esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface Current Analysis  
The performance of a super-wideband (SWB) antenna is analyzed using surface current dissipation, as shown in Figure 8a-e. Current is more concentrated around the feedline region and the edges of the minor elliptical slots, at the resonance frequencies of 24.1 GHz, 67.1 GHz, 129.1 GHz, 249.1 GHz, and 287.1 GHz of the final iteration of the proposed antenna, as shown in Figure 8a. Current flows through the feedline and concentrates at the slot edges depending on the phase angle, as shown in Figure 8b. The magnitude of the current at the feedline is higher at 129.1 GHz, as shown in Figure 8c. At higher resonant frequencies of 249.1 GHz and 287.1 GHz, more current flows to the feed line and ground plane, as shown in Figure 8d,e. The iterations in the antenna design produce a fringing field, which improves the coupling mechanism between the radiating layers. As a result, the impedance of the proposed monopole antenna increases.  
Figure 6: The 3D gain polar plots of the final iterative design of the proposed tapered-fed structure with partial ground plane.  
Figure 7: The 2D far-field patterns in E-plane and H-plane: (**a**) 24.1 GHz, (**b**) 67.1 GHz, (**c**) 129.1 GHz, (**d**) 249.1 GHz, (**e**) 287.1 GHz (red solid—co-polarized E-plane; black solid—cross-polarized E-plane; red dotted line—co-polarized H-plane; black dotted line—cross-polarized H-plane).
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 6 Conclusions  
This paper presents a defect reconstruction and classification method based on CV-CNN, which effectively exploits both amplitude and phase information in eddy current signals. Comparative experiments with the conventional CV-FCNN model validate the superiority of the proposed method. The following major conclusions are drawn:  
* CV-CNN significantly enhances the representation of defect features, providing clearer and more distinguishable signals under varying excitation frequencies, greatly improving the visualization and interpretability of the signals;
* Compared with CV-FCNN, CV-CNN achieves higher overall accuracy (85.0%) and effectively suppresses inter-class confusion. By preserving local features in complex-valued signals, CV-CNN demonstrates stronger stability and robustness, achieving higher precision, recall, and F1-scores across multiple key defect categories.  
In summary, the proposed CV-CNN-based method not only achieves superior performance in defect reconstruction but also demonstrates high accuracy and generalization in the classification of complex eddy current signals, offering a promising solution for industrial NDT.  
Future work will focus on constructing a complex defect dataset with overlapping types, designing more robust CVNN architectures to accommodate signal coupling, and enhancing CV-CNN's generalization and representation in ECT modeling through augmented samples and physical priors.  
Methodology, B.C.; experiment, B.C.; writing--original draft preparation, B.C.; writing--review and editing, B.C. and T.Y.; supervision, T.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Discussion
### Comparison between In-Office and At-Home Dental Bleaching
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Type of the Study** & **Aim of Study** & **Materials** & **Results** \\ \hline [PERSON] et al., & Randomized clinical & Evaluate the effectiveness of home & Twenty volunteers. & Follow-up after a \\
2018 [22] & trial & bleaching treatment & At-home bleaching (30 min twice a day, for 2 weeks). & mouth, a significant \\ \hline [PERSON] et al., & Randomized clinical & Comparison between two techniques: 35\% & Thirty patients. & Effective treatment for \\
2018 [43] & trial & At-home & At-home bleaching (16\% CP) and in-office bleaching (35\% HP). & color regression at 3 \\ \hline [PERSON] et al., & Randomized clinical & Comparison between two techniques: 38\% & Forty patients. In-office bleaching and second & No difference in \\
2018 [60] & trial & At-home & At-home bleaching or in-office bleaching. & bleaching efficacy and \\ \hline [PERSON] et al., & Randomized, triple-blind clinical trial & At-home & At-home bleaching (3 h a day for 21 days). & No difference in \\
2018 [45] & Randomized, triple-blind clinical trial & At-home & At-home bleaching (3 h a day for 21 days). & bleaching efficacy and \\  & & At-home & At-home (10\% CP gel without desensitizer. & \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline  & Comparison between techniques: 1.0\% CP, & Ninety patients divided into two groups: lighter and darker teeth. The teeth in each group were further divided into three groups (n = 15). & The severity of tooth discoloration and the type of trayless system \\ [PERSON] et al., & Randomized clinical trial & At-home & At-home (3 h a day for 21 days). & affected the \\
2018 [51] & Randomized clinical trial & At-home & At-home (3 h a day for 21 days). & effectiveness of \\
2018 [51] & Randomized clinical trial & At-home & At-home (3 h a day for 21 days). & bleaching, while only \\
2018 [51] & Randomized clinical trial & At-home & At-home (3 h a day for 21 days). & the type of trayless system affected color stability. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Reported studies about comparison between in office and at-home dental bleaching [CP: carbamide peroxide, HP: hydrogen peroxide, TS: tooth sensitivity].
