esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Challenges and Future Development
### Satellite Communication  
Few base stations and low network coverage are characteristics of Ag-IoT, which prevents the wireless sensor node from sending data to the central node, using the centralized fault diagnosis strategy, and sending fault information to the central node using the distributed fault diagnosis strategy. In addition, agricultural sensors are frequently deployed in harsh environments, and environmental factors such as sudden changes can cause network disruptions and other problems. With the proposal of Starlink, people began to investigate the commercial application of satellite Internet and the use of satellite communication to solve the problem of no network in sensor deployment areas [149]. Satellite communication has the advantages of long communication distance, being unrestricted by natural conditions, and having high stability, which guarantees the network's continuous availability in sensor fault diagnosis.
# Vulnerability Detection and Classification of Ethereum Smart Contracts Using Deep Learning
## 4 Results and Discussion
### Results of Multi-Label Classification
The use of both performance metrics can provide trusted insights into the algorithm's performance. The highest accuracy value and F1-score were obtained for the VGG19-RGB algorithm in multi-label classification. Therefore, we will use its results for comparison with previous studies. [PERSON] et al. [39] used four algorithms that specifically target the vulnerabilities in smart contracts and classify them in a multi-label classification. Their highest reported accuracy and F1-score were 73.53% and 83.81%, respectively, achieved using the ResNet1D algorithm. Our methodology using the VGG19-RGB algorithm significantly improves these performance metrics, achieving a 15.6% increase in accuracy and a 2.1% increase in the F1-score. Similarly, [PERSON] et al. [22] utilized only the LSTM algorithm for multi-label classification, but their study reported only an F1-score of 77%. Our approach achieves a significant improvement of 8.6% in the F1-score. The results of the performance metrics confirm our clear contribution to studying how effectively the proposed models perform in multi-label and binary classification. Based on the confusion matrices and benchmarks, VGG19 and RF are the best-performing algorithms, demonstrating their superiority in accurately classifying and detecting smart contract vulnerabilities. In addition, using the three approaches in multi-label classification demonstrates the accuracy of each algorithm and the extent to which the results differ in each of the algorithms used. It also emphasizes the importance of choosing the best algorithm based on the task's requirements.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 4 Results and Discussion
### Results for MFCC-Based Features
The method can accurately diagnose different types of faults using the extracted MFCC features and the ELM classifier. The results also provide insights into the optimal combination of MFCC parameters and ELM hyperparameters, which can be used to improve the performance of the method in future studies. These findings demonstrate the model's effectiveness in vehicle fault diagnosis and highlight areas for further optimization.
# Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale
## 4 Results
### Other Parameters  
To more comprehensively evaluate the device performance under different washer shapes, this article also simulates the signal-to-noise ratio, total current (x cut), x cut error, total current (y cut), y cut error, the total fluxoid 1, and the total fluxoid, where the total fluxoid 1 is the applied field flux and the total fluxoid is the response flux.  
The simulation results of the signal-to-noise ratio (SNR, Figure 12) show that, in most diameter ranges, the octagonal washer has a clear advantage, with an average SNR of about 12.5% higher than that of washers of other shapes. This may be due to the advantages of the octagon in signal capture and noise suppression. In addition, as the diameter of the washer increases, the SNR of all shapes increases; among which, the SNR of washers with larger diameters increases most significantly.  
Here is the correlation matrix visualization for the washer parameters (Figure 13). The analysis revealed a substantial positive correlation between the inner diameter (D) of the washer and both its self-inductance and total magnetic flux. This correlation implies that, as the inner diameter of the washer increases, both the self-inductance and total magnetic flux experience a pronounced increase. In our design criteria, a lower self-inductance is preferred, and for the purpose of achieving optimal inductive coupling with the input coil, a smaller inner diameter is also desirable. However, considering the field noise \(\mathrm{S}_{\mathrm{B}}^{\frac{1}{2}}(\mathrm{f})\)  
Figure 11: Comparison of inductance as a function of the washer diameter for various shapes in SQUID; contrasting the simulated results (solid lines) with theoretical predictions based on empirical formulas (dashed lines), the data spans washer diameters from 150 μm to 250 μm.  
related to magnetic sensors, a larger diameter is favored. This necessitates the pursuit of a new equilibrium in subsequent design iterations.  
