esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Fusion Approach
### Measuring Performance with Objective Quality Metrics  
_E(F)_ (Entropy): It assists in the extraction of meaningful information from an image. A high level of entropy indicates that the image carries more than information.  
_AG_ (Average-Gradient): It determines the sharpness and clarity of an image. It shows that when the value of _AG_ is high, the fused image has more clarity and sharpness.  
_CC_ (Correlation-Coefficient): It assesses the similarity of the all-in-focus image to the input images. For a better fusion process, a higher CC value is desired.  
_SSIM_ (Structural-Similarity-Index-Measure): It assists in the correlation of two images' local patterns of the brightness of pixels. _SSIM_ has a range of \(-1\) to \(1\) in its value.  
_Q\({}_{E}\)_ (Edge-dependent Fusion Quality): This metric considers features of the human visual system, such as edge detail sensitivity. A greater \(Q_{E}\) value suggests that the fusion process is more efficient.  
\(SD\) (Standard Deviation): The higher the \(SD\) number, the noisier the final image. Noise is more likely to impact images with lower contrast.  
\(SF\) (Spatial-Frequency): It is used to determine the total intensity of activeness. When the all-in-focus image activity level is really high, it indicates that \(SF\) is quite high.  
\(RMSE\) (Root Mean Square Error): It is useful for calculating the variations per pixel caused by image fusion methods. The value of \(RMSE\) rises as the similarity decreases.  
\(PSNR\) (Peak-Signal-to-Noise-Ratio): It compares the similarity of the produced fused image and the reference image to determine image quality. The better the \(PSNR\) number, the better the fusion results.  
In addition, objective image fusion effectiveness assessment via gradient information [11] is examined. Assessing total fusion performance (TFP), fusion loss (FL), and fusion artifacts (FA), provides a complete analysis of fusion performance. The process intended for calculating these metrics is detailed in [11], and their symbolic representation is presented below:  
\(Q^{AB/F}\) denotes the total amount of data transferred from the source images to the all-in-focus image. The method's performance is good if \(Q^{AB/F}\) values are higher; \(L^{AB/F}\), Total loss of information. The method's performance is good if \(L^{AB/F}\) values are lower, and \(N^{AB/F}\). Due to the fusion process, noise or artifacts have been added to the fused image. The method's performance is good if \(N^{AB/F}\) values are lower.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 4 Methodology
### On-Manifold Adversarial Examples
#### 4.1.1 Case One: Simulation Data  
In the case of simulation, we can define the data distribution, in particular, \(P(Y|X)\) to check the Definition 1. As we like to make our procedure for case one as a basis for case two below, we assume that we know \(P(Y|Z)\) instead of \(P(Y|X)\), where \(Z\) is the random variable belonging to the latent space between an encoder \(\mathcal{E}\) and a decoder \(\mathcal{D}\) (to be described more in detail later). Using [PERSON]'s rule and the transformation of random variables through \(X=\mathcal{D}(Z)\), we can establish the following connections:  
\[P(Y=y|Z) =\frac{P(Z|Y=y)P(Y=y)}{P(Z)}=\frac{P(Z|Y=y)\det(\
abla_{X} \mathcal{D}^{-1}(X))P(Y=y)}{P(Z)\det(\
abla_{X}\mathcal{D}^{-1}(X))} \tag{5}\] \[\approx\frac{P(\mathcal{D}^{-1}(X)|Y=y)\det(\
abla_{X}\mathcal{D }^{-1}(X))P(Y=y)}{P(\mathcal{D}^{-1}(X))\det(\
abla_{X}\mathcal{D}^{-1}(X))}\] \[=\frac{P(X|Y=y)P(Y=y)}{P(X)}=P(Y=y|X)\]  
where \(P(X)=P(\mathcal{D}^{-1}(X))\det\
abla_{X}\mathcal{D}^{-1}(X)\) is the result of transformation of the random variable \(X=\mathcal{D}(Z)\)[51], and \(\det\
abla_{X}\mathcal{D}^{-1}(X)\) is the determinant of the Jacobian of \(\mathcal{D}^{-1}\) regarding \(X\). In the approximation (the second line), we assumed that \(\mathcal{D}^{-1}(X)\approx\mathcal{E}(X)=Z\). The connection in (5) allows us checking the Definition 1, in particular if a perturbed point \(x^{\prime}=\mathcal{D}(z^{\prime})\) belongs to the manifold of \(Y=y\) in the space of \(Z\), that is,  
\[P(Y=y|z^{\prime})>P(Y=y^{\prime}|z^{\prime}),\ \ \forall y^{\prime}\
eq y,\ \ \text{and}\ \ f(\mathcal{D}(z^{\prime}))\
eq y. \tag{6}\]  
For simulation, we define \(P(X|Y=y)\) to be a two-dimensional multivariate Gaussian distribution for each of the four classes \(y\in\{1,2,3,4\}\). To map the latent samples \(z\in\mathbb{R}^{2}\) to the input space (we define the input space to be sixteen-dimensional), we use a simple auto-encoder composed of two fully connected layers in the encoder \(\mathcal{E}\) and the decoder \(\mathcal{D}\). The auto-encoder is trained to minimize the reconstruction error of the latent samples \(z\) (we used 17,500 samples in the experiments):  
\[\min_{w_{\mathcal{E}},w_{\mathcal{D}}}\ \ \mathbb{E}_{z}\Big{[}\|\mathcal{E}_{ w_{\mathcal{E}}}(\mathcal{D}_{w_{\mathcal{D}}}(z))-z\|_{2}^{2}\Big{]}.\]  
Figure 1: The process of generating on-manifold adversarial examples. We separate the cases based on whether we have information on the true \(P(Y|Z)\) or not.  
