esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The regulation of gene expression within this crosstalk involves chromatin remodeling by preventing acetylation of histone 3 lysine residue #9 (H3K9) at the flavonoid pathway gene (FPG) loci (such as _CHS_) in presence of flg22 [18]. H3K9 acetylation is a hallmark of gene activation and was affected at both the promoter and coding region of the MYB TF MYB12, a positive regulator of the CHS. Other MYB TFs were found to be differentially regulated in the context of this crosstalk with functional redundancy. For example, MYB11, 12, and 111 are positive regulators of the flavonoid pathway [19] and all are co-regulated with the marker gene _CHS_ being UV-B inducible and suppressed by flg22. MYB4 and its close relatives MYB87 and MYB32 are negative regulators [20]. The _MYB4_ gene was induced earlier by flg22 than by UV-B, suggesting its functional involvement in the PTI-mediated suppression of the UV-B-induced expression of FPGs [5]. Many MYBs proved to be targeted by microRNAs (miRNAs), a typical feature of TF regulation, whose expression requires frequent control by feedback circuits [21]. Moreover, MBW complexes contain MYB and basic-helix-loop-helix (bHLH) TFs as well as WD40 proteins, and proved to be implicated in the regulation of anthocyanin production [22]. All FPGs contain MYB-responsive _cis_-elements (MREs) and adjacent G-box-like sequences in their promoters [5]. In Arabidopsis and rice, the G-box binds both bZIP and bHLH TFs [23; 24]. It has been assumed by [PERSON] et al. [9] that at least some \"early genes\" (e.g., the _CHS_) required for flavonoid biosynthesis might be regulated by MBW complexes. The crosstalk as well as the postulated model merely relied on studies of in vitro Arabidopsis cell culture systems [5]. The successful establishment of a seedling-based model system [6] allowed us to investigate this crosstalk for the first time also in planta. Thus, we deployed an RNAseq experiment to gain insights into transcriptomic response in Arabidopsis and report here the identification and analysis of several TF genes, which are differentially regulated by flg22 and UV-B treatment. The large number of candidate TFs suggests different regulatory layers are implicated in this crosstalk. The results sup port on the one hand, our previously postulated working model, and on the other hand, demonstrate that the underlying mechanism(s) are much more complex than previously anticipated. For example, bHLH/bZIP and WD40 proteins were identified as potential interactors of the MYB TFs involved in the crosstalk. In addition, this work constitutes a valuable dataset to elucidate the molecular mechanisms underlying plant adaption to different environmental cues of biotic and abiotic origin.
## 4 Results and Analyses
### Rebar Stress  
Figure 16 illustrates the correlation between reinforcement stress and \(P_{0}\). As shown in Figure 16a, an increase in \(P_{0}\) leads to a rise in the maximum tensile stresses in the reinforcement of segments B1, B2, B3, and L2, while segments F and L1 show minimal changes. Specifically, at \(P_{0}\) = 720 kPa, segment B1's reinforcement reaches a maximum tensile stress of 413.66 MPa, and, at \(P_{0}\) = 1080 kPa, segment B2's reinforcement hits a maximum of 458.05 MPa, with both matching the yield strength of the reinforcement. Figure 16b displays the progression of maximum compressive stresses in the reinforcements, indicating a consistent increase across all segments with rising \(P_{0}\). However, the variations in maximum tensile and compressive stresses are more pronounced in segments B2 and L2, yet they do not reach the yield strength. This suggests that under ground surcharge conditions, the reinforcement in the lining structure predominantly experiences tensile stress.  
Figure 17 presents the distribution of rebar stress, where tension is indicated by positive values and compression by negative ones. In Figure 17a, before ground surcharge, the maximum tensile and compressive stresses in segment B1's reinforcement are located  
Figure 16: Variation in rebar stress with \(P_{0}\). (**a**) Tensile stress; (**b**) Compressive stress.  
Figure 15: Distribution of bolt stress. (**a**) Pre-loading; (**b**) Case C2 (\(P_{0}\) = 240 kPa); (**c**) Case C4 (\(P_{0}\) = 480 kPa); (**d**) Case C6 (\(P_{0}\) = 720 kPa); (**e**) Case C8 (\(P_{0}\) = 960 kPa); (**f**) Case C10 (\(P_{0}\) = 1200 kPa).  
on the inner and outer sides of the vault, respectively. Similarly, in segment B2, these stresses occur on the outer and inner sides of the haunch. In segment B3, they appear on the inner and outer sides of the arch bottom. For segments L1, L2, and F, the stresses concentrate near the joints. Figure 17b shows that at \(P_{0}\) = 240 kPa, the stress distribution in each segment mirrors the pre-surcharge condition. Figure 17c-f reveal that as \(P_{0}\) increases, the maximum stresses in all segments are primarily around the vault, haunch, arch bottom, and joints. Notably, the maximum tensile stress in the reinforcement shifts towards the joints with increasing \(P_{0}\). At lower \(P_{0}\) values, the joint deformation is minimal and the segments mainly undergo bending deformation. However, at higher \(P_{0}\) values, significant local compressive deformation at the joints results in increased tensile stress in the rebar. The maximum compressive stress, primarily a result of segment bending, also localizes near the vault, haunch, arch bottom, and joints.  
