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Traditional methods for object detection typically necessitate a substantial amount of training data, and creating high-quality training data is time-consuming. We propose a novel Few-Shot Object Detection network (FSODv2) in this paper that aims to detect objects from previously unseen categories using only a few annotated examples. Attention RPN, Multi-Relation Detector, and Contrastive Training strategy are central to our method (Fan et al., in: CVPR, 2020), which exploit similarity between few shot support set and query set to detect novel objects while suppressing false detection in the background. We also contribute a new dataset, FSOD-1k, which contains 1000 categories of various objects with high-quality annotations to train our network. To the best of our knowledge, this is one of the first datasets designed for few-shot object detection. This paper improves our FSOD model through well-designed model calibration in three areas: (1) we propose an improved FPN with multi-scale support inputs to calibrate the multi-scale support-query feature matching by exploiting multi-scale features from the same support image with different input scales; (2) we introduce a support classification supervision branch to calibrate the support feature supervision, aligning to the query feature training supervision; (3) we propose backbone calibration to preserve prior knowledge while alleviating backbone bias toward base classes by employing classification dataset to help our model calibration procedure, where such dataset has previously only been used for pre-training in other related works. Besides, we propose a Fast Attention RPN to improve evaluation speed and save computational memory during inference. Once trained, our few-shot network can detect objects from previously unseen categories without further training or fine-tuning, resulting in new state-of-the-art performance on different datasets in the few-shot setting. Our method is general in scope and has numerous potential applications. The dataset link is https://github.com/fanq15/Few-Shot-Object-Detection-Dataset. [ABSTRACT FROM AUTHOR]

A low-cost, tri-axial 50 Hz magnetic field monitoring sensor was designed, calibrated and verified. The sensor was designed using off-the-shelf components and commercially available coils. It can measure 50 Hz magnetic fields originating from high-voltage power lines from 0.08 µT to 364 µT, divided into two measurement ranges. The sensor was calibrated both on-board and in-lab. The on-board calibration takes the circuit attenuation, noise and parasitic components into account. In the in-lab calibration, the output of the developed sensor is compared to the benchmark, a narrowband EHP-50. The sensor was then verified in situ under high-voltage power lines at two independent measurement locations. The measured field values during this validation were between 0.10 µT and 13.43 µT, which is in agreement with other reported measurement values under high-voltage power lines in literature. The results were compared to the benchmark, for which average deviations of 6.2% and 1.4% were found, at the two independent measurement locations. Furthermore, fields up to 113.3 µT were measured in a power distribution sub-station to ensure that both measurement ranges were verified. Our network, four active sensors in the field, had high uptimes of 96%, 82%, 81% and, 95% during a minimum 3-month interval. In total, over 6 million samples were gathered with field values that ranged from 0.08 µT to 45.48 µT. This suggests that the proposed solution can be used for this monitoring, although more extensive long-term testing with more sensors is required to confirm the uptime under multiple circumstances. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) rely heavily on sensors to perceive their surrounding environment and then make decisions and act on them. However, these sensors have weaknesses, and are prone to failure, resulting in decision errors by vehicle controllers that pose significant challenges to their safe operation. To mitigate sensor failures, it is necessary to understand how they occur and how they affect the vehicle's behavior so that fault-tolerant and fault-masking strategies can be applied. This survey covers 108 publications and presents an overview of the sensors used in AVs today, categorizes the sensor's failures that can occur, such as radar interferences, ambiguities detection, or camera image failures, and provides an overview of mitigation strategies such as sensor fusion, redundancy, and sensor calibration. It also provides insights into research areas critical to improving safety in the autonomous vehicle industry, so that new or more in-depth research may emerge. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid inductor‐current sensor with fast response and high accuracy using online calibration for 25 MHz BUCK DC‐DC converter. The proposed inductor‐current sensor combines the high accuracy of the senseFET sensing method with the lossless and real‐time response characteristics of the RC filter technique. Comparing to exist techniques, the proposed calibration scheme can make filter coefficient converge even in unsteady state, which improve its effectiveness in practical application. Moreover, the proposed inductor‐current sensor utilizes sample‐based senseFET‐based current sensing to offer calibration reference, which avoid the use of high bandwidth amplifier. Digital current balance control is seamlessly integrated into the 25 MHz 2‐phase digital adaptive time‐controlled (AOT) BUCK DC‐DC converter through the utilization of the proposed hybrid inductor‐current sensor. The 2‐phase BUCK DC‐DC converter is implemented in a standard 0.18‐µm CMOS process. After online calibration, proposed hybrid inductor‐current sensor outputs a full‐wave waveform with high accuracy and low propagation delay of only 9 ns in post‐layout simulation. [ABSTRACT FROM AUTHOR]

Some applications, such as aerospace testing and monitoring the operating conditions of equipment on space missions, require mechanical sensors capable of measuring accelerations at frequencies of several hundred hertz. For such measurements, optomechanical sensors can be used, providing the ability to measure accelerations without calibration. To enable such measurements, improved designs of drum-type sensors with the assigned performance have been elaborated. Such designs make it possible to provide the necessary levels of natural frequencies for optomechanical sensors and eliminate crosstalk. Using mathematical modeling, the dependencies of the mechanical characteristics of the proposed types of acceleration sensors versus their parameters were obtained. The use of such sensor designs ensures their compactness, making their manufacturing more technologically sound and suitable for use, in particular, in space missions. [ABSTRACT FROM AUTHOR]

