esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Vortex Shedding Characteristics by Numerical Simulations
### Solution Setting and Mesh Division  
The CFD simulation utilized transient calculations, with the corresponding mathematical model being the Reynolds time-averaged Navier-Stokes equation. The _SST_\(k\)-\(\omega\) model served as the turbulence model, while the Coupled algorithm was used for pressure-velocity coupling [55]. The second-order windward interpolation was employed as the discrete format, and the computational residual was controlled to \(1\times 10^{-5}\). A sufficiently small time step was necessary to capture the more minor non-constant flow field characteristics and to ensure calculation accuracy. However, excessively small time steps would significantly reduce calculation efficiency. After conducting trial calculations, a time step of \(1\times 10^{-4}\) s was selected to meet the Coulomb number requirement [28; 29].  
The computational domain mesh was generated using the preprocessing software ICAM 15.0, with the computational domain taking the form of an overlapping external and internal region [56]. The external region was 15B long and 15D high, with the inlet boundary being the velocity inlet located 5B from the center of the section and the outlet boundary being the pressure outlet located 10B from the center. The upper and lower boundaries were considered free-slip symmetric boundaries, positioned 7.5D from the center of the section. A structured mesh was employed, and the external region was large enough to minimize the boundary's impact on the internal region's flow field. The internal region was 2B long and 2D high, using an unstructured mesh. The outer wall of the truss girder section and accessory members were treated as smooth wall boundaries. Figure 14 shows the boundary conditions of the computational domain. The number of meshes significantly influenced the calculation results, with excessively sparse meshes affecting calculation accuracy and overly dense meshes significantly increasing the calculation volume and reducing calculation efficiency. This research set the maximum mesh size in the internal region to \(6\times 10^{-3}\) mm. To meet the computational needs of the _SST_\(k\)-\(\omega\) turbulence model, a boundary layer mesh was positioned at the wall surface. The boundary layer consisted of 12 layers with a linear growth rate of 1.2 for the section and accessory components, with the height of the first layer taken as \(5\times 10^{-5}\) mm to ensure a smooth transition of the near-wall mesh [57]. The total number of meshes was 0.22 million, which was sufficient after the trial calculation [28; 29]. Furthermore, the Y-plus values of the truss girder section were all less than one, indicating that the mesh division met the requirements of the _SST_\(k\)-\(\omega\) model [58]. Figure 15 displays the computational domain mesh division.
## 3 Result and Discussion
### Characterization
#### 3.1.2 Textural Properties and Light Absorption
The band gap of the synthesized TiO\({}_{2}\) samples was between 3.1 and 3.22 eV, as shown in Table 2, and these values are in accordance with the band gap of anatase as reported previously [32]. The results of the electrochemical characterization of the samples synthesized at 70 \({}^{\circ}\)C are shown in Figure 4C,D. The material calcined at 500 \({}^{\circ}\)C exhibits the highest photocurrent, followed by the TiO\({}_{2}\) calcined at 600 and 400 \({}^{\circ}\)C. Almost no photocurrent was measured for the uncalcined material (Figure 4C). The TiO\({}_{2}\) calcined at 500 \({}^{\circ}\)C also has the smallest EIS radius (Figure 4D). Both results indicate that the TiO\({}_{2}\) calcined at 500 \({}^{\circ}\)C can separate and migrate photogenerated charge carriers more effectively than the other materials investigated. Figure 4: (**A**) UV-Vis DR spectra of TiO\({}_{2}\) synthesized at 70 \({}^{\circ}\)C and after calcination at 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C or 600 \({}^{\circ}\)C and (**B**) the corresponding Tauc plots for band gap estimation, (**C**) their photocurrent under irradiation with light (\(\lambda\) = 365 nm), and (**D**) the corresponding EIS results.