Figure 17: _Cont._
## 5 Results and Discussion
### Model Validation  
We validated the model using independent data, that is, data that were not assimilated by the system. We validated the DA process by comparing modelled velocity estimates when data from eight drifters were assimilated with non-assimilated observations (i.e., six drifters). It is noted that the velocity output from model simulations that was compared with measured velocities were based on the actual drifter trajectories with 1 min interval (\(\Delta t=1\)). A scatter plot of observed versus simulated horizontal velocities shows good agreement (R\({}^{2}=0.90\)) between velocities derived from the model at drifter locations and velocities of non-assimilated drifters (Figure 6). This agreement is significantly higher than the correlation obtained between model velocity without assimilation and drifter velocities (R\({}^{2}=0.56\)) [28]. The result here further determines a reliable DA performance and shows that the integration of Lagrangian data significantly improves the hydrodynamic model prediction.  
Figure 5: Sensitivity of DA performance to number of drifters complied with the Base-Test configuration.
## 5 Case Study and Discussion  
### Data Preparation  
To verify the model and method proposed in this paper, bearing spacer, as shown in Figure 9 of an M5018A roller press of a building material equipment enterprise in northern China is used as an experiment. This enterprise is a large-scale manufacturing enterprise affiliated to a building material group in China; its products include roller press, cement mill, grate cooler, etc. Given the needs and characteristics of each enterprise, we have helped the group and its subordinate manufacturing enterprises to achieve in-depth informatization. In addition, due to the interconnection of information systems among these enterprises, manufacturing process data of products can be effectively managed and widely shared, which creates conditions for us to verify the theory and method of this paper.  
Figure 9: Bearing spacer of an M5018A roller press of a building material equipment enterprise.  
The process card of bearing spacer is shown in Figure 10. After quenching, microcracks are found on the transverse plane. In addition, the surface roughness of cylindrical, internal cylindrical, and transverse plane do not meet the requirements of design. Consequently, the causes of defects should be analyzed to avoid the same problems. In the bearing spacer, the quality of each surface is influenced by two other surfaces. Therefore, the target gene can be extracted, as shown in Figure 5, according to the process card and manufacturing process quality information.  
**Retrieving knowledge.** In information set \(L\), the values of elements \"1-4\" in the three gene units are applied to retrieve knowledge from the database. If the value of an element \"1-4\" in a knowledge gene equals \"C6\" or \"IC6\" or \"TP2\", then the knowledge gene is taken as a result of retrieval. This method can improve the retrieval range of knowledge, and get more effective reference data. The \"C6\" is in \(GU(1)\), the \"IC6\" is in \(GU(2)\), the \"TP2\" is in \(GU(3)\). In addition, the information set \(R\) is used to associate the complete knowledge genes. Some of the results obtained are shown in Table 4.  
Figure 10: Process card of bearing spacer of an M5018A roller press.  
Where the elements \"6-1\" corresponds to the information set _PE_ and \"7-1\" correspond to the information sets _KCL_. In element \"6-1,\" each value represents a quality defect described by an abbreviation of its name. In element \"7-1,\" each value represents a cause of quality defects, which is also described by an abbreviation of its name. These abbreviations are explained in Table 5.  
Due to the help of modern database technology, the information as shown in Table 4 can be quickly retrieved from the product gene knowledge base. Therefore, this method greatly improves data processing efficiency in the process of quality defect diagnosis.
# Future Is Unlicensed: Private 5G Unlicensed Network for Connecting Industries of Future
## 4 Challenges and Research Directions
### Challenge 5: Multi-Domain Coexistence in a Shared Band  
The two key bands that have emerged for NR-U are the 3.5 GHz shared access band (only in the U.S.) and the global unlicensed 5 GHz band. Military radar is the incumbent users of 3.5 GHz, whereas 5 GHz is popular among WiFi networks. In future IoT-based industries, there will be a wide range of devices and diverse applications based on WiFi, Bluetooth, ZigBee, etc., who will be sharing the same band with NR-U. Thus, NR-U must fairly share the available frequency resource with coexisting devices. The FCC proposed dynamic frequency selection (DFS) to protect incumbent users in the frequency domain, LBT, to share the unlicensed band in the time domain fairly [6]. However, these proposed protocols are limited to single domain applications. To the best of our knowledge, only a handful of research has worked simultaneously on multiple domain coexistence such as time, frequency, and space.  
