esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study investigated the effects of high-pressure processing (HPP) on selected physicochemical properties of Pangasius gels at reduced salt concentrations. Minced pangasius muscle was treated at different HPP conditions (300-500 MPa/10 \({}^{\circ}\)C/10 min) with 1% or 2% added salt, then cooking (90 \({}^{\circ}\)C/30 min) and compared to heat-induced gels (HIGs) with 2% added salt and cooking (90 \({}^{\circ}\)C/30 min). The results showed that HPP significantly improved the texture, whiteness, and water-holding capacity of gels added salt 2% as compared to HIGs. At a reduced salt concentration of 1%, the gel texture, whiteness, and water-holding capacity of pangasius gels are similar to HIGs. SDS-PAGE showed a reduction in myosin band intensity of both SDS-soluble and sarcoplasmic proteins with pressure treatment. Sarooplasmic actin protein was undetectable in all treatments. Fourier transform infrared spectroscopy (FTIR) analysis revealed higher \(\alpha\)-helix content in HPP-treated samples, while SEM images confirmed the formation of a denser, more uniform gel network, particularly at 2% salt. These findings suggested that HPP improved the quality of gel with 2% salt added, while producing gels with similar quality compared to HIGs at reduced salt concentration.  
high-pressure processing; pangasius minced muscle; reduced salt; Fourier-transform infrared spectroscopy; gelation +
Footnote †: journal: Applied sciences
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
raphanipes_ is suitable for growth in neutral or slightly acidic environments, with the optimal pH for mycelial growth being 6.0-7.5 [15]. In recent years, due to people's attention and preference, _O. raphanipes_ has been cultivated and produced in large quantities [16]. [PERSON] et al. [17] conducted a quantitative study on the nutrients contained in _O. raphanipes_, and the data showed that _O. raphanipes_ contains a large amount of proteins and carbohydrates, which accounted for 32.12% and 24.25% of their total content, respectively, which directly indicates that _O. raphanipes_ is highly nutritious. Research by [PERSON] et al. [18] has shown that _O. raphanipes_ also contains rich mineral elements, such as phosphorus, potassium, calcium, magnesium, iron, and zinc. This indicates that _O. raphanipes_ is a high-protein and nutritionally rich edible fungus. It has a high content of umami amino acids and can be developed into a raw material for natural healthy seasonings. [PERSON] et al. [19] reported that _O. raphanipes_ contains a variety of antioxidant bioactive substances, such as polysaccharides, brass, and polyphenols. By measuring the antioxidant activity of the aqueous and alcoholic extracts of _O. raphanipes_, it was confirmed that both extracts have good scavenging abilities for DPPH radicals, hydroxyl radicals, and ABTS radicals. Among them, polysaccharides, as one of the most important macromolecules in organisms, are able to participate in cell growth and senescence and have a wide range of pharmacological activities [20]. Studies have shown that polysaccharides are the main antioxidant active components in _O. raphanipes_[21], as well as having various functions, such as hypopli epidemic, enhancement in body immunity, antibacterial and anti-inflammatory, antitumor, and protection of the liver [22]. Therefore, to better preserve the flavor and nutrients of _O. raphanipes_ and to reduce losses during storage and transportation, drying techniques can be used. Currently, studies on the drying of edible mushrooms mainly focus on the species of _Lentinula Eddes_, _Pleurotus eryngii_, and _Agrocybe aegita_, while there are fewer studies related to the drying of _O. raphanipes_, especially on the effects of different drying methods on _O. raphanipes_. In view of this, this experiment investigated the effects of hot air drying, infrared radiation drying, and vacuum freeze drying on the quality of _O. raphanipes_ from three aspects: (1) evaluation of its rehydration rate, appearance quality, and microstructure; (2) HS-SPME-GC-MS was used to analyze the effects of different drying methods on the volatile flavor components of _O. raphanipes_; (3) the effect of different drying methods was further investigated by determining the physicochemical properties of polysaccharides. In order to optimize the drying method that can maximally maintain the excellent quality of _O. raphanipes_ and provide a theoretical basis for the selection of the drying processing method of _O. raphanipes_, this study will lay the foundations for its further deep processing.
