esa-sceva/satcom-chunk-collection huggingface dataset:
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 3 Proposed Speech Enhancement Algorithm
### Proposed \(K_{0}(n)\) Tuning Method
Since \([\hat{\alpha}^{2}(n)+\hat{\sigma}_{w}^{2}+\hat{\sigma}_{v}^{2}]>>\hat{\alpha }^{2}(n)\) occurs during speech presence (e.g., 0.16-0.33 s or 0.9-1.06 s of Figure 6a), it may be underestimated \(K_{0}^{\prime}(n)\) as in the WGN experiment (Figure 1d). Thus, \(J_{2}(n)\) metric-based tuning of \(K_{0}^{\prime}(n)\) in speech activity of \(y(n,k)\) is inappropriate. As discussed earlier, we carried out tuning biased \(K_{0}(n)\) using the \(J_{1}(n)\) metric during speech activity of \(y(n,k)\). However, our further investigation of the \(J_{1}(n)\) metric-based tuning in Equation (33) reveals that the subtraction of \(J_{1}(n)\) from biased \(K_{0}(n)\) may still produce an underestimated \(K_{0}^{\prime}(n)\). To cope with this problem, at sample \(n\), if \(\zeta(n)=1\), we found a more effective solution for tuning of biased \(K_{0}(n)\) using the \(J_{1}(n)\) metric as:  
\[K_{0}^{\prime}(n) =\tilde{K}_{0}(n)[1-J_{1}(n)], \tag{41}\] \[=\left[\frac{\hat{\alpha}^{2}(n)+\hat{\sigma}_{w}^{2}}{\hat{ \alpha}^{2}(n)+\hat{\sigma}_{w}^{2}+\hat{\sigma}_{v}^{2}}\right]\left[\frac{ \hat{\alpha}^{2}(n)+\hat{\sigma}_{w}^{2}}{\hat{\alpha}^{2}(n)+\hat{\sigma}_{w }^{2}+\hat{\sigma}_{v}^{2}}\right],\] \[=\frac{[\hat{\alpha}^{2}(n)+\hat{\sigma}_{w}^{2}]^{2}}{[\hat{ \alpha}^{2}(n)+\hat{\sigma}_{w}^{2}+\hat{\sigma}_{v}^{2}]^{2}}.\]  
To justify the validity of \(K_{0}^{\prime}(n)\), the numerator and the denominator of Equation (41) are shown in Figure 6b. It can be seen that \([\hat{\alpha}^{2}(n)+\hat{\sigma}_{w}^{2}+\hat{\sigma}_{v}^{2}]^{2}\geq[\hat{ \alpha}^{2}(n)+\hat{\sigma}_{w}^{2}]^{2}\) during speech presence of \(y(n,k)\) (e.g., 0.16-0.33 s or 0.9-1.06 s), which causes \(K_{0}^{\prime}(n)\) to approach 1. To examine the performance of the proposed tuning algorithm in real-life non-stationary noise conditions, we repeated the experiment in Figure 2. It can be seen from Figure 7a that \(K_{0}^{\prime}(n)\) is closely similar to the oracle \(K_{0}(n)\). Specifically, it maintains a smooth transition at the edges and the temporal changes in speech regions are closely matched to the oracle \(K_{0}(n)\). Conversely, the AKF-RMBT method [22] produces a significant underestimated \(K_{0}(n)\) in speech regions. Therefore, the reduced-biased \(K_{0}^{\prime}(n)\) in the proposed method is more appropriate to mitigate the risks of distortion in the enhanced speech than that of the AKF-RMBT method [22]. We also repeated the experiment in Figure 2 except for the utterance sp05 which was corrupted by 5 dB colored (f16) noise. Figure 7b reveals that the biasing effect is reduced significantly in \(K_{0}^{\prime}(n)\) and closely similar to the oracle \(K_{0}(n)\). However, the AKF-RMBT method [22] still produced underestimated \(K_{0}(n)\) in speech regions. In light of the comparative study, it is evident that the proposed method adequately addresses the tuning of biased \(K_{0}(n)\) both in real-life non-stationary and colored noise conditions.
