esa-sceva/satcom-chunk-collection huggingface dataset:
# Wire-Grid and Sparse MoM Antennas: Past Evolution, Present Implementation, and Future Possibilities
## Why MoM?
### The Use of MoM  
In MoM, the boundary value problem for the unknown current distribution over the conductor surface is often formulated as an integral electric field equation. Then, applying suitable basic and test functions, these equations can be transformed into a SLAE with a dense matrix. The computational complexity of solving a SLAE of order \(N\) is O(\(N^{3}\)) by the direct method. When modeling millimeter-scale integrated circuits where \(N\) is fixed and relatively small, it is sufficient to use a traditional direct method for solving the SLAE. After solving it, circuit characteristics, such as \(S\)-parameters, radiation losses, etc., can be obtained from the current distribution. These can be obtained by applying MoM either in the spectral domain [76] or in the spatial domain [77].  
Over time, this method has been used and developed considerably. For instance, [78] presents a method based on the Galerkin realization and is closely related to the approach in the spectral domain. The results are a full harmonic/time electromagnetic analysis of the microstrip, which can be used to evaluate individual microstrip inhomogeneities or, on faster computers, the entire microstrip circuit. The proposed approach is quite effective, since the results of the analysis of simple circuits can be obtained in a reasonable period of time, even by using a personal computer. Then, in [79] the authors also used MoM to solve the integral equation for an accurate full-wave analysis of shielded microstrip inhomogeneities, obtained by applying the reciprocity theory. This paper [80] presents the results of full-wave analysis in the spectral domain using the Galerkin method with vector functions of a triangular subarea as expansion and testing functions of the MoM procedure to investigate various microstrip inhomogeneities. This approach proved itself to be a very accurate method for analyzing compensated microstrip inhomogeneities. A grid structure and database for storing impedance matrix elements that can be used to analyze structures of different geometries are demonstrated. MoM is also used in [81] to solve an integral equation reduced to SLAE, expressed in a modified method of spectral analysis proposed by the authors to calculate three-dimensional microwave structures. Based on the realization of MoM in the spatial domain, a full-wave version was developed for analyzing and determining the circuit characteristics of passive microstrip elements (in an open environment) at microwave frequencies [82].
###### Abstract  
Herein we investigated the cellular responses of the endemic fish species _Alburnus vistonicus_ Freyhof [PERSON], 2007, under the variation of several physico-chemical parameters including temperature (\({}^{\circ}\)C), salinity (psu), dissolved oxygen (mg/L), pH and conductivity (\(\mu\)S/cm), which were measured in situ. Monthly fish samplings (October 2014-September 2015) were conducted in Vistons Lake in northern Greece, a peculiar ecosystem with brackish waters in its southern part and high salinity fluctuations in its northern part. Fish gills and liver responses to the changes of the physico-chemical parameters were tested biochemically and histologically. Heat shock protein levels appeared to be correlated with salinity fluctuations, indicating the adaptation of _A. vistonicus_ to the particular environment. The latter is also enhanced by increased Na\({}^{+}\)-K\({}^{+}\) ATPase levels, in response to salinity increase during summer. The highest mitogen activated protein kinases phosphorylation levels were observed along with the maximum mean salinity values. A variety of histological lesions were also detected in the majority of the gill samples, without however securing salinity as the sole stress factor. _A. vistonicus_ cellular stress responses are versatile and shifting according to the examined tissue, biomarker and season, in order for this species to adapt to its shifting habitat.  
Keywords:Vistonis Lake; physico-chemical parameters; gills; liver; HSPs; MARKs; Na\({}^{+}\)-K\({}^{+}\) ATPase; histology +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Theil Exponential Decomposition  
The Gini coefficient is often used to calculate inequality in general and does not explain the source of inequality (from within or between regions) [66]. Therefore, it is combined with the Theil index, an indicator of income level difference based on the concept of information entropy [67] to reflect the source of regional difference in the degree of city-industry integration. Its size also has a positive correspondence with the regional difference.  
