esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison Based on Performance Metrics  
* **Accuracy**: TCN consistently achieves the highest test set accuracy across most configurations, indicating robust overall performance and generalization capability.
* **Precision**: High precision scores by TCN (up to **0.9770**) reflect its effectiveness in minimizing false positives, crucial in ITS to avoid incorrect detections.
* **Recall**: TCN's high recall values demonstrate its ability to identify true positives reliably, ensuring critical events are not missed.
* **F1 Score**: The balanced F1 Scores suggest that TCN maintains a good trade-off between precision and recall on unseen data, essential for reliable decision-making in ITS applications.
# Federated Pseudo-Sample Clustering Algorithm:
## 2 Related Work
While certain security techniques such as differential privacy [26; 27; 28], secure multi-party computation [29; 30; 31], homomorphic encryption [32; 33], and trusted execution environments [34; 35] can provide some protection, they often incur significant communication and computational costs or rely on specialized hardware devices. In light of these challenges, [PERSON] et al. [16] proposed PerFed-GAN, a personalized federated learning approach based on generative adversarial networks. This method allows each client to share samples generated by the GAN. The central server collects all the generated samples and randomly selects a portion to send back to the local clients for further training. However, the need to harmonize client labeling remains a key issue. If participants have different classification tasks or label orders, the local model may receive incorrect training labels, leading to degradation in model performance. Unlike existing methods, our work addresses collaborative learning between clients while allowing for label personalization. Even when different clients apply different labels to the same type of data, our approach has the ability to further improve model performance. Table 1 presents information on related work, including methods, publication dates, central ideas and results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Time** & **Core Idea** & **Result** \\ \hline \multirow{3}{*}{PerFed-GAN [16]} & \multirow{3}{*}{2023} & The structure of the client model is personalized by sharing pseudo-samples generated by adversarial networks instead of shared parameters. & The average test is 42\% higher than existing methods. \\ \hline \multirow{3}{*}{FedProx [18]} & \multirow{3}{*}{2020} & The stability of overall convergence was improved by adding a proximal term; the system heterogeneity & Compared with FedAvg, the average absolute test accuracy is improved local training. & \\ \cline{1-1}  & & & \(\quad\) 32\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant work.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 9 Smart Port Use Case
### Intelligent Automation (IA) (mMTC)  
Intelligent automation (IA) concerns the intelligent controlling and monitoring of the end devices with the combination of physical entities and application services. The efficiency of the port can be achieved by improving the automation, which will also help to communicate between different parties present on the port. Smart ships are equipped with sensors in order to monitor the position of the ships and environmental data in real time. Containers can also be equipped with sensors to monitor the temperature and to trace their location. These sensors needs to be connected to 5G base station via a 5G customer premise equipment (5G-CPE) wireless terminal. A data analytics mechanism is required for clustering and data aggregation due to the higher volume of data generated by sensor networks placed in the sea port. ML algorithms can be used in this regard.
# Embrace the Era of Drones: A New Practical Design Approach to Emergency Rescue Drones
## 2 Literature Review
### Fuzzy Kano-QFD-FBS Model Fusion Application  
At present, research on the fusion application of the Kano, QFD, and FBS models has achieved certain results, which have been applied to product design, interaction design, and service experience design. [PERSON] et al. [41] developed an integrated method that consists of the QFD, Kano model, and interval-valued intuitionistic fuzzy analytical hierarchy to provide a customer-oriented product design. [PERSON] et al. [42] developed a function that combines the baby stroller design method with the Kano, QFD, and FAST methodologies. [PERSON] et al. [43] used Kano and QFD analyses for autonomous electric cars to increase customer contentment. [PERSON] et al. [44] proposed an online review-driven Kano-QFD method for service design. [PERSON] et al. [45] used dissected interviews and questionnaire surveys to introduce the Kano-QFD research method into UX design research in the area of handicraft ICH apps. [PERSON] and [PERSON] [46] used TRIZ theory with the Kano model and QFD theory to design helmet products. [PERSON] and [PERSON] [47] proposed an enhanced model using the Kano model, QFD, and TRIZ with a component-based approach for systematically designing sustainable and innovative products. [PERSON] et al. [48] used an innovative engineering design process integrated with the QFD and FBS model to assist in the design of transport devices. [PERSON] et al. [49] researched the design of children's swimming auxiliary products via the QFD and FBS models, enabling precise mapping and matching between the objective performance of the final product and user requirements. [PERSON] et al. [50] combined the QFD theory with the FBS model and applied it to the preliminary design of a braille writing modifier, transforming subjective abstract user requirements into concrete structured information and verifying the feasibility of the integrated application of the two in accessible design.  
