esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
Figure 6: Comparison between the signal predicted through MCBF-net and the true signal: (**a**) reconstructed ECG signal by MCBF-net and (**b**) actual ECG signal. Figure 7: Prediction performance of the actual ECG signal and the reconstructed ECG signal. (Blue signal: the actual ECG; red signal: the reconstructed ECG; and green signal: the difference between the actual ECG and the reconstructed ECG.) To evaluate the performance of the proposed method, we used three measures: the MSE, the SNR, and the PSNR. Firstly, the MSE is the average obtained by squaring the difference between the original signal and the reconstructed signal. A lower MSE indicates a better reconstruction performance, while a higher MSE reflects a poorer performance. The SNR represents the ratio between the strength of the signal and the noise. A high SNR indicates a good quality of the reconstruction signal, while a low SNR indicates a poor quality. Finally, the PSNR calculates the peak signal-to-noise ratio between the original signal and the reconstructed signal. This ratio is often used as a quality measure between two signals: the higher the PSNR, the better the quality of the reconstructed signal. As a result of comparing the loss values through the MSE, the conventional CNN with only the FC layer showed an average loss value of 0.0138, while the proposed model with ANFN layer recorded a lower loss value of 0.010. Table 1 lists the performance of the previous two models and the proposed model. As listed in Table 1, the SNR was 7.514 and the PSNR 18.909 for the signal reconstructed using the existing deep network with only the FC layer. Meanwhile, the deep network with the ANFN layer showed a better performance than the previous models, with an SNR of 9.023 and a PSNR of 20.418. Figure 8 and Table 1 show the MSE histograms and the performance table of the predicted ECG signals obtained using the proposed model, respectively. As shown in Figure 8 and Table 1, the design of the deep learning model with an FC layer exhibited a higher error distribution compared to the other methods. The model combining the MODWT and deep learning with FC layers showed the most moderate error distribution out of all three methods. Finally, the proposed model with MODWT and ANFN layers demonstrated the lowest error distribution, indicating a superior reconstruction performance with less deviation from the original signal in comparison to the previous two methods. As a result, in order to reconstruct an ECG signal from a CW radar signal, it is essential to check changes in the frequency domain alongside conducting an analysis in the time domain. Therefore, the reconstruction performance was the lowest when the MODWT was not used. On the other hand, the existing FC method based on the MODWT was sufficiently effective. However, when the FC layer was replaced with an ANFN layer, we achieved reconstruction stability by maintaining the nonlinear characteristics seen in deep learning. Therefore, the proposed method demonstrated that a nonlinearity process through an ANFN layer was more effective than through an FC layer.
## 4 Results and Analysis
### Detection Rate (\(\%\)) of Malicious Nodes  
Table 4 shows the results of detection rate based on our proposed method. The detection rate is an important metric in examining the accuracy of the status packet to detect malicious nodes. The reason for selecting this metric is to show the ability of DDBG to identify the malicious nodes in the network. In Figure 4, the x-axis shows the number of nodes and the y-axis shows the detection rate (the finding accuracy) of DDBG compared to native AODV, LID-AODV, and HPSO-GA. It shows that the detection rate of DDBG is the highest (98%). The reason for this is that our proposed technique received replies from every legitimate node, while the malicious nodes did not reply correctly or dropped the status packet. As soon as the number of malicious nodes increases in the network, the proposed DDBG technique gets more chances to detect the malicious nodes because it sends the false queries to IDS nodes. Therefore, the proposed technique detects the malicious nodes more rapidly than the others, hence the detection rate increases as the number of nodes increases, which is the highest detection rate recorded at 98.15%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ad Hoc On-Demand** & \multirow{2}{*}{**Local Intrusion Detection (LID)-ADOV**} & \multicolumn{2}{c}{**Hybridization of Particle Swarm Optimization-Genetic Algorithm (HPSO-GA)**} & \multirow{2}{*}{**Proposed DDBG**} \\
**Distance Vector** & & & \\
**(AODV)** & & & \\ \hline
85.32\% & 87.97\% & 93.21\% & 95.39\% \\
86.28\% & 88.18\% & 93.65\% & 96.11\% \\
86.94\% & 88.29\% & 93.91\% & 96.98\% \\
87.15\% & 88.63\% & 94.58\% & 97.52\% \\
87.63\% & 89.35\% & 95.13\% & 98.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detection rate evaluation values.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Simulator & NS-2(ver.2.35) \\ Network area & 800 \(\times\) 800 m \\ Normal nodes & 100 \\ Mobility model & Random Walk mobility (Bonn Motion) \\ Routing Protocol & Ad hoc on-demand distance vector (AODV) \\ Simulation time & 600 s \\ MAC type & IEEE 802.11 \\ Traffic type & Constant bitrate (CBR) \\ Agent & UDP \\ Packet size & 512 bytes \\ Mobility & 0.5–1.0 m/s \\ Pause time & 5–20 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The simulation parameters.
