Although digital detection of nucleic acids has been achieved by amplification of single templates in uniform microfluidic droplets and widely used for genetic analysis, droplet-based digital detection of proteins has rarely been reported, largely due to the lack of an efficient target amplification method for protein in droplets. Here, we report a key step towards digital detection of proteins using a highly parallel microfluidic droplet approach for single enzyme molecule detection in picoliter droplets via enzyme catalyzed signal amplification. An integrated microfluidic chip was designed for high throughput uniform droplet generation, monolayer droplet collection, incubation, detection, and release. Single β-galatosidase (β-Gal) molecules and the fluorogenic substrate fluorescein di-β-D-galactopyranoside were injected from two separated inlets to form uniform 20 μm droplets in fluorinated oil at a frequency of 6.6 kHz. About 200 000 droplets were captured as a monolayer in a capture well on-chip for subsequent imaging detection. A series of β-Gal solutions at different concentrations were analyzed at the single-molecule level. With no enzyme present, no droplets were found to fluoresce, while brightly fluorescent droplets were observed under single-enzyme molecule conditions. Droplet fluorescence intensity distribution analysis showed that the distribution of enzyme molecules under single-molecule conditions matched well with theoretical prediction, further proving the feasibility of detecting single enzyme molecules in emulsion droplets. Moreover, the population of fluorescent droplets increased as the β-Gal concentration increased. Based on a digital counting method, the measured concentrations of the enzyme were found to match well with input enzyme concentration, establishing the accuracy of the digital detection method for the quantification of β-Gal enzyme molecules. The capability of highly parallel detection of single enzyme molecules in uniform picoliter droplets paves the way to microdroplet based digital detection of proteins.

Ground control points (GCPs) are generally used to calibrate the installation between the camera and star sensor of a satellite in orbit and improve the geometric positioning accuracy of the satellite. However, the use of GCPs for high-frequency calibration is difficult, and it is particularly difficult to acquire accurate GCPs for the image of a nightlight satellite. In this study, we developed a camera-star sensor installation calibration method that eliminates the need for GCPs. In the proposed method, the camera and star sensor lenses are simultaneously pointed at the star, and the camera-star sensor installation is accurately calibrated by processing the star map obtained by the camera and star sensors. Reference data such as road network and Moon position data were used to verify the proposed method and evaluate its positioning accuracy. The results of the application of the method to the positioning of the Luojia 1-01 satellite indicated an accuracy within 800 m, which is comparable with that of the traditional method.

Owing to changes in the space thermal environment, the optical axis angle between the camera and star tracker (Cam-ST) changes as well, resulting in regular low-frequency attitude errors and a decline in the geometric positioning accuracy of the images. In this study, a star-observation-based low-frequency attitude error correction method is proposed. First, the star was used as the control point to be observed by satellite from different latitudes in an orbit. The star information is extracted using a priori attitude to obtain the camera pointing in inertial coordinates. Second, combined with the optical axis pointing value of the star tracker, the angle change between the camera and two star trackers (Cam-2STs) was calculated. A polynomial model was used to fit the angle change, and the fitting model and camera optical axis pointing compensation algorithm were used to compensate for the camera optical axis. Third, the regular low-frequency attitude error of the camera was realized. The experiment used 20 groups of star observation data from two missions of the Jilin-1 07 video satellite. After compensation, the camera pointing maximum root mean square error was reduced from 30.08" to 7.70", the X -axis attitude error was reduced from 13.26" to 7.09", and the Y -axis from 29.48" to 7.36". The geometric positioning accuracy of Jilin-1 ground images was 25.63 m after correction. The experimental results showed that the proposed method effectively improved the attitude measurement accuracy. This method can thus effectively improve the direct geometric positioning accuracy of a single image.

This article presents an innovative geometric calibration mode for a linear array camera used in the Jilin-1 satellite. Changing the camera from observing the ground to observing space and treating stars as control points allow for the accurate determination of the geometric parameters of the camera. In this article, several important calibration procedures and strategies are presented to gradually eliminate the influence of astronomical and attitude errors. Self-verification of the calibration image results in a star-based calibration accuracy is better than 0.3 pixels. Further verification of the star image that leads to an interior orientation accuracy is better than 0.6 pixels. The accuracy of geometric positioning without control is verified by ground images of different regions to be 30 m. The interior orientation accuracy of ground images processed by star-based calibration parameters is better than 1.5 m. The ground image corrected using the star-based calibration parameters is well overlaid on the reference image. The accuracies obtained with star-based and ground-based calibrations are comparable. However, ground calibration fields require high maintenance costs and may suffer from harsh imaging conditions. Contrarily, star-based calibration is not affected by the weather and climate conditions on the ground, which can be photographed from any location along the satellite orbit. Therefore, star-based calibration can meet the requirements for high-frequency geometric calibration of subsequent satellite constellations construction.

Block adjustment is a key technology for achieving large-scale accurate mapping of space via remote sensing. When block adjustment without ground control points (GCPs) is conducted, the adjustment model has large condition number of design matrix, and an ill-conditioned normal equation, which eventually leads to abnormal convergence of direct least squares solution. In this study, we investigated block adjustment without GCPs based on Rational Function Model (RFM), and applied image space affine model of RFM as the systematic error compensation model of single scene imagery. The value range of each parameter of the affine model was determined by calculating the inverse projection errors of tie points and the adjustment model parameters were solved by the optimization method with attached inequalities constraint. Finally, the abnormal convergence problem was solved by reducing the degree of freedom of convergence. Comparative tests of block adjustment without GCPs using various methods were conducted using multiple regional imagery from Ziyuan-3 (ZY-3), Tianhui-1 (TH-1), Luojia-1 (LJ-1) and Gaofen-3 (GF-3). The results show that the proposed method can achieve rapid and correct convergence, and has strong practicability.