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
###### Abstract  
The Internet-of-Things (IoT) framework has been considered as an enabler of the smart world where all devices will be deployed with extra-sensory power in order to sense the world as well as communicate with other sensor nodes. As a result, smart devices require more energy. Therefore, energy harvesting (EH) and wireless power transfer (WPT) emerge as a remedy for relieving the battery limitations of wireless devices. In this work, we consider a multi-user amplify-and-forward (AF)-assisted network, wherein multiple source nodes communicate with destination nodes with the help of a relay node. All the source nodes and the relay node have the capability of EH. In addition, to cope with a single point of failure i.e., failure of the relay node due to the lack of transmit power, we consider the WPT from the source nodes to the relay node. For WPT, a dedicated energy control channel is utilized by the source nodes. To maximize the sum rate using a deadline, we adopt a joint approach of power allocation and WPT and formulate an optimization problem under the constraints of the battery as well as energy causality. The formulated problem is non-convex and intractable. In order to make the problem solvable, we utilize a successive convex approximation method. Furthermore, an iterative algorithm based on the dual decomposition technique is investigated to get the optimal power allocation and transfer. Numerical examples are used to illustrate the performance of the proposed iterative algorithm.  
energy harvesting; energy transfer; cooperative communication; power allocation strategy; power constraints Article
## 4 Analysis of Experimental Results
### Influence of Inlet Flow on Separation Effect  
At a certain inlet area of the separator, the greater the inlet flow rate of the separator the greater the inlet velocity of the fluid. Figure 12 shows the phenomenon of increasing inlet flow rate separator changes in presented as a graph. Figure 12a-e for five different inlet flows under the separator's work situation site photos. As the flow rate increases, the sand content at the bottom of the separator gradually increases, the mud and sand separation region begins to move up, and the mud and sand settling region at the bottom shows different degrees of mud and sand accumulation.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility
### Real-Time RIS Phase-Shift Optimization
#### 4.1.2 Optimization Problem  
Maximize the SINR by optimizing the RIS phase shifts:  
\[\max_{\mathbf{\Phi}}|h_{d}+\mathbf{g}^{H}\mathbf{\Phi}\mathbf{h}|^{2}, \tag{10}\]  
subject to  
\[\phi_{M}\in[0,2\pi),\quad\forall m\in\{1,2,\dots,M\}. \tag{11}\]
## 2 Methodology
### Calculate Charging Waiting Time for Each Charging Station via Queuing Theory  
Given the initial charging station deployment plan, the K-means clustering results were directly translated into the electric taxi charging selection plan of the charging stations. The charging demand for each of these charging stations was determined by the centroid label assigned to each demand point. To assess the performance of these charging stations, an M/M/s queueing model was employed to calculate the estimated charging waiting time for each station. In this context, the arrival rate of electric taxis seeking to charge was modeled using a Poisson distribution, with the mean number of arrivals in a 12-h daytime interval based on the number of demand points covered by each station. When taxis arrive at a charging station, they are assumed to choose to enter the shortest queue.  
Based on the fact that direct current fast chargers take up to one hour to fully charge electric taxis, the service rate was set to a normal distribution with a range from 30 to 60 min, reflecting the variability in charging times due to factors such as battery condition and charger efficiency. Taxi drivers were assumed to charge their vehicles when there are no customers nearby and the battery power falls below 50%. The queueing model also assumed an infinite queue capacity and an infinite population of customers (electric taxis) and followed the FCFS service discipline.  
The simulation process, implemented using a queueing tool in Python, considered the fact that each charging station is equipped with 20 chargers (s = 20). The output of the queuing model simulation included two key pieces of information: whether each simulated charging demand was fulfilled, and the waiting time for each vehicle.
