esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Fault Analysis and Diagnosis of PEM Fuel Cell Control System
## 4 FDI Algorithm  
In the obtained residuals, a unique combination of faults is developed to obtain the fault signature matrix, as constructed in Figure 7. In the fault signature matrix, the combination of residual signals makes it possible to classify the fault, and a fault detection and isolation algorithm is developed.  
According to the result of the fault analysis, the nonlinearity of the air compressor map makes it difficult to detect faults. In the map shown in Figure 8, the motor speed and pressure ratio of the supply manifold inlet air pressure and outlet pressure are inputs, and the air flow rate is the output. When there was a fault in the pressure variable, the fault must be propagated to the air flow rate variable and to the residual. However, the air flow rate remains the same regardless of the pressure ratio when the motor speed is over 70,000 rpm, and this problem raises the possibility of a fault not being detected by the residual. In this case, an adequate FDI algorithm must be implemented to overcome the problem. Furthermore, the sensitivity of each fault must be considered, and it is expressed as the ratio between the amount of change in the residual and the amount of change in the fault.  
Figure 8: Compressor map.  
Figure 7: Fault signature matrix.  
When the sensitivity of the residual is high, the fault tends to be detected more easily than when the sensitivity is low. For a better classification of the faults, a FDI algorithm is constructed with a top-down method based on the sensitivity of residuals. The flow of the FDI algorithm is presented in Figure 9. The threshold of each residual is chosen empirically rather than by following a general guideline since sensor noise tended to be amplified while being delivered over variables and showing residual overlap.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 5 Results and Discussion
### Prediction  
Based on the previous validation section, Case 1, Case 2, and Case 3 achieved commendable scores, each exceeding 70, indicating relatively reliable models. The remaining cases scored below 60, making those models relatively unreliable. Therefore, this study further evaluates the models using only these three cases, with 47 different soil samples as input factors. Table 6 lists the datasets of 47 soil samples [36; 37]. As listed in Table 6, an extensive dataset comprising 47 different soil samples was consolidated, and each type was assigned a unique identifier ranging from 1 to 47. The data sources for samples 1 through 15 are derived from [PERSON] et al. [36], while samples 16 through 41 are sourced from [PERSON] and [PERSON] [37]. Samples 42 through 47 are based on the soil mechanic experimental data obtained in this study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Soil** \\ **Sample** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Coefficient** \\ **of Curvature** \\ **(Co)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Coefficient of** \\ **Uniformity** \\ **(Cu)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Plasticity** \\ **Index (PD)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Organic Soil** \\ **or Inorganic** \\ **Soil** \\ **of Soil** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Liquid Limit** \\ **(LL)** \\ **No. 4 Sieve** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Percentage** \\ **Passing** \\ **No. 20 Sieve** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Percentage** \\ **Passing** \\ **Classification** \\ \end{tabular} } \\ \hline  
\hline \end{tabular}
\end{table}
Table 6: Datasets of 47 soil samples [36; 37].  
The proposed RF model was employed for analysis and soil classification, with the predicted soil properties compared against actual properties. The RF model's accuracy results for the 47 soil samples across these cases are illustrated in Table 7. The obtained results show that 46 of 47 samples are correctly predicted when none of the factors are missing (Case 1). When Factor 1 (Cc) is missing, the predicted results show that 44 out of 47 samples are correctly predicted. When Factor 2 (Cu) is absent, the predicted results indicate that 45 out of 47 samples are correctly classified. These predicted results support the OOB importance analysis using the proposed RF method. Factor 1 (Cc) and Factor 2 (Cu) exhibited lower importance, thus their absence did not significantly affect the model performance.
## 2 Method
### The Effectiveness of Feature Selection  
In this research, feature selection was performed on the variable factors, and three variable factors with a score less than 0.1 were excluded from the nine variable factors. In order to prove the necessity of feature selection, relevant experiments were carried out. The test data Test1-Test3 were tested for classification accuracy with all variable factors, and variable factors after feature selection, respectively. The compared results are shown in Table 6.  
