esa-sceva/satcom-chunk-collection huggingface dataset:
# Conductive Textiles for Signal Sensing and Technical Applications
## 2 Materials Used to Impart Conductivity on Textiles
### Chemical Vapor Deposition (CVD)
#### 3.1.2 Layer-by-Layer Deposition (LbLD)  
LbLD is a new and simple method of developing a thin film on the substrate at normal temperature and pressure. Alternate deposition occurs using a poly-ion layer on solid substrates by dipping into an oppositely charged solution and washing between each depositing layer. The alternation of the surface charge results in polyelectrolyte layers on the solid. Charged particles are bound by the electrostatic and Van der Waals bond [168; 169; 170; 171]. The LbLD process has already been applied to impart conductivity to textile materials in many applications [172; 173]. Electromagnetic interference shielding of cotton fabric by layer-by-layer (LbL) assembly of positive and negative charge MWCNTs and nickel ferrite (NiFe\({}_{2}\)O\({}_{4}\)) nanoparticles with a PDMS covering to keep the layer stable was reported [169]. A cationic surfactant is employed to create positively charged MWCNT, and EDTA is used to functionalize NiFe\({}_{2}\)O\({}_{4}\), which is controlled by the ionization of both catalysts [169]. The vacuum-assisted spray-LbLD process can deposit MWCNT without any binder, thereby producing a high degree of binding sites and resulting in better sensing capabilities [174]. Poly(ethylenimine)/CNTs and ammonium polyphosphate layer nanocomposite coating were placed LbL on cotton fabrics to generate superior hydrophobic conductive cotton fabric, and PDMS coating imparted an identical energy density throughout the surface [175]. LbL deposited Chitosan-graphene cation and PSS anion layers one after the other on cotton fabric, resulting in an electrical conductivity of 1.67 \(\times\) 10\({}^{3}\) Sm\({}^{-1}\) for 10 layers [176]. V\({}_{2}\)O\({}_{5}\)-nanostructured supercapacitors show low energy density and electrical conductivity. Deposition of graphene layers between V\({}_{2}\)O\({}_{5}\)-coated MWCNT films prevents congregation and improves the energy density (96 Whkg\({}^{-1}\)). The graphene layer acts as a barrier and improves the durability and capacitance of the device [177]. An LbLD process featuring a pad-dry-cure method for developing an electrically conductive Ag nanoparticles-carboxymethyl cellulose composite was reported [178].
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSSs Based on Applications
#### 4.3.6 Textile FSSs  
Although rare good researches on textile FSS are found in literature, however, it is another emerging class after wearable FSS. FSSs printing on flexible and smooth surfaces, like paper or Polyethylene Terephthalate, have been demonstrated while using screen printing, lithographic processes, inkjet printing, and stamping [191]. A unique application is to integrate them into clothes to provide on-body and off-body communications [192]. Textile structures offer benefits of flexibility, lightweight, smoothness, and higher strength-to-weight ratio.  
Fabric production techniques have been used to produce conductive components, and similarly, textile surfaces may be used to construct fabrics to control the propagation of EM waves at microwave frequency regime to embed into buildings [193]. Such fabrics based FSSs that may have short-term use and are easily controllable; find application facilities in conferences, museums, art galleries, and also screening on the clothing. A novel FSS on the scale of the scattering elements by an array of squares is screen printed on a woven textile with conductive inks [191]. Each square printed is of textile weave order, presenting a challenge of printing with high reliability. The squares jointly respond close to their resonant wavelength. Two FSS knitted from conducting yarn on a computerized flat-bed knitting machine are shown in Figure 22a [192]. Figure 22b shows a fabric FSS fabricated by the screen printing technique.