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 4 Frequency Stability Analysis
* BDS-3 IGSO PHM: These clocks have the best daily stability, down to \(0.17\times 10^{-14}\) s/s, but the stabilities for 1000-second and 10,000-second variations are \(11.1\times 10^{-14}\) and \(4.48\times 10^{-14}\) s/s, respectively, which are larger than those of clocks on MEO satellites. * BDS-3 GEO PHM: Although the newest BDS-3 GEO satellites are equipped with PHMs, their frequency stabilities are much worse than those of MEO clocks, probably resulting from the poor accuracy of precise clock products for GEO satellites. The 1000-second, 10,000-second, and 1-day stabilities are \(23.81\times 10^{-14}\), \(7.9\times 10^{-14}\), and \(0.61\times 10^{-14}\) s/s, respectively. * BDS-2 clocks: Being the older clocks of BDS satellites, their frequency stability is worse than those of BDS-3 clocks. The 1000-second, 10,000-second and 1-day stabilities of BDS-2 satellite onboard clocks are \(21.51\times 10^{-14}\), \(7.83\times 10^{-14}\) and \(0.62\times 10^{-14}\) s/s, respectively. Figure 11: OADEV comparison of BDS-3 and BDS-2 clock offsets.
## 3 Experimental Tests and Results
### ZERO(tm) Machine Tests
#### 3.2.1 First-Order Model Validation of ZERO(tm) Machine Tests
The model's performance was quantitatively assessed using the MAE and RMSE metrics, which confirmed the model's validity in both the adjustment phase (22-26 February) and the subsequent validation phase (12-15 February). The lower MAE and RMSE values during the adjustment phase (6.9 \(\upmu\)m and 9.6 \(\upmu\)m, respectively) compared to the validation phase (10.1 \(\upmu\)m and 14.1 \(\upmu\)m) indicate that the model performs well within the calibration range, although there is some discrepancy when it is applied to a different period. Figure 20: Validation of the first-order model output against real measured data from the initial test. From 12 to 15 February.
## 4 Discussion
Notably, the logistic regression model demonstrated promising results by potentially preventing 51% of unnecessary deliveries while experiencing only a minimal 5% loss in correctly identifying acidotic cases. In a similar vein, [PERSON] [16] employed an AdaBoost model that achieved a sensitivity of 92% and specificity of 90%, which closely aligns with our findings, emphasizing the robustness of additive tree models. However, no information is available regarding the potential reduction in cesarean sections through the misclassification of acidosis cases. [PERSON] [38], on the other hand, achieved near-perfect classification results using neural networks, with a sensitivity of 99% and specificity of 97%. Nevertheless, these exceptionally high values would benefit from external validation for further confirmation. In a meta-analysis conducted by [PERSON] [52], it was concluded that the use of AI and computer analysis for EFM interpretation during labor does not improve neonatal outcomes. However, their conclusions were solely based on risk ratio analysis. As illustrated in our study, while overall accuracy measures such as AUC may indicate comparable model performance, a comprehensive validation process is essential to thoroughly assess their effectiveness. The strength of our study lies in the development of a classification model utilizing machine learning algorithms applied to EFM features. Specifically, we combined a 30 min period with variables extracted from the last deceleration before delivery and their progression over the subsequent 30 min. These predictor variables have previously exhibited promising predictive capabilities for acidemia. However, few studies have integrated them into a comprehensive predictive model utilizing various machine learning algorithms. Moreover, our model demonstrated favorable clinical utility, making it applicable in real-world clinical practice. However, our study does have certain limitations. It was conducted retrospectively and relied on data from a single hospital, thus necessitating external validation on a larger scale for wider application.
# BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet
## 3 Methodology
### Proposed Architecture
#### 3.1.3 Weather Sensors  
The common instruments of measurement are an anemometer, wind vane, pressure sensor, thermometer, hygrometer, and rain gauge. The weather measurements are formatted in a special format and transmitted to WMO to help the weather forecast model. The sensory module captures the initially investigated data from the other modules and provides reliable data in a finalized order. The sensory module describes the working with the proper order of the network estimation parameters. The proposed approach enhances the working of the provided dataset with the proposed methodology. Sensory modules relate to weather sensors to promote the proper weather forecasting network parameter settings and enhance the complete functionality of the weather data.  
The proposed model used the novel implemented topology, which makes it different from other proposed models in the VANET. It is a novel research topic that we resolved in this research. The vehicle's and the RSU-based communication architecture enhances the request and response architecture in a VANET. A detailed explanation of the proposed model is elaborated below.  
* Initially, we set up the base topology using a 5G network under the highway that connected the vehicles using the communication protocols.  
Figure 2: Secure routing for weather forecasting information through blockchain.  
* The weather sensors were equipped with RSUs, and vehicles travelling in both directions captured the weather data and forwarded the data to the weather forecasting server.
* The weather forecasting server is connected using the Hyperledger Sawtooth blockchain transaction mechanism to connect and control the transactions. The transactions contain secure weather forecasting information on the road the vehicles are moving towards.
* The weather forecasting information is distributed to all the vehicles connected to RSUs with a distributed blockchain public key to display weather information.
* The private key is only distributed to the vehicles which are part of the network.
* The source-to-destination information is routed based on the methodology presented in Figures 3-5 and Algorithm 1.
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis
### Influence of Target Location on Localization Accuracy  
Since the target location affects the localization accuracy, the GDOP for different target locations is simulated in the single target scenario. The height of the target is fixed at 9 km, and the X-axis and Y-axis of the target change from 20 to 80 km in steps of 4 km. The remainder of the simulation parameters of the RIS-assisted 2-D radar are the same as in Section 4.1.  
When the target is located at [20; 40; 9] km, the SNR of the direct channel is 30 dB, and the coordinates of the RISs are [1.34, 2.68, 0.1] km, [1.34, 2.68, 0.8] km and [1.34, 2.68, 1.5] km respectively. For the other target locations, the target SNR can be calculated by proportional calculation. To ensure that three assisted RISs always stay in the radar detection beam, they are mounted on the UAVs and move with the scanning beam. The relative locations of the target and the RISs with the 2-D radar remain unchanged.  
For each target located in the detection area of \([-100,100]\times[-100,100]\) km, the estimated GDOP are obtained by Monto Carlo simulation with 5000 run times. The simulation results are given in Figure 4. The derived CRLB in (38) is also calculated in Figure 4 as the theoretical estimation accuracy limit, and the differences between the simulated GDOP and the CRLB are shown in Figure 4.  
As shown in Figure 4,b, the closer the target is to the radar, the smaller the GDOP is. It is indicated that the localization accuracy becomes lower when the target becomes further away from the 2-D radar. When the distance between the target and the radar increases, the target SNR will become lower, resulting in the deterioration of target localization estimation accuracy. In addition, the differences between the simulated GDOP and the CRLB, shown in Figure 4, become smaller when the target becomes closer to the radar. The simulation result is consistent with the conclusion of Figure 3.  
