esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Selected Results of Optical Metrology Techniques
### Multiple Simultaneous Viewing Directions Using Four Digital Holographic Microscopes (MuVie)
#### 3.1.2 Robust Interferometric Inspection Using Computational Shear Interferometry (COSI)
We use typically \(n=5\) to 10 cross amplitudes \(M_{n}\) associated with different shears \(\vec{s}_{n}\), to minimize the functional [29]  
\[L(f)=\sum_{n}||M_{n}(\vec{x})-f^{*}(\vec{x})f(\vec{x}+\vec{s}_{n})||^{2} \tag{6}\]  
using an iterative steepest descent algorithm. The complex amplitude found is the minimum least squares solution for the wave field  
\[u(\vec{x})=\arg\min_{f}L(f). \tag{7}\]  
Figure 5: Computational Shear Interferometry: (**a**) Commercially available shear interferometer _Golden Eye_, which creates the shear using a liquid crystal SLM in the Fourier plane of a 4\(f\)-configuration. The interferometer can be attached to any imaging optics and measures with interferometric precision while being insensitive against mechanical vibrations. (**b**) Measured shape of a micro lens array using CoSI in transmission. The system measures relatively steep slopes with a measurement uncertainty of \(\sigma=2.5\) nm. For more details, please refer to the text. in the sense of Equation (6). With the complex amplitude \(u(\vec{x})\), we can employ, for example, Equation (2), to determine the profile of the specimen. Using multiple measurements with varying wavelengths, we can also define a synthetic wavelength \(\Lambda\) and use Equation (3) to overcome limitations rooted in the ambiguity of the phase. As an example, Figure 5b shows the measured shape of a micro lens array. The lenses have a diameter of 537 \(\upmu\)m and relatively steep slopes. We have measured the array in transmission, using a 10\(\times\) microscope objective with a numerical aperture of \(NA=0.21\). The light source was a fiber coupled LED with a wavelength of 625 nm and a fiber diameter of 200 \(\upmu\)m. The measurement uncertainty (1\(\sigma\)) was \(\sigma=2.5\) nm, even though the setup is immune against vibrations.
## 5 Conclusions  
This study presented a new adversarial training strategy for federated learning that is effective even with limited data. The strategy was tested against a wide range of adversarial attacks and made the global model more robust overall. PGD and FGSM techniques were used to generate adversarial samples, and the AQI classification model was used as an example, as it is easy to understand. Monitoring and mitigating air pollution is a primary objective of Sustainable Development Goal 17 (SDG 17).  
Federated learning is a new learning paradigm that protects user privacy and enhances system security. It also shows promise for IoT and smart cities. Experimental findings indicate that FGSM-based adversarial attacks are less effective at reducing the accuracy of global models than PGD-based attacks.  
This research challenged the assumption that adversarial training is only effective with large datasets. It provided valuable insights that can be used to develop new defense scenarios and countermeasures against adversarial attacks in federated learning. Ultimately, this research contributed to improving the security of sustainable smart city applications.  
Nevertheless, the federated adversarial training strategies proposed in this study make the global model more robust to attacks. For example, the fat-pgd and fl-mixed-pgd scenarios have accuracies of 81.13% and 82.60%, respectively, which is only a 10% loss compared to the fl-normal scenario, which achieves an accuracy of 91.34%. This potential drawback may be alleviated by using a more effective baseline model and increasing the number of clients. As more clients join, more data will be involved in the training, and the quality of the global model will be improved.  
Conceptualization, S.U. and P.-A.H.; methodology, S.U.; software, S.U.; validation, P.-A.H. and S.U.; formal analysis, S.U.; investigation, S.U.; resources, P.-A.H.; data curation, A.R.; writing--original draft preparation, S.U.; writing--review and editing, P.-A.H. and S.U.; visualization, S.U.; supervision, P.-A.H.; project administration, H.-C.H.; funding acquisition, P.-A.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been partially supported by research project grants from the National Science and Technology Council, Taiwan, under the project grant numbers NSTC 111-2634-F-194-003 and NSTC 112-2927-I-194-001.  
