esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### AnFIS  
For the purpose of this investigation, the data (training data and testing data) were loaded into the model, a FIS was generated and trained, and then its reliability was tested  
Figure 6: Major steps of implementation of hybrid ANFIS-PSO and ANFIS-GA models.  
with the training data and the testing data, as shown in Figure 7. The Takagi-Sugeno fuzzy inference system (FIS) was used, and eight Gaussian membership functions were used for each input variable, while a linear membership function was used for the output. A total of eight rules were applied to define the FIS, whose structure is shown in Figure 8.  
Figure 7: Testing the FIS with (**a**) training data, and (**b**) testing data. The red asterisk represents the testing of the data in the ANFIS architecture in the MATLAB environment.  
It can be observed from Figure 7 that the set FIS is reliable for training and testing because the errors are negligible compared to the range of total power output.  
Figure 9 displays a rule viewer of the model, while Figure 10 shows the surface viewer for two input parameters, namely ambient temperature (tamb) and direct normal irradiation (DNI). The output power (W_output) in Figure 9 is to be multiplied by \(10^{5}\). The rule viewer also shows reasonable results.  
Figure 8: Structure of the FIS used in this investigation.  
Figure 9: Rule viewer for the trained ANFIS model.  
As expected, it can be observed from the surface viewer that the power output increases as the ambient temperature and the direct normal irradiation increase.  
The ANFIS model appraisal was also conducted by means of the coefficient of correlation, R\({}^{2}\), obtained from the regression plots shown in Figure 11 for training, testing, and overall results.  
Figure 11: ANFIS regression plots for (**a**) training, (**b**) testing, and (**c**) all data. Note: Blue line represents the liner data.  
Figure 10: Typical surface viewer of the trained ANFIS model.  
It emerges from Figure 11 that the ANFIS model performed well, with an overall correlation coefficient of 0.9991.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 3 Results
### The Impacts of SM and VPD on SIF  
This section delves into the correlation between vegetation growth (measured by SIF) and VPD and SM. In Figure 8, red indicates a positive correlation between two variables. This means that as one variable increases, the other variable also increases. The deeper the red, the stronger the positive correlation; conversely, lighter shades of red indicate a weaker positive correlation. On the other hand, blue represents a negative correlation between two variables. This implies that as one variable increases, the other variable decreases. Deeper shades of blue indicate a stronger negative correlation, while lighter shades indicate a weaker negative correlation. We found that except for certain areas in the southwest and a few eastern regions where the opposite occurs, overall anomalies in SIF show a positive correlation with anomalies in SM and a negative correlation with anomalies in VPD. This indicates that in most regions, a decrease in SIF accompanies a decrease in SM and an increase in VPD. Excluding the inconsistencies in the southwest, it can be concluded that the correlation of SM with SIF (R = 0.30) and VPD with SIF (R = \(-\)0.28) are roughly equivalent. This suggests that during compound dry-hot periods, the correlations of SIF with SM changes and SIF with VPD changes are consistent. Although the correlation with SM changes is slightly higher, it does not demonstrate a significant advantage over VPD changes.
## 4 Experimental and Results
### Ablation Experiments and Methodological Analysis  
#### 4.5.1 Ablation Study  
We implemented an ablation study to verify the effectiveness of different scales for HierTTS. We chose comparative MOS (CMOS) with seven points (from \(-3\) to \(3\)) to evaluate the example by pairs. Results are summarized in Table 2: (1) By removing the HAE, HierTTS degrades into a simple cascaded TTS model, which brings a CMOS drop of \(0.24\). The performance loss is mainly manifested in unnatural rhythms and stress. For example, an unexpected pause occurs in the middle of a word, or there are multiple accents in a sentence. Additionally, some words are mispronounced. This may be because the text in the training data are sparse, and the generalization problem can be severe when the latent space is unconstrained, thereby affecting the pronunciation. For example, there are speech pauses within words or even multiple stresses within a sentence. In addition, some words are mispronounced. This may be because the text in the training data is sparse, and when the latent space is not constrained, the generalization issue will be serious, which will affect the pronunciation. (2) Removing the sentence-level latent variable brings a CMOS drop of \(0.33\). The sentence-level information encodes the overall prosody. After removing the sentence level, the prosody fluctuates too much in a sentence, which also leads to unnatural prosody. Occasionally, the speaking speed suddenly becomes fast or slow. (3) Removing word-level latent variables brings a CMOS drop of \(0.22\). After the word level is removed, the model loses the ability to recognize word boundaries, and the most direct impact is wrong pauses, especially for some nouns, such as names of people and places. (4) Removing the subword-level hidden variable brings a CMOS drop of \(0.18\). The rhythm of the generated speech is not coherent enough--e.g., it will be read word by word. (5) Removing staged weight annealing brings a CMOS drop of \(0.42\). KL divergence at the sentence and word level quickly drops to zero, and sampling over the corresponding hidden variable will not affect generated speech. Synthesized speech also suffers from serious prosody problems.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**System** & **CMOS** \\ \hline -HVAE & \(-0.24\) \\ -Sentence level & \(-0.33\) \\ -Word level & \(-0.22\) \\ -Subword level & \(-0.18\) \\ -Staged weight annealing & \(-0.42\) \\ \hline HierTTS & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: CMOS evaluation of ablation studies.  
