esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight End-to-End Speech Enhancement Generative Adversarial Network Using Sinc Convolutions
## 6 Experimental Setup  
### Database  
To assess the performance of proposed Sinc-SEGANs, we report objective measures on the Valentini [39] benchmarks. This publicly available dataset is originated from the Voice Bank corpus [40], which contains 30 speakers, of which 28 speakers are included in the training set while the remaining 2 are included in the test set. For the noisy training set, 40 different noisy conditions are considered by combining 10 sorts of intrusions (2 artificially generated and 8 derived from the Demand database [41]) with 4 different signal-to-noise ratios (SNRs) each (15, 10, 5, 0 dB). For the test set, 20 different noisy conditions are considered by combining 5 sorts of intrusions (all originated from the Demand database)  
Figure 1: Illustration of the addition architecture of **(a)** the generator and **(b)** the discriminator, where the Sinc convolution sits before the first layer of the encoder and the discriminator, and behind the last layer of the decoder. Skip connections with learnable \(a_{l}\) are depicted in pink boxes, which are summed to each intermediate activation of the decoder.  
Figure 2: Illustration of the substitution architecture of **(a)** the generator and **(b)** the discriminator, where the Sinc convolution acts as the substitute of the first standard convolutional layers of the encoder and the discriminator, and the last standard convolutional layer of the decoder. Skip connections with learnable \(a_{l}\) are depicted in pink boxes, which are summed to each intermediate activation of the decoder.  
with 4 SNRs (17.5, 12.5, 7.5, and 2.5 dB) each. There are 10 different utterances in each adverse condition per training speaker; while, per test speaker, there are 20 utterances in each condition. Notably, the test set is entirely unseen by the training set; that is, no overlap exists in either speakers or adverse conditions. All utterances are downsampled to 16 kHz. Table 1 demonstrates the data structure of the employed corpus [39].
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Error Analysis
#### 4.3.2 RMSE, MAE, and \(R^{2}\) Comparisons  
Figure 7 summarizes the comparative performance of all models on the test set across three widely used regression metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (\(R^{2}\)). This unified bar chart provides a holistic view of model accuracy and consistency. The proposed Enhanced ResNet consistently outperforms baseline models across all metrics. Specifically, it achieves the lowest RMSE of 23.02 Mg/ha, significantly improving upon ViT (29.55 Mg/ha), GBDT (29.95 Mg/ha), and RF (30.27 Mg/ha). The addition of multiscale residual blocks (MSRBs) alone reduces RMSE to 25.36 Mg/ha, while integrating the Squeeze-and-Excitation (SE) module further refines the estimate, demonstrating the additive effect of attention-based spectral weighting.  
For MAE, the Enhanced ResNet achieves 17.15 Mg/ha, a substantial reduction compared to 21.52 Mg/ha for ViT and over 27 Mg/ha for RF and GBDT. This reflects improved stability and reduced average deviation, which are crucial for operational carbon accounting. In terms of \(R^{2}\), the Enhanced ResNet attains a score of 0.773, indicating the strongest agreement with ground-truth data. This represents a 7.5% improvement over ViT (0.719), and a 15.1% improvement over GBDT (0.643). Even without the SE module, the MSRB-only variant achieves a solid \(R^{2}\) of 0.741, underscoring the benefits of multiscale spatial modeling in capturing forest heterogeneity.  
These results collectively validate that each architectural component contributes meaningfully to model accuracy and robustness. The consistent improvements across RMSE, MAE, and \(R^{2}\) highlight the advantage of combining spatial feature diversity and adaptive spectral weighting for forest carbon estimation in complex and heterogeneous landscapes.  
Figure 6: Feature importance scores of input variables based on permutation analysis. Top features include Tree Cover, Slope, and Red-edge bands.
# Benchmarking Deep Learning Models for Instance Segmentation
## 3 Background  
### Feature Pyramid Network (FPN)  
Detecting objects of various sizes existing in an image is a key issue in the object detection task. Although various methods have been proposed that can adequately detect an object regardless of its size, it is being argued that the inference speed of current models is slow and that they are memory-intensive [18]. To address this issue, a method has been proposed for detecting objects of various sizes while being less computationally expensive through the FPN [18] (Figure 1(d)).  
