esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Extraction Example  
The extraction process is exactly the reserve of the embedding process, so the message extraction and recovery procedures of the pixel value only need to be reversed. The following is an example of the extraction process:  
First, take a stego image in Figure 7 and use Equation (6) to split it into two images \(I^{\prime\prime}\)_HISB_ and \(I_{\textit{LoSB}}\). The embeddable pixel in the image \(I^{\prime\prime}\)_HISB_ is divided into a gray pixel and a white pixel in a chessboard manner. First, the extract extraction for the white pixel and calculate the backbound prediction error \(e^{back}=x_{2,3}^{(hack)}-p_{2}=21-22=-1\), and according Equation (13), \(s_{8}=0\), and \(x_{2,3}^{(back)}\) is unchanged; therefore, \(x_{2,3}^{(out)}=20+1=21\). Next, calculate the outbound prediction error so that \(e^{out}=x_{2,3}^{(out)}-p_{1}=21-19=2\). According to Equation (14), \(s_{7}=1\), and \(x_{2,3}^{(out)}\) subtract 1 to restore the pixel value, so \(x_{2,3}^{(back)}=21-1=20\). This completes the extraction of the outbound journey. Then, when the recursive parameter \(t_{w}=1\), the backbound prediction error is also calculated, so that \(e^{back}=x_{2,3}^{(back)}-p_{2}=20-22=-2\), which is expressed according to Equation (15); \(s_{6}=1\), and add 1 to \(x_{2,3}^{(back)}\) to restore the pixel value, so \(x_{2,3}^{(out)}=20+1=21\). Finally, calculate the outbound prediction error so that \(e^{out}=x_{2,3}^{(out)}-p_{1}=21-19=2\). According to Equation (16), \(s_{5}=1\), and \(x_{2,3}^{(out)}\) subtract 1 to restore the pixel value, so \(x_{2,3}=21-1=20\). Finally, the secret bitstream extracted from the white pixels is as \(s_{5}s_{6}s_{7}s_{8}\) = \"1110\", and the pixel values are also restored.  
In this example, there are only two pixels \(x_{2,2}\) and \(x_{2,3}\), so the next step is to extract the information from the gray pixel \(x_{2,2}\) when the recursive parameter \(t_{g}=2\). In order to simply express the extraction process, Table 4 is used to show the changes in the values of the gray pixel during the extraction process to extract the secret bitstream \(s_{1}s_{2}s_{3}s_{4}=\) \"1110\". Figure 7 illustrates the whole extraction and recovery processes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter \(t_{g}\)** & **Stego Pixel** & **Predictor** & **Prediction Error \(e\)** & **Secret Bit** & **Restored Pixel** \\ \hline \(t_{g}=2\) & \(x_{2,2}^{(back)}=21\) & \(p_{2}=22\) & \(e^{back}=-1\) & \(s_{4}=0\) & \(x_{2,2}^{(out)}=21\) \\ \hline  & \(x_{2,2}^{(out)}=21\) & \(p_{1}=19\) & \(e^
## 5 Experimental Results and Evaluation  
### Datasets and Query Construction  
In this section, we employ the proposed QGDA model for explainable sentiment analysis of documents related to the Chinese stock market. We compiled a securities news review dataset obtained from the RESSET ([[http://www.resset.com](http://www.resset.com)]([http://www.resset.com](http://www.resset.com)) (accessed on 12 March 2025)) database, which consists of analysts' evaluation articles of the Chinese stock market since 1 January 2000. The articles in this raw dataset inherently come with five sentiment labels based on their overall recommendations: buy, sell, hold, follow, and other.  
Because our raw dataset exhibited significant class imbalance, we implemented a targeted data screening and stratified sampling strategy to ensure robust model training and unbiased evaluation. This process was crucial to obtaining a balanced dataset in which each of the five sentiment categories contained an equal number of samples. The resulting balanced dataset totaling 21,200 documents was then stratified and divided into training (14,835 samples), validation (2120 samples), and test (4240 samples) sets at a ratio of approximately 7:1:2. This division ensured that each sentiment category contained an equal number of samples within each split. After segmenting all the documents into sentences, we utilized BERT's tokenizer to tokenize the words within each sentence.  
