esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### WRPM: Weather-Robust Preprocessing Module
#### 2.4.1 FFA-Net  
FFA-Net is responsible for feature recovery in dense haze regions within the WRFM module. We incorporated multiple residual blocks and a feature attention [39; 40] mechanism into this network, enabling it to extract deep-level features from images and effectively process areas with heavy haze and blurred features. Additionally, we designed a multilevel convolutional and residual connection structure within the network. This mechanism ensures that the trained network prioritizes key regions with dense haze in the image, aiming to restore details and structures in these heavily obscured areas. Figure 3 illustrates the basic structure of FFA-Net.  
The key to the implementation of FFA-Net lies in the feature attention mechanism. We designed two types of attention: Channel Attention (\(CA_{\text{ffa}}\)) and Pixel Attention (\(PA_{\text{ffa}}\)). Equation (4) describes the computation process of Channel Attention, which dynamically adjusts the weights of feature channels to enhance the representation of haze-related feature channels. Equation (5) describes the computation process of Pixel Attention, which allocates attention weights at the pixel level to focus on and enhance features in local pixel regions with higher haze concentration.  
\[CA_{\text{ffa}}=\sigma(\text{Conv}(\delta(\text{Conv}(\gamma(x)))))\odot x \tag{4}\]  
\[PA_{\text{ffa}}=\sigma(\text{Conv}(\delta(\text{Conv}(x))))\odot x \tag{5}\]  
where \(x\) represents the input feature vector, \(\sigma\) denotes the sigmoid activation function, \(Conv\) stands for convolution, \(\delta\) represents the \(ReLU\) activation function, \(\gamma\) indicates average pooling, and \(\odot\) signifies element-wise multiplication. The group structure is composed of multiple block residual modules connected in series, along with skip connections, which  
Figure 3: Detailed structure diagram of FFA-Net. FFA-Net is an encoder-decoder structure and has a unique channel attention and pixel attention mechanism designed to achieve deep learning of image features. The image shows the design of the encoder, decoder, group structure, block structure, and attention mechanism in detail.  
enhance the main network's ability to recover dense haze regions. The outputs of the three-layer group modules are combined and then processed through the feature attention module. This result is further integrated with the initial image via a global residual connection to produce the desired output.
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
## 5 Discussion  
The model developed in this study is based on real data provided by manufacturers of electric vessels available on the market. It operates in discrete timesteps and updates the status and state of charge of each vessel for each timestep and each route. This allows the model to evaluate the minimum number of electric vessels necessary to completely cover the routes that are currently covered by diesel vessels in the summer (the most critical season concerning the frequency of the connections and the number of tourists).  
The results obtained from applying the model to the case study of Orta lake highlight the importance of the charging infrastructure in the transition. Specifically, the charging power of the charging stations plays a crucial role in determining the optimal fleet configuration and operation. Higher charging power allows for the substitution of the entire fleet with the same number of electric boats without any change to the routes or to the timetable, while lower charging power requires more electric boats to cover the same routes.  
These findings have important implications for the planning and implementation of the transition to sustainable inland waterways transport in local lakes. They suggest that adequate charging infrastructure is a key enabler of the transition and that careful consideration should be given to the charging power of the charging stations when planning the fleet configuration and operation.  
The results shown in Figure 4 provide important insights into the fleet configuration and charging needs under different charging power scenarios. In particular, the 500 kW scenario, where only one vessel reaches very low state of charge levels, suggests that more targeted investments in charging infrastructure could be sufficient rather than scaling up all charging points to 1000 kW. For example, installing a 1000 kW charging station at only the port where this one vessel with a low state of charge docks could improve the situation while requiring lower infrastructure costs compared to upgrading all ports to 1000 kW.