According to the test results, in the test data Test1-Test3, the filtering accuracy without feature selection is 96.48%, 88.39%, and 90.02%, and the filtering accuracy after feature selection is 96.54%, 88.50%, and 90.09%. At the same time, the time consumption of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Classification Accuracy of Filtering** \\ \hline  & without feature selection & after feature selection \\ Test 1 & 96.48\% & 96.54\% \\ Test 2 & 88.39\% & 88.50\% \\ Test 3 & 90.02\% & 90.09\% \\ Time consuming of model & 203 min & 96 min \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of filtering accuracy before and after feature selection.  
Figure 8: Filtering results of the test data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Type I Error** & **Type II Error** & **Total Error** \\ \hline Test 1 & 0.0361 & 0.0327 & 0.0346 \\ Test 2 & 0.1804 & 0.0742 & 0.1150 \\ Test 3 & 0.1287 & 0.0965 & 0.0991 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy evaluation of test data.  
filtering model without feature selection is 203 min, and the time consumption of the model after feature selection is 96 min. It can be clearly seen that the time consumption of the model after feature selection is significantly shortened, being less than half of the former.  
It can be concluded that the filter test results using all variable factors are not much different from the filter test results of variable factors after feature selection, and the accuracy of point cloud classification after feature selection is slightly higher, indicating that the variable factor (point cloud 3D coordinates) eliminated in this paper is dispensable for the accuracy of results. However, the required to run the model without feature selection is far from that of the model after feature selection. The total time consumed by the model after feature selection is greatly reduced, which greatly improves the filtering efficiency, indicating that these three excluded variables do not improve the filtering results, but increase the time consumption of the experiment. Therefore, in this research, we used the method of feature selection to screen the variable factors.
# Tensile Properties of As-Built 18 Ni300 Maraging Steel Produced by DED
###### Abstract  
The mechanical behaviour of as-built DED-produced 18 Ni300 Maraging steel was studied by manufacturing a wall-like structure from which three different specimen types were obtained: specimens in which the loading direction was the same as the printing direction (vertical), specimens in which these two directions were perpendicular (horizontal), and bimetallic specimens in which the interface between the AISI 1045 substrate and the 18 Ni300 steel was tested. The yield strength of the produced samples was \(987.9\pm 34.2\), \(925.9\pm 89.7\) and \(486.7\pm 47.2\) MPa for the vertical, horizontal and bimetallic specimens, respectively, while the elongation to failure was \(9.4\pm 1.9\), \(18.3\pm 2.3\) and \(14.06\pm 0.6\)% in the same order. The latter specimen failed within the substrate-comprised portion of the specimen. Additionally, the fracture surfaces were analysed through scanning electron microscopy, concluding that while both surfaces consist of dimples, the horizontal specimen presented microporosities with a reduced diameter. A microhardness analysis in the printed wall-like structure following the printing direction yielded an average hardness of \(392\pm 21\) HV0.3, with fluctuations along the build direction mostly within one standard deviation.  
additive manufacturing; directed energy deposition; maraging steel; tensile tests; digital image correlation; microstructural analysis +
Footnote †: journal: Applied
## 5 Performance Evaluation
### Example 2
At 0 m, it is 22.3 m/s, and it is 10 m/s at 308 m. The velocity drops to 8.8 m/s at 364 m, whereas it grows to 18 m/s at 784 m. This drops to 5.7 m/s at 1134 m and then grows to 21.7 m/s at 1792 m. At 100 s, the velocity is bounded from 12.4 m/s to 17.2 m/s. The velocity at 0 m is 16.1 m/s, and it is 17.2 m/s at 392 m. The velocity drops to 12.4 m/s at 982 m, whereas it grows to 14 m/s at 1540 m. This further grows to 16 m/s at 2000 m. At 200 s, it is bounded from 13.97 m/s to 15.5 m/s. At 0 m, it is 15 m/s, and it is 15.5 m/s at 630 m. The velocity drops to 13.97 m/s at 1302 m, whereas it grows to 14.7 m/s at 2000 m. For 0.0025 