# Margin CosReid Network for Pedestrian Re-Identification
## 4 Experiment
### Comparison with State-of-the-Art Methods  
The proposed re-identification method has been compared with other existing state-of-the-art methods, and the comparison results are shown in Tables 3 and 4. On the Market-1501 dataset, the single-person search rank-1 and mAP of our method reach 91.0% and 75.9%, respectively. On DukeMTMC-reID, the single-person search rank-1 and mAP of our method reach 81.1% and 64.61%, respectively, which are much higher than those of other methods. Compared with PSE [50], the rank-1 and mAP of our method without any personal posture information increase 2.3% and 6.9%, respectively, on Market-1501 and 1.3% and 2.61%, respectively, on DukeMTMC-reID. Furthermore, compared with PNGAN [26] using GAN to generate pedestrian images, rank-1 and mAP of our method increase 0.6% and 3.3%, respectively, on Market-1501 and 7.5% and 11.4%, respectively, on DukeMTMC-reID.  
To show the improvements of the re-ranking method and the multi-query mode used on rank-1, rank-5, rank-10, and mAP, we conduct comparison experiments on the performances of the proposed MCSL with single-query mode (MCSL+single-query), MCSL with reranking (MCSL+RK), MCSL with multi-query mode (MCSL+multi-query), and MCSL with both reranking and multi-query mode (MCSL+RK+multi-query). The experimental results are shown in Tables 3 and 4, indicating that using re-ranking and multi-query mode can improve the performances of our method.  
Figure 7: Pedestrian retrieval results on the Market-1501 dataset. The first column in the black box shows five different identity query images. The other columns show the results sorted according to the similarity in which the sort orders are marked by serial numbers. The green numbers represent the retrieved images that are the same identities as the query, and the red numbers represent those without the same identities.
## 3 The Complex Network of (p)ppGpp Signaling
### Transcriptional Regulation
Evidently, multiple lines of structural and biochemical evidence need to be pursued to determine whether the ppGpp-regulation of transcription occurs through direct interaction, or indirectly, via other proteins (such as DksA). DksA and GreA/B: An Unexpected Similarity  
The transcriptional elongation complex is regulated by a large number of protein factors that control pause and termination, which have been studied in great detail in _E. coli._ As implicated earlier, the _E. coli_ RNAP encompasses two spaces, discernible in its 3D structure, referred to as primary and secondary channels, as recently summarized [78]. While the primary channel binds the template DNA and is the site for nascent RNA synthesis, the secondary channel serves as the entrance for ribonucleotides and for the elongation regulatory factors, such as the DksA and ppGpp. Biochemical and genetic analyses, independent of the stringent response, revealed the existence of protein factor(s) that suppress transcriptional pause. Initially, the use of transcription reactions in vitro showed that cell-free _E. coli_ lysates kinetically suppress the +15/+16 pause at the late promoter (pR') of coliphage lambda without affecting the termination property of the polymerase [79]. Subsequently, a flurry of elegant studies characterized two highly homologous proteins, named GreA and GreB, as pause suppressors [80; 81; 82; 83]. In parallel, structural studies showed that all three proteins (GreA, GreB, DksA) contain large coil-loop-coil (also known as 'coiled-coil') domains (Figure 2) that interact with the secondary channel of the _E. coli_ RNAP and make specific contacts, which may underlie the observed competition among them [84; 85; 86; 87]. The commonality of structure as well as binding of DksA and GreA/B to the same RNAP channel is a remarkable discovery that may have important ramifications on the possible competition between them, which remains to be explored [78].
# Energy-Optimized Content Refreshing of Age-of-Information-Aware Edge Caches in IoT Systems
## 4 Model-Driven Cache-Management Optimization
### Energy-Optimized Cache Refresh
#### 4.1.3 Hybrid Sensors: \(0<\overline{\beta}<1\)  
In this case, both the energy consumption of transmissions and the energy consumption of sensing are different from zero, and therefore there is a trade-off between minimizing the average poll frequency and minimizing the sampling frequency on the device, as the first leads to minimizing \(s\). Indeed the optimization problem chooses \(s\to s_{min}\), whereas the second leads to maximize \(s\).  