The featurized image pyramid method resizes input images to various sizes and extracts feature maps from each resized image, such that feature maps of various sizes can detect large and small objects well. However, because a CNN is applied to each resized image, the method is computation- and time-intensive. The single feature map method extracts feature maps from an input image and detects objects only with the final feature map. Its learning and inference are fast because a CNN is applied only once. However, its performance is poor because only the final feature map with low resolution is used. The pyramidal feature hierarchy method extracts feature maps from an input image and performs object detection on each extracted feature map. Although detection is performed while maintaining information about small objects, the feature map of the already generated upper level is not reused and does not reflect the low-resolution feature with rich semantic information. FPN first extracts feature maps of various scales through bottom-up paths. Then, through the top-down pathway, the low-resolution feature map combines bottom-up feature maps with lateral connection while increasing the resolution. The authors of [18] argue that by combining feature maps with rich semantics information in a top-down manner and feature maps that preserve location information in a bottom-up manner, strong feature maps can be rapidly generated at various scales.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Electrical Quality Metric  
In SRAM design, a balance is required among various electrical characteristics such as static noise margin, access speed, and power consumption, and these characteristics often show a trade-off relationship. For example, increasing the transistor size improves SNM or access speed, but at the same time, the area and power consumption also increases. The performance metrics are summarized in Table 1. To comprehensively evaluate these complex interrelationships, this study proposes an electrical quality metric (EQM) that can quantitatively compare the electrical performance of SRAMs.  
The EQM is expressed as a single numerical value that integrates various design indices such as the RSNM, RAT, WAT, WWTV, HPWR, and Qc, and can quantitatively compare the performance of different SRAM structures. As shown in Figure 14, the WRTU-16T recorded the highest EQM value among the five compared SRAMs, which was up to 136 times higher than that of the SUR-16T and at least 11 times higher than that of the RHSCC-16T [14].
## 4 Results and Discussion
### Prediction Results  
The prediction results of the sub-series for the KSE-100 index closing stock price are presented in Figures 6-9. The prediction accuracy of the low correlated IMFs with the actual stock price is relatively low due to the high amplitude and high frequency of the components, whereas for the high correlated IMFs, including the single monotone residue that represents the long-term trend of the index data the predicted value is close to the actual value.  
According to Figures 6-9, there is a small difference between the predictive and actual values of the sub-components of the KSE-100 index closing price obtained by implementing the hybrid Akima-EMD-LSTM. The proposed new version of EMD has the potential to solve the problem of overshoot and undershoot, and wiggles at both ends using the benchmark EMD, and hence the prediction accuracy improves the prediction accuracy. It can also be seen from Table 8 that the forecast accuracy in terms of RMSE, MAE, and MAPE of our proposed hybrid Akima-EMD-LSTM model is maximum than the hybrid EMD-LSTM, EEMD-LSTM, SEMD-LSTM model and therefore used for the final prediction of the KSE-100 index closing price. The actual prediction results of the KSE-100 index are visualized in Figure 10 below, which shows that our proposed hybrid model not only predicted the stock price values precisely but also predicted its up, and down pattern in a more accurate way than the usual classical time series or any other machine learning methods without hybridization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Epochs** & **Batch Size** & **Dropout** & **Hidden Units** & **Hidden Layers** \\ \hline IMF5 & 250 & 64 & 0.2 & 50 & 04 \\ \hline IMF6 & 250 & 64 & 0.2 & 50 & 04 \\ \hline IMF7 & 200 & 32 & 0.2 & 50 & 04 \\ \hline IMF8 & 250 & 32 & 0.2 & 50 & 04 \\ \hline IMF9 & 200 & 32 & 0.2 & 50 & 04 \\ \hline Residue & 100 & 16 & 0.2 & 50 & 04 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Hyper-parameters of the Akima-EMD-LSTM model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Epochs** & **Batch Size** & **Dropout** & **Hidden Units** & **Hidden Layers** \\ \hline IMF5 & 250 & 32 & 0.2 & 50 & 04 \\ \hline IMF6 & 250 & 32 & 0.2 & 50 & 04 \\ \hline IMF7 & 150 & 32 & 0.2 & 50 & 04 \\ \hline Residue & 150 & 32 & 0.2 & 50 & 04 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Hyper-parameters of SEMD-LSTM model.  
