esa-sceva/satcom-chunk-collection huggingface dataset:
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance
### Delay  
Delay is defined as the end-to-end time. In our study, this refers to the time required for the data packet to be transmitted from the sensor node to the sink. The total delay includes the cluster formation time, the time that the sensor nodes take to send data to the cluster head, and the time that the cluster head takes to transmit data to the sink. An experiment was conducted to analyze the length of the delay that occurs when the number of sinks is increased. The simulation time was set from 0 to 10 min and the data rate created by each source node was 1000 bit/s.  
[PERSON] proposed that packet delay is a very critical factor for the real-time scene [46], and defined delay as the maximum hop (K-hops) count from the network boundary to the sink. With an increase in the hops there will be a greater delay; therefore, an effective method used to shorten delay is to decrease the maximum hop count. In our study, a whole network coverage rate is achieved after the sink adjusts the optimal transmit power; the sink then sends broadcasts to its coverage area, a cluster is formed using the UCEBA algorithm, and all the sensor nodes transmit data to the sink in one hop or two hops. Because the time taken to form the cluster is relatively the same with the number of sinks used, in this experiment we mainly compare the time that the sensor nodes take to send data to the cluster head, and the time that the cluster head takes to transmit data to the sink.  
Because the sensor nodes only need to communicate with the cluster head or sink, when more sinks are deployed in the network the data traffic in the network is effectively reduced, and the load of the network greatly decreased. In addition, the average number of hops is reduced, and therefore the length of the delay is eventually improved.  
Figure 13 compares the delay of the data packet with different numbers of sinks used. Delay of the data packet gradually increases over time but is at its lowest when 12 sinks are used. With an increase in the number of sinks, the delay of the data packet increases at a lower level. In addition, with an increase in the number of sinks, the data transmission route becomes shorter and has a lighter load when using the PCEB-MS protocol, thereby significantly reducing network congestion. Therefore,  
Figure 12: Comparison of network lifetime with different numbers of sinks.  
the probability of network congestion and data delay degree is lower when a larger number of sinks are used.
# S-Band Doppler Wave Radar System
## 2 Coherent Wave Measuring Method
Then, the cross spectrum can be described as follows:  
\[u=U_{0}\cos(k_{r}R+k_{t}y^{\prime}-\omega t)\cos(\alpha_{r}-\alpha)+V_{D}+V_{S} \tag{2}\]1  
The wave height spectrum corresponding to the coming waves and leaving waves can be solved with the following equations:  
\[u=U_{0}\cos(k_{r}R+k_{t}y^{\prime}-\omega t)\cos(\alpha_{r}-\alpha)+V_{D}+V_{S} \tag{2}\]2  
\[u=U_{0}\cos(k_{r}R+k_{t}y^{\prime}-\omega t)\cos(\alpha_{r}-\alpha)+V_{D}+V_{S} \tag{2}\]3  
\[u=U_{0}\cos(k_{r}R+k_{t}y^{\prime}-\omega t)\cos(\alpha_{r}-\alpha)+V_{D}+V_{S} \tag{2}\]4 Schematic illustration of the radar measurement of surface wave velocity. \(I(\omega)\) is defined as follows:  
\[I(\omega)= \begin{cases}1,&-\pi<\text{arg}(\phi_{12})\leq 0\\ 0,&0<\text{arg}(\phi_{12})\leq\pi\end{cases}  \tag{14}\]  
Finally, the relation between the velocity spectrum and wave height spectrum is rewritten as follows:  
\[E(\omega)=(\left|B^{+}(\omega)\right|^{2}+\left|B^{-}(\omega)\right|^{2})/[ \omega^{2}\Gamma(\omega,\theta,a_{r})] \tag{15}\]  
After obtaining the directional wave height spectrum, the spectrum moment method and empirical formulas can be applied to obtain the wave parameters, such as the significant wave height and average wave period. The energy distribution of the directional wave height spectrum can also reveal some other wave parameters, such as the main wave direction.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
###### Abstract  
The reliability of crop-growth modelling is related to the accuracy of the information used to describe the agricultural growing phases. A proper knowledge of sowing periods has a significant impact on the effectiveness of any analysis based on modeled crop growth. In this work, an estimation of maize actual sowing periods for year 2019 is presented, combining the optical and radar information from Sentinel-1 and Sentinel-2. The crop classification was conducted according to the information provided by local public authorities over an area of 30 km \(\times\) 30 km, and 1154 maize fields were considered within the analysis. The combined use of NDVI and radar time series enabled a high-resolution assessment of sowing periods and the description of maize emergence through the soil, by detecting changes in the ground surface geometry. A radar-based index was introduced to detect the periods when plants emerge through the soil, and the sowing periods were retrieved considering the thermal energy needed by seeds to germinate and the daily temperatures before the emergence. Results show that 52% of maize hectares were sowed in late April, while about 30% were sowed during the second half of May. Sentinel-1 appears more suitable to describe the late growing phase of maize, since the radar backscattering is sensitive to the dry biomass of plants while the NDVI decreases because of the chromatic change of leaves. This study highlights the potential of synergy between remote sensing sources for agricultural management policies and improving the accuracy of crop-related modelling.  
