esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Circuit with ASK Modulation
### La Calculation  
To ensure high transmission efficiency, high data transmission rates, and low error rates, the choice of \(L_{\text{a}}\) is constrained by the range of \(\varphi\) and MI. The calculation procedure of \(L_{\text{a}}\) is given below based on these two restriction requirements.  
In ASK modulation, the MI of the output voltage is defined as follows.  
\[\text{MI}=\frac{V_{\text{out}\_H}-V_{\text{out}\_L}}{V_{\text{out}\_H}+V_{ \text{out}\_L}} \tag{16}\]  
The receiver coil's output voltage amplitudes are denoted by \(V_{\text{out}\_H}\) and \(V_{\text{out}\_L}\), which corresponds to high-level data and low level data, respectively.  
According to the above discussion, the optimally tuned point for Class-E PA is defined when the data are at a high level. Assuming the load angle is \(\varphi_{1}\) in this state, \(\varphi_{1}\) is approximately \(49^{\circ}\) in the optimally tuned state. By inserting the value of \(\varphi_{1}\) into (14), the voltage of \(R_{\text{eq}}\) is obtained as follows:  
\[V_{\text{eq}\_m}\approx 1.07V_{\text{dc}} \tag{17}\]  
By substituting Equation (17) into Equation (15), the \(V_{\text{out}\_H}\) is:  
\[V_{\text{out}\_H}=\frac{1}{k}\sqrt{\frac{L_{\text{r}}}{L_{\text{t}}}}(1.07V_{ \text{dc}}\frac{R_{\text{ref}}}{R_{\text{eq}}}) \tag{18}\]  
When the data are low-level, the inductor \(L_{\text{a}}\) is added to the Class-E PA's load network, and the load angle is assumed to be \(\varphi_{\text{a}}\). Using Equation (13), the \(\tan(\varphi_{\text{a}})\) is given as follows:  
\[\tan(\varphi_{\text{a}})=\frac{\omega_{0}(L_{\text{1}}+L_{\text{a}})-(\omega_ {0}C_{\text{t}})^{-1}}{R_{\text{eq}}}=\tan(\varphi_{1})+\frac{\omega_{0}L_{ \text{a}}}{R_{\text{eq}}} \tag{19}\]  
By inserting Equations (19) and (14) into Equation (15), the output voltage \(V_{\text{out}\_L}\) is obtained as follows:  
\[V_{\text{out}\_L}=\frac{1}{k}\sqrt{\frac{L_{\text{r}}}{L_{\text{t}}}}\bigg{[} 1.07V_{\text{dc}}\frac{R_{\text{ref}}}{R_{\text{eq}}}\exp(\frac{-0.47\omega_{ 0}L_{\text{a}}}{R_{\text{eq}}})\bigg{]} \tag{20}\]  
Combining with Equations (16), (18) and (20), the value of \(L_{\text{a}}\) is deduced as below.  
\[L_{\text{a}}=\frac{-R_{\text{eq}}}{0.47\omega_{0}}\ln\bigg{(}\frac{1-\text{ MI}}{1+\text{MI}}\bigg{)} \tag{21}\]  
The MI is proportional to \(L_{\text{a}}\)'s magnitude. To achieve a low BER, MI should be maximized, favoring schemes like OOK modulation. However, higher MI results in reduced power transmission efficiency. Therefore, for efficient WPDT system transmission, MI is typically set at 10% [29].
## 4 Experimental Validation and Discussion
### STFA-PD of Vibration Signals of Rolling Bearings
Figure 9: TFIs obtained using the PWVD from bearing vibration signals of different severity degrees: (**a**) 0.000 in, (**b**) 0.007 in, (**c**) 0.014 in, and (**d**) 0.021 in. Compared to Figures 8-10, Figure 11 shows that the time-frequency concentration and resolution of the STFA-PD were higher than the STFT, PWVD, and WT methods, and there was no cross-term interference problem. Moreover, the STFA-PD filtered out a lot of noise, which made the characteristics of the high-frequency impact more obvious. The differences between the TFIs of different fault severity degrees were higher than those obtained using the other three methods. As a result, the STFA-PD can better express the time-frequency characteristics of the signals, and thus, build a good foundation to extract the texture features for an effective characterization of bearings with different fault severity degrees.