\[T=\frac{1}{n}\sum_{i=1}^{n}\frac{y_{i}}{y}\ln\left(\frac{y_{i}}{y}\right) \tag{33}\]  
\[T=T_{b}+T_{w}=\sum_{k=1}^{k}y_{k}\ln\left(\frac{y_{k}}{n_{k/n}}\right)+\sum_{ k=1}^{k}y_{k}(\sum_{i\in g_{k}}\frac{y_{i}}{y_{k}}\ln\frac{y_{i}/y_{k}}{1/n_{k}}) \tag{34}\]  
\(T\) is the Theil index, and its value range is \([0,1]\). \(y_{i}\) represents the city-industry integration degree of each city, \(y\) is the sample average, \(y_{k}\) is the integration degree of each region, and \(g_{k}\) represents the number of groups. Decomposing the Theil index into inter-group difference \(T_{w}\) and intra-group difference \(T_{b}\) can more intuitively reflect the uneven characteristics of city-industry integration in various regions.
## 3 Experimental Methods
### Flight Configuration and Routes  
The 60 GHz band data transfer was performed across three flight types: hovering, pass-by flight, and platooning flight. Figure 6a-c show both UAVs (A and B) hovering in the air, both UAVs navigating in opposing directions to establish a scenario of their mutual passage, and the follower B maintaining a fixed distance as if following the leader A, respectively.  
The pass-by flight configuration entails UAVs navigating towards disparate destinations, leveraging mmWave or terahertz technology as they cross paths. Within this setup, communication is only feasible within a specific range centered around the point of closest approach, albeit constrained to an exceedingly brief temporal window as they move away from the point. Figure 7 illustrates the positional relationship among the UAVs, with a vertical distance of 5 m and a horizontal distance of 5 m as well. Contrastingly, during the platooning flight, the follower B aligns its destination with that of the leader A. This alignment curtails the freedom of flight for the follower B; however, it enables sustained flight in close formation and keeps the ultra-narrow communication link continuously. This configuration is used to create a situation similar to the hovering when UAVs have to fly toward their common destination, thereby enabling stable communication, and is expected to perform the continuous transmission of voluminous data.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Modulation** & **FEC Rate 1** & **Throughput (Gbps)** & **Receiver Sensitivity (dBm)** \\ \hline \(\pi/2\) QPSK & 11/15 & 2.5813 & \(-61\) \\ \(\pi/2\) QPSK & 14/15 & 3.2853 & \(-58\) \\
16-QAM & 11/15 & 5.1627 & \(-55\) \\
16-QAM & 14/15 & 6.5707 & \(-51\) \\ \hline \end{tabular} 1 Forward error collection (FEC) rate.  
\end{table}
Table 2: Modulation dependent parameters defined in IEEE 802.15.3e.  
Figure 6: Illustration of the flight types featured in the demonstration. The blue arrow indicates the direction of flight, and the red areas indicate the communication range. (**a**) Hovering: UAVs remain stationary in flight. (**b**) Pass-by flight: UAVs navigate in divergent directions to orchestrate a crossing. (**c**) Platooning flight: Both UAVs uphold a specified distance between them.
## 4 Generation of Polarization-Multiplexed Signal Using Intradyne Coherent Transmitter
In the method discussed above, the heterodyne beating is generated by detuning the frequency of the coherent transmitter from the frequency of the coherent receiver through thermal control. We keep \(F_{x}\) and \(F_{y}\) to very small values to minimize any influence of the modulation bandwidth on the coherent transmitter. Thus, the calibration procedure  
Figure 14: Frequency response of ICR integrated in a CFP2-ACO optics module. The calibration is performed using a polarization-multiplexed signal generated by ICT. above will require the ICR and ICT to be located in different coherent transceivers. It is achievable since there are normally multiple coherent transceivers within a host line card. One can connect the Tx output of one module to the Rx input of its neighboring module through an SMF. Moreover, it is possible to implement the calibration procedure using the ICR and the ICT within the same coherent transceiver. As seen from Equation (2), the beating frequency contains two parts: the frequency difference between the Tx LO and the Rx LO, and the average value of \(F_{x}\) and \(F_{y}\). If the LO is shared between Tx and Rx, the first term is zero. One can increase both \(F_{x}\) and \(F_{y}\) simultaneously to increase the heterodyne beating frequency \(F_{set}\). Meanwhile, \(|F_{x}-F_{y}|\) should be kept larger than 0.94 GHz to depolarize the combined output. However, when a large frequency shift is introduced, the combined output power will be reduced due to the limited modulation bandwidth of the MZMs. Most ICTs have integrated photo diodes to monitor the output power and most ICRs have integrated photo diodes to monitor the input power. One can use those photo diodes to monitor the power of the polarization-multiplexed signal, as shown in Figure 15. The influence of the power change can be deducted from the calibration result of the ICR. In this way, the self-contained calibration of the toherent transceiver can be achieved by looping back between its transmitter and its receiver.
