esa-sceva/satcom-chunk-collection huggingface dataset:
### Challenges and Related Works  
Creating adversarial images of the same size as their clean counterparts, as illustrated in Figure 2, is a novel and highly challenging task in terms of speed, adversity, and imperceptibility.  
Firstly, the complexity of the problem grows quadratically with the size of the images. This issue impacts the _speed_ of attacks performed directly in the \(\mathcal{H}\) domain. In [29], an evolutionary algorithm-based black-box attack, that successfully handled images of sizes \(224\times 224\), was tested on a high-resolution image of size \(910\times 607\) via the direct approach illustrated in Figure 2. Despite 40 h of computational efforts, it failed to create a  
Figure 1: Standard attacks’ process, where \(c_{\alpha}\) is the CNN’s leading category of the clean resized image, and \(c\
eq c_{\alpha}\) is the CNN’s leading category of the adversarial image.  
high-resolution adversarial image by this direct method. This indicates that a direct attack in the \(\mathcal{H}\) domain, as described above, is unlikely to succeed. An alternative approach is definitively needed to speed up the attack process in the \(\mathcal{H}\) domain.  
Additionally, the _adversarial noise_ in the high-resolution adversarial image should prevail even when the adversarial image is resized to the input size of the CNN. Finally, the difference between the high-resolution original clean image and the high-resolution adversarial image must be _imperceptible_ to a human eye.  
A first solution to the speed and adversity challenges is presented in [29; 30] as an effective strategy that smoothly transforms an adversarial image--regardless of how it is generated--from the \(\mathcal{R}\) domain to the \(\mathcal{H}\) domain. However, the imperceptibility issue was not resolved.
## 4 Performance Analysis of an FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI fading Channels
### Average Bit Error Rate (\(P_{BER}\))  
The average bit error rate (\(P_{BER}\)) can be defined as the rate at which errors occur in the considered FSO RIS-ACs transmission system. The \(P_{BER}\) of the received SNR for various binary modulation techniques of the FSO RIS-ACs system in F-S \(\mathcal{F}\) TI fading propagation environment can be calculated using ([42], Equation (25)):  
\[P_{BER}(\gamma)=\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\gamma^{p-1}F_{\gamma }(\gamma)e^{-q\gamma}d\gamma \tag{27}\]  
where \(p\) and \(q\) are the parameters of different modulation types. In particular, \(P_{BER}(\gamma)\) for the non-coherent binary frequency shift keying (NBFSK) (\(p=1\), \(q=1/2\)), binary frequency shift keying (BFSK) (\(p=1/2\), \(q=1/2\)), binary phase shift keying (BPSK) (\(p=1/2\), \(q=1\)) and differential binary phase shift keying (DBPSK) (\(p=1\), \(q=1\)) can be obtained from (27). According to (23), \(P_{BER}(\gamma)\) can be written as:\[P_{BER}(\gamma) = \frac{q^{p}}{2\Gamma(p)}\frac{(b_{1}-1)^{b_{1}}{a_{2}}^{a_{2}}(b_{2} -1)^{b_{2}}\tilde{\gamma}^{(b_{1}+b_{2})/2}}{\beta(a_{1},b_{1})\beta(a_{2},b_{2}) }\sum_{k=0}^{a_{1}-1}\binom{a_{1}-1}{k}(-1)^{k}\frac{((b_{1}-1)\tilde{\gamma}^{ 1/2})^{k}}{b_{1}+k} \tag{28}\] \[\times\left(\frac{\left(\frac{1}{\tilde{q}}\right)^{p}\Gamma(p)( \frac{(b_{2}-1)\tilde{\gamma}^{1/2}}{a_{2}})^{a_{2}}\beta(a_{2},b_{2})}{((b_{1 }-1)\tilde{\gamma}^{1/2})^{b_{1}+k}((b_{2}-1)\tilde{\gamma}^{1/2})^{a_{2}+b_{2 }}}-H_{2}\right)\]  
where \(H_{2}\) is given as:  
\[H_{2} = \int_{0}^{\infty}\gamma^{p-1}e^{-q\gamma}\frac{(\frac{a_{1} \gamma^{1/2}}{(b_{1}-1)\tilde{\gamma}^{1/2}})^{a_{2}+b_{1}+k}\beta(a_{2}+b_{1} +k,b_{2})}{(a_{1}\gamma^{1/2})^{b_{1}+k}((b_{2}-1)\tilde{\gamma}^{1/2})^{a_{2} +b_{2}}} \tag{29}\] \[\times\ {}_{2}F_{1}\Bigg{(}a_{2}+b_{2},a_{2}+b_{1}+k;b_{1}+a_{2}+b_{ 2}+k;1-\frac{a_{1}a_{2}\gamma^{1/2}}{(b_{1}-1)(b_{2}-1)\tilde{\gamma}}\Bigg{)} d\gamma\]  
The C-F \(P_{BER}(\gamma)\) expression can be obtained by solving \(H_{2}\) in (29) using ([41], Equation (07.23.26.0007.01)) and ([41], Equation (07.34.21.0088.01)), respectively. The evaluated \(H_{2}\) is derived as:  