## 1 Introduction
Our algorithm introduces a dynamic grouping mechanism with a threshold that is associated with feedback information from agents. At the same time, we add the exploration probability \(c\) that is set to ensure the adaptability to different environmental datasets. DG-MOSS makes full use of the number of arms, rewards are contained in user feedback, and the data with different densities are in different value ranges. It adaptively and dynamically adjusts the balance of exploration and exploitation through the change of full utilization of feedback from agents. Only arms whose error falls within an allowable range are added to the dynamic grouping and simultaneously provided as multiple actions for selection. Moreover, our algorithm does not require additional context information, ensuring the generalization of various data. However, making full use of feedback during training requires sufficient training episodes, which poses a challenge for reducing the training time. Therefore, this paper mainly focuses on the SMAB problem with EE in recommendation, and our main contributions are summarized as follows. 1. We propose a new dynamic grouping within the minimax optimal strategy in the stochastic case (DG-MOSS) algorithm for reinforcement learning and providing multiple action selections. The algorithm uses dynamic grouping to ensure the balance of exploration and exploitation, and fully considers the feedback information in the selection process in a sufficient training recommendation. 2. This paper presents the design of an adaptive episode length to effectively improve the training efficiency so that the parameters of the episode in the training are automatically rather than manually adjusted. 3. We analyze and prove DG-MOSS's upper bound of the regret value, which provides strong theoretical support for the feasibility of the algorithm. 4. Extensive experiments were conducted on four different scales, densities, and field datasets with total rewards and average rewards evaluation metric settings. The experimental results demonstrate that the proposed approach outperforms nine baseline competitors. Furthermore, random attacks and average attacks prove that DG-MOSS has better robustness with sufficiently trained recommendation.
# Routing Design for Pipe System Considering Natural Frequency
## 3 Adaptive Decomposition of Routing Space
### The Adaptive Decomposition of Routing Space  
As depicted in Figures 4 and 5, the routing space is represented as a rectangle, while equipment and prohibited pipe areas are represented by individual rectangles or their combinations. During the decomposition process, we assume that the space has already been randomly divided based on the diameter (\(d_{i-1}\)) of the (\(i\)-\(1\))-th pipe. When selecting the \(i\)-th pipe with a diameter of \(d_{i}\), the routing space is re-decomposed using an integer value n, which can be expressed by the following equation:  
\[n=int\bigg{(}\frac{d_{i}}{d_{i-1}}\bigg{)} \tag{1}\]  
Figure 4: Adaptive decomposition (\(n>1\)).  
Figure 5: Adaptive decomposition (\(n<1\)).  
As shown in Figure 4, the adaptive grid decomposition method is illustrated. When the value of n is greater than 1, the adjacent \(n\,*\,n\) grids in the original grid system are examined. If there are obstacles present within these original grids, the corresponding new grid is considered obstructed and marked with a red rectangle. Conversely, if the original grid is free from obstacles, the corresponding new grid is considered unobstructed and represented by a green rectangle. When \(n\,<\,1\) (Figure 5), the original grid will be disassembled into \(n^{-1}\,*\,n^{-1}\) grids for judgment. If there are obstacles in the original grid, the new \(n^{-1}\,*\,n^{-1}\) grids need to be identified. If the original grid is unobstructed, the new grid is considered unobstructed. This process helps identify and differentiate obstructed and unobstructed regions within the routing space.  
By employing this method, the need to repeatedly decompose the routing space for different pipe diameters is eliminated, resulting in improved algorithm efficiency. Figure 6 demonstrates the outcomes of obstacle identification in an aero engine using the proposed adaptive decomposition algorithm. The final grid size achieved by this algorithm is 4 mm, and the entire process takes a total of 1461 s using a 2.9 GHz CPU computer. Compared with traditional cell decomposition, the efficiency of obstacle identification has been improved more than 50%.