sowing periods; crop phenology; crop monitoring; Sentinel-1; Sentinel-2; soil moisture; radar; irrigated croplands; crop classification +
Footnote †: journal: remote sensing
# Effect of Extraction Method and Thermosonication on Apple Juice Quality
## 3 Results and Discussion
### Cloud Value  
Cloud value is related to particle suspensions, composed of a mixture of protein, pectin, lipids, and other minor components [48], and is an important parameter of apple juice quality. The influence of various treatment methods on cloud value is presented in Figure 6.  
The applied juice preservation methods (P, U1, and U2) increased the cloud value of apple juice by 28% for pasteurized juice as compared to the control sample. In the case of samples subjected to ultrasound, the change in cloud value depended on the intensity. Increased cloud value compared to control was 134% for juice sonicated at 17.5 W-cm\({}^{-2}\) intensity (treatment U1) and 92% at 25 W-cm\({}^{-2}\) intensity (treatment U2). A similar tendency was reported by other authors [46; 49; 50], who also observed increased cloud value of apple and acai juices after ultrasonic treatment.  
Figure 5: Effect of preservation method on solids content in apple juice. Error bars represent standard deviations. Values that share the same letter are not significantly different at P < 0.05.  
Figure 6: Effect of preservation method on cloud value in apple juice. Error bars represent standard deviations. Values that share the same letter are not significantly different at P < 0.05.  
The reason for the increased cloud value in sonicated apple juice is mechanisms induced by cavitation. These phenomena cause a breakdown of solid particles of juice and facilitate its homogenization. [PERSON] et al. [51] suggested that ultrasound may break linear pectin molecules and reduce the molecular weight of complex compounds. Comparing treatments U1 and U2, it is worth noting that increased ultrasonic intensity caused a significant decrease (p < 0.05) in cloud value. This may be related to the increased sonic energy and further reduction of suspended particles, providing greater transparency of apple juice.  
Decreased cloud value under the influence of ultrasound may also be caused by the disintegration of liquid molecules, including lipid molecules, contained in fruit juice. Dispersion system transparency depends on the degree of dispersion of molecules of the dispersed phase in the continuous phase. When the dispersed phase is sufficiently disintegrated, the whole system becomes transparent. The efficiency of ultrasound in creating transparent dispersion systems has been confirmed by numerous authors [52; 53].  
A similar tendency was observed by [PERSON] et al. [54], who also reported an increased cloud value of orange juice after ultrasonic treatment and a slow decrease with increased ultrasound intensity. However, it should be emphasized that in each case of ultrasound treatment, the cloud value remained at a considerably higher level compared with control sample.