## 4 Procedures
### Ill-Conditioning in Coupling Methods
If H is singular or ill-conditioned, its inverse can be approximated by the pseudo-inverse \(\mathrm{H}^{+}=\mathrm{V}\mathrm{S}^{+}\mathrm{U}^{\mathrm{T}}\). In this formulation, \(S^{+}\) represents the pseudo-inverse of the singular values matrix. By setting small singular values to zero, SVD effectively mitigates issues related to ill-conditioned matrix inversions, particularly when input FRFs are significantly affected by noise [38; 39; 40; 41]. In practical applications, mitigating ill-conditioning often involves preprocessing FRF measurements, such as spectral averaging or cross-power spectral density calculations, and carefully selecting interface DoFs to capture dominant dynamic behaviors. While these considerations were not explicitly incorporated into the case studies in this paper, they are critical for extending the methodology to more complex, real-world systems.
## 3 Results and Discussion
### Corrosion Resistance of Composite Coatings  
The samples of blank aluminum alloy, MAO treated aluminum alloy, MAO-PU composite coating, and EP-PU composite coating were immersed in 3.5% sodium chloride solution for 20 min. After the open-circuit voltage was stabilized and the polarization curve was measured, saturated calomel  
Figure 6: Adhesion strength between modified and unmodified MAO samples.  
electrode (SCE) was used as a reference electrode. Figure 7 illustrates the corresponding cathodic and anodic potentiodynamic polarization (PP) curves. Taking into consideration the cathodic branches of the PP curves, it is assumed that the sample MAO-PU (represented by the black curve) demonstrates lower cathodic current densities compared to the blank aluminum alloy (represented by the red curve), indicating that the treatment/application of MAO-PU to the aluminum alloy could possible reduce the cathodic reactions. Furthermore, by studying the anodic branches of the polarization curves, it is mentioned that the sample MAO-PU exhibits lower anodic current densities compared to the blank aluminum alloy sample, meaning an effective inhibition of the anodic reactions. Regarding the corresponding Eco (open circuit potential) values, it is observed that the sample MAO-PU demonstrates nobler Eco values compared to the blank aluminum alloy sample denoting that they are less prone to corrosion after exposure to the corrosive environment. It is important to mention that the PP method was used only for qualitative estimations because the determination of Rp and corrosion current (icorr) values via the linear fitting of the polarization curves according to the Tafel method was impossible, due to the fact that the Tafel method only yields an average uniform corrosion rate and is not appropriate for localized corrosion, such as pitting corrosion; aluminum alloy 7050 is corroded via pitting corrosion [29; 30; 31].
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
###### Abstract  
In recent years, multimodal thermoacoustic imaging has demonstrated superior imaging quality compared to other emerging modalities. It provides functional and molecular information, arising due to electromagnetic absorption contrast, at ultrasonic resolution using inexpensive and non-ionizing imaging methods. The development of optical- as well as radio frequency (RF)-induced thermoacoustic imaging systems would benefit from reliable numerical simulations. To date, most numerical models use a combination of different software in order to model the hybrid thermoacoustic phenomenon. Here, we demonstrate the use of a single open source finite element software platform (ONELAB) for photo- and RF-acoustic computed tomography. The solutions of the optical diffusion equation, frequency domain Maxwell's equations, and time-domain wave equation are used to solve the optical, electromagnetic, and acoustic propagation problems, respectively, in ONELAB. The results on a test homogeneous phantom and an approximate breast phantom confirm that ONELAB is a very effective software for both photo- and RF-acoustic simulations, and invaluable for developing new reconstruction algorithms and hardware systems.  