###### Abstract
To take account of the EG, as presented in Figure 2b, the existing analytical approaches for the joint with EG are mainly based on the assumption that the concrete is much more rigid than EG, and the deformations of EG are much greater than those of the concrete. Therefore, only the compressive strains of EG are taken into consideration, while the compressive strains of the concrete attached to the EG are neglected [19; 20; 21; 22; 23; 24]. In sum, this assumption only considers the stress generated by the compression of the EG instead of the concrete-EG-concrete composite structure. However, according to the constitutive model of EG, the rigidity of EG tends to be increased rapidly with increasing the compressive stress, and the EG can no longer be considered as a flexible material in this condition [16; 25; 26]. This leads to the result that, when the joints are subjected to large loads, the joint rotational stiffnesses calculated by the analytical approaches cannot coincide well with the numerical results [22; 23; 24]. Therefore, the deformations of the EG and the attached concrete should be both taken into consideration in the analytical approach to calculate the joint rotational stiffness. Figure 2: Analytical approaches for the segmental joints: (**a**) joint without EG and (**b**) joint with EG. This study aims to propose an improved analytical approach for the joint rotational behavior with fully consideration of the combined work of EG and concrete. To verify the proposed approach, the analytical results are compared with the experimental results and numerical results. Based on the analytical results, the bearing behavior of the segmental joint with EG is analyzed and compared with that of the segmental joint without EG, and the effect of EG on the joint bearing performance is clarified.
# An Adaptive and Secure Holographic Image Watermarking Scheme
## 6 Conclusions  
Based upon the entropy and edge entropy, an adaptive secure image watermarking method that inserts the encrypted hologram into the SFLCT domain is proposed in this paper. Without using the host image, the watermark can be extracted by using the presented method.  
A novel chaos-based binary CGH encryption technique which provides a huge key space is developed to fabricate a hologram of a watermark. Compared with the encryption techniques based on conventional optical holography, the advantages of the proposed method are: (1) the parameters of chaotic maps which are used as keys make it easy to save and distribute the keys expediently and safely; (2) the proposed CGH cryptosystem has the advantage in being implemented effectively by the use of a computer. By using the initial values and the parameters of chaotic system as secret keys, the security strength of the watermarking approach is heightened.  
In the presented method, the use of entropy and edge entropy helps to choose the suitable embedding positions adaptively for satisfying the invisibility and robustness requirements of the watermarked image. The encrypted hologram watermark is embedded into the SFLCT coefficients with Schur decomposition. The experimental results illustrate that our scheme is not only secure and transparent, but also robust against various kinds of attacks including filtering, JPEG compression, occlusion, unsharp, brighten, darken, blurring, rotation, rescaling and painting attacks, etc.  
C.Y., J.L., X.L., and X.C. commonly finished the manuscript. All authors have read and approved the final manuscript. Funding acquisition, C.Y. and J.L.; Investigation, C.Y. and X.C.; Methodology, C.Y. and J.L.; Validation, C.Y., X.L. and J.L.; Writing--original draft, C.Y. and J.L.; Writing--review & editing, X.L. and J.L.  
This research was funded by the Natural Science Foundation of Guangdong Province under Grant No.2018A0303070009 and No. 2014A030310038), and the Educational and Commission of Guangdong Province under Grant (No. 2015 KTSCX089 and 2018).  
The authors are grateful for the reviewers' careful, unbiased and constructive suggestions.  
The authors declare no conflict of interest.
# LDPC-Coded CAP with Spatial Diversity for UVLC Systems over Generalized-Gamma Fading Channel
## 2 CAP-Based UVLC System
### System Model
After DC bias is removed, the effective SNR per received symbol is [26]  
\[\gamma_{\text{EGC}}^{\text{eff}}=\frac{1}{1+g^{2}}\gamma_{\text{EGC}}=\frac{ \overline{\gamma}\Big{(}\sum_{i=1}^{N}\alpha_{i}\Big{)}^{2}}{N} \tag{8}\]  
where  
\[\overline{\gamma}=\frac{(\eta Ps)^{2}\text{E}\Big{[}x^{2}(t)\Big{]}}{(1+g^{2 })\sigma^{2}} \tag{9}\]  
where \(\overline{\gamma}\) denotes the average SNR per symbol. At the receiver, using two matched filters that were the time-reversed versions of transmitter filters, \(y(t)\) was separated into two orthogonal signals, followed by downsampling, QAM demapping, and LDPC decoding. The transmitted bits were then compared with the decoded bits to estimate the BER.
## 3 Results
### Feature Inference and Extraction from Non-Etched Metallographic Microstructures
#### 3.2.4 Classification of Quality  
Prior to [7], quality evaluations were performed by a metallography expert. Then, they were refined using computational techniques that combine CV and ML. Specifically, thanks to the curve analysis, which is a method that employs the slope of characteristic curves derived from morphological features, samples were classified into quality category ranges (see Section 2.3 for more information).  