## 2 Design of the Wideband GGW Bandpass Filter  
The structure of the proposed wideband Chebyshev bandpass filter based on GGW technology is depicted in Figure 1. It is a fifth-order stepped impedance filter. The bandpass response is obtained by combining the high-pass behavior of the GGW with the low-pass characteristics of the properly centered, distributed, and sized metallic pins along the groove area (\(W\)) of the GGW, which act as impedance inverters. The design steps of this bandpass filter according to the design specifications given in Table 1 are describedbelow. According to the expression of the insertion loss defined in [49], high-order or very narrowband bandpass filters lead to higher lossy structures. Therefore, a fifth-order Chebyshev bandpass filter with \(FBW=36.4\%\) and \(RL=20\) dB was selected.
# Productivity Analysis of Documentation Based on 3D Model in Plant Facility Construction Project
## 4 Productivity Analysis of 3D-Model-Based Documentation
### Case Study
#### 4.1.2 Data Collection and Classification  
The drawings required for the production of a case project consisting of four modules are shown in Table 1. Looking at the types of drawings, most of them are assembly drawing (35.09%), single part drawing (38.92%), and single plate drawing (24.74%).  
Table 2 shows the work productivity data per sheet in two documentation methods. Table 3 analyzes the documentation time according to the type of drawing by using the productivity data of Table 2. In the case of general arrangement drawing, the working time per drawing was 90 min and 60 min. However, due to the small number of drawings, the proportion of total work time was relatively low at 8.58% (5940 min) and 11.80% (3960 min). On the other hand, in the case of assembly drawing, not only does a lot of work time is put in comparison with single part drawing and single plate drawing, but also a large number of drawings. Therefore, the proportion of total documentation work time was very high at 67.08% (46,450 min) and 72.00% (24,154 min). Therefore, this study aims to compare the productivity of 2D-drawing-based documentation method and 3D-model-based documentation method for assembly drawing extraction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & & & **(Unit: EA)** \\ \hline
**Types of Drawings** & **Module A** & **Module B** & **Module C** & **Module D** & **Total** \\ \hline General arrangement drawing & 26 & 14 & 11 & 15 & 66 \\ Assembly drawing & 675 & 420 & 340 & 423 & (1.25\%) \\ Single part drawing & 769 & 466 & 363 & 463 & 2061 \\ Single plate drawing & 495 & 316 & 225 & 274 & (24.74\%) \\ Total No. of Drawings & 1965 & 1216 & 939 & 1175 & 5295 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total number of fabrication drawings  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **(Unit: min/1 drawing)** \\ \hline
**Types of Fabrication** & **2D-Drawing-Based** & **3D-Model-Based** & **Productivity** \\
**Drawings** & **Documentation** & **Documentation** & **Improvement Ratio (\%)** \\ \hline General arrangement drawing & 90 & 60 & 33.33 \\ Assembly drawing & 25 & 13 & 48.00 \\ Single part drawing & 5 & 2 & 60.00 \\ Single plate drawing & 5 & 1 & 80.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Productivity comparison of two documentation methods  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & **(Unit: min/1 drawing)** \\ \hline
**Types of Fabrication** & **2D-Drawing-Based** & **3D-Model-Based** & **Productivity** \\
**Drawings** & **Documentation** & **Documentation** & **Improvement Ratio (\%)** \\ \hline General arrangement drawing & 90 & 60 & 33.33 \\ Assembly drawing & 25 & 13 & 48.00 \\ Single part drawing & 5 & 2 & 60.00 \\ Single plate drawing & 5 & 1 & 80.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time of documentation comparison of two documentation methods
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.4 Power Reduction Method  
NB-IoT devices are intended to have a 10 years battery life to support massive deployment with limited human intervention. In this regard, two features i.e., Power Saving Mode (PSM), (from Release 12), and extended Discontinuous Reception (eDRx) (new feature from Release 13) were supported. These features are intended to extend the UE's battery longevity as follows:  
In PSM, the NB-IoT device is configured to completely sleep while remaining registered online but cannot be reached by the base station signaling. In Release 13, the device can be in PSM mode for approximately up to about 413 days. In eDRX, the device is in an inactive mode for a few minutes to a few hours only.  
In both cases, the partial or complete inability to receiving and sending different signals enhance the battery life longevity; however, choosing either PSM, eDRX or both depends on the corresponding use-case requirement. In this regard, the device can be synchronized to wake up from these modes by either Real-Time Clock (RTC), triggering from sensors, or both.
