esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Hybrid Modeling: Integrating Physics Preprocessing with Data-Driven Model  
### Physical and Data Driven Hybrid Model of CPS  
There are two main modeling methods for CPS: modeling based on physical equations (PE) and modeling based on data. PE modeling uses underlying physics relationships to derive its mathematical representation; the main advantages of the PE model are its interpretability and scalability. Interpretability means that the PE model can better understand the modeling process from input to output, which is a white-box model. The causal relationship between parameters and variables is clear. Scalability is reflected in the complex CPS, which is composed of many physical subsystems. Any system of simple to moderate complexity can be established through the use of this method [35]. However, considering the complexity, uncertainty, and time-varying characteristics of CTC working conditions, the physical model is usually simplified [36] to a rough model that is an incomplete representation of the physical process of the real system. This process is time-consuming, expensive, and requires a high level domain expertise for industrial applications.  
The ML method based on data eliminates the limitation of relying on system dynamics knowledge and completely depends on data, meaning that it is suitable for modeling different types of systems. However, as a complex mechanical system, the vibration signal of CTC usually contains a large amount of redundant information, and it is difficult to extract valuable state features without physical preprocessing. The use of appropriate data preprocessing will enable us to improve the prediction accuracy [37]. Therefore, the misuse of data and the DL model may lead to large errors or even obtain results that violate the laws of physics.  
As shown in Figure 2, in order to fully utilize the advantages of PE and DL, a new hybrid CPS model based on physical preprocessing and data is proposed for CTC condition monitoring and reliability analysis. CPS modeling based on PE consists of three steps: raw vibration signal data fusion, data normalization, and feature extraction based on signal processing. Data fusion uses multi-sensor information, data normalization eliminates the influence of data distribution, and feature extraction can obtain valuable features from the original data. Input data \(X\) are transformed into \(\widehat{X}\) after physical preprocessing. The high-value features of the previous step \(\widehat{X}\) are divided into training sets and test sets and then sent into the DL model via CPS data modeling. Through model training and testing, the optimal DL model is used for the online deployment of SPL. The model training loss \(Y_{ML}\) is calculated using the cross entropy loss function. The cross entropy loss function is expressed as:  
\[Y_{ML}=-\sum_{i=1}^{N}p_{i}(x)\log(q_{i}(x)) \tag{1}\]  
where \(N\) is the number of the classes, \(q_{i}(x)\) denotes predictive probability of the input data \(X\) belonging to \(i\)th class, \(p_{i}(x)\) denotes real probability.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 2 Prerequisites
### Successive Cancellation List Decoder
In the CRC-aided settings, a CRC checksum of \(N_{\textit{crc}}\) bits is appended after the \(K\) information bits, and the \(K+N_{\textit{crc}}\) bits are then polar encoded into an \(N\) bit codeword. The decoder output is then the most likely decoding path in the final list that passes the CRC check.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 4 Results
### Transformer Models Results Comparison  
In the transformer-based model, input text was encoded using tokenizers. The models were fine-tuned by varying the batch size. Full performance results are summarized in Table 6.  
The CAMeLBERT model, with a batch size of 64, delivered the best performance among the transformer models, achieving 90.62% testing accuracy along with F1-score, recall, and precision scores of 0.91.  