Table 7 illustrates the classification results obtained by both the curve analysis method and the ML approach, alongside the corresponding error percentages relative to expert evaluations. The data reveal that the curve analysis method achieves an error rate of approximately 1% for the \"very good\" and \"good\" categories, indicating a high degree of concordance with expert assessments in these top-tier classifications. Notably, for the \"regular\", \"bad,\" and \"very bad\" categories, the curve method exhibits perfect agreement with expert classification, with zero error reported.  
In contrast, the ML method shows slightly higher error percentages in the upper quality classes, that is, 4.2% for \"very good\" and 3.7% for \"good\", suggesting that while machine learning captures broader classification trends effectively, it is somewhat less precise near category boundaries. However, for the middle- and lower-quality classes, ML performance is comparable to that of the curve-based method.  
This result indicates that the curve analysis method provides a robust and interpretable tool for quality classification. The ML approach, despite its obtained accuracy, may benefit  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Mean Absolute Error (\%)** & **Expert Reference** \\ \hline \hline Nodule count & 1.0 & Manual counting \\ Average diameter & 1.1 & Calibrated manual measurement \\ Roundness & 1.2 & Visual expert assessment \\ Circularity & 1.3 & Visual expert assessment \\ Aspect ratio & 1.2 & Visual expert assessment \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of extracted morphological features compared to expert measurements [7].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Quality** & & **Curve Analysis** & **Curve Error \%** & **ML** & **ML Error \%** \\ \hline \hline Very good & 91 & 89 & 1 & 94 & 4.2 \\ \hline Good & 112 & 110 & 1 & 108 & 3.7 \\ \hline Regular & 62 & 62 & 0 & 62 & 0 \\ \hline Bad & 34 & 34 & 0 & 34 & 0 \\ \hline Very bad & 27 & 27 & 0 & 27 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quality calculation, with the curve method and ML percentage error.  
from further training or feature engineering to reduce misclassifications in the higher-quality range. Importantly, both methods maintain high fidelity in identifying lower-quality samples, where microstructural deviations are typically more pronounced.
# Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach
## 4 Experimental Results and Discussion
### Evaluation Metrics  
A confusion matrix is a performance evaluation table used for classification models. It summarizes the number of correct and incorrect predictions across different classes, providing insights into model performance. The structure of the confusion matrix is presented in Table 2.  
Accuracy is a measure of the proportion of instances that are correctly classified [58]. Mathematically expressed:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool Operation** & **Description** & **Speed (RPM)** & **Feed (mm/s)** & **Duration (s)** \\ \hline OP00 & Step drill & 250 & \(\approx\)100 & \(\approx\)132 \\ OP01 & Step drill & 250 & \(\approx\)100 & \(\approx\)29 \\ OP02 & Drill & 200 & \(\approx\)50 & \(\approx\)42 \\ OP03 & Step drill & 250 & \(\approx\)330 & \(\approx\)77 \\ OP04 & Step drill & 250 & \(\approx\)100 & \(\approx\)64 \\ OP05 & Step drill & 200 & \(\approx\)50 & \(\approx\)18 \\ OP06 & Step drill & 250 & \(\approx\)50 & \(\approx\)91 \\ OP07 & Step drill & 200 & \(\approx\)50 & \(\approx\)24 \\ OP08 & Step drill & 250 & \(\approx\)50 & \(\approx\)37 \\ OP09 & Straight flute & 250 & \(\approx\)50 & \(\approx\)102 \\ OP10 & Step drill & 250 & \(\approx\)50 & \(\approx\)45 \\ OP11 & Step drill & 250 & \(\approx\)50 & \(\approx\)59 \\ OP12 & Step drill & 250 & \(\approx\)50 & \(\approx\)46 \\ OP13 & T-slot cutter & 75 & \(\approx\)25 & \(\approx\)32 \\ OP14 & Step drill & 250 & \(\approx\)100 & \(\approx\)34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tools operations collected from M01, M02, and M03 [57].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Actual Positive (1)** & **Actual Negative (0)** \\ \hline Predicted Positive (1) & True Positive (TP) & False Positive (FP) \\ Predicted Negative (0) & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix structure.  
