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Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R²). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. However, LSTM models may be too slow (1.5 h) or computation-intensive for applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m³) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m³) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. These findings suggest that AI/ML models, particularly LSTM models, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is among the most comprehensive studies on AI/ML for air pollutant calibration. We also discussed limitations, applicability to other sensors, and the explanations for good model performances. This research can be adapted to enhance air quality monitoring for public health risk assessments, support broader environmental health initiatives, and inform policy decisions. Full article

This paper presents the implementation of ORB-SLAM3 for visual odometry on a low-power ARM-based system, specifically the Jetson Nano, to track a robot’s movement using RGB-D cameras. Key challenges addressed include the selection of compatible software libraries, camera calibration, and system optimization. The ORB-SLAM3 algorithm was adapted for the ARM architecture and tested using both the EuRoC dataset and real-world scenarios involving a mobile robot. The testing demonstrated that ORB-SLAM3 provides accurate localization, with errors in path estimation ranging from 3 to 11 cm when using the EuRoC dataset. Real-world tests on a mobile robot revealed discrepancies primarily due to encoder drift and environmental factors such as lighting and texture. The paper discusses strategies for mitigating these errors, including enhanced calibration and the potential use of encoder data for tracking when camera performance falters. Future improvements focus on refining the calibration process, adding trajectory correction mechanisms, and integrating visual odometry data more effectively into broader systems. Full article

The primary payload onboard the Gaofen-3-02 (GF3-02) satellite is a C-band Synthetic Aperture Radar (SAR) capable of achieving a maximum resolution of 1 m. This instrument is critical to monitor the marine environment, particularly for tracking sea surface wind speeds, an important marine environmental parameter. In this study, we utilized 192 sets of GF3-02 SAR data, acquired in Quad-Polarization Strip I (QPSI) mode in March 2022, to retrieve sea surface wind speeds. Prior to wind speed retrieval for vertical-vertical (VV) polarization, radiometric calibration accuracy was analyzed, yielding good performance. The results showed a bias and root mean square errors (RMSEs) of 0.02 m/s and 1.36 m/s, respectively, when compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) data. For horizontal–horizontal (HH) polarization, two types of polarization ratio (PR) models were introduced based on the GF3-02 SAR data. Combining these refitted PR models with CMOD5.N, the results for HH polarization exhibited a bias of −0.18 m/s and an RMSE of 1.25 m/s in comparison to the ERA5 data. Regarding vertical–horizontal (VH) polarization, two linear models based on both measured normalized radar cross sections (NRCSs) and denoised NRCSs were developed. The findings indicate that denoising significantly enhances the accuracy of wind speed measurements for VH polarization when dealing with low wind speeds. When compared against buoy data, the wind speed retrieval results demonstrated a bias of 0.23 m/s and an RMSE of 1.77 m/s. Finally, a comparative analysis of the above retrieval results across all three polarizations was conducted to further understand their respective performances. Full article

In this paper, an EML (electro-magnetic log) integrated navigation algorithm based on the HMM (hidden Markov model) and CNLKF (cross-noise linear Kalman filter) is proposed, which is suitable for SINS (strapdown inertial navigation system)/EML/GNSS (global navigation satellite system) integrated navigation systems for small or medium-sized AUV (autonomous underwater vehicle). The algorithm employs the following five techniques: ① the HMM-based pre-processing algorithm of EML data; ② the CNLKF-based fusion algorithm of SINS/EML information; ③ the MALKF (modified adaptive linear Kalman filter)-based algorithm of GNSS-based calibration; ④ the estimation algorithm of the current speed based on output from MALKF and GNSS; ⑤ the feedback correction of LKF (linear Kalman filter). The principle analysis of the algorithm, the modeling process, and the flow chart of the algorithm are given in this paper. The sea trial of a small-sized AUV shows that the endpoint positioning error of the proposed/traditional algorithm by this paper is 20.5 m/712.1 m. The speed of the water current could be relatively accurately estimated by the proposed algorithm. Therefore, the algorithm has the advantages of high accuracy, strong anti-interference ability (it can effectively shield the outliers of EML and GNSS), strong adaptability to complex environments, and high engineering practicality. In addition, compared with the traditional DVL (Doppler velocity log), EML has the advantages of great concealment, low cost, light weight, small size, and low power consumption. Full article

