NAVTEX is a key component in the Global Maritime Distress and Safety System (GMDSS) that automatically transmits urgent maritime safety information such as navigational and meteorological warnings and forecasts to vessels. For the safe navigation of smart ships, this information from different systems should be shared harmoniously in the Common Maritime Data Structure (CMDS). To share NAVTEX messages as CMDS, words in NAVTEX messages must be semantically classified and placed within the CMDS structure. While traditional parsing methods are typically used to understand message semantics, NAVTEX requires natural language processing methods with deep learning due to its unstructured messages. This paper applies six types of Bi-LSTM CRF-based deep learning models to NAVTEX navigational safety messages and analyzes the results to find the most suitable model for understanding the semantics of each word in NAVTEX messages. This technique can be applied to accurately convey the meaning of NAVTEX navigational safety messages to equipment that requires navigational safety information on smart ships without human intervention.

The S-100 standard for Electronic Chart Display and Information Systems (ECDIS) uses Lua scripts to render electronic charts, yet lacks security specifications for script execution. This paper evaluates automated Static Application Security Testing (SAST) tools versus expert manual review for S-100-compliant software. Four SAST tools were applied alongside an expert review of OpenS100, a reference implementation for next-generation ECDIS. While automated tools identified numerous defects, they failed to detect 83% (19/23) of expert-identified vulnerabilities, including an unrestricted Lua interpreter flaw with a Common Vulnerability Scoring System (CVSS) score of 9.3. This vulnerability enables Remote Code Execution (RCE) via malicious portrayal catalogues, verified through Proof of Concept (PoC) development. The analysis demonstrates that SAST tools are constrained by limited maritime domain knowledge and challenges in analyzing cross-language semantic risks at the C++–Lua interface. The findings establish that identified vulnerabilities stem from specification gaps in the S-100 standard rather than isolated coding errors. These results indicate that functional safety certifications require supplementation to address design-level security risks. The evidence supports that the International Hydrographic Organization (IHO) incorporate security controls, such as script sandboxing and library restrictions, into the S-100 framework before the 2029 mandatory adoption deadline.

Maritime transportation requires reliable navigational safety communications to ensure vessel safety and operational efficiency. The Maritime Single Window (MSW) enables vessels to submit all maritime data digitally without human intervention. NAVTEX (Navigational Telex) messages provide navigational warnings, meteorological warnings and forecasts, piracy, and search and rescue information that require integration into automated MSW system. However, NAVTEX transmissions experience message corruption when Forward Error Correction (FEC) mechanisms fail, marking unrecoverable characters with asterisks. Current standards require discarding messages exceeding 4% error rates, resulting in safety information loss. Traditional human interpretation of corrupted messages creates limitations that prevent automated MSW integration. This paper presents the application of Masked Language Modeling (MLM) with Transformer encoders for automated NAVTEX message restoration. Our approach treats asterisk characters as masked tokens, enabling bidirectional context processing to reconstruct corrupted characters. We evaluated MLM against dictionary-matching and n-gram models using 69,658 NAVTEX messages with corruption ranging from 1% to 33%. MLM achieved 85.4% restoration rate versus 44.4–64.0% for statistical methods. MLM maintained residual error rates below the 4% threshold for initial corruption up to 25%, while statistical methods exceeded this limit at 10%. This automated restoration capability supports MSW integration while preserving critical safety information during challenging transmission conditions.

Safety-critical maritime navigation depends on machine-readable data products. Their digital signatures establish provenance and integrity but do not guarantee that authenticated content can be parsed safely: even passive data containing no executable code can drive allocation through file-supplied metadata. Using OpenS100, an open S-100 reference implementation, this paper asks whether passive S-102 products can compromise parser memory safety and whether operating system protection alone preserves availability. This paper analyzed the S-102 bathymetric surface specification, built a structurally valid proof-of-concept file with inconsistent grid metadata, and ran it under strong and weak operating system protection across repeated trials. The file triggered an integer overflow and heap buffer overflow without an executable payload. Under strong memory protection, no tested exploit achieved code execution; process termination dominated and the base trigger produced deterministic denial of service, a Fail-Secure outcome that still removed the navigation display. Under weak protection, existing-function reuse achieved Arbitrary Code Execution (ACE) in 14 of 200 launches (7.0%). Neither preserved safe-navigation continuity in the tested application. A four-layer application-level validation sequence applied before allocation prevented both, with no workload-dominant overhead in three desktop microbenchmarks. Passive data are therefore security-relevant parser inputs; preserving both security and availability requires validation before corruption occurs.

Shipboard gateways send many sensor channels to shore subscribers with different needs over narrow, intermittent links, yet how Message Queuing Telemetry Transport (MQTT) topic structure, broker placement, Quality of Service (QoS), publishing rate, and outage recovery should be combined has not been evaluated on real shipboard data. Traces from five liquefied natural gas carriers were replayed through four topic structures on a mock broker, on amqtt, Mosquitto, and EMQX, across two hosts, and over emulated satellite-like links, with repeated runs. Per-sensor topics cost fewer bytes per useful sample than vessel batches for subscribers needing under 44–48 % of the channels, and an analytical model predicts this break-even from the channel inventory within 0.8 %. Latency tails arose whenever the offered message rate exceeded the broker delivery rate and vanished at matched rates for every QoS level. Brokers differed in queuing or discarding under overload, so latency should be reported with the delivery ratio. Onboard bridge filtering cut forwarded bytes to 6–35 % and, on a 512 kbit/s link, 95th-percentile latency from 12 s to 0.9 s. Event-first recovery delivered events fastest only below a capacity-bounded token rate. The results give researchers a predictive cost model and operators computable gateway settings.

Shipboard data servers sample many sensor channels at a fixed interval, although many channels repeat their value or vary only within a small tolerance. This study examines how far transmission to shore can be reduced while the received stream remains fit for its intended use. Traces from five liquefied natural gas (LNG) carriers with 1300–1870 channels per vessel were cleaned of no-signal sentinels and profiled by value type and temporal behaviour. Seven policies, from full and compressed transmission to change-only, heartbeat, deadband, spike-aware, and adaptive transmission, were applied and reconstructed at the receiver. Indicators derived from the International Organization for Standardization and International Electrotechnical Commission (ISO/IEC) standards 25012 and 25024 were evaluated against acceptance profiles for alarm monitoring, condition-monitoring dashboards, and aggregate evidence. Applied to all channels, the adaptive policy removed 82.8% of the records, with a normalised error of 0.08% on numeric channels and no missed state transition. Savings ranged from 73.9% on all float channels to 98.7% on quasi-static ones. Gzip reduced bytes by 85.9% but removed no records, and compressing the reduced stream removed a further 51–78% of the compressed bytes. Staleness, rather than reconstruction error, limited the reduction, so heartbeats must be set in elapsed time.