Figure 6: Predicted results of correlated subcomponents obtained from the EMD-LSTM model.  
Figure 7: Predicted results of correlated subcomponents obtained from the EEMD-LSTM model.  
Figure 8: Predicted results of correlated subcomponents obtained from SEMD-LSTM model.  
Figure 9: Predicted results of correlated subcomponents obtained from the proposed Akima-EMD-LSTM model.
## 1 Introduction
[19] used numerical simulation to study the mechanical response of a small clear-distance tunnel in Xiamen and compared the mechanical response of the tunnel under different excavation methods, different feeds, and different surrounding rock grades. [PERSON] et al. [20] investigated the interaction of a large-section parallel tunnel in hard clay soils by building a three-dimensional finite element model. [PERSON] et al. [21] used Ansys finite element software to study the excavation sequence problem of a shallow-buried asymmetrical loading small clearance tunnel under different conditions and came up with a reasonable excavation sequence for different spacing, slope, and burial depth. [PERSON] et al. [22] proposed a hybrid construction method for a small clear-distance tunnel and compared it with the CRD construction method using numerical simulation to verify the effectiveness of the hybrid construction method. [PERSON] et al. [4] conducted a numerical simulation of the whole process for a shallow-buried, large-span, double-arch tunnel under the faulted and broken zone of the highway. [PERSON] et al. [23] analyzed the interaction of the two tunnel tubes using the finite element method to derive the relative change processes of stress and strain. [PERSON] et al. [24] analyzed the construction process of the double-arch tunnel using the numerical simulation model as well as on-site monitoring and found the deformation pattern of the support structure. [PERSON] et al. [25] studied the effect of double-arch tunnels on transmission tower foundations via numerical simulation and compared the mechanical correspondence of tower foundations under different construction schemes and different construction sequences. However, in the existing studies, most of the scholars have studied only one type of small clear-distance tunnel or double-arch tunnel. There is a lack of research on small clear-distance, double-arch tunnels, especially in asymmetrical terrain and fractured surrounding rock conditions. Therefore, in this paper, we used numerical simulation to study the behavior of the construction mechanics of a small clear-distance, double-arch tunnel under an asymmetrical load. The aim is to explore its reasonable construction sequence as well as construction methods. The findings of the study can provide references for similar projects.
## 3 Results and Discussion
### Hardening Rate  
The influence of the accelerator additive was analysed according to the early hardening rate of the concrete estimated, since it is an important aspect in optimizing pre-cast concrete manufacturing. To study this parameter, the mixtures RC-30, RC-30-Acel and RC-30-Acel_x2 were compared, since these dosages were designed using the same raw material proportions, with the only change being in the accelerator additive rate. Figure 5 shows the compressive strength evolution over time for the mixtures with different additive rates. Figure 5 is also distributed in two plots according to the two types of concrete analysed: RC-30 (Figure 5a) and PC-45 (Figure 5b).  
Thus, as Figure 5 shows, the effect of the accelerator additive was notable, since an increment of 25% (65.0 MPa versus 52.4 MPa) on the compressive strength was obtained for the mix with accelerator compared to the mixture without it. Note that the use of additive accelerator did not modify the \(\mathrm{W_{ef}/C}\) ratio. Furthermore, the results also show that the analysed additive rate had a limited effect on strength improvement. Thus, despite the use of double the additive rate (from 0.36% to 0.72% of the cement volume), both mixtures reached similar final strength levels, albeit with a significant increase in the cost of the mix (EUR +1.5/m\({}^{3}\)).  
Figure 5: Effect of accelerator additives on RC-30 (**a**) and PC-45 (**b**) mixtures.  