Figure 4: Example histograms of utterance length, utterance average pitch, and pitch standard deviation within an utterance. One-hundred samples were generated from each model.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 5 Battlefield Simulation Model
### Battlefield Applications  
#### 5.2.1 Overview of MANETs in Battlefield Applications  
One of the MANET applications that is most often quoted in the literature is a battlefield (e.g., [PERSON] et al., 2004, [160]; [PERSON] and [PERSON], 2004, [161]; [PERSON] and [PERSON], 2008, [162]; [PERSON] and [PERSON], 2012, [163]; and [PERSON] et al., 2012, [164]). This application exploits the key characteristic of a MANET; that is, the environment is unpredictable, and the nodes are likely to be deployed rapidly. It is unlikely that there will be sufficient time to set up an infrastructure in a battlefield to support a communications network with either power or wired data links. Prior to the actual battle, stealth may be required, precluding any significant engineering works. Any infrastructure that is preinstalled will be highly vulnerable to enemy attack and would therefore not provide a reliable communication system. The nodes (soldiers and possibly vehicles, such as tanks) need to have great flexibility in their movement whilst maintaining communication links with each other and with the command headquarters. Individual nodes are also vulnerable to enemy attack, and this means that at any time, a particular route could be broken, or the network could be partitioned. Thus, the use of a mobile ad hoc network with its self-configuration, dynamic reconfiguration, and deployment flexibility could provide a significantly more reliable communications facility than a conventional network in a battlefield situation.
# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 3 Experiments
### Qualitative Evaluation  
Figures 4 and 5 show the qualitative comparison results of our method with other state-of-the-art methods on real and synthetic datasets, including JSTASR [9] (ECCV'2020), HDCWNet [10] (ICCV'2020), TKL [17] (CVPR'2022), and LMQFormer [18] (TCSVT'2023). As shown in Figure 4, the images restored by the JSTASR method exhibit significant black artifacts and snowflake noise, resulting in poor visual quality. While HDCWNet and TKL are capable of removing a substantial portion of snowflake artifacts, residual noise remains visible. LMQFormer effectively eliminates snowflake interference and produces clean snow-free images, yet minor artifacts persist. In contrast, the algorithm proposed in this paper achieves more precise snow removal while preserving richer background details. The restored images are free from black artifacts and exhibit higher overall quality compared to existing methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Patches** & \(128\times 128\) & \(192\times 192\) & \(224\times 224\) & \(256\times 256\) & \(384\times 384\) & \(464\times 464\) \\ \hline
**Iterations (K)** & \(128\) & \(128\) & \(92\) & \(92\) & \(64\) & \(64\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameter setting table for iterations and patches; “K” represents thousand.  
Figure 4: The qualitative comparison results on the three major benchmark synthetic datasets.  
Figure 5: The qualitative comparison results on the real dataset.  
Figure 5 demonstrates the comparative results between the proposed method and other state-of-the-art approaches on a real-world dataset. It is evident that some existing methods still leave noticeable snowflake particles unremoved. In contrast, our method demonstrates superior precision in identifying and eliminating snowflakes, exhibiting a clear advantage in desnowing performance.
## 6 Conclusions  
This study investigated the compressive strength of the high strength concrete and suggested the estimation equation for non-destructive tests and core strength tests. The conclusions are as follows:  
1. In this study, the compressive strength of high strength concrete (greater than 40 MPa) was evaluated and an estimation equation was suggested that uses a dual regression approach in conjunction with rebound hammer tests and the ultrasonic pulse velocity method. In comparison to previous formulas, the proposed estimation equation showed the highest accuracy and the lowest \(RMSE\) in predicting compressive strength. Thus, the formula suggested in this study can be effectively used to estimate the compressive strength of a high strength concrete member.
