esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Design of 3D-Printed 6-18 GHz Double-Ridged TEM Horn Antenna
###### Abstract  
A robust design of a 3D-printed 6-18 GHz double-ridged TEM horn antenna is proposed in this paper. The designed TEM horn antenna has two parts: an adaptor and a horn aperture. The adaptor is realized using a double-ridged waveguide to extend the operating bandwidth of the dominant mode (TE10 mode). Meanwhile, the horn aperture section is implemented in an exponentially tapered configuration to match the impedance of the double-ridged waveguide with the intrinsic impedance. The performance of the initially designed antenna shows that the reflection coefficient and gain levels are less than \(-13\) dB and greater than 5.5 dBi within the 6-18 GHz band, respectively. The initial design was well done, but the noise factors that may occur during the manufacturing process were not taken into account. To design an antenna considering these noise factors, the parameters of the initial design are optimized by a novel robust design method also proposed in this paper. The robustness of the antenna optimized by the proposed method is approximately 12.4% higher than that of the initial antenna. The validity of the proposed method was tested by fabricating the antenna. A prototype of the optimized antenna with the proposed robust design method is fabricated using a 3D printer with a stereolithographic apparatus attached, and the surface of the frame is covered by a nano-silver plating. The measured results of the fabricated antenna are in good agreement with the simulation results over the operating band. The measured \(-10\) dB reflection coefficient bandwidth of the antenna can cover 6-18 GHz. In addition, the measured gain ranges from 4.42 to 10.75 dBi within the 6-18 GHz band.  
3D-printed antenna; double-ridged waveguide; optimization algorithm; robust design; TEM horn antenna +
Footnote †: journal: Applied
# Towards the Internet of Smart Trains:
## 3 Overview on the Railway Applications Offered by GSM-R  
This section details the steps related to the adoption of GSM-R and reviews the main railway-specific services and requirements.
## 2 Materials and Methods
### Continuous Flow Experimental Setup and Operational Conditions  
Laboratory-scale experiments were conducted in continuous flow mode under optimal pH and dosage conditions previously determined through jar testing by [PERSON] et al. [14]. The experimental configuration was developed according to [PERSON] [32] and is illustrated in Figure 2. It included a 100 mL stirred reactor with a residence time of 5 min, where rapid mixing was facilitated using a magnetic stirrer. The aquaculture effluent was fed into the system at a constant flow rate of 20 mL\(\cdot\)min\({}^{-1}\) via a peristaltic pump, while the coagulant solution was introduced at a rate of 6.8 mL\(\cdot\)min\({}^{-1}\) using a dosing pump. The following optimal concentrations for each coagulant were used: Tanfloc at 25 mg\(\cdot\)L\({}^{-1}\) (pH7), FeCl\({}_{3}\cdot\)6H\({}_{2}\)O at 25 mg\(\cdot\)L\({}^{-1}\), Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\cdot\)14H\({}_{2}\)O at 20 mg\(\cdot\)L\({}^{-1}\), and the CS-based coagulant at 10 mg\(\cdot\)L\({}^{-1}\) (all at pH 8). To evaluate the influence of pH, different concentrations (20, 50, and 100 mg\(\cdot\)L\({}^{-1}\)) were also applied under the natural pH of the water (around pH 6.5). Following coagulation, the mixture entered a 226 mL tubular reactor, designed to mimic the slow mixing phase, with an estimated residence time of 11 min. Settling took place in a U-shaped sedimentation tank equipped with dual outlet valves to separate sludge and the clarified effluent. After allowing the system to stabilize, effluent samples were collected after 45 min and analyzed for parameters such as final pH, color, dissolved organic carbon (DOC), natural organic matter (NOM), nitrite, total nitrogen, and phosphorus.