## 2 Source and Toxicity of Heavy Metal Ions  
### Sources of Heavy Metals in the Environment  
A vast and diverse array of industrial processes, encompassing a wide range of activities such as leather tanning, the electroplating of materials with Cr, the manufacture of batteries, the operation of glass industries, various agricultural activities, the disposal of domestic waste, and the conduct of pharmaceutical industrial processes, have all been conclusively identified as major contributors to the widespread and pervasive dissemination of heavy metals throughout the environment [14; 20]. Toxic heavy metal ions are released into the environment, which poses a significant threat to both human health and the ecosystem. The production of Pb has resulted in the generation of roughly ten million tons of the element [21]. A substantial proportion of the total amount, precisely 85.10%, has been allocatedto the battery industry for utilization. An additional 5.5% has been employed in the development of pigments. The remaining 2.1% has been directed towards miscellaneous industrial processes [4]. Furthermore, Pb is commonly utilized in petrol as tetraethyl and tetramethyl agents, which act as anti-knocking compounds. On the contrary, Cr finds its purpose in the production of steel, wood preservation, chrome plating, pigments, and electroplating [22]. Meanwhile, Cd is primarily employed in the electroplating and battery industry. To tackle the issue of heavy metal discharge in industrial effluent, environmental agencies such as the central pollution control board (CPCB), India, and the state environmental protection administration (SEPA), China, have laid down the maximum permissible limit for heavy metals. The maximum permissible limit of heavy metals in industrial effluent should be 0.2 mg/L (As), 2 mg/L (Cd), 0.1-2 mg/L (Pb), 2 mg/L (Cr), and 3 mg/L (Cu) [23]. The maximum permissible limit of heavy metals in industrial effluent according to the SEPA should be 0.1 mg/L (As), 0.01 mg/L (Cd), 0.05 mg/L (Cr\({}^{6+}\)), 0.1 mg/L (Pb), and 0.5 mg/L (Ni) [4; 24]. Similarly, the United States Environmental Protection Agency (US EPA) has also established the maximum allowable limit of heavy metals present in drinking water as 0.05 mg/L (As), 0.05 mg/L (Cr), 0.1 mg/L (Pb), 0.01 mg/L (Cd), and 0.10 mg/L (Ni) [25]. Any transgressions beyond these permissible limits may result in multiple detrimental effects caused by heavy metals.
## 3 Results and Discussion
### Wettability Alteration of Reservoir Rock  
So far, we have focused on the study of the model rock treated with stearic acid. However, oil-rock interactions include several mechanisms. Experiments with reservoir rock, aged in crude oil, were carried out to prove that mixture of CA and selected surfactant performed well under real conditions. Aged rock was treated with CA, surfactant, and CA-surfactant mixture at 25 \({}^{\circ}\)C and 120 \({}^{\circ}\)C according to Section 2.2.4 and then wettability of treated rock was assessed with Section 2.2.6. Results are shown in Table 4.  