2. Analytical comparison of the concrete core and cylinder specimen revealed that the compressive strength of the concrete core specimen was lower than that of the concrete cylinder. This must be due to the inherent damage caused during the extracting process of the cores, and the lower amount of coarse aggregates within the specimen. Thus, the strength correction factor of high strength concrete should be further researched using more experimental tests or damage image processing. Smaller-sized concrete core specimens can be reasonably used to estimate the compressive strength of concrete with minimum destructive damage to high strength concrete members.
3. This study investigated a strength correction factor (\(\gamma\)) that converts the compressive strength of concrete core specimens to the compressive strength of the concrete cylinder at 28 days. The strength factor conversion coefficient was obtained by averaging the data from an entire mix series for each specific core diameter. This value is not deterministic but is useful in estimating the correlation between concrete cores and cylinder specimens made from high strength concrete. This factor can be further updated and modified when more test results are available.
4. Using a combination of both estimation equation and strength correction factor for high strength concrete may be a good strategy for the practical maintenance of NPP members made of the high strength concrete.  
Figure 10: Average strength ratio for core specimens according to core diameter.  
**Acknowledgments:** The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a grant (#2015R1A2A2A01005286) from the National Research Foundation of Korea (NRF) and a grant (#20171510101910) from the Korea Institute of Energy Technology Evaluation and Planning (KETEP) funded by the Ministry of Trade, Industry and Energy.  
**Author Contributions:** [PERSON] and [PERSON] designed and performed the experiments; [PERSON] and [PERSON] analyzed the data and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
## 3 Simulation Results
### Position  
The ABH effect exerts a significant influence on the flexural wave behavior of its carrier. This is primarily manifested through a reduction in the wave propagation speed \(c_{\text{flex}}\) in the area of the ABH, which consequently leads to a decrease in the wavelengths \(\lambda_{\text{flex}}\) and an increase in the amplitude \(\Delta_{nx,ny}\) within this area. This leads to a drastic shift in the tensions of the panel, which is expressed through changed mode shapes. Given these impacts, the positioning of the ABH is of great importance and is thus the primary parameter to be analyzed.  
The initial ABH presented in Table 3 is moved around in a grid of 3 \(\times\) 3 positions with a distance of 10% of the panel dimensions, as shown in Figure 4a. The starting point P11 is the center of the panel, for which the contour of the ABH with a radius \(a=10\) cm is represented in the figure by the dashed circle. The whole circular ABH structure is shifted, with the thinnest point in the center located at the specified position.  
The investigations conducted in this section were performed in the absence of the air cavity, so the Modal Superposition Solver was used instead of the Full Method Harmonic Response Solver in ANSYS. This approach facilitated the illustration of mode shapes across all the modes, inclusive of the symmetric even modes, which would have been otherwise suppressed at the ABH positions on the symmetry lines.  
A frequency response is considered superior to another if it shows an increase in the normalized mean surface velocity \(\frac{\overline{v}_{x,y}}{p}(f)\). In addition, the responses with the most even frequency spacing of the modal peaks on the logarithmic frequency axis are favored.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Initial ABH** \\ \hline Residual thickness \(h_{\text{ABH}}/\text{mm}\) & 0.1 \\ Radius \(a/\text{m}\) & 0.1 \\ Power-law factor \(m\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Initial Acoustic Black Hole parameters used in the simulations.  
The evaluation of the simulation outcomes needs to be split into smaller subsets. The partitioning is achieved by assigning the results of the three axes, commencing from the center. The sequence of analysis begins with the x-axis, followed by the y-axis, and finally, the diagonal axis. The best results of these preliminary analyses are then compared in a further graph.
# Recent Developments and State of the Art in Flexible and Conformal Reconfigurable Antennas
## 2 Frequency-Reconfigurable Antennas
### Mechanically Frequency-Reconfigurable Antennas  
Aside from the aforementioned challenges, the mechanical stability and rigidity of the frequency reconfigurable antennas based on electrical switches (PIN diodes and varactors) are not sufficient for some applications such as wearable antenna for gadget [63]. The high strength fields in MEMS switches limit the antennas with this type of switch to low-power applications [27]. To overcome these  
Figure 2: The fabricated frequency-reconfigurable antenna on polydimethylsiloxane (PDMS) substrate in [24]: (**a**) front view; (**b**) back view; (**c**) side view; (**d**) bending position.  
issues, the new categories of stretchable antennas such as microfluidic antennas, antennas based on micromesh structures, and origami-based antennas have been introduced. In the following sections, different categories of the mechanical frequency reconfigurable antennas with their existing challenges are presented.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 5 Experimental Evaluations
### Comparative Evaluations
#### 5.5.3 Evaluation on Privacy  
In this subsection, we evaluate location privacy protection algorithms' privacy guarantee by the location privacy attacks metric. Experimental results are shown in Figure 5. A larger ordinate indicates less knowledge that the adversary obtains after observing the obfuscated trajectory dataset, which further implies a stronger defense against privacy attacks. We summarize the results and analyze the reasons, respectively.  