## 3 Evaluation of Process Precision and Accuracy
Many studies have indicated that Taiwan's output value for machine tools ranks number seven worldwide, and its export volume ranks number five worldwide [22]. Central Taiwan is an important stronghold of the machine tool industry. About 70% of manufacturers of machine tools,precision machinery, and their components are situated in central Taiwan. Aiming to cut operating costs and concentrate resources on the core technologies, the machine tool industry outsources some non-core businesses to specialized manufacturers. Meanwhile, the industry focuses on improving its specialized machining processes in order to elevate the entire quality level, efficiency, and competitiveness of the machine tool industry and the entire industry chain [23; 24; 25]. Additionally, the machine tool industry also combines the aerospace technology and intelligent machinery industries. Driven by the high clustering effect, the central region of Taiwan has turned into a complete industry chain of machine tools and plays a significant role in the machine tool industry worldwide [22]. According to various studies, a machine tool contains many important components, including axles, bearings, and gears. These components usually have nominal-the-best quality characteristics (QCs), such as diameter [7]. As mentioned earlier, enhancing the process accuracy index and the process precision index would not only make the process loss lower but also make the process capability higher. This study took the outer diameter of an axle as an example to demonstrate the application of the proposed model, like converting the tolerance into a target value of zero (T = 0), the lower specification limit into \(-1\), and the upper specification limit into 1 by means of variable transformation. Then, the \(\delta-\gamma\) control charts developed by this study were employed to monitor the process accuracy and precision. When the process precision and the process accuracy became stable, the statistical testing method recommended by this study was adopted to evaluate the process capability. Furthermore, the statistical testing method of accuracy and precision indices was also used as the decision-making basis of process improvement. In the example, the outer diameter tolerance of the axle is \(2.8\pm 0.03\), and its 25 groups of statistical process control data are displayed as follows  
\[H_{1}:\delta\
otin A_{\delta}\;\sigma\;\gamma\
otin A_{\gamma} \tag{29}\]4  
where \(j=1,2,\ldots,25\) and \(i=1,2,\ldots,11\). Based on the above developed model, we started monitoring, evaluating, and improving the machining process quality for the outer diameter of the axle by Excel software 2016 and offered manufacturers a direction of decision making on improvement for reference.
# Energy-Water Management System Based on MPC for a Greenhouse in a Mapuche Indigenous Community
## 7 Simulation Results
### Short-Term Climate Prediction  
The model performance results for the testing dataset are presented in Table 2. The maximum values of the results obtained for each variable were used to calculate error values for comparison. Notably, maximum values were used instead of daily averages because each climatic variable was strongly affected by the hour of the day. Therefore, the daily average was less effective for error magnitude quantification than the 10 min trend of these climatic variables. In the case of the one-step prediction, a low error was observed when compared with the maximum values; additionally, at 144 and 288 steps, the error increased, which could be attributed to the intrinsic stochasticity of the variables.  
Figure 4 shows the prediction of the climatic variables one day ahead (for 144 prediction steps). In the case of the predictions made with neural networks (\(T_{ext}\), \(HR_{ext}\), and \(Ir\)), all of the predicted signals displayed RMSEs below 14% of the maximum value, but the MAPE values were higher; this was because of the nature of the MAPE, for which a predicted value was compared with the real value. The difference was especially pronounced for \(Ir\) because of the night values that approached 0. Nevertheless, Figure 4 shows that both \(T_{ext}\) and \(Ir\) displayed daily dynamics. Additionally, although the neural network model for \(HR_{ext}\) displayed dynamic effects similar to those observed for other variables, periods of high error could be observed. Finally, high RMSE and MAPE values were observed for the wind speed outside the greenhouse \(u^{ext}\), but the trend of the results generally matched the actual wind dynamics. This result was expected because of the high stochasticity of this variable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **RMSE** & & & **MAPE (\%)** & \\ \hline Variable & Max & 1 step & 144 steps & 288 steps & 1 step & 144 steps & 288 steps \\ \(T_{ext}\) (\({}^{\circ}\)C) & 19.205 & 1.407 & 2.410 & 2.755 & 0.253 & 15.580 & 18.620 \\ \(HR_{ext}\) (\%) & 99.448 & 2.946 & 13.198 & 11.778 & 1.220 & 12.102 & 11.425 \\ \(Ir\) (W m\({}^{-2}\)) & 571.556 & 36.403 & 70.079 & 76.655 & 14.4 & 33.163 & 33.390 \\ \(u^{ext}\) (m s\({}^{-1}\)) & 9.691 & 2.178 & 2.862 & 3.013 & 40.251 & 52.280 & 55.022 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prediction performance based on the test data.
