esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Transformer-Based Segmentation Model
#### 3.1.1 Swin Transformer Block  
Each Swin Transformer block consisted of a LayerNorm (LN) layer, a multi-head self-attention module, a residual connection, and a 2-layer MLP with GELU non-linearity. In Figure 2b, two consecutive Swin Transformer blocks are presented; the difference between both is in the multi-head self-attention module. The window-based multi-head self-attention (W-MSA) module and the shifted window-based multi-head self-attention (SW-MSA) module were applied in them, respectively. The Swin transformer block was formulated as follows:  
\[\widehat{\mathcal{Z}}^{l}=W-MSA\Big{(}LN\Big{(}\mathcal{Z}^{l-1}\Big{)}\Big{)} +\mathcal{Z}^{l-1} \tag{1}\]  
\[\mathcal{Z}^{l}=MLP\Big{(}LN\Big{(}\widehat{\mathcal{Z}}^{l}\Big{)}\Big{)}+ \widehat{\mathcal{Z}}^{l} \tag{2}\]  
\[\widehat{\mathcal{Z}}^{l+1}=SW-MSA\Big{(}LN\Big{(}\mathcal{Z}^{l}\Big{)}\Big{)} +\mathcal{Z}^{l} \tag{3}\]  
\[\mathcal{Z}^{l+1}=MLP\Big{(}LN\Big{(}\widehat{\mathcal{Z}}^{l+1}\Big{)}\Big{)} +\widehat{\mathcal{Z}}^{l+1} \tag{4}\]  
The variables \(l\) and \(l+1\) denote two consecutive Swin Transformer blocks, respectively, while \(\widehat{\mathcal{Z}}^{l}\) and \(\mathcal{Z}^{l}\) represent the output of W-MSA and MLP in block \(l\), respectively. \(\widehat{\mathcal{Z}}^{l+1}\) and \(\mathcal{Z}^{l+1}\) represent the output of SW-MSA and MLP in block \(l+1\), respectively. Self-attention was calculated similarly to earlier research [44; 45] as follows:  
\[Attention(Q,K,V)=Softmax\Big{(}\frac{QK^{T}}{\sqrt{d}}+B\Big{)}V \tag{5}\]where \(Q,K,V\in\mathbb{R}^{M^{2}\times d}\) denote the query, key, and value matrices. The variables \(M^{2}\) and \(d\) represent the number of patches in a window and the dimension of the query or key, respectively, and \(B\) represents the relative position bias of the patch token.
# Optical Interference Suppression Based on LCD-Filtering
## 3 Interference Reduction
### Ambient Light  
The first type of interference under investigation is solar irradiation. Filtering can be accomplished in the electrical domain, e.g., by placing a high-pass filter after the first stage transimpedance amplifier (TIA). Nevertheless, interference by sunlight is critical since the detected and later on filtered interference power \(P_{i}\) induces receiver side noise. Namely, shot-noise [3] in the PD with variance  
\[\sigma_{\mathrm{shot}}^{2}=2 qRP_{i}B. \tag{9}\]  
Since the responsibility \(R\) of the PD, the electron charge \(q\) and the bandwidth \(B\) are constant for a given receiver, the shot-noise is directly proportional to the interference power \(P_{i}\). The relevance of this effect becomes apparent when comparing the potential high solar irradiation power with the signal power \(P_{s}\). In such a case, \(P_{i}\gg P_{s}\), the communication system is shot-noise limited with [3]:  
\[\mathrm{SNR}=\frac{\left(RP_{s}\right)^{2}}{\sigma_{\mathrm{shot}}^{2}}. \tag{10}\]  
One additional problem is receiver saturation by a large DC bias exceeding the dynamic range of the receiver.  
In order to derive the potential of LCD-based filtering, we assume the sunlight to be purely diffuse and to arrive at the receiver from each direction with equal intensity.  
