esa-sceva/satcom-chunk-collection huggingface dataset:
# Practical Application of Mesh Opportunistic Networks
## 4 Results
### Communication Flow among Participants
#### 4.3.2 Connections, Relevance and Nuclearity (K-Index)  
The connections refer to the number of relationships for each node (Figure 6), and the relevance of each node is calculated on the basis of the number of connections and the relevance of the nodes it is connected to (derived from their number of connections). The nuclearity or k-index measures in which layer of the network a node is and the number of links in a complete mesh responds to the formula N = n (n - 1)/2. The k-index indicates the layer of the network a node belongs to. The layer with the highest k-index is the core of the network. Layer k includes those nodes with at least k connections to other nodes in the same layer [34, 37]. In our case, there were 28 possible bidirectional links with 8 terminals. The core of the network was the layer with the highest k-index and the k layer was made up of nodes with at least k connections among them. When these parameters were represented (Figure 5), a total of 562 connections were made (GT01 made 51 connections; GT02, 49; GT03, 54; GT04, 60; GT5, 40; GT06, 44; GT07, 228; and GT08, 36). The most relevance was attributed to GT07 (1.00), after which two groups were detected: one with a relevance between 0.40 and 0.51 (GT01 obtained a relevance of 0.42; GT02, 0.40; GT03, 0.49; and GT04,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time** & **Type** & **GT Sender** & **GT Receiver** \\ \hline
8:14 & Location & 7 & Everyone \\ \hline
8:20 & Text & 3 & 7 \\ \hline
8:21 & Text & 7 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._0.51); and the other with a relevance below 0.40 (GT05 obtained a relevance from 0.34; GT06, 0.38; and GT08, 0.32). Regarding nuclearity, we identified a first group with a k-index of 44, consisting of GT03, GT04 and GT07, and a second group with a k-index of 41, consisting of GT01, GT02 and GT6. In addition, GT05 had a k-index of 39 and GT08 a k-index of 36. Nuclearity shows differences in the use of the system among participants, and it can be illustrative for future strategies in similar experiments or professional situations.  
Figure 4: Pie chart with the text communication flow done by participants.  
Figure 5: Radial bar chart of the communications, pins, and locations.
## 4 Conclusions  
The primary objective of this study was to evaluate the impact of wheat flour and a combination of whole-grain spelt and wheat flours, along with the addition of microalgae as a functional ingredient at different levels (0.5, 1.0, and 1.5%), on the quality of vegan muffins. The study investigated the effects of flour type and microalgae addition on the rheological properties of the dough, as well as the color, microstructure, texture, and sensory properties of the muffins. Doughs containing spelt flour and microalgae exhibited a higher consistency coefficient index and lower flow index compared to doughs with wheat flour. The doughs with spelt flour and 1.0 and 1.5% microalgae additions demonstrated the highest consistency coefficients (99.7 Pa\(\cdot\)s\({}^{\text{n}}\) and 118.6 Pa\(\cdot\)s\({}^{\text{n}}\), respectively), resulting in muffins with approximately 69% total porosity and a hardness of 52 N. In contrast, muffins made with wheat flour with 1.0 and 1.5% microalgae additions displayed low consistency coefficients (52.3 Pa\(\cdot\)s\({}^{\text{n}}\) and 69.7 Pa\(\cdot\)s\({}^{\text{n}}\), respectively), resulting in greater total porosities (63% and 67%, respectively), more closed porosities, and larger average pores area in their microstructures. The taste and color of all samples were acceptable, but the addition of microalgae imparted a strong aroma. An important finding of this study is the successful development of vegan muffins enriched with microalgae using a blend of whole-grain spelt and wheat flour. With the addition of 1.5% microalgae, viscosity increased and structure and texture improved. The limitation above all is the grassy aroma. Our research shows that the addition of 1% microalgae is optimal for structure, texture, and sensory properties. However, more research is needed to confirm the nutritional value of the newly developed vegan muffins.  
Conceptualization, A.M. and P.K.; methodology, A.M.; software, A.M.; validation, A.M., H.K. and J.K.; formal analysis, A.M.; investigation, P.K.; resources, H.K. and J.K.; data curation, A.M., P.K., H.K. and J.K.; writing--original draft preparation, A.M.; writing--review and editing, A.M.; visualization, A.M.; supervision, A.M. and H.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The research for this publication was performed using research equipment purchased as part of the \"Food and Nutrition Centre--modernisation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (Project No. RPMA.01.01.00-14-8276/17).  
The authors declare no potential conflict of interest with respect to the research, authorship, and/or publication of this article.  
