esa-sceva/satcom-chunk-collection huggingface dataset:
# Blind People: Clothing Category Classification and Stain Detection Using Transfer Learning
## 3 Methodology
### Clothing Type Category Classification Methods
#### 3.1.2 Transfer Learning for Clothing Type Classification  
The main objective of image classification is to classify the image by assigning it to a specific label. It involves the extraction of features from the images.  
CNNs have been used for different image classification problems. Most of them have a small quantity of data and training these networks carries a high computational cost and is a time-consuming task. With this premise, transfer learning appears as an interesting solution, where a pre-trained network is retrained with another dataset, allowing one to train a network faster than training from scratch. Transfer learning could be used in two different approaches, feature extraction and fine-tuning. Regarding the first one, the head of the network, the fully connected layer (FC), is replaced, and it only retrains this part. Moreover, all the weights of the convolutional layers of the network are frozen. On the other hand, in the second approach, the head of the network is replaced, and a new head can be built, which is similar to feature extraction. However, it differs from the first one as the weights of the convolution neural network can be unfrozen and the entire network can be retrained.  
In this work, six implementations of convolutional neural networks were made, specifically those that, to the best of our knowledge, have never been used before in the Fashion Product Images Dataset. The choice of the implementation was based on the different depths and parameters; notwithstanding, all implementations had with the same resolution--see Table 3. Overall, this study used the following networks: RestNet-18, ResNet-50, MobileNetV2, GoogLeNet, Mobilenet V3 (small) and EfficientNet-B0. A comparative analysis of the results obtained with these networks was also carried out.  
Figure 3: Sample images from the Fashion Product Images Dataset.
## 3 Proposed TIGSO-EDS System Architecture
### Security Enhancements  
The proposed model uses a hybrid encryption approach which combines symmetric and asymmetric encryption techniques. The homomorphism encryption method will be utilized to encrypt the raw data with the public key. Through the implementation of a hybrid cryptographic method, the proposed model will enable both public and private key encryption. The suggested approach is meant to accommodate event-driven applications as well as continuous monitoring applications. In an event-driven application, the intermediary nodes verify the availability of the nodes when a sensor node identifies an event. In a system of continuous monitoring, the node's accessibility is validated via public key exchange. The sink node verifies the data's authenticity by decrypting the cyber data  
Figure 3: Hybrid integration of VIKOR GSO model for clustering in TIGSO-EDS.  
using both the public and private keys. When a sink node is unable to decrypt data using the public key, the data are deleted, as they are unauthorized. By employing a hybrid encryption technique, data secrecy is assured in the suggested architecture. The integrity of the data gathered by the intermediate nodes and sink node may be ensured by the homomorphism nature of the proposed encryption algorithm. Lastly, the authorization of the node may be confirmed by the routing algorithm, which is proposed as TIGSO-EDS.
# Antimicrobial and Antibiofilm Properties of Bioceramic Materials in Endodontics
## 3 Bioceramic Root Repair Material
### Biodentine  
The new generation of tricalcium silicate cements contains modifications aimed to eliminate the limitations of MTA and enhance its physico-chemical properties [42]. Biodentine (Septodont, Saint-Maur-des-Fosses, France) is a fast-setting calcium silicate-based material and is recommended for use as a dentin substitute that can be used as a coronal restoration material, perforation repair material, or pulp-capping material [43; 44]. Biodentine contains tricalcium silicate, calcium carbonate, zirconium oxide, and calcium chloride [10]. Zirconium oxide has been used instead of bismuth oxide as a non-inducing discoloration radiopacifier in Biodentine [45].  
The antimicrobial activity of Biodentine has been shown to be dependent on calcium ion release [46; 47]. The setting process of Biodentine also allowed the increase of pH values [30; 48]. The alkaline and calcium release properties allowed Biodentine to kill _E. faecalis_ in a direct contact test [39]. Another recent study showed Biodentine had excellent antimicrobial activity upon initial contact with the bacteria [49]. However, confocal laser scanning microscopy results have shown Biodentine to lose its antimicrobial activity after prolonged exposure to biofilms [49]. The reduction in pH value over time may contribute to the loss of antibiofilm properties as time progresses [49]. Biodentine has also been reported to be ineffective against multi-species biofilm [14]. Incorporating titanium tetrafluoride to Biodentine has been reported to enhance its antimicrobial properties [50].