In the case of PC-45 mixtures, the difference between conventional accelerator and nanocomponents was analysed. Thus, as Figure 5b shows, the use of nanocomponents generates a different behaviour regarding the strength evolution on the hardening rate. Although the improvement in compressive strength at 28 days was not significant (64.2 and 65.4 MPa), the results show a substantial effect on the strength hardening from the curing age of 3 days, with an improvement of 5.5 MPa at 7 days. This effect can be favourable in the stripping process of some concrete structural parts. Thus, nanocomponent additives enhance the compressive strength by around 7% at 7 days, i.e., 90.8% vs. 84.0% of the compressive strength at 28 days for the conventional additive. However, this improvement also entails an increment of EUR 7.1 (+15%) over the control mix, representing an extremely high increase in cost for many manufacturing situations.  
Nonetheless, the results for improving the hardening at early stages, even before the first 24 h, to streamline the production of elements and save costs were unsatisfactory. In the experiment, the strength evolution for both mixtures up to the first 72 h was similar in value and tendency (Figure 5b). Hence, for these experiments, the increase in the unitary cost of this new additive was not justified by either the hardening rate or the final strength increment.
# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
## 3 Results
### Conflicts Spatial Distribution Characteristics
#### 3.1.2 Distribution Characteristics of Two Proposed Conflict Types  
For Conflicts Type 1:  
1. On each conflict heat map of each roundabout, it is found that the area of Conflict Type 1 is larger than Conflict Type 2. Due to e-bikes mainly occupying the two outer vehicle lanes when driving around the roundabout, Conflict Type 1 mainly concentrated on the outermost circulatory lanes. Therefore, when vehicles turn right to exit, conflicts with e-bikes will occur there.
2. In the interweaving area also could be seen conflicts, which mainly occur on the outer vehicle lanes. When vehicles and e-bikes were driving into the roundabout, both of them failed to follow the guidance as e-bikes' moving trajectories are chaotic without regulations which made right-turning vehicles occur in conflicts with e-bikes before they leave the roundabout. For conflicts of Type 2:  
1. The area of Conflict Type 2 is smaller than Conflict Type 1 and mainly concentrated inside of the outermost lane and behind the second stop line. In this situation, left-turn e-bikes are most likely to occur in conflicts with vehicles, because left-turn movement will require the longest detour distance. Considering the efficiency, e-bikes may tend to violate the traffic regulations to choose the shortest vehicle lanes rather than the outermost e-bike lane. Under this situation of mixed traffic of vehicles and e-bikes, conflicts occur when vehicles leave the roundabout.
2. The first reason why Conflict Type 2 occurs less than Conflict Type 1 is that left-turn e-bikes are less than go-straight e-bikes; the second is that left-turn e-bikes need to wait twice when they in the roundabout. As the conflict concentrated area is closer to the secondary stop line and e-bikes are in a state of deceleration, the conflicts in those areas are more discrete than conflicts of Type 1.
3. Among these three roundabouts, the severity of Conflict Type 2 at Chenyangzhai is most serious. This is due to the largest left-turn e-bikes volume and the layout form of the second stop line. The layout form of the second stop line in Chenyangzhai is a parallel layout, which means both inner and outer lanes' stop lines are on a line, and the conflict area is farther away from the stop line position. There are fewer vehicles that would like to slow down, so conflicts are more likely to occur. While the layout of the second stop line at Hanguang and Zhuque roundabout is stepped type (the stop line of the inner lanes is in front and the outer stop line is behind), the vehicles on the outer lane (the main subject that conflicts with e-bikes) will be easier to decelerate and stop when passing through the conflict area.  
(4) Conflict Type 2 could also be seen in the weaving area, and the happen mechanism is the same as Conflict Type 1.
# Partial Discharge Data Augmentation and Pattern Recognition Method Based on DAE-GAN +
## 4 Case Analysis
### Evaluation of Data Augmentation Capability of the DAE-GAN Model  
The effectiveness of data augmentation in the DAE-GAN model is dependent on its ability to generate samples, which is primarily evaluated by the authenticity of the generated data and how well the probability distributions are fitted. To provide a clear comparison of the model's generative capabilities, this study contrasts it with three other models: CWGAN-GP, BEGAN, and VAE. The performance of these models is assessed based on two main factors: the diversity of the samples generated and their similarity to real-world samples. Each of these models was trained using the sample library data presented in Table 1.