With the trained decoder, we can generate on-manifold adversarial examples according to the Definition 1 using (6) (we used 70,000 generated adv-on examples as simulation data in the experiments). After generating the simulation dataset, we split the dataset randomly into trainset with 60,000 instances and testset with 10,000 instances. We separately generate on-manifold adversarial trainset and testset. Figure 2 shows the distribution of sampled latent vectors and the on-manifold vectors, respectively.
## 2 Problem Definition and Proposed Models
### Evaluation Criterion  
In order to assess the performance and accuracy of proposed models, two evaluation criteria are used to measure how close the predicted value by these models are to the real value (target value). Firstly, the \"regression accuracy\" of these models are calculated by means of correlation coefficient \(R\) given by Equation (13). \(R\) is a correlation coefficient, between the actual outputs and the predictions of each model. A correlation coefficient of \(R=1\) shows 100% prediction accuracy, i.e., prediction equals target value:  
\[R=\frac{\sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)\bigl{(}\hat{y}_{i}- \overline{y}\bigr{)}}{\sqrt{\sum_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2} \sum_{i=1}^{n}\left(\hat{y}_{i}-\overline{y}\right)^{2}}} \tag{13}\]  
Figure 5: Artificial Neural Network.  
where \(y\) is the actual output or desired output, \(\hat{y}\) is the value estimated by our ML models, and \(n\) represents the total number of observations.  
Furthermore, the accuracy of the predictions for the three ML models in terms of cumulative distribution function (CDF) of \"percentage prediction error\" is used. Accuracy results in the form of CDFs of error are perhaps more useful from a design and analysis perspective. Typically, the prediction performance of the ML tools is interpreted with a target or admissible prediction error
## 4 Results and Discussion
### Transformer Condition Monitoring
#### 4.8.3 Transformer Oil Level  
Transformer oil is used mainly for insulating and cooling in transformers. Transformer oil should possess a high quality of dielectric strength, thermal conductivity, and chemical resilience. It must maintain these attributes while operating at higher temperatures for a lengthy period. Depreciation in transformer oil may result in overheating of the transformer, resulting in electrical faults and catastrophic failures. Monitoring the oil level is an essential part of transformer condition monitoring to monitor the level of transformer oil inside the transformer [4]. Sustaining a sufficient transformer oil level is critical in order to prevent instances of the transformer oil level dropping below what is required or being excessively low. This level drop may result in insufficient insulation durability [2]. Electrical discharges take place, leading to insulation failure and transformer explosion. The oil level drop may also result in unsatisfactory localized heat transfer for the transformer, leading to localized overheating and complete insulation failure as time passes [1]. Numerous reviewed studies conducted research on this aspect using IoT-compatible sensors to gather real-time data of the transformer oil. The HC-SR04 ultrasonic distance sensor and JSN-SR04T/AJ-SR04M Waterproof Ultrasonic Module are widely used by most researchers.