## 7 Result and Discussion  
In this section, we explain the results of the SLR study and provide answers to the research questions based on the findings of the selected studies.  
RQ1: What Is the Distribution of the Selected Studies According to the Year of Publication, Digital Library, Publication Type and Topic, and Country of Origin?  
We answer this question by providing a list of figures and charts that describe the bibliographic information of the selected studies. Figure 5a depicts the distribution of selected studies according to the year of publication, and Figure 5b shows the allocation of selected studies in the digital libraries. We can observe from Figure 5a that the publication related to the topic of this study is constantly increasing over the years. Looking at the chart, we concluded that the number of published studies in 2020 almost doubled from the year before. After conducting this study in June 2021, we predict a marked increase in the number of studies on ML and DL on RPL published in 2022, indicating the significant role of ML and DL mechanisms in identifying attacks in RPL-based 6 LoWPAN at present and in the future.  
Figure 5b shows that most related studies were published in IEEE Xplore(r) digital library (14), followed by Google Scholar (13) and Springer Link (12), while Scopus and Science Direct returned the same number of published studies (5). Furthermore, it is worth mentioning that there are many studies published by other publishers, such as MDPI and  
Figure 4: The steps and tools for conducting the SLR study.  
[PERSON], but rather than directly extracting them from the publishers' databases, we obtained those studies via the Google Scholar database.  
Figure 5(a),b show the percentage of the selected studies according to the type and topic, respectively. As for the type of published studies, it is evident that most were journal articles, i.e., 30 out of 49 studies (61.22%), while 11 were indexed conference proceedings (22.45%), and only 8 (16.33%) of the studies belong to the book section. Furthermore, regarding the topic of selected studies, it is evident that many published studies emphasize ML mechanisms, which constitute 23/49 (46.94%) of published studies, while 6/49 (12.24%) of studies focus on DL mechanisms, and the last portion, 6/49 studies (12.24%), focus on combined ML and DL mechanisms. In addition, it is worth pointing out that the studies that provide a review on the topic, 8/49 (16.34%) (see Section 2), and the studies that proposed a mechanism without empirical results, 6/49 (12.24%), are not presented with the distribution of studies in Figure 5(b).  
Figure 7 shows the total number of selected studies based on country of origin. As can be observed from the chart, India is the most active country in this field of research, followed by Turkey, USA, Australia, etc.  
Figure 5: Distribution of selected studies according to (**a**) Year of Publication, (**b**) Digital Libraries.  
Figure 6: Distribution of selected studies according to (**a**) Publication Per Type, (**b**) Publication Per Topic.
# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 5 Discussion
### Limitations  
While the proposed approach offers significant advantages, several limitations should be acknowledged. The semantic assignment of variables to \"intuitive\" or \"technical\" categories relies on user expertise and task context, introducing some subjectivity; different users or domains may lead to different choices and, thus, different interpretations. The number of avatar visual features is limited, restricting the range of intuitive variables that can be  
Figure 13: Principal component analysis for the wine dataset based on all technical variables except avatar-assigned ones. The first two principal components are mixtures of several physicochemical properties (PC1: volatile acidity 0.57, density 0.40, sulfates 0.44; PC2: residual sugar 0.59, citric acid 0.52, density 0.45). No clear clusters or quality-based groupings emerge—high- and low-quality wines are scattered across the projection. It highlights the limitation of PCA-based projections for interpretability compared to explicit, user-driven assignments in spatial avatars. Variable abbreviations: VAC—volatile acidity, CAC—citric acid, CLO—chlorides, FSD—free sulfur dioxide, SUL—sulfates, DEN—density, SUG—residual sugar.  