## 9 Samples of the Results
### Leveraging Auxiliary Tasks for Enhanced Navigation in Dynamic Mazes  
[PERSON] et al. [34] proposed a navigation framework leveraging auxiliary tasks, including depth prediction and loop closure detection, to improve the training efficiency and performance of reinforcement learning agents. Using the A3C algorithm, the authors trained agents in visually rich, 3D mazes with dynamic goals, such as random goal mazes and an I-maze inspired by rodent navigation experiments. The auxiliary tasks, predicting depth and detecting loop closures, provided additional learning signals that accelerated the development of spatial reasoning and navigation capabilities. The experiments involved static and dynamic mazes of varying sizes and complexities, with performance measured by cumulative rewards, area under the learning curve, and position decoding accuracy. Key findings demonstrated that incorporating auxiliary tasks improved data efficiency, robustness, and stability, enabling agents to approach human-level performance in some scenarios.  
Figure 20 compares the learning curves of agents with and without auxiliary tasks across different maze configurations. The figure highlights how agents augmented with depth prediction and loop closure detection achieve faster convergence and higher final performance. This result underscores the value of auxiliary tasks in enabling more efficient reinforcement learning in complex navigation domains.
# The Influence of Various Types of Functional Bread on Postprandial Glycemia in Healthy Adults
## 1 Introduction
In addition to starch composition, several factors, including particle size, degree of processing, cooking method, and food composition--dietary fiber, protein, and fat content--affect food's glycemic response [21]. A series of systematic reviews and meta-analyses of studies regarding the influence of carbohydrate quality on health revealed that low glycemic compared with high glycemic diets were associated with a reduced risk of stroke in the general population [22]. Some data indicate beneficial outcomes from low glycemic index interventions on blood pressure, inflammatory markers, kidney function, and intestinal microbiota composition in the prediabetic and diabetic population. Low-GI diets were also shown to improve glycemic control, possibly contributing to reduced body weight, BMI, waist and hip circumferences, and body fat [23]. In turn, a diet with a higher glycemic index was associated with an increased risk of adverse effects, including increased risk of cardiovascular disease and death among both participants with pre-existing cardiovascular disease and those without [24]. The GI of bread varies widely [25], and it might be influenced by the bread's composition and manufacturing conditions (fermentation, gelatinization, baking process). Adding different types of ingredients, such as seeds and grains, may affect the glycemic response and, therefore, contribute to modifying the bread's glycemic index [26]. Thus, determining commercially available bread' glycemic index (GI) is important for several reasons. Firstly, bread remains one of the most well-known and widely consumed food products not only in the population of European countries [27; 28], and secondly, the glycemic index providesinsights into how these products might affect blood glucose levels--a significant factor in preventing and managing metabolic diseases. Moreover, labeling a product as low-GI can be useful in guiding consumers in making conscious food choices. Therefore, this study aimed to evaluate the influence of various types of functional bread, including composition, type of flour, and additives, on postprandial glycemia.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 4 Results and Analysis
### Comparison Experiments of Different Models  
To verify the performance of the improved kiwifruit target recognition algorithm, the four object detection algorithms were evaluated by training different object detection models YOLOv3, SSD, and YOLOv4 on a desktop computer using the same kiwifruit dataset, using the best weights and the same test set for comparison tests (Figure 11). It can  
Figure 9: The Precision-Recall curves using different backbone networks.  
be seen from Figure 11 that the detection of both YOLOv3 and SSD algorithms showed a missed recognition of the distant fruit, the main reason for this result is the small size of the distant fruit. In addition, some kiwi fruits were not recognized due to severe covered by the branches and leaves, as only a limited number of kiwifruits' features could be detected by the feature extraction network. MobileNetV3-YOLOv4 had good detection results with only a few fruit misidentifications, which were due to branch shading and insufficient light on cloudy days. As can be seen from Table 4, the mAP, Recall, and F1 value of the improved GhostNet-YOLOv4 were higher than the other object detection algorithms at an overlap threshold of 50%. The improved GhostNet-YOLOv4 object detection algorithm occupies 42.5 Mb of memory in space, while the other unimproved object detection algorithms in the comparison test had a minimum of 101 Mb, and the YOLOv4 algorithm was six times the volume of the improved algorithm. In terms of detection time, the detection speed of the YOLOv4 model was 36 frames/s, the YOLOv3 model was 41 frames/s, the SSD model was 58 frames/s, and the improved model was 53 frames/s. The mAP value of the YOLOv4 model is higher than the other three object detection models (SSD, YOLOv3, MobileNetV3-YOLOv4), but the YOLOv4 model occupies more memory, the average detection speed was slower, and the relatively large number of parameters was not recommended for porting to embedded devices. In summary, GhostNet-YOLO4 model occupied less memory and has obvious advantages in terms of detection speed and recognition accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Network Model** & **mAP** & **Recall** & \(\mathbf{F_{1}}\) & **FPS** & **Weights** & **Precision** \\ \hline MobileNetV3-YOLOv4 & 91.44 & 86.45 & 88 & 47 & 53.7 & 90.04 \\ GhostNet-YOLOv4 & 93.07 & 92.43 & 92.0 & 53 & 42.5 & 90.62 \\ YOLOv4 & 91.79 & 87.94 & 87.0 & 36 & 244 & 85.26 \\ SSD & 85.10 & 82.88 & 82.0 & 58 & 101 & 80.69 \\ YOLOv3 & 90.95 & 86.49 & 85.0 & 41 & 235 & 84.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of different algorithms.  