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 5 User Study
### Experimental Results  
#### 5.3.1 Assembly Time  
The experimental result of assembly time is summarized in Table 2. The average total assembly time for the paper-based instructions, 3D viewer, and AR-based systems was 358.5, 366.9, and 781.9 s, respectively. The AR-assisted assembly consumed the longest time. The one-way ANOVA showed that there is a statistically significant difference between the three results, F(2, 45) = 49.313, \(p<0.005\) (see Table 3). The post-hoc analyses using the Scheffe test indicated that the average assembly time was significantly shorter in the paper group (mean (M) = 358.50, standard deviation (SD) = 102.48) than in the AR group (M = 781.88, SD = 195.02), F(1, 45) = 25.32, \(p<0.005\). The average assembly time was significantly shorter in the 3D viewer group (M = 366.94, SD = 92.14) than in the AR group, F(1, 45) = 17.65, \(p<0.005\).  
Next, the total assembly time was decomposed into six steps (see Section 5.2) for in-depth analyses. Table 4 lists the experimental result of each step. As shown in Figure 12, for the paper form, the part recognition step consumed the longest time (81.8 s). For the part-fetching step, the AR system took the longest average time (92.1 s). Only the AR system has the next two steps. The 3D viewer system required the longest time in the task recognition step (76.2 s). The AR system yielded the longest time in the actual assembly step. The ANOVA result showed a significant difference among three groups only in the assembly step, F(2, 45) = 15.23, \(p<0.00\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Media** & **\# of Data** & **Average** & **Standard Deviation** & **Min** & **Max** \\ \hline Paper & 16 & 358.50 & 102.48 & 247 & 608 \\
3D Viewer & 16 & 366.94 & 92.14 & 227 & 496 \\ AR & 16 & 781.88 & 195.02 & 588 & 1373 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental result of total assembly time in seconds.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SS** & **df** & **MS** & **F** & \(p\) \\ \hline Between & 1,874,617.125 & 2 & 937,308.563 & 49.313 & 0.000 \\ Within & 855,334.688 & 45 & 19,007.438 & & \\ Total & 2,729,951.813 & 47 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The analysis of variance (ANOVA) result of the total assembly time.
# Effect of Platinum Doping on the Morphology and Sensing Performance for CuO-Based Gas Sensor
## 2 Experimental
### Characterization  
The crystallinity and phase composition of the CuO specimens were analyzed by X-ray diffraction with a monochromatized Cu target radiation resource (\(\lambda\) = 1.5418 A), scanning from 10-80\({}^{\circ}\) (XRD, D8-Advance, Bruker, Germany), as well as X-ray photoelectron spectroscopy (XPS, ESCALAB 250 Xi, Thermo Scientific, London, UK). The morphology of the powders was investigated by field emission scanning electron microscopy (FE-SEM, S-4800, Hitachi Co. Ltd., Tokyo, Japan) at an accelerating voltage of 10 kV. Energy Dispersive X-Ray (EDX) spectroscopy was used to confirm the composition of the CuO nanostructures. The surface areas were measured by the Brunauer-Emmett-Teller method (BET, Tristar II 3020, Micromerities Instrument Co Ltd., Norcross, GA, USA). The concentrations of Pt-doped specimens were determined by inductively coupled plasma atomic emission spectrometer (ICP-AES, A-6300, Thermo Scientific, Waltham, MA, USA).