\[H_{2}=\frac{a_{1}^{a_{2}}\beta(b_{1}+a_{2}+k,b_{2})\Gamma(b_{1}+a_{2}+b_{2}+k )}{((b_{2}-1)\tilde{\gamma}^{1/2})^{a_{2}+b_{2}}((b_{1}-1)\tilde{\gamma}^{1/2 })^{a_{2}+b_{1}+k}}\]  
\[\times\frac{2^{a_{2}+2b_{2}+k-1}}{(2\pi)^{2}q^{p+\frac{1}{2}a_{2}}\Gamma(a_{2} +b_{2})\Gamma(b_{1}+a_{2}+k)\Gamma(b_{1}+k)\Gamma(b_{2})} \tag{30}\]  
By substituting (30) in (28), the C-F \(P_{BER}(\gamma)\) was efficiently derived, whereas the numerical evaluation and further analysis of \(P_{BER}(\gamma)\) are provided in the Numerical Results.
## 1 Introduction
### Comparison with Existing Surveys  
A summary comparison with relevant survey papers is provided in Table 1. Several related survey papers have contributed to the realm of teleoperation, focusing on aspects such as teleoperation research scope, architectures, taxonomies, advanced communication links, and challenges of mobile robots [8; 12; 13] and CAVs [1; 3; 7; 10; 14; 15].  
[PERSON] et al. [13] emphasise architecture, communication links, and situational awareness in teleoperation. While some studies present teleoperation methods and enhancement techniques, [PERSON] et al. [12] review approaches for time delay mitigation, focusing on control theory, user interface designs, and time-series prediction models. Similarly, [PERSON] et al. [8] comprehensively review teleoperation challenges and examine existing teleoperation methods and enhancement techniques (with limited coverage of latency compensation techniques). However, these survey papers revolve around mobile robots, which may not be valid for CAVs.  
Other recent survey studies focus on CAV teleoperation. For instance, [PERSON] et al. [14] provide a taxonomy for the teleoperation systems, outlining recent terminology in the field.  
Likewise, [PERSON] et al. [1] stress integrating remote operator roles into industry-standard taxonomies and use cases for regulatory frameworks. They also highlight the importance of designing control interfaces to maximise operator situational awareness. Additionally, [PERSON] [3] presents a vision of intelligent teleoperation systems powered by artificial intelligence and advanced networks (e.g., 5G), discussing their potential benefits and challenges. [PERSON] and [PERSON] [7] analyse significant challenges in CAV teleoperation and provide design guidelines for future teleoperation interfaces. [PERSON] et al. [10] compile works on the remote operation of road vehicles, categorising them based on the level of human intervention and identifying challenges in deployment across technological, regulatory, and commercial domains. [PERSON] et al. [15] survey remote driving challenges and solutions of latency, driving feedback, support control, and initiatives. They briefly provide the impacts of latency on remote drivers and review a few mitigation strategies. Although the existing surveys acknowledge the significance and challenges in the context of CAV teleoperation, extensive research focusing on network latency as a challenge and mitigation strategies specific to this domain remains scarce. Many of these surveys have insufficiently covered the network latency aspects of CAV teleoperation.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 4 Experimental Studies
### Evaluation of Generative Models
#### 4.1.1 Evaluation Metrics  
There are two aspects that need to be confirmed in the evaluation of the synthesized samples: their realism and their biometric resemblance to the minutiae origins.  
In our considerations, **the realism of synthetic fingerprints** is equal to their visual quality and is estimated by calculating the NFIQ2 scores [49]. Note that the primary objective of NFIQ2 scores is to estimate the utility of fingerprints or, in other words, their effectiveness for the purpose of user authentication. However, it is known that NFIQ2 scores correlate well with the visual quality of fingerprints. The NFIQ2 scores range from 0 to 100, with higher scores indicating higher utility. Fingerprints with scores above 45 are considered perfect, while scores above 35 indicate good fingerprints. Fingerprints with scores lower than 6 are deemed useless due to poor quality.  