## 2 0-1 Test Description  
The test, described in [24; 29; 30], uses as input a time series of \(N\) discrete scalar data \(s(n)\), for \(n=1,2,\ldots,N\), where \(s(n)\) is a 1-dimensional observable of the underlying dynamical system. An observable is any physical quantity that can be measured. If it is possible to measure all the observables that contribute to a given system's dynamical evolution, we could define a state vector in the phase space. However, in general, only an observable or very few are normally available in the experimental scope. In any case, with only one observable, it is possible to obtain information on the system's state since each of them usually contains information from the others, given the mutual coupling between them, whether linear or nonlinear. The 0-1 test supports noisy data directly obtained from the measurement process. The test output is a bi-dimensional system:  
\[p_{c}(n) = \sum_{j=1}^{n}s(j)\cos jc,\] \[q_{c}(n) = \sum_{j=1}^{n}s(j)\sin jc, \tag{1}\]  
where \(c\in(0,2\pi)\) is a constant value, and \(n=1,2,\ldots,N\). The mean squared displacement (MSD) or mean square fluctuation, is, in a sense, a measure of the extent of the system space, the \((p_{c},q_{c})\)-plane shaped by Equation (1), explored by the time series at hand over time; \(M_{c}(n)\), denoting the mean squared displacement, is defined as:  
\[M_{c}(n)=\frac{1}{N}\sum_{j=1}^{N-n}\Bigl{(}[p_{c}(j+n)-p_{c}(j)]^{2}+[q_{c}(j+ n)-q_{c}(j)]^{2}\Bigr{)},\quad n=1,2,3,\ldots \tag{2}\]  
The \(M_{c}(n)\) value changes over time as \(n\) grows. The growth rate, \(K_{c}\), gives the way to know how it changes:  
\[K_{c}=\lim_{n\to\infty}\frac{\log M_{c}(n)}{\log n}. \tag{3}\]  
The \(K_{c}\) values determine the type of deterministic behavior of a dynamic system. If \(K_{c}\approx 0\), the system dynamic behaves regularly; if \(K_{c}\approx 1\), it evolves chaotically. In the case of a dynamic system's regular evolution, the equations, defined by Equation (1), describe bounded paths (bounded space-filling in \((p_{c},q_{c})\)-plane); in a chaotic dynamical system, the evolution undergone by the paths proposed at Equation (1) resembles a 2-dimensional Brownian motion, that is, its evolution is diffusive (unbounded space-filling in \((p_{c},q_{c})\)-plane).  
The mean square fluctuation \(M_{c}(n)\) enables the identification of the growth rate of the paths, whether it be a bounded growth rate (regular case), with fluctuations tending to zero, or a linear growth rate (if chaotic), with random fluctuations typical of erratic motion. In any case, the parameter \(K_{c}\) captures this growth rate.
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 4 Comparative Analysis Results
### Analysis of Irregular Undesired Signals  
If there is an obstacle near the target in the flow field, it will have an impact on the original signal, and the undesired signal is usually chaotic. In this paper, the flow field around a triangle obstacle was used as an irregular undesired signal, as shown in Figure 15. Noise was added in correspondence of the regular undesired signal by linear superposition into the original test sample, as shown in Equation (22).  
\[\chi(n)=\left\{\begin{array}{l}x(n),1\leq n\leq 2200\\ x(n)+T(n),2201\leq n\leq 2400\\ x(n),2401\leq n\leq 3000\end{array}\right. \tag{22}\]  
where \(x(n)\) is the original signal, \(T(n)\) is the undesired signal, \(\chi(n)\) is the final testing original signal series.  