To address the increasing demand for diurnal information on trace gases and aerosols, a series of geostationary (GEO) satellite programs called GEO‐constellation have been initiated, with the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) onboard Geostationary Korea Multi‐Purpose Satellite 2B (GK2B). To assess the sensor performance of GEMS in orbit, the current work suggests employing an inter‐calibration methodology involving the Advanced Meteorological Imager (AMI) aboard its twin satellite, GK2A. Twin satellites have a significant advantage in obtaining collocation data sets across diverse spatiotemporal, angular, and atmospheric conditions, enabling rigorous collocation criteria effectively reducing mismatch uncertainty. The results present robust correlation coefficients over 0.99, revealing the current calibration characteristics of the sensors. This research emphasizes the advantages of the GEO‐GEO inter‐calibration, particularly the capability of analyzing spatial and temporal dependencies. These findings confirm the mutual benefit of utilizing the sensors in similar configurations, highlighting their importance for future satellite monitoring endeavors. Plain Language Summary: Understanding diurnal changes in air pollutants is crucial for grasping how these substances move and disperse in the air, thereby aiding efforts to reduce pollution. In this regard, satellites have a distinct advantage in observation owing to their wide spatial coverage at regular time intervals. GEMS is one of the geostationary sensors providing such information for the Asia‐Pacific region, nearly 7–8 times a day. To evaluate the reliability of GEMS, this study proposes an inter‐calibration method by comparing GEMS observations with those of AMI aboard GK2A. These satellites have a unique advantage as they fly close to each other, observing the Earth with matched optical viewing paths. Scenes simultaneously observed by AMI and GEMS exhibit strong agreement, thus revealing the inherent observation characteristics of each sensor. These findings confirm the mutual benefit of utilizing the sensors in similar configurations for satellite monitoring during the operation. Key Points: Inter‐calibration based on ray‐matching between geostationary satellite sensors onboard twin satellitesImplementation of collocation process in the visible wavelengths and the application of weighted statistics for ensuring collocation stabilityThe novel advantages of the GEO‐GEO inter‐calibration approach for future applications [ABSTRACT FROM AUTHOR]

Context. The recent IRLOS upgrade for VLT/MUSE narrow field mode (NFM) introduced a full-pupil mode to enhance sensitivity and sky coverage. This involved replacing the 2 × 2 Shack-Hartmann sensor with a single lens for full-aperture photon collection, which also enabled the engagement of the linearized focal-plane technique (LIFT) wavefront sensor instead. However, initial on-sky LIFT experiments have highlighted a complex point spread function (PSF) structure due to strong and polychromatic non-common path aberrations (NCPAs), complicating the accurate retrieval of tip-tilt and focus using LIFT. Aims. This study aims to conduct the first on-sky validation of LIFT on VLT/UT4, outline challenges encountered during the tests, and propose solutions for increasing the robustness of LIFT in on-sky operations. Methods. We developed a two-stage approach to focal-plane wavefront sensing, where tip-tilt and focus retrieval done with LIFT is preceded by the NCPA calibration step. The resulting NCPA estimate is subsequently used by LIFT. To perform the calibration, we proposed a method capable of retrieving the information about NCPAs directly from on-sky focal-plane PSFs. Results. We verified the efficacy of this approach in simulated and on-sky tests. Our results demonstrate that adopting the two-stage approach has led to a significant improvement in the accuracy of the defocus estimation performed by LIFT, even under challenging low-flux conditions. Conclusions. The efficacy of LIFT as a slow and truth focus sensor in practical scenarios has been demonstrated. However, integrating NCPA calibration with LIFT is essential to verifying its practical application in the real system. Additionally, the proposed calibration step can serve as an independent and minimally invasive approach to evaluate NCPA on-sky. [ABSTRACT FROM AUTHOR]

In wide-angle X-ray scattering (WAXS) experiments, the use of a one-dimensional arc detector can effectively expand the angle range of collected diffraction or scattering signals. In practice, the relative positions between the centers of arc detector, sample and beam spot may overlap or misalign. Therefore, it is necessary to calibrate the instrument parameters and detection angle of the arc detector. The calibration is routinely performed by standard samples. In this contribution, the equations for angle calibration and error evaluation are constructed based on three standard diffraction peaks, and the corresponding simulation and experiment are performed to verify the feasibility of the calibration. The results indicate that the larger the coverage and the more uniform the dispersion of the three standard diffraction peaks distributed on the detector, the smaller the calibration error. [ABSTRACT FROM AUTHOR]

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor's viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. [ABSTRACT FROM AUTHOR]

Using electrochemical gas sensors for quantitative measurements of trace gas components in ambient air introduces several challenges, of which interference, drift and aging of the sensor are the most significant. Frequent and precise calibration as well as thorough characterization of the sensor helps to achieve reliable and repeatable results. We therefore propose the use of a simple, lightweight and inexpensive setup to produce hydrogen calibration gases with precisely known concentrations in ambient air. The hydrogen is produced by electrolysis with electric current monitoring, and the output can be set to any value between ∼ 3 and ∼ 11 µgH2min-1. With a dilution flow of 500 mL min -1 , for example, this results in a concentration range from ∼ 70 up to ∼ 240 ppm, but concentrations significantly below or above this range can also be covered with accordingly modified dilution flows. This setup can be used not only for calibration, but also for thorough and long-term characterization of electrochemical gas sensors to evaluate sensitivity, zero voltage and response time over extended periods of time. [ABSTRACT FROM AUTHOR]