Furthermore, the analysis has revealed a significant negative correlation between the diameter of the washer (D) and the total current. This negative correlation underscores that, as the washer's diameter increases, there is a discernible decrease in the total current measured on the x and y planes. Such reduced currents are less effective in inducing response voltages across Josephson junctions, leading to a notable reduction in the sensor's sensitivity--a scenario clearly undesirable in our design considerations.
# Transmission Line Fault-Cause Identification Based on Hierarchical Multiview Feature Selection
## 4 Experiments and Discussion
### Overall Classification Performance  
In this subsection, we compare the mentioned dimension reduction approach on the basis of SVM to verify the effectiveness of multiview learning and HMVFS. Two concatenating rules were applied to FSFC. The first rule tries to keep 1:1 proportion of waveform and contextual features. There is one more contextual feature when the total number is odd. The second rule holds the same proportion of waveform and contextual features as that in HMVFS.  
The results in terms of Gmean with different numbers of selected features are shown in Figure 2. By comparing single-view feature selection methods among strategies, we notice that most of them perform best in BSV rather than in FSFC and FCFS. Added fault features from the other view will even degrade their classification, and this indicates that simple concatenation cannot help conventional feature selection methods adapt to multiview classification. A similar conclusion is drawn in [23]. Thus, the introduction of MVL appears vital in particular. HMVFS has comprehensive advantages in the comparison of FSFC and FCFS and achieves the best performance compared with methods in BSV. HMVFS outperforms others in the middle of feature increasing, and its result with 14 selected features is the global or near-global optimum. When features from the other view increase, the performance is degraded to a certain extent, and then it rises to another peak. Most methods in BSV produce a zigzag rise curve and reach their best when almost all view features are selected. They are also inferior to HMVFS in FSFC and FCFS. Relieff is the best competitor that achieves acceptable performance in different strategies. As for KCCA and DCA, their performance is low. Figure 2 illustrates that HMVFS is more capable of obtaining the best performance combining waveform and contextual features.  
Figure 2: Classification comparison between HMVFS and other feature algorithms in strategies: (**a**) BSV; (**b**) FSFC_rule1; (**c**) FSFC_rule2; (**d**) FCFS.  
Due to the limit of yield data condition and fault signature study, irrelevant and redundant features are introduced with increasing feature numbers. This problem is more prominent in the waveform view in both theoretical and experimental studies. The advantage of HMVFS is that it selects features with independent and complementary information of all views, while the single-view methods are easily affected by irrelevant features facing concatenated assembly or meeting the limitation of single-view features. As seen from Figure 2, concatenating and mapping fail to select or transform discriminative features with combined waveform and contextual features. There are two local optimums for HMVFS, and they are better than the performance of competitors, which demonstrates that HMVFS overcomes the negative effect of redundant features in multiview data.
## 5 Experiments and Results
### Noise Recovery Results  
The most challenging tests for the proposed methodology are the images having noise, because the CBIR methodology works directly with the texture, the noise directly affects the CBIR performance. Figure 10 shows the recovery results for three types of images contaminated with salt and pepper noise at 0.1%, 0.3%, and 0.5%, the searched image again appears within the 5 most similar images.The result of the confusion matrix for the noise tests is given in Table 3.
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 2 Materials and Methods
### Performance Evaluation  
In a hypothetical scenario of user authentication, we used a feature vector containing the connectivity values (for PLI and PLV separately), extracted using the upper triangular part of the correlation matrix to compute the biometric performance. As previously reported [12; 25], pair-wise similarity scores between feature vectors were estimated as \(1/(1+d)\), where \(d\) represents the Euclidean distance. Genuine and impostor score distributions were finally used to compute the equal error rate (EER) and the area under the ROC curve (AUC), which allowed us to assess the performance for each band, each condition, and each time window. In particular, the EER is a standard measure to summarize the performance of a biometric system. Considering the False Reject Rate (FRR), the percentage of times that a legitimate subject is incorrectly recognized as an imposter by the system, and the False Accept Rate (FAR), the percentage of imposters incorrectly recognized as genuine by the system, the EER represents the system's error when the FRR equals the FAR. Lower values of EER indicate better classification performance.  