photoacoustic imaging; tomography; thermoacoustic; radio frequency +
Footnote †: journal: Applied  
1
Footnote 1: _Appl. Sci._**2018**, \(8\), 1568; doi:10.3390/app8091568
## 3 Results and Discussion
### Effect of the BGE Diluent on Reaction Kinetics
Thus, the above system maintains the same activity at relatively low temperatures. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**BGE Fraction** **(\%)**} & \multicolumn{2}{c}{\(-\)**ln(\(\beta\)/\(T_{P}\)2) \(-\)1000/\(T_{P}\)**} & \multirow{2}{*}{**Relative Coefficient R2**} & \multirow{2}{*}{**Activation Energy \(E\) (k) mol\({}^{-1}\))**} & \multirow{2}{*}{**Reaction Order \(n\)**} \\  & \multicolumn{2}{c}{\(-\)**ln\(\beta\) -1000/\(T_{P}\) Linear Fitting**} & & \\ \hline \multirow{2}{*}{10.0} & y = \(-\)3.65469 + 5.72375x & 0.998 & \multirow{2}{*}{47.59} & \multirow{2}{*}{0.870} \\  & y = \(-\)17.76639 + 6.57637x & 0.999 & & \\ \hline \multirow{2}{*}{12.5} & y = \(-\)5.68331 + 6.55079x & 0.969 & \multirow{2}{*}{54.47} & \multirow{2}{*}{0.885} \\  & y = \(-\)19.78786 + 7.40054x & 0.976 & & \\ \hline \multirow{2}{*}{15.0} & y = \(-\)4.4576 + 6.06282x & 0.998 & \multirow{2}{*}{50.41} & \multirow{2}{*}{0.877} \\  & y = \(-\)18.56654 + 6.91445x & 0.999 & & \\ \hline \multirow{2}{*}{17.5} & y = \(-\)3.36634 + 5.61983x & 0.998 & \multirow{2}{*}{46.73} & \multirow{2}{*}{0.868} \\  & y = \(-\)17.4921 + 6.47836x & 0.998 & & \\ \hline \multirow{2}{*}{20.0} & y = \(-\)3.32175 + 5.62768x & 0.998 & \multirow{2}{*}{46.79} & \multirow{2}{*}{0.868} \\  & y = \(-\)17.44446 + 6.48505x & 0.998 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Activation energy and reaction order with different mass fractions of the diluent. Figure 3: Linear fitting using the Kissinger equation and Crane equation. (**A**) \(-\)ln(\(\beta\)/\(T_{P}\)2) vs. 1000/\(T_{P}\); (**B**) \(-\)ln\(\beta\) vs. 1000/\(T_{P}\).
# Carbon, Water, and Light Use Efficiency Under Conservation Practice on Sloped Arable Land
## 3 Results
### Interaction Between Carbon, Water, and Light Use Efficiency  
Correlation and principal component analyses were performed to investigate the relationships among the studied indicators. Gross primary productivity (GPP), net primary productivity (NPP), autotrophic respiration (AR), and light use efficiency (LUE) were derived from accumulated biomass data, explaining the strong correlations observed among the first three indicators (Table 7). The calculations of actual evapotranspiration  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant/Crop** & **2021** & **2022** & **2023** \\ \hline Maize CT & 1.78 & 3.29 & 4.12 \\ Maize MT & 4.38 & 4.46 & 6.13 \\ Wheat CT & 2.97 & 2.58 & 3.31 \\ Wheat MT & 5.87 & 2.66 & 3.38 \\ \hline \hline \end{tabular} ANOVA: tillage, significance \(p=0.0635\).  
\end{table}
Table 6: Light use efficiency (g MJ \({}^{-1}\)).  
Figure 4: Accumulated dry biomass (g m\({}^{-2}\)), in the different cultivation options of the studied crops, energy in biomass (MJ m\({}^{-2}\)), and efficiency (LUE, g MJ\({}^{-1}\)) of using photosynthetically active radiation (APAR) for (**a**) 2021, (**b**) 2022, (**c**) 2023.  
(ETa), GPP, and AR incorporate temperature-dependent components, contributing further to their mutual correlations. However, LUE did not show any significant correlation with the other variables. A moderate positive correlation was identified between carbon use efficiency (CUE) and ETA (r = 0.50).  
Principal component analysis (PCA) was conducted to identify the primary relationships among the studied parameters (Figure 5, Table 8). The analysis resulted in two principal components, which together explain 86.50% of the total variance in the dataset. Component 1 alone accounts for 65.65% of the variation in the indicators. The biplot illustrates a strong positive association among net primary productivity (NPP), gross primary productivity (GPP), water use efficiency (WUE), and light use efficiency (LUE). In contrast, carbon use efficiency (CUE) was negatively correlated with LUE and WUE. These findings suggest that, under the experimental conditions, improvements in water and light use efficiency were not associated with enhanced carbon assimilation efficiency.  
