esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Future Trends  
Optimization techniques like genetic algorithm or particle swarm optimization or neural networks can be utilized to optimize the frequency response of the projected diplexer, especially regarding the steepness of each filter for the designed diplexer. Also, Chebyshev parameters, GD parametric studies, and the equivalent lumped element circuit can be employed in future study to explore more about the projected diplexer in this paper. There are many FR4 varieties or alternatives with mostly the same dielectric thickness and dielectric constant but with lower loss tangent values like Kappa substrate and other ceramic materials. These materials can reduce the insertion losses for the band responses of the employed diplexer.  
Apart from that, it is also expected that software-defined diplexers will be on the rise. These diplexers are able to dynamically change and/or adjust their filtering characteristics when the signaling conditions or requirements are somehow changed. This flexibility and adaptability can be a positive factor if the operating conditions are dynamic and constant, or the communication requirements have a tendency to change.  
In light of the enormous interest in multi-band applications of communication infrastructures and the Internet of Things (IoT), alongside the need for diplexers that would be able to handle more than one frequency band at a time, this can become a leading trend in the near future. Such multi-band diplexers can be utilized by modern telecommunication systems for their particular frequency requisitions, bringing in more flexibility and the possibility of compatibility with different devices and applications.  
The performance, diversity, and acceptability of diplexers in various communication systems or technologies is largely increased by foreseeing and integrating these future trends into diplexer design.  
[PERSON] conceptualized the study, conducted data analysis, and wrote the manuscript. [PERSON] contributed to the experimental design and data collection. [PERSON] provided critical feedback and revisions to the manuscript. [PERSON]-M. assisted with data interpretation and analysis. [PERSON]-[PERSON] supervised the research project and provided overall guidance. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: All data are contained in this article.  
The authors would like to express their gratitude for utilizing Quillbot, an AI-driven tool for paraphrasing and correcting grammar, throughout the creation of this work. Quillbot played a crucial role in helping with the paraphrasing and grammar correction of the entire article, enhancing the clarity and accuracy of the language and structure of the document.  
The authors declare no conflict of interest.
## 4 Computational and Mathematical Aspects
### Anisotropic Finite Element Macro-Model
#### 4.3.1 Displacement Functions  
Figure 6 shows the four node isoparametric rectangular finite element model, with nodes 1, 2, 3, 4 numbered in an anticlockwise order. The displacements of a node have two components, namely  
\[\delta_{i}=\left[\begin{array}{c}u_{i}\\ v_{i}\end{array}\right] \tag{10}\]  
Figure 6: Finite element macro-model dimensions.  
and the eight components of element displacements are listed as a vector  
\[\delta^{e}=\left\{\begin{array}{l}\delta_{1}\\ \delta_{2}\\ \delta_{3}\\ \delta_{4}\end{array}\right\} \tag{11}\]  
The displacements within an element have to be uniquely defined by these eight values. The simplest representation is given by two linear polynomials, namely  
\[\begin{array}{l}u(x,y)=\alpha_{1}+\alpha_{2}x+\alpha_{3}xy+\alpha_{4}y\\ v(x,y)=\alpha_{5}+\alpha_{6}x+\alpha_{7}xy+\alpha_{8}y\end{array} \tag{12}\]  
The eight constants \(\alpha_{i}\) (where i = 1, 2,..., 8) can be evaluated easily by solving the two sets of four simultaneous equations, which will arise if the nodal coordinates are inserted and the displacements equated to the appropriate nodal displacements.
###### Abstract  
The design and multi-objective optimization of auxetic sandwich panels (ASPs) are performed to enhance the blastworthiness of armored fighting vehicles (AFVs). Various metastructures in the form of four auxetic geometries are proposed as the sandwich core: re-entrant honeycomb (REH), double-arrow honeycomb (DAH), star honeycomb (SH), and tetra-chiral honeycomb (CH). This paper employs a combination of finite element and machine learning methodologies to evaluate blastworthiness performance. Optimization is carried out using the nondominated sorting genetic algorithm II (NSGA-II) method. The optimization results show significant improvements in blastworthiness performance, with notable reductions in permanent displacement and enhancements in specific energy absorption (SEA). Global sensitivity analysis using SHapley Additive exPlanations (SHAP) reveals that cell thickness is the most critical factor affecting blastworthiness performance, followed by the number of cells and corner angle or radius for CH. The application of optimized ASP on AFVs shows promising results, with no failure occurring in the occupant floor. Furthermore, AFVs equipped with the optimized ASP DAH significantly reduce maximum displacement and acceleration by 39.00% and 43.56%, respectively, and enhance SEA by 48.30% compared to optimized aluminum foam sandwich panels. This study concludes that ASPs have potential applications in broader engineering fields.  