It is possible to compute the optimum values of \(w\) and \(s\) solving (17) using optimization techniques for non-linear integer programming, e.g., branch and bound. Table 2 and Figure 6 show the optimum values of \(w\) and \(s\) computed using the APMonitor solver ([31; 32]) using \(\alpha=0.9\), \(AoI_{a}=420\) s, and considering three exemplary cases where \(\lambda=1/90\) s\({}^{-1}\), \(\lambda=1/180\) s\({}^{-1}\)  and \(\lambda=1/360\) s\({}^{-1}\). We can notice that the optimum values are on the top edge of the feasible region, as in the cases shown in Figure 4. Moreover, we can also notice that several values of \(\overline{\beta}\) can result in the same configuration of the parameters \(w\) and \(s\): Table 2 shows that in all the considered scenarios, there are different values of \(\overline{\beta}\), i.e., different types of devices, that have the same optimum values of \(w\) and \(s\). This follows from the model constraint that \(w\) can only take integer values. However, the resulting values of \(c_{\overline{\beta}}\) are different as they depend on the value of \(\overline{\beta}\), i.e., the energy consumption still depends on the type of device.  
Figure 7 shows \(c_{\overline{\beta}}\) for different types of devices and for the same AoI requirement and five possible network loads: \(\lambda=1/10\) s\({}^{-1}\), \(\lambda=1/180\) s\({}^{-1}\), \(\lambda=1/500\) s\({}^{-1}\), \(\lambda=1/1000\) s\({}^{-1}\) and \(\lambda=1/1800\) s\({}^{-1}\). Clearly, as the request rate decreases also the transmission energy cost decreases. As mentioned above, in all the considered scenarios for values of \(\overline{\beta}\) going from 0 up to 0.9-0.91-0.97, the optimum values of \(w\) and \(s\) are the same, hence their values of \(c_{\overline{\beta}}\) differ only for \(\overline{\beta}\), showing a linear behavior. Instead, when \(\overline{\beta}\to 1\) the optimum values of \(w\) and \(s\) change and, at lower rates we also have that \(f_{\rm T}\to 0\), so \(c_{\overline{\beta}}\to 0\), causing the steep change in the slope of the curve.
## 2 Background and Related Work
### Semantic Interoperability  
Semantic interoperability constitutes a critical element in industrial digitalisation, notably within the scope of SMEs in the manufacturing sector [25]. It empowers diverse systems and applications to effectively share, exchange, and interpret data, a capability that becomes increasingly indispensable as industrial enterprises integrate a range of emerging technologies [11; 26]. The concept of interoperability refers to the innate ability of various systems to communicate effectively, enabling the efficient exchange and utilisation of shared information [12]. This feature holds significant importance in the contemporary technological landscape, particularly in facilitating seamless collaboration among a complex and expanding network of interconnected technologies [11; 27].  
However, semantic interoperability extends this foundational concept by ensuring not only effective exchange but also the accurate interpretation of data between the systemsinvolved [28]. This advanced capability gains prominence for industrial companies looking to maximize the advantages of their digitalisation efforts. It ensures a coherent technological ecosystem capable of operating on shared semantic principles [29].  
In the context of industrial digitalisation, achieving a high degree of semantic interoperability is vital to ensure effective communication and coordination among a wide array of technologies and digital systems [28]. Thus, such enterprises are better equipped to optimize their operational processes, make data-driven decisions, and offer increased customisation and efficiency [30].
