esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area
### Monitoring Data  
Fourteen GPS displacement monitoring points (FJ001-FJ014) were established in the Huanglianshu landslide working area, and the monitoring equipment recorded landslide displacements from May 2012 to June 2013. Since the large-scale local sliding resulted in the destruction of several GPS monitoring devices, we selected the monitoring points FJ003, FJ004, FJ005, FJ006, FJ009, FJ010, and FJ011 with complete records for research to reveal the deformation characteristics of the slope after local deformation and establish an appropriate prediction model. The employed data in this paper was provided by the Center for Hydrogology and Environmental Geology of the China Geological Survey. The geographical location relationship of the monitoring points studied in this paper is illustrated in Figure 2.  
Figure 3 shows the displacement change curve of the monitoring points of the Huanglianshu landslide. There is a complicated nonlinear deformation characteristic of the Huanglianshu landslide. After the local large deformation of the Huanglianshu landslide occurred on 31 May 2012, the deformation characteristics of each monitoring point were different in the following years. Among the points, the monitored average displacements  
Figure 1: Geographical location of the Huanglianshu landslide.  
Figure 2: Schematic diagram of monitoring arrangement of the Huanglianshu landslide.  
on FJ003, FJ004, FJ005, and FJ006 were small, while the monitored average displacements on FJ009, FJ010, and FJ011 were large.  
The deformation curves in Figure 3 illustrate that the displacement changes of monitoring points were closely related to the relative positions of the monitoring points and the deformation area. As is shown in Figure 2, monitoring points FJ010 and FJ011 were located in the local deformation area; monitoring point FJ009 was located on the right side of the deformation area; and the monitoring points FJ003, FJ004, FJ005, and FJ006 were located outside the local deformation area. Within one year after the occurrence of the local deformation, the sliding velocity of the monitoring points within the deformation was approximately 10 times higher than that of monitoring points above the deformation area.
## 2 Antenna Modelling
### Antenna's Performance with Other Existing Antennas  
Comparative Antenna Performance Analysis: Assessing the performance of a new antenna for a certain communication band against existing antennas for the same frequency range is crucial and essential to development. The comparative analysis serves numerous important purposes: Performance benchmarking: Engineers can benchmark the antenna's performance by comparing its properties to existing designs. This benchmarking identifies strengths and flaws. The new antenna's competitive benefits must be highlighted. Whether it has higher gain, wider bandwidth, reduced sidelobes, or improved efficiency, these benefits must be quantified and described. Comparisons to current antennas can reveal areas for improvement in the new design. It is important to iteratively improve antenna parameters and performance. Market positioning requires knowing how the new antenna compares to competitors for commercial purposes. Improving the antenna design can boost sales. In terms of application, the antenna's performance is context dependent. Compared to realistic antennas, the antenna's real-world applicability is assessed to ensure it fits consumers' needs. If regulatory standards require particular performance criteria, the produced antenna must be compared against compliant antennas to assure compliance. Engineering often compares antenna performance using gain, bandwidth, directivity, efficiency, radiation pattern, impedance matching, and environmental durability. The antenna's response to interference and multipath propagation may also be assessed.
## 2 Materials and Methods
### Quantitative Evaluation Indices  
The evaluation of super-resolution results mainly includes subjective evaluation and objective evaluation. We used various image quality evaluation indicators, including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [42], standard deviation (SD), information entropy (IE) [43], root mean square error (RMSE), and spectral angle (SAM) [42], as shown in Table 1. These indicators not only measure the fidelity and differences between real HR images and SR images on the ground, but they can also better explain the amount of information contained in the image and evaluate many subtle differences from the perspective of spectrum. SD and IE are non-reference image quality evaluators. Additionally, three experts with three years of experience in this field compared the naturalness of SR images.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator** & **Formula** & **Remarks** \\ \hline PSNR & \(20\text{log}_{10}\) & \(\frac{255}{\text{RMSE}}\) & / \\ SSIM & \(\frac{\text{K}}{\text{j}}\) (\(\frac{\big{(}2\overline{\text{X}}\overline{\text{Y}}+\text{c}_{1}\big{)}(2 \sigma_{\text{XY}}+\text{c}_{2})}{\big{(}\overline{\text{X}}^{2}+\overline{ \text{Y}}^{2}+\text{c}_{1}\big{)}\big{(}\sigma_{\text{X}}^{2}+\sigma_{\text{Y} }^{2}+\text{c}_{2}\big{)}}}\) & \(j\) & \\ RMSE & \(\sqrt{\frac{1}{\text{K}\times\text{N}}\sum_{i}^{\text{K}}\sum_{i}^{\text{N}} \Big{(}\chi_{i}^{\text{j}}-\chi_{i}\Big{)}^{2}}\) & N is the total number of pixels in each image, K is the number of bands. \\ SAM & \(\frac{1}{\text{N}}\sum_{i}^{\text{N}}\text{arccos}(\frac{\chi_{i}\text{Y}_{i}}{|| \chi_{i}||||_{\text{Y}i}||})\) & / \\ SD & \(\sqrt{\frac{1}{\text{N}}\sum_{i}^{\text{N}}(\text{F}(\text{i})-\overline{ \text{u}})}\) & F(i) represents the pixel value, \(\overline{\text{u}}\) represents the mean pixel value. \\ IE & \(-\sum_{i}^{\text{N}}\text{P}(i)\text{log}_{2}\text{P}(i)\) & P(i) represents the probability of the pixel value. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The equations of quantitative evaluation indices.