Extensometers are critical instruments for accurately measuring small displacements in terrain deformation monitoring. Conventional extensometers often employ eddy current displacement sensors or differential transformer sensors, which are constrained by structural limitations that hinder their ability to meet high-precision requirements. The capacitive micro-displacement sensor has a high precision of up to 0.1 µm, but it is typically limited by its measurement range, making it unsuitable for directly capturing rapidly changing geological phenomena such as earthquakes and landslides. This range limitation can result in exceedance and measurement errors, severely compromising the reliability and timeliness of the data. To address these challenges, this study introduces a novel design for a full-range capacitive sensor extensometer powered by a high-precision ultrasonic motor. The system integrates an ultrasonic motor with high-sensitivity capacitive sensors, enhanced by a grating scale and PID control algorithms. By enabling real-time signal processing and adaptive correction, the proposed design ensures a wide measurement range while significantly improving the measurement stability and accuracy. Laboratory experiments and field validations confirm the extensometer’s performance, achieving a resolution of 2.0 × 10⁻¹¹ strain, a linearity of 0.024%, and a calibration repeatability of 0.06%. These results meet the stringent requirements of terrain deformation observation and establish the extensometer as a robust solution for micro-displacement measurements. This innovative design enhances the reliability of terrain deformation monitoring and contributes to the advancement of rock mechanics observation technologies. Full article

This paper presents a new approach to polarimetric calibration, extending classical PARC-based methods by exploring new matrix combinations that broaden the applicability of the existing techniques. By investigating alternative matrix configurations, the proposed method not only enhances the flexibility of conventional calibration approaches but also identifies matrix combinations that offer superior performance advantages. The influence of the SNR and scattering matrix error of PARC on the proposed method is evaluated by numerical simulations. The results demonstrate that the proposed method is highly accurate for PARCs with an SNR greater than 34 dB and with single-channel scattering matrix deviations less than −40 dB and four-channel scattering matrix deviations less than 0.5 dB. The effectiveness and precision of the method were validated through calibration experiments conducted on the L-band polarimetric synthetic-aperture radar aboard the LT-1 satellite. The experimental results demonstrate that the amplitude and phase estimation errors of channel unbalance are less than 0.6 dB and 4.5°, respectively, and that the crosstalk estimation error is less than −33 dB. Furthermore, the effectiveness of the method is validated through trihedral corner reflector correlation experiments and the synthesis of pseudo-color images via Pauli decomposition. The theoretical polarization characteristics of the reference target exhibited a high degree of agreement with the calibrated polarization characteristics. Full article

Gas turbines are widely used in power generation due to their efficiency, flexibility, and low environmental impact. Modeling, especially in thermodynamics, is crucial for the designer and operator of a gas turbine. An advanced and rigorous thermodynamic model is essential to accurately predict the performance of a gas turbine under on-design operating conditions, off-design or failure. Such models not only improve understanding of internal processes but also optimize performance and reliability in a wide variety of operational scenarios. This article presents the development of a thermodynamic model simulating the off-design performance of a gas turbine. The mathematical relationships established in this model allow for quick calculations while requiring a limited amount of data. Only nominal data are required, and some additional data are needed to calibrate the model on the turbine under study. A key feature of this model is the development of an innovative relationship that allows direct calculation of the mass flow of air entering the turbine and, thus, the performances of the turbine according to atmospheric conditions (such as pressure, temperature, and relative humidity) and the position of the compressor inlet guide vanes (IGV). The results of the simulations, obtained using code implemented in MATLAB (R2014a), demonstrate the efficiency of the model compared to experimental data. Indeed, the model relationships exhibit high determination coefficients (R² > 0.95) and low root mean square errors (RMSE). Specifically, the simulation results for the air mass flow rate demonstrate a very high determination coefficient (R² = 0.9796) and a low root mean square error (RMSE = 0.0213). Full article

This paper presents an adaptive sliding mode control (ASMC) scheme based on strain gauge position feedback for compensating for motion errors in a piezoelectric nanopositioning stages and ensures precise and reliable motion tracking control. The innovation of this scheme lies in calibrating the relationship between the feedback voltage of the strain gauge and the actual stage displacement. Thus, the calibrated feedback displacement is directly used as the position feedback signal for the ASMC scheme. Adaptive rules are employed to adjust the control gains, thereby eliminating the requirement to determine the upper bound of the disturbance. The stability of the ASMC strategy is theoretically proven within the Lyapunov framework. Comparative experiments under external disturbances have confirmed the superiority of the proposed control scheme. Results demonstrate that the proposed control scheme exhibits superior robust tracking performance compared to the traditional sliding mode control (SMC) scheme. Full article

