key-based operation is implemented along with an anti-theft alarm to enhance vehicle security. The EV also includes a three-level gear system and reverse operation for improved usability. The system ensures reliable performance, enhanced safety, and efficient energy utilization. The proposed smart BMS provides a practical and effective solution for modern load-carrying electric vehicle applications.

Magnetic soft fiber robots have demonstrated significant potential in minimally invasive medicine due to their superior navigability in confined lumens. However, integrating functional end-effectors into these systems often leads to control coupling, where the actuation of distal modules inadvertently interferes with the robot's navigation posture. Herein, we propose a decoupled actuation strategy by integrating a photothermal MXene/reduced graphene oxide (RGO) gripper onto a magnetically steerable fiber robot. The distal gripper features a bilayer architecture, comprising a functional MXene@RGO/elastomer composite layer and a passive substrate layer. Leveraging the high photothermal conversion efficiency of MXene nanosheets, the gripper generates significant bending deformation driven by the thermal expansion mismatch under near-infrared (NIR) irradiation. This optical actuation mechanism is physically independent of the magnetic steering system, effectively eliminating signal crosstalk. We demonstrate that the fiber robot can perform precise magnetic navigation through complex tortuous paths and execute on-demand optical grasping of small objects without compromising its structural flexibility. This work presents a robust material interface-based solution for enabling multimodal control in soft robotics, expanding their capabilities for precise remote operations in restricted environments.

The international health crisis that struck the world in early 2020 has resulted in an unprecedented systemic outage and caused the macroeconomic ecosystem to attempt an abrupt and incoherent shift to remote operation. Although there is extensive literature on how damaging such recessions can be on aggregate employment and other conventional industries, one key reason behind this study is to find out the reasons why some digital infrastructure companies became shock-absorbers and recorded unprecedented growth. Considering Zoom Video Communications, this paper explores the niche mechanics that gave a niche enterprise software a chance to monopolize this new macroeconomic demand. The research methodology used in the study is literature review of institutional articles, combined with company-level financial reports. In addition, this research performs a totality analysis of data on it based on the U. S Census Bureau Business Dynamics Statistics (BDS) in order to quantify job reallocation among sectors. The results also indicate that the crisis was a violent accelerant of reallocation of resources destroying employment in the physical sectors, and increasing the information sector considerably. In conclusion, this paper derives that outstanding corporation achievement during a harsh crisis cannot be simply ensured by the efficiency of the product; but by the ideal timing of a frictionless service hitting a colossal exogenous shock.

This study focuses on developing an unmanned forklift steering control system utilizing the Internet of Things (IoT) for remote operations. The objective was to integrate a Proportional Integral Derivative (PID) control system to accurately measure the rotation speed and angular position of the steering wheel. Sensor data is refined using the Kalman Filter technique (KF), which effectively reduces noise and improves the accuracy of steering angle data, leading to smoother and more stable steering control during forklift turns. This study utilizes a rear-wheel steering configuration for autonomous control via microcontrollers and IoT-based remote operation systems. Kinematic analysis governs motion and steering by calculating the Instantaneous Center of Rotation (ICR) based on the vehicle's linear and angular velocities. A limit switch facilitates accurate angular position tracking and ensures system consistency for steering wheel homing. The feedback control system employs a wheel encoder and gyroscopic sensors with a PID controller to maintain precise steering through motor speed adjustments that correct errors. Experimental results demonstrate the ability of the control system to accurately adjust and follow the desired trajectory. Initially, during the turning phase, the vehicle may not adjust its direction in time, but the control system subsequently adjusts to ensure accurate path following. The angular error is high when the vehicle reaches a 90-degree corner but decreases to near zero as it moves along the straight path, indicating effective alignment with the desired direction. Control input shows linear velocity decreases near corners for smoother turns and increases on straight paths, while turning rate is high at corners and decreases on straight paths, reflecting precise steering response. Lateral error increases during turns but decreases to near zero on straight paths, demonstrating the control system's ability to maintain proximity to the desired path.