s\({}^{-2}\), at 12 s, velocity is bounded from 3.8 m/s to 22.5 m/s. At 0 m, it is 21.6 m/s, and it is 22.5 m/s at 252 m. The velocity drops to 4.8 m/s at 392 m, whereas it grows to 18 m/s at 800 m. This drops to 3.8 m/s at 1078 m, which then grows to 5.4 m/s at 1200 m and stays constant up to 1300 m. The velocity grows to 21.7 m/s at 1800 m. At 100 s, it is bounded from 6.7 m/s to 19 m/s. The velocity at 0 m is 16.1 m/s, and it is 19 m/s at 635 m. The velocity drops to 6.7 m/s at 775 m, whereas it grows to 14 m/s at 1000 m. This grows to 14.5 m/s at 1652 m and then further grows to 16 m/s at 2000 m. At 200 s, it is bounded from 12 m/s to 16.84 m/s. At 0 m, it is 14.9 m/s, and it is 16.84 m/s at 868 m. The velocity drops to 12.2 m/s at 1092 m, whereas it drops to 12 m/s at 1204 m, grows to 14.8 m/s at 1653 m, and stays approximately uniform up to 2000 m. Figure 11 demonstrates that the variation in velocity with 0.0025 s\({}^{-2}\) is larger than with 1 s\({}^{-2}\) and shows appropriate evolution. Figure 10: Traffic density behavior with the proposed system on a 2000 m circular road. The solid lines represent the proposed system behavior with the driver sensitivity 0.0025 s\({}^{-2}\)at 0.2 s, 12 s, 100 s, and 200 s. The dotted lines represent the proposed system behavior with the driver sensitivity 1 s\({}^{-2}\) at 0.2 s, 12 s, 100 s, and 200 s.
###### Abstract  
Federated learning enables privacy-preserving model training across distributed clients, yet real-world deployments face statistical, system, and behavioral heterogeneity, which degrades performance and increases vulnerability to adversarial clients. Existing incentive mechanisms often neglect participant credibility, leading to unfair rewards and reduced robustness. To address these issues, we propose a Trust-Aware Incentive Mechanism (TAIM), which evaluates client reliability through a multi-dimensional trust model incorporating participation frequency, gradient consistency, and contribution effectiveness. A trust-weighted reward allocation is formulated via a Stackelberg game, and a confidence-based soft filtering algorithm is introduced to mitigate the impact of unreliable updates. Experiments on FEMNIST, CIFAR-10, and Sent140 demonstrate that TAIM improves accuracy by up to 4.1%, reduces performance degradation under adaptive attacks by over 35%, and ensures fairer incentive distribution with a Gini coefficient below 0.3. TAIM offers a robust and equitable FL framework suitable for heterogeneous edge environments.  
federated learning; incentive mechanism; trust management; edge computing; heterogeneous clients; robust aggregation
## 3 Results and Discussion
### Physicochemical Properties of SDF  
#### 3.3.1 Effect of Three Extraction Methods on the Appearance and Extraction Rate of SDF  
As can be seen from Figure 3, there were significant differences in the appearance of the SDF powders extracted by acid-, alkali-, and enzyme-based methods. The color of the SDF extracted using acid and enzyme was light and yellow-white, while that of the SDF extracted using alkali was darker and yellow-brown. In the process of alkali extraction, alkali could brown the soluble pigments or proteins and produce dark substances, resulting in this dark color. The SDF extracted using enzyme was more delicate and less granular, possessing a good appearance. [PERSON] and [PERSON] (2018) found the same results; the SDF-alkali experienced strong browning, while the SDF-enzyme showed lighter color characteristics than SDF-acid and SDF-alkali.  
As can be seen from Figure 4, the extraction rate of SDF obtained using the acid and enzymatic method was significantly higher than that obtained using the alkaline method. [PERSON] et al. (2018) found that the extraction rates of SDF-enzyme and SDF-alkali were comparable, higher than that of SDF-acid, which differed from the results in this experiment. This was probably because different raw materials and specific experimental conditions resulted in different extraction rates of SDF. Acid might lead to the hydrolysis of insoluble cellulose and hemicellulose into soluble sugars, thus increasing the content of SDF in the extract. In ultrasound-assisted enzymatic extraction, the enzyme facilitated the formation of permeable and loose structure, and the cavitation effect of ultrasound further promoted the dissolution and release of SDF in the SSR (2018).  