The query set \(\mathbb{Q}\) can be defined according to specific task requirements. To ensure that our model aligns with the analytical perspectives of investors and systematically inject domain knowledge, the selection and definition of the query categories followed a structured procedure rather than an arbitrary manual process. This procedure was conducted by financial experts, and involved two key stages: first, the experts performed a qualitative analysis of the financial articles within our dataset to identify recurring themes and pivotal factors that influence stock market sentiment; second, these data-driven observations were synthesized and structured by cross-referencing them with established knowledge and common analytical frameworks used in the financial domain.  
This synthesis resulted in the five query categories shown in Table 1, which represent fundamental aspects considered by investors when evaluating stock-related information. Each query category consists of a set of representative keywords. Crucially, this knowledge injection methodology is adaptable; the query categories and keywords can be readily modified for different datasets or applied to other domains (e.g., legal or medical text analysis), demonstrating the generalizability of our query-guided approach. The subsequent experiments show strong correlation with stock market feedback, serving as empirical validation for the effectiveness of our chosen query categories.
## 4 Conclusions  
In this work, an invar alloy wire modified by the Cr-Mo-V-N alloy was used as raw material, a high-strength invar alloy specimen was fabricated by wire arc additive manufacturing, and its microstructure and mechanical properties were characterized. The main conclusions are presented as follows:  
1. In the WAAM-fabricated invar alloy, there are generally three regions in each deposition unit: the heat-affected zone at the top, the columnar grain region in the middle, and the fine-grain region at the bottom.
2. The precipitates in the WAAM-fabricated high-strength invar alloy specimen are composite phases consisting of VC, VN, Mo\({}_{2}\)C, Fe\({}_{20}\)Mo\({}_{3}\)C\({}_{6}\), and Cr\({}_{20}\)Mo\({}_{3}\)C\({}_{6}\) and are mainly distributed along grain boundaries and the cell wall of the cellular substructure. These precipitation phases can refine the grain and the cellular substructure within grains and can also effectively hinder the movement of dislocation, which contributes to the strength of the material.
3. The average tensile strength of the WAAM-fabricated high-strength invar alloy specimen is 793 MPa, an increase of about 99%, and the impact toughness at low temperature (\(-196\)\({}^{\circ}\)C) is 111 J/cm\({}^{2}\), an increase of about 23%. However, the thermal interaction of adjacent deposition units will cause the mechanical properties of the invar alloy specimen to exhibit anisotropy.  
C.C.: methodology, investigation, writing--original draft, formal analysis, writing--review and editing; C.Z. ([PERSON]): methodology, investigation, formal analysis; Z.S.: methodology, investigation; J.H.: investigation, methodology; W.G.: investigation, methodology; H.X.: methodology; B.L.: methodology; C.Z. ([PERSON]): visualization; H.Z.: visualization. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hebei Province, China (E2022202199 and E2023202233), the High-level Talents Foundation of Hebei Province, China (B20231016), and the Central Guided Local Science and Technology Development Funding Program (236Z1023G and 22281007Z).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors thank Hebei Iron and Steel Technology Research Institute (HBIS) for providing 4]36 welding wire (\(\varphi\)1.6 mm) and technical support.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
WAAM Wire Arc Additive Manufacturing  
GMAW Gas Metal Arc Welding  
PAW Plasma Arc Weld
## 2 Boundary Conditions for Solar Radiation
### Boundary of Solar Radiation Intensity  
#### 2.3.1 Direct Solar Radiation  
Based on the above theoretical basis, the direct solar radiation reaching the Earth's surface can be expressed in the following equation:  
\[I_{s}=I_{0}\cdot P^{1/\sin h} \tag{11}\]  
where \(P\) is the atmospheric transparency coefficient and \(h\) is the solar altitude angle.  
Assuming that the north, west, and up directions of the Sun correspond to the \(x,y\), and \(z\) directions in the Cartesian coordinate system, the direction vector of the Sun in three-dimensional coordinates can be obtained based on the Sun angle calculated above, and the conversion relationship between the Sun angle and the direction vector is:  
\[i_{x}=-\cos(90^{\circ}-h)\sin\gamma_{z} \tag{12}\]  
\[i_{y}=\sin(90^{\circ}-h)\sin\gamma_{z} \tag{13}\]  
\[i_{z}=-\cos\gamma_{z} \tag{14}\]  
Because of the shading effect, the direct solar radiation does not cover the entire structure. The shading effect is illustrated in Figure 3. Shading increases the structural temperature gradient and internal stresses, leading to more complex numerical analyses [16]. The solar irradiated and shaded areas are divided by the solar radiation direction vector.  