## 3 Results and Discussion
### Microwave-Based Detection of Aging State Dependent Water Adsorption at Low Temperatures
% water containing feed gas, measured with a fresh catalyst sample. Since the surface area, which decreases with aging due to sintering effects, may have an influence on the sorption of water, a BET analysis of catalysts samples that were analogously aged was conducted. The BET surface area of the milled fresh catalyst including the cordierite honeycomb is 49.2 m\({}^{2}\)/g. Aged at 850 \({}^{\circ}\)C, it is reduced by about 33% to 33.0 m\({}^{2}\)/g and after the second aging by about 48% compared to the fresh state to a value of 25.4 m\({}^{2}\)/g. Figure 2 had shown that between 150 and 290 \({}^{\circ}\)C the resonance frequencies at \(\lambda=1.02\) are slightly lower than the one at \(\lambda=0.98\). A different amount of adsorbed water depending on the oxidation state of the ceria may be an explanation for this effect. Since the influence of water on the resonance frequency is not negligible at those temperatures, and since the surface area in the fresh state is higher than the one in an aged state, this small effect disappears when the catalyst sample gets aged. However, this explanation needs further support by appropriate experiments. All obtained data are summarized in Figure 7 in a strongly normalized representation, so that all values lie in a range between 100% and 200%. Red bars denote fresh catalysts, blue bars stand for samples aged at 850 \({}^{\circ}\)C (aging state ), and black bars indicate samples aged at 1050 \({}^{\circ}\)C (aging state ). Whereas the first three groups (light off temperature, oxygen storage capacity, BET surface) are classical catalyst data, as they are used for catalyst characterization, the microwave-derived data are evaluated in this study for the first time. It is astonishing how similar they behave. Especially the changes of the oxygen storage capacity (derived at 500 \({}^{\circ}\)C) and the resonance frequency at 80 \({}^{\circ}\)C seem to correlate quite well. These observations may open the door not only to control an engine with the microwave-based method, but also to determine catalyst-aging processes with this method, which is required for on-board diagnosis (OBD). Figure 6: Temperature dependent resonance frequency differences to water-free feed gas compositions for all three aging state. The demonstrated effect is based on a correlation between surface area and water adsorption capacity of the washcoat. Since such a correlation is expected for all kinds of high surface area catalysts, the proposed OBD procedure is not limited to three way catalysts. Rather the method should be useful for different catalyst technologies such as diesel oxidation catalysts, catalytically coated filters, NO\({}_{x}\)-storage catalysts or SCR catalysts. The method might be especially advantageous for ceria-free catalysts such as diesel oxidation catalysts, since there is currently a lack of proven OBD methods for these catalysts.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 2 Cut-In Scenario Classification and Cut-In Possibility Quantification
### Quantification of Cut-In Possibility Based on Lateral Distance and Lateral Relative Velocity
#### 2.3.2 Relative Lateral Velocity Criterion  
There can be a minor misjudgment in recognizing cut-in behavior only using the lateral distance. As shown in Figure 8, in the curve scenarios, for the cut-in vehicle, if the cut-in recognition is only performed using lateral distance, the turning behavior of the nearby car will be recognized as a cut-in behavior, which is obviously unreasonable. Therefore, the relative lateral velocity of the nearby car is used as another reference factor.  
During the cut-in process of a nearby vehicle, similar to the lateral distance, the lateral speed of the nearby vehicle also gradually increases, and then gradually decreases until the cut-in maneuver is successful [26]. At the same time, in curve scenarios, a nearby vehicle also has a large lateral speed. Therefore, in order to avoid judging the turning behavior of a vehicle as a cut-in behavior, this paper uses the relative lateral speed between the host vehicle and the nearby vehicle as a reference.  
\[v_{rellat}=v_{plat}-v_{blat} \tag{7}\]  
where \(v_{rellat}\) is the relative lateral velocity, \(v_{plat}\) is the relative lateral velocity of the nearby vehicle, and \(v_{blat}\) is the lateral velocity of the host vehicle.  
Similar to the lateral distance judgment method, as shown in Figure 9, the sigmoid function is used to fit the relationship between cut-in probability and the relative lateral distance.  
\[P_{cutin}\_v_{rellat}=\frac{1}{1+e^{v_{rellat}-c}} \tag{8}\]  
where \(P_{cutin}\_v_{rellat}\) is the cut-in possibility of the lateral relative velocity and \(c\) is the parameter.  