Figure 4: _Cont._
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Duty Cycle  
#### 3.5.1 Principles  
In ODYSSE, source nodes and gateway are always active while routers are duty cycled. The maximum duration of the active state is fixed, while the sleep period follows a random uniform distribution. The detailed functioning of a node, starting from the active state, is described hereafter:  
* If the node is idle (has no data packet to send), it waits for Beacon messages:
* IF after ACTIVE_PERIOD (the maximum active period of routers) no Beacon message is received, it returns to the sleep state.
* Otherwise, IF the node has replied to a Beacon, it waits for a Data message, and then:
* IF it does not receive a Data message after WAIT_DATA_PERIOD, it returns to the sleep state
* Otherwise, it will forward the Data message as follows:
* IF the node has data to send: it sends Beacon messages periodically. It collects replies as described in Section 3.4, until a forwarder is selected and then the Data packet is successfully sent (receiving MAC layer acknowledgements). After that, the node has no data packet to send and returns to the sleep state.
# On Hybrid Nanogrids Energy Management Systems--An Insight into Embedded Systems
## 4 Energy Storage Systems
The emphasis on extended lifecycles has driven the exploration of storage solutions based on reversible physical processes, such as compressed air energy storage or pumped hydro, as alternatives to electrochemical batteries. This shift is driven by the challenges associated with battery aging and recycling. In transportation applications, the key performance factors revolve around portability, scalability, energy, and power density. As a result, despite various challenges, including limited lifetime,batteries continue to be seen as the most practical choice for transportation applications due to their modular nature and portability.
## 3 Results and Discussions  
### Selection of Climatic Variables  
The results of subset regression analysis suggested that the maximum air temperature was the highest contributor among selected variables in estimation of ETo. For all sites, R\({}^{2}\) was in the range of 0.70-0.74 between ETo and maximum air temperature. Higher R\({}^{2}\) indicated the strong predictive power of maximum air temperature in estimation of ETo. A study by [PERSON] et al. [35] explained the significance of temperature data in reference to ETo modeling. They used only temperature data to estimate ETo. These results are in agreement with the findings of [PERSON] et al. [35] and confirmed the relevance of temperature data for modeling ETo. Relative humidity was the second largest contributor in estimation of ETo as it increased the R\({}^{2}\) by 0.11%, 0.13, 0.14, 0.10, and 0.12 for Saint Peter, Harrington, North Cape, Summerside, and PEI data sets, respectively (Table 2). By increasing the number of variables from 2 to 5, there were minor increases of 0.02, 0.01, 0.02, 0.02, and 0.02 in R\({}^{2}\) for Saint Peter, Harrington,North Cape, Summerside, and PEI data sets, respectively. Based on subset regression analysis results, only two variables, namely maximum air temperature and relative humidity, were selected in training of recurrent neural networks. One of the objectives of this study was to decrease the number of variables to a possible extent without compromising the overall accuracy. A study by [PERSON] et al. [34] concluded that there was no major improvement in deep learning models predictive accuracy by increasing the number of variables from 2 to 4.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & bit error rate \\ GCM & generalized color modulation \\ CSBM & color-space-based modulation \\ LEA & light emitting array \\ LED & light emitting diode \\ LOS & line-of-sight \\ MIMO & multiple-input multiple-output \\ ROI & region of interest \\ SER & symbol error rate \\ SNR & signal-to-noise ratio \\ V2V & vehicle-to-vehicle \\ V2X & vehicle-to-everything \\ VLC & visible light communication \\ \end{tabular}
## 3 Results and Discussion
### pH and EC  
Different treatments had little influence on the pH value of the composting process. The pH value of all treatments increased significantly in the first week and finally maintained a stable stage of 7 to 8 (Figure 3). In the early stage of the co-composting of KW and GW, the pH value of the materials was about 4 due to the acidification phenomenon of KW during the early storage. With a rapid biodegradation of easily degradable organic matter, small molecular organic acids were produced. With the rise of temperature, organic acid volatilization and mineralization of organic nitrogen will gradually increase pH [19]. In this study, due to the higher temperature and continuous aeration accelerating the generation of NH\({}_{3}\) during the high temperature phase, organic acids volatilized and neutralized more thoroughly, resulting in the initial increase in substrate pH. In the later stage of composting, the pH value of all treatments no longer increased in a large range due to the increased activity of nitrifying bacteria, which promoted the conversion of a small amount of NH\({}_{4}\)+ into NO\({}_{3}\)- [20].  