Class-wise results for CAMeLBERT with batch size 64 are provided in Table 7. Similar to the BiLSTM model, baseet and taweel attained the highest F1-score of 0.96, while rajaz had the lowest at 0.80. The confusion matrix is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Batch** & \begin{tabular}{c} **Training** \\ **Size** \\ **Seconds** \\ \end{tabular} & \begin{tabular}{c} **Validation** \\ **Accuracy** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Testing** \\ **Time (in** \\ **Seconds**) \\ \end{tabular} & \
\begin{tabular}{c} **EarlyStopping** \\ **Epochs** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Accuracy** \\ **(\%)** \\ \end{tabular} & **Recall** & **Precision** & **F1-Score** \\ \hline \multirow{3}{*}{AraBERT} & 32 & 2561.87 & 84.36 & 66.83 & 10 & 85.24 & 0.85 & 0.85 & 0.85 \\  & 64 & 2017.41 & 79.91 & 60.01 & 10 & 79.95 & 0.80 & 0.79 & 0.79 \\  & 128 & 1505.03 & 78.76 & 61.73 & 10 & 78.85 & 0.78 & 0.78 & 0.78 \\ \hline \multirow{3}{*}{Arabic-BERT} & 32 & 2167.33 & 81.44 & 81.31 & 9 & 62.71 & 0.81 & 0.81 & 0.81 \\  & 64 & 1566.64 & 78.93 & 62.03 & 8 & 79.04 & 0.79 & 0.79 & 0.79 \\  & 128 & 1148.03 & 76.08 & 57.26 & 8 & 75.92 & 0.76 & 0.76 & 0.76 \\ \hline \multirow{3}{*}{AraELECTRA} & 32 & 2204.53 & 87.59 & 60.23 & 14 & 87.14 & 0.87 & 0.87 & 0.87 \\  & 64 & 1901.09 & 83.46 & 56.83 & 10 & 83.72 & 0.83 & 0.83 & 0.83 \\  & 128 & 1623.86 & 79.63 & 55.83 & 12 & 79.60 & 0.79 & 0.79 & 0.79 \\ \hline \multirow{3}{*}{ARBERT} & 32 & 2299.88 & 72.92 & 58.11 & 9 & 73.21 & 0.73 & 0.74 & 0.73 \\  & 64 & 1546.04 & 78.01 & 57.02 & 8 & 78.37 & 0.78 & 0.79 & 0.78 \\  & 128 & 1094.12 & 77.57 & 53.76 & 8 & 77.23 & 0.77 & 0.77 & 0.77 \\ \hline \multirow{3}{*}{CAMeLBERT} & 32 & 2431.07 & 90.59 & 35.18 & 12 & 90.26 & 0.90 & 0.90 & 0.90 \\  & 64 & 1769.59 & 90.49 & 54.08 & 12 & 90.62 & 0.91 & 0.91 & 0.91 \\  & 128 & 1232.03 & 90.57 & 52.28 & 12 & 90.21 & 0.90 & 0.90 & 0.90 \\ \hline \multirow{3}{*}{MARBERT} & 32 & 1875.58 & 83.95 & 59.64 & 7 & 83.43 & 0.83 & 0.83 & 0.83 \\  & 64 & 1348.81 & 82.79 & 57.97 & 7 & 82.69 & 0.82 & 0.83 & 0.82 \\ \cline{1-1}  & 128 & 929.73 & 83.07 & 62.54 & 7 & 83.35 & 0.83 & 0.83 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of the transformer models in the proposed study.  
Figure 10: Confusion matrix of the BiLSTM model with 6 layers.
# Ultra-Wideband Bandpass Filters Using Tapered Resonators
###### Abstract  
In this paper, ultra-wideband bandpass filters using tapered transmission line resonators are presented. The proposed filters are based on short-circuited tapered transmission line stubs. The use of tapered transmission resonators resulted in a noticeable length reduction, a wider controlled operational bandwidth, and better stopband characteristics for filter applications. Three filters are designed with different tapered ratios using exponential tapered transmission line resonators. The other three filters are also designed using equivalent linear tapered transmission line resonators. The designed filters are simulated and fabricated on a Roger RT5880 substrate with a 2.2 relative permittivity and 0.78 mm thickness. Approximately, the same performance is obtained using either exponential or linear tapered resonators. A good agreement between the simulated and measured results is reported. The realized 112.22% fractional bandwidth filter has a return loss (S\({}_{11}\)) better than 17 dB and an insertion loss (S\({}_{12}\)) better than 1 dB over the entire UWB spectrum. Notable improvements in stopband characteristics are also achieved.  
short-circuited stubs; tapered transmission lines; tapered resonators; ultra-wideband filters +
Footnote †: journal: Applied Physics Letters
## 2 Proposed SALDL Based on GCM
### GCM-Based Residual Subnetwork for Base Layer Generation  
To realize the proposed GCM for base layer generation, a GCM-based residual subnetwork was designed, as shown in Figure 4. To implement the proposed GCM, as shown in Equation (4), Gaussian filtering was first applied to the input halftoned image. In existing deep learning tools, it can be easily implemented through a convolution layer where the convolution filter is fixed as a Gaussian filter. The Gaussian-filtered halftoned image was passed through the GCM-based residual subnetwork to output the residual layer.  
\[\mathbf{x}^{(\eta_{b})}=f_{\theta}^{\text{GCM}}\Big{(}\mathbf{x}^{i}\otimes\mathbf{k}^{g} \Big{)} \tag{5}\]  
where \(\mathbf{x}^{(\eta_{b})}\) is the predicted residual layer for base layer generation, and \(f_{\theta}^{\text{GCM}}\) denotes the GCM-based residual subnetwork to be trained. Herein, parentheses in superscripts indicate the predicted values. The standard deviation of the Gaussian filter \(\mathbf{k}^{g}\) was set to 1 and the filter size was \(5\times 5\).  