By integration over a sphere with  
\[\mathrm{d}A=\sin\!\theta\mathrm{d}\theta\mathrm{d}\varphi, \tag{11}\]  
we obtain  
\[F_{\mathrm{norm}}=\int_{-\pi/3}^{\pi/3}\int_{\pi/6}^{5\pi/6}\mathrm{d}A \tag{12}\]  
as reference. This value represents the solar irradiation without an LCD in the optical path and with only a static aperture with an FOV of \(120^{\circ}\) remaining. For the interference, we can distinguish two portions, as shown in Figure 4. The first part  
\[F_{1}=\int_{-\pi/3}^{\pi/3}\int_{\pi/6}^{5\pi/6}\eta_{1}\left(\Psi\right) \mathrm{d}A \tag{13}\]  
is interference leaking to the PD owing to \(\eta_{1}\left(\theta\right)>0\). The angle to the normal of the PD is given with:  
\[\Psi=\arccos\left(\sin\!\theta\mathrm{cos}\varphi\right). \tag{14}\]  
The second part  
\[F_{0}=\int_{-\theta\left(0\right)/2}^{\theta\left(0\right)/2}\int_{\Psi+\pi/ 2-\theta\left(\Upsilon\right)/2}^{\Psi+\pi/2+\Theta\left(\Upsilon\right)/2} \eta_{0}\left(\Psi\right)\mathrm{d}A \tag{15}\]  
is interference arriving at the PD by passing the LCD at the pixel positions opened for receiving the desired signal. By relating the remaining interference (13) and (15) to the case of not using a filter (12), we obtain the results on the SIR improvement by using the LCD-based filter approach:\[\Delta\text{SIR}\left(\Psi\right)=\frac{\eta_{0}\left(\Psi\right)F_{\text{norm}}}{F_{0 }+F_{1}}. \tag{16}\]  
The factor \(\eta_{0}\left(\Psi\right)\) in (16) stands for the attenuation of the wanted signal by the LCD filter in transmissive state. Analytically derived results are presented in Figure 5, with the interference normalized with \(F_{\text{norm}}\).
## 3 Results  
A total of 3023 participants were examined and interviewed. The mean age was \(31.6\pm 13.1\), and among all the subjects, 2058 were male (68.1%) and 965 were women (41.9%). Most participants were born in Nigeria, followed by Bangladesh, Pakistan, Somalia, Mali and Senegal (Figure 1). The overall oral pain prevalence was 48.2%. The prevalence of patients who claimed poor oral hygiene was 32.4%; 36.2% of the subjects consumed high sugar; and 26.7% saw their dentist for a check-up in the last year. At the clinical examination, 68.9% of patients had caries experience (considering decay of deciduous teeth, and caries of permanent teeth and teeth with fillings), with 32.2% showing pulpal involvement. Low levels of oral hygiene were also found at the clinical examination, with 46.5% of patients presenting plaque and calculus. The trend of the DMFT index, which expresses the mean value of dental pathology, was found to be 5.41, determined by the curious component (Decay: 2.21), the lack of permanent teeth (Missing: 1.78), and the teeth with fillings (Filling: 1.42) (Figure 2). The multiple regression model statistically significantly predicted an increase in DMFT scores correlated with an increase in age (\(p\)-value = 0.003) (Table 1). Periodontal health has been expressed with the CPI (Community Periodontal Index) when going to evaluate gingival bleeding, calculus, and periodontal pockets (3 mm < PD < 6 mm). Good periodontal health (CPI = 0) was present in approximately 33.5% of patients. The CPI = 1 index reported bleeding from gingivitis in 37% of patients; tartar was found in 27% of patients (CPI = 2). The percentage of patients with CPI = 3 was 3.6%. Just over half (52.2%) of the migrants examined had malocclusion, and only 0.7% had a malocclusion in treatment.  
Figure 1: Percentage of subjects coming from different countries.
# Forecasting Irregular Seasonal Power Consumption.
## 3 Results
### Application of Models with Regular Seasonality
#### 3.2.3 Application of nHWT Models  
To perform the analysis using the nHWT models, a proprietary tool developed in MATLAB(r) is used. This tool comes in the form of a toolbox, but it has not been published yet. Models with one and two seasonalities are tested.  
The results show that the model that best adapts to this series presents a single seasonality, with the parameters \(\alpha=\ 0.0001\), \(\gamma=\ 0.0001\), \(\delta_{10}=\ 0.4856\) and \(\varphi_{AR}=0.9286\). The damping factor \(\varrho\) is set to 0. Models including this parameter were tested, but results were not improved, thus it was removed from the model. The RMSE of the fit process is 54.93.  