Figure 8: Curve scenarios.  
Figure 9: Cut-in possibility of lateral velocity.  
In a straight lane scenario, as a nearby vehicle cuts in, the lateral velocity of the vehicle gradually increases, but the lateral velocity of the host vehicle is 0, so the relative lateral velocity between the host car and the nearby vehicle gradually increases, and the cut-in possibility becomes greater. In a curve scenario, both the host vehicle and the nearby vehicle have large lateral speeds, so the relative lateral velocity is small, which means that the cut-in possibility is low and the situation where a nearby vehicle that is driving normally on a curve is judged as a cut-in vehicle is avoided. If the nearby vehicle performs a cut-in maneuver, the vehicle needs to generate a greater lateral velocity than the host vehicle to cut in the front of the host vehicle successfully. Therefore, the relative lateral velocity criterion is effective when judging the cut-in possibility.
## 3 Effective Medium Parameters Extraction Method  
The EM simulator CST was applied for the mathematical and geometrical analyses based on a definite integration procedure. This method provided tractability in different composite symmetrical structure modelling and integrated the arbitrary distribution of materials with their properties. Simultaneously, a frequency-domain solver using a tetrahedral mesh in CST was used to simulate the proposed structure. The EM waves were fixed to promulgate to the z-axis. Perfect electric and magnetic fields were applied along the x- and y-axes as a boundary condition to simulate 2.0-18.0 GHz of the resonance frequency. Additionally, transmission coefficient (S21), permittivity (\(\epsilon_{\mathrm{r}}\)), and refractive coefficient (n\({}_{\mathrm{r}}\)) were evaluated to determine the EM properties of the proposed structure using the Nicolson-Ross-Weir technique [PERSON] and [PERSON] (1961).  
\[\mathrm{Permitivity},\epsilon_{\mathrm{r}}=\frac{\mathrm{c}}{\mathrm{j\pi d} \mathrm{d}}\times\frac{\{1-(\mathrm{S21}+\mathrm{S11})\}}{\{1+(\mathrm{S21}+ \mathrm{S11})\}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Dimension (mm)** & **Parameter Dimension (mm)** \\ \hline a & 9 & G 0.50 \\ b & 9 & g 0.20 \\ W1 & 8 & W 0.50 \\ W2 & 8 & y 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Unit cell parameters.  
Figure 1: (a) Unit cell and (b) Simulation arrangement.  
\[\begin{split}\text{Permeability},\ \mu_{r}=\frac{c}{j\pi fd}\times\frac{\{1-(S21-S11)\} }{\{1+(S21-S11)\}}\\ \text{And}\ n_{r}=\sqrt{\varepsilon_{r}\mu_{r}}\\ \text{Or},\ n_{r}=\frac{c}{j\pi fd}\sqrt{\frac{\{1-(S21+S11)\}}{ \{1+(S21+S11)\}}\times\frac{\{1-(S21-S11)\}}{\{1+(S21-S11)\}}}\\ =\frac{c}{j\pi fd}\sqrt{\frac{(S21-1)^{2}-S11^{2}}{(S21+1)^{2}-S11 ^{2}}}\end{split} \tag{3}\]  
where, \(c\), \(f\), and \(d\) represent the speed of light, frequency, and the thickness of the substrate, respectively. S11 represents the reflection coefficient, and S21 is denoted the transmission coefficient. To obtain the refractive index (n\({}_{r}\)), relative permittivity (\(\varepsilon_{r}\)), and permeability (\(\mu_{r}\)), MATLAB codes were established based on Equations (1)-(3). The results of the parameters extracted through the NRW method was compared with the CST results.
