esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 6 Methodological and Technical Details
### Rule Base and Fuzzy Inference System  
A structured fuzzy rule base was developed to govern decision-making in the system. For instance, rules such as \"IF energy is _Low_ AND queue length is _High_, THEN switch to _Vacation mode_\" were applied. These rules mapped fuzzy inputs to outputs such as bulk service activation, service slowdown, or cost behavior.
## 5 Simulation Results and Discussion
### SNR Performance: Robustness Under Mobility and Prediction Failures  
Figure 7 presents the time evolution of the total SNR (dB) achieved by the DE-ACSW-R system compared to the baseline LR/SVR-enhanced MUSIC approach. Both systems exhibit similar SNR trends during steady near-linear user movement (indices 6-26), where predictive and beamforming models track the trajectory without interruption. However, the distinction between the two approaches becomes pronounced during abrupt mobility changes.  
Figure 6: The DE-ACSW-R system dynamically selects between enhanced MUSIC and full MUSIC based on prediction confidence.  
Figure 7: Time evolution of total SNR (dB) for DE-ACSW-R (proposed) versus baseline LR/SVR-enhanced MUSIC.  
A key difference is observed following the sharp AoA jump at index 27. The baseline system, lacking robust jump detection and fallback logic, continues to track with the outdated prediction, causing the enhanced MUSIC estimator to steer the beam incorrectly and resulting in a severe SNR collapse (dropping below \(-8.75\) dB at indices 28-29). In contrast, the DE-ACSW-R model detects the anomaly and immediately disables enhanced MUSIC, switching to full MUSIC-based estimation. For index 28, the baseline system reports a total SNR of \(-8.75\) dB, whereas the DE-ACSW-R approach maintains the SNR at 23.98 dB by leveraging prediction-aided fallback. This proactive fallback prevents SNR degradation, with the system rapidly restoring link quality to above 20 dB within just two time slots after the jump.  
The key advantages demonstrated by the DE-ACSW-R framework are substantial. Most notably, the prediction-aware fallback logic effectively eliminates the risk of severe outages by preventing catastrophic SNR drops that would otherwise result from abrupt user movement or prediction errors. Furthermore, the system exhibits rapid SNR recovery, as its ability to quickly re-centre on the correct angle of arrival (AoA) using full MUSIC ensures that optimal beamforming performance is restored with minimal latency, even following extreme mobility events. Over the entire user trajectory, the DE-ACSW-R framework consistently maintains high SNR, substantially outperforming the baseline model, particularly during periods of trajectory instability. These results underscore the critical importance of adaptive prediction-aided fallback mechanisms for O-RAN deployments, where unpredictable user mobility and the need for robust link maintenance are essential for achieving system reliability and a high-quality user experience.
# Mechanical Behavior of Bi-Layer and Dispersion Coatings
## 5 Conclusions  
The best mechanical, plastic, and elastic properties were achieved for the multi-wall carbon nanotubes coating combined with titania particles. This coating possesses the highest Young's modulus, close to that of natural human bone, and the best resistance to plastic deformation so that it substantially improved the capability of the MWCNTs coating to accommodate stresses appearing under applied load during surgery or further use of an implant. The addition of nanocopper to the MWCNTs coating made the material more susceptible to plastic deformation; nevertheless, it also improved the MWCNTs coating resistance to chipping off under deflection loading.  
There is a general rule that more homogenous coatings form on pure metals than on their alloys. The latter materials are more complex in terms of chemical and phase composition, but also demonstrate a much harder surface compared to the pure titanium. Therefore, the observed different mechanical behavior of coatings on the different substrates is related to the difference in surface composition of the substrate and its influence on the EDP process, mainly on the adhesion of the coating.  
The applications of obtained coatings may be different, but they can be considered plausible as coatings for implants for orthopedic applications. Such coatings need to be both elastic and resistant to relatively high stresses during implantation surgery and possess Young's modulus close to that of a bone. From a mechanical point of view, the tested coatings, in particular MWCNTs-titania nanocomposite structure, can be particularly considered useful for medical implantology if biological properties become appropriate.  
Conceptualization, D.R.-W. and B.M.-M.; methodology, D.R.-W. and B.M.-M.; software, D.R.-W.; validation, D.R.-W., B.M.-M., A.Z., and B.J.J.; formal analysis, D.R.-W.; investigation, D.R.-W. and B.M.-M.; resources, D.R.-W.; writing--original draft preparation, D.R.-W. and A.Z.; writing--review and editing, A.Z. and B.J.J.; visualization, D.R.-W.; supervision, A.Z.; and project administration, B.M.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 8: The diagram comparing the H\({}^{3}\)/Er\({}^{2}\) parameter for the MWCNTs, MWCNTs/TiO\({}_{2}\) bi-layer, and the MWCNTs_Cu dispersion coating deposited on Ti (present work) and Ti13 Nb13 Zr [47], examined under indentation load of 10 mN.