The result is not surprising since the nHWT models are structural and do not allow for a relaxation of seasonality. Once seasonality is established, the model will continue to develop the same pattern, even though this is not really reflected in the series. When using a single seasonality, the information provided by the second seasonal pattern is lost, but it  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Neural Network** & **Parameters** & **Fit RMSE** \\ \hline \multirow{4}{*}{NARX} & 1 input layer & \\  & 1 hidden layer with 20 neurons & \\  & 1 output layer & \\  & TDL = 3 & \\ \hline \multirow{4}{*}{NLR} & 1 input layer & \\  & 1 hidden layer with 20 neurons & \\  & 1 output layer & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Neural network parameters and RMSE for fitted results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & **Fit RMSE** \\ \hline AR & \(\begin{array}{c}\phi_{1}=-0.145;\ \phi_{2}=\ -0.472;\\ \phi_{3}=\ -0.190;\ \phi_{p}=\ 0.170\end{array}\) & \\ \hline MA & \(\begin{array}{c}\theta_{1}=\ 0.861;\ \theta_{2}=-0.601;\\ \theta_{3}=0.443;\ \theta_{4}=0.174\end{array}\) & \\ \hline SAR & \(\begin{array}{c}\Phi_{1}=-0.487;\ \Phi_{2}=0.067;\\ \Phi_{3}=-0.260;\ \Phi_{4}=-0.067\end{array}\) & \\ \hline SMA & \(\begin{array}{c}\Theta_{1}=0.142;\ \Theta_{2}=0.069;\\ \Theta_{3}=-0.292;\ \Theta_{4}=\ -0.065\end{array}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: ARIMA parameters and RMSE for fitted results.  
has less influence than the error caused by a second seasonality that does not coincide with the real seasonality of the series.
## 5 Experimental results and discussion
### Convolutional neural network hyperparameter optimization experiments
#### 5.2.1 Benchmark dataset  
The TGA, FA, and the proposed EE-TGA and \(E^{3}\)-FA methods were tested and validated on the MNIST benchmark dataset [70]. The MNIST database is a large database of handwritten digits from zero to nine; each image is in grayscale; and it consists of \(28\times 28\) pixels. The dataset consists of 70,000 labeled samples; in the experiment, 50,000 examples were used for training, 10,000 examples for validation, and 10,000 examples for testing purposes. The grayscale images that had pixel values between zero and 255 were normalized in the range from zero to one. Samples of the digit images are shown in Figure 5.
## 5 Conclusions  
In this paper, to achieve a better subjective quality of a restored image, we proposed an improved method that uses edge detection and SVD for tamper detection and median filtering during image recovery. With the SVD and edge detection characteristics applied, the watermark block is classified to ensure it contains sufficient texture and edge information. After a three-level tampering detection process, the tampered region can be located accurately. Using the inverse process of the watermark embedding method and the filtering operation, the recovery result is finally obtained. Under the effect of the median filter and proper threshold selection, the proposed method objectively achieves high quality. The performance of the proposed method is tested on eight classic images under six different types of tampering attacks and its results compared with the results of the methods in References [7; 8; 9]. The experimental results show that the tampered region can be accurately located but has a slightly higher false alarm rate than do other methods due to the sensitivity of edge detection. Using the adopted filtering operation, the restored result has a better visual effect without losing its objective quality. However, these operations are not suitable for images where the tampered regions contain details represented in a single pixel or by small pixel areas. In this case, the better subjective quality is obtained only at the cost of eliminating such details. In future research, we will further explore the effects of different filters on image recovery quality and discuss a more automated method for selecting the threshold.  
[PERSON], [PERSON] and [PERSON] conceived the algorithm and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the results; [PERSON] drew the block diagrams; [PERSON] and [PERSON] drafted the manuscript; [PERSON], [PERSON] and [PERSON] revised the manuscript. [PERSON] supervised the project. All authors read and approved the final manuscript.  
Figure 15: Relationships of pixel threshold to the PSNR value in different experiments.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. 61702303); the Shandong Provincial Natural Science Foundation, China (No. 2R2017 MF020); and the 13 th student research training program (SRTP) at Shandong University (Weihai), China.  
**Acknowledgments:** The authors thank [PERSON], [PERSON], [PERSON] and [PERSON] for their help in revising this paper. The authors also gratefully acknowledge the technical assistance of DL850E ScopeCorder administrated by [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
In summary, we have designed and fabricated an OPO module. The module is compact and confined, with dimensions and a weight of 220 \(\times\) 60 \(\times\) 55 mm\({}^{3}\) and 2.5 kg. The experiment results show that the module had a wide tuning range, a high power, and a stable output when a commercial Yb fiber laser was used as the pump laser. The MIR tuning ranges were 2416.17-2932.25 nm and 3142.18-3452.15 nm when two channels of a periodically poled lithium niobate (PPLN) crystal, with poling periods of 31.59 and 30.49 \(\upmu\)m channels, respectively, were used. At the maximum pump power (50 W), the highest output power of the module exceeded 10.4 W at 2.7 \(\upmu\)m and 9.1 W at 3.3 \(\upmu\)m. To our knowledge, this is the best performing CW-OPO based on PPLN. The 10 h power stability at 3.31 \(\upmu\)m was measured to be 2.13% RMS under outdoor environment. We believe that such a compact, tunable, stable, and high-power OPO module can be used as the core part of a number of mid-infrared laser sources. Its promoting effect in the relevant industries is foreseeable.  