# Layer-Level Knowledge Distillation for Deep Neural Network Learning
## 3 Proposed Method
### Auxiliary Structure Learning (ASL)
#### 3.2.2 Layered Inter-Class Gram Matrix  
The layered inter-class Gram matrix models the relationship between two classes within the same layer. When a teacher network is pre-trained, training images can be fed into the model class by class to extract feature maps of each class. Then, the feature representation of each class can be obtained by calculating the average feature using all features of the same class. Once we have the feature representation of each class, the layered inter-class Gram matrix computes the inner product between two class features. An entry value close to zero indicates that two class features are different. This means that the trained class features are more diverse and discriminative. An illustration of the layered inter-class Gram matrix is depicted in Figure 3.  
Given class \(c\) in a dataset and layer \(l\) in a pre-trained CNN model, denote the average feature by \(f^{l}_{c}\in\mathbb{R}^{K}\). \(K\) is the feature dimension. Then, the layered inter-class Gram matrix \(H\) can be defined by:  
\[H^{l}(c,\hat{c})=\frac{\left<f^{l}_{c},f^{l}_{\hat{c}}\right>}{K} \tag{7}\]  
Figure 3: Illustration of the layered inter-class Gram matrix.  
where \(\forall c,\ \hat{c}\in C\) and \(c\
eq\hat{c}\). This means that only entries of the upper triangle matrix in \(H^{l}\) without the diagonal entries are counted, since \(H^{l}\) is symmetric and the diagonal entries are all 1. The class diversity can be calculated for a given layer \(l\) by:  
\[H^{l}_{class}=\frac{\sum_{c,c\in C,\ c\
eq c}H^{l}(c,\hat{c})}{\left(\begin{array} []{c}C\\ 2\end{array}\right)}. \tag{8}\]  
By jointly considering the inter-layered Gram matrix and the layered inter-class Gram matrix, the objective function can be defined as:  
\[\mathcal{L}_{LSP}(l)\ =G^{l}_{diversity}+H^{l}_{class}. \tag{9}\]  
To select the representative intermediate layer, we find the layer index \(l\) that minimizes the objective function.  
\[l^{*}=\underset{l}{argmin}\mathcal{L}_{LSP}(l). \tag{10}\]
## 3 Results
### KP505 Propeller in Open Water Test
#### 3.1.2 Grid Independence Test
This indicates that the current CFD method and setup is the most suitable for the \(J\) = 0.8 condition. In actuality, for \(KT\) of \(J\) = 0.7, the \(U_{G}\) is only slightly smaller than \(\mid E\%D\mid\) of \(S_{1}\) with a less than 1% difference. Thus, the comparisons between the CFD results themselves are only slightly less confident than comparisons with the experiment. This is also because the \(KT\) is slightly deviated from \(D\) somehow from \(S_{2}\) to \(S_{1}\), i.e., when refining the grid from medium to fine grid. The \(\mid E\%D\mid\) increase is very small, also less than 1%. In fact, if comparing \(U_{G}\) with \(\mid E\%D\mid\) of \(S_{2}\), it can be considered to be validated, i.e., \(U_{G}>\mid E\%D\mid\) of \(S_{2}\). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(D\) & \(R_{G}\) & \(U_{G}\%D\) \\ \hline \(J\) = 0.5 & 0.28354 & 0.28316 & 0.29838 & 0.285 & \(-\)0.0249 & 2.67\% \\ \(E\%D\) & 0.51\% & 0.65\% & \(-\)4.70\% & & O.C. & Validated \\ \(J\) = 0.7 & 0.19189 & 0.19045 & 0.20169 & 0.185 & \(-\)0.128 & 3.04\% \\ \(E\%D\) & \(-\)3.72\% & \(-\)2.95\% & \(-\)9.02\% & & O.C. & Not Validated \\ \(J\) = 0.8 & 0.14030 & 0.13949 & 0.14805 & 0.137 & \(-\)0.0947 & 3.13\% \\ \(E\%D\) & \(-\)2.41\% & \(-\)1.82\% & \(-\)8.07\% & & O.C. & Validated \\ \hline \end{tabular}
\end{table} 
Table 6: Grid independence test result for \(KT\). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(S_{1}\) & \(S_{2}\) & \(S_{3}\) & \(D\) & \(R_{G}\) & \(U_{G}\%D\) \\ \hline \(J\) = 0.5 & 0.39950 & 0.39988 & 0.41204 & 0.437 & 0.0312 & 0.00350\% \\ \(E\%D\) & 8.58\% & 8.49\% & 5.71\% & & M.C. & Not Validated \\ \(J\) = 0.7 & 0.31273 & 0.31159 & 0.32422 & 0.311 & \(-\)0.0902 & 2.03\% \\ \(E\%D\) & \(-\)0.56\% & \(-\)0.19\% & \(-\)4.25\% & & O.C. & Validated \\ \(J\) = 0.8 & 0.25185 & 0.25114 & 0.247 & 0.247 & \(-\)0.0657 & 2.22\% \\ \(E\%D\) & \(-\)1.96\% & \(-\)1.68\% & \(-\)6.11\% & & O.C. & Validated \\ \hline \end{tabular}
