Streptomyces, the dsDNA translocase FtsK of gram‐negative bacteria, and the genome‐packaging motor in mimivirus. These motors exhibit an asymmetrical hexameric structure for transporting the genome via an inch‐worm sequential action. This review intends to delineate the revolving mechanism from a perspective of conformational changes and electrostatic interactions. In phi29, the positively charged residues Arg‐Lys‐Arg in the N‐terminus of the connector bind the negatively charged interlocking domain of pRNA. ATP binding to an ATPase subunit induces the closed conformation of the ATPase. The ATPase associates with an adjacent subunit to form a dimer facilitated by the positively charged arginine finger. The ATP‐binding induces a positive charging on its DNA binding surface via an allostery mechanism and thus the higher affinity for the negatively charged dsDNA. ATP hydrolysis induces an expanded conformation of the ATPase with a lower affinity for dsDNA due to the change of the surface charge, but the (ADP+Pi)‐bound subunit in the dimer undergoes a conformational change that repels dsDNA. The positively charged lysine rings of the connector attract dsDNA stepwise and periodically to keep its revolving motion along the channel wall, thus maintaining the one‐way translocation of dsDNA without reversal and sliding out. The finding of the presence of the asymmetrical hexameric architectures of many ATPases that use the revolving mechanism may provide insights into the understanding of translocation of the gigantic genomes including chromosomes in complicated systems without coiling and tangling to speed up dsDNA translocation and save energy.Investigations of the parallel architectures of biomotors in both prokaryotic and eukaryotic systems suggest a similar revolving mechanism in the use of ATP to drive translocation of the lengthy double‐stranded (ds)DNA genomes. This mechanism is exemplified by the dsDNA packaging motor of bacteriophage phi29 that operates through revolving but not rotating dsDNA to “Push through a one‐way valve”. This unique and novel revolving mechanism discovered in phi29 DNA packaging motor was recently reported in other systems including the dsDNA packaging motor of herpesvirus, the dsDNA ejecting motor of bacteriophage T7, the plasmid conjugation machine TraB in  E. coli and Streptomyces). The motors made of asymmetrical 6 subunits translocate lengthy double‐stranded (ds)DNA. The dsDNA is moved in a circular motion around the inner surface of the motor channel subunits. The translocation involves conformational changes and electrostatic interactions between the motor subunits and DNA.The revolving DNA motors of ATPase have been found in viruses  and bacteria ( These include the proteins necessary for manipulating nucleic acids in such processes as repair, recombination, transcription, and translation. Some of the ATPase proteins that directly interact with DNA contain a hexameric ring that encloses DNA and translocates it. The proteins of this type are generally termed DNA motors. These biomotors are of special interest because their efficacy in transporting large biomolecules can be applied for therapeutic and industrial use. Additionally, these hexameric motors may be valuable drug targets, since their high stoichiometry and dependence on sequential mechanisms for transport make them easier targets than monomeric proteins.1 More recently, a third mechanism was revealed, in which DNA travels around the inner channel of the hexameric ring, and translocation through the ring is driven by conformational changes of the ring proteins without the need for the DNA or the channel to rotate. Such an action offers several advantages to the revolution mechanism compared with the rotation mechanism, including avoiding supercoiling that requires the work of topoisomerase enzymes to correct.Transporting long strands of DNA is no easy task. In order to overcome the energetic barriers of DNA transportation, ATP is used. However, the exact mechanisms for how these motors worked remained elusive for many years. Initially, it was thought that there were two mechanisms for moving DNA, the linear and the rotational mechanism. For the linear mechanism, a DNA‐binding protein “walks” along a DNA strand, while in the rotational mechanism, the hexameric ring acts as a nut and the DNA acts as a bolt.2 More Among these, the motor of bacteriophage phi29 was the first to be constructed as a defined in vitro system. The packaging of the phi29 DNA genome and the production of the infectious virion through this in vitro system provide the initial evidence for the hypothesis stating that viral DNA packaging does not rely on host proteins and enzymes. The study about the function of the viral DNA packaging protein gp16 in 1986 demonstrated the following key features about the DNA packaging enzymes of the bacteriophage. First, the DNA packaging enzymes were discovered to be ATPases DNA‐packaging motors of prokaryotic cells and their viruses that pack the genomes against an accumulating internal pressure in an entropically unfavorable manner have evolved common structures and mechanisms.11 Amos Figure . Second, Several clues pointed to the existence of the revolving mechanism rather than the rotational “nut and bolt” mechanism. For a nut and bolt to be effective, the nut must fit tightly around the bolt. This was not the case in the phi29 motor. The diameter of the motor channel is almost twice as large as the diameter of dsDNA. Additionally, for a nut and bolt to be functioning, they have to be threaded the same way. The phi29 motor channel displays left‐handed chirality, opposite of the right‐handed chirality of dsDNA. Lastly, for the nut and bolt mechanism to hold true, either the nut or the bolt has to rotate. Low‐resolution Cryo‐EM images showed dsDNA bending around the portal region, and a tilting of dsDNA when passing through the connector channel. Further evidence defining the revolving mechanism came from the following functional analyses. First, when the T4 motor active in dsDNA packaging was fused to the procapsid protein, thus rendering it incapable of rotating, the motor was still able to translocate dsDNA. Second, single‐molecule force spectroscopy combined with polarization spectroscopy studies did not show any evidence of the motor or DNA rotating. Third, bead clusters attached to both ends of the DNA substrate showed no sign of complete rotation during the active packaging of dsDNA, and the dsDNA substrate twists about 1.5°/base pair (bp) instead of a full 360°/complete helical turn. In fact, this finding verifies a revolving instead of a rotational mechanism because if one helical turn of dsDNA is made of ∼10.5 bases, then 1.5° /bp × 10.5 bp/turn = 15.7°, which is far smaller than 360°. The data from several studies indeed demonstrate a revolving mechanism by which the dsDNA substrate revolves forward through the motor channel. Fourth, translocation of the dsDNA substrate is one‐way traffic coordinated by the various motor components. The directional translocation is maintained by a ratchet‐like mechanism making use of multiple functional features of a channel, ATP‐coupled conformational alternations, and the recognition of the 5′‐3′ strand for single‐directional movement. Moreover, this revolving mechanism of translocation is executed by biomotors in a great variety of biological systems, such as bacteria and the dsDNA viruses (bacteriophages) as well as eukaryotic dsDNA viruses  demonstrate that TraB is an FtsK‐like ancestor protein, suggesting the adaptation relationship between FtsK/SpoIIIE and TraB. For the bacterial cellular motor, FtsK that comprises a family of motor proteins coordinates the replication and segregation of the chromosome during cell division. FtsK helps resolve bacterial chromosome dimers during cell division by translocating dsDNA in a mechanism similar to filament treadmilling. Of the viruses in the eukaryotic system, herpesvirus possesses a genome packaging machinery in which terminase complex displays a hexameric ring structure, an architecture suggesting a sequential revolving motion for DNA translocation. The dsDNA packaging motors of mimivirus exist in the mimiviridae, a highly diverse family of dsDNA viruses infecting many aquatic eukaryotes. They are evolutionarily related to FtsK ,35 ande Figure . The thrs Figure . The thr It is also possible for keeping the connector in contact to prepare for ejection during the infection process. The exact mechanism for gating in the one‐way valve function is poorly understood, thus demanding further investigation. Yet, the revolving “push‐through one‐way valve” mechanism entails the separate roles of the connector and the ATPase, both of which are embraced by pRNA into an integrated motor active in DNA translocation through the interplay of conformation alternations and electrostatic interactions among the motor parts . When no DNA is present, FtsK exists as a monomer. In the presence of dsDNA, the α and β domains assemble into hexameric rings with an outer diameter of ∼120 Å. The inner channel of the FtsK hexamer has a diameter of ∼30 Å, which is much wider than the diameter of ds DNA helix (∼20 Å). The native FtsK motor protein used in biochemical studies was not well‐suited for structural work due to its low solubility; so a soluble mutant was created. Negative stain transmission electron microscopy and analytical ultracentrifugation were used to show that this soluble mutant existed as monomers in the absence of DNA but oligomerized into hexamers when combined with dsDNA. Additionally, X‐ray crystallography shows that the β portion was similar in structure to other oligomeric ATPases. The X‐ray crystal structure of the hexamer exhibits that each monomer had its own active site, in contrast to other hexameric motors where the active site is located between two monomers. Interestingly, electron microscopy revealed that the FtsK motor was able to form double rings around strands of DNA, with the rings aligned β to β. The significance of the double rings is currently unknown.Highly conserved among all bacteria,53 theThe combination of X‐ray crystallography and electron microscopy provides clues on the mechanism for the FtsK motor. The X‐ray crystal structures of the FtsK monomer show the α and β domains acting as a hinge. When the hinge opens, the α domain moves 5.5 Å away from β, which correlates to 1.6 base pairs of dsDNA. Based on this observation, Massey et al. suggested that FtsK translocates dsDNA in a rotary inchworm mechanism. In this model, DNA first binds to the α domain. ATP binding and hydrolysis initiate a conformational change that allows DNA to bind to β and opens the hinge to move α away from β. As the hinge opens, the DNA is pulled through the channel. At the end of the catalytic cycle, the DNA remains bound to β and is completely disengaged from α. Since the DNA is helical, the same α domain will not be able to bind the DNA again, so another monomer goes through