esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 5 Results and Discussion
### Actual Wave Energy Harvesting Plant Experiment
#### 5.3.1 Data Segmentation  
Ocean waves, unlike waves in a tank, are irregular in nature and have a wider frequency spectrum. To handle the frequency variation, we adopted wider window sizes so that low-frequency components could be considered. The data were collected at 10 Hz, and the sizes of the windows were 600, 1200, 1800, 2400, 3000, 3600, 4200, 4800, 5400 and 6000 samples, as shown in Table 4.  
We used three regressors to investigate the effect of the window size on the prediction accuracy. Figure 10 illustrates that the regressors tended to perform better as the window size increased. The maximum R\({}^{2}\) value, 0.962, was obtained at the window size of 6000 samples by the ANN regressor.  
Since the performance of the prediction models varies with the nature of the dataset and complexity, we have noticed that the prediction accuracy of the models fluctuates as the window size changes. This implies that the complexity of the dataset is altered with the window size. In general, the performance of the models varies with the nature of the algorithm used and the nature of the dataset used.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Window Size** & **600** & **1200** & **1800** & **2400** & **3000** & **3600** & **4200** & **4800** & **5400** & **6000** \\ \hline Number of training & 3625 & 1813 & 1208 & 906 & 725 & 604 & 518 & 453 & 403 & 362 \\ Number of testing & 1554 & 777 & 519 & 389 & 311 & 260 & 222 & 195 & 173 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training and testing sample sizes of the experiments.  
Figure 10: Performance evaluation on different window sizes for prediction.
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 6 Conclusions  
In this research, the use of the 1.8 MW/10.8 MWh PSS of the Kinmen power grid was investigated as an additional up- and down-spinning reserve capacity, in contrast to traditional economic dispatch. The optimization problem was modeled so that the charging and discharging of the PSS achieved the peak-shaving function in order for the diesel generators to provide more reserve to the system.  
At low load power conditions, the Kinmen power grid may only have two to three units operating, and if all these units are operating at their minimum capacity, it will not be able to cope with drastic load changes. Therefore, this study proposed a more robust power dispatch by including the PSS in both up and down-spinning reserve constraints.  
This study has shown how the PSS was able to maintain the reserve especially in the transition stages of the system. The PSS was found to have prevented too many generators online, or the early turning-on or late turning-off of generators, so that it could reduce the operating cost of the system by charging the PSS at off-peak hours and discharging during peak hours. The PSS can also provide a maximum reserve capacity of twice its available power range for the flexibility of the system.  
**Author Contributions:** Conceptualization, Y.-T.C. and C.-C.K.; Data curation, Y.-T.C.; Formal analysis, Y.-T.C.; Investigation, Y.-T.C. and J.-Z.J.; Methodology, Y.-T.C. and C.-C.K.; Project administration, Y.-T.C. and C.-C.K.; Software, J.-Z.J.; Validation, Y.-T.C. and C.-C.K.; Visualization, J.-Z.J.; Writing-original draft, Y.-T.C.; Writing-review & editing, Y.-T.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the Ministry of Science and Technology, Taiwan, under Grant MOST 109-2622-E-011-017-CC2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data are already provided in the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Multiple Edge Computing Platforms Enabled Air-Ground Network Architecture for Vehicular Scenarios
However, HAP performance can be reduced by longer communication distances, additional channel loss in terms of rain fading high deployments, and maintenance time and costs. The HAPs coverage area can depend upon its altitude. In general HAP platform can provide coverage between a few 10 s of Kms to up to several 100 s of Km [56, 57]. Communication, Computation and Storage Characteristics  
Each EC platform layer of the proposed network architecture can adapt different computation and communication strategies. Several virtualization techniques (i.e., Virtual Machines (VMs), containers) can be used for the efficient utilization of EC resources. Furthermore, an SDN-based centralized control approach can be applied for managing the computation and storage resources of individual EC platforms. Multiple operators based communication technologies can be adapted for enabling the communication between EC nodes of the same and distinct layers. Table 3 lists the most important characteristics of individual EC layers considered in the proposed network architecture.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### Mesh Tests
#### 5.3.4. Park  
In this experiment, the nodes were put around a park, as seen in Figure 13. Nodes were using spreading factor 7 and were about 110 m from each other. In this experiment, packets could take more than one route to arrive at their destination.  