## 3 Data Processing Methods
### GB-SAR Deformation Detection with SAR Images
#### 3.4.2 Atmospheric Phase Analysis and Simulation  
When electromagnetic waves traverse the atmosphere, the diverse media encountered can significantly affect their propagation paths, resulting in phase differences (i.e., \(\varphi_{atm}\) in Equation (13)). The calculation of the atmospheric phase between the target and the antenna is conducted through integration along the propagation path [67]. Assuming that at that time, the atmospheric phase of a point target is as follows:  
\[\varphi_{a}=\frac{4\pi}{\  
where \(T\) is the dry temperature (in Kelvin), \(P\) is dry pressure (in millibars), and \(e\) is water vapor pressure (in millibars). The conversion relationship between water vapor pressure \(e\) and relative humidity \(H\) (percent per hundred) is as follows:  
\[e=\frac{H}{100}\times 6.107\times e^{\frac{(17.27\times(T-273.15)}{T-3.86}} \tag{17}\]  
In the experiments, the area observed by the CBS is relatively small, and the time interval for data acquisition is exceedingly brief. Consequently, it can be assumed that the atmospheric distribution remains uniform and stable throughout the observation period; thus, the atmospheric phase can be simplified as follows:  
\[\varphi_{a}=\frac{4\pi r}{\lambda}n(t)=\frac{4\pi r}{\lambda}\left(7.76\times 1 0^{-5}\frac{P}{T}+3.73\times 10^{-1}\frac{\frac{H}{100}\times 6.107\times e^{ \frac{17.27\times(T-273.15)}{T-3.86}}}{T^{2}}\right) \tag{18}\]  
As can be seen from the above analysis, the atmospheric phase is related to dry temperature, dry air pressure, and relative humidity. Based on the actual conditions of outdoor experiments, we analyzed the impact of changes in these parameters on the atmospheric phase through simulation. Assuming that at a distance of \(r=150\) m, \(T=293.15\) K, \(P=1013\) hPa, and \(H=50\)%, the atmospheric phase variations resulting from changes in these parameters and their impacts on deformation monitoring are shown in Figure 7. Based on the simulation results, it can be observed that when conducting observations over a small area, the observation time of the CBS typically does not exceed one second. During this brief duration, temperature, atmospheric pressure, and relative humidity remain relatively constant, resulting in a minimal influence of atmospheric phase on deformation detection. Consequently, the atmospheric phase can be disregarded during the data processing stage.
# Water Jacket Systems for Temperature Control of Petri Dish Cell Culture Chambers
## 2 Materials and Methods
### Theoretical Model Describing the Heat Transfer Inside the Petri Dish  
The heat transfer within the water jacket system is governed by convection through the water jacket facilitated by the temperature change between the inlet/outlet ports to compensate for the free convection at the surfaces of the system, which are exposed to the surrounding air. The heat transfer between the water jacket chamber and the Petri dish is facilitated by conduction via the sidewalls and the rubber washer. Similarly, heat transfer across the radial axis of the Petri dish is facilitated by conduction via the sample as well as the thin surface of the Petri dish (Figure 1).  
In this regard, the sample inside the Petri dish behaves as a heat sink [25; 26; 27]. Ignoring the temperature gradient along the height of the sample, the temperature distribution along the radial axis of the Petri dish can be expressed using the following simplified differential equation:  
\[\frac{1}{r}\frac{\partial}{\partial r}\bigg{(}r\frac{\partial T}{\partial r} \bigg{)}=\frac{h_{air}}{k_{sample}\;H_{sample}}(T-\;T_{air}) \tag{1}\]  
\[\left.\frac{\partial T}{\partial r}\right|_{r=0}=0 \tag{2}\]  
\[T|_{r=R_{ Petri dish}}=T_{ side\;wall} \tag{3}\]  
where, \(r\), \(R_{ Petri dish}\), and \(H_{sample}\) are the location along the radial axis radius, Petri dish radius, and sample height inside the Petri dish, respectively, \(h_{air}\) is the free convection heat transfer coefficient, \(T_{air}\) is the ambient temperature, \(T_{ side\;wall}\) is the temperature of the sample along the side walls of the Petri dish, and \(k_{sample}\) is the thermal conductivity of the sample. Solving this equation, the temperature profile along the radial axis of the Petri dish is obtained as below, in which BesselJ is the Bessel function of the first kind:  
\[T(r)=\;T_{air}+\frac{\text{BesselJ}(0,\alpha\;r)}{\text{BesselJ}(0,\alpha\;R _{ Petri dish})}\;(\;T_{ side\;wall}\;-T_{air}) \tag{4}\]  
\[\alpha=\sqrt{\frac{h_{air}}{k_{sample}\;H_{sample}}} \tag{5}\]
## 4 Case Study
### Comparison Analysis
#### 4.4.2 Evaluation Using Only One Type of Nature  
In a search to understand the importance of the integration of both qualitative and quantitative nature in the evaluation, two more evaluations were carried out, one of them performing the alternatives under only quantitative criteria and another considering only the ordinal inputs regarding qualitative criteria. Table 18 presents the criteria weights obtained in each model of evaluation, respecting the amount of weights for each performance, where \(\sum\ w_{j}=1\).  