the catalytic cycle. In this way, all 6 monomers work together to drive DNA through the channel in a revolving manner.56 The two DNA‐binding loops made of the four subunits in the hexameric ring form a pair of spiral staircases holding the two DNA strands inside the ring , of which pRNA not only braces the ATPase and the connector but also plays a role in motor conformation dynamics. This section discusses the interplay between the force generation and the resultant conformational changes of the motor components that drive DNA translocation.The parallel mechanisms of the viral and cellular dsDNA motors involve interaction between force generation and conformational alterations of the motor components for DNA translocation. Similar to other biological activities, the force generation is powered by energy released from ATP hydrolysis by the ATPase domain.63 DNA3.1 As the conformational change of some biomotors was found to be associated with ATP binding without ATP hydrolysis, the process of conversion of chemical energy into motion by the biomotors is still a conundrum. The discovery of the DNA‐dependent activity of the phi29 motor ATPase gp16 of the hexameric AAA+ family ,72 the3.2 The activity of this ATPase provides energy to the motor through ATP hydrolysis into ADP and inorganic phosphate. Initially, the gp16 ATPase binds DNA in the presence of ATP or γ‐S‐ATP as shown by EMSAs  folded into a complex structure containing two major domains  motif is located in the center and branches out to hold the gp16‐binding domain and the connector‐binding domain of the right‐hand and left‐hand loops  may generate artificial conformational changes in the portal proteins. Further investigations are required to exclude this possibility. Although the meaning of gating in the context of viral genome packaging and invasion is not fully understood, it can be assumed that translocation of the viral genome into the procapsid encounters the internal pressure of the packaged DNA that increases the probability of DNA contacts with the C‐terminal flexible domain. Such contacts induce conformation changes with the stepwise gating reduction to avoid dsDNA leakage from the capsid. It is also possible for the connector‐DNA contacts to prepare for ejection during the infection process. The ejection force may mostly build up during DNA packaging.Many protein pores and ion channels use gating to regulate ion transportation through cell membranes. Gating is controlled in different ways, for example, ligand‐gated ion channels respond to the binding of chemical signals to open or close, and voltage‐gated ion channels respond to electrical potential differences near the channel for activation.134 Thn Figure .[1369 Figure .[40]n Figure , a three4.4 The electrostatic force from ATP binding is likely to affect the ATPase conformation. The binding of ATP increases the affinity of the ATPase for DNA, as revealed by studying the electrostatic potential of the hexameric RecA‐ATP complex. RecA in this complex displays a high positive potential along the internal region in which the negatively charged ssDNA is bound. This suggests that the ATP‐bound conformation of the ATPase has a higher affinity for DNA than the unbound ATPase as a result of the increased positive charge on the DNA binding surface of the ATPase. One possible mechanism for the ATP‐binding increased affinity of the ATPase for DNA is dielectric allostery. Particularly, the dielectric allostery model was derived from molecular dynamics simulations of myosin in response to ATP binding, which is essential for force generation. Upon ATP binding to a motor protein of allostery such as myosin, the net negative charge of ATP induces such an extensive anisotropic dielectric response in the protein that the electrostatic potential in a distant region of that protein is altered. This alteration includes the increase of the positive charge of the actin‐binding loop and retraction of the loop. Accordingly, the ATP binding to the DNA motor proteins increases the affinity of the ATPase for DNA plausibly through the dielectric allostery. The binding of the gp16 ATPase to DNA is possibly regulated allosterically by ATP binding, the allosteric regulation that has been found in the DNA motor systems of the helicase superfamily II. Besides, the DNA binding to the ATPase facilitates ATP hydrolysis by the ATPase, plausibly by another allosteric mechanism as observed in the ATPase activity of Escherichia coli Rep helicase. After ATP hydrolysis, the ATPase becomes unbound, and the conformation changes back to the initial state, which has a low affinity for DNA. This low affinity conformation ATPase could generate a negatively charged binding surface that repels the negatively charged DNA substrate. The ATPase loses its affinity to dsDNA, and dsDNA departs for the next subunit of the ATPase. Taken together, the motor ATPase may be subjected to the allosteric regulations by the binding of the two substrates, ATP and DNA. ATP binding increases ATPase affinity for DNA by adding positive charges to the DNA binding surface of the ATPase via dielectric allostery. The bound DNA, in turn, facilitates ATP hydrolysis that reduces ATPase affinity for DNA. Whether the dielectric allostery is involved in dsDNA translocation by the DNA motors remains to be determined.The binding of ATP to the gp16 ATPase is most likely mediated by electrostatic force.139 Th4.5 However, several key experiments discovered that the rotation mechanism was unlikely for nanopores.The phage connector can function as a nanopore that translocates DNA. Initially, it was proposed that the nanopore acted similarly to a nut and thus dsDNA as a bolt, using a rotational force to drive dsDNA into the procapsid.150 Ho The authors acknowledged that in these studies, the force with which DNA travels through the pore might be able to sever the engineered crosslinks, leaving room for debate on whether the rotation mechanism was viable. An additional study aimed to further investigate the mechanism of DNA transport. Hugel et al. used single‐molecule fluorescence spectroscopy to observe whether the nanopore rotated with respect to the viral prohead. In this work, several fluorescent dye molecules were attached to the nanopore. The prohead was immobilized on a glass coverslip. Magnetic beads were attached to dsDNA molecules, and magnets were placed on the objective of a fluorescent microscope to stretch the DNA polymer toward the objective. Even with the magnetic bead on dsDNA and the dye molecules on the nanopore, the nanopore was able to package DNA under the precaution that the fluorescent label on the nanopore could not move substantially on its own. Particularly, the excitation source was rotated in two orthogonal directions for polarization measurements. Changes in excitation polarization were considered for the dye observation. Upon incubation with ATP‐γS, a nonhydrolyzable ATP analog, the nanopore failed to transport DNA. During this incubation, the signal intensity of the dye remained unchanged based on variations in excitation polarization. This observation indicated that the dye molecule could not move by itself. In the presence of ATP, the nanopore was able to transport DNA as reflected by the fluorescent signal coming from the magnetic bead. However, the emission intensities of the dye molecules on the nanopore did not change, demonstrating that the nanopore remained still.For the rotational mechanism to be true, either the nanopore or DNA would have to rotate. Bauman et al. tested this by creating several different constructs of a protein found in the nanopore. These constructs had regions that were tethered to the head of the bacteriophage so that the nanopore could not spin. However, even with the nanopore immobilized, the bacteriophage was still able to package DNA.151 Th Taken together, the data indicate that the translocation of dsDNA through nanopores most likely operates in the revolving mechanism.Moreover, if the nanopore and the dsDNA were acting as a nut and bolt, the diameter of the nanopore would fit snugly around the dsDNA so that the nanopore and dsDNA would be “threaded” in the same chirality. Yet the experimental results with nanopores did not support this thought. Rotational biomotors display channels smaller than 2 nm, similar to the diameter of dsDNA. However, crystal structures of the phi29 channel diameter reveal 3.6 nm, larger than that of dsDNA. Furthermore, the channel displays a left‐handed chirality, opposite to the right‐handed chirality of dsDNA.153 Ta5 other than the traditional belief that ATP is the starter to initiate a power stroke. Evidently, these nanomotors appear to operate in the revolving mechanism as exemplified by the phi29 motor to accomplish intricate transportation of the lengthy dsDNA genome. A summary of the biomotors discussed herein is presented in Table The architectural and functional similarities are unveiled in many eukaryotic and bacterial motors. Both types of motors have asymmetrical hexameric rings of ATPases with the internal diameters greater than the external diameter of dsDNA. The transacting arginine fingers are required for sequential DNA translocation through the revolving motion. In fact, the arginine finger acts as the starter of the motor to translocate dsDNA,43 oth For example, the phi29 connector channel has been used for chemical sensing, identifying single amino acid differences in peptides, and nucleotide sequencing. Recently, an array of phi29 connector protein channels was inserted into a polymer membrane. This has the potential to be used for high‐throughput chemical sensing and peptide sequencing. Additionally, the discovery of the structural pRNA and its ability to form a variety of structures sparked the field of RNA nanotechnology. The pRNA has been used as building blocks for drug delivery vehicles and as scaffoldings that act as “molecular circuit boards” to organize and regulate proteins. The role of the arginine finger in the phi29 biomotor is becoming more defined, and information on how the arginine finger controls traffic through the phi29 channel yield valuable information on how to control the motion of biomotors. The field of nanotechnology is advancing at a rapid pace as lessons are learned from nature's biomotors. Overall, the similarities and differences shown in the biomotors presented herein can lay the groundwork for the rational design of future nanomachines.There is still much work to be done to further unravel the mechanisms of these biomotors and to harness them for biomedical applications. As further insight is gained into the molecular architecture and mechanisms of how these motors work, their properties can be fine‐tuned for specific uses. The phi29 biomotor is especially appealing since its components can be easily assembled in vitro.154 FoPeixuan Guo is the consultant and licenser of Oxford Nanopore Technologies, and the cofounder and member of the Board of Directors of ExonanoRNA, LLC.