**The biometric resemblance with the minutiae origin** is also referred to as reconstruction success. In order to measure reconstruction success, we apply VeriFinger SDK [50] to calculate matching scores between reconstructed synthetic samples and the origins of minutiae. Given a pair of fingerprints, the Verifinger's outcome is a similarity score ranging from 0 to \(\infty\), with 0 for maximal dissimilarity. The decision threshold for confirming that fingerprints belong to the same identity is set based on the desired security level of a biometric system. The threshold is set in a way that the false acceptance rate (FAR) does not exceed 0.1%, 0.01%, or 0.001%, with lower values indicating higher security. The Verifinger thresholds at the aforementioned FAR levels are 36, 48, and 60, respectively.  
For each generative model under evaluation, we calculate the fingerprint reconstruction success rate as the ratio of fingerprint pairs (synthesized vs. origin) for which matching scores are higher than a pre-defined threshold in all tested fingerprint pairs. For the purpose of comparability with other studies, this ratio is also referred to as the true acceptance rate (TAR). In state-of-the-art studies, two types of TAR are estimated: Type 1 and Type 2. Type 1 TAR involves calculating matching scores between reconstructed and original fingerprints, while Type 2 TAR involves calculating matching scores between the reconstructed fingerprint and other impressions of the original fingerprint.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### LQE Classification Based on PRR  
The PRR measurement is used as a classification parameter in the LQE prediction model [31; 32; 33; 34; 35]. In the experimental scenario presented in Figure 1, the transmitting node periodically sent 100 test packets to the receiver node that calculated PRR. The LQE was related to the rate of packets received successfully. Forecast categories were divided according to (2). We considered that a PRR smaller than 0.89 is not acceptable for our industrial application; 2500 random samples were collected for each of the four LQE categories.  
\[y=f_{(\text{PRR})}=f(x)=\left\{\begin{array}{ll}1,&Best&\text{PRR}=1\\ 2,&Good&0.96<\text{PRR}\leq 0.99\\ 3,&Common&0.89<\text{PRR}\leq 0.96\\ 4,&Bad&\text{PRR}\leq 0.89\\ \end{array}\right. \tag{2}\]  
Figures 2-5 show the scatterplots of the experimental measurements for distance, transmission rate, power, and data payload against BER. The LQE classification is denoted by numbers in the scatterplots, where the best subset is denoted by number 1, good by number 2, common by number 3, and bad by number 4, as explained in (2). The BER observation range in the scatterplots was chosen to identify the four forecast categories, and we can see that the BER mean value was low because experimental data were gathered with low noise working conditions, i.e., from (1), we can see that BER = 0 when PRR = 1. The scatterplot in Figure 2 shows that BER decreased with distance. In Figure 3, we did not see a strong relationship between transmission rate and the BER variable; however, forecast categories 1 and 2 had low BER values. On the other hand, increasing the transceiver's power improved the quality of transmission with better BER values, i.e., more data packets without errors arrive at the receiver, whilst data from categories 1 and 2 tended to have lower BER values, as shown in Figure 4. For the NRF24L01 transceiver, a high payload increased the transmission quality, and finally, Figure 5 shows a clear division between forecast categories. We selected values of rate, payload, and power based on the NRF24L01 transceiver characteristics.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Rate** & **Power** & **Distance** & **Payload** & **BER** \\  & _x\({}_{1}\)_ & _x\({}_{2}\)_ & _x\({}_{3}\)_ & _x\({}_{4}\)_ & _x\({}_{5}\)_ \\ \hline count & 10 k & 10 k & 10 k & 10 k & 10 k \\ mean & 1101.9 kbps & 272.9 mW & 8.9 m & 13.9 bytes & 0.0144 \\ std & 699.2 kbps & 376.7 mW & 4.4 m & 11.4 bytes & 0.0328 \\ min & 250 kbps & 15.85 mW & 1 m & 2 bytes & 0.0 \\ max & 2 Mbps & 1000 mW & 16 m & 32 bytes & 0.470 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical information of the input data.  
Figure 4: Scatterplot of transmission power against BER.  
Figure 5: Scatterplot of payload against BER.  
Figure 3: Scatterplot of transmission rate against BER.  
Figure 2: Scatterplot of distance against BER.