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 2 Ece-Tts
### Model Architecture
#### 2.2.2 Model Inference  
To achieve content and emotion-controllable speech synthesis, our model integrates the following key inputs: reference audio spectrogram \(x_{\text{ref}}\), corresponding transcript \(y_{\text{ref}}\)  
Figure 2: An overview of ECE-TTS training.  
target transcript \(y_{gen}\), and emotion prompt \(E\). The reference audio provides crucial information about the target speaker's timbre and prosody, the text prompt guides the content of the generated speech, and the emotion prompt enables precise control over emotional expression.  
In Text-to-Speech models, sequence length \(N\) (i.e., speech duration) is a key factor that directly affects length control in speech synthesis. Unlike other models that require a separately trained duration predictor, we directly predict the duration based on the character ratio between \(y_{gen}\) and \(y_{ref}\). This approach simplifies the model architecture, introduces greater randomness, and enhances the naturalness of the generated speech. We assume that the total character length does not exceed the Mel-spectrogram length. Therefore, similar to the training process, we concatenate \(y_{gen}\) and \(y_{ref}\), then pad the character sequence to match the Mel-spectrogram length. Additionally, following the emotion-adaptive coordinate transformation method introduced in Section 2.3.1, we extract emotion-adaptive spherical coordinates from the emotion reference audio \(e_{ref}\) and use them as the emotion feature input \(E\) for the model.  
To sample from the learned distribution, we use the converted Mel features \(x_{ref}\), the concatenated extended character sequence \(z_{ref\cdot gen}\), and the emotion dimension sequence \(E\) as conditional inputs in Equation (8), obtaining  
\[v_{t}(\psi_{t}(x_{0}),c)=v_{t}\big{(}(1-t)x_{0}+tx_{1}\mid x_{ \textit{ref}},z_{\textit{ref}\cdot gen},E\big{)}, \tag{10}\]  
As illustrated in Figure 3, the inference process begins with sampling from simple noise \(x_{0}\), with the goal of gradually generating the final output \(x_{1}\). Given the differential equation condition \(d\psi_{t}(x_{0})/dt=v_{t}\big{(}\psi_{t}(x_{0}),x_{\textit{ref}},z_{\textit{ ref}\cdot gen},E\big{)}\), we employ an ODE solver to iteratively recover the target state from the initial condition \(\psi_{0}(x_{0})=x_{0}\) to \(\psi_{1}(x_{0})=x_{1}\). During inference, flow transformation steps are executed in a structured manner; for example, by uniformly sampling a predefined number of time steps from 0 to 1, based on the Number of Function Evaluations (NFE) setting.  
Figure 3: An overview of ECE-TTS inference.  
After generating the Mel-spectrogram using the model \(v_{t}\) and ODE solver, we discard the reference audio component \(x_{ref}\), retain only the generated speech component \(x_{\text{gen}}\), and finally convert the Mel-spectrogram into waveform audio using a vocoder.