\end{table} 
Table 7: Grid independence test result for \(KQ\). For \(KQ\) of \(J=0.5\), a very small \(U_{G}\) value is found, which reflects that \(S_{1}\) and \(S_{2}\) are extremely close to each other. Thus, the confidence level for the comparison of the CFD results themselves is much higher than that of comparison with the experiment. As Table 6 reveals, \(E\%D\) rises to close to 9% as the grid number increases from \(S_{3}\) to \(S_{2}\), and then to \(S_{1}\). This is related to the fact that constant \(C_{D}=0.01\) is unsuitable for low \(J\) condition, previously discussed. A larger drag coefficient would increase propeller torque in low \(J\) condition because the high angle of attack. A severe increase in error can be observed in Figure 8 and Table 5 as well. Future development of the propeller model, e.g., a more sophisticated \(C_{D}\) correction, is required.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 5 Polarimetric Analysis
### Benefit of Polarimetry for Building Monitoring
#### 5.2.3 Results
(**a**) Cristal Tower (**b**) Perspective II Tower. Figure 15: Images of the buildings and detail of its facade, orientaion of the facade from the ground truth and results of estimation of polarimetric orientation. (**a**) Keller Tower (**b**) Espace 2000 Tower.
## 2 Materials and Methods
### Feature Extraction and Classification Algorithms
#### 2.5.4 Classifier Design and Fusion Strategy  
Considering that the extracted features require classification, two classifiers were selected based on the characteristics of the respective features. For features extracted using the CSP algorithm, SVM was adopted, as the CSP aims to maximize variance between two classes, making an SVM a suitable choice for binary classification. In contrast, the features derived from the CCA algorithm tend to have higher dimensionality due to the presence of multiple stimulation frequencies; therefore, the XGBoost algorithm, an ensemble learning method, was chosen to better handle this complexity. Accordingly, in this study, an SVM and XGBoost were used to classify features extracted by the CSP and CCA algorithms, respectively.  
XGBoost is well suited for handling a large number of features and can automatically learn nonlinear relationships among them, making it particularly effective for complex and high-dimensional datasets. In contrast, SVMs excel at identifying optimal decision boundaries, especially in binary classification tasks. Given the nature of the combined features--CCA-derived features represent multi-class frequency information, while CSP-derived features capture binary spatial energy differences--XGBoost may outperform an SVM when classifying CCA features, whereas an SVM may be more effective than XGBoost for CSP features. Therefore, a weighted (SVM + XGBoost) classifier is proposed to predict the combined FB(CSP + CCA) features. This hybrid approach enables the classifier to learn the relative importance of different feature types, potentially enhancing the model's ability to recognize asynchronous states. The detailed design is as follows:  
\[p=(1-q)*SVM\Big{(}T_{f,\varphi}\Big{)}+q*XGBoost\Big{(}T_{f,\varphi}\Big{)} \tag{28}\]  
In this equation, \(p\) represents the final predicted probability for the combined feature \(T_{f,\varphi};q\) denotes the weighting coefficient. \(T_{f,\varphi}\) is the fused feature vector constructed from both CSP and CCA features. \(SVM\Big{(}T_{f,\varphi}\Big{)}\) and \(XGBoost\Big{(}T_{f,\varphi}\Big{)}\) correspond to the predicted probabilities generated by the SVM and XGBoost classifiers, respectively, based on the input feature \(T_{f,\varphi}\).