**Data Availability Statement:** The dataset is available at: [[https://www.kaggle.com/datasets/adarshrouniyar/air-pollution-image-dataset-from-india-and-nepal](https://www.kaggle.com/datasets/adarshrouniyar/air-pollution-image-dataset-from-india-and-nepal)]([https://www.kaggle.com/datasets/adarshrouniyar/air-pollution-image-dataset-from-india-and-nepal](https://www.kaggle.com/datasets/adarshrouniyar/air-pollution-image-dataset-from-india-and-nepal)) (accessed on 27 June 2023).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Results and Analysis
### Training Results  
Figure 3 illustrates the successful acceptance ratio of VNRs to the physical network when training SN-VNE model. The acceptance ratio is at the low level and fluctuates severely when the mapping just begins. As the training epoch increases, the curve increases until epoch is 70. Finally, the acceptance ratio leveled off and stabilized at 0.66.  
Figure 4 illustrates the average revenue, which is low, reaching 810, and fluctuates greatly. As the training epoch increases, the corresponding value of the curve also gradually increases. When the epoch is 70, the curve begins to level off and finally stabilizes at 1100.  
The long-term average revenue/cost is a measure of revenue on investment that expresses the revenue received from an investment compared to the cost of the investment. The calculation of the revenue/cost can help evaluate the effect of the model. Figure 5 describes the changes in the revenue/cost during model training. At the beginning, the lowest value of the curve is 0.355 and the highest value is 0.39. The curve fluctuates greatly and remains at a low value for a long time. As the training epoch increases, the curve gradually increases and finally levels off.  
Figure 4: The Long-term average revenue of policy network training process.  
Figure 3: The VNR acceptance ratio of policy network training process.  
This is because the initial model parameters are usually initialized randomly. This randomness triggers to unsteady model performance in the initial stage. Then, the initial stage model is more inclined to explore to discover better strategies. The models try different virtual network mapping strategies resulting in fluctuations in acceptance ratio, long-term average revenue and long-term average revenue-to-cost. As training progresses, the model gradually strengthens the learned experience and begins to make more use of the effective strategies learned, thereby improving performance. Finally, after a certain number of training iterations, the training curves tend to a relatively stable level when Epoch is 70, that is, the model has found a relatively better strategy in the virtual network mapping problem. To evaluate the stability of the algorithm, we collect data from 50 independent experimental runs and sample 30 epochs of data after the model converges. We calculate the confidence interval using formula \(\bar{x}\pm z\frac{\sigma}{\sqrt{n}}\) and use the mean of 50 runs as the final result. The statistical results show that the 95% confidence intervals for ACC, LR, and LR/C are [0.636, 0.664], [1044.921, 1117.782], and [0.388, 0.402], respectively. This demonstrates that the SN-VNE algorithm exhibits good stability in the later stages of training.
## 2 System Model
During data transmission, the received composite signal at the CBS on subchannel \(SC_{m}\) is given by  
\[y_{m}\left(t\right)=\sqrt{P_{1m}\left(t\right)}x_{1m}\left(t\right)h_{1m}+ \sqrt{P_{2m}\left(t\right)}x_{2m}\left(t\right)h_{2m}+\mathbf{\omega}_{m}, \tag{1}\]  
where \(P_{lm}\left(t\right)=e_{im}^{tr}\left(t\right)/_{\tau_{Tr}}\left|i\in\left\{1,2\right\},m\in\left\{1,2,...,M\right\}\) is the transmission power of \(CU_{i}\) in NOMA group \(G_{m}\), in which \(e_{im}^{tr}\left(t\right)\) is the transmission energy assigned for \(CU_{im}\) in time slot \(t\); \(x_{im}\left(t\right)\) denotes the transmit signal of \(CU_{im}\) in time slot \(t\), \(\left(\mathbb{E}\{\left|x_{im}(t)\right|^{2}\}=1\right)\); \(\mathbf{\omega}_{m}\) is the additive white Gaussian noise (AWGN) at the CBS on subchannel \(SC_{m}\) with zero mean and variance \(\sigma^{2}\); and \(h_{im}\) is the channel coefficient between \(CU_{im}\) and the CBS. The overall received signal at the CBS in time slot \(t\) is given by  
\[y\left(t\right)=\sum_{m=1}^{M}y_{m}\left(t\right). \tag{2}\]  