The KKF-VSM and RBF-NNM were used to filter the irregular undesired signal and predict the original test sample. The results are shown in Figures 16 and 17. The results indicated that the irregular undesired signal could be filtered, and the change trend of the original samples could be predicted very well by both models. Furthermore, the MSE of  
Figure 14: Noise sensibility of KKF-VSM comparing with RBF-NNM.  
Figure 15: The irregular undesired signal spectrum.  
RBF-NNM was \(1.625\times 10^{-4}\), and that of three-order KKF-VSM was \(1.374\times 10^{-4}\). That indicated that the robustness of the three-order KKF-VSM is higher than that of RBF-NNM.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing
### Model Evaluation Indicators  
The selection of appropriate and accurate model evaluation criteria is crucial for selecting the optimal prediction model and evaluating its performance. The results of the evaluation parameters determine the final modeling method. The commonly used standard parameters for model evaluation are the coefficient of determination (\(R^{2}\)) and root means square error (\(RMSE\)).  
The corresponding definitions are as follows:  
\[R^{2}=\left[1-\frac{\sum_{i=1}^{N}\left(y_{c}-y_{p}\right)^{2}}{\sum_{i=1}^{N} \left(y_{c}-\overline{y_{p}}\right)^{2}}\right]\times 100\% \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(y_{p}-y_{c}\right)^{2}}{N}} \tag{3}\]  
In Formula (2) and (3): where \(y_{c}\) represents the actual value of the sample, \(y_{p}\) represents the predicted value of the prediction model, \(\overline{y_{p}}\) represents the average of predicted values, and \(N\) represents the number of test samples. The closer to 1 the correlation coefficient is, the smaller the \(RMSE\), the better the prediction performance of the model.
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 6 Applications
### Digital Engagement  
Human engagement is drawing increasing interest due to the surge in digital interfaces, such as personal computers and smart phones, in the modern era. Engagement empowers critical attributes in designing and configuring digital applications ranging from accessibility of a web page to e-learning and online meetings. As specified previously, monitoring learner engagement can be particularly useful for class management and providing live feedback to instructors [56; 57]. Using off-the-shelf eye trackers to offer feedback has been shown to be useful in educational settings. For example, students could be prompted to  
Figure 3: Example applications enabled by engagement estimation. (**A**) E-learning can benefit from automatic learner engagement evaluation. (**B**) Driver drowsiness detection is critical for safety and reducing road accidents (data from [68]). (**C**) Engagement estimation plays a key role in designing human–computer interfaces, social robots, and autonomous systems for HRI.  
re-focus when attention lapses were detected [141]. Due to the COVID-19 pandemic, there has been a sharp rise in remote working and online meetings which calls for automated participant engagement estimation [142]. [PERSON] et al. [143] noted that engagement is an essential requirement, as well as one of the most important benchmarks, in website design. Online interactions for meeting, learning, and entertainment will keep thriving in a digital future. We envision the seamless integration of human engagement estimation in smart devices and software systems will be inevitable to meet this digital culture.
## 3 Results and Discussion
### Advanced Energy Management Systems and Control Architectures
#### 3.1.2 Real-Time Monitoring and Control Platforms (SCADA and AMI)  
Real-time monitoring in microgrids has advanced through the deployment of SCADA and AMI platforms [16; 17], enabling continuous observation of key system variables. These platforms offer real-time visualization of operational conditions and support decision-making based on updated system data [47; 58; 59]. The integration of AI has further expanded their capabilities, allowing these systems to anticipate possible failures rather than simply record historical information [22; 60].  
The use of digital twins has also become an important tool for creating virtual models of physical systems, enabling the simulation of complex scenarios and analysis of contingency plans without affecting actual operations [27; 29; 31]. The implementation of these technologies requires secure and stable communication infrastructures to preserve data integrity and system availability, minimizing exposure to cybersecurity risks [28; 42; 61]. Recent studies have explored the application of embedded systems in distributed environments to enhance local control and enable remote monitoring functions [32; 62].
