esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Thick Level Set Damage Model
### On the Choice of TLS Parameters  
The TLS model is a model with few, but very important parameters to be chosen properly. In fact, only three parameters need to be identified: (i) the damage profile with respect to the level set \(d(\Phi)\); (ii) the characteristic length \(l_{c}\) ; and (iii) the critical energy release rate \(Y_{c}\). Several approaches can be adopted to do so. In the present study, the adopted approach relies on confronting the TLS model with fracture mechanics.  
We start by fixing the damage profile \(d(\Phi)\). Several choices are illustrated in Figure 3. Although the choices are countless, the damage profile is chosen to be a good fit and true to the targeted application. Choosing for instance the fourth order polynomial profile \(d(\Phi)=(\frac{\Phi}{l_{c}})^{4}\) yields a short almost non-existent process zone ahead of the crack tip. One could easily explore more sophisticated profiles such as the arc-tangent profile \(d(\Phi)=c_{1}atan(c_{2}(\frac{\Phi}{l_{c}}-c_{3}))+c_{4}\), illustrating a longer process zone, but should keep in mind that such profiles are tricky to handle. They introduce more parameters \((c_{i})_{i\in\{1,2,3,4\}}\) to be identified and might face numerical integration problems due to the large slack zone. The third order polynomial profile \(d(\Phi)=3(\frac{\Phi}{l_{c}})^{2}-(\frac{\Phi}{l_{c}})^{3}\) is a good intermediate and is adopted throughout this paper following several tests performed prior to this study.  
Next, for a given fracture energy \(G_{c}\), a couple \((Y_{c},l_{c})\) is identified using the relation proven in [23]:  
\[G_{c}=2 AY_{c}l_{c} \tag{16}\]  
with \(A\) being the area under the curve \(d(\Phi)\): \(A=\int_{0}^{l_{c}}d(\Phi)d\Phi\).  
In other words, every couple \((Y_{c},l_{c})\) verifying the above relation leads to the same dissipated energy and the same load-CMOD curve. Generally, the characteristic length \(l_{c}\) is chosen to respect \(l_{c}=3.agg_{max}\) where \(agg_{max}\) is the maximum aggregate size.  
Note that although the mesh size \(h\) is not a parameter of the model, it is highly recommended to chose it significantly smaller than the characteristic length \(l_{c}\) in the problematic zone (\(h\leq 4.l_{c}\)). This ensures the best representation of the transition between the damage and crack initiation.
# A Hybrid Watermarking Approach for DICOM Images Security
## 4 Experiment Results
### Evaluation Metrics
#### 4.1.3 Execution Time Analysis  
Table 6 presents the processing time for embedding the watermark in the RONI region and the processing time for extraction of the watermark from the RONI region after the attacks.  
As shown in Table 6, the execution time for embedding the watermark is, on average, 10.8567 s, and the execution time of the extraction process of the watermark is, on average, 11.9031 s.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Attacks** & \multicolumn{4}{c}{**NC Values (\%)**} & \multicolumn{4}{c}{**BER Values (\%)**} & \multicolumn{4}{c}{**AVG**} & **AVG** \\  & **Img 1** & **Img 2** & **Img 3** & **Img 4** & **Img 1** & **Img 2** & **Img 3** & **Img 4** & \\ \hline Median filtering 3 \(\times\) 3 & 98 & 100 & 98 & 100 & 0.6 & 0.8 & 0.7 & 0.5 & 0.7 & 99 \\ Salt and pepper noise (0.01) & 99 & 97 & 97 & 97 & 0.7 & 0.8 & 0.7 & 0.6 & 0.7 & 98 \\ Salt and pepper noise (0.03) & 98 & 99 & 99 & 99 & 0.7 & 0.6 & 0.5 & 0.5 & 0.6 & 99 \\ Average filtering (3 \(\times\) 3) & 98 & 98 & 99 & 99 & 0.8 & 0.7 & 0.9 & 0.4 & 0.7 & 99 \\ Cropping left top corner (Wa1) & 99 & 99 & 99 & 99 & 0.7 & 0.8 & 0.7 & 0.4 & 0.6 & 99 \\ Cropping left top corner (Wa2) & 97 & 98 & 97 & 96 & 0.8 & 0.7 & 0.7 & 0.8 & 0.7 & 97 \\ Gaussian filtering (3 \(\times\) 3) & 99 & 100 & 100 & 100 & 0.8 & 0.6 & 0.8 & 0.9 & 0.8 & 100 \\ Histogram equalization & 90 & 89 & 97 & 98 & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 & 96 \\ Gaussian noise (0.05) & 99 & 99 & 98 & 98 & 0.6 & 0.5 & 0.5 & 0.5 & 0.5 & 99 \\ Gaussian noise (0.01) & 98 & 99 & 99 & 99 & 0.4 & 0.4 & 0.4 & 0.6 & 0.5 & 99 \\ Gaussian noise (20) & 99 & 99 & 9
## 2 Related Work  
Smart grid applications [6; 7; 8; 9; 10; 11; 12; 13; 14; 15] can use state-of-the-art cellular technology to facilitate communication of power system components over IP-based networks such as the Internet. The communication performance should be validated to find out if the real-time requirements are met. This is particularly important for communication-based protection applications which require R-GOOSE messages to be communicated strictly during certain times.  