General Background: Hygiene of baby feeding equipment is essential due to infants’ low immunity and high risk of bacterial contamination. Specific Background: Conventional cleaning and drying methods are often ineffective and less hygienic, while existing sterilization systems lack integrated remote monitoring. Knowledge Gap: Previous studies have not fully integrated UV-C sterilization, heating-based drying, and Internet of Things control in a single system. Aims: This study aims to develop an IoT-based UV-C sterilization device using ESP32 to monitor and control sterilization and drying processes. Results: The drying system reduced moisture to a completely dry state, while sterilization testing showed bacterial counts below SNI thresholds. The IoT system operated up to 60.2 km with an average delay of 4.86 seconds, and the DS18B20 sensor achieved an average error of 0.25°C. Novelty: The integration of UV-C sterilization, heating drying, and long-distance IoT control with real-time monitoring represents the main contribution. Implications: This system provides a practical and hygienic solution for maintaining baby equipment cleanliness with reliable remote operation Keywords: Uv C Sterilization, Internet of Things, Esp32, Baby Equipment, Temperature Sensor Key Findings Highlights Drying process achieves complete moisture removal after extended duration Bacterial testing confirms compliance with microbiological safety standards Remote operation remains stable across multiple long-distance locations

An integrated STPA-STRIDE-BN framework for cybersecurity risk analysis: A case study of ship remote pilotage operations

Efficient and environmentally friendly energy use for base transceiver stations (BTS) in remote areas is essential for telecommunication network development. This study simulates and compares two BTS configurations: a conventional grid-powered system and a hybrid solar-grid system, focusing on energy efficiency, operational cost, and carbon emissions. The simulation was conducted over a one-year operational period using Python-based modeling with realistic input parameters. The results indicate that the hybrid system can supply approximately 74% of the annual energy demand using solar power, achieving 24.4% operational cost savings and reducing carbon emissions by 73% compared to the grid-only system. These findings confirm that the hybrid BTS system is a feasible and sustainable solution to support telecommunication expansion in remote areas with lower cost and environmental impact.

Cold-water corals (CWCs) are key ecosystem engineers in deep-sea habitats, yet their distribution in the Gulf of Naples remains poorly known. Here, we applied a Maximum Entropy (MaxEnt) model with high-resolution environmental predictors to investigate the fine-scale suitability of scleractinian CWCs within the Gulf of Naples and adjacent areas. Presence records were derived from Remotely Operated Vehicles (ROV) video analyses, while predictors included bottom current velocity from a Regional Ocean Modeling System (ROMS) simulation and geomorphological variables from multibeam bathymetry (bathymetric position index, slope, roughness, aspect, and backscatter). The model predicted ∼0.43km2 of suitable habitat (suitability index >0.6) corresponding to 0.09% of the entire study area, mainly along canyon walls and elevated seabed features of the Dohrn Canyon. Additional suitable areas were identified in the deeper canyon sectors and south of Ischia Island. Current velocity at the bottom influenced the most our model, with high suitability values obtained from 0.10 to 0.18m/s, suggesting these as favorable conditions for sediment removal and food supply. The variable response curves documented that Bathymetric position index and roughness contributed to the model, with preferences for elevated seabed features and heterogeneous seafloor topography. These findings highlight the role of bottom current velocity and topographic complexity in shaping CWCs habitats in the study region and reveal unexplored areas with high potential for coral occurrence. Model outputs provide a scientific basis for Natura 2000 site designation and support conservation and restoration strategies for vulnerable deep-sea ecosystems in the area.

Abstract Rosalinda incrustans (Kramp, 1947) is a poorly known, deep-sea capitate hydrozoan whose biology, morphology, and ecological traits have remained largely obscure due to the rarity of collected material and its small, inconspicuous epibiontic habitus. Since its original description, the species has been collected only twice and recorded two additional times with Remotely Operated Vehicles (ROVs), invariably observed as an epibiont of the crab Anamathia rissoana (Roux, 1828). Here, we report a detailed morphological description of the species based on newly collected material, adding information from in situ pictures, as well as from ecological, structural, and histological perspectives. For the first time, the species is documented growing on substrates other than the crab, including polychaete tubes and scleractinian skeletons, revealing a broader host range and a non-obligate association with A. rissoana . These findings, supported by the analysis of an extensive ROV archive, allow a reevaluation of the species’ life habit, its symbiotic relationships, and its role as an early coloniser of deep-sea hard substrates. The combination of new host records, morphological plasticity of the hydrorhiza, and overlapping diagnostic traits also suggests a possible synonymy with the Atlantic species Rosalinda williami (Totton, 1949).