## 3 Results
### Effects of Gefitinib and Canertinib on Cellular Viability Using Alamar Blue Dye Assay  
To study the effect of gefitinib and canertinib on the cell viability of EGFR and HER-2 positive asciti-derived cells, we examined the cellular conversion of the Alamar blur dye, which was converted by cells from blue into pink colour, as an indirect predictor of cell viability. As shown in Table 3A, gefitinib and canertinib selectively reduced viability in the EGFR positive asciti cells. Of nine asciti cell samples, four gefitinib-treated asciti cell samples (patient numbers 1, 4, 17, 19; \(p<0.05\)) had lower viability than the control cells. Four canertinib-treated cells (patient numbers 4, 6, 17, 19; \(p<0.05\)) also showed a reduction in cellular viability compared to the control cells. Both gefitinib and canertinib reduced viability in three patient cells (patient numbers 4, 17, 19). Canertinib (\(74.9\%\pm 4.3\%\), \(p<0.05\)) reduced viability to a greater degree than gefitinib (\(84.6\pm 0.8\)) in patient number 4.  
Table 3B shows that asciti cells expressing both EGFR and HER-2 did not reduce cellular viability after exposure to gefitinib. However, three out of eight EGFR and HER-2 positive asciti cell samples (patient numbers 9, 11, 16; \(p<0.05\)) treated with canertinib had lower cellular viability than the control cells. Similarly, the EGFR and HER-2 positive cell line SKOV-3, used as an internal control cell line, showed a decrease in cellular viability only with canertinib treatment (Table 3B; \(p<0.05\)). These results may suggest that a dual inhibition of EGFR and HER-2 with canertinib may be a better option than gefitinib to inhibit ovarian cancer cells in asciti fluids that are EGFR and HER-2 positive. Out of three low EGFR and HER-2 expressing cells, only patient number 2 showed reduced cellular viability by both gefitinib and canertinib (Table 3C; \(p<0.05\)).  
Figure 4: Morphological appearances of asciti fluid-derived cells from selected patients are cultured in a cell monolayer and a 3D cell suspension. Cells cultured in the 3D cell suspension display three distinct patterns consisting of compacted spheroids, small clusters and loose single cells (**A**). Ascitic fluid derived cells from selected patients cultured in a 3D cell suspension positively express CA125, cytokeratin-18, N-cadherin, E-cadherin and vimentin (**B**).
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.2. Gaps between Infill and Outline  
There are visible gaps on the print between the infill and the wall, in which they do not meet or bond together. This is usually due to the infill overlap not set or set at 0, having an incorrect print order of infill and outer perimeters, the type of filament used, and/or the nozzle height set too high. Solutions include the following:  
1. Changing the infill or perimeter overlap from the default 15% to 30%. This setting is directly linked to the extrusion width. When adjusting this setting, keep it below 50% to prevent any overlap on the outer perimeter of the print.
2. Reverse the printing instruction to \"inside-out\" by printing the infill before the perimeter shell. For thin models, print the perimeter first with a minimum wall thickness to avoid the infill from ghosting on the surface (refer to 3.3.7: (1) Prints with thin wall structure).  
Figure 11. Walls not touching.  
Gaps can be due to the type of filament used. This is more commonly encountered when using advanced materials. Increase the printing temperature by 5 to 10 \({}^{\circ}\)C and reduce the print speed on the top layer to avoid gaps.
4. If gaps occur on the first layer, recalibrate the build platform to decrease the Z-distance (refer to 3.1.2: (1) Incorrect built platform levelling).