Figure 12. Positioning of nodes in quarry test.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measurement** & **Value** \\ \hline PDR & 100\% \\ RSSI (db) & [\(-\)101; \(-\)89] \\ SNR & [4; 7] \\ \hline \hline \end{tabular}
\end{table}
Table 5. Results obtained from the quarry test.  
Table 6 contains the data gathered from this test. Packets were all delivered to node \(R\) by the route that used the right node at the fork.  
This test shows how our solution could be used to gather data around a somewhat open environment. For example, the mesh network could be used to monitor the number of people at the park at different times through presence detection, and these data could be used to provide better services at the park and improve security measures for the citizens.
## 3 Results and Discussion
### Thermal Conductivity Analysis  
The thermal conductivity analysis of the coconut oil + \(n\)-hexadecane Mixed SSPCMs using TGi at the temperature of 10 \({}^{\circ}\)C is shown in Figure 6. As shown in Figure 6, the thermal conductivities of the Mixed SSPCMs are 1.70, 1.61, and 1.23 W/mK. The Mixed SSPCMs appear to follow the tendency that the more coconut oil they contain, the greater the thermal conductivity because the pure coconut oil has larger thermal conductivity than pure \(n\)-hexadecane. The thermal conductivities of pure coconut oil and \(n\)-hexadecane are 0.321 and 0.154 W/mK, respectively [23; 24]. The overall Mixed SSPCMs have larger thermal conductivities which are 284% higher than the pure PCMs. This implies that the xGnP led to an enhancement of the thermal conductivity of coconut oil and \(n\)-hexadecane. Consequently, we concluded that Mixed SSPCMs can be usefully applied to various categories such as buildings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mixed SSPCM (Coconut** & **First Peak of Derivative** & **Second Peak of** & **Total Decomposition** \\
**Oil: \(n\)-hexadecane** & **Weight (\({}^{\circ}\)C)** & **Derivative Weight (\({}^{\circ}\)C)** & **Rate (\%)** \\ \hline
70:30 (wt \%) & 165.14 & 363.68 & 71.59 \\
50:50 (wt \%) & 172.62 & 357.68 & 70.73 \\
30:70 (wt \%) & 178.38 & 339.79 & 68.53 \\ \hline \hline \end{tabular}
\end{table}
Table 6: TGA analysis of Mixed SSPCMs.  
Figure 5: Thermogravimetric analysis (TGA) graphs of Mixed SSPCMs.
## 4 Energy Utilization  
Throughout the entire manufacturing process, while maintaining a constant laser power and considering the scanning time of the laser, it is possible to calculate the total energy released through the laser. In the SLM method, the energy consumption during part fabrication is primarily attributed to the melting and solidification of powder. By utilizing the temperature field variations in the simulated impeller structure, this study calculates the energy consumption required for the fabrication of the impeller. Consequently, by comparing the actual energy absorbed by the turbine pump impeller with the total energy released by the laser, the energy utilization efficiency of the turbine pump impeller's SLM manufacturing process can be determined.  
When the laser power remains constant and the scanning speed increases, the energy utilization efficiency also changes accordingly, as shown in Figure 8. While the relationship between laser scanning speed and energy utilization efficiency still exhibits an approximate linear trend, unlike thermal deformation, the energy utilization efficiency gradually increases with an increase in laser scanning speed. Consequently, within a certain range of power, energy utilization efficiency can be enhanced by raising the scanning speed. This enhancement in energy utilization efficiency also facilitates a reduction in manufacturing time. However, it is worth noting that an increase in scanning speed results in reduced interaction time between the laser and the material, which may lead to partial melting of the material and a portion remaining in an unmelted state. This can eventually lead to an increase in porosity, affecting the mechanical properties of the final part.  
