esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, this study presented a novel design for a high-frequency sheet beam dual-cavity coupled EIO that operated at the dual frequencies of 94 GHz and 140 GHz. The dual-frequency operation is achieved through coupling two extended interaction circuits with a common coupling cavity, with output power delivered through a standard rectangular waveguide. Under different electron injection conditions of 3 A at 34 kV and 3 A at 56 kV, the circuit demonstrated stable output powers of 6 kW at 94 GHz and 42 kW at 140 GHz, respectively.  
This dual-frequency design significantly broadened the potential application of EIO technology by enabling operation at two distinct frequency bands. Additionally, the use of a sheet beam to increase the cross-sectional area of the electron beam not only enhances power efficiency but also provides a practical solution for scaling up the output power of the oscillator.  
Compared to traditional single-frequency extended interaction circuits, the dual-frequency EIO proposed in this study enables high power output at two distinct frequency bands, offering new possibilities for practical applications in millimeter-wave and terahertz technologies. This design, especially when operating at higher frequencies, increases the power capacity of the EIO and provides new insights for the design of multi-frequency and dual-frequency EIO circuits. This work addressed the limitations of traditional single-frequency EIO in terms of bandwidth and high power output.  
This dual-frequency EIO design opens new avenues for high-power, broadband applications, particularly in the millimeter-wave and terahertz frequency ranges, and serves as a foundation for future research on multi-frequency, multi-mode EIO circuits. It highlights the importance of optimizing circuit design and electron beam parameters to achieve reliable, high-power output across multiple frequency bands, addressing the limitations found in previous single-band EIO configurations.  
Conceptualization, J.L. and Q.Z.; methodology, J.L.; resources, X.L. ([PERSON]), X.L. ([PERSON]), Q.Z., R.L. and S.S.; data curation, Q.Z.; formal analysis, Q.Z. and X.L. ([PERSON]); writing--original draft preparation, J.L. and Q.Z.; writing--review and editing, Q.Z. and S.S.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the Natural Science Foundation Project 62361019, 62001131, the Guangxi Natural Science Foundation 2024 GXNSFAA010502, the Dean Project of Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Grant Nos:  
Figure 11: Output power, efficiency, and frequency versus voltage at 140 GHz.  
GXKL06190102, Guangxi, and the major special project plan of Guilin City in 2022, fund number 20220101.  
**Data Availability Statement:** Data are within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
### Model Testing and Evaluation
As shown in Table 8, the BERT-RGCN and Mordred-RGCN models exhibited high AUROC values, ranging from 0.9942 to 0.9977 and from 0.9971 to 0.9995, respectively. Lastly, AUPRC measures the trade-off between precision and recall in various threshold settings. The values range from 0 to 1, with higher values indicating better precision and recall. The BERT-RGCN model demonstrated robust performance with AUPRC values ranging from 0.9896 to 0.9972. Meanwhile, Mordred-RGCN prevailed with high AUPRC (i.e., 0.9973 to 0.9996 shown by \(AmD_{F}\)) as shown in Table 8.  
One might be curious as to what the performance of the RGCN model using combined Bert and Mordred features will be. To answer the aforementioned concern, we performed another experiment using the concatenated features (i.e., Bert and Mordred features) and the test performance is as shown in Table 5. In summary, the BERT-RGCN and Mordred-RGCN models consistently performed well across different feature combinations, showcasing high accuracy, sensitivity, specificity, MCC, AUROC, and AUPRC values. These results suggest the effectiveness of the models in predicting new antimalarial drugs against Plasmodium falciparum in various scenarios based on different sets of features of drugs and potential antimalarial targets.
# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion
### Evaluation Index  
In this paper, we employ three metrics to assess the denoising performance: signal-to-noise ratio (SNR), correlation coefficient (r), and root mean square error (RMSE) [41]. The definitions of these metrics are as follows.  
(1) The SNR represents the ratio of clean signal to noise in decibels (dB). A higher SNR indicates a reduced noise component. The corresponding formula is delineated as follows:  
\[\text{SNR}=10\text{log}_{10}\left(\frac{\sigma_{s}}{\sigma_{n}}\right) \tag{7}\]  
where \(\sigma_{s}\) denotes the energy of the clean signal and \(\sigma_{n}\) denotes the energy of the noise.  