When the aggregation of preferences is realized, a new total outranking for each evaluation is obtained. Table 19 exposes the new ranking and the obtained net flows regarding alternatives.  
As observed in Table 19, considering the performance only under qualitative criteria, there is a change of the ranking between the alternatives Tiger HAD, VIPER, and T129 compared to the original evaluation is noticeable. Meanwhile, when the evaluation is realized only regarding quantitative criteria, there is a change of positions regarding all alternatives from the set, where alternatives with low performance in the original evaluation, presented as the most favorable when considered only quantitative nature.  
This procedure aims to explore the importance of considering not only quantitative data but also qualitative data, as performed by the proposed model. In this way, when there is the integration of both natures into a model, and it is taken into account the decision-maker subjectivity, a more realistic process is performed, providing an assertive final decision.  
Figure 8: Complete ranking in the PROMETHEE method, adapted from [86].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluation Based Only in** & \multicolumn{2}{c}{**Evaluation Based Only in**} \\
**Qualitative Criteria** & \multicolumn{2}{c}{**Quantitative Criteria**} \\ \hline
**Criteria** & **Weights** & **Criteria** & **Weights** \\ \hline _Technological level_ & 0.144 & _Maximum speed_ & 0.137 \\ _Main common system_ & 0.211 & _Cruising speed_ & 0185 \\ _Missles system_ & 0.211 & _Range_ & 0.137 \\ _Armanent subsystems_ & 0.211 & _Autonomy_ & 0.137 \\ _Armor level_ & 0.211 & _Service ceiling_ & 0.048 \\ _Employment flexibility_ & 0.012 & _Maximum payload_ & 0.220 \\  & & _Troop capacity_ & 0.088 \\  & & _Length_ & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Criteria weights in scenarios considering only quantitative or qualitative nature.
# Impact of Die Radius in a Streamlined Die during Wire Drawing
## 1 Introduction
[PERSON] and [PERSON] [24] investigated the effect of die shape on die wear during wire drawing. Their results showed that the streamlined die decreases die wear because it decreases the concentration of die pressure during wire drawing. [PERSON] et al. [25] analyzed the twin parabolic die for wire drawing using the upper bound analysis. Although there are some studies on the streamlined die as mentioned above, no study has reported a comprehensive investigation of streamlined dies compared to the conventional die. From a practical point of view, the results are thus difficult to apply  
Figure 1: Schematic explanation of (**a**) the conventional die, (**b–d**) streamlined dies, and (**e**) the variations in die angle along the drawing direction during wire drawing. in industrial fields. Therefore, the present study focuses on investigating the effects of streamlined dies on the distribution of strain, stress, temperature, drawing force, and damage value of wire during wire drawing, for a better understanding of streamlined die and an improvement in the drawing quality of wire. In addition, from a practical point of view, it is necessary to find the optimum design for a streamlined die considering all the design factors. A numerical simulation was performed for streamlined dies with various die radii. To validate the accuracy of the numerical model used in this study, a wire drawing test was also conducted using the conventional die, and the drawing force and center temperature of the wire obtained from the experiment and those obtained by numerical simulation were compared. Finally, the strain, stress, damage value, temperature, and drawing force were compared as a function of the die radius of the streamlined die during wire drawing to derive a design concept for better quality of wire, rod, and bar products.