Figure 5: Sensorial evaluation of vegan muffins. Explanations: CW—100% wheat flour, SW—whole spelt flour (50%), and wheat flour (50%), microalgae addition: a0.5, a1.0, and a1.5%.
# Devising Mobile Sensing and Actuation Infrastructure with Drones
## 4 Implementing and Evaluating DaaG
### Empirical Evaluation
#### 4.4.2 Examining Observations  
As a validation, we have analyzed the delay of our system with a series of experiments. In the analyses, both the main focusing delay and the total service delay are considered, so that the observations made for the field experiment match those for the simulations. In our scenario, the main actuator can be Actuator #1, because Sensor #1 is registered in PSL and Actuator #1 is close to Sensor #1 compared to Actuator #2.  
Figure 12 shows the result of delay measurement based on our experiment. In the result, it can be seen that DaaG shows a better result compared to nearest-based path-planning with respect to the main focusing delay. The entire delay of our system for moving the drone and operating the actuator is 46.46% shorter than that of the nearest-based path-planning system. In our experiment, the nearest-based drone visited Actuator #2, which is not the main actuator. It can be seen from Figure 12 that the total service delay of DaaG is only 2.08% longer than that of the nearest-based path-planning system.  
With these results, we should notice that the essential purpose of this system is to serve a new service by connecting separate networks. Following this purpose, the main focusing delay is more important than the total service delay, because a short main focusing delay can provide the priority service rapidly. The results shows that our platform is much faster than the nearest-based path-planning system, so that the main service can be rapidly provided with our platform. Naturally, the total service delay is also important to increase the quality and accuracy of the service, as a lengthy delay can negatively affect the service quality. However, our system requires only 2.08% additional time, which is negligible, compared to the nearest-based path-planning system.
# Attention-Based Transfer Learning for Efficient Pneumonia Detection in Chest X-ray Images
## 2 Materials and Methods
### Datasets  
For the training, we employed three different domains of datasets. We employed the structure of pre-trained models that were each trained on ImageNet [15], Chest X-ray14 [28], Custom datasets [29]. The ImageNet dataset is well known for being easily accessible and large. It contains 1,281,167 data classified as 1000 classes. The Chest X-ray 14 dataset is the largest publicly available chest X-ray dataset released by the National Institute Of Health(NIH). It contains 112,120 X-ray images of 30,805 patients and classified into 14 chest pathology labels, including pneumonia. The Chest X-ray 14 dataset has a similar domain as our test dataset. The Custom dataset is classified into 11 classes and contains a total of 43,956 images. This dataset was used to demonstrate the generalization ability of our framework. With our framework, even pretrained models in unrelated domains can also be used to implement the target task. For the testing, we used publicly available chest X-ray images collected from pediatric patients between 1 to 5 years old from Guangzhou Women and Children's medical center [32], which was not used for the pre-trained model. This dataset was collected by researchers that have achieved approvals from the Institutional Review Board (IRB) and Ethics Committee on data collection and experimentation. The dataset consists of 1583 normal images and 4273 pneumonia images. Of the 5856 images, 5216 images are used for framework training, and 16 images are used as data to evaluate models during training. The size of the validation set was too small, so it was used for reference only, and cross-validation was not performed separately. Cross-validation is used to avoid overfitting. However, this process takes a lot of time. So, we chose not to use the cross-validation procedure. Instead of cross-validation, we used the validation set to determine the optimal number of epochs to avoid overfitting. Finally, 624 images were used to conduct tests on the framework (shown in Table 1). Pneumonia is divided into bacterial and viral pneumonia. However, we focus on the binary classification task of classifying normal and pneumonia by defining two pneumonia categories as one pneumonia category due to lack of data. Images of the normal class are much smaller than images of the pneumonia class. We used F-score to evaluate our approach properly given  
Figure 7: Overall process of ECA.  
the class-imbalanced dataset. Although we used a class-imbalanced dataset, there was no major problem with model performance. A sample image of the dataset is shown in Figure 8.
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
## 4 Performance Evaluations
### Experimental Models and Characteristics  
We conducted a performance comparison to evaluate the effectiveness of our dynamic clip generation method in sign language recognition, using four widely adopted DNN-based models: 3D CNN, R(2+1)D, Video Swin Transformer (VST), and Multiscale Vision Transformers (MViT). These models were selected for their distinct approaches to video recognition and their relevance to sign language recognition tasks. Table 7 provides a comprehensive overview of the key features, computational efficiency, and corresponding references for each model.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Dataset Feature** & **Value** \\ \hline Number of Training Samples & 9888 \\ Number of Validation Samples & 2472 \\ Total Number of Samples & 12,360 \\ Number of Categories (Classes) & 67 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dataset statisticssummary.  