The main difference between experiments with model and reservoir rock is the ability of plain CA to alter wettability towards a water-wet state. We can conclude that CA can rupture other bonds which bind oil components to the rock surface, such as surface precipitation, polar interactions, or acid-base interaction. The most probable mechanism is the breaking of acid-base interactions, as deprotonated carboxylic groups in CA are strong  
Figure 10: Caverns formed by plain CA (upper row) and flat marble cubes after CA–surfactant treatment (lower row) at 120 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Contact Angle, \({}^{\circ}\)** & \\ \cline{2-3}
**Sample** & **25 \({}^{\circ}\)C** & **120 \({}^{\circ}\)C** \\ \hline Control sample, hydrophilic & & 57.11 \(\pm\) 7.19 & \\ Control sample, hydrophobic & & 82.17 \(\pm\) 5.80 & \\ Hydrophobic sample, treated with plain CA & 77.52 \(\pm\) 4.10 & 98.34 \(\pm\) 4.73 \\ Hydrophobic sample, treated with CA-surfactant mixture & 35.48 \(\pm\) 5.05 & 27.83 \(\pm\) 4.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contact angle values before and after treatment.  
conjugated bases which can interact easily with protonated acids [9], which are present in adsorbed oil high-molecular components.  
AO surfactant alone has little influence on the rock's wettability, which stays hydrophobic after the treatment. This result is close to the results of the model experiment. Synergism between CA and surfactant is absent for this pair of rock and crude oil. Nevertheless, values of \(R\) for the surfactant-CA mixture lie in the hydrophilic range at both temperatures, and they are very close to each other at 120 \({}^{\circ}\)C. This fact means that the surfactant does not influence CA performance negatively, which is a sign of optimal selection of surfactant type and concentration.
# Development of a New Evaporation Exposure Model: Chemical Product Evaporation Model (CPEM)
## 3 Material and Methods
### Input Data for Simulation  
Input parameters for the CPEM were divided into chemical-specific or product-specific parameters. The chemical-specific parameters were vapor pressure, molecular weight, and mass transfer rate, and the product-specific parameters were product mass, molecular weight of the residual material, weight fraction of the substance, release area, and product mass reduction rate. One air freshener product containing 2-phenoxyethanol was selected for simulation (Table 1). The weight fraction of the chemical in this product was 3.58%, 2-Phenoxyethanol is a chemical commonly used in air fresheners, with a molecular weight of 138.17 g/mol and vapor pressure of 1.33 Pa (20 \({}^{\circ}\)C). The mass transfer rate was calculated using the Thibodeaux method [7] equation \(K=\frac{1}{2}\times\frac{1}{60}\times\sqrt[3]{0.018/M}\) in the ConsExpo evaporation model documentation [3]. The initial product mass and mass reduction rate were measured in this study. To obtain \(M_{r}\), the average molecular weight of the other substances in the product was required. It was not possible to obtain the average molecular weight of each material, and therefore the method suggested by the RIVM report [8] was used. Two cases were established, one in which the material to be evaluated was the main solvent of the product and a second in which it was not. The basic formula for calculating \(M_{r}\) is \(1/(\sum_{i}w_{i}/M_{i})\), where \(w_{i}\) is the content ratio of other materials in the product, and \(M_{i}\) is the molecular weight of other materials in the product. If the material to be evaluated is not the main solvent of the product, \(M_{r}=M_{Main\ solvent}/w_{Main\ solvent}\), where \(M_{Main\ solvent}\) is the molecular weight of the main solvent, and \(w_{Main\ solvent}\) is the content ratio of the major solvent in the product. If the material to be evaluated is the main solvent of the product, \(M_{r}=M_{Main\ solvent}\). For the content ratio w of the substance in the product, the minimum value of the content of the target chemical in the product was applied based on data from the Chemistry Dashboard and Consumer Product Information Database of the US Environmental Protection Agency (US EPA). Because it is difficult to know the release area of every product on the market, three cases with respective release areas of 9, 25, and 49 cm\({}^{2}\) were assumed. Finally, a ventilation rate of 1 h\({}^{-1}\) and room volume of 30 m\({}^{3}\) were assumed as in [PERSON]'s study [9]. The temperature was assumed to be 293.15 K.
## Appendix A Mathematical Compensation  
This section gives a short overview of a purely mathematical approach which uses no diffusers. This method works well in the simulation if one has a precise calibration of the camera and projector optics (e.g., focal length, distortion parameters, etc.). In reality, one often cannot achieve these parameters with the required precision.  