This paper proposes an adaptive threshold segmentation algorithm for the magnesium ingot stack based on image overexposure area correction (ATSIOAC), which solves the problem of mirror reflection on the surface of magnesium alloy ingots caused by external ambient light and auxiliary light sources. Firstly, considering the brightness and chromaticity information of the mapped image, we divide the exposure probability threshold into weak exposure and strong exposure regions. Secondly, the saturation difference between the magnesium ingot region and the background region is used to obtain a mask for the magnesium ingot region to eliminate interference from the image background. Then, the RGB average of adjacent pixels in the overexposed area is used as a reference to correct the colors of the strongly exposed and weakly exposed areas, respectively. Furthermore, in order to smoothly fuse the two corrected images, pixel weighted average (WA) is applied. Finally, the magnesium ingot sorting experimental device was constructed and the corrected top surface image of the ingot pile was segmented through ATSIOAC. The experimental results show that the overexposed area detection and correction algorithm proposed in this paper can effectively correct the color information in the overexposed area, and when segmenting ingot images, complete segmentation results of the top surface of the ingot pile can be obtained, effectively improving the accuracy of magnesium alloy ingot segmentation. The segmentation algorithm achieves a segmentation accuracy of 94.38%. With its excellent properties such as low density, high specific strength and specific stiffness, and good thermal conductivity, magnesium alloy is widely used in robotics ,2, imageThe segmentation object in this paper is a magnesium ingot with a trapezoidal cross-section, and the specific dimensions are shown in Currently, in the industrial field, machine vision  technoloOn the other hand, the disorderly placement of metal parts also requires high stability and accuracy of the algorithm. Hsu et al.  developeCurrently, there is little research on large metal ingots such as magnesium ingots. Due to the large surface area of magnesium ingots, which are more susceptible to light effects, research on the segmentation and recognition of small metal workpieces is not applicable to magnesium ingots. In addition, the disorderly placement of metal workpieces may lead to difficulties in extracting complete edges. The industrial-grade 3D sensors are expensive, and when using depth cameras to capture images, the high reflective areas on the magnesium ingot surface cause depth information loss and create holes. Therefore, considering the comprehensive factors, this paper proposes an adaptive threshold magnesium ingot stack segmentation algorithm based on image overexposure region correction, using color cameras to capture images. This algorithm can quickly and robustly segment magnesium ingots in an image. The main contributions of this paper are as follows:Firstly, a color correction algorithm based on overexposure detection is proposed. This algorithm accurately identifies the areas of magnesium ingot overexposure under various lighting conditions and corrects the color information of the target ingot. As a result, the grayscale gradient of the image becomes more uniform, leading to improved image quality. Secondly, based on this color correction algorithm, an adaptive threshold segmentation algorithm for stacking magnesium ingots is designed. This algorithm utilizes threshold segmentation and morphological processing techniques to segment the top surfaces of the corrected images of the ingots. It effectively addresses the issues of segmentation results sticking when magnesium ingots are arranged and distributed and the segmentation challenges caused by overexposure.The other chapters of this article are described as follows: In the second section, the construction of the experimental light source environment is firstly introduced, then the overexposure area detection algorithm and color correction algorithm are introduced in detail for the experimental object. Finally, an adaptive threshold magnesium ingot stack segmentation algorithm is introduced, which is suitable for the image that the color of the overexposure area is corrected. In the third section, we quantitatively compare our algorithm with other image segmentation algorithms, and design multiple sets of controlled experiments to verify the reliability and accuracy of our algorithm. The fourth section summarizes the main contributions of this article and draws conclusions.This paper proposes an algorithm for detecting and correcting overexposed regions, and designs an adaptive threshold magnesium ingot stack segmentation algorithm based on image overexposed region correction, which can effectively solve segmentation failures caused by object overexposure. The algorithm flowchart is shown in In order to meet the high-quality requirements for the original image of the image recognition, in visual inspection systems, it is often necessary to design appropriate lighting methods with the help of professional visual light sources to illuminate the target object to be detected. Building a suitable light source environment can enhance the distinction between the target object and the background in the captured image, form an imaging effect conducive to image processing, obtain more information about the target object, and reduce the difficulty of image-processing algorithms ,24.In natural environments, differences in time, weather, and the indoor environment can cause uneven brightness distribution in the target area in the captured image, directly affecting the segmentation results of the target object . AccordiAccording to the arrangement of magnesium ingots and the frame structure of the equipment, two sizes and lengths of strip light sources, 800 mm and 1200 mm, are selected and placed symmetrically. They are surrounded by a high angle above the target magnesium ingot for illumination. In order to ensure a clean background for the captured image and to better highlight the target object, the target magnesium ingot is placed on a solid color background. The constructed light source environment has a simple structure and is convenient for debugging, which can more effectively save space without affecting the movement of the mechanical arm above the magnesium ingot. The surrounding lighting scheme can effectively prevent the impact of shadows generated by the movement of the robot arm on the captured image, and avoid uneven brightness caused by shadows generated on the captured image. Because the magnesium ingot is located at the middle of the light source environment and the surface of the metal magnesium ingot is smooth, the parts of the magnesium ingot that are close to the light source may experience varying degrees of overexposure. However, the overexposure positions in the image are relatively concentrated, located at both ends of the magnesium ingot and at a few positions on the edge of the magnesium ingot, as shown in We generally believe that the overexposed area in an image is white , and theIn this paper, we use Lab color space to map the brightness and chrominance information in an image to determine whether there is a possibility of overexposure of pixels in the image, obtaining strong and weak exposure areas. Compared to traditional segmentation methods using single color information, this method can better handle the transition from overexposed regions to adjacent regions.Lab color space is a uniform color space model for visual perception, independent of device differences. It is a digital method for describing human visual perception based on physiological characteristics , where Lnge of −1, 127. ThBefore converting an image from RGB color space to Lab color space, it is necessary to normalize the In addition, the conversion between RGB color space and Lab color space is an indirect process, which requires mapping the RGB color space to the XYZ color space. The conversion relationship is shown in Equation (3).Finally, the conversion of the XYZ color space to the Lab color space, and the conversion relationship is shown in Equation (4):In Equation (4), The strong exposure area in the image has strong brightness, and just performing color correction on the strong exposure area will lead to uneven color transition of the magnesium ingot. Therefore, this paper divides the overexposed area in the image into a strong exposure area and weak exposure area, wherein the strong exposure area is the area that still has the possibility of overexposure in the weak exposure area.We map the brightness and chroma information of the image The strong exposure area The critical value of weak exposure probability In order to obtain the optimal parameters of Algorithm 1: Overexposure detection