Targeting the problem of road pothole detection and distance estimation under controlled experimental conditions, this paper proposes a method for pothole identification and ranging based on monocular vision and the ground-plane intersection method. The camera is calibrated using Zhang Zhengyou's checkerboard calibration method to obtain its intrinsic and extrinsic parameters along with distortion coefficients. This is combined with the YOLOv8 object detection model to achieve pothole identification. Furthermore, a pothole distance estimation model based on the ground-plane intersection method is constructed. The model is validated through sandbox experiments conducted on a smart-car platform equipped with a Raspberry Pi 5B. Experimental results demonstrate the preliminary feasibility of the proposed method for short-range pothole detection and distance estimation in a controlled sandbox environment, while further validation under more complex real-road conditions is still needed.

The development of sustainable and climate-resilient food systems increasingly relies on robust analytical methodologies capable of integrating environmental, biochemical, and management-related signals. In this study, a multi-isotope framework based on δ[2]H, δ[18]O, δ[13]C, and δ[15]N was applied to assess its capacity to discriminate between contrasting raspberry production systems and to provide chemically grounded indicators of agroecosystem functioning. Raspberry fruits (Rubus idaeus L.; cultivars Opal and Delniwa) were collected during the 2024-2025 growing seasons from two distinct systems in Romania: an organic open-field system and a rainfed agroforestry system. Stable isotope ratio analysis revealed system-dependent isotopic patterns, with the strongest differentiation observed for δ[15]N. Nitrogen isotope composition (δ[15]N) provided the strongest discrimination, with enriched values in organic fruits (2.73-9.77‱) and depleted values in agroforestry fruits (-3.01 to 0.62‱), reflecting differences in nitrogen sources and cycling pathways. Hydrogen and oxygen isotopes (δ[2]H: -60.46 to -4.62‱; δ[18]O: -6.19 to 10.41‱) were consistent with hydroclimatic variability and evaporative fractionation processes associated with soil-plant-atmosphere interactions. Carbon isotopes (δ[13]C: -28.14 to -22.62‱) provided complementary insights into plant water-use conditions. Multivariate statistical analysis supported the separation between production systems, while short-term fertilisation effects were secondary to system-level controls. The results suggest that raspberry fruits preserve an integrated isotopic fingerprint of production environment and management practices. From an analytical chemistry perspective, this work highlights the relevance of multi-isotope approaches as transferable tools for food authentication, traceability, and sustainability assessment, contributing to the broader application of stable isotope techniques across complex biological systems.

The use of time-lapse electrical resistivity tomography (ERT) in environmental studies has increased considerably in recent years. This method serves to produce 2D or 3D images of the subsurface's electrical properties, making it possible to monitor the evolution of hydrologic or biogeochemical processes over time. However, the high cost and limited flexibility of commercial equipment has restricted its accessibility. OhmPi addresses these issues by offering a cost-effective, open hardware resistivity meter designed for laboratory and mid-scale field monitoring experiments. Since its launch in 2020, OhmPi has evolved significantly in response to user requirements. The latest version, OhmPi v2024, features a redesigned architecture with a new multiplexer and an upgraded measurement board. This improves performance and accuracy while maintaining affordability, with a price tag of under €1500 for 16 electrodes. The software has been completely redesigned to offer advanced features such as remote control via a graphical interface, and an integrated web server with MQTT protocol for network integration, ensuring flexibility and ease of use. Backward compatibility has been maintained to ensure a seamless transition for existing users. Extensive testing with ground analogue circuits and small-scale field experiments has confirmed OhmPi 's improved sensitivity, stability and robustness. These upgrades further establish OhmPi 's position as a reliable and adaptable tool for environmental ERT applications, providing an affordable alternative to commercial systems without compromising on quality.

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857.

In this study, a location and altitude-based warning and collision awareness and advisory prototype was designed and two functional prototypes were developed to reduce the risk of collisions that may occur when hot air balloons cannot see each other in the air. Each prototype used a Raspberry Pi-4, 7-inch touch screen, LoRa communication module, BMP180 pressure sensor, MPU6050 accelerometer, and a GY-GPSV3 NEO-8 M Global Navigation Satellite Systems (GNSS) module. The balloons' location, altitude, and speed information were mutually broadcast via the LoRa module; the data of the intruder balloon located within a horizontal 500 m radius was visualized with an interface similar to the Traffic Alert and Collision Avoidance System (TCAS) in aircraft. The system offers a resolution advisory (RA) at distances of 100 m and closer. It produces a traffic advisory (TA) at distances between 100 and 200 m. In the tests carried out on the ground and in the hot air balloon, the system's location detection accuracy, the functionality of the warning algorithms, the detection of flight phases, and the information levels in the designed interface were analyzed. The findings have shown that this system, which leverages low-cost GNSS technology for cooperative awareness, is open to development and represents a novel application of positioning technology to fill a gap in preventing accidents that may occur after balloons come into contact with each other, making a contribution to the hot air balloon industry.