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 3 DAA Algorithm Design
### Access Switching Mechanism  
When the coverage time of a single satellite is not enough to transmit data from an ES, it is necessary to switch access to a new satellite. An ES also needs to switch ESL to a new access satellite in the following cases:  
1. The remaining resources of \(\tilde{v}\) are below the threshold, such as memory capacity and energy. The ES can receive the ACKs correctly for this case;
2. ESL interruption or access satellite failure. In this case, ES cannot receive the ACKs.  
Figure 5: Flow chart of the algorithm to calculate the \(\mathbf{Q}\).
# Hydration, Setting and Crack-Resistance Properties of Activated HVFACs at Early Stages
## 4 Models and Discussion
### Mechanism Analysis of Crack-Resistance of FAC Using Na\({}_{2}\)SO\({}_{4}\)  
As described previously, the use of FA in concrete improves the crack devolvement of the concrete at the early age effectively, such as postponing the time of wider cracks or delaying cracks and reducing the total cracked area. On the other hand, moderate CaO and Na\({}_{2}\)SO\({}_{4}\) promote FA in FACs to obtain better strength development and setting time, but also increase the hydration and cracking of concrete, as well. Therefore, the authors suggest using about up to 50% FA and 10% CaO + 1.5% Na\({}_{2}\)SO\({}_{4}\) can make FACs more acceptable and have better comprehensive properties. In summary, based on this and previous studies, the crack-resistance mechanism of FACs can be explained from the following four aspects, shown in Figure 15.  
Filling effect of FA: Comparing to the particles of Portland cement, FA particles with smaller diameters can fill the fissures between aggregates and pores, which then can improve the compactness of concrete and reduce its porosity, and can affect the development of potential crack, shown as 1 in Figure 15.  
Chemical effect of FA: FA can generate more hydrated calcium silicate (C-S-H) and hydrated calcium aluminate in concrete to improve the compactness and hardness of concrete as the increase of curing time. The process reduces the effective water/binder of concrete and improves the compactness of concrete, shown as 2 in Figure 15.  
Lubrication action of FA: As described previously, FA can improve the workability of concrete, which then avoids the excessive concentration of coarse aggregates and un-hydrated cement. This can mitigate the internal deformation of the mixture caused by the internal stress generated from the uneven distribution of coarse aggregates, especially for the mixture around these aggregates, shown as 1 in Figure 15  
Moderate activator CaO and Na\({}_{2}\)SO\({}_{4}\) in FAC: If FA in concrete is activated moderately via adjusting the dosages and type of chemical ACs, the hydration of concrete is accelerated, its hydration heat increased suitably and the setting time of concrete reduced with a low degree, and more C-S-H can be obtained to improve the compactness of concrete at early stages, shown as 1 in Figure 15. The process is attributed to the combined activator CaO and Na\({}_{2}\)SO\({}_{4}\) increasing the hydration reaction  
Figure 14: Predictions of [PERSON] and proposed models for FACs with 1.5% of Na\({}_{2}\)SO\({}_{4}\).  
of the active Al\({}_{2}\)O\({}_{3}\) and SiO\({}_{2}\) of fly ash and the hydration products of cement. Meanwhile, lime CaO increases the reaction resource of the productions of hydrated calcium aluminate and hydrated calcium silicate.
# Analysis of Cement Sheath-Rock Damage Mechanism--A Case Study on Water Injection Wells
## 6 Conclusions  
Based on the current developmental status in Field Y, this study investigates the damage mechanism of interfacial rock in water injection wells. By employing theoretical analysis and numerical simulations considering fluid-solid coupling, the evolution of interfacial rock damage is comprehensively explored. The research findings lead to the following conclusions:  
The interfacial rock in the cemented zone undergoes damage under the effect of fluid-solid coupling. The numerical simulation results indicate that water breakthrough outside the casing is caused by the evolution of interfacial rock damage.  
This study establishes a shear stress correction model for cement-rock interfaces, which improves the accuracy of numerical simulations by more than 5% compared to traditional models. The modified model enables a more scientifically reasonable calculation of the shear stresses at cement-rock interfaces.  
Through comparing the variation patterns of peak stress under different conditions, the linear fitting slopes of peak stress with respect to different confining pressures, widths of interfacial rocks, and modulus ratios are 3.84, \(-\)1.51, and 19.99, respectively. A larger slope indicates a more pronounced influence of the parameter on the peak stress of interfacial rocks. Thus, it can be inferred that the damage to the interfacial rocks of the injection well is primarily related to the Young's modulus ratio of cement to rock.  