## 3 Results and Discussion
### Performance Comparison Based on the Arbitrary Scale  
It is very important to evaluate the performance test results from a different perspective, such as the arbitrary scale, so that the optimum RAP content to be applied in HMA design can be determined. The arbitrary scale was developed to assimilate the five major performance test results by assigning a weightage for the obtained test values compared to the standards and other justifications. The arbitrary scale was based on the following aspect:  
* If a test has a certain pass/fail value, it was given 1.0 point. However, if there is no standard requirement, the value of the R0 was assumed to be 1.0 point. Then, the points for other mixes were determined accordingly. For example: (1) according to PWD, the standard stability value is 8.0 kN. As the stability value of the R100 mix was 10.2 kN, thus the R100 mix gained 1.28 points; (2) PWD specifications do not have a standard M\({}_{\text{R}}\) value. According to the test result, the M\({}_{\text{R}}\) value of R0 was 13,544 mPa and was given 1.0 point. Since the M\({}_{\text{R}}\) value of the R100 mix was 16,966 mPa, therefore R100 mix gained 1.25 points.  
Figure 7 shows the comparison of the overall performance of the five mixes based on the arbitrary scale. The overall performance of all RAP mixes was higher than the R0 and more than the minimum of five points in total. The performance increased approximately to 7.4% with the increment in RAP content. Understandably, the collective performance of the five mixes was above the lower limit set by an arbitrary scale. Based on these findings, all RAP mixes can be recommended for the road construction industries where up to 100% RAP can be used in HMA design without significantly reducing the performance of the asphalt mix.  
Figure 6: ML testing result for the five mixes.
# An Energy-Efficient and Robust Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks
## 4 Energy-Efficient and Robust Multipath Routing
### Route Decision
* \(E_{rx}\) is the energy required for receiving a packet and is given by \(E_{rx}\) = (1.15 \(\times\) packet size in bits)/(2 \(\times\) 10\({}^{6}\)) J. * \(E_{sut}\) is the energy consumed for channel switching and is given by \(E_{sut}\) = \(P_{sw}\)\(\times\)\(t_{sw}\)\(\times\)\(\mid\)\(F_{2}\)\(-\)\(F_{1}\)\(\mid\), where \(P_{sw}\) is the power dissipation for channel switching, \(t_{sw}\) is the time for channel switching of the unit bandwidth, and \(F_{2}\) and \(F_{1}\) are the frequencies of the channels switched to or from, respectively. * \(E_{idle}\) is the energy loss while listening to a channel and is given by \(E_{idle}\) = 0.8 \(\times\)\(E_{rx}\). Regarding the stability of the path, we consider the time during which PU is in OFF state for the next time period such that the corresponding channel can be used by SUs. In other words, it is the access duration available for SU communication. The higher the access duration, the more stable the link. Hence, the stability, \(S\), can be measured as  
\[S=\sum\
olimits_{l=1}^{k}T_{off}(l), \tag{12}\]  
where \(k\) is the number of links in the path, and \(T_{off}(l)\) is the period during which a channel is available to SUs for link \(l\), which follows from Equation (7). We use this approach to evaluate the cost function for each path. Consequently, the path with the maximum value of the cost function is taken as the primary path. The backup path is the path with the second-highest value of the cost function, and it is taken in case the primary path suffers from route failure.
## 4 Results
### Model Evaluation Phase
#### 4.2.2 Post Fine-Tuning Evaluation  
The results following fine-tuning reveal a striking enhancement in model performance, with both fine-tuned gpt-4o (ft:gpt-4o) and its smaller variant, gpt-4o-mini (ft:gpt-4o-mini), achieving a remarkable accuracy of 98.6%. This represents an improvement of 86.3% and 74.3%, respectively, over their base models, underscoring the significant impact of task-specific tuning. While the GPT-4o model initially showed very low accuracy in zero-shot classification--less than half that of the mini counterpart--it overcame its challenges and demonstrated remarkable improvement, achieving the same 98.6% accuracy as the smaller model after fine-tuning. However, the identical accuracy of both models led to further investigation, as a more pronounced difference was expected due to their varying parameter sizes. To ensure the accuracy of these results, we conducted multiple rounds of model re-fine-tuning and verification using OpenAI's panel, each time confirming the same accuracy. This consistency suggests that the specific dataset and task may have enabled gpt-4o-mini to achieve a performance level equal to its larger counterpart, despite the fewer parameters.  
These findings have compelling implications for model efficiency in targeted applications. The comparable performance of gpt-4o-mini suggests that for certain well-defined tasks, smaller models may serve as cost-effective alternatives to larger ones without significant sacrifices in accuracy. This parameter efficiency could benefit scenarios where computational resources or deployment costs are limited.  