The above research focuses mainly on the integration of the Kano-QFD model or the QFD-FBS model, while there is less research on the integration of the fuzzy Kano-QFD-FBS model. The fuzzy Kano model can screen out key user requirements, ensuring that the design process closely aligns with user requirements. The QFD model can construct a quality house, conduct quantitative analysis on key user requirements, and obtain product design elements and weights. The FBS model can accurately analyse key design elements and translate them into objective and specific design guidance, solving the problems of high subjectivity and low product matching in the product design process. The integration of the fuzzy Kano-QFD-FBS model can comprehensively capture user requirements, achieve accurate mapping from user requirements to product design elements, and translate into objective and specific design guidance. Therefore, this article combines the fuzzy Kano-QFD-FBS model to carry out research on the styling design of emergency rescue drones.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 3 Proposed Method
### Complexities  
Ordinarily, the complexity of a full SVD execution in serial environments is considered to be exceedingly large, albeit it is polynomial, namely \(\mathrm{O}\left(\left(\right)n^{3}\right)\), where \(n\) is the longest dimension of \(\mathbf{A}\)This is attributed to the fact that every right eigenvector of both the Gramian matrix and its transpose has to be computed in order for the factors \(\mathbf{U}\) and \(\mathbf{V}\) to be computed. Nevertheless, in our experiments, only the singular values, namely the diagonal entries \(\sigma_{k,k}\), need to be computed in order to assess which feature is essential. This reduces the full SVD problem to the computation of the eigenvalues of the Gramian matrix or those of its transpose, whichever is shorter in dimensions. In turn, this can be reduced to computing the eigenvalues of an equivalent companion matrix. Due to the special structure of the latter, its eigenvalues can be computed in a quadratic number of steps.  
Moreover, in a distributed system such as Spark, each node can undertake the computation of a chunk of eigenvalues with much lower complexity. This can be accomplished through a number of ways. For instance, given a set of orthonormal vectors as a starting point, the power method can be used in order to computed each eigenvalue. The upper bound for this method is pseudolinear in the size of the Gramian matrix or its transpose, but it may require more communication. On the other hand, the companion matrix method may require more operations but is cheaper in terms of communication. At any rate, the SVD implementation of the MLlib is quite efficient and takes full advantage of the underlying distributed system.
## 5 Experiments
### Results and Discussion
#### 5.3.1 Results of the Different Model  
In this section, we finally selected the baseline model of the full text by comparing the parameter quantity and accuracy of several ResNet networks. ResNet18 and ResNet50 are very popular residual networks widely used for computer vision tasks. They are both part of the ResNet, which uses residual blocks in deep neural networks. Compared with ResNet50, ResNet18 has a lighter network structure, fewer parameters, and less calculation. Therefore, ResNet18 is easier to deploy on devices with limited resources. We compared the performance of the network model regarding signature verification using the CEDAR public dataset and the self-built Uyghur dataset. Through these experiments, we chose the baseline model that is most suitable for our research purposes.  
According to the experimental results shown in Table 3, on the CEDAR dataset, the accuracy rate of ResNet18 is 95.08%, and the accuracy rate of Resnet101 is the highest, which is 95.64%. However, Resnet101 has the largest number of parameters and the most memory usage, and its verification effect has no obvious advantage compared with ResNet18.  
