esa-sceva/satcom-chunk-collection huggingface dataset:
# On Orthogonal Double Covers and Decompositions of Complete Bipartite Graphs by Caterpillar Graphs
## 2 Symmetric Starters  
Let \(\mathcal{A}\) be a subgraph of \(K_{x,x}\) and \(\delta\in\mathbb{Z}_{x}\). Then, the graph \(\mathcal{A}+\delta\) with \(E(\mathcal{A}+\delta)=\{(\lambda+\delta,\mu+\delta):(\lambda,\mu)\in E(\mathcal{ A})\}\) is called the \(\delta\)-translate of \(\mathcal{A}\). \(\mathcal{A}\) is called a half starter with respect to \(\mathbb{Z}_{x}\), if (i) \(|E(\mathcal{A})|=x\) and (ii) the lengths of all edges in \(\mathcal{A}\) are mutually dissimilar and equal to \(\mathbb{Z}_{x}\); that is, \(\{d(e):e\in E(\mathcal{A})\}=\mathbb{Z}_{x}\). The following results have already been proven in the literature [22]:  
1. If \(\mathcal{A}\) is a half starter, then the union of all translates of \(\mathcal{A}\) forms an edge decomposition of \(K_{x,x}\); that is, \(\cup_{\delta\in\mathbb{Z}_{x}}E(\mathcal{A}+\delta)=E(K_{x,x})\). Hereafter, a half-starter \(\mathcal{A}\) can be represented by the vector \(u(\mathcal{A})=\big{(}u_{\omega_{0}},\ldots,u_{\omega_{x-1}}\big{)}\in \mathbb{Z}_{x}^{\lambda}\). Two half-starter vectors \(u(\mathcal{A}_{0})\) and \(u(\mathcal{A}_{1})\) are said to be orthogonal if \(\{u_{a}(\mathcal{A}_{0})-u_{a}(\mathcal{A}_{1}):\alpha\in\mathbb{Z}_{x}\}= \mathbb{Z}_{x}\).
2. If two half-starter vectors \(u(\mathcal{A}_{0})\) and \(u(\mathcal{A}_{1})\) are orthogonal, then \(\mathcal{G}=\{\mathcal{A}_{\delta,\beta}:(\delta,\beta)\in\mathbb{Z}_{x}\times \mathbb{Z}_{2}\}\) with \(\mathcal{A}_{\delta,\beta}=\mathcal{A}_{\beta}+\delta\) is an \(ODC\) of \(K_{x,x}\). The subgraph \(\mathcal{A}_{s}\) of \(K_{x,x}\) with \(E(\mathcal{A}_{s})=\{(\lambda_{0},\mu_{1}):(\mu_{0},\lambda_{1})\in E(\mathcal{ A})\}\) is called the symmetric graph of \(\mathcal{A}\). If \(\mathcal{A}\) is a half starter, then \(\mathcal{A}_{s}\) is also a half starter. A half starter \(\mathcal{A}\) is called a symmetric starter with respect to \(\mathbb{Z}_{x}\) if the following vectors \(u(\mathcal{A})\) and \(u(\mathcal{A}_{s})\) are orthogonal.
3. Let \(x\) be a positive integer and \(\mathcal{A}\) a half starter represented by the vector \(u(\mathcal{A})=\big{(}u_{\omega_{0}},...,u_{\omega_{x-1}}\big{)}\). Then, \(\mathcal{A}\) is the symmetric starter if \(\{u_{\omega}-u_{-\omega}+\omega:\omega\in\mathbb{Z}_{x}\}=\mathbb{Z}_{x}\).  
The symmetric starter can be used to decompose the balanced complete bipartite graphs \(K_{x,x}\) by several graphs and relies on the idea of \(\delta\)-translate and half starters that are strongly related to [PERSON]'s labeling and his cyclic decompositions of the complete graph [25]. The symmetric starter vectors method is an easy method compared to the direct method, function-half starter method, and matrices method. Some graphs do not have a symmetric starter vector; so, the orthogonal double covers cannot be constructed by these graphs based on the symmetric starter method. Hence, the limitation of the method is that some classes of graphs cannot be represented by vectors; thus, we try to apply the methods above.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
## 3 Case Analyses and Discussions  
In this section, systematic parametric analyses of the piezoelectric metamaterial are presented using the semi-analytical equations derived and finite element simulations. All propagating wave modes in lattice/periodic structures can be captured by restricting the dimensionless wavenumber to the first Brillouin zone due to the periodicity. Thus, the dispersion curves are plotted based on wavenumbers within this zone. COMSOL Multiphysics 5.3 is adopted, which is widely used in multi-field coupled investigation and piezoelectric metamaterial analysis, to analyze the dispersion relation and the forced frequency responses of the piezoelectric metamaterial.  