The received signals at the CBS on different sub-channels are independently retrieved from composite signal \(y_{m}(t)\) using the SIC technique. In particular, the CU's signal with the highest channel gain is firstly decoded, and then it will be removed from composite signal at the CBS, in a successive manner. The CU's signal with the lower channel gain in the sub-channel is treated as noise of the CU with the higher channel gain. We assume perfect SIC implementation at the CBS. The achievable transmission rate for the CUs in NOMA group \(G_{m}\) are  
\[ \begin{array}{l}R_{1m}\left(t\right)=\frac{\tau_{Tr}}{T_{tot}}\times b_{m} \left(t\right)\times\log_{2}\left[1+\frac{P_{1m}\left(t\right)\mathbb{E}_{1m} }{P_{2m}\left(t\right)\mathbb{E}_{2m}+\sigma^{2}}\right]\\ R_{2m}\left(t\right)=\frac{\tau_{Tr}}{T_{tot}}\times b_{m}\left(t\right) \times\log_{2}\left[1+\frac{P_{2m}\left(t\right)\mathbb{E}_{2m}}{\sigma^{2}} \right],\end{array}  \tag{3}\]  
where \(b_{m}\left(t\right)\) is the amount of bandwidth allocated to subchannel \(SC_{m}\) in time slot \(t\), \(g_{im}=\left|h_{im}\right|^{2}\) is the channel gain of \(CU_{im}\) on subchannel \(m\), and \(g_{1m}\geq g_{2m}\). Since the channel gain of \(CU_{1m}\), \(g_{1m}\), is higher, \(CU_{1m}\) has a higher priority for decoding. Consequently, the signal of \(CU_{1m}\) is decoded first by treating the signal of \(CU_{2m}\) as interference. Next, user \(CU_{1m}\) is removed from signal \(y_{m}\left(t\right)\), and the signal of user \(CU_{2m}\) is decoded as interference-free. The sum achievable transmission rate of NOMA group \(G_{m}\) can be calculated as:  
Figure 2: Time frame of the cognitive users’ operations. \[R_{m}\left(t\right)=R_{1m}\left(t\right)+R_{2m}\left(t\right). \tag{4}\]  
The sum achievable transmission rate at the CBS can be given as follows:  
\[R\left(t\right)=\sum_{m=1}^{M}R_{m}\left(t\right). \tag{5}\]
# Strength Correlation and Prediction of Engineered Cementitious Composites with Microwave Properties
## 4 Mathematical Modelling of Experimental Results  
### Microwave Conductance Modelled by Bi-Exponential Regressions  
The authors modelled the temporal responses of effective conductance shown in Figure 5, using a bi-exponential regression function, as given by  
\[g_{2}(t)=A1\ exp\left(-\frac{t}{b}\right)+A2\ exp\left(-\frac{t}{c}\right),\ 1\leq t\leq 8 \tag{4}\]  
where \(g_{2}(t)\) is the regression function intended to model the reduction of conductance at 2 GHz as shown in Figure 7, and \(t\) represents the time variable. A1, A2, \(b\), and \(c\) are the regression coefficients.  
The bi-exponential function consists of two terms characterizing the decay in conductance with age. The first term is an approximate ramp function that imitates the steady slow decay in the later response, namely, conductance decreases linearly from Day-4 to Day-8, whereas the second term is a first-order exponential decay function that imitates the rapid decay of conductance in the first 3 days. It is observed that the bi-exponential functions have excellent goodness of fits, as verified by the values of \(R^{2}\), than the single time-constant exponential functions used in [20,21]. All the fitting coefficients of (4) and goodness of fits are summarized in Table 4.  
To understand the physical meanings of the modelling functions, especially, the conductance variations and their decaying rates against the strength development at early ages as well as the effect of water-to-binder ratio (\(w/b\)), the step responses of the second term of regression functions were examined, as shown in Figure 8a. It is observed that the acquired time constants of the normalized curves (second terms) are distinguishable and varied as a function of \(w/b\). We hence conclude that the responses are reasonable against the initial water contents of the ECC mixes, and the time constant (\(c\)) can be used to monitor the changes of moisture inside each specimen. As shown in Figure 8a, the decay time constant (\(c_{\rm i}\)) indicates the time/age for the bulk electrical conductance of an ECC material decreasing in value to \(exp(-1)\) or 36.8% from a step decrease. The regression coefficients \(A1\) and \(c\) varies linearly with increasing \(w/b\), as shown in Figure 8b. The coefficient \(A1\) is the initial value of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**2 GHz** & \(w/b\) & \(A1\) & \(b\) & \(A2\) & \(c\) & \(R^{2}\) \\ \hline PE1 (\(g_{2,1}\)) & 0.200 & 3.535 & 60.96 & 25.44 & 0.6358 & 0.9999 \\ PE2 (\(g_{2,2}\)) & 0.255 & 3.601 & 71.43 & 4.913 & 0.8003 & 0.9997 \\ PE3 (\(g_{2,3}\)) & 0.300 & 3.649 & 72.32 & 4.794 & 0.8708 & 0.9993 \\ \hline \end{tabular}
\end{table}
Table 4: Regression coefficients and goodness of fits for Equation (4).  