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 1 Space-Air-Ground Integrated Network
### Architecture for Space-Air-Ground Integrated Network  
#### 1.2.1 Introduction of SAGIN Architecture  
SAGINs can deal with sophisticated environments in the future, which makes them a developmental tendency in the future communication networks. Figure 1 shows the architecture of a space-air-ground integrated network, it consists of a space network, air network, and ground network, which realizes the sharing of global information andresources by connecting numerous networks to shape a complicated topology [5]. The specific details of each network are as follows:  
1. Space Network  
The space network is made up of various communication satellites distributed in different orbits [6]. Satellite networks can be classified by two indicators: altitude and channel width. Altitude categories include geostationary orbit (GEO), middle-Earth orbit (MEO), and low-Earth orbit (LEO) satellites. Channel width categories include narrowband and broadband. The main advantage of the space layer is that it provides a routing bypass function when direct communication between the space layer and the ground layer is impaired. This bypass function enables the implementation of SAGINs.
2. Air Network  
The air network consists of a variety of aerial communication equipment, such as balloons and unmanned aerial vehicles. The aerial layer has three characteristics: small resource cost, easy deployment, and large coverage [6]. Because of these characteristics, the aerial layer can provide regional wireless access services and routing functions for the ground layer, which make it preferable compared with a base station on the ground.
3. Ground Network  
The ground network consists of users and various ground communication networks including wireless local area networks and cellular networks. The ground layer provides high data rates; however, it fails to service some remote areas.
# Research and Analysis of MEMS Switches in Different Frequency Bands
## 3 The Research Status of MEMS Switches in Different Frequencies  
This section will summarize the research status of MEMS switches in different frequency bands (centimeter, millimeter, and submillimeter) and their reliability (dielectric charging, contact failures, and temperature instability). Finally, some multiband switches are introduced. The research on different bands focuses on the insertion loss and isolation of MEMS switches. Due to the wide band of the millimeter wave, it has been divided into the Q band (30-50 GHz), V band (50-75 GHz), and W band (75-110 GHz).
## 3 Results and Discussion
### Frog Trace  
Figure 3A shows the observed FROG trace, which mainly consists of five components. From the tilt angle of the SD FROG trace, the pulse emitting at 267 nm was found to be positively chirped by a group delay dispersion (GDD) of 1500 fs\({}^{2}\), and was eventually extended to 147 fs. The trace retrieved from the observed data using the standard SD FROG algorithm is shown in Figure 3B. The result of the retrieved trace was very similar to the observed one, with a FROG error of 0.86%. The temporal profile was then calculated, the result of which is shown in Figure 4. A train of pulses was generated,  
Figure 2: Emission spectrum for the Raman sidebands. Red, observed data; black, retrieved spectrum; blue, retrieved phase.  
and the spacing between the pulses was 8 fs, corresponding to the vibrational period of molecular hydrogen (a reciprocal value of the vibrational energy of 4155 cm\({}^{-1}\)). This result suggests that coherent Raman sidebands were generated as the result of the molecular phase modulation of hydrogen by the two-color pump beam, and the frequency modulation of the probe pulse. The pulse width in the train, which was calculated by a half width at half maximum (HWHM), was ca. 4 fs at nearly the center of the pulse. The value calculated from the shifted baseline was 2.6 fs at around 25 fs in Figure 4. These results suggest that the intensities of the spectral components (240, 267, 300 nm) are very different from each other, and that the three pulses are not temporarily (or spatially) overlapped, resulting in the generation of short optical pulses only at around 25 fs where the three components would have a flat intensity distribution due to a slight mismatch in the temporal overlap. In fact, the relative intensities of the Raman sidebands (Stokes and anti-Stokes) against the intensity of the probe beam in the retrieved spectrum are much smaller than those in the observed data (cf. Figure 2B), suggesting that the temporal and spatial overlaps are not sufficient among the pulses.
## 3 Results
### Experimental Results and Comparisons
#### 3.2.3 Defect Segmentation Results of Improved SSR Image Enhancement and Region-Growing Algorithm
The detection effect of each method on this image is shown in Figure 14. The segmentation method based on the genetic algorithm could roughly recover the edge contour, but the recovery ability was low at the edge and was too complex, which may lead to the destruction of the edge continuity. Segmentation methods based on edge detection algorithms could detect the general contour of defects, but the connectivity was poor, and simple image morphological processing could not recover the defect edges, which needed to be specifically edge connections. In fact, edge connectivity is more difficult than edge detection. In contrast, the proposed segmentation method eroded from the normal surface to the inside of the defect and stopped growing after obtaining the defect edge. The defect edge information could be better restored.
# Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3
## 2 Research Method
### Defect Detection and Measurement Using YOLOv3  
This research used YOLOv3 [19] as the basis for die defect detection and measurement. The basic idea of YOLOv3 is shown in A.2 of Appendix A. The YOLOv3 model is a one-stage method, end-to-end training process that can be realized using a single network. The inference can predict the center coordinate, width and height of each bounding box where the defect is located, and the confidence that each bounding box contains a defect.  
After YOLOv3 output the predicted bounding boxes, the study would further measure the defects of the corresponding patches and sort the quality of a die, as shown in Figure 4. The process included Ostu binarization [18], the estimation of the bounding ellipse, and the calculation of the major and the minor axis. The process was able to potentially assist to conduct the sorting of the dies in accordance with the quality specifications of the customers. For example, there were three classes of die products: an excellent die had no particle defect after inspection; a qualified die had particle defects with the major axis length between 50 and 149 \(\upmu\)m and the minor axis length less than 20 \(\upmu\)m; an unqualified die had particle defects that exceeded the quality specification.
# Phase Change Materials and Their Benefits in ETICS
## 1 Introduction
### PCM Materials  
In pure chemicals, three types of phase changes are possible: melting and solidification, vapor and condensation, and sublimation and desublimation. For technical reasons of the production, phase change between solid and liquid is the most useful. Materials that change state and thereby release or receive large latent heat are called PCMs (Phase Change Materials) and they are used in various fields [23; 24].  
Thus, PCMs are latent heat-absorbing materials based on phase change. At certain melting and freezing temperatures, the substance is able to absorb and then release a large amount of energy. Heat is absorbed and released when the material changes from solid to liquid and vice versa (the so-called phase conversion of the first kind) [25; 26; 27].  
Different types of paraffins (PCMs) were tested. The PCMs utilized were purchased from Microtek Lab. The core is paraffin and the shell is a polymer (see Figure 3). These PCMs are all in microencapsulated form and physically have an appearance of a white powder.  
Figure 3: Micronal microcapsule production scheme [28; 29].  
Main criteria for the selection of PCMs are in Table 1.  
**Thermo-Physical Properties**  
* Phase change temperature suitable for desired application, in the desired operating temperature range [30; 32].
* High latent heat of fusion per unit volume [30].
* High thermal conductivity to assist in charging and discharging of PCM within the limited time frame [30].
* High specific heat so that additional energy in the form of sensible heat is available to the thermal energy storage system [30].
* PCM should melt completely (i.e., congruent melting) during phase transition so that the solid and liquid phases are homogenous [30].
* Thermally reliable (i.e., cycling stability) so that the PCM is stable in terms of phase change temperature and latent heat of fusion and can be used in the long run [30].
* Good heat transfer [32].
* Small vapor pressure at operating temperature [32].
* Favourable phase equilibrium and no segregation [32].
* Complete melting [31].  
Kinetic properties  
* High rate of nucleation so as to avoid super cooling of the PCM in liquid phase [30; 32].
* High rate of crystal growth so that heat recovery from the storage system is optimum [30; 32].  
Chemical properties  
* Chemically compatible with construction/encapsulated materials [30].
* No degradation after large number of thermal (freeze/melt) cycles so as to assure long operation life [30].
* Non-toxic, non-flammable and non-explosive so as to assure safety [30].
* Corrosion resistant to construction/encapsulated materials [30].
* Complete reversible melt/freeze cycles [32].
* Chemical stability [31].
* High freeze/melt stability [31].  
Environmental properties  
* Low environmental impact and non-polluting during service life [30].
* Low embodied energy [32].