Structural cracks threaten the safety and long-term durability of civil infrastructure, yet manual inspection remains slow, subjective, and unreliable on complex surfaces. This study presents a training-centric strategy to improve a lightweight YOLOv11-N detector (2.6 M parameters) by transferring knowledge from a high-capacity YOLOv11-L teacher, without modifying the student architecture. Two semi-supervised mechanisms are investigated: pseudo-labeling and knowledge distillation. Using a crack dataset enlarged via systematic data augmentation, pseudo-labeling combines high-confidence teacher predictions with available ground-truth annotations through IoU and NMS filtering, while the distillation approach guides the student using both hard labels and teacher-derived soft signals to strengthen localization behavior. Experimental results show that both strategies enhance the baseline student model, with pseudo-labeling providing more stable training dynamics and stronger overall gains, whereas distillation primarily improves convergence behavior and sample efficiency. Ablation analyses highlight that the benefit of pseudo-labeling is data-dependent and requires a minimum pseudo-label density to achieve consistent improvements. Finally, edge-device (Raspberry 3B + and Nvidia Jetson Nano Kit) benchmarks validate that the resulting lightweight detector is suitable for deployment on resource-constrained platforms, enabling practical UAV- and mobile-oriented crack inspection across diverse edge computing tiers.

Accurate time synchronization is essential in distributed electrical signal monitoring, where phase coherence and event correlation depend on precise timing agreement between acquisition nodes. Conventional approaches often rely on a single synchronization source, typically internet-based Network Time Protocol (NTP) or GPS-disciplined clocks, which is impractical in isolated, offline, or cost-sensitive scenarios. This paper introduces an autonomous offline synchronization architecture for multi-node monitoring systems built on Raspberry Pi 5 (RPI5) platforms connected to a private Ethernet network. Instead of depending on one timing method, the system integrates several complementary mechanisms: battery-backed RTC persistence via the J5 interface, deterministic orchestration through systemd services, automated boot time recovery, chrony-managed NTP discipline, and Precision Time Protocol (PTP) hardware timestamping using PTP Hardware Clock (PHC). Synchronization performance is validated through continuous multi-day measurements of long-term stability, inter-node phase coherence, and short-term jitter. Controlled power-loss scenarios are also included to verify recovery behavior. The system maintains sub-microsecond alignment between nodes using only commodity hardware and no external time source. To further confirm inter-node timestamp alignment at the signal level, both hardware-based reference signal injection and software-based synchronized signal emulation are employed, providing ground-truth validation alongside scalable and reproducible evaluation. The results show that low-cost embedded hardware can support reliable, long-duration synchronization in fully offline installations.

Odometry estimation, which calculates the trajectory of a moving object across timeframes, is a critical and time-consuming function in SLAM (Simultaneous Localization and Mapping) systems. Although LiDAR-based sensing is most popular for outdoor and long-range applications because of its ranging accuracy, the sparsity of laser point cloud poses a significant challenge to feature extraction and matching in odometry estimation. In this paper, we investigate odometry estimation from two aspects, i.e., algorithm optimization, and system design/implementation. In algorithm optimization, we present an image feature-assisted odometry estimation scheme that leverages the richness of image information captured by a companion camera to enhance the accuracy of laser point cloud matching. This also serves as a screening mechanism to reduce the matching size and lower the computing complexity for a higher estimation rate. In addition, various schemes, such as adaptive threshold in image feature point selection, principal component analysis (PCA)-based plane fitting for laser point interpolation, and Gauss-Newton optimization for calculating the transform matrix, are also employed to improve the accuracy of odometry estimation. The performance of improved odometry estimation is verified using an existing FLOAM (Fast Lidar Odometry and Mapping) framework. The KITTI dataset for autonomous vehicles with ground truth was used as the test bench. Simulation results indicate that the translation error and rotation error can be reduced by 16.6% and 1.3%, respectively. Computing complexity, measured as the software execution time, also reduced by 63%. In system implementation, a hardware/software (HW/SW) co-design strategy was adopted, where complexity profiling was first conducted to determine the task partitioning and time-consuming tasks are offloaded to a hardware accelerator. This facilitates real-time execution on a resource-constrained embedded platform consisting of a microprocessor module (Raspberry Pi) and an attached FPGA board (Pynq Z2). Efficient hardware designs for customized DSP functions (adaptive threshold and PCA) were developed in an FPGA capable of completing one data frame in 20ms. The final system implementation met the target throughput of 10 estimations per second, and can be scaled up further.

This data article presents a labelled flow-based network traffic dataset collected from a controlled Internet of Things (IoT) laboratory environment. The dataset captures network communication generated by Raspberry Pi-based IoT nodes configured to emulate service and client roles. Traffic was recorded during normal operations and during the execution of predefined cyberattack scenarios within an isolated experimental network. Network traffic was recorded at the packet level using passive network monitoring and stored in PCAPNG format. The packet captures were subsequently processed into bidirectional network flows, producing flow records with statistical and temporal attributes derived from the observed packet exchanges. Cyberattack-related flows were labelled using the experimental ground-truth markers recorded during each attack campaign, complemented by the fixed attacker node IP address. Flows outside the marked intervals were labelled as benign and corresponded to regular device communication. This combined labelling approach reduces the potential for overlap between benign and attack activities. The dataset covers nine attack scenarios grouped into six attack categories. It is released through a structured repository containing raw packet captures, labelled flow files, and supporting metadata for flow-based IoT traffic analysis, cyberattack detection research, and optional re-labelling.