The positions of millisecond pulsars (MSPs) can be determined with sub-milliarcsecond (mas) accuracy using both Very Long Baseline Interferometry (VLBI) and timing, referenced to the International Celestial Reference Frame (ICRF) and planetary ephemerides frame, respectively, representing kinematic and dynamical reference frames. The two frames can be connected through observations of common celestial objects, MSPs observed with VLBI and timing. However, previous attempts to establish this connection were unreliable due to the limited number of MSPs observed by both techniques. Currently, 23 MSPs have been precisely measured using both multiple timing and VLBI networks. Among them, 17 MSPs are used to link the two reference frames, marking a significant three-fold increase in the number of common MSPs used for frame linking. Nevertheless, six MSPs located near the ecliptic plane are excluded from frame linkage due to positional differences exceeding 20 mas measured by VLBI and timing. This discrepancy is primarily attributed to errors introduced in fitting positions in timing methods. With astrometric parameters obtained via both VLBI and timing for these MSPs, the precision of linking DE436 and ICRF3 has surpassed 0.4 mas. Furthermore, thanks to the improved timing precision of MeerKAT, even with data from just 13 MSPs observed by both MeerKAT and VLBI, the precision of linking DE440 and ICRF3 can also exceed 0.4 mas. The reliability of this linkage depends on the precision of pulsar astrometric parameters, their spatial distribution, and discrepancies in pulsar positions obtained by the two techniques. Notably, proper motion differences identified by the two techniques are the most critical factors influencing the reference frame linking parameters. The core shift of the calibrators in VLBI pulsar observations is one of the factors causing proper motion discrepancies, and multi-wavelength observations are expected to solve it. With the improvement in timing accuracy and the application of new observation modes like multi-view and multi-band observations in VLBI, the linkage accuracy of the dynamical and kinematic reference frames is expected to reach 0.3 mas. Full article

The inertial navigation system (INS) is a robust and reliable navigation strategy to provide position, attitude and velocity information of a carrier with signal acquired from inertial sensors without external assistant. However, the lack of external correction information leads to the accumulation of navigation errors, thereby limiting the reliability and applicable range of INS. In a typical INS, the accuracy and robustness of INS are mainly hindered by sensor’s measuring accuracy, installation misalignments and navigation algorithm effectiveness. To address the limitations of navigation accuracy degradation caused by sensor measurement errors, a calibration and compensation method of the gyro bias was proposed to improve the navigation accuracy. Through analyzing the influence of individual navigation errors, we found that the bias noise of gyroscope is the dominant factor in degrading the navigation accuracy. Aiming to improve the performance of navigation, a rotational modulation method is employed to eliminate the influence of gyro bias drift. Specifically, the rotational modulation could average the gyro bias to zero through the periodic rotational mechanism. Furthermore, the rotational turntable output angle can be used to correct navigation-resolved attitude results, which has a highly precise angle and can be used to calibrate the gyro drift. By compensating for gyro bias in a navigation algorithm, the performance of the navigation results is improved by a matter of one order from 7 km to less than 1 km over a period of 6 h. Several individual navigation experiments were also conducted, and the results prove the effectiveness of our method. The theoretical and experimental results show that the proposed error analysis and the compensation method are feasible and can been applied to the practical navigation system. Full article

For the purpose of reducing the cost, size, and weight of the integrated navigation system of an AUV (autonomous underwater vehicle), and improving the stealth of this system, an integrated navigation algorithm based on a propeller tachometer is proposed. The algorithm consists of five steps: ① establishing the resistance model of AUV, ② establishing the thrust model, ③ utilizing the measured speeds obtained from the AUV’s voyage trials for calibration, ④ discrimination and replacement of outliers from the tachometer measurements, and ⑤ establishing a linear Kalman filter (LKF) with water currents as state variables. This paper provides the modeling procedure, formula derivations, model parameters, and algorithm process, etc. Through research and analysis, the proposed algorithm’s accuracy has been improved. The specific values of the localization error are detailed in the main text. Therefore, the proposed algorithm has high accuracy, a strong anti-interference capability, and good robustness. Moreover, it exhibits certain adaptability to complex environments and value for practical engineering. Full article