It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higher-density sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO 2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O 3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO 2 and NO sensors are extremely cross-sensitive to O 3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being R2 = 0.90 ± 0.05 and root mean square error (RMSE) = 3.4 ± 0.9 ppb for NO 2 and R2 = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance being R2 = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb for NO 2 and R2 = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O 3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O 3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors. [ABSTRACT FROM AUTHOR]

We present a method for precise monitoring of the loop gain of transition edge sensors (TES) under electrothermal feedback. The measurement is implemented on the ICE DfMux electronics and operates simultaneously with Digital Active Nulling (DAN). It uses one additional bias sinusoid per TES and does not require any additional readout channels. The loop gain monitor is being implemented on the Simons Array and is an integral part of the baseline calibration strategy for the upcoming LiteBIRD satellite. [ABSTRACT FROM AUTHOR]

The X-ray Integral Field Unit (X-IFU) onboard the future European X-ray telescope Athena will be the first space instrument carrying an array of more than a thousand transition edge sensors. One of the key challenges of the X-IFU is the measurement of narrow X-ray atomic lines to determine velocity shifts at an unprecedented level of accuracy. For this reason, the energy scale of the instrument needs to be known with extreme accuracy, of 0.4 eV (1 σ ) up to 7 keV. The energy scale will be measured on the ground through a dedicated calibration campaign using fiducial X-ray sources. Though calibrated, the energy scale is extremely sensitive to the environmental conditions around the TES array, and drifts in the readout chain electronics. Uncorrected, the energy scale can naturally drift up to hundreds of eVs. Changes of the TES gain will be monitored via onboard X-ray calibration sources, and the energy scale will be corrected either per pixel, or within a small groups of pixels. Although simulations show that a 0.4 eV level can be achieved, the very high accuracy required by the X-IFU calls for experimental validation. A dedicated measurement campaign has been performed by NASA Goddard Space Flight Center to characterize the energy scale of a prototype kilo-pixel array of X-IFU-representative TESs. The analysis of the data demonstrated the ability to correct for various drifts using two fiducial lines to track the temporal gain variation. In this paper, we propose to extend this study on the same data set by investigating multi-parameter correction techniques based on both the pulse-height of the fiducial line and the prepulse baseline level, using the knowledge of the TES energy scale at reference temperature/magnetic field set points acquired on the ground. Investigations on the co-adding of pixels to perform a joint correction over pools of pixels is also explored. [ABSTRACT FROM AUTHOR]

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Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors, it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method. [ABSTRACT FROM AUTHOR]

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data. [ABSTRACT FROM AUTHOR]

A panchromatic and multispectral sensor (PMS) and a wide-field-of-view (WFV) sensor were fitted aboard the Gaofen6 (GF6) satellite, which was launched on 2 June 2018. This study used the Landsat9-Operational Land Imager 2 and Sentinel2-Multispectral Instrument as reference sensors to perform radiometric cross-calibration on GF6-PMS and WFV data at the Dunhuang calibration site. The four selected sensor images were all acquired on the same day. The results indicate that: the calibration results between different reference sensors can be controlled within 3%, with the maximum difference from the official coefficients being 8.78%. A significant difference was observed between the coefficients obtained by different reference sensors when spectral band adjustment factor (SBAF) correction was not performed; from the two sets of validation results, the maximum mean relative difference in the near-infrared band was 9.46%, with the WFV sensor showing better validation results. The validation of calibration coefficients based on synchronous ground observation data and the analysis of the impact of different SBAF methods on the calibration results indicated that Landsat9 is more suitable as a reference sensor for radiometric cross-calibration of GF6-PMS and WFV. [ABSTRACT FROM AUTHOR]

Calibration of 22 gamma camera units was performed at 15 hospitals in southern and western Sweden to estimate 137Cs contamination in humans in a supine static geometry, with a new developed calibration protocol and phantom. The minimum detectable activities (MDAs) and the estimated committed effective doses (CEDs) were calculated for each calibration. Generic calibration factors were calculated for five predetermined groups based on the detector type and manufacturer. Group 1 and 2 included NaI-based gamma cameras from General Electrics (GEs) with a crystal thickness of 5/8′′ and 3/8′′ respectively. Group 3 and 4 included NaI-based gamma cameras from Siemens Healthineers with a crystal thickness of 3/8′′, with a similar energy window as the GE NaI-based cameras and a dual window respectively. Group 5 included semiconductor-based gamma cameras from GE with a CdZnTe (CZT) detector. The generic calibration factors were 60.0 cps kBq−1, 52.3 cps kBq−1, 50.3 cps kBq−1, 53.2 cps kBq−1 and 48.4 cps kBq−1 for group 1, 2, 3, 4, and 5 respectively. The MDAs ranged between 169 and 1130 Bq for all groups, with measurement times of 1–10 min, corresponding to a CED of 4.77–77.6 μ Sv. A dead time analysis was performed for group 1 and suggested a dead time of 3.17 μ s for 137Cs measurements. The dead time analysis showed that a maximum count rate of 232 kcps could be measured in the calibration geometry, corresponding to a CED of 108–263 mSv. It has been shown that semiconductor-based gamma cameras with CZT detectors are feasible for estimating 137Cs contamination. The generic calibration factors derived in this study can be used for gamma cameras of the same models in other hospitals, for measurements in the same measurement geometry. This will increase the measurement capability for estimating internal 137Cs contamination in the recovery phase following radiological or nuclear events. [ABSTRACT FROM AUTHOR]