algorithm.Input: image Output: strong exposure area 1:  convert 2:  for do3:    calculate weak exposure probability value 4:    if5:      6:      pixels 7:    else8:      pass9:    end if10:   end for11:   12:   13:   return The Pseudocode of the overexposure detection algorithm is shown in Algorithm 1.Compared to the segmentation by single threshold, the proposed method can segment more complete regions, including most of the regions in the image where brightness and chroma differ. In addition, the proposed method has a better ability to handle transition regions and obtain more complete segmentation results.Since the overexposed regions in the image are mainly concentrated at both ends and edge positions of the magnesium ingot, the region in the magnesium ingot whose color information is normal is used as a reference to determine the correction color, filling this color into the segmented overexposed region. Finally, the correction image of strong exposure and the correction image of weak exposure are fused according to the pixel WA .Due to the shape and surface physical characteristics of magnesium ingots, which are affected by the surrounding lighting and lighting methods, the overexposed areas in the image are mainly concentrated at both ends of the magnesium ingot. Therefore, to correct the color of the overexposed areas, it is necessary to obtain the appropriate color in the normal color area of the magnesium ingot. First, it is important to eliminate the background effect in order to better obtain the appropriate color of the magnesium ingot.The main objects in the collected images have significant color differences, and the HSV color space is used to segment the image background. After color space conversion of the image, the three channels of the HSV color space are separated and represented as binary images. Histograms of the three channels are drawn, as shown in The threshold segmentation of images utilizes the “bimodality” characteristic of image histograms. The binary image histograms in The threshold segmentation result obtained from the S channel binary image, namely the magnesium ingot region mask, The color correction of an image is a process of inferring and filling to the color and texture information of the region to be corrected based on known region information. The color of the overexposed area of the magnesium ingot region differs from the normal region of the middle portion, and there is a significant color transition from both ends to the middle. A secondary filling method is used for color correction of this portion. Different correction colors are used to fill strong and weak exposure areas, respectively. Compared to single large area filling, processing for areas with different overexposure levels can achieve a uniform color transition effect.A. Calculating the threshold of color reference area.The reference area for color correction is obtained from the exposure probability values of the underexposed pixels in the magnesium ingot. Taking color correction in the weakly exposed areas as an example, the algorithm is detailed as follows:Using the idea of mask superposition, the original image is overlaid with the non-magnesium ingot region mask, B. Obtaining color reference areas.We superposition the original image with C. Calculating the correction color of the area to be corrected.The filling color of the region to be corrected D. Filling color.Algorithm 2: Color correction algorithm (taking color correction for weak exposure as an example).Input: image Output:1:  convert 2:  s_image ← the S channel image of image 3:  4:  5:  6:  for do7:    calculate the weak exposure probability value, 8:    9:    calculate the weak exposure probability value, 10:     11:     if12:      13:      pixels 14:      15:     else16:      pass17:     end if18:   19:   end for20:   returnThe Pseudocode of the color correction algorithm is shown in Algorithm 2.As can be seen from the The image that is corrected by single exposure intensity has a certain degree of uneven color transition. From Compared to the original image, the fusion result shows that the overexposure phenomenon on the magnesium ingot region is reduced and the color transition is uniform. From Compared to the original image, the brightness is more uniform and color information is more complete for the corrected image, which is conducive to the segmentation of magnesium ingot regions. In this paper, an adaptive threshold segmentation algorithm  is used The result of color correction in To verify the image segmentation effect of the algorithm in this paper, experiments were conducted on an automatic magnesium ingot sorting device, as shown in (1) Verifying the impact of illumination on the algorithm when the magnesium ingot is at different positions.Due to the method of lighting on all sides, the distance between magnesium ingots at different locations and the light source is different, and magnesium ingots close to the light source are significantly affected by light, making overexposure more likely to occur. By controlling the number, placement position, and placement posture of magnesium ingots, the experiment was designed. And the segmentation results of magnesium ingots are shown in In (2) Verifying the influence of light intensity on the algorithm.The intensity of the light source will directly affect the area of the overexposed area on the surface of the magnesium ingot. In order to verify the adaptability of the algorithm in this paper to different overexposed areas and to be able to adapt to the interference of light sources with different intensities, a control experiment is designed for different illumination. The brightness of the light source of the strip light is adjusted through a dimmer controller. And the segmentation results of the magnesium ingot are shown in As can be seen from the input image in (3) Verifying the effectiveness of the algorithm under actual operating conditions.In an industrial production environment, magnesium ingots are arranged in a vertical cross shape and in multiple layers. Due to the limit of experimental conditions, the number of magnesium ingots is insufficient. Take a double-layer arrangement of magnesium ingots as an example, and the specific arrangement is shown in In (4) Comparing with other algorithms to verify the reliability of this algorithm.This experiment selects four magnesium ingot images with different postures and compares the segmentation results of the four algorithms. They are the Otsu threshold , TrianglFrom We collected a total of 250 magnesium ingot images, including various light source brightness, various magnesium ingot postures, and magnesium ingot arrangement methods. When each magnesium ingot in the image is recognized independently, the image is considered as a fully recognized image. The calculation for the segmentation accuracy in this paper is the percentage of fully recognized images out of the total number of captured images. Comparative experiments were conducted between the three threshold segmentation algorithms and ours, and their segmentation accuracies are shown in From the collected images, a total of 225 images were selected. They were divided into two groups of experiments according to different the postures and light source brightness of magnesium ingot images, comparing the recognition rates of the four methods.In In addition, during the magnesium alloy smelting process, the basic production requirement is to deliver two magnesium ingots per minute. The real-time requirement for the identification process is not high, and our current identification efficiency can meet the production requirements.This algorithm can be applied to the recognition of magnesium ingots with high reflectivity on the surface, solving the segmentation difficulties caused by overexposure of magnesium ingots due to changes in lighting. This paper proposes an algorithm for detecting and correcting overexposed regions for high reflection of magnesium ingots, and designs an adaptive threshold magnesium ingot stack segmentation algorithm based on image overexposed region correction. This algorithm identifies overexposed areas in an image through the mapping relationship between image brightness and chrominance information, and corrects them based on their neighborhood color information. The corrected image effectively improves segmentation accuracy, and this algorithm can effectively improve the antienvironmental interference ability of image segmentation processing.In the experiment presented in this paper, we achieved satisfactory segmentation results for the magnesium ingots with our current shape and size under the current lighting environment. However, in order to obtain even better experimental results, we plan to further explore the segmentation of magnesium ingots with different shapes and sizes under more complex lighting conditions.