## 5 Feasibility Study Through System Simulations
### Simulation Process
#### 5.6.1 Main Execution Flow of the Simulator
In order to present a simple and compact structure for the simulator, we avoid adding additional functions and parameters passing variables to the functions. Then, the definition of the scenario is in part distributed across the hard-coded scenario dimensions presented in the function \(FTileCenters()\) (Pseudocode 1) and the base station positions hard-coded into the function \(FTile()\) (Pseudocode 4). Figure 14: Block diagram of the main execution flow of the simulator. Numbers inside hexagons represent the execution steps, these are explained in the main text. In step 3, the fixed tile centers returned by the function \(FTilecCenters()\) are used to create a table mapping each one of the fixed tiles that covers the positioning area to the respective center positions of the tile and to a suitable class label to identify the tile. This information is used in pattern-matching method 4-MultFingIn-ClassOut, which is a classification-based method. * In steps 4 and 5, the datasets of fixed tiles FTT and FTV, introduced in Section 5.2.3, are created by calling the function \(FFixedTiles()\) (Pseudocode 2). In steps 6 and 7, the moving tile datasets MTV and MTT, introduced in Section 5.2.4, are created by calling the function \(FMovingTiles()\) (Pseudocode 3). * In step 8, the function \(FModelTraining()\) is called. This function is in charge of implementing a selected NN structure for a given pattern-matching method and is in charge of performing the training of the NN model. For convenience and compactness in the description, this function also executes the testing phase, consisting of the actual calculation of the estimated positions for the given dataset of moving tiles for testing, MTT. In addition, the error distances for the estimated positions with the dataset MTT are calculated. This step is executed for each one of the four pattern-matching methods introduced in Table 6 (Section 4.4) independently. For simplicity, for the model training, we present the simulation flow description at a general level, suitable for use with any of the pattern-matching methods considered. Therefore, we omit defining the actual pattern-matching methods as part of the block diagram.
## 4 Results
### ECG Results  
Figures 8 and 9 display the normal and abnormal ECG samples of both real and synthetic data generated via the WGAN-GP model, illustrating the similarity between real and generated samples.  
Table 6 presents the classification performance of ECG datasets (real, synthetic, and a combination of both) using SVM and RF classifiers. The datasets include 1200 real ECG samples, each containing 140 data points, and 1200 corresponding synthetic samples  
Figure 8: Real and synthetic normal ECG samples.  
Figure 9: Real and synthetic abnormal ECG samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Original data + \% Synthetic Data** & **Concentration** & **Relaxation** & **Average** \\ \hline
25\% & 95.39 & 100 & 97.69 \\
50\% & 92.44 & 100 & 96.22 \\
75\% & 90.77 & 100 & 95.39 \\
100\% & 88.22 & 100 & 94.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average classification accuracy(%) for mental states—concentration and relaxation—for varying synthetic data percentages added to the original dataset for a GAN model.  
generated via the WGAN-GP model. The combined dataset, comprising both real and synthetic data, totaled 2400 samples.  
For the SVM classifier, the real dataset achieved the highest classification accuracy at 98%, while the synthetic dataset alone achieved 95.8%. Combining real and synthetic data resulted in an accuracy of 97%, suggesting that real data alone may provide more informative features for SVM. However, combining synthetic data still yielded robust performance.  
In contrast, the RF classifier performed best with the synthetic dataset, achieving an accuracy of 98.57%, which outperformed the real dataset's accuracy of 97%. When the combined dataset was used, RF maintained a high accuracy of 98.40%. This indicates that RF benefits from the diversity provided via synthetic data and can effectively integrate both sources for robust classification.  
Overall, the results suggest that, while SVM performs optimally with real data, RF demonstrates superior performance with synthetic data and remains highly effective when real and synthetic datasets are combined. Although the accuracy improvements from incorporating synthetic data for ECG are not as pronounced as those for EEG, the 1-2% increase still highlights the value of synthetic data for enhancing classification performance.
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 4 Experiments and Evaluations
### MSE Performance in an Extended Range of SNR  
We evaluated the MSE performance over an extended SNR range compared to the training dataset, to investigate the ability to generalize to uncertain SNR values. As shown in Figures 9 and 10, we compared the MSE of several algorithms to different SNR ratios in the KAU1- and NOF1-channel environments. The analysis and simulation results verified that the LS method performs poorly in the case of UWA, because it is inversely proportional to the SNR. While it is easy to implement, this simplicity comes at the cost of relatively low precision. The DNN model was more accurate than the traditional LS algorithm, and its MSE performance at SNR = 30 dB in KAU1 and NOF1 improved by about 184.9%  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 8 kHz \\ \hline Channel bandwidth & 6–10 kHz \\ \hline Number of subcarriers & 512 \\ \hline Number of symbols/frame & 14 \\ \hline Subcarrier spacing & 7.8125 Hz \\ \hline Symbol duration & 128 ms \\ \hline Cycle prefix length & 30 ms \\ \hline Modulation & QPSK \\ \hline \end{tabular}
\end{table}
Table 2: UWA-OFDM System Parameters.  