blasworthiness; auxetic structure; sandwich panels; protective structures; finite element; machine learning; armored fighting vehicle
## 4 Structural Analyses Results
### Comparisons of Limit States  
In this study, the target displacement values for the different number of storeys were obtained from the structural analyses for each country, considering the limit states in Eurocode 8 for six different countries. The comparison of target displacements of sample RC models for Albania is given in Table 17.  
The target displacements suggested by the seismic design code for the first three earthquakes in Albania provide target displacements in which the acceleration values measured in earthquakes are taken into account. However, the target displacements predicted for the structure for the last two earthquakes and the target displacements obtained under the effect of the earthquake were exceeded. This suggests that the target displacements are adequately represented for some earthquakes, while it is not sufficient for others. The comparison of target displacements of sample RC models for Bosnia and Herzegovina is given in Table 18. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Number of**} & \multicolumn{3}{c}{**Suggested**} & \multicolumn{3}{c}{**Measured**} \\ \cline{5-10}  & & & **D1 (m)** & **SD (m)** & **NC (m)** & **DL (m)** & **SD (m)** & **NC (m)** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{1969} & \multirow{2}{*}{Bosnia and Herzegovina} & 4 & 0.055 & 0.076 & 0.154 & 0.111 & 0.151 & 0.284 \\  & & & 6 & 0.112 & 0.146 & 0.257 & 0.196 & 0.253 & 0.443 \\  & & & 8 & 0.154 & 0.197 & 0.342 & 0.263 & 0.337 & 0.584 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{1962} & \multirow{2}{*}{Bosnia and Herzegovina} & 4 & 0.055 & 0.076 & 0.154 & 0.145 & 0.194 & 0.360 \\  & & & 6 & 0.112 & 0.146 & 0.257 & 0.245 & 0.316 & 0.552 \\  & & & 8 & 0.154 & 0.197 & 0.342 & 0.326 & 0.418 & 0.725 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{1981} & \multirow{2}{*}{Bosnia and Herzegovina} & 4 & 0.055 & 0.076 & 0.154 & 0.179 & 0.237 & 0.436 \\  & & & 6 & 0.112 & 0.146 & 0.257 & 0.293 & 0.378 & 0.661 \\  & & & 8 & 0.154 & 0.197 & 0.342 & 0.389 & 0.499 & 0.866 \\ \hline \multirow{2}{*}{4} & \multirow{2}{*}{1969} & \multirow{2}{*}{Bosnia and Herzegovina} & 4 & 0.058 & 0.082 & 0.164 & 0.003 & 0.004 & 0.007 \\  & & & 6 & 0.119 & 0.155 & 0.272 & 0.006 & 0.008 & 0.014 \\  & & & 8 & 0.163 & 0.209 & 0.362 & 0.009 & 0.012 & 0.020 \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{2019} & \multirow{2}{*}{Bosnia and Herzegovina} & 4 & 0.096 & 0.132 & 0.251 & 0.013 & 0.016 & 0.029 \\  & & & 6 & 0.175 & 0.226 & 0.397 & 0.026 & 0.033 & 0.058 \\  & & & 8 & 0.235 & 0.302 & 0.524 & 0.036 & 0.046 & 0.081 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: The obtained target displacements of sample RC models for Albania.