# Multi-Scale Features for Transformer Model to Improve the Performance of Sound Event Detection
## 5 Conclusions  
In this study, we propose a method to incorporate multi-scale features in the Transformer model for SED by employing a feature-pyramid component in the DNN architecture. To confirm the effect of the multi-scale features on SED, we implemented two types of Transformer models: a basic Transformer and a mean-teacher Transformer model. The mean-teacher Transformer model can make use of unlabeled training data to further improve the performance of the basic Transformer model. Regarding the basic Transformer model, the use of multi-scale features led to a 9.5% relative improvement in the average F-score on the evaluation data. We found that the multi-scale features contributed to stabilizing the performance variation occurring in the baseline Transformer model as NEB varied from one to three. Regarding the mean-teacher Transformer model, we obtained an 11.6% relative improvement in the average F-score on the evaluation data by employing the multi-scale features, which is better than that observed in the basic Transformer model.  
From the experiments, we found that the proposed multi-scale features contributed to improving the performance of the recent Transformer model for SED. The improvement manifested more in the mean-teacher-based model, which is now popularly used for SED owing to its advantage of using a large amount of unlabeled training data. Although the experiments were performed using only data from the DCASE 2019 challenge, the results appear to be sufficient enough to indicate the importance of multi-scale features in the Transformer model for SED.  
For further studies, we will try some ablation experiments to determine the optimal hyper-parameters of the Transformer encoders in the multi-scale feature framework for better performance. In addition, the architecture of the multi-scale feature extraction part need be jointly optimized with the Transformer encoder.  
Conceptualization, Y-J.C.; methodology, S-J.K. and Y-J.C.; software, S-J.K.; validation, S-J.K. and Y-J.C.; formal analysis, Y-J.C.; investigation, Y-J.C.; data curation, S-J.K.; writing--original draft preparation, Y-J.C.; writing--review and editing, Y-J.C.; visualization, S-J.K.; supervision, Y-J.C.; project administration, Y-J.C.; funding acquisition, Y-J.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Bisa Research Grant of Keimpung University in 2021.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Wireless Energy Harvesting with Cooperative Relaying under the Best Relay Selection Scheme
## 4 Performance Analysis  
### Spectral Efficiency (SE)  
Taking into account the presence of feedback errors, the spectral efficiency can be expressed as follows:  
\[SE=\frac{1}{2}\sum_{m=1}^{M}\log_{2}^{\left(M_{m}\right)}F_{m}, \tag{15}\]  
where \(F_{m}\) represents the probability of selecting the \(m\)-th modulation model for the transmission. This probability can be represented as follows: \(F_{m}=P_{\gamma}(\gamma_{m+1})-P_{\gamma}(\gamma_{m})\), where \(\gamma_{m}\) is the threshold of SNR and \(P_{\gamma}(\cdot)\) is the cumulative distribution function (CDF) of received SNR, which can be computed by integrating the PDF in (14).
# A Method for Synthesizing Ultra-Large-Scale Clock Trees
## 2 IB-KSR Algorithm
### Imst
11: Apply [PERSON]'s algorithm to the sorted edge list \(E_{k}\) to generate the incomplete minimum spanning tree (IMST)
12: Return the set of edges \(E_{IMST}\), which forms the IMST of the point network
```  
**Algorithm 1** Incomplete Minimum Spanning Tree (IMST)  
Since IMST only considers the \(k\) nearest neighbor edge information for each node and omits the computation and storage of distant edges, the resulting structure not only saved computational resources but also naturally adapted to the requirements of clock tree synthesis. However, this incomplete edge information also results in the output structure of IMST differing from that of an MST. Specifically, the result of IMST is a forest consisting of multiple trees rather than a single complete tree, as shown in Figure 2.  