The JBW framework, with Si/Al ratio of 1, is an ideal benchmark system as a stepping stone towards more complicated zeolites. The presence and mobility of water and extraframework species directly affect NMR fingerprints. Excellent agreement between theoretical and experimental spectra is obtained provided dynamic methods are employed with hydrated structural models. This work shows how NMR is instrumental in characterising aluminium distributions in zeolites at operating conditions.An important aspect within zeolite synthesis is to make fully tunable framework materials with controlled aluminium distribution. A major challenge in characterising these zeolites at operating conditions is the presence of water. In this work, we investigate the effect of hydration on the  Discovering the aluminium location is a challenging endeavour in zeolites, as overlapping resonances complicate spectral assignment. Combining theoretical and experimental NMR is indispensable in spectral interpretation while the inclusion of configurationally diverse water molecules is essential. The JBW zeolite is therefore perfectly suited to unambiguously study the effect of water and crystallographic T‐site symmetry on the NMR signals. The synthesis of the JBW sample was described in a previous paper,27Al, 29Si, 39K and 23Na NMR and Rietveld refinements may be found. The aim of the study at hand is to benchmark a combined computational and experimental characterisation protocol for aluminium in zeolites using NMR spectroscopy at operating conditions, critically assessing the role of water. From a computational point of view, both static and dynamic models are employed to compare computational NMR parameters with operando spectroscopic 1D MAS NMR (magic angle spinning NMR) signals of 27Al and 29Si nuclei in JBW at various water loadings. The Si/Al ratio of 1 together with the high level of crystallinity result in very well resolved individual quadrupolar Pake patterns , which is rare in zeolites. The 27Al spectrum was measured at different levels of hydration of the material, which provides unique insight into the effect of water on the aluminium NMR parameters. Through comparison of the experimental and computational chemical shift and quadrupolar coupling constant (QCC), the need for operando modelling techniques that explicitly take into account the presence of water is shown. The insights obtained in this study are important for future efforts in tuning the aluminium distributions in zeolites at operating conditions.In this work, we study the JBW−Na,K zeolite with a Si/Al ratio of 1. The crystallographic analysis of this sample by X‐ray diffraction (XRD) revealed exceptional levels of crystallinity and order. The framework consists of one‐dimensional eight‐ring (8R) channels separated by dense anhydrous layers of six‐ring (6R) pores, as is shown in Figure 27Al MAS NMR spectra at specific water loadings along the procedure. All experimental NMR measurements with various hydration levels were carried out following the methodology as detailed in the SI (section S1). 29Si, 23Na and 39K NMR spectra were recorded for the as‐synthesised system and the fully anhydrous system. XRD measurements were employed to obtain the space group, atomic positions and unit cell parameters of the as‐synthesised and dehydrated systems with the purpose of obtaining a starting point to build structural models for the computations. Any reported hydration levels were measured using 1H NMR.1H NMR hydration measurements the experimental water content at the saturation point is underestimated due to chemical exchange between free water and adsorbed water.To assess the influence of water on the NMR spectra, the hydrated as‐synthesised JBW sample was dried and rehydrated to the point of saturation, while collecting 2O/Al (anhydrous) to 0.33 H2O/Al (4 water molecules per unit cell). The saturation point of water was determined through grand canonical Monte Carlo, the details of which may be found in the SI (section S2). The computational point of saturation amounts to one water molecule per potassium site, which is in agreement with the XRD measurement performed on our as‐made JBW sample where potassium and water molecules alternate inside the 8‐ring channel as well as with older XRD measurements on the Na,K‐JBW system.The computational structural models were built by taking the atomic positions from the XRD experiments and subsequently loading the structures with different amounts of water. This resulted in a total of six systems of which one triclinic anhydrous cell and five orthorhombic systems with water loading ranging from 0.0 HThe six resulting structural models were then used as input for a static model and for a dynamic model. In the static model, the atomic positions and cell parameters are first optimized using density functional theory (DFT) at the PBE−D3 level of theory. The optimized structures are subsequently used to calculate the NMR parameters through linear response. The dynamic model, on the other hand, uses the XRD structures as input for first‐principles molecular dynamics runs at 300 K. Then a set of snapshots from the trajectories is selected to obtain time‐averaged NMR properties for each atom in the structures. Details of both static and dynamic workflows are given in the SI (section S2). The conversion of calculated chemical shieldings to chemical shifts which can be compared to experiment is detailed in the SI (section S3).Pmn21 space group showing an orthorhombic unit cell. If the system is dried for 16 hours under vacuum (1 mbar) at a temperature of 200 °C to the point where no NMR‐measurable amount of water is present, the symmetry of the system breaks and it resorts in the P1‾space group with a triclinic unit cell. This breaking of symmetry affects the number of distinct T‐sites in the system, with the orthorhombic system having two distinct T‐sites occurring in a ratio of 2 : 1 (T1:T2), whereas the triclinic system has three T‐sites in ratios of 1 : 1 : 1 (T1A:T1B:T2) .The space group of the JBW zeolite depends on the degree to which the sample is dehydrated. The as‐made JBW sample has the 27Al NMR parameters is found at low water loadings. Beyond 6 mg H2O, the influence of adding additional water to the rotor on the aluminium NMR parameters is negligible, as can be seen in Table 1H NMR measurements. The 27Al NMR spectra for the orthorhombic system as shown in Figure 29Si NMR spectrum in Figure 27Al and 29Si NMR spectra properly. Based on the assignment in the orthorhombic case, the lineshape with the highest chemical shift (65.01 ppm) is assigned to aluminium residing in T2 sites. Purely based on experimental data, it is impossible to assign the other two lineshapes to T1A and T1B sites. The assignment of the triclinic case in Table The strongest influence of water on the 27Al chemical shift and QCC between the static model, dynamic model and the experimental fitting data for the anhydrous triclinic cell. The quadrupolar asymmetry parameter and 29Si chemical shift may be found in the SI (section S4). Based on the data in Figure Figure 27Al chemical shift and QCC for all measured and simulated hydration levels are shown in Figure 29Si chemical shift data may be found in the SI (section S4). Additionally, we have produced a plot that shows the correlation between the average T−O−T angle and the chemical shift, which may be found in the SI (section S5). Note that for each hydration level, 8 T1 and 4 T2 data points are plotted, however, some of them overlap. The computational 27Al chemical shift and QCC show a clear splitting between resonances originating from T1 and T2 sites. Moreover, it is clear that both the static and dynamic model can reproduce the trend of decreasing chemical shift upon increasing water loading. For the QCC, there is no obvious trend when changing the hydration level. While the individual resonances of the aluminium sites change upon the addition of water, the overall distribution of values does not show a systematic change. This is also the case for the experimentally fitted values as these only change by 0.1 MHz overall, which is small compared to the spread on computational QC values. An important phenomenon to notice is that there are three distinct resonances in the static model for the anhydrous case  no diffusion of water is present in the model system and (b) the time scale of the dynamic simulations is too short to attain the same configurational diversity as in the experimental system. In the real system, more configurational diversity is present so that over time all individual atoms with the same T‐label, on average, see the same water configurations around them, which then translates into a single NMR resonance. While these long time scales are not accessible in the dynamic model , the necessary configurational diversity of water can be mimicked by further averaging over T‐sites within one crystallographic label, as will be explained in the section Bridging experiment and theory.27Al chemical shift of the individual sites in the dynamic model of the orthorhombic system . The data indicates that our model even predicts further distinction within the T1A and T1B labels, as within these labels there exist sites with distinct Al−K distribution functions. Furthermore, no potassium diffusion through the channel is observed in the trajectory of the system. We therefore conclude that mobility is hindered in the triclinic cell, leading to discrepancies in NMR parameters between models using a single K distribution and experiment. Interestingly enough, this is consistent with previous observations in the JBW system that upon rehydrating the anhydrous triclinic cell, the orthorhombic cell is not retrieved,27Al NMR spectrum and model NMR parameters derived from theory for the orthorhombic case. As argued before, the T1 (respectively T2) sites at each specific water loading are nearly equivalent in the orthorhombic dynamic models. Therefore, it is appropriate to further average over T‐sites within the T1 (respectively T2) label to obtain a representative computational T1 (T2) resonance which can be compared to the experimentally fitted T1 (T2) data. The reasoning behind this, is that in the real system, the NMR parameters are measured on much larger time and length scales than in the computational models. In our dynamic model no diffusion of water molecules past the potassium ions in the channel is observed on the time scale accessible by these dynamic models (around 100 ps), as these are calculated at the DFT level. Water does diffuse in the experimental system during measurement otherwise no substantial change in NMR spectra would be observed upon hydration. The mobility of water and potassium species and a comparison to the static single‐configuration case is shown in Figure In this section