## 3 Results and Discussion  
### Analyses of SF Events  
An example of flare-induced phase and amplitude perturbations, measured for the NAA/24.00 kHz signal on an active day of 10 May 2013, is given in Figure 2 (red lines on both panels). The unperturbed daytime values of phase and amplitude were measured on 9 May 2013 (black lines on both panels in Figure 2). There were visible changes in the VLF daily signal during the duration of M1.3 SF (peak time, 12:56 UT) and C2.5 SF (peak time, 14:37 UT). One can see visible variations in amplitude and phase. SID VLF changes at the time of the maximum of SF of M1.3 and C2.5 SF classes are \(\Delta A\) = 4.2 dB, \(\Delta P\) = 68.19 deg and \(\Delta A\) = 1.06 dB, \(\Delta P\) = 19.20 deg, respectively (see Table 1 and Figure 3c,d). It can be seen that during SF class C, amplitude and phase disturbances are not well defined, but are still noticeable. Figure 3 shows simultaneous variations of the sharpness \(\beta\), effective reflection height \(H^{\prime}\), amplitude and phase (recorded and simulated), electron density at reference height (74 km) and X-ray flux, during the occurrence of two successive flares on 10 May 2013 from Figure 3a-e, respectively. For the period around SFs on 10 May 2013, we have calculated the time-dependent \(H^{\prime}\) and \(\beta\) parameters as shown in Figure 3a,b. During M1.3- and C2.5-class SFs on 10 May 2013, the change of \(H^{\prime}\) with time has normal behaviour. After the start of the SFs it falls to a minimum, and after X-ray peak it keeps growing to a preflare value. Sharpness behaviour is connected with form of registered increase of VLF amplitude, i.e., \(\beta\) rises sharply to a maximum, and after the peak of X-ray flux it drops to a preflare value. At 12:56 UT, i.e.,at peak time of the M1.3-class SF, with \(I_{x}\) = 1.36\(\cdot\)10\({}^{-5}\) Wm\({}^{-2}\), \(H^{\prime}\) decreases to a value of 67 km and the \(\beta\) rises to 0.41 km\({}^{-1}\). At C2.5-class SF peak time (14:37 UT) with \(I_{x}\) = 2.59\(\cdot\)10\({}^{-6}\) Wm\({}^{-2}\) the \(H^{\prime}\) decreases to 72 km and the \(\beta\) increase to 0.32 km\({}^{-1}\). It can be noted that the reflection height and the sharpness are in correlation with X-ray flux shape. Figure 4 shows the height profile, i.e., vertical electron-density profile before, after and during the M1.3- and C2.5-class solar flares that occurred on 10 May 2013. Electron-density altitude profile changes describe the variation of ionization at the D layer due to SF events and are relevant for mapping the low ionosphere [30,31] and moreover are important for checking the validity of the method and results [32] as shown in many examples. For unperturbed (preflare) ionospheric conditions (blue line) and post flare, there is a moderate increment in \(N_{e}\) (from 2.8\(\cdot\)10\({}^{7}\) m\({}^{-3}\) at \(h\) = 60 km height, to 6.7\(\cdot\)10\({}^{8}\) m\({}^{-3}\) at the upper part of
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 3 Antenna Analysis
### Safety Consideration  
When the proposed antenna is implanted into the human body, specific absorption rate (SAR) for safety concerns should be evaluated. The IEEE C95.1-2005 standard limits the SAR average over any 10 g of tissue in the shape of a cube to less than 2 W/Kg (SAR 10g, max \(\leq\) 2 W/kg) [26]. Therefore, through calculation, the maximum 10-g averaged SAR value is 81.5 W/kg at 2.45 GHz on condition  
Figure 11: Performance of the proposed antenna with different \(L5\) values: (**a**) Reflection coefficient; and (**b**) AR.  
Figure 10: Performance of the proposed antenna with different \(L4\) values: (**a**) Reflection coefficient; and (**b**) AR.  
that the power delivered to the proposed antenna is set to be 1 W, meaning that the delivered power should be below 24.5 mW to meet the IEEE C95.1-1999 standard.
## 4 Experimental Results
### Experiment on the Effect of the Improved PSO Algorithm
#### 4.3.1 Comparison with Basic Particle Swarm Algorithm
In general, as the number of inputs increases, the number of iterations increases. The improved PSO algorithm achieves a better performance in terms of the mean, median, standard deviation, and maximum and minimum numbers of iterations. However, the improved PSO still occasionally falls into a local optimum, such as the case in which \(k=25\)  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{\(k\) = 10} & \multicolumn{2}{c}{\(k\) = 15} & \multicolumn{2}{c}{\(k\) = 20} & \multicolumn{2}{c}{\(k\) = 25} \\  & **PSO** & **Proposed** & **PSO** & **PPSO** & **PSO** & **PPSO** & **PSO** & **Proposed** \\ \hline
1 & 3042 & 31 & 4986 & 61 & 5406 & 25 & 7290 & 100 \\
# Small-Target Detection Based on an Attention Mechanism for Apron-Monitoring Systems
## 3 Materials and Methods
### Feature Fusion: BiFPN  
The smallest feature layer is 32 times smaller than the original image, which is not conducive to the detection of small targets on the apron. When the area of small targets is less than 32\({}^{2}\), the extracted features are compressed to less than one pixel, essentially losing the features of the small targets. Deep convolutional layers contain more semantic information about small targets; this helps with target classification. Shallow convolutional layers are more likely to be activated by local textures and can capture more specific details, which is suitable for detecting small targets. Therefore, combining both layers improves the detection of small targets.  