The satellite laser geometry calibration method based on terrain matching (terrain matching calibration) has been extensively employed in satellite laser geometry calibration for its simplicity and lack of need for ground probes. In this study, the key factors for the accuracy of the above-mentioned calibration method, i.e. namely the terrain slope and the number of laser points, are examined with the GaoFen-7 (GF-7) satellite as an example. Terrain is classified into six levels in according with the slope classification standards, i.e. Flat (<2°), Micro-slope (2°-5°), Gentleslope (5°-15°), Moderate-slope (15°-25°), Slope (25°-35°) and Steep-slope (35°-55°). Moreover, a different number of laser points are randomly selected from each the respective terrain slope for calibration. The accuracy of calibration is verified using the true laser pointing obtained based on the ground detector calibration method. As indicated by the experimental results, the terrain matching calibration achieves the optimal experimental conditions when there are over 50 laser points with a terrain slope greater than 15°, or there exist over 20 laser points with a terrain slope greater than 25°. In both cases, the laser pointing accuracy after calibration can exceed 3 arc seconds. This study can provide technical guidance for high-precision terrain matching calibration. [ABSTRACT FROM AUTHOR]

We discuss how the dual-wavelength differential detection (DWDD) of fiber Bragg grating sensors can be used to build standardized high-resolution, high-accuracy, large-measurement-range, multichannel instruments and associated sensors. We analyze the system resolution and experimentally show that the high signal-to-noise ratio can allow the design of sensors with a ratio of range to resolution superior to 14 bits, and temperature measurement ranges of more than 180 °C. We propose a scheme for real-time signal correction to cancel the drift of the instrument using two internal reference sensors, and a calibration method using centralized golden sensors that allows traceability to international standards for all instruments and sensors, allowing the creation of a global sensor/instrument ecosystem. [ABSTRACT FROM AUTHOR]

Electrical tomography sensors have been widely used for pipeline parameter detection and estimation. Before they can be used in formal applications, the sensors must be calibrated using enough labeled data. However, due to the high complexity of actual measuring environments, the calibrated sensors are inaccurate since the labeling data may be uncertain, inconsistent, incomplete, or even invalid. Alternatively, it is always possible to obtain partial data with accurate labels, which can form mandatory constraints to correct errors in other labeling data. In this paper, a semi-supervised fuzzy clustering algorithm is proposed, and the fuzzy membership degree in the algorithm leads to a set of mandatory constraints to correct these inaccurate labels. Experiments in a dredger validate the proposed algorithm in terms of its accuracy and stability. This new fuzzy clustering algorithm can generally decrease the error of labeling data in any sensor calibration process. [ABSTRACT FROM AUTHOR]

Many countries use low-cost sensors for high-resolution monitoring of particulate matter (PM2.5 and PM10) to manage public health. To enhance the accuracy of low-cost sensors, studies have been conducted to calibrate them considering environmental variables. Previous studies have considered various variables to calibrate seasonal variations in the PM concentration but have limitations in properly accounting for seasonal variability. This study considered the meridian altitude to account for seasonal variations in the PM concentration. In the PM10 calibration, we considered the calibrated PM2.5 as a subset of PM10. To validate the proposed methodology, we used the feedforward neural network, support vector machine, generalized additive model, and stepwise linear regression algorithms to analyze the results for different combinations of input variables. The inclusion of the meridian altitude enhanced the accuracy and explanatory power of the calibration model. For PM2.5, the combination of relative humidity, temperature, and meridian altitude yielded the best performance, with an average R2 of 0.93 and root mean square error of 5.6 µg/m3. For PM10, the average mean absolute percentage error decreased from 27.41% to 18.55% when considering the meridian altitude and further decreased to 15.35% when calibrated PM2.5 was added. [ABSTRACT FROM AUTHOR]

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems and to provide high spatial and temporal resolutions. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated whether NO 2 , which creates PM 2.5 by means of chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM 2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors and that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than 2 years, since July 2017. Based on the operating periods of the PA-II units, we then chose the period of January 2018 to December 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM 2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM 2.5 data. In the per-month analysis, three key features – namely temperature, relative humidity (RH), and NO 2 – were considered. The NO 2 , called collocated NO 2 , was collected from the reliable instrument collocated with the PA-II unit. The per-month analysis showed that the PA-II unit had a good correlation (coefficient of determination R2>0.819) with the BAM-1020 during the months of November, December, and January in both 2018 and 2019, but their correlation intensity was moderate during other months, such as in July and September 2018 and August, September, and October 2019. NO 2 was shown to be a key factor in increasing the value of R2 in the months when moderate correlation based on only PM 2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies, as well as their multiplicative terms. The addition of NO 2 had a much larger effect than that of RH when both PM 2.5 and temperature were considered for calibration in both models. When NO 2 , temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. In addressing the feasibility of utilizing distant NO 2 measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO 2 when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO 2 data proves to be challenging or costly. We assessed the performance of different PA-II units to determine their efficacy. Our investigation reveals a significant enhancement in calibration performance across different PA-II units upon integrating NO 2. Importantly, this improvement remains consistent even when employing models trained with different PA-II units within the same location. Overall, this investigation emphasizes the significance of NO 2 in improving calibration for low-cost PM 2.5 sensors and presents insights into leveraging distant NO 2 measurements as a viable alternative for calibration in the absence of collocated data. [ABSTRACT FROM AUTHOR]

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory. [ABSTRACT FROM AUTHOR]