The unit-cell is made of aluminum substrate (\(25.38\times 25.38\times 3.13\) mm\({}^{3}\)) and two piezoelectric ceramic transducers (\(21\times 21\times 0.55\) mm\({}^{3}\)) bonded onto the center of the top and bottom surfaces of the substrate. Inductance element is connected to the top and bottom surfaces of the piezoelectric transducer, as the shunt circuit. The conventional unit-cell of piezoelectric metamaterial has a solid substrate. In this research, a through-hole cavity is incorporated underneath the substrate of the unit-cell (Figure 3a). The material parameters are chosen as: piezoelectric mass density \(\rho_{p}=7500\) kg/m\({}^{3}\), piezoelectric Young's modulus \(E_{p}=65\) GPa, piezoelectric constant \(d_{31}=-320\) pC/N, dielectric constant \(\beta_{33}=2.98\times 10^{8}\) Vm/C, host medium mass density \(\rho_{b}=2730\) kg/m\({}^{3}\), and host medium Young's modulus \(E_{b}=69\) GPa. Without loss of generality, the inductance load for unit-cellsis given as \(L=0.13\) H to generate a bandgap around 3250 Hz. In practice, the value of inductance load can be arbitrarily modified for the creation of bandgap at other frequencies.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art
### State-of-the-Art in the Image Aspect
#### 3.2.2 Gray-Scale versus Color Image  
Several algorithms that focus specifically on gray-scale image encryption have been proposed such as one that interestingly utilizes the concept of water waves [98] and another that uses the integer wavelet transform [99]. If color image encryption is required, then gray-scale-specific algorithms will typically not suffice. Algorithms that encrypt color images employ a wide range of techniques such as matrix convolution [100] and 4D memristive hyper-chaos [82]. An approach for encrypting multiple colored images has also been proposed [101], as well as a unique procedure for encrypting and transmitting color images using audio signals [102].
###### Abstract  
This paper presents a radiofrequency (RF) energy harvesting system based on an ultrawideband Archimedean spiral antenna and a half-wave Cockcroft-Walton multiplier circuit. The antenna was proved to operate from 350 MHz to 16 GHz with an outstanding performance. With its use, radio spectrum measurements were carried out at the Telecommunication Engineering School (Universidad Politecnica de Madrid) to determine the power level of the ambient signals in two different scenarios: indoors and outdoors. Based on these measurements, a Cockcroft-Walton multiplier and a lumped element matching network are designed to operate at 800 MHz and 900 MHz frequency bands. To correct the frequency displacement in the circuit, a circuit model is presented that takes into account the different parasitic elements of the components and the PCB. With an input power of 0 dBm, the manufactured circuit shows a rectifying efficiency of 30%. Finally, a test is carried out with the full RF energy harvesting system to check its correct operation. Thus, the RF system is placed in front of a transmitting Vivaldi antenna at a distance of 50 cm. The storage capacitor has a charge of over 1.25 V, which is enough to run a temperature sensor placed as the load to be supplied. This demonstrates the validity of the RF energy harvesting system for low-power practical applications.  