Figure 4: Extraction rate of the soluble dietary fiber (SDF) extracted by three methods.  
Figure 3: Appearance of the soluble dietary fiber (SDF) extracted by three methods.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.3 Measuring End-to-End Delay for Data Transmission  
End-to-end delays rise with an increase in users, which slows down data transfer across the network. In 5G mmWave networks, optimized hybrid beamforming can minimize this delay by increasing signal efficiency and decreasing traffic. SIM-ADMT, and MP-TCP function in the transport layer, while UL-SCH + PUSCH functions in the physical and MAC layers. From the perspective of end-to-end latency, they are not comparable because they are working at different tiers. Nevertheless, both methods have an impact on the network's overall performance. MP-TCP maximizes multi-path data transmission, which impacts transport-layer performance, while UL-SCH + PUSCH impacts radio link transmission efficiency. Such protocols can therefore be present in the same network, and in maximizing them at once, 5G mmWave networks become more efficient all in all, considering latency.  
Figure 5 and Table 7 represent the suggested system that has the least delay, starting at 20 users, at 52.5 (ms), in contrast to UL-SCH + PUSCH at 58 (ms), MP-TCP at 60 (ms), and SIM-ADMT at 63 (ms). The suggested strategy keeps an acceptable latency of 61.5 (ms) at the halfway point of 60 users, whereas SIM-ADMT climbs to 65 (ms), MP-TCP climbs to 70 (ms), and UL-SCH + PUSCH increases to 78 (ms). The suggested scheme's latency by the final measurement at 100 users is 70 (ms), which is higher than at lower user counts but still beats out SIM-ADMT at 80 (ms), MP-TCP at 90 (ms), and UL-SCH + PUSCH at 98 (ms). For high-density user scenarios in 5G mmWave networks, the suggested approach consistently provides improved end-to-end delay performance, proving its efficacy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of User (x-Axis)**} & \multicolumn{4}{c}{**Round-Trip Time (ms) (\(y\)-Axis)**} \\ \cline{2-5}  & **SIM-ADMT** & **FB-TCP** & **UL-SCH + PUSCH** & **Proposed** \\ \hline
20 & 95 & 85 & 100 & 65 \\ \hline
40 & 130 & 100 & 120 & 85 \\ \hline
60 & 120 & 110 & 135 & 58 \\ \hline
80 & 135 & 125 & 150 & 90 \\ \hline
100 & 180 & 150 & 175 & 120 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Numerical results of round-trip time (ms).  
Figure 4: Number of users vs. round-trip time (ms).
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
###### Abstract  
Sensors and actuators are becoming important components of Internet of Things (IoT) applications. Today, several approaches exist to facilitate communication of sensors and actuators in IoT applications. Most communications go through often proprietary gateways requiring availability of the gateway for each and every interaction between sensors and actuators. Sometimes, the gateway does some processing of the sensor data before triggering actuators. Other approaches put this processing logic further in the cloud. These approaches introduce significant latencies and increased number of packets. In this paper, we introduce a CoAP-based mechanism for direct binding of sensors and actuators. This flexible binding solution is utilized further to build IoT applications through RESTlets. RESTlets are defined to accept inputs and produce outputs after performing some processing tasks. Sensors and actuators could be associated with RESTlets (which can be hosted on any device) through the flexible binding mechanism we introduced. This approach facilitates decentralized IoT application development by placing all or part of the processing logic in Low power and Lossy Networks (LLNs). We run several tests to compare the performance of our solution with existing solutions and found out that our solution reduces communication delay and number of packets in the LLN.  
Internet of Things; CoAP; sensor/actuator binding; RESTlets; IoT application; in-network processing; resource observation +
Footnote †: journal: Sensors  
Article
## 5 Experimental Results
### Power Generation Comparison  
Since a higher vibration level of the BWT can generate higher power, applying a magnetic field to the MREs when the wind speed is over 2.6 m/s can increase the energy production efficiency. If the wind speed is lower than 2.6 m/s, the BWT should operate in the nominal MRE state to maximize energy harvesting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **B = OFF** & **B = ON** & **Increment (\%)** \\ \hline Structural resonance (\(f_{BWT}\)) & 2.8 Hz & 4.6 Hz & 65\% \\ Lock-in wind speed (\(v\)) & 2.0 m/s & 3.3 m/s & 65\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The changes in the resonant frequency and the lock-in wind speed of the BWT in different magnetic field modes.  