CT dose reduction protocols have been developed amongst researchers and a variety of strategies have been established to minimize CT dose without compromising diagnostic image quality [25; 26; 27]. These include reducing tube current (mAs) and tube voltage (kVp), automatic attenuation-based tube current selection, decreased pitch factor, increased slice collimation and improper positioning [28; 29]. Several researchers have proposed CT dose reduction by increasing the pitch value combined with the iterative reconstruction method to obtain significant dose reduction of the normal chest and abdominal scans while preserving the consistency of the image quality [28; 30; 31]. The vigorous action by the institution in setting the best optimization protocols, particularly for pediatric patients, is an indispensable part in reducing dose exposure in CT examination. Therefore, the study highlights the current finding in optimizing radiation doses from altering pediatric CT CAP scan parameters in terms of tube voltage, tube current, slice collimation, pitch, and effect of tube current modulation (TCM) application, as well as the effect on image quality indices in regard of image noise, signal to noise ratio (SNR), and contrast to noise ratio (CNR).
## 3 Results and Discussion
### Cooking Loss and WBSF
\begin{table}
\begin{tabular}{c c c c} \hline
**HT Method** & **CT (Min)** & _CL (\%)_ & **WBSF (N)** \\ \hline CON & 47.24 \(\pm\) 3.54 & 37.12 \(\pm\) 2.56 e & 44.29 \(\pm\) 3.45 d \\ SV55 & 360 * & 22.55 \(\pm\) 2.84 b & 26.47 \(\pm\) 3.18 a \\ SV65 & 240 * & 26.28 \(\pm\) 3.08 c & 28.26 \(\pm\) 3.85 ab \\ SV75 & 180 * & 31.19 \(\pm\) 2.87 d & 32.84 \(\pm\) 3.26 c \\ \(\Delta\)T20 & 135.25 \(\pm\) 8.28 & 18.78 \(\pm\) 3.12 a & 29.65 \(\pm\) 2.98 b \\ \(\Delta\)T40 & 82.21 \(\pm\) 5.28 & 19.54 \(\pm\) 3.28 a & 33.68 \(\pm\) 3.77 c \\ R75 & 116.22 \(\pm\) 11.26 & 21.32 \(\pm\) 2.49 ab & 29.84 \(\pm\) 2.46 b \\ \hline \end{tabular}  
*–based on preliminary experiments, letters (a, b...) show the significant differences in a column (\(p\leq\) 0.05).  
\end{table} 
Table 2: The impact of the heat treatment (HT) method on cooking time (CT), _CL_, and WBSF of the product obtained from ST muscle (mean \(\pm\) SD). The denaturation of meat proteins, which involves structural changes, primarily depends on the rate at which heat penetrates the surface of the meat and the internal heat conduction within the product during heat treatment [2; 43]. [PERSON] et al. [44] show that the denaturation of key proteins, including myosin, sarcoplasmic proteins, collagen, and actin, takes place at different temperature ranges. The degree of protein denaturation during HT is determined by the heat flux supplied to the product as well as the time of exposure to the higher temperature of the heating medium [45]. [PERSON] [1] also observed that protein denaturation is influenced by both the heating temperature and the type of protein and indicated that myosin denatures at temperatures between 54 and 58 \({}^{\circ}\)C, while sarcoplasmic proteins undergo denaturation between 65 and 67 \({}^{\circ}\)C. Additionally, actin denatures at a higher temperature range, occurring between 80 and 83 \({}^{\circ}\)C. When collagen is heated in the presence of water, it dissolves, leading to the loosening of connective tissue and an improvement in meat tenderness. However, it is important to note that the denaturation of myofibrillar proteins can have the opposite effect, increasing meat hardness [46] by causing thermal shrinkage of both muscle fibers and connective tissue [47; 48].
## 3 Methodology
### System Model
#### 3.1.1 Node Deployment  
This phase denotes the overall range and procedure of the node's deployment for the proposed protocol. For testing analysis and purpose, the range of the proposed nodes is introduced. With different random clusters, these nodes are deployed underwater. A total of 250 nodes are deployed underwater in three layers, with each layer consisting of three clusters. This results in a total of nine (9) clusters. Twelve sink nodes are deployed on the ocean's surface to collect data from the underwater nodes. One base station is installed on the ground near the surface of the ocean.