Figure 7: Non-linear regressions of electrical conductance at 2 GHz using bi-exponential functions.  
first term of the bi-exponential regression, which can be considered the extrapolation point of the slow declining ramp function.
# Study of Mesh Pattern for Optically Transparent Flexible Antenna with Feedline
## 2 OT of the Mesh  
There are various methods of fabricating meshes utilizing microfabrication such as inkjet printing, laser etching, electroplating, and so on [20; 21]. The shape of the mesh is mainly used in the form of a square [14; 15; 16; 17; 18; 19]. However, there is a disadvantage that haze and moire have to be considered at a specific size for realizing high transparency of a square mesh [22]. Therefore, diamond mesh is a commonly encountered pattern as an alternative [23; 24]. In general, the optical and electrical characteristics of the mesh pattern are investigated. In this paper, we proceed with the specification of manufacturable samples where the maximum sheet resistance of the mesh pattern does not exceed 0.02 \(\Omega\)/sq. [25].  
Detailed parameters for the square mesh and diamond mesh are shown in Figure 1. As shown in Figure 1, an open space in the form of a square with a side length \(g_{s}\) is regularly arranged at intervals of a specific width \(w_{s}\). The open space of the square mesh allows visible light to go through, while the remaining part, copper, guarantees the high conductivity of the square mesh. Figure 1b shows an arrangement of diamond-shaped open spaces. It shows the width \(w_{d}\) of the copper grid. The vertical length of the diamond is denoted by \(g_{d,\text{ {{{{\text{{{vert}}}}}}}}}\), and the horizontal length, parallel to the direction in which the signal is transmitted in the transmission line, is denoted by \(g_{d,\text{ {{{{hor}}}}}}\). Similar to the square mesh, it has OT through open space, and the remaining copper guarantees a high conductivity.  
Figure 1: Structure of the mesh pattern for schematic representation and OT: (**a**) square mesh; (**b**) diamond mesh.  
OT is calculated as the ratio of the area of its open space to the total area of the mesh. This simplified equation considers only the conductor layer, and the thickness of the conductor is not considered, but it is accurate enough [26]. In the case of square mesh in Figure 1a, many papers have already used the calculation formula to find it. As reported in [26], the OT can be calculated as the following Equation (1).  
\[\text{OT of square mesh }=\frac{B}{A}=\left(\frac{g_{s}}{w_{s}+g_{s}}\right)^{2}. \tag{1}\]  
Likewise, in the case of diamond in Figure 1b, it can be obtained as the ratio of the total area (A\({}^{\prime}\)) of unit cell and the open space area (total sum of B) as the following Equation (2).  
\[\text{OT of diamond mesh }=\frac{B_{total\ sum}}{A^{\prime}}=\frac{4g_{d,\ ver}g_{d,\ hor }}{\left(\sqrt{2}w_{d}+2g_{d,\ ver}\right)\left(\sqrt{2}w_{d}+2g_{d,\ hor }\right)}\;. \tag{2}\]  
In this paper, we proceeded with design parameters with OT of 70-90% for square mesh and diamond mesh, respectively. ITO has an average OT of 72% in the wavelength of the EM wave, which covers an entire visible range from 380 to 750 nm [25]. It has a thickness similar to that of the simulation model.
# Eyelid Dynamics Characterization with 120 GHz mmW Radar
## 1 Introduction  
Millimeter wave radar (mmW radar) technology, which uses electromagnetic waves with wavelengths in the millimeter range, has a number of applications, including estimation of the range and velocity of objects [1; 2; 3], beam forming for location detection [4], meteorology [5], geodesy [6], air traffic control [7], and seafaring [8]. In this paper, the use of mmW radar for biomedical applications, specifically measuring eye blink rate, is explored. The ability to remotely observe the micrometric deformation of objects is utilized to investigate the characterization of eye blinking dynamics as a potential tool for detecting neurological disorders [9]. This research includes optimization of the radar system, electromagnetic modeling, and experimental evaluation. Eye blinking is a reflex response triggered by a corneal stimulus that helps keep the cornea clean and healthy by stimulating the secretion of mucin and meibum, which are components of the tear film [10]. Studies have categorized eye blinking as spontaneous, reflex, and voluntary [11]. Factors such as stress can affect blink rate [12] and can be used to determine one's fitness for tasks such as driving. In order to accurately measure eyelid movement and gain sufficient data to understand the dynamics of blinking, this research will employ a two-step approach using Frequency-Modulated Continuous Wave (FMCW) radar. This method aims to overcome the limitations of previous techniques, such as high-speed cameras and magnetic coils, which have difficulty in certain lighting conditions and require significant computational effort to extract the parameters of interest. For a successful evaluation of blinking with regard to specifying neurological disorders, it is crucial to detect the inter-blink interval (IBI), the duration of a blink, and the dynamics of a blink (i.e., the duration and speed of opening and closing the eyelids and the duration of the eye being closed).