With an unchanged scanning speed, increasing the laser power leads to a decrease in energy utilization efficiency. As shown in Figure 9, when the laser power reaches a certain level, the energy utilization efficiency stabilizes. Therefore, within a specific range, to ensure that the material can absorb enough energy for melting and guarantee the mechanical performance of the formed part, it is possible to appropriately increase the laser power without causing a significant decrease in energy utilization efficiency.  
Due to the combined effect of laser power and scanning speed, different power and scanning speed combinations can achieve similar energy utilization efficiencies, as shown in Figure 10. Under the combined influence of laser power and scanning speed, the energy utilization efficiency changes within a certain range. In the actual processing process, the material absorbs a portion of the laser energy for melting, and heat conduction occurs  
Figure 7: The combined effects of laser power and scanning speed on deformation.
## 1 Introduction
This paper proposed a hybrid autoencoder named stacked sparse denoising autoencoder (SSDAE) which combines SAE and DAE for more representative features extraction and health condition recognition. The effective fault features are extracted using a hybrid method blending EEMD and MPE. EEMD is used to preprocess the raw signals, and the screening of IMFs related to characteristic frequency is carried out. MPE of both the selected IMFs and the raw signals are obtained and fused to input into the proposed SSDAE model for training and classification. The rest of this paper is organized as follows: Section 2 presents a fault diagnosis SSDAE model that combines EEMD and MPE, with a detailed overview of the proposed method. In Section 3, two kinds of bearing dataset are adopted to validate the effectiveness of the proposed method. The superiority of the proposed method is exhibited through some comparisons. Conclusions are drawn and future work is suggested in Section 4.
# 6G to Take the Digital Divide by Storm: Key Technologies and Trends to Bridge the Gap
## 4 The Technologies for Bridging the Service-Fruition Divide
### Brain-Computer Interfaces  
Brain-Computer Interfaces (BCI) have been used extensively in assisting elderly or disabled people. While wired BCIs have been active for years, wireless BCIs are less supported due to their stringent QoS and Quality of Experience (QoE) requirements [26]. Over the last years, we can observe a handful of wireless BCIs implemented through short-range communication technologies in LANs, for use cases like home automation and digital healthcare [27]. However, these technologies are far from bridging the service-fruition divide, as their pervasiveness is limited to localized areas and hardcoded functions (e.g., switching on and off smart bulbs, etc.). With the perspective of 6G, requirements for deploying BCIs at large could be met and the deployment of a set of compelling applications in, for instance, urban environments could become a reality. Just like (and probably more than) XR, BCIs may be the new frontier of Human-Computer Interaction, involving Internet of Things (IoT) devices that will pervade our urban realities and will enable 6G ultra-low-latency connections. This also involves applications in healthcare that have, so far, only been conducted in controlled environments [28]. Such a giant leap in overcoming the service-fruition divide is envisioned to mark the end of the smartphone era, in a decade from now. Using wireless Brain-Computer Interaction technologies instead of smartphones, people will interact with their environment and other people using discrete devices, some worn, some implanted, and some embedded in the world around them.