As shown in Figure 6, on the Uyghur language dataset, Resnet101 still has the largest number of parameters, but it can only achieve 86.61% accuracy, 9.04% lower than that of ResNet18. Compared to the others, ResNet18 achieved the best results of two different signature datasets and had the lowest number of parameters and the lowest memory usage. Considering both computational performance and model effectiveness, ResNet18 was chosen as the baseline model in this paper.
# Detection of Frozen Soil Using Sentinel-1 SAR Data
## 3 Description of Data  
### Sentinel-1 Images  
A database of Sentinel-1 (S1) images collected over the Orgeval Basin situated near Paris was used (centered on lat.: 48.86\({}^{\circ}\)E and long: 3.17\({}^{\circ}\)N, Figure 1). Relatively flat, the Orgeval Basin is mainly composed of cereal fields (wheat, colza, maize, barley, etc.) and forest. The soil texture is relatively  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Type**} & \multicolumn{3}{c}{**Soil Texture (\%)**} & \multirow{2}{*}{\(\rho_{b}\)} & \multirow{2}{*}{\(\rho_{s}\)} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**B**} \\ \cline{2-2} \cline{5-7}  & **Sand** & & & & & & \\ \hline Silty-clay & 6.83 & 45.76 & 47.41 & 1.62 & 2.60 & 11.3301 & 0.6166 \\ Silt-loam & 28.58 & 51.46 & 19.96 & 1.58 & 2.58 & 5.2752 & 0.5675 \\ Sandy-loam & 50.73 & 39.61 & 9.66 & 1.59 & 2.63 & 2.6945 & 0.6104 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Coefficients A and B derived for three soil types by [PERSON] et al. [14].  
uniform over the whole basin, with clay 20%, silt 70% and sand 10% [29]. Sixty-one S1 images obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (frequency = 5.406 GHz, wavelength \(\sim\)6 cm) were collected for the period between 1 November 2016 and 1 March 2017. Over our site, S1A and S1B provide three images every four days, on average. One image is in the Descending mode (acquisition time \(\sim\)05:58 TU), with an incidence angle of approximately 38\({}^{\circ}\); one is in the Ascending mode (acquisition time \(\sim\)17:40 TU), with an incidence angle of approximately 42\({}^{\circ}\); and one is in the Ascending mode (acquisition time \(\sim\)17:31 TU), with an incidence angle of approximately 33\({}^{\circ}\). The 61 S1 images used were acquired in the IW imaging mode with the VV and VH polarizations. In addition, the images were generated from the high-resolution Level-1 Ground Range Detected (GRD) product with a spatial resolution of 10 m \(\times\) 10 m. These Sentinel-1 images are available via the Copernicus website ([[https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home)]([https://scihub.copernicus.eu/dhus/#/home](https://scihub.copernicus.eu/dhus/#/home))). The Sentinel-1 Toolbox (S1 TBX), developed by the ESA (European Spatial Agency), was used to calibrate the S1 images. The calibration aims to convert the digital number values of S1 images into backscattering coefficients (\(\sigma^{\circ}\)) in a linear unit. The radiometric accuracy of Sentinel-1 SAR backscattering coefficient is approximately 0.70 dB (3\(\sigma\)) for the VV polarization and 1.0 dB (3\(\sigma\)) for the VH polarization [30].  
A 2017 land cover map based on field trips was used in this study [29]. This map is a thematic vector file with one value per type of land cover. For each vector/segment (one cereal field, one forest stand, etc.), the mean backscattering coefficient was calculated from each calibrated S1 image by averaging the \(\sigma^{\circ}\) values of all pixels within that vector/segment.
## 2 Current Supervisory or Regulatory Technologies
### Definition of Several Concepts: Fintech, RegTech, SupTech
#### 2.1.3 SupTech (RegTech for Supervisors)  
The fast development of RegTech presents significant opportunities for supervisory agencies. SupTech refers to the application of technology by regulatory authorities to enhance their supervisory and regulatory functions. It involves the use of data analytics, AI, ML, and other technologies to collect, analyze, and monitor large volumes of data from regulated entities with \"a shift away from current approaches based on past data, lengthy on-site inspections, and often delayed supervisory action, towards a pro-active, forward-looking supervision that relies on better data collection and sophisticated data analytics\" [12]. SupTech can improve the efficiency and effectiveness of regulatory processes such as risk assessment, market surveillance, fraud detection, and compliance monitoring. It also enables regulators to have a more proactive and data-driven approach to supervision.  