# Vibration Energy Harvesting from Plates by Means of Piezoelectric Dynamic Vibration Absorbers
###### Abstract  
In this paper, the possibility of harvesting energy from the vibrations of a plate is analyzed. The harvester takes the form of a cantilever dynamic vibration absorber equipped with a piezoelectric layer and tuned by means of a tip mass to the first mode of vibration of the plate. A mathematical model of the coupled system composed of the plate and the harvester is presented. The validity of the proposed harvester is proved by means of simulations carried out with the modal expansions approach. Simulation results highlighting the effects of harvester tuning and location are presented as well. Then, the validity of the harvester is confirmed by experimental tests carried out both with a concentrated impulsive load and with a distributed pressure load. Simulations and experimental tests are performed on the cantilever piezoelectric dynamic vibration absorber and on the same piezoelectric layer directly bonded to the plate surface. Results show an improvement in terms of generated voltage when the proposed novel device is used in place of the simple layer.  
piezoelectric harvester; plate vibrations; dynamic vibration absorber +
Footnote †: journal: Applied Sciences
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 3 UAV-Assisted Non-Terrestrial Networks
### UAV Mobile Edge Computing in NTN Communications
However, this also brings about security issues since the line-of-sight links are prone to attacks by other platforms. Nevertheless, the data links usually work in a broadcast manner, which might be used by eavesdroppers to intercept the data. For secure MEC processes [64], considered UAV placement and RF power to comprehensively solve the maximum security capacity problem. Non-orthogonal multiple access (NOMA)-based UAV MEC have advantages in channel efficiency, thus enabling more users in scenarios of limited resources. To resolve the secure communication of the NOMA-based UAV MEC problem [65] proposed an optimizing paradigm for maximizing secure capacity through jointly optimizing UAV trajectory and resource allocation. Furthermore, the authors of [66] proposed a security enhancement method emphasizing an airborne eavesdropper scenario in NOMA UAV MEC systems.
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 2 Preliminaries
### Routing Metrics

Table 3: Comparison of four kinds of games used in routing for wireless sensor networks (WSNs). Network lifetime is defined as the period when a WSN remains fully functional and operative. We can also consider network lifetime as the time up to when the first node is alive, because the death of a node can reduce certain functionalities. According to [55], this is defined as the maximum time duration that the deployed sensor nodes can monitor the phenomena of interest. The routing protocol for WSNs should be designed such that the network lifetime is prolonged. After the deployment of sensor nodes, a considerable amount of time is required to set up the network and for it to subsequently attain a steady state. A certain reliable route must be established from the source to the destination. Different approaches have been used in various routing protocols for route discovery and establishment. Latency in first-time route establishment is not acceptable, as WSNs are deployed in mission critical cases. Therefore, it is also a considerable routing metric for WSNs to design a routing protocol. Throughput can be considered the amount of data transferred per unit of time. It is the rate of successful message delivery over a communication channel. The routing approaches in WSNs that are based on game theory consider two or more routing metrics simultaneously. We can denote this as a cumulative metric. For example, energy consumption and packet delivery ratio can be considered together as a routing metric to design a routing protocol. Simultaneous considerations of these metrics can ensure a robust path selection in a complex routing scenario.
# Context-Aware End-User Development Review
## 4 Application Building Techniques
### Simple Flow of Components  
Diverse approaches simplify models proposing a simple flow of components (e.g., commands, actions, language-based). End-users create applications in easy steps by connecting components in sequence or parallel, e.g., pipeline [80]. The development tool includes techniques for flow validation, such as a list of compatible components and icons or colors for compatible connections/actions.  
S-APIs (Semantic APIs), presented by [PERSON] et al. [63], is a command-based programming abstraction for building smart space applications. End-users connect commands, forming sequences and branches, and the system provides composition input/output validation. End-users can build _command-oriented_ and _goal-oriented_ applications using UI editors that facilitate the use of S-APIs. _Command-oriented_ programming provides two visual approaches: graph-based editor for desktops (Smart Modeler) and list-based editor for mobiles (RDFScript Creator). In graph-based development, a user attaches and connects command/branches, creating the application sequence. The editor provides type compatibility between input/output and checks inputs configuration. In list-based development, a user creates a command sequence by adding/removing commands, defining inputs, and connecting the command to the output branch of the previous command. _Goal-oriented_ programming is performed in the RDFScript creator, enabling end-users to define goals achieved by executing a sequence of commands, e.g., if location changes, and I arrive home; then, turn on the TV.  