## 4 Improvement of Thermal Performance  
### Encapsulation  
The practical use of solid-liquid PCMs impedes, due to some deterrents, characteristics, such as leakage of the liquid phase. To overcome these problems, new types of PCMs forms have been investigated, such as form-stable (or shape-stabilized) PCMs and encapsulated PCMs [20]. Encapsulation methods are used in order to overcome the leakage problems by keeping the material suitably contained inside a coating, a shell, or a matrix (form-stable PCMs). Encapsulation of PCMs can bring great improvement in enhancing the stability and heat transfer efficiency of the PCM, as well as facilitating its utilization, storage, and transportation.  
The obstruction of material interaction with the environment not only prevents the leaking of the material, but also protects it from interacting with the environment, which could provoke degradation or corrosion during the melting-freezing cycles [2; 21]. Furthermore, encapsulation increases the surface area of the PCM, which enhances the heat transfer efficiency. Moreover, the capsule form gives the capability of controlling the volume changes during melting, as well as the possibility of increasing thermal conductivity by inserting additives such as nanoparticles or C-based materials. Another great capability of manufacturing composite PCMs is that they can be applied directly in applications without any extra devices. For example, the fabrication of a PCM can be in forms such as powders or pastes, and they can be used directly as additives in constructional materials [21; 22].  
The encapsulated PCMs are composed of the core material, which is the PCM, and the shell material, which can be inorganic or organic. Their shape can be spherical, tubular, oval, or irregular, depending on the materials and the fabrication process. Additionally, a core-shell composite can have single or several cores within the capsule and multiple shells. A classification of encapsulated PCMs is related to their size. The PCMs can be characterized as macroencapsulated when their size is bigger than 1000 \(\upmu\)m, microencapsulated when their size is between 1 and 1000 \(\upmu\)m, and, finally, nanoencapsulated when their size is less than 1 \(\upmu\)m [21]. The most interesting encapsulated PCMs to investigate are microencapsulated and nanoencapsulated PCMs because of the higher surface area per volume unit, which seems to facilitate thermal transfer and lead to better thermal performance. Another reason is that small particle sizes are more durable to mechanical stress [22].
* We develop Parallel Encoders, including Location Encoder and the Sailing Status Encoder, to capture more information from longitude, latitude, COG, SOG, and sailing distance. * We develop the Ship-Oriented Decoder and the Semantic Location Vector (SLV). The Ship-Oriented Decoder can utilize the SLV to generate accurate prediction results, which better represent the spatial correlation of historical track points. * We implement comparative experiments with several baseline models. The experimental results show that our model is superior to them both quantitatively and qualitatively.
## 3 Methodologies  
### Modelling Procedure  
The flowchart of this study, shown in Figure 3, outlines five key stages. In the first stage, data preparation and factor reclassification, all the influencing factors were converted into 30 m-resolution raster data using ArcGIS. The frequency ratios (FRs) for continuous factors were calculated using smaller intervals, and subcategories with similar FR values were grouped to establish factor classifications.  
For the sampling strategy of landslide and non-landslide data in the second stage, landslide samples were generated by buffering around landslide points, with the buffer radius determined by the area of each landslide. Buffered areas within slope units containing landslides were retained, whereas those within slope units without landslides were excluded. Non-landslide samples were identified using five distinct methods. In the \"Slope\" dataset, samples were selected from slope units without landslides. In the \"Buffer\" dataset, samples were extracted from areas outside an 800 m buffer around landslide points, the buffer distance based on the largest recorded landslide in the study area, which measured 1600 m in length and 1300 m in width; and the non-landslide samples were extracted from the zones of very low susceptibility identified by the LR, SVM, and GBDT models, forming the \"Pre-LR\", \"Pre-SVM\", and \"Pre-GBDT\" datasets, respectively.  
In the third stage, the average method to reduce model selection uncertainty refers to averaging the susceptibility indices from different models to mitigate the uncertainty in the model selection process.  
For the LSM in the fourth stage, the LSM performance of the LR, SVM, GBDT, and RF models was compared using the \"Buffer\" and \"Slope\" datasets through combining the aforementioned landslide and non-landslide sampling strategies. Additionally, the RF model was applied to compare LSM performance across the \"Pre-LR\", \"Pre-SVM\", and \"Pre-GBDT\" datasets.  