While FinTech focuses on innovation in financial products and services, RegTech addresses compliance challenges faced by organizations, and SupTech involves using technology by regulatory bodies to improve their supervisory functions. However, these terms are not mutually exclusive, and there can be overlap and synergy among them as technology continues to shape the financial industry.  
\begin{table}
\begin{tabular}{c l l} \hline  & \multicolumn{1}{c}{**FinTech**} & \multicolumn{1}{c}{**RegTech**} \\ \hline _Time Period_ & 1993–Now & 2015–Now \\ \hline _Focus_ & Deliver innovative financial products, services, and solutions & Address regulatory compliance challenges faced by organizations \\ \hline _Aim_ & Enhance and automate various financial processes, increase accessibility, improve efficiency, and provide new business models. & Help organizations meet their compliance obligations more efficiently, accurately, and cost-effectively. \\ \hline _Sector Scope_ & Finance only, negligible tangible products involved & Varied and often involve tangible products that generate unique data \\ \hline _Key Technologies_ & Well-developed e.g., application program interface, artificial intelligence, machine learning, internet of things, big data analytics, distributed ledger technology, smart contracts, cloud computing, cryptography, biometrics & Everything in Fintech, but it is still evolving \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of Differences Between FinTech and RegTech.
# Deep-Learning-Based Stream-Sensing Method for Detecting Asynchronous Multiple Signals
## 5 Learning-Based Asynchronous Stream Sensing
### Input Data Generation
Additionally, for \(\mathbf{I}_{\text{R}}\) and \(\mathbf{I}_{\text{I}}\), the covariance matrices are redefined as \(\mathbf{D}_{\text{R},(i,s)}\) and \(\mathbf{D}_{\text{I},(i,s)},i\in[1,N_{\text{sym}}],s\in[1,N_{\text{FFT}}]\), respectively. The input datum \(\mathbf{I}\) has \(N_{\text{sym}}\) sample groups, each of which has \(N_{\text{FFT}}\) covariance matrices. In a sample group, covariance matrices have the frequency domain information. Sample groups include information from the time-domain perspective. Therefore, the 4D input allows our network to simultaneously observe the correlation in terms of both time and frequency. Eigenvalues of the input data are represented by \(\mathbf{I}_{\text{eig}}\in\mathbb{R}^{N_{\text{FFT}}\cdot N_{\text{R}}\times 1}\) and generated by normalizing the eigenvalues of expected covariance matrices, \(\mathbf{D}_{\text{exp},s}=\sum_{i=1}^{N_{\text{sym}}}\mathbf{D}_{i,s},\forall s\). Specifically, if \(\mathbf{I}_{\text{eig},s}\in\mathbb{R}^{N_{\text{R}}\times 1}\) is a vector that includes normalized eigenvalues of \(\mathbf{D}_{\text{exp},s}\), then \(\mathbf{I}_{\text{eig}}=[(\mathbf{I}_{\text{eig},1})^{\text{T}},(\mathbf{I}_ {\text{eig},2})^{\text{T}},\ldots,(\mathbf{I}_{\text{eig},N_{\text{FFT}}})^{ \text{T}}]^{\text{T}}\).
# Stimuli-Responsive Photonic Crystals
## 3 Fabrication Techniques  
Photonic crystals can be fabricated by following two main approaches: (1) top-down methods; and (2) bottom-up methods. In particular, bottom-up techniques can be more conveniently used on the laboratory scale, while top-down approaches rely on the use of microfabrication methods permitting development of microstructures with selected size and shape from bulk materials [16; 17]. Besides these advantages, both of them also show some disadvantages. Bottom-up methods usually suffer from a relatively low throughput, whereas top-down techniques require substantial initial investment in terms of money and person hours for dedicated setups. For these reasons, it is thus essential to select the most suitable approach according to the desired goal. Self-assemble techniques are surely the most used bottom-up methods, combining building blocks such as nanoscale structures (e.g., nanoparticles) or block copolymers. These techniques are particularly suitable for the fabrication of responsive photonic crystals as, in this way, one can combine different unitary structures and materials to integrate different functionalities in a one single photonic device. A list of the most used techniques is reported in Table 1.