displayed directly, which may necessitate dimensionality reduction or the omission of some information. When many variables are treated as \"anonymous\" and incorporated via PCA, interpretability can be lost, since principal components represent linear combinations rather than original features. Moreover, expressive avatars may introduce emotional or cognitive biases, and for very large or high-dimensional datasets, visual clutter or computational limits could arise. Finally, variable assignment is currently manual; integrating automated or user-guided assignment tools remains an important direction for further work.  
Although a formal user study has not yet been conducted, the method has been informally evaluated among collaborating researchers. These preliminary trials indicated that certain avatar features--such as face color or hair color--are readily distinguished and effective for communicating differences, while others, including smile curvature or subtle facial traits, may be less easily perceived. This observation highlights the importance of meticulous glyph design and perceptual testing to enhance interpretability. Future work will involve systematic user studies to quantitatively assess the recognizability of individual avatar features and guide further refinements of the visual encoding.  
Addressing these limitations is a natural path for development, including more robust assignment strategies, alternative glyphs to reduce bias, and enhanced support for complex or large-scale datasets.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.4 Array Configurations  
The array configuration is crucial for both the scaling and optimization of holographic metasurfaces. As an extension of HMIMO communications, multilayer designs have been proposed (ref. [70]) using stacked intelligent metasurfaces (SIMs). These systems adopt quasi-continuous aperture control via dynamic waveform shaping across SIM layers, significantly enhancing spectral and energy efficiency (ref. [80]). With its integrated framework of electromagnetic signal processing incorporated into the metasurface structure, SIMs outperform conventional MIMO systems by more than 150% in both channel capacity and spectral efficiency. Another new configuration uses holographic metasurface-based antennas (HMA) in XL-MIMO systems to handle near-field and spatial-wideband effects (ref. [76]). This approach adopts spherical wave propagation modeling together with iterative beamforming algorithms and shows robust and efficient performance in complex transmission environments. Moreover, reconfigurable refractive surfaces (RRSs) (ref. [79]) are optimized for energy efficiency in multi-user HMIMO systems, offering huge advantages over conventional phased arrays.
# Effect of Plasma Treatment of Titanium Surface on Biocompatibility
## 3 Results  
### Sample Preparation  
Scanning electron microscopy (SEM) disclosed no structural changes on the titanium disk surface after plasma irradiation (Figure 1).  
Figure 1: Surface morphology of titanium and plasma-modified titanium by SEM. SEM disclosed no structural changes on the titanium disk surface after plasma irradiation.  
No changes in surface roughness were detected on the test disks observed by scanning probe microscopy (SPM) (Figure 2).  
X-ray photoelectron spectroscopy (XPS) revealed that the intensity of the O1s peaks increased while that of the C1s peaks decreased in response to plasma irradiation (Figure 3).  
There was a clear difference between the contact angle of the plasma-treated titanium disks (titanium; 32\({}^{\circ}\), plasma-modified titanium; 0) and those of the untreated disks (Figure 4).
## 9 **Handover**  
The handover process is conducted by controlling positioning algorithms. Each BSC measures some information, such as the downlink signal strength and quality of the mobile user and the downlink signal strength and quality of the user's six best neighbors, when making a handover decision. The \"Handover failure Rate\" index provides valuable information, such as the efficiency between two neighbors, the strength or weakness of the typical radio path between two neighbors, issues related to interference at the border between two neighbors, etc. In addition, to identifying weak neighbors in the radio path, handover optimization is used to improve the call failure percentage due to handover failure. Therefore, the incorrect design of neighborhoods, traffic congestion at the destination, and the fault of swapped sectors in implementing antennas lead to handover failure.