energy harvesting; Archimedean spiral antenna; Cockcroft-Walton multiplier; parasitic elements modeling; energy storage +
Footnote †: journal: Sensors
# Study on Permeability Stress-Sensitivity in
## 3 Numerical Simulation of Tight Sandstone Gas Reservoirs
### Governing and Constitutive Equations
#### 3.1.2 Mathematical Model of Stress Field  
Rock deformation may transition from an elastic to a plastic state during gas reservoir development. The elastic-plastic deformation of rock is governed by the following equations [30; 31]:  
\[\sigma_{ij,j}^{\prime}-\big{(}\alpha\delta_{ij}p_{t}\big{)}_{j}+f_{i}=0 \tag{3}\]  
\[\epsilon_{ij}=\frac{1}{2}\big{(}u_{ij}+u_{j,i}\big{)} \tag{4}\]  
\[\{{\rm d}\sigma\}=\big{[}D_{\rm ep}\big{]}\{{\rm d}\epsilon\} \tag{5}\]  
\[\big{[}D_{\rm ep}\big{]}=\left[[D]-\frac{[D]\Big{\{}\frac{\partial Q}{\partial \sigma}\Big{\}}\Big{\{}\frac{\partial F}{\partial\sigma}\Big{\}}^{\rm T}[D]}{A +\Big{\{}\frac{\partial F}{\partial\sigma}\Big{\}}^{\rm T}[D]\Big{\{}\frac{ \partial Q}{\partial\sigma}\Big{\}}}\right] \tag{6}\]  
where \(\sigma_{ij}\) is the effective stress tensor, \(f_{i}\) is the volume force tensor, \(\alpha\) is the effective stress coefficient, \(\delta_{ij}\) is the Kronecker delta, \(\epsilon_{ij}\) is the strain tensor, \(u\) is the displacement component, \([D]\) is the elastic matrix, \([D_{\rm ep}]\) is the elastic-plastic matrix and \(A\) is the strain hardening parameter. According to the law of associated seepage, when the yield function, \(F\), and plastic potential function, \(Q\), in the matrix are equal, \([D_{\rm ep}]\) is a symmetric matrix.  
Combining the mechanical properties of rocks in the target formation [32; 33] the Mohr-Coulomb yield criterion is chosen considering the elastic-plastic deformation of the formation rocks.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 2 Dataset and Methods
### Machine Learning Algorithms
#### 2.2.2 The Gradient Boosting Regressor Algorithm  
Gradient boosting is a popular ML technique for both regression and classification. The model is built incrementally, meaning every new model is constructed with the purpose of rectifying errors caused by the preceding versions. The fundamental principle involves the amalgamation of many weak learners to construct a robust learner, usually decision trees. This algorithm generally starts with a constant, \(F_{0}(x)\), aiming to minimize the loss function. Then, at each stage m up to stage'm', a decision tree \(h_{m}(x)\) is adapted to the loss function's negative gradient, the basis of the gradient boosting process. The model isiteratively revised with the learning rate v, which scales the contribution of each tree [52; 53]. The equation of the model is demonstrated in Equation (1).  
\[\mathrm{F_{m}(x)=F_{m-1}(x)+v\cdot h_{m}(x)} \tag{1}\]  
After'm' iterations, the final model, \(\mathrm{F(x)}\), emerges as the sum of the initial model and all subsequent learners. Stochastic gradient boosting, an extension of this method, adds randomness by subsampling the training data at each stage, thereby increasing the robustness and generalization ability of the model [53]. As highlighted by [48], this approach is part of a broad ensemble learning framework in which each new tree is built on the sum of all previous trees, minimizing the overall prediction error. This structured methodology allows gradient boosting to effectively handle complex nonlinear interactions, making it a versatile tool for regression and classification in various data types.
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results
#### 4.2.1 Text Addition Attack  
A text addition attack introduces some of textual information to a watermarked image--the word \"LENA\" in this experiment. The images in Figure 7 show the experimental results and Table 2 gives the PSNR and SSIM values of the four different methods.  
By comparing the four images listed in Figure 7e-h, it is obvious that tamper detection is more accurate when using watermarks with texture information. However, the addition of edge information causes tamper detection more sensitive to edge changes, resulting in a higher false alarm rate. From the recovery results shown in Figure 7i-l, it can be seen that the final results of [8,9] do not fully recover the tampered region. But with neighborhood adjustment, both the proposed method and the method in Reference [7] restore the detection area without any visible missing information. However, the PSNR and SSIM values in Table 2 show that the quality of the final image is slightly better after the filtering operation.  
\begin{table}
\begin{tabular}{c|c c|c c|c c|c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Test** \\ **Images** \\ \end{tabular} } & \multicolumn{2}{c|}{\(N_{\text{SB}}\)} & \multicolumn{2}{c|}{\(N_{\text{TB}}\)} & \multicolumn{2}{c|}{**PSNR (dB)**} & \multicolumn{2}{c}{**SSIM**} \\ \cline{2-9}  & **[PERSON] et al.** & **Proposed** & **[PERSON] et al.** & **Proposed** & **[PERSON] et al.** & **Proposed** & **[PERSON] et al.** & **Proposed** \\ \hline Lena & 8561 & 8372 & 7823 & 8012 & 39.82 & 39.74 & 0.9636 & 0.9985 \\ Goldhill & 5038 & 4637 & 11,346 & 11,747 & 38.91 & 39.08 & 0.9734 & 0.9984 \\ Car1 & 10,516 & 9998 & 5868 & 6386 & 40.41 & 40.07 & 0.9650 & 0.9978 \\ Car2 & 13,572 & 12,872 & 2812 & 3512 & 42.20 & 40.76 & 0.9627 & 0.9981 \\ Venice & 6062 & 5758 & 10,322 & 10,626 & 39.15 & 39.21 & 0.9627 & 0.9993 \\ Clock & 12,366 & 11,842 & 4018 & 4542 & 41.22 & 40.49 & 0.9575 & 0.9991 \\ Airplane & 10,946 & 10,673 & 5438 & 5711 & 40.56 & 40.40 & 0.9629 & 0.9986 \\ Barbara & 7321 & 7163 & 9063 & 9221 & 39.52 & 39.42 & 0.9697 & 0.9987 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of watermark embedding results along four aspects.