Figure 4: GCM-based residual subnetwork for base layer generation.  
Figure 3: Histogram comparison of two types of residual layers generated using the conventional additive model and the proposed GCM.  
To train \(f_{\theta}^{\text{GCM}}\), a loss function is defined as follows:  
\[L=\frac{1}{m}\sum_{i=1}^{M}\left\|\mathbf{x}_{i}^{(r_{b})}-\mathbf{x}_{i}^{r_{b}}\right\| ^{2} \tag{6}\]  
where \(i\) denotes a training sample, \(M\) is the batch size, and \(\left\|\cdot\right\|\) is the \(l_{2}\)-norm. Compared with the additive model, the proposed GCM-based residual subnetwork can narrow the output range of the residual layer.  
For the pretrained GCM-based residual subnetwork, the base layer was generated as follows:  
\[\mathbf{x}^{(b)}=\mathbf{x}^{(r_{b})}+\mathbf{x}^{i}\otimes\mathbf{k}^{\delta} \tag{7}\]  
where \(\mathbf{x}^{(r_{b})}\) is the output of the pretrained GCM-based residual subnetwork \(f_{\theta}^{\text{GCM}}\), and \(\mathbf{x}^{(b)}\) is the predicted base layer. This equation indicates that the base layer is the sum of the Gaussian-filtered halftoned image added to the predicted residual layer through the GCM-based residual subnetwork. For reference, the entire architecture, as shown in Figure 4, was not trained. Based on heuristic experiments, it was discovered that the learning of the entire architecture did not yield good results.
# Study on Effects of Energy Deposition and Thermal Shock Wave by Electron Beam Irradiation
## 3 Energy Deposition of Electrons in Materials
### Electron Beam Radiation Energy Deposition in Multilayer Structures
#### 3.4.2 Electron Beam Radiation Energy Deposition  
The Monte Carlo method is used to calculate the energy deposition rate of a multi-energy-spectrum electron beam in a multilayer structure under different energies and incident modes (as shown in Figure 11). \(\eta\) is the energy deposition rates, and x is the multilayer thickness, and \(E_{0}\) is energy electrons. Incidence is divided into the normal incidence and oblique incidence, the latter of which has an incidence angle of 40\({}^{\circ}\), with the total energy deposition rate shown in Table 2, which shows the escape energy is the sum of the reflected energy of the front surface and the penetrating energy of the rear surface.  
It can be seen that for the same incident energy, the total energy deposition rate of oblique incident electrons of no more than 0.5 MeV is lower than that of the normal incident electrons, while the total energy deposition rate of the electrons of larger than 0.5 MeV is higher than that of the normal incident electrons. The energy deposition rate of oblique  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Incident Energy (MeV)**} & \multicolumn{2}{c}{**Escape Energy (MeV)**} & \multicolumn{2}{c}{**Energy Deposition Rate (\%)**} \\ \cline{2-5}  & **0\({}^{\circ}\)** & **40\({}^{\circ}\)** & **0\({}^{\circ}\)** & **40\({}^{\circ}\)** \\ \hline
0.200 & 0.004 & 0.012 & 98 & 94 \\
0.400 & 0.020 & 0.036 & 95 & 91 \\
0.500 & 0.049 & 0.063 & 90.2 & 87.4 \\
0.600 & 0.135 & 0.128 & 77.5 & 78.7 \\
0.800 & 0.379 & 0.327 & 52.6 & 59.1 \\
1.000 & 0.636 & 0.550 & 36.4 & 45 \\ \hline \end{tabular}
\end{table}
Table 2: Total energy deposition rate in the multilayer structure.  
Figure 11: Energy deposition rates of different energy electrons in multilayer structures. (**a**) 0.4 MeV. (**b**) 0.6 MeV. (**c**) 0.8 MeV. (**d**) 1 MeV.  
incident electrons in each layer is higher than that of normal incident electrons near the light-facing surface. With the continuous penetration of electrons, the electron energy decreases, and the energy deposition rate of oblique incidence electrons is lower than that of normal incidence electrons.  
For the same incident angle, the total energy deposition rate of electrons with different incident energies is different in the multilayer separation structure. The higher the electron energy, the lower the energy deposition rate in the multilayer structure. The energy deposition rate of the lower-energy electrons in the light-facing surface is higher than that of the higher-energy electrons, while the energy deposition rate in the layer far away from the illuminated surface is lower than that of the higher-energy electrons.
# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
## 2 Materials And Methods
### Data Pre-Processing
Therefore, after carefully examining some of the cases, we noticed that intersections occur in the case of a more general land type associated with the parcel whose areas are much larger than the other intersected parcel. Hence, we discarded the one with the larger area and more general land type. As a result, we ended up with 43,000 shapefiles to further continue our ground truth LUM calculation. Additionally, to ensure that we have enough data, as mentioned in the previous chapter, we applied the quadrats method and quadrat tests to eliminate fishnets without sufficient land registry data for ground truth LUM calculation. As a result, out of 1862 fishnets, 158 of them were discarded from the evaluation. We encountered data limitations as well. First of all, we could only calculate LUM for some fishnet eyes. We could not calculate LUM because there is not a sufficient number of POIs in the fishnet to draw a Voronoi. There must be at least four points to draw a Voronoi. After examining the places with less than four POIs, we saw that green areas, wetlands, or agricultural areas mostly cover those areas. In addition to that, the other reason could be the lack of point-based data, which we collected from an ad listing website for the sale/rent of houses and offices and Google Maps API for various shops, workplaces, and business locations with our best effort. Lastly, we also saw that some areas do not have an official record of the registry on the land registry website. In Figure 3, we depict all the available land registry data, but on the left of the figure, it is clear that there is either an apartment or a villa, but the official sources do not confirm. Although it is an official data source and each parcel indicates registered land according to the land registry office, we discovered issues of collisions between parcels. In Figure 3, it can be seen that one big shapefile overlaps with other small shapefiles. In general, we saw that the bigger shapefile usually has a generic label such as land or field. That is why we decided to discard the shapefiles with a bigger area in the case of an intersection. Figure 3: **(a)** An urban area covered by all the available official land registry shapefiles. **(b)** An example of shapefiles having overlaps with each other, indicating a problem with the official data source, overlapping area zoomed within the blue rectangle for better clarity.
# Federated Learning for Clients' Data Privacy Assurance in Food Service Industry
## 3 Methodology
### Federated Averaging (FedAvg)  
FedAvg [21] is the most common and well-known method used in the implementation of FL to aggregate local weights coming from clients and to update the global model on the server. Generally, FedAvg comprises four steps. In the first step, the global model is sent synchronously from the server to a randomly chosen subset of clients. Then each client, in parallel, performs the gradient descent steps during training on their local data and then updates the model with a determined learning rate, epoch number, and batch size, as shown in (1). After that, each client returns the updated model weights to the server. Finally, the server aggregates the client model weights (2) and updates the global model, sending it back to the clients for the next round. These four steps make one round of communication and are repeated several times until the global model converges. In each communication round, the FedAvg takes a weighted average of the weights from the local models based on the size of each client's local dataset. This means clients with more training data contribute more to the global model update.  
\[w\gets w-\eta\
abla\ell(w;b) \tag{1}\]  
\[w_{t+1}\leftarrow\sum_{k=1}^{K}\frac{n_{k}}{n}w_{t+1}^{k} \tag{2}\]  
In (1), \(\eta\) is the local learning rate, \(w\) is the model weights, \(b\) is the local batch size, and \(\ell()\) is the local loss function. In (2), \(K\) is the total number of selected clients, \(k\) shows each client, \(n\) denotes the total number of data samples, and \(n_{k}\) is the sample size of each client. Despite the simplicity of FedAvg, this algorithm has proven to be not only robust enough to cope with unbalanced data that is not \"independent and identically distributed\" (non-IID) but also able to decrease the frequency and the amount of data transferred to that server that is normally required for training a deep neural network model [21].
# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 2 Materials and Methods
### Secure SASV Module
#### 2.2.4 Anti-Spoofing  
The core objective of this module is to detect fraudulent identity verification attempts resulting from logical spoofing attacks, such as voice cloning or other audio deepfake techniques. The anti-spoofing module also relies on a DNN model trained to determine whether the input audio used during the authentication phase was produced by a genuine speaker or synthesized using voice generation technologies. The DNN model considered for anti-spoofing is based on a Self-Supervised Learning (SSL) upstream model [64], focused on computing an embedding representation of the input audio, and a downstream classifier, trained for the anti-spoofing task. Specific details about the neural architecture can be found in [65]. The trained model produces a score representing the similarity between the input audio and a reference vector representing a set of genuine samples. The computed score is finally encrypted using the public key and returned to the SSI module.  