The direct solar radiation in the shaded region is 0 and the direct solar radiation received in the sunlit region is \(I_{D}\):  
\[I_{D}=I_{s}\cos\beta \tag{15}\]
## 3 Example Verification
### Applicability of Obliquely Incident Wave Fields  
In seismic response studies under near-field conditions and damage detection of welded seams, the seismic wave incidence angle is not always perpendicular to the free boundary. For this reason, the effect of obliquely incident waves should be highlighted in the simulation of seismic wave fields under near-field conditions [56], in rock layer fracture damage investigation [57], and in structural non-destructive testing [58]. To verify the attenuation effect of the MVDB on obliquely incident pulse waves, a numerical model of homogeneous linear elastic and isotropic soil oblique incidence angles was established. Typically, three source wave fields are involved in the problem of obliquely incident P waves in a single-layer medium at the free boundary. To avoid the interference caused by the wave field superposition and to facilitate the observation of the wave energy attenuation, the source was set at the junction of the absorbing boundaries to eliminate the transmissive wave field in the opposite direction. \(\Delta l\) is taken as the maximum grid size (Equation (12)), \(k\) is set at 1.08, and \(L\) is determined as two times the minimum wavelength (\(2\lambda_{min}\)), and the excitation scheme is shown in Figure 11.  
For example, at an incident angle of \(30^{\circ}\) and \(45^{\circ}\), in this paper, snapshots of the wave fields at 0.063 s, 0.112 s, 0.189 s, 0.28 s, 0.36 s, and 0.45 s are obtained. As shown in Figure 12, the P wave reaches the bottom and side boundaries at \(t\approx 0.189\) s and traverses the bottom  
Figure 10: Key processes of the CALM and MVDB. (**a**) CALM. (**b**) MVDB.  
boundary at \(t\approx\) 0.28 s, and no reflection occurs in this duration. The S wave is about to reach the lateral boundary at \(t\approx\) 0.189 s and continues to propagate towards infinity. Similarly, no reflections are generated, which is identical to expectations.
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 2 Detailed Design
### The Untimodal Extraction Model of M2 ER
#### 2.1.2 Audio Modality Preprocessing and Feature Extraction  
The representation of audio signals is quite diverse, and the way audio signals are described greatly impacts the performance of subsequent feature extraction and emotion recognition. The purpose of the preprocessing is to transform audio signals with different quality into signals with smooth and uniform representative characteristics, which is convenient for the subsequent feature extraction. Preprocessing includes pre-emphasis, framing, and windowing. The next step is to process the data by transforming the raw audio into spectrograms, which contain both temporal and frequency domain information. These spectrograms are fed into a pre-trained model. Due to its excellent performance in audio emotion recognition, the pre-trained DenseNet [19] network model was selected for extracting emotional features from the spectrograms. The overall steps for audio emotion feature extraction are illustrated in Figure 4.  
Figure 3: Text feature extraction model.  
Spectrograms visualize audio signals, and they can be regarded as color images in terms of their representation. By using the two-dimensional image to describe the three-dimensional information of time, frequency, and energy, the differences between different audio data can more effectively captured. Moreover, spectrograms are two-dimensional, colorful images, making them suitable for feature extraction using CNNs. The spectrograms corresponding to the seven emotions are shown in Figure 5.  
Finally, each spectrogram corresponding to each short-time frame is input into DenseNet to extract emotional features from the spectrogram. The detailed architecture of the DenseNet used for feature extraction is shown in Figure 6.  
Figure 4: Audio feature extraction model.  
Figure 5: Spectrogram of seven emotions. The horizontal axis of the spectrogram represents the temporal information of the audio signal, while the vertical axis represents the frequency of the audio signal. The two-dimensional coordinates in the spectrogram represent the frequency of the audio at a specific moment, and the intensity of the coordinates also reflects the energy of the audio. The darker the color in the spectrogram, the higher the energy.  
Figure 6: Structure of DenseNet in our method.
# Bounded-Error Parameter Estimation Using Integro-Differential Equations for Hindmarsh-Rose Model
## 3 Hindmarsh-Rose Model
### Parameter Estimation  
In this section, enclosures of different integrals in the set-membership framework are obtained, by the interval extension of the trapezoidal classic method.  
For the simulations, the following values are taken: \(a^{*}=3\), \(b^{*}=4\), \(d^{*}=5\), \(u(t)=3.25\)A, \(e^{*}=0.20\), \((x_{1}(0,\theta),x_{2}(0,\theta),z(0,\theta))=(0.2;0.7;4)\). The time interval is \([0,1000]\), with a step size \(h=0.01\)s.  