(2) The r represents the correlation coefficient that measures the degree of similarity between the denoised and the reference signals. A value approaching one indicates a stronger correlation, suggesting that the denoised seismic signal closely resembles the waveform of the reference seismic signal. The corresponding formula is delineated as follows:  
\[\text{r}(\text{X},\text{Y})=\frac{\text{Cov}(\text{X},\text{Y})}{\sqrt{\text{ Var}[\text{X}]\text{Var}[\text{Y}]}} \tag{8}\]  
where X denotes the reference signal and Y denotes the denoised seismic signal predicted by the model. \(\text{Conv}(\text{X},\text{Y})\) denotes the covariance of the reference signal and the denoised signal, \(\text{Var}[\text{X}]\) denotes the variance of the reference signal, and \(\text{Var}[\text{Y}]\) denotes the variance of the denoised signal.  
(3) The RMSE represents the root mean square error at each sample point between the reference signal and the denoised signal predicted by the model. A lower RMSE value indicates the higher resemblance of the denoised seismic signal to the reference signal. The extent of the waveform distortion after signal denoising is characterized by the deviation of each sampling point. The corresponding formula is delineated as follows:  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{\text{n}}\left|\text{X}^{\prime}(\text{ i})-\text{X}(\text{i})\right|}{\text{n}}} \tag{9}\]  
where \(\text{X}^{\prime}(\text{i})\) denotes the denoised seismic signal output by the model prediction, \(\text{X}(\text{i})\) denotes the reference seismic signal, and n is the number of signal sampling points.
## 2 Materials and Methods
### Ability of SIF to Monitor Drought-Induced Changes in NPP Dynamics
#### 3.2.3 Ability of SIF to Monitor Dynamic Changes in NPP over Different Time Scales  
The SIF/NPP ratio exhibited dynamic changes in different drought events and severity levels, and the change trend showed seasonality, similar to the results of SIF/GPP studies [54; 63]. To further investigate the capability of SIF to monitor NPP dynamic changes at different drought timescales, we analyzed the correlation between SIF and NPP at 8-day intervals during two drought events that lasted for 8, 16, 24, and 32 days until the end of the drought. The correlation analysis experiment on the 8-day interval during the two drought events showed a similar relationship between SIF and NPP. The correlation between SIF and NPP did not change substantially between adjacent 8-day intervals. Therefore, we displayed the correlation analysis results using a 40-day interval (Figures 10 and 11). At the beginning of the drought event, the monitoring capability of the SIF for NPP exhibited significant regional heterogeneity. As the duration of the drought event increased, the correlation between the SIF and NPP gradually strengthened. This correlation change indicates that SIF can effectively monitor long-term droughs. This conclusion is consistent with the findings of vegetation productivity research that plants grown in regions with favorable water and thermal conditions are extremely sensitive to long-term drought [64]. Therefore, when using SIF to monitor dynamic NPP changes during drought events, the timescale of the drought should be considered.  
Figure 10: Correlation between GOSIF and GLASS NPP during drought events of 40 (**a**), 80 (**b**), 120 (**c**), 160 (**d**), 200 (**e**), 240 (**f**), 280 (**g**), and 320 days (**h**) from October 2013 to September 2014.
# Multi-Information Fusion Indoor Localization Using Smartphones
## 5 Experimental Verification and Analysis
### Discussion and Analysis  
#### 5.2.1 Step-Counting Detection  
To validate the performance of step-counting estimation for different pedestrians with different devices, the two volunteers collect the acoustic signal and inertial data along each planned path at normal speed with VivoY85a and Honor60 devices. Figure 7 shows the results of the peak and valley detection for two volunteers in the first experimental scenario. The peak and valley of the pedestrian acceleration are marked with red and green circles, respectively, in the figure. Figure 7 shows that the peaks and valleys can be detected accurately for pedestrians of different heights. Figure 7a-d show that good performance is achieved when counting steps with both devices. These results are primarily achieved because the threshold can identify invalid peaks and valleys.  
Figure 8 shows the peak and valley detection results for the two volunteers in the second scene. Peaks and valleys can be detected accurately with different devices and different heights. Once the precise peaks and valleys are obtained, steps can be counted.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technical Information** & **VivoY85a** & **Honor60** \\ \hline Operating system & Android 8.1.0 & Android 11 \\ CPU & Snapdragon 450 & Snapdragon 778 \\ RAM + ROM & 4 G + 64 G & 8 G + 256 G \\ Screen & 6.26 inch & 6.67 inch \\ Image resolution & 2280 x 1080 & 2400 x 1080 \\ Battery capacity & 3260 mAh & 4800 mAh \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mobile phone technical information.  
Figure 7: Peak and valley detection results with Honor60, VivoY85a, and two different height pedestrians in the first scene. (**a**) Volunteer #1 with a VivoY85a cell phone. (**b**) Volunteer #2 with a VivoY85a cell phone. (**c**) Volunteer #1 with an Honor60 cell phone. (**d**) Volunteer #2 with an Honor60 cell phone.  