MicroApps, presented by [PERSON] et al. [55], is a tool to create applications in mobile phones through a jigsaw-based visual editor. The tool integrates pre-existing applications managing services (e.g., camera, contact, mail), making them available, exposing their list of actions (e.g., camera.take, camera.preview). Icons represent actions for dragging and dropping in a composition area. End-users can connect actions in sequential or parallel and define preconditions (triggers), inputs/outputs of actions, and implicit loops, i.e., implicit objects generated based on outputs. The visual editor helps through the composition process. It highlights action icons by compatibility, disables not compatible columns, and represents inputs/outputs with shapes and colors (e.g., a red bullet means text string, a cyan bullet means contact data, a triangle means an available parameter for composition). The visual editor also includes aids such as color descriptions when a user long-press an action icon, allowance to associate parameters in parallel actions and undo operation to remove the last action or association [81].
# A Quality Control Method for High Frequency Radar Data Based on Machine Learning Neural Networks
## 2 Materials and Methods  
### High-Frequency Radar  
In our study, we employed the OSMAR-S100 high-frequency compact ground-wave radar system developed by Wuhan University [17]. The system was manufactured by Beijing Highlander Digital Technology Company Limited in Beijing, China. Compared to large line-of-sight radar systems, this compact high-frequency radar boasts multiple advantages, including reduced equipment size, decreased physical space requirements (with antenna aperture no larger than several-hundred meters), lower energy consumption, and the convenience of antenna equipment installation and maintenance. The OSMAR-S100 HF radar operates as a networked radar with a working frequency range from 13 to 16 MHz. As depicted in Figure 1, a total of 19 radar units were installed, distributed across cities like Haikou, Sanya, Ledong, Changjiang, and Wenchang. The synthesized detection area (107.5\({}^{\circ}\) E-111.5\({}^{\circ}\) E, 17.5\({}^{\circ}\) N-21.5\({}^{\circ}\) N) spans over 40,000 square kilometers.  
The data used in this research were collected from 1 January 2022, to 31 October 2022, with a time resolution of 12 min and a spatial resolution of 5 km. Due to factors such as equipment maintenance and varying environmental disturbances at radar stations, the data collection rate differed across locations. In subsequent processing, we selected points with a data collection rate exceeding 80% to ensure data reliability. For the purpose of training and testing our machine learning models, the data were averaged into hourly values.
# Smart Bandwidth Assignation in an Underlay Cellular Network for Internet of Vehicles
## 6 Discussion  
The aim of this discussion section is to summarize the meaningful results presented in the previous section. Regarding the throughput results, we could have validated that V-UEs' throughput is independent of the C-UEs' traffic pattern. This conclusion comes from the linear trend followed by the V-UEs' throughput for all the different C-UEs' packet size, as it is shown in Figure 4. In addition, the total throughput results provide evidence that the RRM we propose provides the highest values of total throughput, as it is presented in Figure 5.  
On the other hand, in Figure 6, the V2V's efficiency results prove that the proposed methodology performs better when the number of V-UEs is high and that V2V's efficiency is also independent of C-UEs's traffic pattern. Additionally, the total efficiency results demonstrate that our approach is more efficient than the other alternatives, as it is demonstrated in Figure 7.  
The last metric evaluated is the energy efficiency, which results shown in Figure 8 provide evidence that the proposed methodology performs better than the compared ones. In addition, the energy efficiency of V2V links is independent of the C-UEs' traffic pattern. On the other hand, the total energy efficiency results provide evidence that energy efficiency of V2V communications is higher than the common cellular ones, which is a coherent consequence of a V2V communication because the distance between transmitter and receiver is usually lower than in a cellular one.  