The integrals are evaluated, by using 29 points. In the output equation, \([\eta(t)]\) is given by three successive intervals: \([0.00008,\ 0.00012]\), \([0.0004,\ 0.0006]\) and \([0.0005,\ 0.0015]\), then System (10) is solved by using SIVIA's algorithm (see Section 2.3). For each instance of this value, the estimated intervals of \(\bar{a}\), \(\bar{b}\), \(\bar{d}\), \(\bar{\varepsilon}\) and the widths of the estimated intervals are given in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \([\eta(t)]\) & \(\bar{\varepsilon}\) & \(\bar{a}\) & \(\bar{b}\) & \(\bar{d}\) \\ \hline \([0.00008,\ 0.00012]\) & \([0.1949,\ 0.2029]\) & \([2.9300,\ 3.0600]\) & \([3.9790,\ 4.1156]\) & \([4.9399,\ 5.0681]\) \\ \hline \([0.0004,\ 0.0006]\) & \([0.1934,\ 0.2035]\) & \([2.9398,\ 3.0633]\) & \([3.9089,\ 4.0225]\) & \([4.9407,\ 5.0654]\) \\ \hline \([0.0005,\ 0.0015]\) & \([0.1905,\ 0.2024]\) & \([2.9563,\ 3.0643]\) & \([3.8557,\ 4.1684]\) & \([4.9409,\ 5.0670]\) \\ \hline \([-0.0001,\ 0.0001]\) & \([0.1369,\ 0.2026]\) & \([2.9909,\ 3.2119]\) & \([3.3170,\ 5.1301]\) & \([4.9709,\ 5.1399]\) \\ \hline \([-0.0001,\ 0.0001]\) & \([0.18,\ 0.2026]\) & \([2.9909,\ 3.2119]\) & \([3.6,\ 4]\) & \([4.9709,\ 5.1399]\) \\ \hline \end{tabular}
\end{table}
Table 1: Parameter values obtained with the method presented in Section 2.  
Figure 2: These two diagrams summarize the differences between the procedure presented in [5] and the one presented in this paper. (**a**) Diagram summarizing the stochastic procedure, presented in [5]. (**b**) Diagram summarizing the interval procedure, presented in this paper.  
The impact of the width of the interval chosen for the measurement noise is analyzed through the width of the estimated.  
The intervals for initial values are given by \(\epsilon\in[0.18,\ 0.22]\), \(a\in[2.7,\ 3.3]\), \(b\in[3.6,\ 4.4]\) and \(d\in[4.5,\ 5.5]\).  
All the intervals in Tables 1 and 2 contain the true values. Tables 1 and 2 highlight the impact of the width of the measurement noise interval on the width of the estimated; the larger the size of this interval is, the larger the size of the estimates.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 1 Introduction
### Optical Frequency Combs: A Background  
An optical frequency comb is a set of evenly-spaced lines in the frequency domain, \(f_{n}\), whose location is given by \(f_{n}=nf_{r}+f_{0}\), illustrated in Figure 1. In the time domain, this corresponds to a train of optical pulses. In the expression above, \(f_{r}\) defines the line spacing, which relates to the inverse of the period in the pulse train (repetition rate). Note that the frequency lines are offset from zero by the quantity \(f_{0}\). This offset indicates that the oscillation frequencies of the spectral lines are not necessarily a multiple integer of the repetition rate. Since the offset frequency \(f_{0}\) is in the most general case independent of \(f_{r}\), there are two degrees of freedom in an optical pulse train that define the absolute position of the lines in the frequency domain.  
The origin of the offset frequency depends on the specific comb generation technology used. For mode-locked lasers, it depends on the relation between the group and phase velocity in the resonating cavity, while for combs generated as modulation spectra around a central frequency \(f_{c}\), it depends on the absolute value of this central frequency. Here, it should be noted that for the latter kind of frequency combs, \(f_{c}\) is often used instead of \(f_{0}\) to define the absolute position of the comb. This definition is also commonly used in telecom applications.
## 3 Results
### Solution by Levenberg-Marquardt Method  
As was developed by the above section, the problem regarding NLS was solved by using the Levenberg-Marquardt method. Figure 8 displays the implemented algorithm based on the Levenberg-Marquardt method. Simulations were developed considering the same initial vectors for the previous solution method. Table 6 shows the results for the three main simulations developed by using the Levenberg-Marquardt method.  