In contrast, the fine-tuned BERT (ft:bert-adam) and CNN (ft:cnn-adam) models produced notably different results. Ftbert-adam achieved 97.5% accuracy, indicating a strong performance, while ft:bert-cnn lagged significantly with only 58.6% accuracy. This disparity highlights the critical role of model architecture and pretraining in the effectiveness of fine-tuning for specialized tasks. Transformer-based architectures like GPTs and BERT have an inherent advantage in NLP and classification tasks due to extensive pretraining on diverse, large-scale datasets. Conversely, CNN models, although highly effective for structured or image-based data, often require a large volume of labeled data to excel in more complex language-based tasks, where subtle semantic patterns are crucial for accurate classification.  
The relatively low accuracy of the CNN model in this study may thus be due not to architectural limitations, but rather to an insufficient amount of labeled data to learn and recognize the intricate fake news patterns in the task. Unlike pre-trained transformers, which come with a broad foundational understanding from large, generalized datasets, CNNs must acquire such nuance from labeled data specific to the task. In applications like fake news categorization, where recognizing subtle semantic variations is essential, CNNs likely benefit from substantial labeled datasets to achieve high accuracy, highlighting the importance of dataset size and quality for such architectures.
# The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China
###### Abstract  
As the observation accuracy of parameters in the ionosphere cannot be directly checked, the comparison with other observations is the main way to evaluate the data quality of satellite measurements. Through the comparative analysis between the in situ electron density (Ne) observed by the China Seismo-Electromagnetic Satellite (CSES) and Ne at about 500 km altitude detected by Qujing Incoherent Scatter Radar (ISR), it was found that the pattern of CSES Ne is consistent with that of ISR Ne, and the correlation coefficient between the two sets of data is above 0.88 for different groups according to the magnitude. The value of CSES Ne is lower than that of ISR Ne, and the median value of the ratio for the difference between the conjugate data is 84.04%. Based on the comparison in the daytime between CSES Ne and ionosonde observations in China, it was found that the trend of the two datasets is mostly similar, and the correlation coefficient in some locations can reach up to 0.7. The distribution of CSES Ne and correlation coefficients at different latitudes show that the relationship is relatively better around the peak of the equatorial ionization anomaly (EIA). The differences in the value between CSES Ne and ionosonde data also exist, the relative change of which is about 80-95% in the daytime.  
CSES; electron density; incoherent scatter radar; ionosonde Article  
The Comparison of Electron Density between CSES In Situ and Ground-Based Observations in China. _Remote Sens._**2022**, 14, 4498. [[https://doi.org/10.3390/rs14184498](https://doi.org/10.3390/rs14184498)]([https://doi.org/10.3390/rs14184498](https://doi.org/10.3390/rs14184498))
# Towards a Reference Architecture for Cargo Ports
## 4 Patterns in a Cargo Port
### Container Terminal Physical Structure
#### 4.3.7 Known Uses  
Most ports worldwide have the infrastructure and container handling equipment presented in this pattern. The ports of Los Angeles [50] and Long Beach in California, USA [51], as well as the ports of Miami and Port Everglades in Fort Lauderdale [53], present the described infrastructure.  
Port Everglades is the #1 seaport in Florida by revenue as well as one of the top container ports in the state. The port is also South Florida's main seaport for petroleum products, including gasoline, jet fuel, and alternative fuels. It has 44 available berths with a draft depth of 44 ft. Port Everglades' existing seven gantry cranes in the Southport area, where most of the containerized cargo operations take place, are 151 feet high and can reach containers stacked six high on deck and sixteen containers across [53]. As part of its expansion program (fall 2020), they have currently purchased an additional six Super Post-Panamax container gantry cranes, the largest of their kind worldwide. On November 17, Broward County's Port Everglades received three. In comparison with the existing ones, the three 175-foot-high gantry cranes, valued at $13.8 million each, have the ability to handle containers stacked nine high from a ship's deck and reach 22 containers across. This will enable the port to handle larger ships and gain economies of scale [53].  
Figure 10: Class diagram of a container terminal’s physical structure.