# Roll Control of Morphing Aircraft with Synthetic Jet Actuators at a High Angle of Attack
###### Abstract  
Flow separation and dynamic stall occurring at a high angle of attack will lead to difficulty in control and maneuverability for morphing aircraft. This study proposes a novel active flow control technology using a synthetic jet actuator for the roll motion of morphing aircraft. With the help of the computational fluid dynamics method and vortex lattice method, the roll control model of morphing aircraft undergoing large shape change at a high angle of attack is established. In this model, both the array of the actuator with an optimized cavity shape and morphing span, which are subject to the input saturation constraint, are used to mimic the conventional control surface. Integrated flight control based on the sliding mode control method is designed to ensure the desired closed-loop asymptotic stability, wherein the radial basis function neural network is employed to provide the compensation induced by the input saturation constraint. To demonstrate the effectiveness of the control scheme, various control strategies for different combinations of input are proposed to maintain the roll motion. The numerical results show that the designed control law could track the target signal well, which suggests that the virtual control surface is an effective tool for maintaining the high flight performance of morphing aircraft.  
morphing aircraft; synthetic jet actuator; sliding mode control; input saturation constraint
## 2 System Model
### Display Screen Extraction  
The stego images created at the control room are fed to a display screen inside the object showcase room. The surveillance camera present inside the object showcase room captures the display screen along with the precious object and its surroundings. Now, the captured image is given to the object protection system, which is located inside the object showcase room, for message extraction. The process of extracting the display screen from the captured image is done at the sub-center. Various steps involving the display extraction from the captured image are given below.  
Step 1: Store the captured image containing a display screen with minimum geometric distortion as a reference image in the grayscale form.  
Step 2: Capture the image containing the display screen and convert into its grayscale form.  
Step 3: Apply Harris corner detection for reference and captured images. This step gives the corner features of both the images.  
Step 4: Extract the detected corner features of both images into two variables F1 and F2.  
Step 5: Match the features between the two images using the variables F1 and F2. Let the matched features of the two images be M1 and M2.  
Step 6: Find the valid matched points from the set of M1 and M2. Let the valid points be V1 and V2.  
Step 7: Estimate the geometric transform required to restore the captured image as a reference image using V1 and V2.  
Step 8: Apply the geometric transform to the captured color image.  
Step 9: Perform edge detection on the restored image and find all the edges in the image.  
Step 10: Apply Hough transform based on the results of edge detection. This gives Hough transform matrix H, line distance \(\rho\) and line inclination \(\theta\).  
Step 11: Find Hough peaks in the obtained Hough transform.  
Step 12: Select lines having minimum line distance (borders of the display screen) inclining 0 \(\pm\) 5\({}^{\circ}\) (vertical lines) or 90 \(\pm\) 5\({}^{\circ}\) (horizontal lines).  
Step 13: Lines obtained from Step 12 are used to extract the display screen from the captured image.  
The extracted display screen is considered as the received stego image. This stego image is given to the proposed extraction algorithm, and the embedded data is extracted from the stego images. A flowchart representation of the display extraction process is given in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pattern** & **Key** \\ \hline SU & 000 \\ SD & 001 \\ SF & 010 \\ SB & 011 \\ FU & 100 \\ FD & 101 \\ FF & 110 \\ FB & 111 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three bit key for 8 patterns.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 3 Method
### Metric Function  
We employed a metric function that combines the MS-SSIM and \(\ell_{1}\) proposed by [PERSON] et al. in [32]. SSIM [33] is a widely used indicator for measuring the structural similarity between images. SSIM mainly considers three key features of images: luminance, contrast, and structure. MS-SSIM uses multiple different Gaussian filters (\(\sigma=\sigma_{1},\ldots,\sigma_{M}\)) on the basis of SSIM. Specifically, for input images x and y of the same size, the luminance similarity of each pixel p is defined as:  
\[l(p)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+C_{1}} \tag{2}\]  
where \(C_{1}\) = 0.01 is a constant used to prevent the denominator from being zero, \(\mu_{x}\) and \(\mu_{y}\) represent the weighted sum of patches centered at pixel \(p\) in image \(x\) and image \(y\), respectively. \(\mu_{x}\) is defined as follows:  
\[\mu_{x}=\sum_{i=1}^{N}\omega_{i}x_{i} \tag{3}\]  
where the weight coefficient \(\omega_{i}\) is obtained from a Gaussian function.  