## 5 Results
### Evaluation of EmoSC
#### 5.2.2 Speech and Emotion Recognition Performance Across Datasets  
Table 4 presents the SER and ASR performance of EmoSDS fine-tuned on each dataset. EmoSC achieved the highest accuracy according to F1 (98.0%), WER (0.1%), and CER (0.1%), demonstrating the benefits of conducting training on a well-balanced, emotionally expressive dataset. StyleTalk also performed well, achieving high F1 (91.1%), though its WER (9.6%) and CER (6.6%) indicated slightly lower transcription accuracy.  
In contrast, DailyTalk recorded the lowest F1 score (55.4%), with higher WER (13.0%) and CER (7.6%), suggesting reduced emotional distinctiveness and a more challenging recognition task. The smaller sample size in both StyleTalk and DailyTalk further hindered speech recognition performance.  
Figure 7 illustrates confusion matrices for SER performance. StyleTalk and EmoSC exhibited strong alignment with ground truth labels, confirming well-separated emotional categories. In contrast, DailyTalk showed frequent misclassifications, particularly among neutral, happiness, and sadness categories, further supporting its weak emotional distinctiveness and data sparsity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Input F1 1 \(\uparrow\)** & **WER \(\downarrow\)** & **CER \(\downarrow\)** \\ \hline StyleTalk & 91.1\% & 9.6\% & 6.6\% \\ DailyTalk & 55.4\% & 13.0\% & 7.6\% \\ EmoSC & 98.0\% & 0.1\% & 0.1\% \\ \hline \hline \end{tabular} \({}^{1}\) Weighted F1 score on input emotion prediction.  
\end{table}
Table 4: Stage 3: SER and ASR results on each dataset. An upward arrow (\(\uparrow\)) indicates a higher value, signifying better performance, while a downward arrow (\(\downarrow\)) indicates the opposite.  
Figure 6: Emotion distributions of three datasets. The numbers are unique speech samples of each emotion category. (**a**) StyleTalk, (**b**) DailyTalk, and (**c**) EmoSC.  
Figure 7: Input emotion prediction confusion matrices. (**a**) DailyTalk, (**b**) StyleTalk, and (**c**) EmoSC.  
These results confirm that large and emotionally expressive datasets enhance both SER and ASR performance, whereas datasets with imbalanced emotions and limited samples hinder accurate recognition.
# Automated Anomaly Detection and Causal Analysis for Civil Aviation Using QAR Data
## 6 Experimental Evaluations
### Results and Analysis  
#### 6.2.1 Overall Performance Evaluation  
For multidimensional data classification and prediction problems, in addition to the XGBoost model, the Random Forest [19] and LightBGM [20] models are also tree-based ensemble learning methods widely used in data mining and machine learning tasks for prediction and classification problems. This section conducts an overall performance evaluation of the above methods, using different models to classify abnormal events for the same data.  
The performance comparison is shown in Figure 7. It can be seen that all three models have an accuracy of over 90%, but their main contribution to accuracy comes from the fact that the model can almost recognize all normal data, which accounts for a very high  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F1** & **Accuracy** \\ \hline Unbalance & 0.955 & 0.687 & 0.799 & 0.992 \\ Balanced & 0.844 & 0.781 & 0.812 & 0.993 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model evaluation on test dataset.  
Figure 6: Confusion matrix of the model: (**a**) unbalanced; (**b**) balanced.  
proportion of the overall data. In terms of precision and recall, the XGBoost model performs significantly better. At the same time, MAD-XFP outperforms the other three models.