# The Prediction of the Performance of a Twisted Rudder
## 4 Results and Discussions
### Validation of the Flow around a Flat Rudder
#### 4.1.3 Forces and Moment  
The measured and predicted lift and drag forces and moment for the flat rudder at 30 knots and cavitating flow condition are shown in Figure 6. The moment was measured with respect to the rudder stock axis that is the center of rotation of the rudder. In Figure 6a, the lift coefficients are seen to linearly change when both starboard and port rudders have no or slight cavitation occurrence. At rudder angles larger than \(\delta_{\rm R}=8^{\circ}\) in absolute value for each side of the twin rudder with significant cavitation, it is seen that the lift forces vary nonlinearly and decrease somewhat because of massive cavitation occurrence. The zero-lift angles are placed at around \(-4.69^{\circ}\) and \(4.91^{\circ}\), respectively, which was due to the inflow angles induced by the propeller. The zero-lift angle can be expected to become around \(0^{\circ}\) for a twisted rudder applied with appropriate inflow angles. The trend of this measured lift force is closely followed by the current numerical results. The difference between the experimental and numerical results was caused by the limitation of the current numerical approach where cloud type cavitation occurs as discussed earlier. For each rudder with surface cavitation, the predicted lift forces are smaller than the experimental values because the predicted cavitation was nearly attached to the rudder surface unlike the cloud type cavitation which detached off the rudder surface, as observed in the experiment, and increased the suction side pressures. In Figure 6b,c, similar trends are also seen in the comparisons of the drag force and moment. Where the cavitation effect is not significant, the predicted forces and moment show a very good agreement with the experimental data. When the grid dependence in the results for the rudder angles of \(\delta_{\rm R}=0^{\circ}\), \(8^{\circ}\), and \(16^{\circ}\) was examined, the difference between the grids was not significant. From these results, it can be noted that the current numerical approach shows a reasonable accuracy enough to qualitatively estimate the hydrodynamic forces of rudders.
# Adaptive Pitch Controller of a Large-Scale Wind Turbine Using Multi-Objective Optimization
###### Abstract  
This paper deals with the control problems of a wind turbine working in its nominal zone. In this region, the wind turbine speed is controlled by means of the pitch angle, which keeps the nominal power constant against wind fluctuations. The non-uniform profile of the wind causes tower displacements that must be reduced to improve the wind turbine lifetime. In this work, an adaptive control structure operating on the pitch angle variable is proposed for a nonlinear model of a wind turbine provided by FAST software. The proposed control structure is composed of a gain scheduling proportional-integral (PI) controller, an adaptive feedforward compensation for the wind speed, and an adaptive gain compensation for the tower damping. The tuning of the controller parameters is formulated as a Pareto optimization problem that minimizes the tower fore-aft displacements and the deviation of the generator speed using multi-objective genetic algorithms. Three multi-criteria decision making (MCDM) methods are compared, and a satisfactory solution is selected. The optimal solutions for power generation and for tower fore-aft displacement reduction are also obtained. The performance of these three proposed solutions is evaluated for a set of wind pattern conditions and compared with that achieved by a classical baseline PI controller.  
multi-objective optimization; multi-criteria decision making; genetic algorithms; gain scheduling control; wind turbine; PI controller +
Footnote †: journal: Applied Sciences
# Design of a Trusted Content Authorization Security Framework for Social Media
## 4 Safety Analysis and Experimentation
### Multi-factor Token Generation  
Firstly, the uploaded text file (critical information summary of the content to be published) is converted into a string and processed using a specific method. Then, the user's current timestamp, address label, and network IP address are obtained. These data will be integrated and spliced in a specific format, including text content, current time, address label, and IP address. Then, the HMAC-SHA1 algorithm calculates the summary of the spliced string as a message, and the corresponding key is used to ensure data security and integrity. Finally, the generated summary is Base64-encoded to form the final token (user token), effectively providing an authentication mechanism to ensure the security and reliability of information transmission. The process and results are shown in Figure 5.
## 4 Results and Discussion
### Micro-Analysis
#### 4.5.4 SEM  
Figure 20 shows the SEM photos of RPSG with different slag contents after standard curing for 28 days under the condition of fixed alkali equivalent and modulus. It can be observed that the SEM photos of RP-S30-M1.3N6 show smaller pores, and the structure is denser. In combination with the analyses in Figures 17-19, it is clear that slag promotes the dissolution of the silica-aluminum component of the RPSG and the formation of hydrated gels such as NASH and CASH with low polymerization, resulting in a denser structure of the specimen RP-S30-M1.3N6. Some unbound loose stroma can be seen in the lower right corner of RP-S0-M1.3N6, while no similar result is observed in RP-S30-M1.3N6. This indicates that the slag particles in RP-S30-M1.3N6 and RP particles have been fully bonded after 28 days. This is because slag rapidly dissolves a large number of calcium ions in the alkaline environment, which rapidly reacts with active SiO\({}_{2}\) dissolved from the RP to form geopolymer gel with a network tetrahedron structure, making the matrix denser [73; 74].
## 4 Implementation Details  
### Interference Detector and SNR\({}_{fe}\) Estimator (IDSE)  
As mentioned in previous sections, central to the adaptive receiver is the Interference detector and SNR estimator. IDSE is active during the preamble. Power in adjacent, alternate and desired signal bands is measured non-coherently. \(\hat{P}_{adj}\) is the power measured in adjacent channels, \(\hat{P}_{alt}\) is the total power in alternate channels and \(\hat{P}_{sig}\) is the power in the desired signal's channel.