Agro-ecosystem models are useful tools to assess crop diversification strategies or management adaptations to within-field heterogeneities, but require proper simulation of soil water dynamics, which are crucial for crop growth. To simulate these, the model requires soil hydraulic parameter inputs which are often derived using pedotransfer functions (PTFs). Various PTFs are available and show varying performance; therefore, in this study, we calibrated and validated an agro-ecosystem model using the Hypres PTF and the German Manual of Soil Mapping approach and adjusting bulk density for the top- and subsoil. Experimental data were collected at the “patchCROP” landscape laboratory in Brandenburg, Germany. The daily volumetric soil water content (SWC) at 12 locations and above ground biomass at flowering were used to evaluate model performance. The findings highlight the importance of calibrating agro-ecosystem models for spatially heterogeneous soil conditions not only for crop growth parameters, but also for soil water-related processes—in this case by PTF choice—in order to capture the interplay of top- and especially subsoil heterogeneity, climate, crop management, soil moisture dynamics and crop growth and their variability within a field. The results showed that while the impact of bulk density was rather small, the PTF choice led to differences in simulating SWC and biomass. Employing the Hypres PTF, the model was able to simulate the climate and seasonal crop growth interactions at contrasting soil conditions for soil moisture and biomass reasonably well. The model error in SWC was largest after intense rainfall events for locations with a loamy subsoil texture. The validated model has the potential to be used to study the impact of management practices on soil moisture dynamics under heterogeneous soil and crop conditions. Full article

Efficient monitoring of soil moisture is crucial for optimizing water usage and ensuring crop health in agricultural fields, especially under rainfed conditions. This study proposes a high-throughput soil moisture monitoring method that integrates LoRa-based wireless sensor networks with region-specific statistical models. Wireless sensors were deployed in the top 0–0.2 m soil layer to gather real-time moisture data, which were then combined with the Biswas model to estimate soil moisture distribution down to a depth of 2.0 m. The model was calibrated using field capacity and crop wilting coefficients. Results demonstrated a strong correlation between model predictions and actual measured soil moisture storage, with a coefficient of determination (R²) exceeding 0.94. Additionally, 83% of sample points had relative errors below 18.5%, and for depths of 0–1.2 m, 90% of sample points had relative errors under 15%. The system effectively tracked daily soil moisture dynamics during maize growth, with predicted evapotranspiration relative errors under 10.25%. This method provides a cost-effective and scalable tool for soil moisture monitoring, supporting irrigation optimization and improving water use efficiency in dryland agriculture. Full article

Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand–eye calibration is required to know the transformation between the camera and the robot flange. The calibration procedure is complex and must be conducted after each intervention (when the laparoscope is dismounted for cleaning). In the field, the surgeons and their assistants cannot be expected to do so. Thus, our approach is a procedure for a robot-based multi-view 3D reconstruction without hand–eye calibration, but with pose optimization algorithms instead. In this work, a robotic arm and a stereo laparoscope build the experimental setup. The procedure includes the stereo matching algorithm Semi Global Matching from OpenCV for depth measurement and the multiscale color iterative closest point algorithm from Open3D (v0.19), along with the multiway registration algorithm using a pose graph from Open3D (v0.19) for pose optimization. The procedure is evaluated quantitatively and qualitatively on ex vivo organs. The results are a low root mean squared error (1.1–3.37 mm) and dense point clouds. The proposed procedure leads to a plausible 3D model, and there is no need for complex hand–eye calibration, as this step can be compensated for by pose optimization algorithms. Full article

One of the challenges in calibrating millimeter-wave radar and camera lies in the sparse semantic information of the radar point cloud, making it hard to extract environment features corresponding to the images. To overcome this problem, we propose a track association algorithm for heterogeneous sensors, to achieve targetless calibration between the radar and camera. Our algorithm extracts corresponding points from millimeter-wave radar and image coordinate systems by considering the association of tracks from different sensors, without any explicit target or prior for the extrinsic parameter. Then, perspective-n-point (PnP) and nonlinear optimization algorithms are applied to obtain the extrinsic parameter. In an outdoor experiment, our algorithm achieved a track association accuracy of 96.43% and an average reprojection error of 2.6649 pixels. On the CARRADA dataset, our calibration method yielded a reprojection error of 3.1613 pixels, an average rotation error of 0.8141°, and an average translation error of 0.0754 m. Furthermore, robustness tests demonstrated the effectiveness of our calibration algorithm in the presence of noise. Full article