# A Two-Step Algorithm for Handling Block-Wise Missing Data in Multi-Omics
## 2 Methods
### Two-Step Optimization Procedure  
Our goal is to learn \(\beta=(\beta^{1},...,\beta^{S})\) and \(\alpha=(\alpha^{1},...,\alpha^{S})\) from the data \((X^{1},...,X^{S},\mathbf{y})\). To achieve this, we employ the same methodology as in [6] [Section 2.2]. In particular, we apply a two-stage optimization model based on a regularization and constraint approach, which are utilized to enhance model performance, reduce overfitting, and incorporate prior knowledge into the analysis. In fact, in [7,8] the authors introduce the key components required to solve an optimization algorithm based on a unified feature learning model for heterogeneous block-wise missing data, which conducts both feature-level and source-level analysis at the same time. The problem that needs to be solved is as follows:  
\[\min_{\mathbf{\alpha},\beta}\frac{1}{|pf|}\sum_{m\in pf}\frac{1}{n_{m}}\varphi \left(\sum_{i=1}^{S}\alpha_{m}^{i}\mathbf{X}_{m}^{i}\mathbf{\beta}^{i},\mathbf{y}_{m} \right)+\lambda\Omega_{2}(\mathbf{\beta}) \tag{4}\]  
subject to  
\[\Omega_{1}(\mathbf{\alpha}_{m})\leq 1\quad\forall m\in pf. \tag{5}\]  
Here, \(\varphi\) can be any convex loss function, including the commonly used least squares loss function, among others. In addition, \(\Omega_{1}\) and \(\Omega_{2}\) are norm functions, which are used to apply regularization techniques and constraints that promote sparsity and control model complexity. Furthermore, \(\lambda\) serves as the regularization parameter, playing a crucial role in mitigating overfitting by imposing a penalty on the complexity of the model. By controlling the magnitude of the coefficients, \(\lambda\) helps ensure that the model generalizes well to unseen data, thus improving its predictive performance and stability across different datasets.  
The primary components required to compute the parameters \(\beta\) and \(\alpha\) are detailed in [6], Section 2.2, and [9, 10, 11, 12, 13, 14]. Nevertheless, for the sake of completeness and to enhance clarity for the reader, we include them partially here.
# Automated Gluten Detection in Bread Images Using Convolutional Neural Networks
## 2 Related Work  
### Classification of Food Images Lising CNNs  
Transfer learning is a machine learning technique that leverages a model trained on one task and adapts it to a related task [18]. As part of deep learning and specifically CNN, instead of training a CNN from scratch, which is computationally intensive and requires a large labeled dataset, existing models trained on massive datasets (e.g., ImageNet) can be used as a starting point [19]. The process typically involves freezing the model's earlierlayers, which have learned to detect basic features, such as edges, textures, and shapes, and then fine-tuning only the later layers. Using this technique, the model can retain its general image recognition capabilities while adjusting to more specific details of the new task, such as food classification [20]. Several studies have explored the application of transfer learning and CNNs for food recognition and classification.  
For example, [PERSON] et al. [21] evaluated SqueezeNet and VGG16 for food image classification. The VGG16 achieved 85.07% accuracy by fine-tuning hyperparameters and augmenting the data. [PERSON] et al. [22] collected a dataset called Sabah Food, which contains 11 categories of popular Sabah food. VIREO-Food172, which contains 172 food categories, was also used. Ten popular Chinese dishes were examined. EfficientNet achieved the highest classification accuracy of 94.01% on the Sabah Food dataset and 86.57% on the VIREO-Food172 dataset.  
A study by [PERSON] et al. [23] applied the MobileNetV2 architecture to identify 190 food categories with an accuracy level of approximately 77.2%. Another study by [PERSON] and [PERSON] [24] applied the MobileNetV3 model to approximately 2700 images of Indian food, achieving an accuracy level of up to 93.3%. Additionally, [PERSON] et al. [25] compared seven CNN architectures for classifying a custom dataset of 41,949 images across 20 food classes, with DenseNet emerging as the top performer, achieving a training accuracy of 74%. In the study of [PERSON] et al. [26], 304 images from 76 tray boxes were used, categorized into four classes (blank, fried rice, egg, and cucumber). The proposed ResNet-18 model outperformed the GoogLeNet model. Lastly, [PERSON] et al. [27] used a modified version of ResNet-50, called MResNet-50, in conjunction with natural language processing (NLP) algorithms, such as Word2 Vec and Transformers, to classify images and extract ingredient information from them. MResNet-50 outperformed ResNet-50, achieving higher accuracy.  