This study provides theoretical evidence for the mechanisms of water channeling at cement-rock interfaces in water injection wells, which contributes to the management and control of water influx outside the casing in oil fields. The shear force correction model for cement-rock interfaces enables oilfield engineers to accurately assess the damage at cement-rock interfaces. The related technological improvements provide guidance for the prevention and control of water channeling and water flooding in oilfield water injection operations, promoting advances in research on technologies related to water channeling and water flooding in oil fields.  
Conceptualization, L.Z. and Q.L.; methodology, L.Z. and X.L.; investigation, Q.L. and X.L.; writing--original draft preparation, L.Z.; writing--review and editing, Q.L. supervision, Q.L.; funding acquisition, X.L. 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 Nos. 51741407).  
Not applicable.
## 3 Results and Discussion
### Analysis of Numerical Simulation Results
#### 3.2.5 Analysis of Crack Extension During Cyclic Loading and Unloading Simulation  
The built-in crack analysis function of PFC can help researchers conveniently analyze the expansion of cracks and the types of cracks. Figure 21 visualizes the dynamic changes of crack numbers and the generation of different types of cracks throughout the simulation process. Figure 22 shows the changes in the acoustic emission (AE) parameters. The two sets of pictures visualize the crack evolution of sandstone under cyclic loading conditions.  
Figure 21: Cont.  
Figure 21: _Cont._  
Figure 23 plots the changes in the percentage of crack types with the freeze-thaw cycles under different loading and unloading conditions.  
As can be seen from Figures 21 and 22, the emergence of cracks shows a significant correlation with the magnitude of axial stress. When the axial stress exceeds a specific value, cracks appear in large numbers. The energy accumulation within the sandstone reaches a certain degree and is more likely to be released in large quantities at the later stage of cyclic loading. Hence, cracks tend to appear in the last 2-3 stress cycle levels.  
Figure 22: Cont.  
The number of tensile cracks dominated throughout the cyclic loading process. The proportion of shear cracks showed an increasing trend with the increment of freeze-thaw cycles (Figure 23). It can be attributed to the formation of shear rupture zones in the sandstone damage process. The shear rupture zone is a high-incidence zone for shear cracks. Thus, shear cracks briefly dominate at a given stage of rock damage. In addition, after the 50 th freeze-thaw cycle, the growth rate of tension cracks decreased, and the growth rate of shear cracks increased. This further indicates that the higher the freeze-thaw cycles, the more prone the sandstone is to shear damage.  
Figure 22: Variation of acoustic emission parameters during loading and unloading of specimens with different numbers of freeze–thaw cycles. (**a**) 0 times, (**b**) 20 times, (**c**) 40 times, (**d**) 60 times. (**e**) 80 times.
## 2 Results
### Identification of Allium Species Non-Volatile Metabolites via UPLC/PDA/qTOF-MS
#### 2.3.4 Identification of Phenolic Acids  
_Allium_ species are known to accumulate phenolic acid derivatives, i.e., caffeic and ferulic acids commonly reported in metabolite profiling studies of many plant extracts. In this study, phthalic acid (L14, \(m/z\) 165.019 [M \(-\) H]\({}^{-}\)), caffeic acid (L20, \(m/z\) 179.0346 [M \(-\) H]\({}^{-}\)), ferulic acid (L27, \(m/z\) 193.0509 [M \(-\) H]\({}^{-}\)) and caffeic acid dimethyl ether (L36, \(m/z\) 207.0658 [M \(-\) H]\({}^{-}\)) were identified. Phthalic acid volatile derivatives are the major constituent of _A. atroviolaceum_[18] and its non-volatile acid form is reported for the first time in _Allium_. A peak for diethyl phthalate acid identified from GC/MS (Table 1) in commercial garlic preparation \"Tomex\" was also detected via UPLC/MS (L40, \(m/z\) 223.0962, C\({}_{12}\)H\({}_{15}\)O\({}_{4}\) [M \(+\) H]\({}^{+}\)) though at a much lower response in positive ionization mode.