Figure 5: Comparison of the vibration acceleration of the BWT without and with magnetic field (**a**) at an input frequency of 2.8 Hz and (**b**) an input frequency of 4.6 Hz.  
To analyze the power generation efficiency for different wind speeds, an electric coil of 1000 loops was used to induce electric current from the BWT's oscillation.  
The distributions of the induced voltage over a range of excitation frequencies for differently tuned BWTs are plotted in Figure 6. According to the voltage-frequency response in Figure 6, the BWT tuned with B = OFF had the highest voltage generation of around 2.6 Hz, and the BWT tuned with B = ON had the highest voltage peak at 4.3 Hz. There was a slight decrement in the overall resonance frequencies because additional mass was attached to the BWT during the power generation test.  
When applying a frequency lift force of 2.8 Hz (wind speed of 2.0 m/s), the induced voltage had a root mean square voltage of 6.9 mV, and the vibration energy, the total of kinetic and potential energy, was calculated to be 7.5 mJ. As the frequency of the input excitation increased to 4.6 Hz (wind speed of 3.3 m/s), the induced voltage was reduced to 2.8 mV by a factor of 2.5 times. Also, the vibration energy decreased to 1.8 mJ by a factor of three times. In this mistuned state, the magnetic field was turned on to tune the BWT's resonance to match the excitation frequency. As expected, the tuned BWT showed higher power generation, which increased the voltage to 5.3 mV by a factor of 1.9 and increased the energy by a factor of 4, as shown in Figure 7 and Table 3.  
Figure 6: Comparison of the induced voltage at a wind speed of 3.9 m/s (5.5 Hz) between the nominal BWT and a re-tuned BWT with 170 mT.  
Figure 7: Comparison of the vibration energy and induced voltage when excited at 2.0 m/s and 3.3 m/s.
## Appendix B Addressing Data Conflicts and Rationale for Data Discarding
### Justification for Data Exclusion  
The rationale for discarding data was based on the need to maintain a high standard of validity and reliability in the study's findings. By excluding incomplete, inconsistent, or unreliable responses, we ensured that the dataset represented only those participants who answered thoughtfully and meaningfully, thus preserving the accuracy of our analysis. Excluding data with extreme inconsistencies also helped to prevent skewed results that could have compromised the study's applicability. Furthermore, ensuring that participants took the survey seriously (e.g., by excluding those who completed it too quickly or inconsistently) guarantees that the study's findings are based on valid user insights rather than noise. Ultimately, the process of discarding data was transparent, objective, and intended to enhance the robustness of our conclusions, ensuring that the final dataset reflected a reliable sample of participants who were engaged with the survey and whose responses were valid.  
The data reduction process is illustrated in Figure 11.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 2 Medical Imaging Methods
### Pet  
A positron that is emitted from a radionuclide-labeled compound (commonly 18 FDG) annihilates with an electron resulting in two 0.511-kev photons. These two photons are emitted in nearly opposite directions (180-degree angle apart, at one particular location in liver), and are registered simultaneously by the ring detector around the patient, so that the PET can accurately locate, analyze and quantify the distribution of radioactively-labeled drugs in the body. After computer reconstruction, three-dimensional human body images are obtained [5]. Changes in the physiological and biochemical levels of liver cancer and normal liver tissue cells can be determined noninvasively, quantitatively and dynamically in vitro through C, N, O, F and other nuclide markers of glucose required for tissue metabolism, with the aim of evaluating the distribution and activity of liver cancer cells in patients. Therefore, PET is a functional molecular imaging technique with high sensitivity, and can effectively aid in characterizing the metabolism of liver cancer, detecting recurrence and evaluating the outcome of radiofrequency or microwave ablation therapy.