## 4 The Settlement Monitoring Results of the ELH Bridge  
### Settlement Time Series Obtained Based on GB-RAR  
Figure 4 shows the power profile of the IBIS-S interferometer radar line-of-sight direction. In Figure 4, multiple peaks are within the radar monitoring range, and these peaks correspond to the position of monitoring points (such as piers) with good electromagnetic reflectivity. In order to evaluate the safety condition of shield when crossing the ELH bridge, the settlement time series and settlement rate of No. 7 and 8 were monitored and analyzed. The thermal signal-to-noise ratios (SNR) of No. 7 and 8 piers were 71.5 and 54.2 dB, respectively, indicating a high phase stability of the monitored bridge piers.  
We used the geometric projection method to obtain the settlement deformation of piers (namely, the vertical deformation component of line-of-sight deformation) because the GB-RAR technology can only obtain the deformation in the direction of the radar line of sight. Given that the ELH Bridge is an important high-speed railway bridge, a train passes through it at high speed every 10 min during the monitoring period. Therefore, the pier was  
Figure 4: Power profile of the ELH Bridge in the radar line of sight direction.  
affected by the vertical load of high-speed train operation and the vibration generated by shield tunneling machine. The atmospheric variation and other random noises also have an impact on radar signals. The settlement observation value sequence obtained based on the GB-RAR technology was seriously affected by various noises due to the above-mentioned reasons. In this work, sym4 wavelet was used to denoise the GB-RAR-derived settlement time series.  
Figure 5 shows the settlement time series of No. 7 and 8 piers obtained based on the GB-RAR technology. During the monitoring period, the settlement changes were essentially within 1 mm. The vibration generated by the shield tunneling machine will have a certain impact on the deformation monitoring of No. 7 and 8 piers because the shield tunneling machine passes through between No. 7 and 8 piers (as shown in Figure 1). In addition, the noise in the radar signal will increase with the increase in the radar monitoring distance. Because of the above reasons, the wavelet analysis method used to reduce the noise of the settlement time series of No. 7 and 8 piers. After wavelet denoising, the SNR of the settlement time series of No. 7 and 8 piers was increased from 9.7/4.4 to 19.5/9.6, and the change law of settlement time series was clearer. The settlement time series of No. 7 and 8 piers showed a nonlinear downward trend. The standard deviations of the settlement time series of No. 7 and 8 piers obtained based on the GB-RAR technology were 0.19 and 0.18 mm, respectively. The settlement monitoring accuracy of GB-RAR technology was typically high.
## 3 Results and Discussion  
### Characterization of the Developed Electrospun Membranes  
#### 3.1.1 FESEM Analysis  
In order to evaluate the surface morphology and diameters of the developed electrospun fibers as well as the presence of NPs, FESEM analysis was performed (Figure 3).  
As shown in Figure 3a, defect-free PCL fibers were successfully developed with average diameters of 1.81 \(\upmu\)m. Figure 3b represents the PCL membranes impregnated with Ag NPs, where the presence of these NPs with some agglomerates onto the fibers is clearly visible. In this case, the Ag NPs were synthetized through the reduction of AgNO\({}_{3}\) by PEG, which also acted as a stabilizer and dispersing agent [26; 28], this being the solution used to impregnate the PCL membranes. Thus, by these images it is possible to confirm the successful synthesis of Ag NPs as well as their attachment to the PCL electrospun membranes. A decrease in fibers' diameters was observed with the incorporation of Ag NPs (1.78 \(\upmu\)m). From Figure 3c,d, it is also possible to detect the presence of NPs covering the fibers' surface, which correspond to TiO\({}_{2}\) and MgO NPs, respectively. Additionally, a more dense and uniform deposition was observed using TiO\({}_{2}\) NPs. The impregnation with TiO\({}_{2}\) and MgO NPs led to a slight increase in fibers' diameters to 2.09 and 2.02 \(\upmu\)m, respectively.