# State Estimation for DC Microgrids using Modified Long Short-Term Memory Networks
## 5 Experimental Setup of the Proposed Modified LSTM-Based State Estimator
### Modified LSTM-Based State Estimator Specification  
The specification for the proposed modified LSTM-based state estimator is shown in Table 1. Additionally, the MLP-based state estimator and the normal LSTM-based state estimator were also simulated in comparison to the proposed modified LSTM-based state estimator. Initially, the number of layers and neurons were chosen arbitrarily from low number, then the number was gradually increased until the parameters that worked best for the specific dataset were found.  
One of the most common problems in deep learning is the overfitting of the network model. Overfitting can happen when the model learns the training dataset too well but cannot generalize on another hold-out dataset [24; 26]. We could diagnose whether a network model experienced overfitting by evaluating the network model's performance on the training dataset and a validation dataset during the training period. In an overfitted model, the performance on the training dataset would usually be good and would continue to improve. At the same time, the performance on the validation dataset would improve up to \(n\) epoch, after which it would degrade. In this case, it is desirable to stop the training of the network model before the performance on the validation dataset starts degrading [26]. This research used this early-stopping technique to overcome the overfitting problem, which improved the generalization of the deep-learning-based state estimator. The model with the highest validation accuracy is set as the final model.
# Personalization in Digital Ecomuseums: The Case of Pros-Eleusis
## 7 Evaluation Methodology
### Process  
The following process was followed while proceeding with the evaluation:  
1. Participant recruitment through online invitations.
2. Meeting with the evaluators in the city of Eleusis and introductory activities: 1. Introduction to the study objectives and process; 2. Brief presentation of the Pros-Eleusis app; 3. Signing consent forms; 4. User profile questionnaire.
3. City tour using the personalized map view--the user was escorted by one evaluator.
4. Questionnaire and interview.  
For participant recruitment, an invitation was sent to the our academic and personal mailing lists for adult individuals that would be interested in visiting Eleusis and touring the town with the support of the mobile application. They were invited to bring along friends or family members if they wished.  
Participants were invited to arrive in Eleusis to meet with the evaluators at pre-agreed dates and times. After a brief introduction regarding the objectives of the study and the process, they were given a brief questionnaire recording participant information, including age, gender, whether they had visited Eleusis in the past, and general interest in history and other topics relevant to the ecomuseum themes, as well as how able and accustomed they were with walking tours. They were then asked to freely use the personalized map view of the mobile app and explore the city. They were advised to use the app at their own discretion and decide on the duration of the tour, possible breaks, number of POIs visited, etc. The evaluators explained the fact that the app offers recommendations for POIs to visit and that not all available POIs are visible at the same time on the map in personalized map view. The participants were also made aware of other two functionalities of the app, thematic routes and map view; however, they were asked not to use them.  
Each visitor was discrerely escorted by one of the evaluators, who recorded user comments and issues with the app during the tour. When they felt that they would like to end the tour, they were interviewed by the evaluator escorting them (see Appendix A) about their experience and given a brief questionnaire to record their general opinion of the tour as well as more specific user experience aspects in relation to the application, content, and recommendation approach.  
Apart from using the interview and questionnaire as means to record the visitor assessment of the tour, the application was designed to log data related to the user choices and visited POIs. These are presented in the following section.  
Logged Data  
During the tour using the personalized map view, specific information was anonymously recorded for each user. This was relevant to the itinerary and duration of the tour, as well as to the recommendation algorithm and included:  
* The ordered list of visited POIs;
* Time spent at each POI;
* The total number of visited POIs;
* Total visit duration;
* POI recommendation system score;
* Indication if the participant rated the POI;
* Participant rating for the POI.