# Bond Strength Assessment of Concrete-Corroded
## 2 Summary of the Existing Test Results  
Many experimental tests were conducted to investigate the bond strength of corroded reinforced concrete and the indices such as surface crack width and corrosion level were proposed for bond strength evaluation. Owing to the different research purposes, their parameter settings are quite different, and it is therefore difficult to develop a generic model that considers all experimental observations. To better understand the existing studies of bond degradation of concrete structures caused by rebar corrosion and to perform an analytical analysis, [PERSON] et al. [1] conducted a comprehensive review and summarized the known knowledge and the gaps. According to the test results collected by them, the existing data show a scattering trend, especially for cases with stirrups. In this study, data including those collected by [PERSON] et al. and others were used, and the bond strength was normalized with the case without corrosion and plotted with respect to the corrosion level and the maximum crack width on the surface of concrete, respectively, as shown in Figure 1. The results show a consistent tendency for corrosion level in the unconfined case and enhancement is noticed when the corrosion level is low, as presented in Figure 1a. Besides, the crack width seems to show a better agreement with a decrease in relative bond strength, and a linear relationship can be observed. This is the main reason many researchers regard the maximum crack width as a critical parameter. However, for cases with stirrups confinement, a larger scattering is observed, regardless of the corrosion level or crack width. This implies that more parameters should be considered for precise bond strength evaluation. An intelligent method that can include multiple parameters is thus necessary and a soft computing method such ANN has intrinsic advantages for the evaluation.  
Figure 1: Experimental database for bond strength degradation with corrosion level and crack width: (**a**) bond strength versus corrosion level in unconfined case; (**b**) bond strength versus crack width in unconfined case; (**c**) bond strength versus corrosion level in confined case; and (**d**) bond strength versus crack width in confined case; [1, 26, 46, 47, 48, 49].
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 4 Results and Discussion
### Resonance Frequency Change of the Reactance Insertion Stub  
Figure 9 shows the measurement results of \(\mid\!S_{21}\!\mid\) and \(\mid\!S_{11}\!\mid\) of the rectangular stub with a series inductance. The three types of data indicated by line (d) show the case where the thickness of the diaphragm is fixed at \(n=0.7\) mm, and the aperture width is changed to \(g=19.4\) mm, \(7\) mm, and \(0.7\) mm in pattern (d) in Figure 5. It should be noted that the case \(g=19.4\) mm means that there is no diaphragm, which is equivalent to the case of \(Z_{1}/Z_{0}=1/4\) of pattern (a) in Figure 5. The two types of data indicated by line (e) show the cases where the aperture width is fixed at \(g=0.7\) mm, and the diaphragm thickness was changed to \(n=4\) mm and \(8\) mm at pattern (e) in Figure 5. It can be seen that the transmission zero frequency is shifted to the left as the aperture width \(g\) is narrowed. Since narrowing the aperture width is equivalent to increasing \(L\) of the circuit, it can be seen that the transmission zero frequency can be lowered without changing the resonance length, so that the size can be reduced. Furthermore, it can be seen that as the diaphragm thickness \(n\) is increased, the transmission zero frequency is shifted further to the left. As \(n\) increases, it is equivalent to increasing the value of \(L\). It is also found that as the inductance becomes larger, the size of the resonator can be reduced. Table 2 shows the change of the bandwidth with respect to the threshold \(T=-10\) dB and the change of the transmission zero frequency \(f_{0}\).  
The numerical values in parentheses represent the bandwidth expansion ratio and the increase/decrease ratio of the transmission zero frequency, compared with the case \(Z_{1}/Z_{0}=1\) as a reference. From the table, it can be seen that the narrower the aperture width, the narrower the bandwidth. As can also be seen from \(Q\) in Equation (4), the larger the \(L\), the larger the \(Q\) value, and the sharper the resonance is. From the above results, it is found that there is a trade-off relationship between miniaturization and broadband.  
Figure 8: Measured \(\mid\!S_{21}\!\mid\) and \(\mid\!S_{11}\!\mid\) results of rectangular stubs, with different impedance ratios \(Z_{1}/Z_{0}\) as shown in Figure 5a–c.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Circuit Pattern** & \(Z_{1}/Z_{0}\) & \(-10\) dB BW [GHz] (\({}^{\ddagger}\)) & \(f_{0}\) [GHz] (\({}^{\ddagger}\)) \\ \hline (a) & 2 & 0.35 (0.6) & 2.71 (1.1) \\ (a) & 1 & 0.60 (1.0) & 2.56 (1.0) \\ (a) & 1/2 & 1.05 (1.8) & 2.56 (1.0) \\ (a) & 1/4 & 1.75 (2.9) & 3.86 (1.5) \\ (b) & 1/4 & 2.15 (3.6) & 2.96 (1.2) \\ (c) & 1/4 & 2.15 (3.6) & 2.96 (1.2) \\ \hline \hline \end{tabular} \({}^{1}\) Indicate the magnification for \(Z_{1}/Z_{0}=1\).  
\end{table}
Table 1: Stop bandwidths in the case of rectangular stubs.