Finally, Table 9 collects the average results obtained for the presented metrics.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 7 Discussion
#### Adaptive Clustering in C-SHAP  
In this study, we employed a fixed number of clusters for K-means clustering based on the observation that the datasets used are of similar size and complexity. While this approach simplifies implementation and ensures consistency across experiments, it may not be optimal for larger or more complex datasets, where the fixed number of clusters could limit the method's scalability and adaptability.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy** & **LIME (Sec)** & **SHAP (Sec)** & **C-SHAP (Sec)** \\ \hline Random Forest & 0.84 & 0.09 & 19.07 & 0.36 \\ \hline SVC & 0.80 & 0.09 & 41.53 & 0.11 \\ \hline XGBoost & 0.83 & 0.08 & 0.09 & 0.09 \\ \hline LR & 0.83 & 0.06 & 11.49 & 0.03 \\ \hline K-NNs & 0.78 & 0.07 & 16.61 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Vertebral Column dataset performance, presenting accuracy metrics and execution times (seconds) for LIME, SHAP, and C-SHAP.  
To address this limitation, adaptive clustering strategies can be incorporated into C-SHAP. Adaptive clustering dynamically determines the number of clusters based on the characteristics of the dataset, such as its size, dimensionality, or intrinsic structure. This allows the method to adjust to varying dataset complexities, improving both interpretability and computational efficiency. For instance, techniques like the elbow method, silhouette analysis, or gap statistics can be used to determine the optimal number of clusters by evaluating clustering performance metrics [57; 58].  
However, implementing adaptive clustering introduces its own challenges. Dynamically determining the optimal number of clusters can increase computational overhead, potentially offsetting the efficiency gains achieved by clustering. Furthermore, the choice of clustering metrics (e.g., silhouette score or Davies-Bouldin index) may affect the results, leading to variability in feature attributions. These trade-offs highlight the need for further exploration of adaptive clustering strategies to balance scalability with interpretability.  
In the context of C-SHAP, adaptive clustering aligns well with the method's objectives. By dynamically adjusting the number of clusters, it ensures that the clustering process captures the underlying patterns in the data while minimizing redundancy in the computation of SHAP values. This would enhance the method's practical applicability, especially in scenarios involving highly diverse or large-scale datasets.
## 2 Materials and Methods  
### Formulation of the Optimization Function  
For the optimization of the dynamic characteristics, the function \(J_{0}\) and a set of constraints (related to frequency level \(O_{1}\) and the design variable domain) are defined. The optimization problem can be described as a set of design variables (1) for which the optimization function (2) reaches a global minimum.  
\[X=[x_{1},x_{2}\ldots x_{i}\ldots x_{n}], \tag{1}\]  
where:  
\(n\)**--**number of design variables (geometrical parameters);  
\(x_{i}\)**--**_i_th design variable from design domain \([x_{i}]_{MIN}<x_{i}<[x_{i}]_{MAX}\),  
\[\min_{X}J_{0}(X), \tag{2}\]  
where:  
\(J_{0}\)**--**optimization function;  
\(X\)**--**design variable vector.  
The desired frequency level of the nozzle guide vane system is driven by the Campbell diagram, presented in Figure 4. The aim is to reach a natural frequency above the excitation level coming from rotor imbalance.  
\[O_{1}\to f_{1}\geq 125\;\mathrm{Hz}, \tag{3}\]  
where: \(f_{1}\)**--**natural frequency of the nozzle guide vane, i.e., the first system mode.  
The optimization function used in this work is described by Equation (4), including a penalty formula due to the constraint imposed on the frequency:  
\[J_{0}=\int_{\Omega}d\Omega*\gamma_{freq}\rightarrow\min_{X_{A}}J_{0}(X_{A}), \tag{4}\]  
where:  
\(\Omega\)**--**model area;  
Figure 3: Experimental Campbell diagram with noticeable dynamic response of the nozzle.  
\(X_{A}\)--set of geometrical design variables;  
\(\gamma_{freq}\)--penalty function defined according to constraint \(O_{1}\).  
The penalty function is represented by Equation (5):  
\[\gamma_{freq}=\left\{\begin{aligned} & If_{1}<125\;then\;(125-f_{1})*2\\ & else\;1\end{aligned}\right. \tag{5}\]  
In the next section, the design parameters and optimization algorithm used to solve the defined optimization problem are described.