In [10; 11], authors analyzed the communication delay of unicast R-GOOSE messages published from ABB equipment and transmitted over private 5G communication and tested network infrastructure. In this paper, a software application is developed to publish multicast R-GOOSE messages exchanging over the Internet by utilizing the commercial 5G network. Since R-GOOSE messages are multicast messages, the network architecture solutions are also implemented for communicating these messages over the Internet, and associated delays are measured. While in [10; 11], authors did not discuss securing R-GOOSE messages, our developed software application can generate secured R-GOOSE messages. Moreover, the secure communication path is also established for multicast R-GOOSE communication, and the delays imposed by applying security mechanisms (additional communication headers and processing times) are also considered. This paper also measures communication delay for secured R-GOOSE messages transmitted in secure communication paths. Our measurement results are presented in terms of statistical key values (1 st centile, mean, and 99 th centile), instead of presenting them as plain scalar values, and interpreted for Logic Selectivity and Loss-of-Main protection schemes.  
To measure communication delay, a synchronized time reference is required between sender and receiver devices. In [10; 11; 14; 15], authors measured communication delay while using a single time server for sender and receiver devices. The authors of [14; 15] measured delay by using the system time without using the external time synchronization setup. In [10; 11], authors utilized time synchronization, but both sender and receiver devices are synchronized with the same \"Stratum 0\" [16] device. This paper proposes a time synchronization system which can use multiple Stratum 0 devices instead of just one. In our proposed system, time synchronization is expandable and not tied to the device locations. Thus, our setup can be used for measuring delay not only inside the laboratory but also in the field.
# A Survey of Photoplethysmography and Imaging Photoplethysmography Quality Assessment Methods
## 4 SQI for cPPG
### Rule-Based  
In this section, we focus on the SQI algorithm as a set of criteria computed from the PPG signal. These criteria rely on the expected behavior or on specific properties (resulting from statistical analysis). These criteria reflect morphological features (see Section 4.1.1  
Figure 5: Example of application of an SQI assigning a quality score or label to filter irrelevant data. In this figure, an SQI assigns a “bad-quality” label (in red) for a noisy PPG signal or a signal affected by a motion artefact to be suppressed and a “good-quality” label (in green) for the signals to be kept in the dataset.  
and Figure 6). The SQI consists here in verifying if their values or variations are within a plausible physiological range. These physiological ranges are defined from a statistical study from the literature or datasets. They can also consist in the detection of events unlikely to have a physiological origin, such as clipping [44; 45; 65; 66], i.e., truncation due to the saturation of the signal. The stability of the PPG waveform is also an important property of the signal, used as a criterion to evaluate its quality.
# Single-Pixel Color Imaging Method with a Compressive Sensing Measurement Matrix
## 5 Conclusions  
A pseudo-random loop measurement matrix based on chaotic sequence is designed by utilizing the external determinism and inherent randomness of chaotic sequences. Compared with other deterministic measurement matrices, it has a remarkable recovery effect and advantages in hardware integration. On the basis of the integration of this measurement matrix, a single-pixel imaging method is proposed in this paper, which can accurately reconstruct the RGB image. Image restoration and reconstruction methods based on FPGA will be investigated in future research to improve the imaging rate of the single-pixel camera and achieve real-time video filming function.  