Figure 5: Principal component analysis of CUE, LUE (g MJ\({}^{-1}\)), WUE (kg C mm\({}^{-1}\)), NPP (kg C m\({}^{-2}\)), GPP (kg C m\({}^{-2}\)), AR (kg C m\({}^{-2}\)), and ETA (mm). The grey dots at the ends of the blue vectors represent the contributions (loadings) of various variables to the first two principal components (PC1 and PC2).
## 2 Modelling and Methods
### ANN Development Protocol
#### 2.5.1 Input Data Selection and Organisation  
As mentioned previously, data selection and organisation are the first steps towards the development of a functional and effective ANN. The importance of careful input selection is underlined, with a particular focus on data significance and independence. Although there are various statistical ways of determining the significance of data, the use of previous experience and domain knowledge is deemed acceptable and valid [9]. In this case, the independent variables selected are all known to have a considerable contribution to the development of the final values of the dependent variable.  
Data filtering and clustering can reduce the number of input parameters, enabling the development of a more efficient and computationally manageable algorithm [32]. Such techniques ensure that any interdependence present in input variables is identified and either removed or merged, reducing the dimensionality of the necessary analysis. The number of independent variables used in the present study is considered small. Although, at present, some dependency between variables is known to exist (i.e., brick density with its thermal conductivity coefficient), the data is structured in a way that allows for the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Sample Ref**} & \multirow{2}{*}{**Brick Density**} & \multicolumn{2}{c}{**Thermal**} & \multirow{2}{*}{**Thermal**} & \multirow{2}{*}{**Insulation**} & \multirow{2}{*}{**Insulation**} & \multirow{2}{*}{**Insulation**} \\  & & **Conductivity** & & & & **Thickness** \\  & & **Coef.** & & & & \\ \hline Test Sample 1 & 2000 & 0.8 & 0.9 & 25 & EPS & Ext \\ Test Sample 2 & 2000 & 0.8 & 0.9 & 25 & EPS & Int \\ Test Sample 3 & 2000 & 0.8 & 0.7 & 0 & Nolns & AbsIns \\ Test Sample 4 & 2000 & 0.8 & 0.3 & 0 & Nolns & AbsIns \\ Test Sample 5 & 1500 & 0.6 & 0.9 & 0 & Nolns & AbsIns \\ Test Sample 6 & 1500 & 0.6 & 0.7 & 75 & EPS & Int \\ \hline \hline \end{tabular}
\end{table}
Table 5: Additional FE model parameters.  
introduction of further test cases able to remove such issues (i.e., varying combinations of brick density and thermal conductivity that are not proportionally related).
## 2 Results and Discussion  
### Phytochemical Profile of C. longa  
The results obtained by GC-MS revealed the predominance of sesquiterpenes in the hexane (HEXCURC), dichloromethane (DCCURC), and ethanol (ETOHCURC) extracts. In the HEXCURC extract, the major compounds annotated were \(\alpha\)-curcumene (14.3%), \(\beta\)-turmerone (58.8%), and \(\alpha\)-turmerone (2.9%). \(\beta\)-Turmerone (23.6% and 26.6%), ar-turmerone (20.8% and 27.2%), and \(\alpha\)-turmerone (16.0% and 14.3%) were the main constituents annotated in the DCCURC and ETOHCURC extracts, respectively. These findings align with previous results described by our group [20]. The present study also focused on the curcuminoid-rich profiles of the DCCURC and ETOHCURC extracts, as determined by LC-MS chromatographic analysis. Based on the structural characteristics of curcuminoids, these compounds contain two keto groups at positions C3 and C5, with variations in the presence of double bonds within the central chain and substituents attached to the aromatic ring. The three curcuminoids identified via LC-MS HR belong to this group and were annotated as curcumin (**1**), demethylox Curcumin (**2**), and bisdemethoxycurcumin (**3**). Their identification was based on ions detected in negative mode [M-H]\({}^{-}\) at \(m/z\) 307.0949 (C\({}_{19}\)H\({}_{17}\)O\({}_{4}\)), 337.1055 (C\({}_{20}\)H\({}_{19}\)O\({}_{5}\)), and 367.0952 (C\({}_{21}\)H\({}_{21}\)O\({}_{6}\)), respectively. The predominant compounds in DCCURC were **2**, accounting for 78%, and **1**, comprising 12%. Meanwhile, in ETOHCURC, the primary constituents were **1** (65%) and **3** (28%). These compounds differ in the number of methoxy groups: **1** contains two, **2** one, and **3** none.  