Therefore, step counting achieves good performance with different devices in different scenes and good universality without device heterogeneity.
## 4 Simulations Results and Discussion with Performance Evaluation
### Performance Metrics  
In this section, the proposed and existing protocols are evaluated based on these defined terms.  
_Active nodes_ represent the nodes which are taking part in communication.  
_Delay_ is defined as the time taken by the packets to reach the destination. It includes the following delays in transmission:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Area & 2000 m \(\times\) 2000 m \\ Noise of Ship & 0.2 db \\ Wind & 5 m/s \\ Number of Nodes & 150 \\ Number of Cross Nodes & 25 \\ Sinks & 5 \\ Layers & 6 \\ Number of Iterations & 10 \\ Frequency & 914 \(\times\) 10\({}^{6}\) Hz \\ Total Energy & 1000 \(\rfloor\) \\ Initialized Energy per Node & 0.667 \(\rfloor\) \\ Transmission Power & 0.66 mW \\ Receiving Power & 0.035 mW \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
* Delay in data packet transmission
* Delay in data propagation
* Delay of packet holding
* Delay in data Processing  
In the present scenario, multiple surface gateways for multiple data packets are destined. These data packets pass from different gateways and experience delay. However, the highest delay among these delays is considered in the current scenarios.  
_EC_ is the amount of energy used by the nodes during whole communication in UWSNs. Total EC is the accumulation of all sensor nodes energy that takes part in communication.  
_RPER_ is the number of erroneous bits allowed in data packets to be received at the destination. Surface gateways or sonobuoys drop the data packets if erroneous bits exceed the RPER limit.  
_PRR_ is the ratio of successfully received packets at destination sink.
## 6 Conclusions
### Implications  
The findings of this study have several important academic and practical implications. First, from an academic viewpoint, based on previous research, this study highlighted the factors that induce SNS fatigue among SNS users and confirmed the theoretical basis of the factors that affect SNS fatigue by analyzing the relationships between them. Although SNS use has become more common and interaction time through SNSs has increased, existing research has mainly focused on identifying the positive aspects of SNSs. Thus, this study's significance stems in part from the fact that it empirically analyzed the negative aspect of SNSs. This study is also significant because, while previous studies have mainly examined the characteristics of SNS-related intentions and uses, it examined the negative emotional and behavioral impacts of SNS use. We expect our findings will contribute to future studies of the relationships between SNSs, users, environmental characteristics, and dysfunction on SNSs. This study also has academic implications related to the development of theory. Previous research on fatigue has examined life, work, and use behaviors separately, but we framed these factors as a series of processes. Importantly, it remains necessary to formulate theories about the negative perceptions of individual SNS users regarding specific services.  
In addition, this study has some implications for practitioners trying to establish policies related to SNS side effects and fatigue. First, our results highlight the need for efforts to minimize the adverse effects of negative emotional factors such as SNS fatigue and living disorder. For this purpose, it is important for users to develop the ability to regulate SNS use on their own. However, it is difficult for users to perceive what factors cause the fatigue that they feel from using SNSs. Therefore, it is necessary to study subjective characteristics that can cause SNS fatigue and living disorders, and to provide guidelines to help users prevent these problems and protect themselves. Second, the government and SNSs should encourage users and hold campaigns that warn that excessive use of SNSs can harm their health and cause mental fatigue. Excessive use of social networking produces negative social and economic consequences. Therefore, to foster a healthier social culture and prevent economic losses, excessive use of SNSs must be recognized as a social disease that should be dealt with seriously. In this respect, in addition to the campaign mentioned above, SNS users should receive education and guidelines regarding the correct use of SNSs. On a practical level, the findings of this study also suggest that technologies should be implemented to automatically prevent the spread of illegal information, curtail the infinite expansion of social relations on SNSs, and block users from SNSs after they exceed certain usage times.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 2 Background and Literature Review  