The forest structure of IMST reflects its essential characteristics:  
* Natural cutting of long edges: Since each node only establishes connections with its \(k\) nearest neighbors, connections between distant nodes are automatically omitted. * Preliminary clustering: Each independent tree in the forest is composed of nodes that are geographically close to each other, thus naturally forming a preliminary cluster. These characteristics make IMST technology more advantageous in the context of clock tree synthesis. IMST significantly reduces the number of edges, thereby decreasing memory usage and computational overhead. Moreover, in the forest generated by IMST, each tree naturally forms a cluster of neighboring nodes, providing an efficient starting  
Figure 2: (**a**) The tree obtained using the MST algorithm, while (**b**) depicts the forest composed of two trees obtained by using the IMST algorithm. point for subsequent clustering steps (such as buffer insertion). In short, the improvement of IMST lies in replacing the fully connected initialization of traditional MST with a non-fully connected sparsification: by screening \(k\)-nearest neighbors to retain strongly correlated edges. The generated IMST exhibits a topology similar to MST and is more suitable for subsequent clock tree clustering.
## 3 Results and Analyses
### Responses of GW Activities to Solar Activity, QBO Wind, and ENSO over the Globe  
The response of the variation in GW Ep at 20-35 km to that of the solar activity represented by the F10.7 indexes is shown in Figure 9b. Contrary to the GW Ep trends during the 14 years, which are mainly negative, GW Ep in the mid and high latitudes of the two hemispheres generally show significant positive responses to the solar activity within the altitude range of 20-35 km. In the low latitudes of 30\({}^{\circ}\) S-30\({}^{\circ}\) N, significant positive responses of GW Ep to solar activity also exist at 30-35 km. Additionally, in terms of the magnitudes of the responses, the positive responses of the GW Ep to the solar activity become stronger in the region closer to the poles. Our results are consistent with [PERSON] et al. [30], who got positive correlations of GWs with the solar activities by using the radio drift observations at 52\({}^{\circ}\) N.  
Figure 9c presents the responses of GW activities to QBO at 20-35 km over the globe. It can be seen that at the latitude range of 20-35 km, the responses of GW Ep values to the QBO eastward wind phases are mainly negative at most latitudes. At the altitude range of 30-35 km, significant negative responses of GW Ep to the QBO eastward wind phases occur at the latitudes of 30\({}^{\circ}\) S-25\({}^{\circ}\) N, and the negative responses at 0\({}^{\circ}\)-25\({}^{\circ}\) N are smaller than those at 0\({}^{\circ}\)-30\({}^{\circ}\) S, which is basically consistent with [PERSON] et al. [9]. At the altitude range of 20-30 km, the significant negative responses extend to the mid and high latitudes in the SH, while at the low latitudes of 15\({}^{\circ}\) S-15\({}^{\circ}\) N, the responses of GW Ep values to the QBO eastward wind phases are significantly positive.  
The response of the GW Ep at 20-35 km to ENSO, which is represented by the MEI index, is shown in Figure 9d. It can be seen that over low latitudes of 15\({}^{\circ}\) S-15\({}^{\circ}\) N, GW Ep values are of positive responses to ENSO in 20-35 km, which is consistent with [PERSON] et al. [9]. In addition, the response of the GW Ep to ENSO is significantly negative in 30-35 km over  
Figure 9: Latitude–height distributions of the deseasonalized trends of GW Ep at 20–35 km during the 14 years from 2007 to 2020 (**a**) and the responses of GW Ep to solar activity (**b**), to QBO (**c**), and to ENSO (**d**). The regions where the results are statistically significant at the confidence level of 90% are shown as protuberance.  
the latitudes of 15\({}^{\circ}\) N-30\({}^{\circ}\) N, which is also consistent with the findings of [PERSON] et al. (2019). The significant negative response near 50\({}^{\circ}\) N in our results should be partly due to the weakening in GW activity after 2016.