In this context, multiple domains can also be exploited for maximizing the fairness and spectral efficiency of the unlicensed band [8], as shown in Figure 6. We argue that for successful coexistence, the networks should possess flexibility in at least one of the above-listed areas. For example, WiFi is coexistence friendly as it exhibits agility in the time-domain, owing to its LBT operation. On the contrary, LTE (coexistence unfriendly) lacks this merit due to its fixed-length frames, and the nearest slot boundary is the earliest time it can react to coexistence-related challenges. However, one can introduce flexibility in other dimensions to compensate for the inflexibility in one dimension. A network with time-domain inflexibility such as LTE/LAA can add coexistence compensation in the frequency or space domain or both. In the above case, LTE can reduce coverage areas to combat the challenge of unfairness in a given area. In [51], the adaptive duty cycling (ADC) and dynamic channel switch (DCS) mechanisms were adopted. The joint time and frequency domain approach was proposed to support a fair channel access opportunity through muting (ADC mechanism) and increased efficiency of the spectrum through avoiding the most congested channel (dynamic channel switch). Moreover, the algorithm is reinforced by Q-learning techniques for support in a highly dynamic environment. A similar approach can be adopted for NR-U in shared channels. A more ambitious option is to exploit all available dimensions and increase the coexistence capability of a network. Note that a desirable  
Figure 5: A flowchart of the LBT mechanism proposed by 3 GPP.  
property for a coexistence scheme is that it does not require substantial changes to the existing protocols and especially in the end-user hardware. Hence, it becomes more challenging to exploit integrated domains by using already available functions and components of technology.
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments
### Methodology
#### 4.2.1 Expectation Maximization Imputation (EMI)  
This technique was proposed by [28] to handle the missing data problem. The general idea behind the EM algorithm is to iteratively estimate parameters required to predict the missing values in a given distribution by calculating the mean and variance between parameters [26]. The application of the EM algorithm usually aims at solving missing data problems. However, research has also shown the use of this algorithm in solving complex problems for complete data sets. For example, multilevel linear models, structural equation model and finite mixture [36; 37; 38; 39].  
The EM algorithm constitutes iterative processes built on two steps: the expectation step (\"E-step\") and maximization step (\"M-step\"). The initial values available in a system are required to initiate the estimation process. The E-step begins with the construction of a linear regression model, using the co-variance matrix and initial mean vector to produce estimates for missing values based on the values from the observed data. The M-step follows after the E-step and produces new parameter values for the data that have been estimated. The algorithm saves the last co-variance matrix and mean vector to determine the next E-step and builds a new regression model from the results which is then used to determine new estimates for missing values. The M-step will subsequently run again to determine new parameters from the updated estimates. The EM algorithm will iterate these two steps until the values of the co-variance matrix and mean vector no longer changes or converges, where the converged value corresponds to that of the value of the maximum likelihood estimates. The number of iterations in the EM algorithm is dependent on the number of missing values in the dataset [40; 41]. Research conducted by [28] shows a detailed description of the EMI algorithm.
# GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks
## 2 System Model
### Architecture  
According to the characteristics of WMN, this paper proposes a reliable routing algorithm that combines the improved breadth-first search and GraphSAGE to improve reliability and increase network resource utilization. As shown in Figure 2. the algorithm architecture consists of two parts: (1) All shortest paths from the source to the destination are iterated by a multi-path routing algorithm based on the improved breadth-first search. (2) The GraphSAGE-based performance optimization algorithm is used to learn the network graph information, determine the network performance of each path, and finally select the path with the best network performance for data transmission from all shortest paths. In other words, the multi-path routing algorithm based on the improved breadth-first search generates the set of candidate paths, and the GraphSAGE-based performance optimization algorithm determines the subset of the paths to be used.
## 5 Experiments
### Experimental Analyses of Monitoring Indicators
#### 5.1.1 Influence of Interference on \(C/n_{0}\)  
When noise and RFIs are superimposed on the GPS digital intermediate frequency signal, with different RFIs and the increase of interference signal power, the output \(C/N_{0}\) of GPS software receiver will change. Figure 3 shows the curves that express the relation between the equivalent \(C/N_{0}\) and the interference to signal ratio (ISR), respectively, under conditions of continuous wave (CW) interference, broadband FM interference, narrowband FM interference, sweeping frequency CW interference, and uniform spectrum interference for the PRN3 satellite.  