Figure 5: Behavior of discrepancy error and solution vector for [PERSON]’s method-simulation 1. (**a**) Behavior of discrepancy error respect to iterations. (**b**) Behavior of values for solution vector respect to iterations.  
Figure 6: Behavior of discrepancy error and solution vector for [PERSON]’s method-simulation 2. (**a**) Behavior of discrepancy error respect to iterations. (**b**) Behavior of values for solution vector respect to iterations.  
Figure 7: Behavior of discrepancy error and solution vector for [PERSON]’s method-simulation 3. (**a**) Behavior of discrepancy error respect to iterations. (**b**) Behavior of values for solution vector respect to iterations.  
As same for [PERSON]'s method, the Levenberg-Marquardt algorithm is an iterative solution method that requires the assumption of an initial \(\mathsf{x}^{\mathsf{o}}\) vector. Therefore, many simulations were developed for achieving the best performance and obtain results comparable with the application of [PERSON]'s method. Based on the results presented in Table 6, the LMM applied for the solution of the NLS problem presented in this work shows moderate positive performance. In simulation 2 and 3, this  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation**} & **Initial** & **Initial** & **Final** & **Final** & **No** & \multirow{2}{*}{**CT (s)**} & **Final Error** & **Converged** \\  & \(\mathbf{\Lambda_{12}}\) & \(\mathbf{\Lambda_{21}}\) & \(\mathbf{\Lambda_{12}}\) & \(\mathbf{\Lambda_{21}}\) & **Iterations** & & \\ \hline
1 & 5.000 & 5.000 & NaN & NaN & 312 & 24.171 & NaN & No \\
2 & 1.000 & 1.000 & 0.689 & 0.798 & 1370 & 94.312 & \(9.92\times 10^{-8}\) & Yes \\
3 & 0.500 & 1.000 & 0.689 & 0.798 & 1667 & 4.263 & \(9.99\times 10^{-8}\) & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for simulation by using Levenberg–Marquardt method.  
Figure 8: Proposed algorithm for the solution of the problem based on Levenberg–Marquardt method.  
algorithm reached converged towards the same solution (\(\Lambda_{12}=0.688\); \(\Lambda_{21}=0.798\)) that was obtained by employing [PERSON]'s method. Taking into account results for simulation 1, LLM also shows that if the initial solution vector is far from the final values of it, the algorithm could diverge or employ several iterations (and computational time) to achieve the real solution. Figures 9 and 10 show curves for discrepancy error and solution vector respect to iterations for simulations 2 and 3 in the LMM, respectively.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 5 Conclusions  
In this study, we first used air pollution elements (SO2, NO2, PM10, PM2.5, O3, and CO) to calculate the air quality index to quantify the air pollution level, then used wavelet transform noise reduction to optimize the data quality of meteorological indicators (temperature, humidity, barometric pressure, and wind speed), and finally used the detrended correlation analysis (DCCA) technique to calculate the detrended correlation number DCCA to evaluate the correlation between air pollution and meteorological variables, representing the development of a DCCA-LSTM model that successfully filters and preserves changed information at short scales. Using DCCA as a variable weighting factor enhanced the multivariate single-step prediction accuracy of air pollution AQI values at three scales at medium and long scales.  
The DCCA-LSTM model was then used to compare the multivariate single-step prediction effects with those of the LSTM model. The simulation plots, mean squared errors, and running times demonstrated that the accuracy of the prediction model improved by 1%, 1.6%, 2%, and 5% on the short, medium, and long scales with window sizes of 24, 48, 168, and 5000 h, respectively, and that the efficiency improved by 5.72%, 8.64%, 8.29%, and 3.42%, indicating the DCCA-LSTM model derived by the DCCA correlation analysis was effective and efficient for the multivariate single-step prediction of air pollution index variables.  
Conceptualization, X.H.; methodology, software, Z.Z.; validation, formal analysis, H.C.; writing--original draft preparation, writing--review and editing, Z.Z.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of the Application Foundation of Sichuan Science and Technology Department, China, grant number 2019 YJ0455 and the Graduate Innovation Fund of Xihua University, China, grant number YCJJ2021072.  
Not applicable.  
On behalf of all the authors, the corresponding author states that our data are available upon reasonable request.  
We express our gratitude to the management of the School of Electrical and Electronic Information at Xihua University for providing us with the necessary support and research facilities to complete this work.  
The authors declare no conflict of interest.