Different levels of features are directly added when performing feature fusion in an FPN; this operation causes different levels of features to have the same weight. This, in turn, ensures that different feature layers contribute equally to the detection results. By adding a top-down path, shallow features can fuse powerful semantic information with deep features. However, because of the relatively large number of convolutions from the bottom to the top layer, dozens or even hundreds of convolution layers are required; thus, many detailed features of the target are lost in the process. Therefore, a lack of more specific details on deep features affects the detection accuracy of the network. BiFPN adds low-to-top channels and weighted fusion, which compensates for the lack of detailed information and produces more effective multiscale feature fusion.  
In Figure 5, \(C_{i}\) and \(f_{i}\) represent the features output by the feature extraction network and the fused feature, respectively. After \(C_{i+1}\) is up-sampled, it is fused with \(C_{i}\) according to (3), and then the intermediate feature \(f_{i}^{tmp}\) is obtained through 3 \(\times\) 3 convolution. Then the same method is used to fuse \(C_{i}\), \(f_{i}^{tmp}\) and \(f_{i-1}\) to generate \(f_{i}\). The specific formula is shown in (4).  
\[F=\sum_{i}\frac{\omega_{i}}{\varepsilon+\sum_{j}\omega_{j}}\cdot I_{i} \tag{3}\]  
\[\left\{\begin{array}{l}f_{i}^{tmp}=conv\Big{(}\frac{\omega_{1}C_{i}+\omega _{2}^{*}up(C_{i+1})}{\omega_{1}+\omega_{2}+\varepsilon}\Big{)}\\ f_{i}=conv\Big{(}\frac{\omega_{1}^{\prime}C_{i}+\omega_{2}^{*}N_{i}^{tmp}+ \omega_{3}^{*}down(N_{i-1})}{\omega_{1}^{\prime}+\omega_{2}^{*}+\omega_{3}^{ *}+\varepsilon}\Big{)}\end{array}\right. \tag{4}\]
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion
### Corrosion Inhibition
#### 3.3.2 Atomic Adsorption Spectrometric (AAS)  
The concentration of Fe+ ions in the corroduct solution of both types are presented in Table 3.  
Maximum inhibition (81.74%) was found at 600 ppm which was reduced to 28% at 100 ppm. These results showcase the presence of a protective layer on mild steel immersed in _C. tagal_ extract with varying concentrations of Fe+ ions in the corroduct solution based on concentration. Variations in the amounts of ions in both the solutions (without and with plant extract) were due to the presence of a protective film which barred the corrosion to about 82% (8.25 mg/I of mild steel corroduct) [52]. The inhibition was concentration dependent, since a decrease in \(\eta\)% was observed at lower concentrations ranging from 28 to 72% for 200-500 ppm. The change was due to the acid attack which had occurred by larger exposure of the mild steel surface. Usually organic compounds with sulfur, nitrogen, and oxygen atoms are linked to aromatic rings forming a conjugate system in order to provide electronegative space for interacting with the metal surface to shield them from corrosive environment. This type of shielding has been observed in biomass (_Pseudomonas chlororaplis_) and plants (_Senna cana_, _Zanthoxylum syncarpum_ Tull., _Dimorphandra gardneriana_ Tul., and _Mangifera indica_ L.) via their aminophenol derivatives and heterocyclic compounds (phenols, alkaloids, anthocyanidins, and saponins) being adsorbed onto the metal surface decreasing the chance for oxidation and preventing corrosion [53; 54].