we aim to obtain a direct comparison between the experimental The result of this averaging procedure at each water loading for the dynamic model is shown in Table Δ(T2-T1). The dynamic model spectrum was simulated using a fixed ratio of T1 to T2 lineshape contributions of two to one. Following this procedure, it is clear that the dynamic computational model can reproduce the experimental spectrum with great accuracy with an overall root‐mean‐square deviation (rmsd) that is comparable to the experimental fits using two lineshapes.The simulated model spectrum at saturation, if artificially broadened as is routinely done to fit the experimental spectra, compares well with the experimental spectrum. This can be seen in Figure 27Al 1D MAS NMR measurements were performed at different hydration levels going from as dry as possible to fully hydrated, revealing the dependency of the NMR parameters on the water content. The chemical shift is the parameter which is most affected by the specific water content, showing a steep decrease upon water loading. Both static and dynamic models were employed to model this behaviour, explicitly taking into account the presence of water. While both models are able to reproduce the trend in chemical shift, the dynamic model gave the best agreement with the experiment as in this approach the mobility of the potassium species and water molecules is taken into account. Furthermore, the spread on individual resonances of the atoms is large in both models. We have proposed a way to enhance the comparison to experiment, by averaging over sites in the model that are located at crystallographically equivalent positions. This is especially relevant for systems with very high aluminium content, similar to the system at hand, where many aluminium atoms are located at sites with the same framework symmetry. As such, the experimental 27Al NMR spectrum can be reproduced with great accuracy, provided the comparison is done at saturated water content and including many different local water configurations.In this work, we have investigated the JBW zeolite with a Si/Al ratio of 1. Owing to its fully defined aluminium distribution at Si/Al=1, this zeolite serves as a good benchmark system to determine the influence of water on the NMR spectra and to set up a protocol for reliably determining computational NMR properties. Experimental operando NMR parameters, i. e. at experimental conditions. Under these conditions dynamic averaging cannot be neglected, as many different water configurations contribute to the experimental signal which cannot properly be taken into account in the static model. The obtained results serve as a protocol on how to include the effect of water and its dynamic rearrangement in computational models that aim to determine the NMR fingerprint of aluminium distributions in zeolites under realistic conditions. To extend the work to other zeolites, systems with symmetrically inequivalent T‐sites at various Si/Al ratios should be considered to further elucidate the impact of the aluminium distribution on the NMR properties. In this respect, computational modelling of the aluminium distribution and NMR spectra is vital, as NMR spectra cannot be deconvoluted with certainty in these cases due to the aluminium atoms being located at a plethora of symmetrically inequivalent sites. Moreover, in zeolites with a Si/Al ratio greater than 1, grouping T‐sites into symmetrically distinct classes is a challenge on its own as the definition of symmetry equivalent sites depends on how many coordination shells are taken into account to produce the symmetry labels. While the computational protocol at hand is in principle applicable to such cases, the grouping of sites can become tedious and it might be better to consider each aluminium site on its own and aim to derive well‐averaged NMR parameters by including as much configurational diversity of water clusters as is computationally possible. If, by some definition of equivalent sites, some atoms can be grouped to represent one equivalent T‐site, one can still apply the averaging protocol. Additionally, there is no reason why the presence of water would not have any influence on the NMR spectrum in different zeolitic systems as well, especially in the case of protic zeolites, where it is expected that water plays an even bigger role as it can change the nature of the acid site.[The data in this work highlight the importance of explicitly taking into account water in the simulation of zeolitic systems when one is interested in obtaining cid site.33, 3429Si chemical shift), Al−K pair distribution functions for the triclinic system, experimental 23Na and 39K NMR for the as‐synthesised orthorhombic and the anhydrous triclinic systems, detailed analysis of unit cell symmetry using molecular dynamics, convergence tests for the DFT‐GIPAW method with respect to computional settings and number of snapshots, space group transformations used when averaging over T‐sites. For an overview of which structural models and input files are available, we refer the reader to the SI (section S11).The following supporting information is available: methodological details on experimental NMR, overview of computational methodology, information on the conversion of shielding to shift values, additional data for the triclinic and orthorhombic systems  should be addressed to the authors.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.Supporting InformationClick here for additional data file.


The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high‐frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process. CPCC‐phycocyaninLODlimit of detectionMImembrane inletIMSion mobility spectrometryODoptical densityOTRoxygen transfer rateUV‐VisUltraviolet‐Visible spectroscopy1off‐line procedures that require sample collection, sample preparation, or dilution of sample. A summary of analytical techniques that are frequently used to monitor biological processes is presented in Table The sustainable, industrial use of algae and their cell ingredients for the production of valuable compounds and active substances is a promising alternative to fossil raw materials and synthetically manufactured products . Pharmaconline monitoring of micro algae cultivation. The developed method bases on algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. The practical benefit is the possibility for predicting the optimal harvest time in systems with varying conditions such as open raceway ponds. Furthermore, early detection of possible disturbances permits the user to check the system before the product yield reduces. The ion mobility spectra are recorded every 10 min and the resulting three‐dimensional data matrix is evaluated using principal component analysis (PCA). Therefore, the developed procedure can be automated and integrated into biotechnological processes.Ion mobility spectrometry (IMS) in combination with a membrane inlet (MI) was successfully introduced as tool for The oxygen transfer rate (OTR) is widely used to study the growth of microbial or plant cell cultures . In photUltraviolet/Visible spectroscopy (UV‐Vis) can be used at fairly low cost to determine the concentrations of substrates, metabolites, and products. UV‐Vis spectroscopy can also be used to estimate the concentration of C‐phycocyanin (CPC) or its purity . UnfortuLimnospira platensis (L. platensis) .. This trend is mirrored by peaks c and e to a lesser extent. These three peaks all show a strong light dependence. Peaks c, d, and e rise when the culture is illuminated and growing (days 0–5 in white marked areas) and then drop when the illumination is turned off (gray marked areas). Finally, a new peak, peak g, is observed as the cultivation reaches a maximum cell concentration at around day 6. This peak continues to increase as the cell concentration starts to decrease.The intensities of the seven relevant peaks over the course of cultivation can be seen in Figure 0 Figure . CompariOD750 of 0.11 ± 0.01 and an increase in cell concentration was observed until day 5. The maximum cell concentration was then reached between day 5 and 6 before a decrease in cell concentration was observed. The maximum intensity of peak d was consistently measured on day 5 or 6 and was normally observed just before the maximum cell concentration was achieved. This indicates that peak d is a marker for the growth rate and when the maximum cell concentration has been reached. The intensity of peak g was seen to rise in all cultures from the onset of the stationary growth phase into the cell death phase. The presence of peak g above a threshold value can, therefore, be used in process control to bring an end to the cultivation as the culture has now started to die.These trends in peak intensities were consistently observed across different cultivations performed under identical conditions. The cultures all started with an OD750 (R2 > 0.95). This correlation shows both that peak d is an important peak in the peak pattern associated with growth, but also that the MI‐IMS based system works effectively at low cell concentrations. This is in contrast to other online sensors such as scattered light sensors. These sensors are used to measure the growth of a culture; however, they normally require higher cell concentrations to show a clear correlation between the sensor signal and the cell concentration. The backscattered signal is often disturbed by absorption or cell agglomeration [Changes in peak intensity can be used to approximate the cell concentration in the growth phase. The intensity of peak d rises in the first days of cultivation. In Figure meration .A student's parametric t‐test was performed to check whether the intensity changes of peaks a–g were significant changes over the growth phases. The results of this can be found in Table L. platensis (Spirulina) and C. vulgaris (Chlorella) differ significantly from each other. Peaks a–g can clearly be observed in the two ion mobility spectra of Spirulina between relative drift times of 1.2 and 1.6. By contrast, the major peaks in the two ion mobility spectra of Chlorella occur before 1.2. This shows that the ion mobility spectrometer has sufficient resolving power to differentiate the VOC profiles of two different microalgae. Although this work focuses on using IMS as on online monitoring tool for the process control to maximize production, these results show the potential for using IMS to detect contaminations, such as grazers, in a cultivation.Whilst microalgae share similar metabolic pathways, a simple proof of concept to test the suitability of IMS for measuring the VOCs produced by microalgae was to sample the headspace above two different microalgae cultivations. As seen in Figure As a result of these investigations it could be shown that (1) different