Although in the traditional ROC curve, where a True Positive Rate (TPR) is plotted against a False Positive Rate (FPR), a higher AUC indeed indicates better performance. In the biometric context, the ROC curve here refers to a plot of False Accept Rate (FAR) versus False Reject Rate (FRR), where a lower AUC reflects better performance.
## 2 Materials and Methods
### Outcome Measures
#### 2.3.2 Secondary Outcomes  
Secondary outcomes included the evaluation of various clinical and therapeutic factors that may influence sensory recovery. Specifically, we examined whether the timing of symptom onset, the removal of the causative factor (e.g., implant or prosthesis), the severity of the nerve injury based on the Sunderland classification, the total duration of treatment, and the type of treatment modality were associated with the degree of improvement in the VAS ratios.  
In addition, exploratory analyses were conducted to assess the potential associations among the continuous variables, including the time from symptom onset to initial visit, the treatment duration, the Sunderland grade, and the VAS scores. Detailed results of these analyses are presented in the Results Section.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grade** & **Structural Damage** & **Functional Loss** & **Recovery Potential** & **Typical Clinical Features** \\ \hline Grade I & Local conduction block & Temporary & & & Mild paresthesa; \\  & (neuropraxia); no axonal & sensory/motor loss; no & & & spontaneous resolution \\  & disruption & Wallerian degeneration & & & expected \\ \hline Grade II & Axonal disruption; & Wallerian degeneration & Good prognosis; axons & & Hypoesthesia; Tinel’s \\  & endoneurium intact & distal to injury site & & & sign may progress \\  & & & & & distally \\ \hline Grade III & Axonal and endoneurial & Disrupted axonal & & & \\  & damage; perineurium & guidance and moderate & & & \\  & preserved & conduction loss & & & \\ \hline Grade IV & Loss of axon, & & & & \\  & endoneurium, and & Severe loss of continuity & & Poor spontaneous & Chronic neuropathic \\  & perineurium; epineurium & and conduction & & & symptoms; poor or \\  & intact & & & & absent regeneration \\ \hline Grade V & Complete transection & & & & \\  & (neurotmesh) with loss & & & & \\  & of all nerve layers & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sunderland classification of peripheral nerve injury [16].
# A Novel Approach to Short-Term Stock Price Movement Prediction using Transfer Learning
## 6 Experimental Results
### Effectiveness of Different Types of Input Features
Meanwhile, top companies of the US are companies working in the information technology field (e.g., Apple Inc., Alphabet Inc Class A (Mountain View, CA, USA), Microsoft Corp. (Redmond, WA, USA), and Facebook Inc. (Menlo Park, CA, USA)). Therefore, considering CI+HMC may not affect to JPMorgan Chase & Co.'s stock price movements, and may make irrelevant input features. As we can see in Table 5, in case of JPMorgan Chase & Co., CI+HMC obtains slightly lower performance than CI, which does not use information about other stocks. Moreover, in our study, we train the prediction model with CI+RND. In this type of input, we choose other companies randomly (i.e., they may have no relationship to the COI stock). From the empirical results presented in Table 5, CI+RND provides lower performance than CI in terms of average prediction accuracy over all study periods. It proves that adding unnecessary information does not help improve the model's performance. One should note that CI+CS, CI+SF, and CI+HMC attain higher prediction accuracy than CI, and COI achieves the lowest performance. It indicates that by adding more relevant information, the performance of the prediction model is generally enhanced. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Stock Name** & **COI** & **CI** & **CI+CS** & **CI+SF** & **CI+HMC** & **CI+RND** \\ \hline Samsung Electronics Co & 49.84 & 53.36 & 57.51 & 59.74 & 58.15 & 55.91 \\ SK Hynix Inc & 53.02 & 54.63 & 58.46 & 60.38 & 58.78 & 55.91 \\ Celltrion & 55.77 & 55.28 & 58.17 & 61.02 & 56.87 & 55.27 \\ Hyundai Motor Co. & 58.16 & 60.07 & 62.94 & 64.54 & 61.98 & 58.79 \\ LG Chem & 51.43 & 56.23 & 59.75 & 60.05 & 60.00 & 56.23 \\ \hline Boeing Company & 50.93 & 55.56 & 59.57 & 60.19 & 57.71 & 55.86 \\ Apple Inc. & 55.87 & 60.49 & 57.41 & 59.88 & 59.88 & 54.63 \\ Amazon.com Inc. & 55.56 & 57.72 & 60.19 & 62.65 & 61.11 & 58.33 \\ Johnson \& Johnson & 53.08 & 54.94 & 57.71 & 59.26 & 57.10 & 55.25 \\ JPMorgan Chase \& Co. & 53.71 & 55.25 & 58.02 & 58.64 & 54.01 & 52.47 \\ \hline Average & 53.74 & 56.35 & 58.91 & 60.64 & 58.59 & 55.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Average accuracy of the different types of input variables over all study periods.
# Optimal Waveform of the Partial-Respond Signal Based on Minimum Out-of-Band Radiation Criterion
## 2 Optimized Mathematical Modeling of the Optimized Waveform
### Additional Constraints of the Pan-Function
#### 2.2.2 Restriction on the Single-Symbol Signal Energy Condition  
Considering the signal \(a(t)\) with the length of \(4T\), the energy per transmission can be expressed as  
\[E=\int\limits_{-2T}^{2T}a^{2}\left(t\right)dt \tag{4}\]
## 3 Implementation of the M&S Software  
The feasibility of the proposed design method is shown through implementation of the message broker-based multi-radio integrated navigation system M&S software for GPS, KNSS, eLoran, Loran-C and DME/VOR [9; 10; 26; 27; 28]. MFC library and sockets were used in the implementation [29; 30].  
The M&S message broker and function modules were implemented in the tasks. All the tasks included threads and queues, as shown in Table 4. The M&S message broker could be represented in the task, as shown in Figure 10. The function modules could be similarly represented.  
Figure 8: Coverage analysis.  
Figure 9: Navigation algorithm.  
Figure 11 shows the GUI for navigation warfare scenario parameters setting. Initial values and motion types were set. If the load button was clicked, the receiver independent exchange format (RINEX) files of the GPS and the files of the location data of the ground-based navigation system stations were loaded. If the navigation warfare scenario was edited, the location, operating time and power of jammer could be set as in Figure 11.  
The GUI for measurements, signal sources, navigation results, and navigation warfare situation is shown in Figure 12. The measurements of the navigation systems are shown in the left upper window.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Task Name**} & \multicolumn{3}{c}{**Thread**} & **Queue** \\ \cline{2-4}  & **Number** & **Name** & \\ \hline M\&S message broker & 6 & Destination finder, destination selector, 4 I/Fs & 9 \\ Navigation environment generation & 10 & Encoder, decoder, I/F, reference trajectory generation, 6 & 16 \\  & & raw measurement generations & 16 \\ Coverage analysis & 5 & Encoder, decoder, I/F, & 6 \\  & & coverage analysis, gap filling & 6 \\ Navigation algorithm & 10 & Encoder, decoder, I/F, 6 navigation algorithms, & 14 \\  & & integrated navigation algorithm & 14 \\ GUI & 23 & Encoder, decoder, I/F, 20 threads for display and parameter setting & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Task, thread, and queue in the implementation.  
Figure 10: M\&S message broker task.  
The sky plots of the GPS and KNSS satellites are shown in the left middle window. The navigation result of the GPS is shown in the left lower window. The horizontal path of the vehicle, the locations of the ground-based navigation system stations, and the jamming area are shown in the right window.