Figure 10: The recognition results before and after applying improvement on YOLOv4.
## 2 Experimental Design
### Experimental Scheme  
(1) Soaking Test: To simulate shallow groundwater quality, dilute hydrochloric acid with a pH value of 5.0 was used as the acidic solution. Plastic bottles were utilized as individual containers, each with the same solution volume, and only one rock sample per bottle. The temperature test chamber used was a DW-40 low-temperature test chamber, capable of adjusting temperatures within the range of \(-\)55 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. Based on the geological environment of the area, the test chamber was set to water temperatures of 5 \({}^{\circ}\)C, 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C. The plastic bottles containing the rock samples were placed in the low-temperature test chamber and soaked for durations of 1 day, 3 days, 7 days, and 14 days. Before testing, the specimens were dried in an oven at 105 \({}^{\circ}\)C for 12 h and subsequently oven-dried at 105 \({}^{\circ}\)C for 24 h post-immersion; this was defined as one immersion cycle.  
(2) Uniaxial Compression Test: After soaking, uniaxial compression tests were conducted, The instrument used for the uniaxial tests was an electronic universal testing machine (electronic universal testing machine, Shenzhen Sanyuan Zongheng Technology Co., Ltd, Shenzhen, China), with a maximum vertical load capacity of 300 kN. The load measurement error was within \(\pm\)0.5%, the load resolution was 1/450,000, the displacement measurement error was within \(\pm\)0.5%, and the displacement resolution was 0.04 \(\upmu\)m. The uniaxial compression tests employed a displacement-controlled loading method, with a loading rate set at 0.05 mm/min. Vertical load was applied until the specimen failed, and photographs of the damaged rock were taken.  
(3) Microscopic Test: After soaking, the concentrations of Na\({}^{+}\), K\({}^{+}\), and Ca\({}^{2+}\) ions in the solution were measured using an ion concentration meter, model PXSJ-270F (PXSJ-270F Ion Meter, INESA Scientific Instrument Co., Ltd., Shanghai, China). The ion concentration measurements were accurate to \(\pm\)0.0001. (The experimental setup is illustrated in Figure 3.)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Elastic** & **Density \(\rho\)** & **Quartz (\%)** & **Albite (\%)** & **Calcite (\%)** & **Potash** & **Anorthite** & **Clay** \\
**(GPa)** & **(g/cm\({}^{3}\))** & & & & **Feldspar (\%)** & **(\%)** & **Minerals (\%)** \\ \hline
6.51 & 2.21 & 42.1 & 7.5 & 7.2 & 15.2 & 10.4 & 17.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of argillaceous siltstone.  
Figure 2: Argillaceous siltstone samples.
# Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
## 3 System Architecture, Design, and Implementation
### Data Acquisition and Statistics  
For this study, the KITTI Dataset and a fusion sensor dataset from an autonomous wheelchair guidance system for individuals with disabilities were selected after reviewing several options. These datasets were deemed most appropriate for aligning with the study's objectives and environment.  
As shown in Table 1, the KITTI Dataset, which is widely used in autonomous driving research, provides high-resolution video and LiDAR data across diverse road environments for obstacle recognition, thereby enhancing the model's generalizability in the complex road conditions encountered by electric wheelchairs. Additionally, the autonomous wheelchair fusion sensor dataset, which reflects real-world operational data, provides critical validation for obstacle recognition technology performance.  
Figure 12: A prototype of an electric wheelchair equipped with a forward situational awareness function was developed in this study.  
Figure 13: Forward situational awareness operation situation by object recognition technique.  