microalgae cultivations lead to fundamentally different spectra. (2) The intensity ratios of the peaks within the ion mobility spectra significantly vary over the course of an algae cultivation. (3) The cultivation of the same species of microalgae yields identical and reproducible ion mobility spectra and comparable intensity courses during cultivation.From these results, some general practical conclusions can be drawn for the application of IMS with respect to information about the bioprocess: (1) When selected peaks reach their maximum intensity, the maximum growth rate is reached. (2) The peak intensities (in particular at low cell concentrations) correlate with cell concentration. (3) A drop in all growth‐related peaks without new peaks normally reflects the day/night cycle. (4) The appearance of new peaks in the ion mobility spectrum indicates change in cell functions, for example, nutrient limitation or lysis.3.2While individual peaks can be correlated effectively with changes in the cell concentration, it has already been shown that certain peaks, for example, peaks c–e, correlate strongly with each other. In addition, there are over 1000 data points within the ion mobility spectrum corresponding to relative drift times between 0.7 and 3. A peak is therefore made up of multiple data points which, under ideal conditions, lead to a gaussian peak shape in the spectrum. This leads to two different strategies to process the data to maximize the number of data points used in the analysis and minimize the influence of signal noise. The data can be modeled using a combination of multiple gaussian peaks with fixed relative drift times. Alternatively, principal component analysis (PCA) can be used to identify the peaks which correlate with each other and build principal components (PC). In both cases, all data points after the reactant ion peak are included in the data set. This means that, for each spectrum, the intensity measured at over 1000 relative drift time points is evaluated as opposed to the maximum intensities of, in this case, seven peaks. In the gaussian peak model, the data matrix is compared to a model built using peaks with a specific center point and width. This allows the noise in the measurement of the intensity to be reduced and, in this case, seven more reliable data points to be generated for each spectrum in the time series. Using PCA, the dimensionality of the data set is reduced from more than 1000 x number of spectra in the time series (**** x ****) to a data set of, in this case, **** x 4. Reference data are then used to link either the 4 PC Vectors or the seven peaks from the gaussian model to a recommendation for the process control.A custom Python code was written using the sklearn package see . The staAs seen in Figure These results can be used as a basis for process control. New spectra taken can be characterized using the PC transformation obtained from the training data set. The process control system can then give recommendations or automatically trigger control events. (1) If PC 1 rises and PC 2 remains constant, the culture is growing and the cell concentration is increasing. Therefore, no changes in the system are necessary. (2) If PC 1 reaches maximum and PC 2 remains constant, the culture achieved maximum cell concentration and the practical recommendation is: Harvest or add more culture medium. (3) If PC 2 increases, the cell concentration is decreasing. The recommendation from process control system would be: Harvest, add more culture medium or check for contamination. (4) If PC 2 is strongly negative, there is an unknown system status and the user is prompted to check the system.3.3L. platensis uses the carbonate ions in the culture medium as a carbon source to produce sugars from photosynthesis which then allow growth. As already shown, the absence of illumination leads to a reduction in peak intensities in the IMS spectra and also a reduction in PC 1. It should not however be assumed that higher levels of illumination lead to increased growth rates. Above a species‐specific maximum level of illumination, the photosystems in Arthrospira sp. are saturated and the rate of growth does not increase. Furthermore, under intense illumination, concentrations of CPC decrease [Illumination is essential for the phototrophic growth of the algae cultures. In all the cultures presented, decrease , 55, 56,Cultivations 5 and 6 were therefore performed under low and high levels of illumination and the changes in the IMS spectra and PC scores were observed. As seen in Figure The presence of peak f, and obviously a change to the metabolic processes under high illumination, could result from the degradation of CPC for use as a nitrogen source. Further investigation is however required to determine if the addition of a further nitrogen source to the culture medium prevents peak f from forming and allows high levels of growth without CPC degradation under high illumination.4IMS equipped with a MI is a promising technology to allow reliable and accurate process monitoring and control of microalgae cultivations. The VOCs can be automatically sampled when the IMS is coupled in‐line and data acquisition occurs every 10 min with zero “hands‐on” time required from an operator. This high frequency of measurement allows process control to occur in a timely manner. The automated, cost‐effective and stable VOC spectra acquired from MI‐IMS can then be used to characterize the condition of the cultivation and provide recommendations for the process control. PCA was performed on a training data set of 6 cultures and the first three PCs could be used to determine: if the culture was growing; if the maximum cell concentration had been reached; if the cell concentration was decreasing and if the level of illumination was too high. This application of a single sensor system to provide multiple process control signals sets MI‐IMS apart from the analytical techniques listed in Table The authors declare no competing interests.L. platensis performed on identical equipment at different locationsSupplementary Figure 1. Comparison of IMS‐spectra from two cultivations of Supplementary Table 1: Results of statistical analysisClick here for additional data file.


Equipping Galileo satellites with a VLBI transmitter (VT) will allow to observe satellites next to quasars with Very Long Baseline Interferometry (VLBI) radio telescopes. This concept will facilitate the direct estimation of the satellite orbits in the celestial reference frame. Moreover, these observations along with usual Galileo observations can be used to transfer the space tie between the VT and the antenna on the Galileo satellite to the Earth surface realizing the frame tie at the geodetic site with VLBI radio telescope and Galileo antenna. In this study, we assess the accuracy of that frame tie by simulating the estimation of station coordinates from VLBI observations to Galileo satellites next to quasars. We find that at least two or three satellites need to be equipped with a VT with the best results if all satellites with a VT are placed in the same plane. Concerning the ratio between satellite and quasar observations within a schedule, the results suggest that the optimal ratio is around 30% to 40% satellite observations out of the total number of observations in order to have enough observations for the estimation of the station coordinates but still enough quasar observations to ensure a sufficient sky-coverage for the estimation of troposphere parameters. The best scenario with two satellites yields repeatabilities for the east and north components between 7.5 and 10 mm, and for the up component between 9.5 and 12 mm. In case there is a third satellite with a VLBI transmitter in the same plane, the repeatabilities are reduced by up to 2 mm for the horizontal components and up to 3 to 4 mm for the up component. Rotating the schedules over the constellation repeat cycle of Galileo of 10 days reveals that there are differences between the individual days, but there are no days with a significantly worse precision of the estimated station coordinates. Very long baseline interferometry (VLBI) is a space geodetic technique observing the emission from extragalactic radio sources, mostly quasars, with a network of radio telescopes. The mounting of a VLBI transmitter (VT) on board of satellites will allow to observe satellites next to quasars with VLBI radio telescopes, opening new possibilities but also new challenges. Besides Satellite Laser Ranging (SLR), Doppler Orbitography by Radiopositioning Integrated by Satellite (DORIS), and Global Navigation Satellite Systems (GNSS), VLBI is one of the four space geodetic techniques contributing to the determination of the International Terrestrial Reference Frame  alone. Then, we use VLBI observations to the satellite to derive station positions, i.e., we determine VLBI station coordinates in the dynamic frame realized with the satellite orbits. These coordinates are then compared against VLBI station coordinates from quasar observations, thereby providing information about the tie between the dynamic and the kinematic (quasar) frame. With the absence of real observations and based on simulations, we focus on the precision of station coordinates from VLBI observations to satellites to get an estimate of the accuracy of the frame tie. This concept was used by Plank et al.  for diffIn the last years, real VLBI observations to satellites were carried out as first attempts. Haas et al. , 2015 anHaas et al.  investigNowadays, no satellite mission with a dedicated VLBI transmitter (VT) is in operation, which could be observed with VLBI radio telescopes. However, besides Genesis, there are plans to mount a VT on board of Galileo satellites. Galileo is Europe’s Global Navigation Satellite System consisting of three orbital planes inclined at an angle of 56° to the equator and spaced by 120° to each other, see Fig. Several studies already investigated VLBI observations to GNSS satellites with respect to the realization of frame ties between the VLBI and the satellite frame  as well as the coordinates of the quasars and the satellite orbits are fixed. We are using sp3 files from the International GNSS Service  every 60 min with a relative constraint of 1.3 cm between these offsets. This is done for all stations except for the station with the highest number of observations, which is selected as reference clock. The troposphere parameters are estimated from both observation types, namely satellite and quasar observations. The zenith wet delays are estimated every 10 min as PWLOs with a relative constraint of 1.5 cm and the northern and eastern gradients are estimated every 20 min as PWLOs with a relative constraint of 0.05 cm. Earlier studies have shown, that the large number of scans with VGOS