# Time Series Feature Selection Method Based on Mutual Information
## 3 Experiments
### Target Variable Construction  
Two issues need to be considered in the construction of the target variable of the system. One is that the variable needs to be able to reflect the change in system operability, and the other is that the variable needs to ensure monotonicity and irreversibility in the whole life cycle. For monitoring and control systems, in most cases, the monotonicity, predictability, and irreversibility of a single sensor signal cannot be guaranteed, nor can it fully reflect the system operability. Therefore, this paper uses the sensor information fusion method based on PCA dimension reduction and kernel smoothing to extract the system target variables, thus ensuring the monotonicity and irreversibility of the degradation trajectory [48].  
It should be noted that the degradation trajectory extracted using PCA and KR is the system operability mentioned above, which reflects the overall operational status of the system. The specific approach is to first fuse the multidimensional monitoring variables of the system using PCA to extract the principal component that contains the most system information. At the same time, in order to ensure the monotonicity and irreversibility of the system operability, KR is used to smooth the extracted principal components, so as to obtain the curve of system operability, as shown in Figure 5.  
Figure 5 shows the system operability of engines from numbers 1 to 10 in the dataset. It can be seen from the figure that by extracting the system degradation curve as the target variable, the monotonicity and irreversibility of the variable can be guaranteed, and the system operability can reflect the health status of the system to a certain extent.  
Figure 4: The trends in the 9 variables after normalization (figure drawn by matplotlib [47]).  
Figure 5: Target variables for engines 1 to 10 based on PCA dimensionality reduction (figure drawn by matplotlib).
## 4 Solar Field Calculations
### Absorbed Solar Radiation Calculations  
The absorbed heat \(\left(S_{absorb,loop}\right)\) by thermal oil passing through the absorbers for one loop is calculated as follows:  
\[S_{absorb,loop}=DNI\ A_{c}IAM\ cos\theta\ \eta_{opt,0}\ \eta_{track}f_{end,loss}f_{row,shadow}\ f_{ dust}f_{add}\ f_{clean} \tag{7}\]  
where:  
\(DNI\):Direct normal irradiance [\(W/m^{2}\)], the \(DNI\) data is measured at the Andasol 2 power plant site in Spain. Then, these measured data are entered into the APROS model.  
\(A_{c}\): Mirror aperture area for one loop [m\({}^{2}\)]; the value of \(A_{c}\) used in Equation (7) is 3270 m\({}^{2}\). _IAM_: Incidence angle modifier, used to modify additional optical and geometric losses due to an incident angle more than 0\({}^{\circ}\). It is calculated from this equation:  
\[IAM=\frac{K}{cos\theta_{i}}\]  
where \(K=cos\theta_{i}-0.000525\ \theta_{i}-0.00002859(\ \theta_{i})^{2}\)  
\(\theta_{i}\): Angle of incidence [deg], the angle between the incident solar radiation on a surface and the plane perpendicular to the aperture of the parabolic trough.  
\(\eta_{opt,0}\) : A peak optical efficiency determined at an incidence angle of zero. The value used in Equation (7) is 0.81, based on Garcia IL correlation.  
\(\eta_{track}\): The tracking coefficient is a measure of how accurate a solar tracking system is to get the best amount of solar radiation. It ranges between 0 and 1, where a value of 1 indicates that the tracking system follows the sun with high accuracy. The unity value is used in this study.  
\(f_{end,loss}\): End loss factor refers to the ratio between the effective length and the actual length of the mirror in a solar collector assembly. It is calculated depending on STUETZLE TA formula.  
\(f_{row,shadow}\): Shadow factor represents the ratio between the effective width of trough aperture and its real total width. It is computed based on STUETZLE TA equation.  
\(f_{dust}\): This coefficient is used to describe the absorption due to dust on the absorber glass cover. Its value ranges between zero and unity. Zero values mean that the absorber is dusty and there is no absorption. The value 1 means the absorber is clear and there is complete absorption. The value of 0.98 was chosen in this study.  
\(f_{add}\): This factor is used to calculate the additional reduction in the absorbed solar radiation. It ranges between 0 and 1, where the value of 1 is the ideal case and used in this work.  
\(f_{clean}\): This factor refers to mirror cleanliness. The values range of this factor is between zero and 1, where the zero value represents no solar radiation of the mirrors to the absorber tube and the unity value denotes that the whole solar radiation falling on the mirrors is reflected. The value of 0.97 was selected in this work.  
The total heat losses of the pipes and absorbers are calculated based on experimental equations in [20; 21]. The results obtained in this work are calculated depending on subtraction the total thermal losses in the SF from the absorbed heat in 156 loops in order to send the net thermal power to the PB.