For the validation and comparison process in the fifth stage, the receiver operating characteristic (ROC) curves and area under the curve (AUC) values for each scenario were calculated to assess model performance and evaluate the impact of different sampling strategies on LSA. A confusion matrix of the model was generated and exported.  
Figure 3: Research flowchart. LR, logistic regression model; SVM, support vector machine model; GBDT, gradient boosting decision tree model; RF, random forest model.  
Furthermore, the distribution and proportion of existing landslide points within each susceptibility zone were recorded, along with the distribution and proportion of pixels for each susceptibility zone.
## 2 Working Principle of a Rolling Shear and Schematic of an Electro-Hydraulic Servo System  
### Working Principle of a Rolling Shear  
A hydraulic rolling shear is a device used for shearing steel plates in rolling production lines. It is mainly composed of an upper blade, connecting rods and two hydraulic cylinders (Figure 1). Figure 2 illustrates the work cycle of a rolling shear. The two hydraulic cylinders push the connecting rods to drive the upper cutting edge to perform the rolling motion. At the beginning of the work cycle, the hydraulic cylinders on both sides are retracted, while the upper blade is at the highest position (Figure 2a). Then, the left-hand hydraulic cylinder extends, and the upper blade descends to start cutting a steel plate (Figure 2b). When cutting, the hydraulic cylinders on both sides harmoniously drive the upper blade to create the rolling motion on the steel plate (Figure 2c). When the cutting finishes, the right-hand hydraulic cylinder retracts, whereas the upper blade is lifted up to prepare for the next work cycle (Figure 2d). Figure 3 shows the position curves of the two hydraulic cylinders during one work cycle.  
Figure 1: (**a**) Photograph of a hydraulic rolling shear; (**b**) schematic of its mechanism.  
Figure 2: Work cycle of a rolling shear: (**a**) the initial state; (**b**) the beginning of the cutting process; (**c**) cutting being performed; (**d**) the end of the cutting process.
# Photovoltaic Roundabouts for Enhancement of Self-Sufficiency and Resiliency
## 2 Materials and Methods
### Calculation of the Energy Demand for Lighting Roundabout  
To determine the electricity demand for the lighting of the roundabouts and the roads that lead into them, we used DIALux (version 9.2), a free software package that can be used to determine the technical characteristics of a street lighting system.  
The software provides a constantly updated archive of lighting fixtures from the world's leading manufacturers, described in detail with all technical data such as power, intensity and luminous flux, luminance diagrams and isometric isolux curves. The specific placement of the lamps allows to realize a complete lighting project. As for the lighting of roundabouts and legs, DIALux allows modelling, simulation and obtaining accurate results in compliance with the current legislation, EN 13201: 2015, which defines the classes of lighting systems for street lighting that meet the visual needs of road users and allow the consideration of the environmental aspects of street lighting.  
In this particular case, the simulations were performed for the configurations summarized in Table 2.  
The choice of the 100 m spacing on each leg was motivated by the desire to consider the range of influence of roundabouts on user behavior. In fact, road users approaching the roundabout begin to slow down from 100 m from the center of the roundabout itself. [30]. Therefore, leg lighting at a distance of 100 m is undoubtedly an important means to ensure safe entry into the roundabout at night.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline \multirow{3}{*}{**Type of** **Roundabout**} & \multirow{3}{*}{**Roundabout**} & \multicolumn{3}{c}{**Roundabout**} & \multicolumn{3}{c}{**Roundabout**} \\  & & **Roundabout** & **(High Mast Pole)** & **(High Mast Pole)** & **(Poles) and Legs** & **(Poles) and Legs** \\  & & **Only (High** & **Only (Poles)** & **and Legs** & **and Legs** & **(Single-Sided** & **(Double-Sided** \\  & & & **(Single-Sided** & **(Double-Sided** & **Arrangement) * & **Arrangement) * \\  & & & **Arrangement) * & & & \\ \hline
20 m \(\leq\) Diameter \(\leq\) 50 m & - & - & - & \(\times\) & - & - \\ (5 m increments) & - & - & - & - & \(\times\) & - \\ \hline \hline \end{tabular}  
* for a longitudinal extension of 100 m on each leg.  
\end{table}
Table 2: Configurations chosen for the simulations (roundabouts and lighting system).