DOI: 10.5281/zenodo.18365189
title: Cyber-Physical Cold-Chain Risk Dataset

description: This dataset contains high-resolution operational records collected from a cyber-physical cold-chain logistics monitoring environment spanning October 2023 to October 2025. The data represent real-world refrigerated transportation workflows where physical degradation processes, logistics operations, and cyber–network conditions jointly influence shipment integrity and compliance. Measurements are recorded at an 8-minute temporal resolution, enabling fine-grained analysis of short-term dynamics and long-term degradation trends.

The dataset integrates physical sensing data, logistics context indicators, and network and integrity telemetry within a unified time-aligned structure. Each record corresponds to a rolling observation window associated with a specific logistics client and shipment segment. The dataset reflects realistic characteristics observed in operational cold-chain systems, including non-IID client behavior, class imbalance, rare extreme events, and episodic cyber anomalies, which are common challenges in distributed industrial environments.

Data Scope and Structure





Temporal coverage: 2023-10-01 to 2025-10-31




Sampling interval: 8 minutes




Observation window: 64 minutes (rolling)




Clients: Multiple independent logistics clients with heterogeneous data volumes




Shipments: Time-varying shipment identifiers per client and day



Each row represents the state of a shipment monitoring window and includes identification metadata, physical measurements, derived degradation indicators, cyber-network statistics, and outcome labels.

Feature Categories

1. Identification and Time Metadata





Client identifier




Shipment identifier




Window start and end timestamps




Window duration (minutes)



2. Physical State Measurements





Product temperature (°C)




Ambient temperature (°C)




Relative humidity (%)




Cooling system activity ratio




Power supply status




Door open ratio




Vibration intensity (RMS)



3. Thermal Degradation and Dynamics





Temperature change rate




Maximum temperature excursion




Excursion duration




Temperature exposure area (AUC)




Thermal recovery time




Rolling temperature variance




Humidity change rate



4. Logistics and Operational Context





Product category




Target temperature bounds




Remaining travel time




Distance to destination




Stop frequency




Container utilization ratio




Handling event count



5. Network Quality and Communication Performance





Packet loss rate




Uplink latency




Latency jitter




Message interval mean




Message interval variance




Out-of-order packet ratio



6. Data Integrity and Consistency Indicators





Checksum error ratio




Time synchronization offset




Sensor cross-consistency score




Physical feasibility flag




GPS feasibility flag



7. Rolling Window Descriptors





Mean, minimum, maximum, and standard deviation of temperature




Temperature trend slope



Labels and Outcome Variables

The dataset provides multiple labels to support both predictive modeling and diagnostic analysis:





Spoilage risk score: A continuous value in the range [0, 1] representing shipment degradation risk




Attack state: Categorical indicator of operational and cyber anomaly conditions




Compliance status: Discrete compliance outcome (Compliant, Near-Violation, Violation)