Figure 8: Comparison of the estimated and true channel samples’ normalized amplitude in KAU 1 and NOF 1.  
and 246% compared to LS, because of its strong fitting ability; however, there are also non-convex-optimization and gradient-vanishing problems in fully connected structures, which makes it less stable when handling complex and variable scenarios, such as the simulation-environment NCS1 channel and KAU1 channel. By contrast, the CNN model in the ChannelNet model has strong feature-extraction ability and denoising network, which ensures more accurate channel estimation at lower SNR = \(-10\) dB, such as KAU1 and NOF1, which improved MSE performance by about 175% and 171.4% compared to DNN. Compared to the Channelnet-dimension-expansion network, our proposed algorithm has a stronger feature-extraction ability, and its MSE performance has also been further improved. For real-world applications, it is important that DL-based models have good generalization capabilities, so that they can work efficiently when the online UWA environment does not exactly match the UWA channel used in the training phase. The experimental results show that the NOF1 channel has a simple multipath structure, slow time-varying dynamics, and little improvement. However, in the experiments with KAU1, the model could provide significant performance benefits, due to its excellent channel-reconstruction ability, due to the more challenging channel structure and fast time-varying properties.  
Figure 10: MSE vs. SNR for NOF1 channel.  
Figure 9: MSE vs. SNR for KAU1 channel.
## 3 Results and Discussion
### System's Parameters  
The values of the most important parameters of the system, including those related to the wheel and rail, as well as to the operating conditions, are listed in Table 1. A total duration of 2 s is used in the numerical time integration, which turned out to be sufficient for all test cases to reach a final stabilized stationary regime, or limit cycle, as will be discussed later. A steel monobloc metro wheel with diameter 86 cm is used and its modal base, containing 37 modes, is obtained with a finite element model for the frequency range 0 to 6250 Hz. The natural frequency and the damping factor of the first three modes, which are related to the wheelset, are chosen to be 10 Hz and 0.8, respectively, and the damping factors of most modes above 400 Hz are set to \(1\times 10^{-4}\). The natural frequencies of significant wheel modes, as well as the corresponding damping ratios are displayed in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter [Units]** & **Description** & **Value** \\ \hline \(V_{x}\) [km/h] & Rolling speed & 30 \\ \(\alpha\) [mrad] & Angle of attack & 11 \\ \(N\) [kN] & Normal load per wheel & 51 \\ \(D_{w}\) [m] & Wheel diameter & 0.86 \\ \(\Delta y_{c}\) [mm] & Lateral offset of the contact point & 15 \\ \(\beta\) [rad] & Contact plane angle & \(-\)0.0145 \\ \(R_{r,trans}\) [m] & Rail transverse radius & 0.3 \\ \(E\) [Gpa] & Young modulus & 210 \\ \(\
u\) [-] & Poisson’s ratio & 0.3 \\ \(\mu_{0}\) [-] & Static friction coefficient & 0.3 \\ \(\kappa\) [-] & Parameter for friction law & 0.05 \\ \(\lambda\) [-] & Falling ratio on saturated regime & 0.2 \\ \hline \end{tabular}
\end{table}
Table 1: Main system parameters.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
## 4 Experiments
### Node Disambiguation  
In this section, we investigate the following questions: \((\mathbf{\mathrm{Q}}_{1})\) Quantitatively, how does our method perform in identifying ambiguous nodes compared to the state-of-the-art and other heuristics? (Section 4.2.2); \((\mathbf{\mathrm{Q}}_{2})\) Qualitatively, how reliable is the quality of the detected ambiguous nodes compared to other methods when applied to real world datasets? (Section 4.2.3); \((\mathbf{\mathrm{Q}}_{3})\) Quantitatively, how does our method perform in terms of splitting the ambiguous nodes? (Section 4.2.4); \((\mathbf{\mathrm{Q}}_{4})\) How does the behavior of the method change when the degree of contraction of a network varies? (Section 4.2.5); \((\mathbf{\mathrm{Q}}_{5})\) Does the proposed method scale? (Section 4.2.6); \((\mathbf{\mathrm{Q}}_{6})\) Quantitatively, how does our method perform in terms of node deduplication? (Section 4.3.1).