LEO satellites are referred to satellites that have a circular orbit from 500 km to 2000 km above the Earth. In contrast, Medium Earth Orbit (MEO) satellites are located in the region above the LEO and below the geosynchronous orbit (GEO), having an altitude of 2000 km to 35,786 km, and Highly elliptical orbit (HEO) satellites have an elliptic orbit with high eccentricity around the Earth. The radius of the footprint of a communications satellite in LEO varies from 3000 to 4000 km. Therefore, the maximum visible time of an LEO satellite is up to 25 min to a user during a single pass. In this case, LEO satellites must hand over much more frequently than other types of orbit or communication must be locked to a specified time window for a single satellite. On the other hand, MEO satellites have wider service coverage and longer visible time window to the user when compared with LEO satellites, while HEO satellites are specific to serve high latitude regions [10]. In order to simplify the structure of the IoT satellite system and reduce its cost, LEO satellites using a shared band is considered to be the most suitable solution for IoT satellite communication, and it is suggested by many researchers [1; 2; 11]. This is because the slant range in LEO is shorter when compared with other orbits. Thus, the path-loss in LEO satellite communication is minimized, leading to a host of useful traits such as; lower transmission power, longer battery life, simpler satellite architecture, lower launch costs, and simpler antenna designs on both the satellite and the IoT device, and it highly reduces the complexity of the ground IoT devices and satellites allowing the construction of Nano-satellite, low power consumption and direct communication from IoTs to satellite system. In such cases, it would be feasible for the IoT device to have a small aperture antenna or a wire antenna without the need for the costly and sometimes infeasible, parabolic antenna setup. Despite all the pros of using LEO satellites for low-power IoT communication, several challenges are currently hindering the full deployment of shared-spectrum LEO satellites, including; (i) the high interference level from terrestrial devices when sharing the spectrum, (ii) a reduced satellite availability time due to the inherited reduction in the low orbit nature, (iii) Doppler shifts due to the relatively large relative velocity of LEO satellites.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 3 Results of Predicting the Number of Heatwave-Related Patients
### Model Comparasion  
The RF model was trained on the basis of the determined hyperparameters, and test data were applied to the regression model. The linear regression relationship between the predicted data from the model and test data is shown in Figure 4. The black line in the graph represents the regression line. The \(x\)-axis represents the weekly predicted number of patients with heat-related diseases in a specific region, as predicted by the model, and the \(y\)-axis indicates the weekly number of real patients with heat-related diseases in the region. The translucent band around the regression line area indicates the size of the confidence interval, which was 95% in this case. The red dotted line indicates when the model accurately reflected reality (slope: 1). The linear fitting slope of this RF model was 1.11. In particular, when high values were predicted, they tended to be underestimated compared to the observed values; however, the models were confirmed to be relatively reasonable.  
Figure 4: Linear fitting results of test data.  
Figure 3: Hyperparameter optimization in the RF regression model: (**a**) training curves with respect to number of trees and (**b**) training curves with respect to maximum depth of trees.  
To compare the accuracy of the regression model more quantitatively, Table 2 compares its results with those of other regression models. In particular, the RF model is compared with linear regression, decision tree, and support vector machine (SVM) models. All models were trained using the same training set, and all the trained models were evaluated by the same test set. However, some of the values predicted by the SVM model were negative; because the values must be greater than or equal to zero, we treated all negative values as zeros. As shown in Table 2, the best values for all the considered metrics, including MAE (3.816), RMSE (8.655), RMSE (0.645), and \(R^{2}\) (0.803), were obtained for the RF model. This means that the RF is more accurate than other models for making predictions, and the \(R^{2}\) value of 0.803 proves that this model is reasonable.
## 3 Simulation and Results
### Area-B
#### 3.5.2 Area Characterization  
Figure 13 shows the CQI versus SINR curve for terrestrial APs. Equations (12) and (13) show the constants \(a1\) and \(b1\) that represent the environmental characteristics, where R-squared and RMSE are found to be 0.7 and 2.5, respectively, for NLoS and 0.5 and 3.4 for \(LoS\).  
\[CQI_{NLoS}=\begin{cases}\begin{aligned} 1&if\ SINR\leq-25\\ 0.4097\ SINR+8.165&if-25<SINR\leq 20\\ 15&if\ SINR>20.\end{aligned}\end{cases} \tag{12}\]  
\[CQI_{LoS}=\begin{cases}\begin{aligned} 1&if\ SINR\leq 0\\ 0.3622\ SINR+1.77&if\ 0<SINR\leq 45\\ 15&if\ SINR>45\end{aligned}\end{cases} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terrestrial APs** & **Customized CDL** & **Standard CDL** & **Standard TDL** \\ \hline \multirow{2}{*}{Average CQI} & \multirow{2}{*}{7} & 11 & 9 \\  & & (Percent Error = 57\%) & (Percent Error = 29\%) \\ \multirow{2}{*}{Average Network DL} & \multirow{2}{*}{9.44} & 16.4 & 12.52 \\ \multirow{2}{*}{throughput (Mbps)} & & (Percent Error = 74\%) & (Percent Error = 33\%) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison between different channel profiles.  
Figure 12: Variation in CQI between standard TDL and CDL channel profiles, Area-B.  
Figure 13: Relation between SINR values and resulting CQI, Area-B.