# Fabrication Tolerances' Impact on an ODAC-Based PAM-4 Transmitter
## 2 Proposed Model
#### Data Extraction and Analysis  
The procedure for data extraction and analysis is presented in Figure 7. Since the machine can be controlled using software, the process in Figure 7 follows repeated iterations to mine 1000 oDAC PAM-4 transmitters. Specifically, while varying the value of \(\sigma_{n}\) in the devices, the signal powers at the four levels of each of the 1000 emulated transmitters are extracted from the iPronics FPPGA machine through the power meter for offline processing.  
The obtained signal power levels are then processed offline as optical transmitted signals and detected through a PIN photodetector. A sketch of the decision circuit showing the waveform of the bit fluctuation across the four levels of the PAM-4 signal is presented in Figure 10 in Appendix A, where \(P(xy|ab)\) is the probability of deciding signal \(xy\) when signal \(ab\) is received and \(\mu_{i}\) is the Gaussian pulse of the PAM-4 signal levels.  
The generalized analytical expression for estimating the SER of a PAM-4 signal based on the schematic in Figure 10 is given by Equation (A2) in Appendix B.  
Therefore, solving for all the \(P(I_{j}|I_{i})\) in Figure 10, as presented in Equation (A2) using Equation (A4), we obtained Equation (A5) in Appendix C. By cancelling out some parameters in Equation (A5), we obtained the final equation to estimate the signal SER as Equation (A6).  
Figure 6: PAM-4 signal amplitude levels and ER against varying phases of the PMs in Figure 4 when K = 0.36. (**a**) Amplitude levels against phase of the PM\({}_{1}\). (**b**) Amplitude levels against phase of the PM\({}_{2}\). (**c**) ER of the PAM-4 eyes against phase of PM\({}_{1}\). (**d**) ER of the PAM-4 eyes against phase of PM\({}_{2}\).  
The \(\sigma_{i}\) in Equations (A4)-(A6) is the receiver's electrical noise, which is estimated as the sum of the thermal and short noise for a typical 10 GHz PIN receiver following the expression in [27] and it is assumed to be constant across the four PAM-4 levels. We have further assumed a typical detection of a 10 Gb/s signal at 1550 nm with a receiver sensitivity of \(-28\) dBm at SER = \(10^{-3}\) to adjust the receiver's noise. In addition to this, the P2P power level of the signal from the machine is normalized to increase the impact of the signal noise before detection. An SER benchmark of \(10^{-3}\) is used to determine the failed or passed chips, as is clearly illustrated in Figure 7. Therefore, using Equation (A6) for SER calculation, all the 1000 mimed transmitters are analysed after detection. Signals detected with an SER above the threshold (\(10^{-3}\)) are classified as having failed while those below the threshold are classified as having passed, as shown in Figure 7.
## 2 Materials and Methods
### Data Management and Outcome Variables  
Data were uploaded to the manufacturer's web-based system. Using the accelerometer values and time spent in a posture, the manufacturer's algorithm (MoveMonitor version 3.0) analyzed the data to classify body postures and activities. This analysis produced information about periods of non-wearing, sitting, standing, and locomotion, and about movement parameters (i.e., length of time in each body position and number of steps).  
The study statistician chose four days from the total measurement days to best represent activity during the hospitalization period when it lasted six days or more, three days when it lasted five or four days, and two days when it lasted only three days. These representative days were referred to as time points 1-4 (see Figure 1 for an illustration of the time points distribution). The use of time points throughout the hospitalization period enabled us to measure change over time. Due to the variation in hospitalization length, time points were chosen individually for each patient. For each time point, data for the morning (7:00-15:00) and evening (15:00-23:00) shifts were extracted. Separating the morning and evening shift data enabled us to get a better understanding of how activity changed throughout the day.  
The variables to describe patients' activity were: (1) sitting time (in minutes), (2) time in an upright position (in minutes), (3) number of steps, (4) longest sitting period (in minutes), and longest walking period (in minutes). Time in an upright position consisted of the sum of all the positions or activities that were not lying (i.e., sitting, standing, shuffling, and walking).