In this paper, a six-degree-of-freedom analog tactile probe with a new, simple, and robust mechanical design is presented. Its design is based on the use of one elastomeric ring that supports the stylus carrier and allows its movement inside a cubic measuring range of ±3 mm. The position of the probe tip is determined by three low-cost, noncontact, 2D PSD (position-sensitive detector) sensors, facilitating a wider application of this probe to different measuring systems compared to commercial ones. However, several software corrections, regarding the size and orientation of the three LED light beams, must be carried out when using these 2D sensors for this application due to the lack of additional focusing or collimating lenses and the very wide measuring range. The development process, simulation results, correction models, experimental tests, and calibration of this probe are presented. The results demonstrate high repeatability along the X-, Y-, and Z-axes (2.0 µm, 2.0 µm, and 2.1 µm, respectively) and overall accuracies of 6.7 µm, 7.0 µm, and 8.0 µm, respectively, which could be minimized by more complex correction models. [ABSTRACT FROM AUTHOR]

Structured light sensing systems, as one of the most common optical-based nondestructive evaluation techniques, have been widely applied for inline pipeline inspection. The sensor can be inserted inside the pipe to generate 3D visualisation and evaluate the cracks in the materials. The precise calibration of the camera-projector measurement system is of great significance to ensure the measurement accuracy of the 3D sensing system. Conventional calibration methods for structured light sensors involve complicated and time-consuming procedures and are easily affected by ambient light. The paper presents a novel algorithm to automatically calibrate the projection module and estimate the stereo parameters between the camera and the projector. The calibration algorithm exploits the cylindrical nature of the inspected pipe to create a set of geometric constraints and automatically calibrate the sensor without the need for reference calibration points. Experimental and simulation results showed that the algorithm could successfully estimate the projector's intrinsic and extrinsic parameters by simply acquiring the data inside a cylindrical pipe with a known diameter. The proposed algorithm highly reduces the data collection time for the calibration (only 53 s), improves the accuracy, and simplifies the calibration process. [ABSTRACT FROM AUTHOR]

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful. [ABSTRACT FROM AUTHOR]

Aditya-L1 is the first Indian space mission to explore the Sun and solar atmosphere with seven multi-wavelength payloads, with Visible Emission Line Coronagraph (VELC) being the prime payload. It is an internally occulted coronagraph with four channels to image the Sun at 5000 Å in the field of view 1.05 - 3 R ⊙ , and to pursue spectroscopy at 5303 Å, 7892 Å and 10747 Å channels in the FOV (1.05 - 1.5 R ⊙ ). In addition, spectropolarimetry is planned at 10747 Å channel. Therefore, VELC has three sCMOS detectors and one InGaAs detector. In this article, we aim to describe the technical details and specifications of the detectors achieved by way of thermo-vacuum calibration at the CREST campus of the Indian Institute of Astrophysics, Bangalore, India. Furthermore, we report the estimated conversion gain, full-well capacity, and readout noise at different temperatures. Based on the numbers, it is thus concluded that it is essential to operate the sCMOS detectors and InGaAs detector at - 5 ∘ and - 17 ∘ C, respectively, at the spacecraft level. [ABSTRACT FROM AUTHOR]

Using electrochemical gas sensors for quantitative measurements of trace gas components in ambient air introduces several challenges, of which interference, drift and aging of the sensor are the most significant. Frequent and precise calibration as well as thorough characterization of the sensor helps to achieve reliable and repeatable results. We therefore propose the use of a simple, lightweight and inexpensive setup to produce hydrogen calibration gases with precisely known concentrations in ambient air. The hydrogen is produced by electrolysis with electric current monitoring and the output can be set to any value between ~3 µgH2/min and ~11 µgH2/min. With a dilution flow of 500 mL/min, for example, this results in a concentration range from ~70 ppm up to ~240 ppm, but concentrations significantly below or above this range can also be covered with accordingly modified dilution flows. This setup can be used not only for calibration, but also for thorough and long-term characterization of electrochemical gas sensors to evaluate sensitivity, zero voltage and response time over extended periods of time. [ABSTRACT FROM AUTHOR]

The European X-ray Free Electron Laser (European XFEL) is a cutting-edge user facility that generates per second up to 27,000 ultra-short, spatially coherent X-ray pulses within an energy range of 0.26 to more than 20 keV. Specialized instrumentation, including various 2D X-ray detectors capable of handling the unique time structure of the beam, is required. The one-megapixel AGIPD (AGIPD1M) detectors, developed for the European XFEL by the AGIPD Consortium, are the primary detectors used for user experiments at the SPB/SFX and MID instruments. The first AGIPD1M detector was installed at SPB/SFX when the facility began operation in 2017, and the second one was installed at MID in November 2018. The AGIPD detector systems require a dedicated infrastructure, well-defined safety systems, and high-level control procedures to ensure stable and safe operation. As of now, the AGIPD1M detectors installed at the SPB/SFX and MID experimental end stations are fully integrated into the European XFEL environment, including mechanical integration, vacuum, power, control, data acquisition, and data processing systems. Specific high-level procedures allow facilitated detector control, and dedicated interlock systems based on Programmable Logic Controllers ensure detector safety in case of power, vacuum, or cooling failure. The first 6 years of operation have clearly demonstrated that the AGIPD1M detectors provide high-quality scientific results. The collected data, along with additional dedicated studies, have also enabled the identification and quantification of issues related to detector performance, ensuring stable operation. Characterization and calibration of detectors are among the most critical and challenging aspects of operation due to their complex nature. A methodology has been developed to enable detector characterization and data correction, both in near real-time (online) and offline mode. The calibration process optimizes detector performance and ensures the highest quality of experimental results. Overall, the experience gained from integrating and operating the AGIPD detectors at the European XFEL, along with the developed methodology for detector characterization and calibration, provides valuable insights for the development of next-generation detectors for Free Electron Laser X-ray sources. [ABSTRACT FROM AUTHOR]