Contrast and structural similarity can be represented by a single formula:  
\[cs(p)=\frac{2\sigma_{xy}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}} \tag{4}\]  
where \(C_{2}\) = 0.03 is also a constant used to prevent the denominator from being zero, \(\sigma_{x}^{2}\), \(\sigma_{y}^{2}\), and \(\sigma_{xy}\) represent the variance and covariance of patches centered at pixel \(p\) in image \(x\) and image \(y\). \(\sigma_{x}^{2}\) and \(\sigma_{xy}\) are defined as:  
\[\sigma_{x}^{2}=\sum_{i=1}^{N}\omega_{i}(x_{i}-\mu_{x})^{2} \tag{5}\]  
\[\sigma_{xy}=\sum_{i=1}^{N}\omega_{i}(x_{i}-\mu_{x})(y_{i}-\mu_{y}) \tag{6}\]  
Figure 3: Illustration of the residual attention module.  
We use the index \(j=1,\ldots,M\) to represent different Gaussian filters. MS-SSIM is defined as:  
\[\textit{MS-SSIM}(p)=l_{M}(p)\cdot\prod_{j=1}^{M}cs_{j}(p) \tag{7}\]  
During the training phase, for an image of size \(H\times W\), the MS-SSIM loss can be expressed as follows:  
\[\mathcal{L}_{\textit{MS-SSIM}}=\frac{1}{H\times W}\sum_{p}1-\textit{MS-SSIM}(p) \tag{8}\]  
The total loss is calculated by adding the \(\ell_{1}\) loss multiplied by the Gaussian filter and the weighted MS-SSIM loss. This formula is shown as Equation (9).  
\[\mathcal{L}_{total}=\alpha\cdot\mathcal{L}_{\textit{MS-SSIM}}+(1-\alpha)\cdot G _{\sigma_{M}}\cdot\mathcal{L}_{\ell_{1}} \tag{9}\]  
where \(\alpha\) represents the weight coefficient.  
During the inference stage, we calculated the anomaly localization by computing the MS-SSIM and \(\ell_{1}\) error for each pixel.
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 1 Introduction
### Contribution  
This study presents a 5G network architecture and next-generation wireless networks (NGWN) communication architecture model. Besides this, we also present 3 GPP-based physical layer services and functions, 5G massive MIMO and beam-forming techniques to enhance data rates with efficient spectrum sharing, 5G crosstalk issues and solutions, 5G-supporting signaling techniques, and security and privacy in 5G. This survey aims to understand and emphasize the emerging methods of ML for upcoming 5G use cases. In addition, the article discusses how forthcoming 5G technologies will meet their demanded goals using shared information and ML-based techniques and expected future challenges  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Key Performance Indicator (KPIs)** & **4G-LTE** & **5G** & **6G** \\ \hline Deployment year & 2000 & 2020 & Yet to be implemented \\ Core architecture & Internet & Internet & Internet \\ Core networking & Internet & Internet & Internet \\ Multiplexing bandwidth & OFDMA/SC-FDMA & BDMA/FBMC & OMA/NOMA \\  & (1.4 Mhz–20 Mhz) & (60 GHz) & (up to 3 THz) \\ Per device peak data rate & 1 Gbps & 10 Gbps & 1 Tbps \\ Switching & Packet switching & Packet switching & Packet switching \\ Forward error correction & Turbo codes & LDPC codes & LDPC codes \\ E2E latency & 100 ms & 10 ms & 1 ms \\ Maximum spectral efficiency & 15 bps/Hz & 30 bps/Hz & 100 bps/Hz \\ Mobility support & Up to 350 km/h & Up to 500 km/h & Up to 1000 km/h \\ Satellite integration & No & No & Fully supported \\ AI supported & No & Partially supported & Fully supported \\ Autonomous vehicle supported & No & Partially supported & Fully supported \\ XR supported & No & Partially supported & Fully supported \\ Haptic communication & No & Partially supported & Fully supported \\ Visible light communication (VLC) & No & No & Yes \\ Maximum frequency & 6 GHz & 90 GHz & 10 THz \\ Architecture & MIMO & Massive MIMO & Intelligent surface \\ Service Level & Video & AR,VR & Tactile \\ Connectivity density & \(10^{5}\) Devices/km\({}^{2}\) & \(10^{6}\) Devices/km\({}^{2}\) & \(10^{7}\) Devices/km\({}^{2}\) \\ Area traffic capacity & 0.1 Mb/s/m\({}^{2}\) & 1 Mb/s/m\({}^{2}\) & 1 Gb/s/m\({}^{2}\) \\ Network energy efficiency & \(1\times\) & 10–100\(\times\) of 4G & 10–100\(\times\) of 5G \\ Spectrum efficiency & \(1\times\) & \(3\times\) of 4G & 5–10\(\times\) of 5G \\ Reliability & 99.99 & 99.999 & 99.99999 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of 4G, 5G, and 6G communication systems.  
due to the combined architecture models. At the end, we also discuss emerging challenges and future directions.