Figure 1: Example visualizations of keypoint representations extracted from video frames.  
The rationale behind selecting these models is based on their ability to address different challenges within video recognition tasks, particularly in sign language recognition:  
* 3D CNN was one of the first deep learning approaches to introduce 3D convolutions for spatio-temporal feature extraction, making it highly effective for gesture and motion-based tasks [25].
* R(2+1)D improves upon 3D CNN by factorizing the spatio-temporal convolution, which has been shown to enhance action recognition performance while maintaining computational efficiency [26].
* Video Swin Transformer (VST) leverages shifted window-based self-attention mechanisms to better capture long-range dependencies in video sequences, which is particularly beneficial for sign language recognition tasks where gesture continuity is essential [27].
* Multiscale Vision Transformers (MViTs) introduce an efficient multi-scale attention mechanism that balances computational cost and recognition accuracy, making them suitable for real-time applications [28].
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 3 Design and Implementation of Access Control Method
### NDN Router  
The NDN router can change the NDN cache node to aggregate proxy re-encryption. The forwarding operation of the data packet and the interest packet by the NDN route cache node is based on the name routing and forwarding. The content name is an opaque binary code sequence for the router. When forwarding the data packet or the interest packet, the name and the information in the FIB need to be processed to match. For example,/university/computer/access control, when it matches the interest packet that needs to perform proxy re-encryption service, it will complete the corresponding operation through the agent module to achieve the purpose of the consumer requesting data, complete efficient data distribution, and optimize the network system performance. Figure 4 illustrates the relationship of the NDN cache node and its main components in the access control method.
# Research on Short-Term Wind Power Forecasting by Data Mining on Historical Wind Resource
###### Abstract  
In order to enhance the accuracy of short-term wind power forecasting (WPF), a short-term wind power forecasting method based on historical wind resources by data mining has been designed. Firstly, the spoiled data resulting from wind turbine and meteorological monitoring equipment is eliminated, and the missing data is added by the Lomonaofski optimization model, which is based on the temporal-spatial correlation of meteorological data. Secondly, the wind characteristics are analyzed by the continuous time similarity clustering (CTSC) method, which is used to select similar samples. To improve the accuracy of deterministic prediction and prediction error, the radial basis function neural network (RBF) deterministic forecasting model was built, which can approximate nonlinear solutions. In addition, the wind power interval prediction method, combining fuzzy information granulation and an Elman neural network (FIG-Elman), is proposed to acquire forecasting intervals. The deterministic prediction of the RBF-CTSC model has high accuracy, which can accurately describe the randomness, fluctuation and nonlinear characteristics of wind speed. Additionally, the mean absolute error (MAE) and root mean square error (RMSE) are reduced by the new model. The interval prediction of FIG-Elman results show that the interval width decreased by 18.85%, and the coverage probability of interval increased by 10.94%.  
Lomonaofski norm; RBF neural network; FIG-Elman network; deterministic wind power predication; wind power interval predication +
Footnote †: journal: Applied Sciences
# An Efficient Document Retrieval for Korean Open-Domain Question Answering Based on ColBERT
###### Abstract  
Open-domain question answering requires the task of retrieving documents with high relevance to the query from a large-scale corpus. Deep learning-based dense retrieval methods have become the primary approach for finding related documents. Although deep learning-based methods have improved search accuracy compared to traditional techniques, they simultaneously impose a considerable increase in computational burden. Consequently, research on efficient models and methods that optimize the trade-off between search accuracy and time to alleviate computational demands is required. In this paper, we propose a Korean document retrieval method utilizing ColBERT's late interaction paradigm to efficiently calculate the relevance between questions and documents. For open-domain Korean question answering document retrieval, we construct a Korean dataset using various corpora from AI-Hub. We conduct experiments comparing the search accuracy and inference time among the traditional IR (information retrieval) model BM25, the dense retrieval approach utilizing BERT-based models for Korean, and our proposed method. The experimental results demonstrate that our approach achieves a higher accuracy than BM25 and requires less search time than the dense retrieval method employing KoBERT. Moreover, the most outstanding performance is observed when using KoSBERT, a pre-trained Korean language model that learned to position semantically similar sentences closely in vector space.  