Large datasets, such as Cityscapes, BDD100K, and A2D2, were reviewed for broader testing. The Cityscapes dataset supports urban scene understanding and obstacle detection, while BDD100K enhances the model's adaptability with its diverse weather and road conditions. A2D2, developed by Audi, supports improved obstacle detection near roadways. Finally, the KITTI and autonomous wheelchair datasets were selected to align with the requirements of this study, ensuring robust performance evaluation in real-world settings for electric wheelchair applications.
# Image Captioning with Word Gate and Adaptive Self-Critical Learning
## Methodology
### Adaptive Self-Critical Sequence Training
In order to stabilize the estimation of baseline, we introduce a factor \(\alpha\) for \(R(\hat{w})\) as follows:  
\[\
abla_{\theta}J(\theta)=(R(w^{s})-\alpha R(\hat{w}))\
abla_{\theta}logp_{ \theta}(w^{s}). \tag{14}\]  
We argue that there exist at least two criteria for the factor \(\alpha\): on one hand, it should be able to normalize \(R(\hat{w})\) so that \(R(\hat{w})\) will not shift severely; on the other hand, it must stabilize the gap  
Figure 4: The above two figures show the unstable training problem of SCST. The \(x\)-axis is the number of iterations, and the \(y\)-axis is the CIDEr score. Image **(a)** shows the plot of CIDEr score over iterations of SCST and our adaptive SCST, and image **(b)** shows the rewards (sample reward and greedy reward) of each iteration of SCST. All models were trained with the MSCOCO dataset [8]. between \(R(\hat{w})\) and \(R(w^{s})\) to ensure that the gradient will not change rapidly. To this end, we formulate the factor \(\alpha\) in such a way that it not only takes account of the history of \(R(\hat{w})\), but also considers the average level of \(R(w^{s})\). So, the factor \(\alpha\) is formulated as:  
\[\alpha=\frac{\mathbf{E}(R(w^{s}))}{\mathbf{E}(R(\hat{w}))}, \tag{15}\]  
where \(\mathbf{E}(R(\cdot))\) denotes the expectation of the reward. In this formulation, \(\mathbf{E}(R(\hat{w}))\) is used to normalize the current greedy baseline \(R(\hat{w})\) with its expectation, and \(\mathbf{E}(R(w^{s}))\) ensures the baseline has a similar level with the expectation value of \(R(w^{s})\). It is not realistic to compute \(\mathbf{E}(R(w^{s}))\) and \(\mathbf{E}(R(\hat{w}))\). Instead, we use the mean value of \(R(\cdot)\) in previous \(v\) iterations (once the optimization for a mini-batch is defined as an iteration) as a estimation for the expectations. Although the history factor \(\alpha\) can normalize the greedy baseline and stabilize the gap between \(R(\hat{w})\) and \(R(w^{s})\), it may lead the absolute value of the gap to an incorrect level (e.g., the loss may keep a negative value). Thus, we further introduce another factor \(\beta\) to adjust the tradeoff of \(R(\hat{w})\) and \(R(w^{s})\). The final equation is shown as follows:  
\[\
abla_{\theta}J(\theta)=\mathbb{E}_{w_{s}\sim p_{\theta}}[R(w^{s})\
abla_{ \theta}\log p_{\theta}(w^{s})]. \tag{7}\]0  
where \(R_{i}(\cdot)\) is the \(R(\cdot)\) in the \(i\)th iteration. Here we assume that \(t\) is the current iteration and \(h\), which is called \"history factor\", determines how many previous rewards are used to estimate the difference. Different from the history factor, \(\beta\) is a hyper-parameter which was empirically set to be 0.9. Our experiments show that the adaptive SCST method became more stable and reliable than the SCST method. It could reduce the variance of the gradient estimate, and we got better performance based on the adaptive SCST model.
## 3 Results and Discussion
### Particle Transport and Deposition
#### 3.2.2 Effect of Attachment Efficiency  
To verify whether sand grains can capture suspended particles, we evaluated the effect of particle-sand attachment efficiency using DLVO theory to calculate the secondary minimum (\(\varphi_{\text{min}}\)). Both the suspended particles and the quartz sand were negatively charged, and our experiments were performed under conditions unfavorable for attachment. After calculating the energy distribution of DLVO interactions with the separation distance at different size distributions (\(d_{10}\), \(d_{50}\), \(d_{90}\)), we observed that the depth of \(\varphi_{\text{min}}\) increases as the size increases (Figure 4). Because the particle deposition increases as the depth of \(\varphi_{\text{min}}\) increases [25], the retention also increases as the particle size increases. As expected, at an ionic strength of 10.6 mM, the depth of \(\varphi_{\text{min}}\) was \(-0.029\)\(k_{\text{B}}T\) at 4.80 \(\upmu\)m, \(-0.202\)\(k_{\text{B}}T\) at 32.4 \(\upmu\)m, and \(-0.589\)\(k_{\text{B}}T\) at 96.6 \(\upmu\)m, with corresponding separation distances at around 39.0 nm.  