## Materials and Methods
### Preparation of Microparts  
A Brabender internal batch mixer (C.W. Brabender Instruments, South Hackensack, NJ, USA), equipped with two counter-rotating blades, was used to prepare different carbon fillers loaded polymer composites. According to a previous study [25], the \(p_{\mathrm{c}}\) for PS/CNT microparts ranges from 5-7 wt %. Thus, the concentration of total carbon fillers in PS was selected at 3, 5 and 10 wt %, respectively. However, the weight ratio of CNT to CB was systematically altered at various combinations (i.e., 100/0, 30/70, 50/50, 70/30 and 0/100). The obtained samples were named as \(x\)-PS/CNTy0/CB20, where \(x\) is the weight fraction of total carbon fillers, \(y\) and \(z\) are the weight ratio of CNT and CB in the total carbon fillers. To put it simply, 3 wt %-PS/CNT30/CB70 represents the sample with 3 wt % total carbon fillers and the weight ratio of CNT to CB is 3:7, i.e., PS(97 wt %)/CNT(0.9 wt %)/CB(2.1 wt %). The nomenclature also applies to other carbon filled systems. The compounding process was carried out at 200 \({}^{\circ}\)C and 50 rpm for 10 min. The obtained composite was mechanically crushed and used for \(\upmu\)IM. The micromolding machine, Battenfeld Microsystem 50 (Wittmann Battenfeld GmbH, Kottingbrunn, Lower Austria, Austria), features a plunger injection system, which consists of a screw melting unit, a metering unit and an injection unit [13]. The melt temperature and mold temperature were 260 and 80 \({}^{\circ}\)C, respectively. The injection speed was 100 mm/s. Figure 2a displays 3D schematic view of a final micropart. All sections of the micropart have a same width of 2.40 mm and the thickness of thick, middle and thin sections is 0.85, 0.50 and 0.20 mm, respectively. The thick and middle sections have a length of 5.00 mm while the thin section has a length of 4.80 mm.  
Figure 1: The morphology of (**a**) high structure carbon black and (**b**) multi-walled carbon nanotubes.
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 4 Conclusions  
This article proposes a DIES-based diagnosis method for an inter-shaft bearing of an aero-engine. This method is developed by extending the improved envelope spectrum. It is fundamentally based on spectral correlation analysis. By employing the spectral noise subtraction method on the faulty NIES and the baseline healthy NIES, a subtracted NIES signature can be derived. Furthermore, an adaptive threshold-based denoising strategy is introduced to mitigate the impact of prominent interfering frequency components and random noise on the spectral line features within the subtracted NIES. The effectiveness and generalization capability of the proposed DIES method are validated through experimental studies on both the inter-shaft bearing vibration dataset and the incipient fault bearing vibration dataset. Comparative analyses reveal that the DIES method outperforms the SKES and IES methods, as well as a more recent ITD-SVD denoising method. Additionally, the proposed DIES method provides a clearer visualization of fault-related spectral components, whereas the spectra produced by the compared methods appear more cluttered.  
Conceptualization, D.L. and L.C.; Methodology, D.L. and L.C.; Formal analysis, D.L.; Data curation, D.L.; Validation, D.L.; Writing--original draft preparation, D.L.; Writing--review and editing, D.L. and L.C.; Funding acquisition, L.C. and J.T.; Supervision, H.Z. and J.X.; Project administration, J.T. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52305144).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
We gratefully acknowledge: 1. Technical support from The State Key Laboratory of Precision Manufacturing for Extreme Service Performance (Changsha, China) for providing computational resources and experimental testing platforms. 2. The open-source inter-shaft bearing dataset shared by [PERSON]'s team at the School of Astronautics, Harbin Institute of Technology (Harbin, China). 3. The open-source bearing dataset provided by the Intelligent Maintenance Systems Center (IMS Center) at the University of Cincinnati (Cincinnati, OH, USA).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCF & Cross-Correlation Function \\ CFSS & Cyclic Frequency Spectrum Slice \\ DIES & Denoised Improved Envelope Spectrum \\ EES & Enhanced Envelope Spectrum \\ FCF & Fault Characteristic Frequency \\ FFT & Fast Fourier Transform \\ HP & High Pressure \\ IES & Improved Envelope Spectrum \\ IESFOgram & Improved Envelope Spectrum via Feature Optimization-gram \\ INS & Improved Noise Subtraction \\ ITD & Intrinsic Time-Scale Decomposition \\ LP & Low Pressure \\ \end{tabular}