# Smart Healthcare Network Management: A Comprehensive Review
## 4 Smart Healthcare Network Management Protocols
### OpenFlow in IoT for Healthcare  
OpenFlow [86] is a foundational SDN protocol that dynamically updates flow tables in switches, allowing fine-grained traffic management. In healthcare IoT, it ensures optimal routing and prioritization of critical health data traffic, reducing latency and ensuring reliability. The flow table \(F\) is defined as  
\[F(i)=\{M_{i},A_{i},P_{i},S_{i}\},\quad\text{where }M_{i}=\text{Match fields},A_{i}=\text{ Actions},P_{i}=\text{Priority},S_{i}=\text{Statistics}. \tag{22}\]  
The controller dynamically programs flows:  
\[F^{\prime}(i)=h(M,\text{Traffic Type},\text{Device Priority}). \tag{23}\]  
One of the critical applications of OpenFlow in healthcare IoT networks is prioritizing emergency health data from IoT devices, ensuring that vital patient information, such as alerts from heart rate monitors or emergency notifications, is transmitted with minimal latency. This prioritization helps healthcare providers respond promptly to critical situ  
Figure 4: RESTful protocol.  
ations. Additionally, OpenFlow plays a crucial role in managing traffic in hospital IoT networks, enabling dynamic resource allocation and optimizing network performance to prevent congestion. By intelligently routing data flows, it ensures efficient communication between connected medical devices, hospital systems, and cloud platforms. Furthermore, OpenFlow facilitates the dynamic control of video streaming for remote patient monitoring, ensuring that bandwidth is allocated efficiently to support high-quality video feeds for telemedicine applications. This capability is essential for continuous patient observation and remote consultations, improving accessibility to healthcare services.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Combinatorial Optimization Problem Classification System
#### 2.3.1 Number of Objectives: Single Objective Versus Multi-Objective  
From a perspective of the number of objectives, the COP can have two behaviors: (1) single-objective and (2) multi-objective combinatorial optimization problems (MOCOP) [40; 41; 42; 43; 44; 45]. In a single-objective problem, the fittest solution is easily determined by the best value returned by the fitness function. In contrast, in multi-objective problems, the optimization process must consider all objectives simultaneously [38]. The performance of a solution in each criterion is not enough, and an analysis that considers all criteria together must support this process. Moreover, the objectives may potentially conflict and concur with each other [19; 24; 40; 41; 42; 46; 47]. Thus, MOCOPs require additional efforts to assess, compare, and determine which solution performs better than others, balancing multiple criteria.  
In practical terms, assessing the solutions' quality is performed by a function that supports the selection process. Essentially, this function receives a set of solutions and the problem's objectives. As a result, it returns a set of \"best\" solutions that are chosen by a specific assessment approach. Several approaches can be used to perform this evaluation. They can be split into two categories of methods: (1) Pareto and (2) non-Pareto [45].  
**Remark 1**.: _These functions used to assess multi-objective problems are generic. Consequently, other optimization problems can reuse these functions because they often are problem-agnostic. However, rarely a particular problem may provide a specific multi-objective assessment. For instance, one method that considers the other fitness values in a weighted formula._
## 2 Materials and Methods
### Characterization of the Nanoemulsion
#### 2.4.2 Zeta Potential (\(\zeta\))  
The Zetasizer Nano-ZS90 instrument from Malvern Instruments Ltd., Worcestershire, UK, was used to determine the zeta potential. Electrophoretic mobility measurements were executed with disposable cuvettes. For sample preparation, 1 mL of nanoemulsion was combined with 10 mL of distilled water and thoroughly mixed. The Zeta potential measurements were performed in triplicate, and the resulting average value was computed.