Low-cost sensors (LCSs) of Geekcreit PM1/PM2.5/PM10 (based on a PMS5003 sampler) and BOHU BH-1 models A3 and B3 (based on a Pando G7 sampler) were compared for different aerosol size ranges using a research-grade instrument (Grimm 1.109) under controlled laboratory conditions. An aerosol generator was utilized to produce various sizes of monodispersed particulate matter (PM), which was introduced into a laboratory smoke chamber under resistance heating/cooling and/or varying RH conditions. In addition, the accuracy of the air temperature (T) and relative humidity (RH) sensors of the LCSs were assessed against calibrated, laboratory-grade instruments. The study LCSs showed generally accurate readings for PM2.5, irrespectively of the slow T and/or RH changes, which provided apt conditions for accurate calibration slopes (S) and low intercepts/bias (b) of the linear fits. On the other hand, PM1 and PM10 readings slightly deviated from those observed with the reference monitor, likely due to the lower detection efficacy of the LCSs towards fine and coarse PM. Varying RH influenced the S and b values, showing its impact on the detection efficacy of LCSs. Under low/medium RH, homoscedastic calibration curves of PMx were found, whereas rather heteroscedastic calibration plots were observed at high RH. For T calibration, low RH in the smoke chamber provided more reproducible conditions in terms of lower measurement bias for LCSs as recorded against a calibrated, reference-grade thermometer. [ABSTRACT FROM AUTHOR]

The article deals with the development and determination of the uncertainty of a calibration tool for tilt sensors for the control of motor vehicle instability. The stability of motor vehicles is critical, especially when driving on inclined terrain, when instability and overturning of the vehicle can occur. For this purpose, it is necessary to install and calibrate a suitable tilt sensor. [ABSTRACT FROM AUTHOR]

This paper suggests a new onboard calibration procedure for star sensors installed on low Earth orbit satellites. In this procedure, all the error factors are investigated, and an accurate model of the star sensor is presented. Then, a new scenario is proposed for producing real data from a star sensor installed on a satellite while the satellite is moving in orbit. These sensor data are entered into the unscented Kalman filter as measurement data, and the sensor parameters, including focal length and principal point, are estimated. In the following, the estimated parameters are entered into the linear Kalman filter, and the misalignment error is estimated. Finally, the proposed procedure is simulated, and its performance is checked. The simulation results show the high accuracy and convergence speed of the suggested algorithm. [ABSTRACT FROM AUTHOR]

In order to combat greenhouse gas emissions, the sources of these emissions must be understood. Environmental monitoring using low-cost wireless devices is one method of measuring emissions in crucial but remote settings, such as peatlands. The Figaro NGM2611-E13 is a low-cost methane detection module based around the TGS2611-E00 sensor. The manufacturer provides sensitivity characteristics for methane concentrations above 300 ppm, but lower concentrations are typical in outdoor settings. This study investigates the potential to calibrate these sensors for lower methane concentrations using machine learning. Models of varying complexity, accounting for temperature and humidity variations, were trained on over 50,000 calibration datapoints, spanning 0–200 ppm methane, 5–30 °C and 40–80% relative humidity. Interaction terms were shown to improve model performance. The final selected model achieved a root-mean-square error of 5.1 ppm and an R2 of 0.997, demonstrating the potential for the NGM2611-E13 sensor to measure methane concentrations below 200 ppm. [ABSTRACT FROM AUTHOR]

Most calibration laboratories prefer the Direct Comparison Transfer Method (DCTM) for a reliable and accurate calibration of power sensors in the radio frequency (RF) scope. Most studies suggest using this calibration method, with its automatic power level control (APLC) of RF signal generators. The APLC is preferred to keep the output power level of the signal generator the same, while the power sensor is calibrated and the reference power sensor is connected to the measurement system. The known APLC mechanisms are also explained for the DCTM, and a comparison of the calibration factor values carried out with and without the automatic power level control process in the DCTM is also given in this study. RF power sensor calibrations with coaxial and waveguide connector types are examined with DCTM in this study as well. This study shows that the DCTM, unless with APLC, should be applied for the waveguide power sensor's calibration at millimeter wave frequencies. [ABSTRACT FROM AUTHOR]

A vision-based guidance methodology is proposed for precise positioning of the tool center point (TCP) of heavy-duty, long-reach (HDLR) manipulators. HDLR manipulators are non-rigid structures with many nonlinearities. Therefore, conventional rigid-body–based modeling and control methods issue challenges for accurate TCP positioning. To compensate for these errors, we compute the pose error between the TCP and an object of interest (OOI) directly in the camera frame, while using motion-based local calibration to find the extrinsic sensor-to-robot correspondence. The proposed pipeline for local calibration is twofold: first, the detected tool is oriented perpendicularly with respect to the OOI. Second, range adjustment is performed in the local planar plane by exploiting the visual measurements. Two methods for adjusting the range were examined: a line equation–based method and a trajectory matching–based method. Real-time experiments were conducted using a HDLR manipulator with a 5 m reach, and visual fiducial markers were used as detectable objects for the visual sensor. The experimental results demonstrated that the proposed methodology can provide sub-centimeter positioning accuracy, which is very challenging to achieve with HDLR manipulators due to their characteristic uncertainties. [ABSTRACT FROM AUTHOR]