The receiver operating characteristic (ROC) curve is a graphical representation used to evaluate the performance of a classification model, especially in binary classification problems. It plots the true positive rate (TPR) against the false positive rate (FPR) at various threshold levels, showing the trade-off between sensitivity and specificity.  
TPR: Measures how well the model correctly identifies positive instances. Mathematical expression:  
\[TPR=\frac{TP}{TP+FN} \tag{15}\]  
FPR: Measures the proportion of negative instances that are incorrectly classified as positive. Mathematical expression:  
\[FPR=\frac{FP}{FP+TN} \tag{16}\]
## 2 Related Work
### Propaganda Detection at Document and Sentence Level  
Historically, propaganda detection focused primarily on document-level analyses, often labeling entire news articles based on source credibility [17]. [PERSON], et al. [30] introduced a neural network model with language infusion to distinguish verified versus uncertain tweets, highlighting the linguistic complexity of social media data. Similarly, Proppy and Character \(n\)-gram models were early attempts to automate propaganda identification in online news [7]. However, relying solely on the source of publication has limitations, as even propagandistic outlets occasionally publish factual content, and credible outlets may occasionally disseminate biased information [11]. This realization spurred a shift toward finer-grained analysis.  
Contemporary methods increasingly focus on sentence-level classification. Recent models, such as fine-tuned BERT variants, address these challenges by incorporating context-aware embeddings [23]. [PERSON], et al. [31] linked CNNs with GloVe embeddings to detect propaganda fragments, while [PERSON], [PERSON], and [PERSON] [30] proposed heterogeneous models integrating social media interactions with linguistic features.  
Moreover, residual bi-directional LSTM architectures combined with ELMO embeddings have demonstrated improvements in capturing contextual dependencies [32]. These innovations significantly advance the granularity and reliability of propaganda detection tasks. However, despite these advances, most current models are constrained by the training dataset's biases and fail to generalize effectively across domains. Furthermore, there is a lack of models that simultaneously exploit syntax, pragmatics, and semantics in a holistic manner, limiting their real-world applicability.
## 4 SMS-Based Telemetry Data Transmission Protocol  
While the protocol defined in this section is ostensibly generic, the chosen application for our communications protocol is to send medical telemetry from a patient to the healthcare providers. For our protocol to have a purpose, there must be an appropriate implementation of a telemetry system and any such system should contain the following components integrated to address the following requirements:  
\begin{table}
\begin{tabular}{c l} \hline
**Msg Type** & **Description** \\ \hline
1 & Request message from base to remote device (poll) \\
2 & Response message from remote device to base (poll response) \\
3 & Remote originated message (_i.e._, a message sent from the remote patient \\  & with no associated request from the medical centre) \\
4 & Receipt message acknowledging receipt of an earlier message (using the \\  & message ID to identify the message that is being acknowledged) \\
5 & Control message send \\
6 & Control message receipt \\
7 & Fault message \\ \hline \end{tabular}
\end{table}
Table 2: Allowable values of the message type octet.  
* Allow the gathering of data from patient-based sensors.
* Provide a mechanism for the collection and formatting of the data into appropriate protocol messages.
* Allow for the transmission of the data to a remote monitoring site.
* Allow for the receipt and appropriate processing of the data at some remote point.  
Moreover, we must consider overall capacity, the robustness and reliability of the medium, the availability of the carrier mechanism, and the tolerance of the system for faults and corruption. Ultimately, all of these factors must be considered in any system aiming to use SMS for the purposes of data transmission.
## 5 Experimental Simulation and Analysis
### Performance of the SLP-MACGT Model
#### 5.2.2 Effect of the Communication Radius on Performance  
The following experiment investigates the effect of the communication radius on performance. As shown in Figure 5(a), when the communication radius increases, the communication range between nodes expands, and the probability of an attacker contacting the network also increases. Therefore, a larger transmission radius may lead to a shorter safety time, and it will be easier for the attacker to obtain the source location information.  
As shown in Figure 5(b), a larger communication radius means that data may be delivered through fewer intermediate nodes, thus reducing latency. As shown in Figure 5(c), the change in the communication range may affect the direct communication ability between nodes. If the communication range is reduced, the direct connections between nodes may decrease, resulting in a lower success rate of data forwarding. In contrast, an increase in the communication range may increase the direct connection between nodes and may help improve the data forwarding success rate.