Keywords:natural language processing; deep neural networks; question answering; document retrieval +
Footnote †: journal: _Sciences_
## 1 Introduction
The paper summarizes 150+ references in a decision tree, linking data availability, model intrusiveness, and computational costs, and cites case studies where surrogate-based polynomial chaos expansion (PCE) cuts the runtime by 10-100\(\times\) without a loss of accuracy. It also draws attention to new demands, such as firm control of active magnetic bearings and Bayesian model updates with sparse measurements. However, the study is descriptive only; no novel standards, standard measures, or test validations are presented. Thus, numerical comparisons between competing approaches and their viability for real applications are still open problems. In another paper [20], the authors compare the efficiency of the stochastic polynomial chaos surrogate method and the interval optimization method for predicting sheet metal springback. With a third-order polynomial on a sparse Gauss-Legendre grid, they show that Monte Carlo statistics are approximated using only 625 complete FEM runs, while the interval branch offers worst-case bounds more than twice as wide. The study underscores that tooling clearance dominates output variability (about 99%), which confirms the value of UQ in process calibration. However, the approach is demonstrated only with four uncertain parameters and one forming process, and thus, extends to highly nonlinear,multi-process workflows that are untested. Experimental evidence is not presented, and the method does not address defect-driven uncertainties such as die wear or material thinning. An interval modal superposition method (IMSM) that propagates bounded uncertainties in disk unbalance and bearing stiffness through a two-disk aero-engine rotor model was examined in the work [21]. IMSM reduces the central processing unit (CPU) time from \(2\cdot 10^{5}\) s to 50 s while enclosing Monte Carlo envelopes within 1%. Its companion, the interval perturbation module (IPM), produces closed-form critical-speed bands. The method is appealing for setting conservative alarm thresholds and quick, data-poor design audits. The sensitivity to the number of retained modes, the conservative widening of response intervals, and the exclusion of parameter correlations and physical defects are among the disadvantages. Therefore, more research is required to extend to high-dimensional uncertainty spaces or nonlinear fault features (such as breathing cracks). The literature shows that classical and modern techniques each address different parts of the diagnostic challenge, yet neither alone delivers a complete, universally applicable solution for high-power rotor equipment operating under real-world uncertainties. Therefore, this review aims to critically analyze and comprehensively systematize methods for energy-consuming rotor equipment condition monitoring that will enhance the efficiency of the management of the technical conditions' management of the main components of modern energy systems.
## 5 Conclusions  
The TETSO solution presented in this paper works for a wide range of applications in the domain of CPS, where the problem has multiple objectives that must be optimized to generate efficient solutions. In this article, we have presented an effective approach for the calculation of a Pareto optimal set of threshold values in the ternary event-based estate estimation. In the state estimation problems, the triggering mechanism depends on a predetermined threshold value; hence, selecting an efficient threshold value has become a significant matter. To achieve this, we proposed a multi-objective framework called ternary event triggering swarm optimization (TETSO), which effectively selected the TET mechanism's threshold values. The suggested approach yields a non-Gaussian distributed event-based approximation framework that is also systematic. The simulation results confirm that the suggested technique is capable of designing and optimizing the TET mechanism's threshold values and, furthermore, can be applied to any TET mechanism. The proposed technique relies less on the initial threshold value to start the optimization and limits human involvement in the optimization process. Finally, the proposed framework presents an effective technique of intelligently using hybrid sets of information, resulting in concurrent decrements in communication overhead as well as MSE.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Group** & **I** & **II** & **III** & **IV** & **V** & VI & **VII** & **VIII** \\ \hline IE & 6 & 3 & 20 & 4 & 5 & 22 & 8 & 11 \\ QM & 186 & 170 & 166 & 181 & 189 & 166 & 176 & 182 \\ RM & 8 & 27 & 14 & 15 & 6 & 12 & 16 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total number of communicated observations in terms of idle epoch (IE), transient (QE), and raw measurements (RM).  
Figure 5: The MSE comparison across communication rates achieved by TETSO.  
**Author Contributions:** Writing--original draft preparation and investigation, software, methodology, visualization: S.D.; writing--review, methodology and supervision, T.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All codes developed in this work will be made available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
List of abbreviations used throughout this paper:  
**Abbreviation**  
**Definition**  
ADAI  
Adaptive distributed artificial intelligence  
APSP  
Adaptive Particle Swarm Optimization  
CPS  
Cyber-physical system  
EBE  
Event-based estimation  
IE  
Idle epoch  
KF  
Kalman filter  
MSE  
Mean square error  
PF  
Particle filter  
PSO  
Particle swarm optimization  
QM  
Quantize measurements  
RM  
Row measurements  
SOD  
Send on delta  
TET  
Ternary event triggering  
Terney event triggering swarm optimization