observations allows short estimation intervals of troposphere parameters  directions, and further the repeatability is determined as standard deviation of the estimated station coordinates. The different scenarios and schedules with various ratios between satellite and quasar observations are compared and assessed based on these repeatabilities.In total 30 schedules are created for the selected VGOS network considering different satellites and ratios between satellite and quasar observations. As an example, Fig. The ratios of 10% to 60% satellite observations out of the total number of observations are fulfilled for the total number of satellite observations at all stations. It does not imply that for all individual stations the ratio between satellite and quasar observations is exactly fulfilled, for instance 10%. The colors in Fig. A good distribution of the scans over the sky above the stations is very important for the determination of the troposphere parameters and for the estimation of the station coordinates. The sky-coverage can be assessed with a so-called sky-coverage score. This score is calculated in VieSched++ by splitting the sky above a station into smaller areas of approximately equal size, analyzing which areas are covered by at least one scan, and dividing the covered areas by the total number of areas. The sky-coverage score ranges from zero to one, with one denoting a perfect sky-coverage, meaning that every area is covered with at least one scan (Schartner and Böhm It is clearly visible that for all scenarios the sky-coverage score becomes smaller representing a worse distribution of scans over the sky with an increasing percentage of satellite observations in a schedule. Due to the reason that satellite observations can be carried out either to three or two satellites only, the distribution of these observations over the sky at a station is limited. A higher percentage of satellite observations in a schedule thus means more observations to satellites, which cannot be well distributed. Therefore, the sky-coverage is degrading with more satellite observations.A comparison of the different scenarios in terms of sky-coverage shows that for a ratio of 10% the results are similar. However, with increasing the ratio of satellite observations, the scenarios with one satellite per plane A, B, C and one satellite in A and two satellites in B have smaller values than the scenario with three satellites in plane A. This difference can be related to the ground tracks of these three scenarios; see Fig. In Fig. Figure The different performances of the three scenarios with three satellites in terms of the precision of the estimated station coordinates can be explained by the ground tracks of the satellites over 24 h, see Fig. The comparison of the results for the different components shows that the precision of the east component is better compared to the precision of the north and up component. We attribute this feature to the mostly north–south oriented ground tracks of the satellites. For stations at low latitudes, the up component can be estimated more precisely than the north component, due to the so-called north hole of the Galileo space segment caused by an inclination of 56° to the equator. This phenomenon is evident from the skyplots for the stations ISHIOKA and WESTFORD in Fig. The best result for the scenario with two satellites in plane A is achieved for a ratio of 30% and for the scenario with one satellite per plane A and B for a ratio of 20%. This is due to the smaller number of satellites and similar ground tracks of these two satellites, leading to a worse sky-coverage for higher ratios of satellite observations. The reason for only displaying results for ratios up to 40% for the scenario with one satellite in plane A and one in B is that the repeatabilities for higher ratios are much higher (30 mm and far above; Fig. The repeatabilities of the station coordinates for the scenario with one satellite per plane A and B are significantly worse compared to the results of the scenario with two satellites in A. This is due to the very similar observation geometries between the stations and the satellites as the trajectories of these satellites are very similar and just shifted in longitude for one satellite in plane A and B; see Fig. The results of the scenario with one satellite in plane A are not shown here as these do not yield to sufficiently good results. However, for this scenario three schedules are investigated with a ratio of 20%, 30% and 40% of satellite observations. A ratio of 20% yields to the best results with repeatabilities between 23 and 56 mm for the east component, 29 and 61 mm for the north component, and 38 and 68 mm for the up component. If the ratio of satellite observations is increased to 40% the repeatabilities are between 79 and 318 mm for the east component, 115 and 304 mm for the north component, and 130 and 270 mm for the up component.The Galileo space segment has a repeat cycle of 10 days. Within this period the constellation of the satellites and therefore the visibilities and observation geometries are changing. After these 10 days, the system constellation repeats again. Therefore, an investigation of the precision of the station coordinates over this cycle is of major importance.For both cases, two and three satellites being equipped with a VT, the best scenarios, which are two satellites in plane A with a ratio of 30% and three satellites in plane A with a ratio of 40% are analyzed over the repeat cycle of Galileo, namely 10 days starting on August 27, 2022. The station coordinate repeatabilities for the east, north, and up components for both scenarios over the repeat period are shown in Fig. The precision of the estimated station coordinates varies over the days due to the aforementioned change of the ground tracks of the satellites resulting in different visibilities and observation geometries. For the scenario with three satellites and considering all three components, the overall results are best for the first 2 days. Over the remaining days the precision is worse for the north and up component, while for the east component there are days when the precision is even better. However, there is not 1 day with significantly worse results. The scenario with two satellites achieves the best precision for all components on August 31, 2022. The day with the worst precision is September 2, 2022. On that day, especially the north component is affected at some stations. By comparing the results of three and two satellites in plane A over the repeat period of Galileo, it is clearly visible that the scenario with three satellites performs significantly better, which was to be expected. For the scenario with only two satellites in plane A, the performance on most of the days is significantly worse compared to a few days when all components are determined with a precision better than 13 mm.The results for two satellites in the same plane are less precise than the results for three satellites in the same plane. Although two satellites being equipped with a VT are already sufficient to derive station coordinates in the satellite frame, three satellites will lead to a higher precision. Further, four satellites in the same plane lead to a small improvement of the station coordinate repeatabilities by about 2 mm compared to three satellites. With in total five satellites, station coordinate repeatabilities improve by 0.5 mm compared to four satellites. However, having the fifth satellite in the same plane does not lead to an improvement. There is no gain in precision with every additional satellite.The network used in this study see Fig.  consistsThe performance of the scenario with three satellites in plane A with a ratio of 30% satellite observations is investigated using this 15 station network. The precision of the station coordinates is improved for all stations and for all three components east, north, and up. The repeatabilities are between 3.5 and 5.6 mm, which represents an improvement between 2 and 5 mm for the individual stations and components compared to the results of the same scenario using the 12 station network. These results show, that a globally extended network has the potential to further improve the estimation of station coordinates from satellite observations.This study examines the estimation of station coordinates from VLBI observations to Galileo satellites next to quasars. By determining the station coordinates from satellite observations alone, we assess the frame tie between the satellite and the quasar frame at the level of station coordinates. This approach provides information how well space ties from the Galileo satellites can be transferred to the Earth surface. In particular, we address three specifications, i.e., the number of Galileo satellites which have to be equipped with a VT, their distribution over the planes, and the ratio between satellite and quasar observations within a schedule.The simulation results show that equipping one satellite with a VT is not enough, at least if it is observed over 24 h only. The station coordinate repeatabilities from 24 h sessions are above 30 mm. An interesting finding is that the optimal placing of two or three satellites with a VT would be within the same plane. Having two satellites with a VT in the same plane leads to better results than having three satellites with a VT in different planes. This shows that a higher number of satellites does not necessarily mean better results, but a proper placing of the satellites is more important. Future studies will also deal with the stacking of solutions from 24 h sessions addressing the question, how many weeks of satellite observations do we need to achieve frame ties at the Earth surface at the 1 mm level.In future studies, we will also address the influence of errors in the space ties and other parameters on our estimates. Moreover, we will investigate the simultaneous estimation of the right ascension of the ascending node of the satellite orbits and of Earth orientation parameters and its impact on the solutions.This study also clearly demonstrates that quasar scans are very important for the determination of the troposphere parameters and therefore for the estimation of station coordinates. A schedule with 100% satellite observations does not yield the best results for the estimation of station coordinates as the troposphere is dependent on a good sky coverage which is only guaranteed by including quasar observations in a schedule. If the individual stations do not achieve a good sky coverage caused by many satellite observations to the same satellites, the results of the station coordinates are degrading. In this study ratios of around 30–40% satellite observations of the total number of observations lead to the best results.