As depicted in Figure 5, (i) generally, the location privacy guarantee increases with the decrease of privacy budget \(\epsilon\). (ii) UDPT performs better than its competitors. The reasons for the experimental results are as follows.  
(i) According to the definition of differential privacy, a smaller privacy budget usually brings about more noise, a.k.a, larger randomness, being injected into the mobility features of the obfuscated trajectory dataset \(D_{O}\). Consider the worst case where the privacy budget was the smallest. The trajectories in \(D_{O}\) would be highly random, and the mobility features in \(D_{O}\) would converge to population averages rather than maintaining the data utility in the actual trajectory dataset \(D_{A}\). However, recall the description of the privacy metric in Section 5.4, the adversary was assumed to have already obtained the mobility features of population average as a prior belief before observing \(D_{O}\). Therefore, in this case, the adversary gained no extra knowledge after observing \(D_{O}\), which indicated that the strongest guarantee of privacy was provided. On the contrary, if the privacy budget were largest, the adversary would gain much knowledge after observing \(D_{O}\), which indicated that the privacy guarantee was weakest. This explains the observation that the location privacy guarantee increases with the decrease of privacy budget \(\epsilon\).  
(ii) AdaTrace generates obfuscated trajectories based on grid trajectories, where each obfuscated location would be uniformly chosen from the grid at random. Since the grid is a plane having infinite locations, the obfuscated locations have a great chance to be unique locations that only appear in \(D_{O}\) rather than \(D_{A}\). Therefore, the posterior probability distribution \(\Pr_{l|D_{O}}\) regarding the individuals who visit the sensitive location \(l\in\mathcal{S}\) might have a larger difference from the prior probability distribution \(P_{l}\). A larger difference implies a more serious threat of location privacy attacks, in other words, a lower privacy guarantee of AdaTrace. In contrast, UDPT's generation of obfuscated locations is guided under the multi-objective optimization algorithm as well as the Viterbi algorithm, which brings much less randomness to the obfuscated trajectory dataset than AdaTrace. This is the reason for the observation that UDPT performs better than its competitors in terms of privacy metrics.
# Temporal Convolutional Network for Carbon Tax Projection: A Data-Driven Approach
## 2 Related Work
### Deep Learning for Time Series Forecasting  
The application of deep learning techniques to time series forecasting has gained significant traction in recent years. [PERSON] et al. [16] introduced the temporal convolutional network architecture, demonstrating its effectiveness in sequence modeling tasks across various domains. Their work showed that TCNs could outperform traditional recurrent neural networks in many sequence modeling tasks, while offering better parallelism and more stable gradients.  
The TCN architecture addresses several limitations of traditional recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. By using dilated causal convolutions, TCNs can efficiently capture long-range dependencies in the input sequence, without the vanishing gradient problems often associated with RNNs. Moreover, the convolutional structure allows for parallel processing of inputs, leading to faster training and inference times.  
[PERSON] et al. [17] applied convolutional neural networks to financial time series forecasting, showcasing the potential of these architectures for capturing complex temporal dependencies in financial data. Their work provides a foundation for applying similar techniques to carbon price forecasting, given the financial nature of carbon markets.  
The authors demonstrated that convolutional neural networks (CNNs) could effectively capture both short-term and long-term patterns in financial time series data. By treating the time series as a one-dimensional image, the CNN could learn hierarchical features that represent different temporal scales. This approach showed promising results in predicting stock prices and volatility, outperforming traditional time series models in many scenarios.  
In the context of environmental and energy-related forecasting, [PERSON] et al. [18] applied deep learning techniques to predict wind power generation. Their study compared various deep learning architectures, including CNN, LSTM, and hybrid models, demonstrating the potential of these approaches in handling the complex, non-linear relationships inherent in renewable energy forecasting.  
Our study builds upon these foundations, leveraging the strengths of TCNs in capturing long-range dependencies and handling non-linear relationships to improve the accuracy of carbon tax price projections. By adapting the TCN architecture to the specific challenges of carbon pricing, we aim to provide a more robust and accurate forecasting tool for policymakers and stakeholders in the carbon market.