Conceptualization, [PERSON]; Methodology, T.J. and D.C.; Software, J.W.; Validation, D.X.; Formal Analysis, T.J.; Investigation, T.J.; Writing-Original Draft Preparation, T.J. and D.C.; Writing-Review & Editing, J.W. and D.X.; Visualization, J.W.; Supervision, T.J.; Project Administration, T.J.; Funding Acquisition, T.J.  
This research was funded by the National Natural Science Foundation of China under Grant U1613214 and party funded by Shenyang City Science and Technology Plan under Grant F16-2051-04 by the 111 Project under Grant B16009.  
This research was supported by the National Natural Science Foundation of China under Grant U1613214 and partly supported by the Fundamental Research Funds for the Central Universities of China under Grant N170402008, by the Fund for the Shenyang City Science and Technology Plan under Grant F16-2051-04. We would like to thank the Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry.  
The authors declare no conflicts of interest.  
Figure 8: Results obtained by single-pixel color camera, and the target image is colored Wi-Fi logo: (**a**) recovered images using LCS-MM, PSNR = 23.40 dB, SSIM = 0.13; (**b**) recovered images using LCS-CMM, PSNR = 20.52 dB, SSIM = 0.07; (**c**) recovered images using LCS-PRC-MM, PSNR = 23.81 dB, SSIM = 0.14.
# Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising
## 2 Review on Time-Series Forecasting Studies
### Artificial Intelligence Model (AI)  
Artificial Intelligence (AI) refers to a machine that can mimic or act like a human when provided with the human-like capabilities such as perception, manipulation, communication, learning, and problem-solving [105]. Comparing to the statistical models, the AI model learns from past data to improve its performance instead of identifying the optimal parameters based on probability as in the statistical models. In addition, the AI models are widely applied in time-series forecasting to identify the hidden data patterns to make an accurate approximation [8]. Two subcategories of AI models are the Machine Learning (ML) models and Deep Learning (DL) models.
# Insights from the Experimentation of Named Data Networks in Mobile Wireless Environments
## 4 Experimentation Setup and Results
### NDN Using Multiple Radio Wireless Interfaces
Despite this increase in performance, it is clear that trying to retrieve content in a bulk, as is the case of the pipelined application, can be detrimental to the behavior of the network, and thus presents significant challenges to overcome which are not entirely related to the architecture itself, but instead are affected by it. The results show that there are grounds to believe that the use of several communication technologies to forward content simultaneously is beneficial to the well fare of architectures using NDN communications in mobile networks.
## 3 Materials and Methods
### Introduction of EcaNet  
Attention mechanisms have been proven to be an effective method to improve deep convolutional neural networks; channel attention, in particular, plays an extremely important role. Attention mechanisms such as SENet [30] and CBAM [31] have obtained effective performance through complex structural design.  
Based on YOLOX, this paper uses the effective channel attention module of the EcaNet (effective channel attention for deep convolutional neural networks) [32] deep convolution neural network. Different from the above attention mechanism, the purpose of ECA is to learn effective attention channels while reducing model complexity.  
In channel attention, there is local periodicity of channel features, and dimensionality reduction can negatively affect the network's ability to learn inter-channel relationships. The changes in channel dimension and the interactions between channels can affect the performance of channel attention to some extent. Keeping the channel dimension constant supports network learning. The effective attentional cross-channel interaction can reduce network complexity without performance loss. ECA reduces the complexity of the algorithm and improves the efficiency of learning attention by adding local cross-channel interaction and channel sharing parameters. The channel weights are calculated as follows:  
\[\omega_{i}=\sigma(\sum_{j=1}^{k}\alpha_{j}y_{i}^{j})\,\ y_{i}^{j}\in\Omega_{i}^{k} \tag{2}\]  
Figure 2: The detailed structures of DCoTNet.  
where \(\Omega_{i}^{k}\) is the \(k\)-domain channel of \(y_{i}\), \(y_{i}\) is the feature representation of channel \(i\) after global average pooling, \(\alpha\) is the shared parameter, \(\omega_{i}\) is the weight of channel \(i\), and \(\sigma\) is the Linear rectification function.  