# Spam Classification Based on Signed Network Analysis
## 1 Introduction
To test our hypothesis, we employ two social psychological theories for signed networks--structural balance theory and social status theory--and the concept of surprise is adopted to quantitatively analyze the given network according to these theories. These surprise measurements are then used as the main features for spam classification. Through extensive experiments with a Twitter dataset, we observe that the proposed spam classification scheme achieves an accuracy of 93%, a precision of 95%, and a recall of 91%: These metrics are higher than those of conventional spam detection schemes. Since the proposed scheme basically utilizes edge sign information, we cannot directly apply it to unsigned networks. To handle this issue, we additionally develop a graph-converting method. Through the analysis of the Epinions dataset, we discover the fact that the edge signs have strong correlations with PageRank and Conductance scores, and based on that, we propose the assignment of positive and negative signs to the edges according to those scores. Extensive real-world dataset experiments verify that the proposed graph-converting method works properly for unsigned networks. The main contributions of this paper are as follows:  
1. To the best of our knowledge, the proposed scheme is the first spam classification method that utilizes the structural balance theory and social status theory. We verify the feasibility of the proposed scheme through extensive experiments with Twitter and Epinions datasets and a comparison with five social-network-analysis-based spam detection approaches. For applying our scheme to unsigned networks, we develop a graph-converting method. To do this, we utilize the fact that the edge signs have strong correlations with PageRank and Conductance scores from the analysis of the Epinions dataset. The remainder of this paper is organized as follows. In Section 2, we summarize several spam classification schemes related to this paper. Section 3 provides background information on the structural balance theory and social status theory. The feature validation is described in Section 4, and Section 5 presents the results of a performance evaluation. Finally, we conclude this paper in Section 6.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 6 Results
### Single Speaker Model Results  
Four speakers were selected (see 1-S in Table 4), using 1000 sentences for each speaker. Our proposed networks were trained with an 80%, 10%, and 10% split for training, validation, and testing, respectively. Corr2D, PESQ, OOSQ, and STOI were applied for evaluation, as discussed in Section 5.3. Additionally, to provide a visual comparison of the reconstructed results, one sample was selected from each speaker and the reconstructed spectrogram compared with the original. The results are shown in Table 5.  
Table 5 compares GaborFea2 Speech, GaborCNN2 Speech, and Lip2 AudSpec. While there are fluctuations for individual speakers, the average shows that our proposed GaborFea2 Speech and GaborCNN2 Speech models demonstrate slightly improved performance compared to Lip2 AudSpec across all measures. GaborCNN2 Speech achieves the highest scores in Corr2D (66.28) PESQ (1.689), and OOSQ (0.299), while GaborFea2 Speech outperforms GaborCNN2 Speech in the STOI measure (0.261 vs. 0.256) and Lip2 AudSpec only achieves 0.236. This demonstrates that the proposed models better preserve speech intelligibility and similarity to the original speech. Regarding OOSQ, GaborCNN2 Speech achieves the highest average score of 0.299, followed by GaborFea2 Speech with 0.282. Lip2 AudSpec obtains the lowest average score of 0.071. This is the largest difference between models and indicates that the proposed models effectively reduce speech distortion and enhance objective speech quality. The IQR values highlight the variation in model performance across different measures. GaborCNN2 Speech generally demonstrates a more consistent performance based on lower IQR values, suggesting its stability across the evaluated measures. Conversely, GaborFea2 Speech and Lip2 AudSpec exhibit larger IQR values, indicating more dispersion in their results. Figure 13 shows examples of reconstructed spectrograms. Figure 13a(1)-a(3) shows the original spectrograms of four selected speakers (S1, S25, S27) along with their corresponding sample sentences: \"bin blue by f 8 please\"; \"bin green at 1 q now\"; \"bin blue with x 8 now\"; \"bin blue by 8 now\". Figure 13b(1)-b(3) shows reconstructed GaborFea2 Speech spectrograms, Figure 13c(1)-c(3) shows the spectrograms reconstructed with GaborCNN2 Speech. For comparison, Figure 13d(1)-d(3) shows equivalent Lip2 AudSpec spectrograms. The comparison clearly shows that our proposed models outperform the baseline model in terms of spectrogram reconstruction. Our spectrograms exhibit enhanced clarity and sharper spectral peaks, indicating a higher fidelity in reproducing the original speech characteristics. Notably, our GaborFea2 Speech method demonstrates superior accuracy in reconstructing the frequency space and maintaining the temporal continuity of phonemes across window samples. It also demonstrates superior accuracy in reproducing the original speech characteristics, especially for capturing high-frequency information. In contrast, the baseline model struggles to retrieve high-frequency information.