Transport experiments using a Na-Ca electrolyte system demonstrated that graphene oxide was retained by collected particles, and \(\varphi_{\text{min}}\) was less than \(-0.030\)\(k_{\text{B}}T\), which is lower than the depth of \(\varphi_{\text{min}}\) (\(-0.589\)\(k_{\text{B}}T\)) in this study [30]. Additionally, a transport experiment performed under unfavorable attachment conditions showed the adsorption of sand particles by saturated sand, and \(\varphi_{\text{min}}\) calculated with DLVO theory was \(-10.6\)\(k_{\text{B}}T\), which is an order of magnitude higher than the depth of \(\varphi_{\text{min}}\) in this study [10]. Prior research also demonstrated that different types of suspended particles clogged aquifers to various degrees; for example, organic particles were adsorbed by porous media during  
Figure 4: Change in contact efficiencies with size distributions of \(d_{10}\), \(d_{50}\), and \(d_{9}\) (**a**); change represented as free energy and second energy minimum on the surface of suspended particles and sand (**b**).  
transport, and their \(\varphi_{\rm min}\) was \(-2.69\)\(k_{\rm B}T\)[19]. Therefore, the depth of \(\varphi_{\rm min}\) in our study falls within the range conducive to the retention of particles in saturated porous media, indicating that the particles used were easily captured by the column during contact. In particular, the larger particles were more easily adsorbed.
## 4 Results and Discussion
### ECG-ID Dataset Results  
The proposed model was trained on this varied dataset to determine the optimum number of samples to be chosen. The model's performance in reconstructing ECG signals was evaluated across a range of sampling rates from 18 to 120 samples per second (sps), which provides interesting patterns in the Pearson's correlation coefficient (r), mean squared error (MSE), and root mean squared error (RMSE). These metrics offer a thorough assessment of the degree to which the reconstructed signal closely resembles the original ECG signal.  
Figures 9-12 illustrate the optimal time points selected for resampling the ECG signal of Person_30 in the database at two extremely different sampling rates: 18 sps and 120 sps. Figures 9 and 11 demonstrate the distribution of optimal time points when the signal is reduced to 18 sps for both TCN and transformer models, while Figures 10 and 12 present the optimal time points for a higher rate of 120 sps for both models, respectively. These figures highlight how the sampling density affects the selection of time points to ensure the reconstructed signal maintains high fidelity to  
Figure 11: ECG optimization using the transformer model at 18 sps for Person_30 in the database.  
Figure 12: ECG optimization using the transformer model at 120 sps for Person_30 in the database.  
Figure 10: ECG optimization using TCN Model at 120 sps for Person_30 in the database.
# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 5 Case Studies
### Quantifying the Benefits of Passive and Active Measures in Engineering Mode
#### 5.5.3 Effect of Hydrophobic Coatings (EM1-C and EM2-C Cases)  
This subsection describes an engineering mode application to the same GRID 1 model, and it simulates the deployment of hydrophobic coatings to the critical lines identified by base cases EM1 and EM2. Simulations are run for four values (3, 6, 9, and 12) of the time delays TLIM, in hours, assured by the coatings, assuming an air temperature of \(-0.6\)\({}^{\circ}\)C.  
For the severe storm (EM1 and EM1-C), Figure 10 shows the total LOL risk and the system resilience indicator (in dB with base level \(10^{-2}\)) accounting for different time delays (from 3 to 12 h).  
Figure 10: Risk and resilience metrics for different time delays for severe storm (EM1 and EM1-C).  
Figure 9: Risk of loss of load (LOL) for severe (S1) and moderate (S2) storm scenarios, and two different inter-device distances—cases EM1, EM1-T, EM2, and EM2-T.  
It is worth noticing that a 3 h delay already decreases the total LOL risk by 60%; with a 9 h delay the risk reaches a very low value. Simulations on moderate storm scenarios indicate that a significant enhancement of system resilience can be obtained with a 6 h time delay: Using a 9 h time delay determines only a limited improvement of resilience.