Notably, the extracts demonstrated a solvent-dependent distribution of stereoisomers within the turmerone group (Supplementary Material, Figure S1). A similar trend was observed for curcuminoids. Consequently, the extraction methodology proved effective in partitioning the sesquiterpenes and curcuminoids across the different extracts. This distribution significantly influenced the biological activities described below, underscoring the importance of extraction methods in determining the bioactivity of plant-based preparations.
_Article_  
**Appraisal of the Spatial Resolution of**  
**2D Electrical Resistivity Tomography for**  
**Geotechnical Investigation**  
[PERSON] 1,*, [PERSON] 1 and [PERSON] 2(c)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Received: 22 May 2020; Accepted: 22 June 2020; Published: 26 June 2020  
**Abstract:** In the past decade, the 2D electrical resistivity tomography (ERT) has been extensively used in the investigation and monitoring of geotechnical engineering and environment engineering, but there are many uncertainties hidden behind its vivid color earth-resistivity profiles. In order to use the 2D ERT in the scale of geotechnical engineering effectively, the accuracy and spatial resolution capability of measurements must be enhanced, or at least these uncertainties should be mastered to avoid overreading the measurement results. There were seven common geological models built in this study to discuss the variance in spatial analysis capability of 2D electrical resistivity profiles under different geologic conditions. The findings show that the resolution capability of 2D electrical resistivity profiles was influenced by depth, and in different strata, it may be influenced by the resistivity ratio, layer depth, covering depth, interlayer thickness, tilt angle, medium size, and noise intensity. Generally speaking, the relatively low resistance stratum had better resolution capability; if the relatively high resistance stratum was located under the relatively low resistance stratum, its resolution capability declined. In different strata, the resolution capability may be degraded under the effect of different factors. In addition, any noise in the course of measurement resulted in a random jump of the electrical resistivity profile, which worsened as the noise increased. These circumstances should be paid special attention to avoid misrecognition of electrical resistivity profile images.  
**Keywords:** electrical resistivity tomography; spatial resolution; non-destructive
## 3 Results and Discussion
### Pressure Drop Analysis  
The air-side pressure drops of the two MCHXs are compared in Figure 9. The rapid growth of the frost layer led to a blockage of the free airflow passage, resulting in the rapid rise of the air-side pressure drop. As discussed earlier, RH has a significant effect on the frost accumulation rate and heat transfer rate. Similarly, RH also has a significant effect on the rate of increase in pressure drop. Irrespective of the inlet coolant temperature, air velocity, and fin structures, the effect of RH on pressure drop is significant. It can be seen from Figure 9b, d that the pressure drop increases rapidly in the early frosting stage. The pressure drops for RH = 90% are about 16.5-98.1% higher than that of RH = 80% at 20 min. At a higher RH, the increase in frost growth rate led to the increased air-side pressure drop. The impact of air velocity on pressure drop was also obvious, and the pressure drop increased with the increase in air velocity. For all the experimental conditions, the pressure drop at u\({}_{\text{a}}\) = 1 m/s was 26.1-56.4% higher than that of the u\({}_{\text{a}}\) = 0.6 m/s. Similarly, at u\({}_{\text{a}}\) = 1.5 m/s, an increase in pressure drops of 60.6-195.4% and 27.3-71.3% was observed as compared to u\({}_{\text{a}}\) = 0.6 m/s and u\({}_{\text{a}}\) = 1 m/s, respectively. The inlet air velocity had the greatest effect on pressure drop.  