Abstract Dissolved oxygen (O 2 ) has been declining in coastal and open‐ocean regions, including the Santa Barbara Channel (SBC) in the Southern California Borderland. While previous studies have investigated the biogeochemical consequences of declining O 2 in the SBC, its spatial distribution and temporal variability in the region remain poorly characterized. Because O 2 dynamics shape the cycles of redox‐sensitive elements such as iron (Fe), improved characterization is essential for understanding regional biogeochemistry. We integrate observations of dissolved O 2 spanning 1985–2023 using Autonomous Underwater Vehicle Sentry and the Remotely Operated Vehicle Jason . This new data set provides the first spatially resolved characterization of dissolved O 2 across the SBC and Santa Barbara Basin. Above the Basin's sill, O 2 exhibits a vigorous seasonal cycle, with low concentrations during springtime upwelling superimposed on a long‐term declining trend linked to decadal variability. This contrasts with the seasonal cycle below the sill, where higher O 2 concentrations are observed in spring, reflecting reoxygenation by intermittent flushing events, which have weakened and become less frequent over time. Severe hypoxia and anoxia are widespread in the deep Basin, punctuated by coherent pockets of oxygenated waters, likely the remnants of flushing events. By combining reconstructions of the O 2 distribution with a benthic Fe flux parameterization, we estimate the release of ∼24,000 tons of Fe annually from the sediment—far exceeding the Fe requirements of net primary production in the region. Our study provides new insights into O 2 dynamics and their implications for biogeochemistry in the SBC, with relevance to global coastal deoxygenation.

The study presents the development and verification of an adaptive data transmission system for controlling unmanned surface vehicles (USVs) in unstable communication channels. The work aims to overcome the limitations of existing technologies, which include LTE networks and satellite systems that fail to deliver stable service quality for USV remote control operations. The proposed adaptive routing algorithm evaluates communication channel status through three vital indicators, which include delay, packet loss, and availability. The algorithm selects the best channels according to changing weight parameters. Experimental results confirmed a significant reduction in data transmission delays, stable real-time video streaming with a delay of 1–4 seconds, and a reduction in packet loss to below 2 %. In addition, the system implements the use of modern video coding standards (e.g., H.265) and secure VPN channels, which increase bandwidth efficiency and the level of cybersecurity. The results confirm the practical suitability of the proposed system for USV operation in real marine conditions, as well as its potential for use in critical scenarios that require stable, low-latency communication.

Offshore oil, gas, and emerging renewable energy operations are increasingly exposed to severe fire and explosion risks due to the handling of large volumes of flammable materials, complex processing systems, and remote operating environments. Effective offshore emergency response therefore depends critically on the availability of specialized firefighting vessels capable of rapid deployment, high operational stability, and sustained suppression performance. This review examines the optimization of Trimaran firefighting vessels with specific reference to operational demands within the Gulf of Guinea, a region characterized by intensive offshore activity, relatively benign sea states, and growing safety and environmental concerns. The paper synthesizes existing literature on offshore firefighting requirements, vessel speed and response time, hydrodynamic resistance, stability, seakeeping, and firefighting system integration, with emphasis on the inherent limitations of conventional Monohull designs. Particular attention is given to the trade-offs between speed, fuel efficiency, stability, and fire monitor effectiveness, which often constrain Monohull performance during high-intensity firefighting operations. The review highlights how multihull configurations, especially Trimarans, offer superior hydrodynamic efficiency, enhanced transverse stability, increased deck area, and improved operational safety through the decoupling of resistance and stability functions. In addition, the study discusses regulatory frameworks governing maritime fire safety, common offshore fire scenarios, and the performance characteristics of various firefighting agents and systems relevant to offshore applications. A comparative assessment of Monohull, catamaran, Ttrimaran, and alternative advanced hull forms demonstrates the Trimaran’s suitability for high-speed, cost-effective, and stable firefighting operations in the Gulf of Guinea. The review identifies a clear research gap in region-specific optimization of Trimaran firefighting vessels and provides a structured foundation for future design, numerical analysis, and performance optimization studies aimed at improving offshore emergency response capability, safety, and sustainability.