The use of CNNs in food classification has been widely demonstrated, with high accuracy yielded across a broad range of food datasets. Despite these advancements, none of the existing studies specifically identify the presence of gluten in food.
## 4 Discussion  
In this study, we developed a novel approach that combines NAR neural networks with SVR to optimize time series forecasting. The results obtained demonstrate the effectiveness of this approach in improving the accuracy of time series predictions compared to traditional models such as ARIMA or machine learning such as SVR or NAR.  
By applying the SVR-based correction to the error, we observed significant improvements in the performance of the superficial neural networks and classical models. This improvement was evident across all analyzed time series data, as indicated by a substantial decrease in both RMSE and MAE. Furthermore, the goodness-of-fit of the NAR-SVR model exhibited a remarkable enhancement.  
The consistent success achieved across a variety of time series data suggests that our proposed forecasting model holds great promise for predicting various types of time series. However, it is important to note that the utilization of the time series error for improving the NAR-SVR neural network restricts its effectiveness in smaller networks with fewer than 100 observations. In such cases, the support vector machine may not have enough data to detect patterns and effectively correct errors, leading to reduced performance.  
It is not the purpose of this work to compare the result with other ML models, as there are many. However, we compare our results with [68], which utilized polynomial approximations on the same time series of SARS-CoV-2, and our correctly adjusted NAR-SVR network exhibits higher accuracy (see Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline NAR & 0.952 & 1599.15 & 2093.40 \\ NAR–SVR & 0.953 & 1576.55 & 2082.80 \\ ARIMA(1,2,1) & 0.952 & 1590.71 & 2094.93 \\ SVR & 0.000 & 30,533.27 & 32,457.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for Bitcoin price time series, 1 January 2021/31 March 2021.  
Figure 4: Prediction chart for Bitcoin price time series, 1 January 2021/31 March 2021.  
Similarly, when compared to [72], which employed ARIMA methods and different long short-term memory (LSTM) neural networks (single and multilayer) on the Bitcoin dataset, our NAR-SVR model achieved lower RMSE (see Table 5).
# A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
## 3 Materials and Methods
### Forwarding Strategy  
In-group forwarding and out-group forwarding are the two ways that nodes forward messages. In-group forwarding is used when the source and destination nodes of the message are in the same group, and out-group forwarding is used otherwise. Two stages make up the message delivery process: first, the message is delivered to the group node, and then it is transmitted from the group node to the message destination.  
1. Out-group forwarding  
When the sender and the recipient of the communication are not in the same group, forwarding is determined by the probability that the sender and the recipient are going to meet at the same place. If node \(N_{a}\) carries message m and encounters node \(N_{b}\), and \(fc(N_
## 3 Results
### Overall Performance  
The ablation study's overall performance (accuracy, macro f1 score, and kappa) at 1\(\times\) and 2\(\times\) is summarized in Table 2(a) (validation set) and Table 2(b) (test set).  
Based on these results, we observe that the baseline configuration--NCB + FPE--exhibits limited performance due to two key issues: FPE restricts data diversity by applying a fixed offset, and NCB amplifies class imbalance by preserving the natural class distribution. In contrast, RPE, when replacing FPE, enhances training diversity through random offsets. CBS, when substituting NCB, mitigates majority-class bias by enforcing uniform class sampling. Both modifications lead to individual performance gains over the baseline. When combined, the full RPE + CBS scheme achieves the greatest improvements across all evaluation settings.  
In particular, the test set under the 2\(\times\) condition is the most challenging, given not only domain differences but also reduced patch context. Nevertheless, RPE + CBS raises accuracy from 0.4857 (baseline) to 0.8033 (+31.76%p), the macro F1 score from 0.4394 to 0.7166 (+0.2772), and Kappa from 0.4227 to 0.7687 (+0.3410). Such a large improvement indicates that random patch extraction and class-balanced sampling remain highly effective, even when the patch context is reduced and the domain differs substantially.