As expected, the pressure drop of MCHX-B was usually greater than that of MCHX-A because MCHX-A has a smaller blocking ratio than MCHX-B during frosting. MCHX-B can have a maximum pressure drop difference of 8 Pa greater than MCHX-A under all frosting conditions. The difference in pressure drop between two heat exchangers is affected by frosting conditions and frosting time. In summary, it is shown that the MCHX with  
Figure 9: Air-side pressure drop variation with time for different MCHXs under frosting conditions [(**a**) T\({}_{\text{c,in}}\) = \(-\)10 \({}^{\circ}\)C, RH = 80%, (**b**) T\({}_{\text{c,in}}\) = \(-\)10 \({}^{\circ}\)C,RH = 90%, (**c**) T\({}_{\text{c,in}}\) = \(-\)7 \({}^{\circ}\)C, RH = 80%, (**d**) T\({}_{\text{c,in}}\) = \(-\)7 \({}^{\circ}\)C, RH = 90%].  
extended fins on the windward side can effectively delay the frost-blocking rate and reduce the pressure drop.
## 4 Computations for Model Example with Linear Dynamics  
### Cooperative Solution  
Consider the model with dynamics (3) and the objective function (5). The optimization problem is solved using the Pontryagin maximum principle (PMP) [12], which is widely recognized as one of the central tools for determining optimal strategies. We mention the papers [13; 14] to give an idea of just a couple of applications of this celebrated technique. We define the Hamiltonian function as  
\[H(\psi,u)=\psi\left(-\sum\limits_{i=1}^{n}u_{i}\right)+\left(\sum\limits_{i=1 }^{n}c_{i}\sqrt{u_{i}}\right). \tag{9}\]  
The optimal controls \(u^{*}\) are computed from the first-order optimality conditions \(\frac{\partial H}{\partial u_{i}}=0\) as  
\[u_{i}^{*}=\bigg{(}\frac{c_{i}}{2\psi}\bigg{)}^{2},\quad i=\overline{1,n}. \tag{10}\]  
Noting that the adjoint variable is constant according to optimality condition \(\frac{\partial H}{\partial x}=0\), \(\psi=\psi_{0}=const\) and, furthermore, has to satisfy \(\psi>0\). Then, by substituting the expression of optimal control into system (3), we obtain \[\psi_{0}=\sqrt{\frac{(T-t_{0})}{4(x_{0}-x_{f})}\sum\limits_{j=1}^{n}c_{j}^{2}}. \tag{11}\]  
The optimal cooperative strategies and trajectory is thus  
\[u_{i}^{*}=\frac{c_{i}^{2}(x_{0}-x_{f})}{\sum\limits_{j=1}^{n}c_{j}^{2}(T-t_{0})}. \tag{12}\]  
\[x^{*}(t)=\bigg{(}\frac{x_{f}-x_{0}}{T-t_{0}}\bigg{)}(t-t_{0})+x_{0}. \tag{13}\]  
Further, we check the control constraints according to the of the problem, i.e., \(u_{i}\in[0;u_{max}]\). For the admissible controls, the following condition should be satisfied:  
\[x_{f}\geq x_{0}-\frac{\sum\limits_{j=1}^{n}c_{j}^{2}}{c_{i}^{2}}u_{max}(T-t_{0 }),\quad i=\overline{1,n}. \tag{14}\]  
The optimal value of the total payoff then equals  
\[K(x_{0},x_{f},T-t_{0},u^{*})=\sqrt{\sum\limits_{j=1}^{n}c_{j}^{2}(x_{0}-x_{f}) (T-t_{0})}. \tag{15}\]
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 2 5G Massive MIMO at Sub-6 GHz
### 5G Smartphone mMIMO Array Antenna Techniques  
Many techniques support massive MIMO systems in smartphone applications working in the sub-6 GHz bands, which are classified depending on the number of elements.