The anti-spoofing model was trained using the combination of ASVSpoof2019 [66] (111 h of speech from 107 speakers), ASVSpoof2021 [67] (133 h of speech from 107 speakers), and a subset of MLAAD corpus [68] (24 h of selected multilingual spoofed samples). This MLAAD subset was selected to include multilingual information in four languages: Swedish, Greek, Spanish, and French, in order to adapt the model to those specific languages. The training corpus is completed with an additional 24 h of synthetic speech generated using the VITS TTS model [69]. The training process employed the one-class softmax loss function [70], a dropout rate of 0.2 where applicable, and the ADAM optimizer with a learning rate of \(3\times 10^{-4}\).
## 3 Implementation Method of Proposed Macrodiversity System for Maritime Wireless Sensor Networks  
In this section, we introduce the reliable transmission strategies of the macrodiversity reception scheme in detail. First, the frame format and reliable transmission scheme in the sensor node are designed. Second, the detailed steps of identifying the coded frame ID and transmission package ID using majority-voting decision in the receiver are introduced. Finally, a cascaded combining scheme is proposed, and its specific implementation process is explained in detail.
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
## 3 Comprehensive Sensitivity Analysis
### Optimization Parameters
In the optimization, limited by the installation size, the rotor outer diameter is fixed. When the rotor outer diameter and the slot size remain unchanged, the core may be saturated with the change in the stator outer  
Figure 4: Influence curves of single factors on efficiency and thermal load. (**a**) Air gap length; (**b**) core length; (**c**) conductors per slot. diameter. In order to avoid this situation, the optimization range of stator outer diameter is limited between 840 mm and 880 mm. To obtain a suitable back EMF, the variation range of the core length is set from 900 mm to 940 mm. Due to the constraints of the stator and rotor assembly, the mechanical air gap length should not be too small, and if the air gap length is too large, then the magnetic field of the air gap will be small, which does not meet the requirements. Therefore, the variation range of the air gap length is limited to 2-5 mm. If the number of conductors per slot is too small, then it will not meet the efficiency requirements. On the other hand, if the number of conductors per slot is too large, then it will lead to an increased thermal load. Therefore, the number of conductors per slot is set to vary between 8, 10, and 12.
## 6 Network Simulation and Results
### Results  
This section describes the most relevant results, observed in the performed simulations of various jitter distributions (no jitter, \"standard\" jitter and window jitter) under shortest-path RREQ forwarding (Section 6.2.1) and shortest-delay RREQ forwarding (Section 6.2.2) configurations.  
Figure 15: Pdfs for the four examined jitter settings.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations
### Occlusion Attack and Noise Attack Experiments  
The occlusion attack and noise attack are considered and utilized for the robustness analysis of the proposed cryptosystem. To simplify the calculation, the 27 th encrypted band is considered and utilized in occlusion and noise attack, analysis. In fact, the other encrypted band can also obtain the same result. In the occlusion attack experiment, the decryption approach is employed by using the  
Figure 4: The (**a**) original 27 th band and (**b**) the decrypted 27 th band. The peak signal-to-noise ratio (PSNR) value of (**a**) and (**b**) is 295.7293.  
Figure 5: The PSNR curve calculated by different values of parameter \(\alpha\).  
correct key from the partly occluded 27 th band image, which is displayed in Figure 6a. Note that the occluded pixels are substituted with 0 in the experiment, and the corresponding recovered result is presented in Figure 6b. Apparently, the outline information of the secret image is recognizable.  
To complete the noise attack experiment, a noise model is designed as follows:  
\[I^{\prime}(x,y)=I(x,y)[1+p\cdot\sigma_{0,1}(x,y)], \tag{6}\]  
where the function \(I(x,y)\) is the 27 th band of the encrypted data and \(I^{\prime}(x,y)\) denotes \(I(x,y)\) with noise. The function \(\sigma_{0,1}(x,y)\) represents the random data-satisfied uniform distribution. Besides, the parameter \(p\) is the coefficient representing the noise intensity. By using this noise model, the noise data can be added into the secret encrypted 27 th band image with different intensity noise. The corresponding PSNR curve is depicted in Figure 7 by decrypting the data \(I^{\prime}(x,y)\) with various values of \(p\). In addition, the two decrypted images drawn in Figure 7 are calculated with the noise intensity \(p\) = 0.4 and \(p\) = 0.9, respectively. The main information of the 27 th band image can be recognized.  
Figure 6: The experiment result of occlusion attack: (**a**) the occluded 27 th band encrypted image and (**b**) decrypted image.  
Figure 7: The PSNR curve of noise attack including the decrypted 27 th band with \(p\) = 0.4 and \(p\) = 0.9.