This work was supported by the National Key Research and Development Program of China (No. 2017 YFB0405200), the National Young 1000 Talent Plan, National Natural Science Foundation of China (No. 91321312, No. 11621091, No. 11674169), the Ministry of Science and Technology of the People's Republic of China (No. 2017 YFA0303700), the International Science and Technology Cooperation Program of China (ISTCP) (No. 2014 DFT50230), the Key Research Program of Jiangsu Province (No. BE2015003-2), and Special Funds for Fundamental Scientific Research Business Fees in Central Universities.  
[PERSON] and [PERSON] conceived and designed the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON] wrote the paper, and [PERSON] supervise the whole work.  
The authors declare no conflict of interest.
## 3 System Design and Hardware Prototype
### Power Source and Protective Casing  
The SafeStride fall detection system is designed to operate on a LiPO battery, which was chosen due to its high energy density, lightweight construction, and rechargeable capability. The LiPO battery provides the necessary power capacity to support the device's operation for extended periods without frequent recharges. To facilitate charging, a charging circuit is incorporated using power from a micro-USB port. In addition, a boosting circuit is employed to elevate the battery's voltage from 3.7 volts to the 5 volts required to power the circuit. It is important to note that the logic levels used within the circuit remain at 3.3 volts.  
To ensure the protection and integration of the system components, a PCB design has been developed. The components are carefully planned to be integrated and enclosed within a 3D-printed case, providing secure and robust housing. This protective case is specifically designed to withstand potential impacts from falls and protect the electronics inside. Special precautions are taken to securely fasten the case to the walker using Velcro straps to ensure stability during use.  
The prototype communicates with the server using the HTTPS protocol, ensuring secure and encrypted data transfer. The system's integration of both Wi-Fi and BLE enhances versatility and offers a fallback communication method. If the Wi-Fi connection has problems, the prototype can pair with smartphones through BLE to relay data to the server via the cellular network. This design choice ensures reliable and secure data transmission.
# Bearing Fault Diagnosis Using Grad-CAM and Acoustic Emission Signals
## 2 Proposed Method
### Envelope Analysis  
Since the impulses generated by bearing failures are amplitude-modulated, AE signals should be first demodulated to extract pure burst signals. As shown in Figure 2, the Hilbert-transform-based envelope analysis was used to demodulate the AE signal [18, 19]. First, the Hilbert-transform was applied to the AE signal as follows [18]:  
\[\hat{x}(t)=\frac{1}{\pi}\int_{-\infty}^{\infty}\frac{x(\tau)}{t-\tau}d\tau, \tag{1}\]  
where \(t\) is the time, \(x(\tau)\) is a sample of the input signal at \(\tau\), and \(\hat{x}(t)\) is a sample of the Hilbert-transformed signal at time t. Hilbert-transform shifts the phase of the input signal by 90 degrees.  
Figure 1: Flowchart of the proposed approach.  