The EC value is an indicator of the soluble salt concentration in the aqueous extract of compost. Too high an EC value can cause toxic effects on crops [21,22]. The EC value for T1 treatment increased significantly in the first few days. This may be due to the rapid decomposition and mineralization of organic matter into small molecules, which increased the salt ion content. A downward trend was observed in the two subsequent treatments, which was due to the volatilization of organic acids and NH\({}_{4}\)+ and humification [23,24]. By the end of composting, the EC values of all treatments were less than 3 mS/cm, meeting the safe composting standard (<4 mS/cm). Compared with T1 treatment, T2 treatment can effectively reduce the EC value within 21 days of composting, which may be due to the improvement of physical and chemical properties of the composting materials, thus accelerating the humification process.  
Figure 2: Evolution of temperature during composting.
# 3D Convolution Recurrent Neural Networks for Multi-Label Earthquake Magnitude Classification +
## 2 Our Approach  
In this work, to comprehensively categorize the earthquake magnitudes, we propose to treat earthquake magnitudes categorization as a multi-label classification task. Moreover, a 3D-CNN-RNN incorporating recurrent layers is constructed to undergo this task, which simultaneously frames and classifies earthquake magnitudes, allowing multiple labels to belong to a single earthquake category. To attain higher dimensionality in the temporal and spatial domain, we convert the raw seismic signals into log-Mel-based spectrograms, feed the extracted features to a 3D-CNN-RNN architecture and perform multi-label classification predictions. The complete architecture of our proposed method is presented in Figure 2. The network comprises three parts, i.e., feature engineering, the 3D-CNN, and recurrent layers assimilated with convolutional layers. The feature engineering part extracts the log-energies in the Mel scale and creates a log-Mel spectrogram. The convolution layers extract the spatial information, while recurrent layers capture the temporal features from seismic waveforms.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
###### Abstract
Several statistical models have been proposed in the literature to fully characterize the turbulence fading. The log-normal [20] distribution has been accepted as an accurate model for weak turbulence conditions whereas negative exponential [21] and Rayleigh [22] distributions have been used to model strong turbulence. For this reason, there have been remarkable research efforts to establish a common statistical model to characterize any turbulence condition. In this context the Gamma-Gamma distribution represents an appealing model that allows tractable analysis of the link performance while modelling turbulence conditions ranging from weak to strong turbulence [21]. Finally, the last adverse effect in FSO links is produced by noise. Background noise is the dominant one in most optical links. This noise is present due to the fact that the receiver collects some undesirable radiations such as reflected or scattered sunlight from hydrometeors or other objects. This radiation is mitigated by means of narrow spectral bandpass and spatial filtering prior to the photo-detector (PD). However, a non-negligible background noise might fall within the spatial and frequency ranges of the detector causing a random electrical signal that is added to the desired signal. This noise term can be modeled according to a Poisson distribution [23]; nevertheless, when the number of received photons associated with this background radiation is high enough, the Poisson distribution can be approximated by a Gaussian distribution [24]. These facts motivate the inclusion of an additive white Gaussian noise (AWGN) model in FSO links.
## 4 Discussion
### Limitations and Uncertainty
Second, how to distinguish between critical signal components and noise-induced components can be critical to the performance of the approach and the results from its output. Significance tests for EEMD components are available but unvalidated, which is a necessity for a universal processing tool [60].