## 5 Discussion
### Limitations and Future Directions for Crop Classification and Rotation Analysis  
Despite these advances, several challenges remain:  
First, complex cropping practices, including intercropping, sequential planting, and variable sowing dates, often generate spectral mixtures that degrade classification accuracy, especially for short-cycle crops like sesame. As noted in other studies [17, 23], pixel-based approaches may underperform in highly heterogeneous regions. Future efforts should integrate crop-specific phenological modeling, sub-pixel unmixing, and temporal consistency constraints to reduce classification noise and error propagation in rotation analysis.  
Second, conventional rotation matrices based on annual majority crop labels may oversimplify actual management patterns, especially in cases of short-cycle or overlapping crops. This can lead to systematic biases in transition detection. Studies such as [PERSON] et al. (2017) [22] and [PERSON] et al. (2022) [17] have stressed the importance of sub-annual classification and object-based methods in capturing fine-scale rotation details. Our results reaffirm this need and suggest integrating ancillary field data (e.g., farmer interviews, planting records) as a promising avenue.  
Third, although multi-sensor fusion improved classification performance, hyperspectral imagery, UAV-based validation, and deep learning methods such as LSTM or CNNs offer promising routes to further enhance spatial and temporal resolution [9, 20, 47]. These tools can address class imbalance, intra-class variability, and mixed pixels more effectively, especially in complex terrain.  
In conclusion, our study contributes a scalable and interpretable framework for crop mapping in smallholder-dominated, topographically diverse regions and provides high-resolution evidence on cropping patterns and rotation dynamics. Building on this foundation, future research should focus on the following: (i) refining rotation tracking in intercropped or multi-harvest systems, (ii) enhancing classification robustness across years and geographies, and (iii) leveraging AI-driven classification with expanded ground truth datasets to better support agricultural management and policy in ecologically fragile zones.
# Decoding Visual Motions from EEG Using Attention-Based RNN
## 2 Related Work
### Data Augmentation for EEG Signals  
DL models contain a large number of learnable parameters, thus, requiring a significant amount of training data to optimize. Many recent works have investigated data augmentation methods for creating additional EEG data. [PERSON] et al. [25] proposed a rotational data augmentation method for EEG data, similar to rotational distortions for image data augmentation. The approach increased the signal classification performance for BCI systems. Other image data augmentation methods, such as noise addition and random cropping, were also evaluated by the subsequent studies [24; 26]. Some novel data augmentation methods were presented for EEG signals. [PERSON] et al. [27] established a Conditional Wasserstein GAN (CWGAN) framework to generate augmented EEG data. [PERSON] et al. [29] proposed an approach for augmenting EEG signals from their covariance matrices using Riemannian geometry.  
In this paper, we will design a novel BCI paradigm based on motion perception and utilize an attention-based RNN to decode visual motions from EEG within one trial or a few trials. In contrast to the earlier BCI paradigms that utilize motion stimuli, we choose four types of video-depicted motions (contraction, expansion, rotation, and translation). The durations of the videos are only 1 s. Our attention-based RNN applies temporal attention directly to the raw EEG signals to the avoid attention bias problem, unlike the aforementioned attention-based RNN models. Besides, the input signals do not need any transformation, so our approach is simpler and more efficient. The online data augmentation proposed in this study generates artificial signals with different SNR during the model training. This method has not been considered and investigated in the above studies yet.
## 4 Experiments and Discussions
### Construction of Multi-DOF Automatic Image Acquisition System  
The multi-DOF automatic image acquisition system is shown in Figure 11. Figure 11a shows a hardware structure of the multi-DOF automatic image acquisition system, which mainly includes motion control and light source control. The motion control component mainly includes an electric guide rail, driver and 8-axes motion control card. The light source control component mainly includes flat light source, ring light source, light source controller and STM32 single chip microcomputer. The key components and related parameters used in the system are shown in Table 4.  
Figure 11b shows the user interface designed according to the multi-DOF automatic image acquisition system. The interface mainly includes an image display area and parameter setting area. The image display area is used to display the fabric image in real-time. The parameter setting area mainly includes camera parameters (exposure, frame rate, gain, etc.), illumination (light source, brightness), image saving path, and the number of collected images.