The \(k\) value as a key parameter can be adjusted to determine the range of interaction between channels by adjusting its own size, and the range of interaction increases with the increase in channel dimension. Assuming that the interaction range, \(k\), is proportional to the channel dimension, \(C\), the relationship between them can be extended to a nonlinear one, as in the formula:  
\[C=\varnothing(k)=2^{(r\times k-b)} \tag{3}\]  
The \(k\) value denotes the convolution kernel size, which can be calculated adaptively as:  
\[k=\left|\frac{log_{2}C+b}{\gamma}\right|_{odd},\ \gamma=2,\ b=1 \tag{4}\]  
In the formula, \(C\) is the number of channels, \(\left|\cdot\right|_{odd}\) means to take the nearest odd number, \(\gamma\) and \(b\) are the hyperparameters, and the best results are obtained by experiments with \(\gamma=2\), \(b=1\).  
The ECA operation is expressed as:  
\[\text{Eca}(x)=\sigma\Big{(}Conv_{1d}^{1\times 1}(\text{GAP}(x))\Big{)}\times x \tag{5}\]  
In the formula, GAP denotes the global average pooling operation, and \(x\) is the input tensor.  
Figure 3 shows the general illustration of the EcaNet method, where the EcaNet module is set between the backbone network and the enhanced feature extraction network of YOLOX after experimental validation.
## 3 Results and Discussion
### Synthesis of AgNPs and Ag(II)PRO Nanoparticles
We have synthesized the high-valence Ag(II)PRO nanoparticles by a previously published method from our group [20; 21]. The 20 nm size of the Ag(II)PRO nanoparticles was achieved by altering the water-to-surfactant molar ratio (R). The microemulsion composition containing a ternary system of water/AOT/heptane with R = 10, the weight ratio of microemulsion composition should be 2.4/6.0/91.6 (water/AOT/heptane). The freshly synthesized Ag(II)PRO chemical and high-valence silver nanoparticles was shown in Figure S2a. The shape and size of the Ag(II)PRO nanoparticles were confirmed by TEM and particle size distribution which are shown in Figure S2b and Figure 4b. These studies confirmed that the synthesized Ag(II)PRO nanoparticles size were 20 nm. The universally accepted method UV-Vis absorption spectroscopy is used to confirm the Ag(II)PRO nanoparticles and it showed strong absorption peak at 270 nm with characteristic peaks at 320 nm as shoulder peak (Figure 4a). The absorption pattern of Ag(II)PRO nanoparticles is different from the starting material propamidine ligand. The stability of the synthesized Ag(II)PRO nanoparticles was also confirmed by the UV-Vis spectroscopy. There was no significance difference between the freshly synthesized Ag(II)PRO nanoparticles and one-month aged nanoparticles stored under room temperature conditions. Figure 3: XPS (X-ray Photoelectron Spectroscopy) spectrum of the high-valence Ag(II)PRO complex. To compare the antibacterial activity we have prepared silver nanoparticles (AgNP) by using co-reduction method to get the 20 nm size of the silver nanoparticles [28]. The co-reduction method typically involves the two different reducing agents, sodium borohydrate used as primary reducing agent and trisodium citrate used as secondary reducing agent. It also serves as the stabilizing agent for the nanoparticles. The reduction was achieved at two different temperature conditions during silver nanoparticles synthesis. In this method, we have mixed required volume of sodium borohydrate and trisodium citrate in conical flask and heated to 60 \({}^{\circ}\)C for 30 min with vigorous stirring. After 30 min the aqueous solution of silver nitrate was added dropwise to the mixture and temperature was increased to 90 \({}^{\circ}\)C. Then the pH was adjusted to 10.5 by using 0.1 M sodium hydroxide. The stirring was continued until the color was changed to yellow which indicated that the silver nanoparticles are formed (Figure S2c). As shown in Figure 4c, UV-Vis spectroscopy exhibits a sharp absorption peak at 398 nm wavelength corresponds to 20 nm size of silver nanoparticles. The size and morphology of AgNPs were confirmed by TEM (Figure S2d and Figure 4d). In this case, the particle size of 20 nm silver nanoparticles were synthesized and compared the antibacterial activity with similar size (20 nm) of the Ag(II)PRO nanoparticles.