This manuscript describes reflectance-based vicarious calibration exercise of the Airborne Visible InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) sensor during its second phase of observation campaigns over the Indian subcontinent. Vicarious calibration methods are one of the significant approaches that have been practiced successfully for the absolute radiometric calibration of various spaceborne and airborne sensors to ascertain its desired performance in terms of data quality and its accuracy. Calibration campaigns were performed for AVIRIS-NG sensor during its phase-2 campaign overpass at the Desalpar (25th March 2018) and Amarapur (27th March 2018) calibration sites of Gujarat, India. Results from ground-based measurements of atmospheric conditions and surface reflectance including descriptions of the test sites are summarized in this work. Based on the in-situ data and images from the AVIRIS-NG sensor, the at-sensor apparent radiances are simulated using the 6SV radiative transfer model. The comparison shows good agreement between AVIRIS-NG measured radiance and 6SV simulated apparent radiance at the two calibration sites using desert aerosol model. The gains (spectrally) derived agree to within ∼ 2 % each other. Furthermore, an analysis is performed to determine or identify systematic and random errors, and the overall uncertainty is evaluated for reflectance-based method (total uncertainty of less than 4 % spectrally). [ABSTRACT FROM AUTHOR]

It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higherdensity sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO2 and NO sensors are extremely cross-sensitive to O3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being of R² = 0.90 ± 0.05 and RMSE = 3.4 ± 0.9 ppb for NO2, and R² = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance for NO2 being R² = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb, and R² = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors. [ABSTRACT FROM AUTHOR]

A new calibration device using a double-ball rotary structure and three contact displacement sensors is proposed to measure the spatial errors of six-axis serial robot. The error model is divided into kinematics part and static stiffness part, established by Modified DH (MDH) model and virtual joint method. The MDH parameter errors are compensated by modifying the angles of each axis with Newton Raphson compensation method and ridge estimation. The stiffness parameters are also identified by the least square method. The self-designed double-ball rotary device is calibrated by coordinate measuring machine (CMM), while the three-point measuring device is calibrated by gauge block. Then, the kinematic and static stiffness calibration and compensation experiments are carried out on EPSON C4 A901 robot in turn. The spatial error under none load and 1.0 kg, 1.5 kg, 2.0 kg gravity load are measured separately. The experiment results show that the motion accuracy of the robot is improved to the range from 0.0163 mm to 0.0236 mm under the gravity load conditions between weights of 1.0 kg and 2.0 kg. Finally, by comparing the measuring results from the laser displacement sensor and laser interferometer, the measurement accuracy of each axis of the self-designed device is equivalent to the laser displacement sensor and the laser interferometer in micron range. • The self-designed measuring device consisting of three-point probing component and double-ball rotary component is proposed for measuring the spatial error of TCP of 6-axis joint industrial robot. • The compensating processes of spatial error are performed by the kinematic calibration under no-load condition and the static stiffness calibration under different loads, respectively. • After dynamic compensation, the average errors of the robot's TCP are improved to the range from 0.0163 mm to 0.0236 mm under the gravity load conditions between 1.0–2.0 kg weight. [ABSTRACT FROM AUTHOR]

Landsat 9 (L9) was launched on 27 September 2021. This spacecraft contained two instruments, the Operational Land Imager-2 (OLI-2) and Thermal Infrared Sensor-2 (TIRS-2), that allow for a continuation of the Landsat program and the mission to acquire multi-spectral observations of the globe on a moderate scale. Following a period of commissioning, during which time the spacecraft and instruments were initialized and set up for operations, with the initial calibration performed, the mission moved to an operational mode This operational mode involved the same cadence and methods that were performed for the Landsat 8 (L8) spacecraft and the two instruments onboard, the Operational Land Imager-1 (OLI-1) and Thermal Infrared Sensor-1 (TIRS-1), with respect to calibration, characterization, and validation. This paper discusses the geometric operational aspects of the L9 instruments during the first year of the mission and post-commissioning, and compares these same geometric activities performed for L8 during the same time frame. During this time, optical axes of the two sensors, OLI-1 and OLI-2, were adjusted to stay aligned with the spacecraft's Attitude Control System (ACS), and the TIRS-1 and TIRS-2 instruments were adjusted to stay aligned with the OLI-1 and OLI-2 instruments, respectively. In this paper, the L9 operational adjustments are compared to the same operational aspects of L8 during this same time frame. The comparisons shown in this paper will demonstrate that both instruments aboard L8 and L9 performed very similar geometric qualities while fully meeting the expected requirements. This paper describes the geometric differences between the L9 imagery that was made available to the public prior to the reprocessing campaign that was performed using the new calibration updates to the sensor and to ACS and TIRS-to-OLI alignment parameters. This reprocessing campaign of L9 products involved data acquired from the launch of the spacecraft up to early 2023. [ABSTRACT FROM AUTHOR]

A differential angle sensor is newly developed to calibrate the pitch deviations of a linear scale grating with a nominal pitch of 1.6 µm on an ultra-precision lathe. The angle sensor is composed of two angle detection units based on the laser autocollimation method. A collimated laser beam with a diameter of 1 mm, which is output from a laser diode with a wavelength of 685 nm, is projected onto the linear scale grating. The positive and the negative first-order diffracted beams from the scale are received by the two angle detection units, respectively. The X-slide of the ultra-precision lathe is employed to generate the necessary scanning motion for the calibration. Based on the fact that the pitch deviations will cause changes in the positive and the negative first-order diffraction angles, which are equal in magnitude and opposite in sign, the pitch deviations can be obtained from the differential output of the angle sensor. The tilt error motion of the X-slide, which is a major error factor in on-machine calibration, can also be removed in the differential output. The robustness of the developed angle sensor for on-machine calibration has been confirmed by testing the basic performances of the sensor on the machine tool. The feasibility of the on-machine calibration result of pitch deviations has been verified through comparing with the off-machine calibration result. [ABSTRACT FROM AUTHOR]