To obtain the analytical signal, \(z(t)\), the Hilbert-transformed signal, \(\hat{x}(t)\), and input signal, \(x(t)\) were combined as complex numbers [18]:  
\[z(t)=x(t)+i\hat{x}(t),\;i=\sqrt{-1}. \tag{2}\]  
Then, the envelope signal, \(e(t)\) was computed as \(\left|z(t)\right|\). Finally, the envelope spectrum, \(f(\omega)\) was calculated as the square root of the fast Fourier transform of \(e(t)\) as follows:  
\[f(\omega)=\int_{-\infty}^{\infty}x(t)e^{-i\omega t}dt. \tag{3}\]
## 3 Discussion
### Comparison of Method Sensitivity, Selectivity, Reproducibility, and Efficiency
SBSE was less efficient than SPME and HSSE for sample preparation due to the manual step of removing non-volatile residues from the PDMS stir bar, but the subsequent GC-MS runs were completed more quickly as a result of the offline sample preparation. HSSE was more efficient than SBSE since the headspace samples did not require the non-volatile residue removal step, but the transfer of the stir bar from the extraction vial to the desorption tube still added time to the sample preparation compared to SPME. SPME was arguably the simplest and therefore the most efficient of the four methods as the volatile extraction and introduction to the GC-MS system could be fully automated by the software and the only manual steps involved were the preparation of the vials and appropriate aliquots of each sample. A major benefit of the automation was evident in the reproducibility of the methods. Several factors, such as extensive sample handling, contact with various glassware, and greater overall exposure to the lab environment, may have been reasons that even with careful preparation and procedures, SAFE was the least reproducible of the four extraction methods. SAFE also required a manual concentration step to achieve its sensitivity, which can present further challenges with volatility bias and reproducibility [20]. The reproducibility issues can be corrected for in a targeted analysis where appropriate internal standards may be selected and applied but may present greater challenges for untargeted approaches. The major benefit of SPME is that it is highly automatable and reproducible, making for very efficient analyses when many samples need to be analysed [12]. SBSE was similar in terms of reproducibility compared to SPME for the extraction of volatiles from sheep milk.
## 3 Classical Methods for Generating OAM Waves in RF Domain
### Circular Phased Arrays  
The last approach concerns the direct generation of OAM beams through the application of uniform circular arrays (UCA) of radiating elements with a particular feeding system that allows specifying the required phase shift for each radiator corresponding to the generation of the selected OAM state. The number of radiators in the array has an additional influence on the OAM antenna, since it determines the largest OAM mode that can be generated. In 2007, [PERSON] et al. numerically designed the first microwave OAM radiator using a circular distributed radiator array [27]. UCA allows generating several different OAM modes by changing the phase shift from the feeding system and to shift the OAM state without changing the antenna configuration. However, the main disadvantage of UCA is the high divergence feeding system complexity. Later, other works have been presented on the generation of OAM vortex waves based on UCA [34; 35; 36; 37; 38; 39]. In [34], instead of feeding all the  
Figure 6: Three dimensional (3D) printed dielectric structure mimicking placed on a flat perfect electrical conductor (PEC) surface mimicking a helicoidal parabolic reflector. The near-field and far-field results show a broadband frequency operation from 8 GHz to 16 GHz, thanks to the use of non-resonant dielectric structures [44].  
radiators at the same time with a particular phase-shift between the elements, the radiators are fed sequentially with the same amplitude and phase. The first experimental results on UCA were presented in [36], where eight radiators were used in the array to generate mode \(l=-1\) (Figure 7). Other types of radiating elements have been further used in the circular arrays, such as horn antennas [38] and single-arm spiral antennas [39].  
Since the vortex beam diverges rapidly along propagation direction, different solutions have been proposed to reduce the divergence angle and increase the gain. For instance, a Fabry-Perot cavity in which a highly-reflective surface served as the superstrate, was numerically evaluated to generate a high gain vortex wave [45]. Afterwards, electromagnetic band-gap (EBG) superstrate was used to achieve high gain OAM vortex waves [46]. Lenses based on the use of metasurfaces [47; 48] and all-dielectric materials [49; 50] have further been applied to reduce beam divergence of OAM vortex waves generated by phased antenna arrays.
## 4 Experiments and Results
### Results
#### 4.2.2 Object Detection as Stream Processing
In summary, the consistent deployment of the same object detection model and the application of identical data preprocessing steps ensure a standardized approach to object detection across all layers. Moreover, network latency is not a relevant factor, as the workloads are input from the same machine. Thus, the primary factor contributing to the observed differences lies in the varying computational resources available in each layer. Differences in hardware capabilities can directly impact the prediction time and detection rate. The computational resources, processing power, memory, and storage capacity play a pivotal role in determining the efficiency and speed of executing resource-intensive tasks associated with object detection. The cloud layer does not showcase accelerated performance despite using a more powerful VM type. Differences in the underlying infrastructure, hardware specifications, or hypervisor configurations between the cloud VM and fog layer VM contribute to the performance outcomes. The edge layer is capable of accommodating a stream processing platform and its applications while exhibiting variations due to more constrained computational environments. \begin{table}
\begin{tabular}{c c} \hline \hline
**Computing Layer** & **Detection Rate** \\ \hline Edge & 4.49 \\ \hline Fog & 35.50 \\ \hline Cloud & 16.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection rate (percentage). Figure 6: Average prediction time per frame.