## 3 Introduction of Basic Principles
### Residual Gated Tanth Unit  
Residual gated tanh module is a gating unit based on residual logic. The specific structure is shown in Figure 5a. Each unit is composed of multiple blocks connected in series. The data are processed by stacking multiple units in series. The block is composed of two linear layers, a sigmoid layer, and a Tanth layer, with residual mechanisms added. Within the block, the data are mapped at different latitudes by two Linear layers, and then pass through Tanh layer and Sigmoid layer respectively. Among them, Sigmoid layer is one of the common Activation functions in neural networks, commonly used for hidden layer neuron output, with a value range of (0,1). It can map inputs to the interval of (0,1). This method is the core of the gating mechanism, which can realize Feature selection of multi-layer data. If the output of Sigmoid is closer to 1, the data information of that layer is retained more. If the output of Sigmoid is closer to 0, the changed data information is more  
Figure 4: The flowchart of data repairment.  
discarded. In addition, to improve the efficiency of multi-layer model, residual structure is added to the element. The calculation formula of the gating unit is as follows:  
\[\mathrm{h}_{l}(X_{l})=\tanh(X_{l}*W_{l}+b_{l})\otimes\sigma(X_{l}*V_{l}+c_{l}) \tag{4}\]  
where \(X_{l}\) and \(\mathrm{h}_{l}(X_{l})\) is the \(l\)-th unit input and output, \(\mathrm{h}_{l}(\cdot)\) is the function of the \(l\)-th unit, \(W_{l}\) and \(V_{l}\) are the weight of two linear layers, \(b_{l}\) and \(c_{l}\) are the offset of two linear layers, \(\sigma\) represents the Sigmoid algorithm, and tanh symbolizes the hyperbolic tangent function.
## 3 Numerical Model
### Model Establishment  
As shown in Figure 10, a two-dimensional numerical model of 150 m \(\times\) 54.43 m is established according to the engineering geological background of the strap joint chamber of Tashan Coal Mine. The model mesh size is between 0.5 and 1.5 m, and the Hoek-Brown strain softening model is adopted for the coal and rock mass. The node of the model is 23400. The horizontal displacement on the two sides of the model is constrained, while the vertical displacement on the bottom boundary is constrained. A uniform vertical load of 12.5 MPa is applied on top to replace the rock mass overlying 500 m. The edge of large-section chamber is 70 m away from the two-side boundaries, which is enough to eliminate the boundary effect.
# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 4 Experimental Scheme
### Experimental Evaluation Scheme  
In order to comprehensively evaluate the performance of the model, this paper chooses commonly used evaluation metrics of the YOLO algorithm, including precision, recall rate, average precision (AP), mean average precision, and F1 score. These metrics are based on many predicted results, which are statistically classified and calculated. The predicted results in the statistical process can be divided into four categories: true positive (_TP_) for correctly predicting positive samples, false negative (_FN_) for wrongly predicting negative samples, true negative (_TN_) for correctly predicting negative samples, and false positive (_FP_) for wrongly predicting positive samples. Here, positive samples represent targets, while negative samples represent backgrounds.  
(1) Precision  
The precision means the proportion of true positives among all samples classified as positive, which indicates the accuracy of the predictions. The calculation formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{2}\]  
(2) Recall rate  
The recall rate indicates the proportion of being correctly classified as true positives among all actual positive samples, indicating whether detected targets are complete or not. The calculation formula is as follows:  
\[R=\frac{TP}{TP+FN} \tag{3}\]  
(3) AP  
The average precision can evaluate the comprehensive performance of the detection accuracy and recall rate of the model on different categories. Based on the area under the precision-recall (_PR_) curve, it is calculated. The value of the average accuracy ranges from 0 to 1, where a higher value indicates a better detection performance.  
(4) mAP  
Figure 8: Training loss variation curves.  
As an important indicator for measuring the performance of the target detection algorithm, the mean average precision indicates the comprehensive calculation of precision at different recall rates for each category. The calculation formula is as follows: \(n\) represents the number of categories, \(m\) represents the number of targets under this category, and \(P(r)\) means that the precision value of the recall rate is \(R\). The higher the \(mAP\) value, the better the performance of the algorithm in detecting targets of each category.  
\[mAP=\frac{1}{n}{\sum}\bigg{[}\frac{1}{m}{\sum}P(r)\bigg{]} \tag{4}\]  
(5) F1 score  
The F1 score considers both precision and recall, and it is the harmonic mean of precision and recall. The calculation formula is as follows:  
\[F1=\frac{2\times P\times R}{P+R} \tag{5}\]  
The above indicators can evaluate the model in terms of detection accuracy, positioning accuracy, and other aspects, thereby expressing the overall performance of the model. In addition, the FPS index is required to evaluate the detection speed of the model. The FPS value represents the number of images that the model can process per second.