With the advancement of China's satellite technology, the HuanJingJianZai-2 A/B (HJ-2 A/B) satellites, equipped with whisk-broom infrared sensors, represent a significant leap forward in environmental monitoring and Earth observation capabilities. This technological leap, however, introduces new challenges in calibration. The unique structure of the HJ-2 A/B infrared spectroradiometer (IRS) necessitates innovative calibration techniques, as traditional methods primarily focused on exterior orientation parameters (EOPs) and often overlooked the importance of interior orientation accuracy, which is essential for accurate multispectral band registration and color rendering. Addressing this gap, we introduce an innovative multi-focal-plane-array joint calibration method specifically designed for whisk-broom cameras. Our method involves selecting a master band from each focal plane array for accurate focal length calibration and deriving ground control points from image matching and altitude interpolation for comprehensive bundle adjustment. This adjustment refines EOPs and interior orientation parameters (IOPs), ensuring globally optimal EOPs and enhanced IOPs calibration stability. The application of our method to the HJ-2 A/B IRS yielded substantial improvements in georeferencing and band registration accuracies, surpassing traditional methods. This paper details the multi-focal-plane-array joint calibration method, describes the IRS and experimental setup, presents the experimental results, and concludes with the implications and potential applications of our findings. [ABSTRACT FROM AUTHOR]

A method to perform measurements of microphone responses directly in the array of a sensor system is described. It can be applied in reverberant environments and does not require high instrumentation effort. Due to the use of internal hardware of the sensor system, the whole signal chain of microphone–preamplifier–analogue-to-digital converter is characterized. The method was successfully tested for calibration of two types of planar arrays constructed with micro-electromechanical system (MEMS) microphones. Presented experimental results illustrate achieved performance, and possible application scenarios are discussed. [ABSTRACT FROM AUTHOR]

The JUNGFRAU detector is a well-established hybrid pixel detector developed at the Paul Scherrer Institut (PSI) designed for free-electron laser (FEL) applications. JUNGFRAU features a charge-integrating dynamic gain switching architecture, with three different gain stages and 75 µ m pixel pitch. It is widely used at the European X-ray Free-Electron Laser (EuXFEL), a facility which produces high brilliance X-ray pulses at MHz repetition rate in the form of bursts repeating at 10 Hz. In nominal configuration, the detector utilizes only a single memory cell and supports data acquisition up to 2 kHz. This constrains the operation of the detector to a 10 Hz frame rate when combined with the pulsed train structure of the EuXFEL. When configured in so-called burst mode, the JUNGFRAU detector can acquire a series of images into sixteen memory cells at a maximum rate of around 150 kHz. This acquisition scheme is better suited for the time structure of the X-rays as well as the pump laser pulses at the EuXFEL. To ensure confidence in the use of the burst mode at EuXFEL, a wide range of measurements have been performed to characterize the detector, especially to validate the detector alibration procedures. In particular, by analyzing the detector response to varying photon intensity (so called 'intensity scan'), special attention was given to the characterization of the transitions between gain stages. The detector was operated in both dynamic gain switching and fixed gain modes. Results of these measurements indicate difficulties in the characterization of the detector dynamic gain switching response while operated in burst mode, while no major issues have been found with fixed gain operation. Based on this outcome, fixed gain operation mode with all the memory cells was used during two experiments at EuXFEL, namely in serial femtosecond protein crystallography and Kossel lines measurements. The positive outcome of these two experiments validates the good results previously obtained, and opens the possibility for a wider usage of the detector in burst operation mode, although compromises are needed on the dynamic range. [ABSTRACT FROM AUTHOR]

The applicability of the calibration method for heat flux sensors based on anisotropic bismuth thermoelements and a heterogeneous copper—nickel structure using a reflected shock wave to determine the volt–watt coefficient is demonstrated. The coefficient obtained for a sensor based on anisotropic thermoelements is close to the stationary calibration data, and for a sensor based on a heterogeneous structure, to the results of numerical simulation. [ABSTRACT FROM AUTHOR]

High-voltage electric field measurement technology has certain applications in electric field measurement of power systems, but due to the limitation of its measurement accuracy and bandwidth, it cannot be used for the measurement of lightning-impulse voltage. In order to calibrate the nonlinearity of the MV-level lightning-impulse voltage measurement system, this paper proposes the design and implementation of a high-precision inductive wideband electric field measuring sensor (EFMS). The influence of the metal shell on the electric field distribution was simulated, and the influence of the electric field non-uniformity coefficient was studied. The characteristics of the EFMS were tested, and the results showed that the EFMS can accurately reproduce the waveform of lightning-impulse voltage and power-frequency voltage, with a proportionality coefficient of 0.05664 V/(kV/m). In mostly uniform and extremely non-uniform fields, the nonlinearity of the EFMS for impulse voltage is less than ±0.25%, and the nonlinearity of the EFMS for power-frequency voltage is less than 0.1%. It is shown that the EFMS can be used for the nonlinearity calibration of ultra-high voltage impulse measurement devices. [ABSTRACT FROM AUTHOR]