The mathematical approach assumes that the focal length \(f\) of the mirrors are known as well as the directions (\(\vec{i}\) and \(\vec{\sigma}\)) of both the incoming and reflected light rays. These directions can be obtained if we assume that both the camera and projector are intrinsically calibrated and that their relation to each other is known. These calibrations can be obtained by using a calibration toolkit such as the one from [PERSON] and [PERSON] and [PERSON] (2017). Please note that, by knowing these parameters, we still don't have a calibrated display, as these parameters only describe the relation between a projector and camera and not between the projector and the display. After finding the correspondences using the structured light approach from Section 3, one can calculate both \(\vec{i}\) and \(\vec{\sigma}\) by deprojecting their corresponding projector and camera pixels.  
While the DDHOE represents an array of concave mirrors, the actual surface of the DDHOE is flat. As such, we can model the DDHOE as a flat surface with a surface normal that behaves like the surface normal of a concave mirror. This is illustrated in Figure 11. This property allows us to easily define the calculation of the offset \(d\).  
Given the incoming ray vector \(\vec{i}\) and the outgoing ray vector \(\vec{o}\), we can calculate the normal \(\vec{n}\) around which the light ray is reflected as follows:  
\[\vec{n}=\frac{\vec{o}-\vec{i}}{2} \tag{1}\]  
the offset \(\vec{d}\) can then be calculated by:  
\[\vec{d}=\frac{2f\hbar_{yz}}{\hbar_{x}} \tag{2}\]  
where \(\hat{n}\) is the normalized vector of \(\vec{n}\). \(\hbar_{yz}\) is the length of \(\hat{n}\) after projecting it onto the display plane, while \(\hat{n}_{x}\) is the length of \(\hat{n}\) after projecting it onto the display normal. The plane and its normal can be calculated by triangulating the projector and camera correspondences and fitting a plane through the corresponding point cloud.  
The actual position of \(C^{\prime}\) on the display can now be calculated as follows: \(C^{\prime}=C-\vec{d}\). In the calibration process from Section 3, we can now use this position \(C^{\prime}\) as a value for \((X,Y)\).  
The main drawback of this approach is the requirement of an accurate initial calibration of the intrinsic parameters and the lens distortion of both projector and camera. A small variation in one of these parameters will result in a change of the direction of \(\vec{i}\) or \(\vec{o}\), causing an incorrect estimate of the offsets. As a result, the light field reconstruction is often still incorrect with a relation to the physical objects. Instead of looking at a mathematical compensation, we looked at a more accurate physical compensation.
# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 2 Related Work  
### SR for Remote Sensing Images  
Since SRCNN [19] first applied convolutional neural networks (CNNs) to the image super-resolution (SR) task and demonstrated superior performance to traditional methods, researchers have proposed numerous deep-learning-based network architectures to further enhance the quality of image reconstruction. For example, [PERSON] et al. [20] introduced subpixel convolution, while [PERSON] et al. [12] combined ResNet with GANs to propose models such as SRGAN. [PERSON] et al. proposed a single-image super-resolution (SISR) method based on the residual dense backprojection network (RDBPN) [21]. These networks sig nificantly enhance the representation of image features by designing fine-grained deep learning frameworks such as residual blocks [22], dense blocks, and recursive blocks [23]. To improve the perceptual quality of the generated images, some methods introduce adversarial learning. For example, [PERSON] et al. added an edge enhancement module to the GAN architecture to improve the detail representation of remote sensing images and make the reconstruction results more visually realistic [24]. The TDEGAN [25] paper uses multi-level dense connections, residual connections, and shuffled attention (SA) to improve feature extraction capabilities. Meanwhile, the application of attention mechanism has also become a key technique to enhance the reconstruction fidelity [26; 27]. [PERSON] et al. achieved significant improvement in detail reconstruction by employing channel attention and window-based self-attention schemes, which enabled the model to focus more on key regions of the image [28].
## 3 Materials and Methods
### Baseline Particle Classification Methods
#### 3.5.1 Cut-Off  
Considering the fact that average EM cluster pt is much higher than PU or QCD cluster pt as shown in Figure 6, the simplest approach to particle classification is to apply linear selection criteria: (pt) cut-off value can be used to accept or reject clusters as EM, as it is likely that the low pt cluster is background. The test is performed with two cut-off values: the first is the maximum PU (QCD) cluster pt, and the second is the third quartile PU (QCD) cluster pt value.