This data article presents an experimental dataset of scattered images, obtained using a low-cost, open-source, Raspberry Pi-based optical system. Each data sample includes two grayscale images of 256 × 256 resolution: the (i) scattered image, and (ii) original projected pattern as ground truth. The system projects diverse patterns using various optical diffusers with different scattering coefficients and physical thicknesses. The dataset includes geometric shapes, digits, and textures to increase variability and generalization. This variety allows the analysis of distinct scattering regimes and evaluation of image recovery models under varying optical complexities. The dataset supports deep learning research focused on inverse problems in optics. It is particularly useful for training and benchmarking image restoration models in scattering environments.

Monitoring mechanical vibration is crucial for ensuring the structural integrity and optimal performance of unmanned aerial vehicles (UAVs). This study introduces a portable and low-cost system that enables integrated acquisition and analysis of UAV vibration data in a single step, using a Raspberry Pi 4B, data acquisition (DAQ) through a MCC128 DAQ HAT card, and six accelerometers positioned at strategic structural points. Ground-based engine tests at 2700 RPM allowed vibration data to be recorded under conditions similar to those of real operation. Data was processed with a Kalman filter, a Hann window function application, and frequency analysis via Fast Fourier Transform (FFT). The first and second wing bending natural frequencies were identified at 12.3 Hz and 17.5 Hz, respectively, as well as a significant component around 23 Hz, which is a subharmonic of the propulsion system excitation frequency near 45 Hz. The results indicate that the highest vibration amplitudes are concentrated at the wingtips and near the engine. The proposed system offers an accessible and flexible alternative to commercial equipment, integrating acquisition, processing, and real-time visualization. Moreover, its implementation facilitates the early detection of structural anomalies and improves the reliability and safety of UAVs.

Monitoring experimental animals is essential for ethical, scientific, and financial reasons. Conventional observation methods are limited by subjectivity and time constraints. Camera-based monitoring combined with machine learning offers a promising solution for automating the monitoring process. This study aimed to validate and assess the performance of a machine learning model for analyzing drinking behavior in minipigs. A novel, vision-based monitoring system was developed and tested to detect drinking behavior in minipigs. The system, based on low-cost Raspberry Pi units, enabled on-site video analysis. A dataset of 5297 images was used to train a YOLOv11n object detection model to identify key features such as pig heads and water faucets. Drinking events were defined by the spatial proximity of these features within video frames. The multi-class object detection model achieved an accuracy of above 97%. Manual validation using human-annotated ground truth on 72 h of video yielded an overall accuracy of 99.7%, with a precision of 99.7%, recall of 99.2%, and F1-score of 99.5%. Drinking patterns for three pigs were analyzed using 216 h of video. The results revealed a bimodal drinking pattern and substantial inter-pig variability. A limitation to the study was chosen methods missing distinguishment between multiple pigs and the absence of quantification of water intake. This study demonstrates the feasibility of a low-cost, computer vision-based system for monitoring drinking behavior in individually housed experimental pigs, supporting earlier detection of illness.

This study introduces a custom-built uncrewed aerial vehicle (UAV) designed for precision agriculture, emphasizing modularity, adaptability, and affordability. Unlike commercial UAVs restricted by proprietary systems, this platform offers full customization and advanced autonomy capabilities.

Taro production is predominantly manual, creating a need for automated defect detection in processing lines to improve efficiency and product quality. Traditional computer vision methods lack accuracy under practical industrial production conditions, while deep learning approaches, though robust, are often computationally expensive. This study presents a modified YOLOv8n model designed for automated defect detection of taro strips in industrial production environments where real-time processing and high accuracy are critical. The proposed model integrates several architectural innovations: a Bi-directional Feature Pyramid Network (BiFPN) to facilitate improved multi-scale feature fusion, the VoV-GSCSP module to replace the conventional C2f block and reduce computational complexity, and a shared parameter detection head to further lighten the model. In addition, an embedded Wise Intersection over Union (WIoU) loss function is used to accelerate convergence and improve prediction accuracy by optimizing bounding box alignment with ground truth data. Multiple data augmentation strategies, such as noise, motion, lighting, and background, are used in training to mitigate the risk of overfitting and enhance model scalability. Both offline and online experimental results demonstrate that the optimized model achieves an average mean detection accuracy (mAP50) of over 99%, with a precision and recall of over 0.99, and 3.23 G FLOPs. These results are significantly better than those of the original YOLOv8n, which achieved an mAP50 of 94.63%, a precision of 0.955, and a recall of 0.934. When deployed on a Raspberry Pi 5, the modified model demonstrates good robustness and generalization by accurately detecting defects in previously unseen taro-strip data. These results clearly show the advantages of the proposed method in terms of accuracy and computational efficiency, making it ideal for real-time taro-strip defect detection.