The experiment demonstrates that the influences on the equivalent  
In order to better analyze the impact on _COP_ from RFIs, Figure 6 depicts the relation curves of _COP_\(\sigma\) to _COP_ mean ratio under conditions of different RFIs and interference strength.  
The experimental results show that, with the increase of interference strength, _COP_ gradually decreases until the receiver loses lock. Therefore, the _COP_ can be used as an indicator of interference monitoring. In the experiment, the _COP_\(\sigma\) measured after the receiver stably tracking does not increase with the rising of interference strength, but show a decreasing trend This is because of the gradual decreasing of _COP_. As is shown in Figure 6, with the increase of interference strength, the _COP_ mean decreases faster than the _COP_\(\sigma\). This indirectly illustrates that the _COP_ jitter is aggravating.
## 3 Results
### Discussion
Figure 5 shows the comparison of the simulated efficiencies of both the segmented module and the unsegmented module. With these results, the segmentation is said to be successful. The segmented module has a 72% and 70.7% increase in the calculated and simulated conversion efficiency, respectively. Figure 6 shows the comparison of the calculated and the maximum simulated conversion efficiency at three different temperature gradients for the segmented TEG module. The maximum simulated values are lower than the calculated values. The higher values of calculated efficiencies are due to the fact that the thermal and electrical contact resistances associated with the TEG module are not included in the calculations. Figure 5: Comparison of the simulated efficiencies for the segmented module and the unsegmented module. Figure 6: Comparison of the conversion efficiencies for the segmented TEG. Having confirmed (calculated and simulated) that the segmented TEG modules have a higher conversion efficiency than the unsegmented TEG modules, the other output parameters, such as output voltage, current passing through the device, and power, are determined. Figure 7 shows the plot of the output current versus the output voltage at three different temperature gradients. As expected, as the voltage increases the current decreases. Figure 8 shows the power plot against the load resistance. The power reaches maximum when the load resistance is equal to the internal resistance. After that point, the power starts decreasing until it reaches a point where it will no longer decrease. Figure 9 shows the plot of the output voltage against the load resistance. Here, as the voltage reaches its maximum (open circuit value), it remains constant irrespective of the load resistance. Figure 8: Output power versus load resistance. Figure 7: Current versus output voltage. Figure 10 shows the plot of efficiency against voltage. The efficiency increases until it reaches \(\frac{R_{l}}{R}=\sqrt{1+ZT_{av}}\), then it decreases to zero at the open circuit voltage value. Figure 11 shows the plot of efficiency against current. Figures 12 and 13 show the plot of output power against current and output power against voltage, respectively. By comparing Figure 11 with Figure 12, and Figure 10 with Figure 13, it can be said that the efficiency and the output power reach their maximum at different points. This confirms that the condition for maximum efficiency differs to that of the maximum power. Figure 10: Conversion efficiency versus output voltage. Figure 9: Output voltage versus load resistance. Figure 11: Conversion efficiency versus current. Figure 12: Output power versus current. Figure 3:0 Output power versus voltage.
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
###### Abstract  
With the development of science and technology, inspection robots have attracted more and more attention, and research on the automatic reading of pointer instruments through inspection robots has become particularly valuable. Aiming at the problems of uneven illumination, complex dial background and damping fluid interference of the collected instrument images, this paper proposes a dial gauge reading algorithm based on coordinate positioning. First, the multi-scale retinex with color restoration (MSRCR) is applied to improve the uneven illumination of the image. Second, a circle detection algorithm based on the arc-support line segment is proposed to detect the disc to obtain the coordinate of the center and radius of the circle. Then, a pointerless template is used to obtain the pointer, and the concentric circle algorithm is applied to locate the refined pointer. Finally, the automatic reading is calculated using the relative position of the pointer and the zero scale. The experimental results prove that the proposed algorithm can accurately locate the center of the circle and the pointer and obtain readings automatically.  
coordinate positioning; MSRCR; arc-support line segment; substation pointer instrument +
Footnote †: journal: Applied Sciences  
Article  
Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 3 Building Blocks  
Integrated photonics paves the way for low-cost large-scale OPA chips. As presented in Figure 4, typically, there are four necessary parts in an OPA: laser source, beam splitters, phase shifters, and emitters [12], for efficient light generation, manipulation, and chip-to-free-space transmission. We will discuss each part in detail in this section. Then a brief overview about overlayers and buried oxide (BOX) layers is given. The optimized thicknesses of the overlayers and BOX layers enhance the upward radiation of WGAs.