esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Aeration Type  
The effect of preliminary saturation by air in an ejector of Venturi type is shown in Figure 11. In terms of power density, the results seem better than those obtained by direct aeration, cf. Figure 10. Both experiments were carried out under the same conditions in the same fuel cell.  
The situation in the small-size cylindrical fuel cell was different. Some results obtained by simple direct aeration in the cathode space of this fuel cell are shown in Figure 12. Obviously, in this case direct aeration was better than the sophisticated Venturi tube because two times higher power density was attained. However, the direct aeration was accomplished in a small-scale fuel cell, i.e., cathode space of 80 cm\({}^{3}\).  
Figure 10: Polarization curve. Membrane Celgard 3501. Single fuel cell. Direct aeration, 0.5 dm\({}^{3}\)/h. Active electrode and membrane area—650 cm\({}^{2}\). Feed flow rate—0.4 dm\({}^{3}\)/h. Anode volume 800 cm\({}^{3}\). (\(\bullet\))—cell voltage; (\(\Circle\)) power density; 230 mg dm\({}^{-3}\) sulfide solution. Temperature 20 \({}^{\circ}\)C.
# Non-Parallel Articulatory-to-Acoustic Conversion Using Multiview-Based Time Warping
## 5 Results and Discussion
### Objective Evaluation  
First, we evaluated the quality of speech predicted from the PMA signals in the evaluation set using the objective metrics described in Section 4.4. For TRANSIENCE, three variants were evaluated depending on the latent-space similarity loss function employed: \(\mathcal{L}_{cca}\), \(\mathcal{L}_{mmi}\), and \(\mathcal{L}_{cl}\) loss functions described in Section 3.1. Furthermore, for each of the three variants, we also evaluated the effect of the autoencoder-based loss described in Section 3.2 and the introduction of the private variables described in Section 3.3. For comparison purposes, we also evaluated the canonical time warping (CTW) technique described in [39], which is a particular case of TRANSIENCE combining standard (linear) CCA with DTW, thus not being able to model nonlinear latent mappings. We also provide the results obtained by an oracle system, in which the PMA and acoustic signals in the training dataset are aligned by using the _ideal_ warping paths computed by applying DTW over the speech signals recorded by the same subjects. These signals are available for the healthy subjects in our database because the original recordings contained both (time-synchronous) speech and PMA signals.  
Table 2 shows the objective results for the different systems. TRANSIENCE using the CL loss yields the best objective results, outperforming the rest of the similarity metrics and, even, the oracle system. In particular, relative gains of 18.36% and 21.46% are achieved in the MCD metric w.r.t. using the CCA and MMI losses. Unfortunately, it seems that the introduction of the autoencoder-based loss and the private latent variables do not improve the results, which may be due to the dimensionality of the private variables not being enough to capture the peculiarities of each view. It is also surprising that the simple (linear) CTW technique outperforms its nonlinear version TRANSIENCE-CCA. In future work, we should look at this issue.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 3 Vulnerability of Deep Learning-Based Medical Image Analysis  
Although the deep learning-based medical image analysis has had a breakthrough in healthcare sector, it is still vulnerable to various security threats, including model inversion attack [69], poison attack [70] and several security vulnerabilities [71; 72]. Among the reported attacks on medical imaging, adversarial attacks have attracted the most attention from the community of deep learning-based medical image imaging handling security concerns, as it raises a series of potential safety and security issues. Apart from disrupting the inference stage of deep learning algorithms, adversarial attacks can very easily bypass the manual check carried out by experts because of the visual similarity to its clean counterpart. Adversaries can manipulate the input medical images to deceive the deep learning models. By making subtle modifications to the images, they can cause the models to produce incorrect or misleading results, potentially leading to misdiagnosis or compromised patient care [71; 73].
## 3 Results And Discussion
### Handover Failure Probability  
The graph shown in Figure 4 indicates handover failure probability under different velocities of the mobile node. Handover failure probability increases the drop rate and has effects on delay; on the other hand, it reduces the throughput of the system. This analysis uses four kinds of wireless channels with different groups of nodes. The speed of the portable hub (mobile node) increases the effect on the performance of the node and the network-based scenario. In the case of different velocities, the condition of handoff changes, though it is extremely hard to decide which sort of sensor group handovers effectively reduce the delay and its effect on the parameter of handover failure. This decision results in the use of static, dynamic, and optimized base algorithms. It further consists of improving the bandwidth use by selecting an effective sensor channel or vertical handovers, and selecting an effective bandwidth that employs hybrid and single optimization approaches. In the proposed approach, the use of hybrid optimization helps in improving the static base algorithms, such as RSS. The use of RSS helps in the selection of a static threshold, and this static threshold is optimized via an optimization algorithm. In most of the cases, the optimization approach does not converge and provides a random answer, which further reduces the performance of time delay. In the proposed approach, the cuckoo search and genetic algorithm are also used. This methodology iteratively improves the performance of cuckoo search. The graph indicates a significant improvement in handover failure  
Figure 3: Delay of the hybrid model.  
using the proposed hybrid approach compared to the existing approach and other single optimization methodologies.
## 3 Results
### Main Outcomes  
The systematic review explored the effects of PAPE protocols on vertical jump performance, drawing on data from eight studies with a total of 224 participants. The key findings demonstrate that PAPE protocols can lead to short-term improvements in jump performance, although the magnitude of these improvements varies depending on the specifics of the protocol and the characteristics of the participants.  
Most studies reported significant increases in jump height following the application of PAPE protocols. For instance, a study on elite female volleyball players observed improvements in countermovement jump height after using a protocol involving half-squat exercises at 90% of one-repetition maximum (1 RM). Similar positive effects were noted in high-level volleyball players who experienced enhanced CMJ performance after performing hack squats with heavy loads. These results suggest that high-intensity exercises targeting major muscle groups involved in jumping can effectively induce the PAPE effect.  
However, not all studies found significant effects. For example, a study on semi-professional basketball players reported no substantial changes in jump height, peak relative power, or movement depth following PAPE interventions. This variability in outcomes indicates that the effectiveness of PAPE is influenced by factors such as the type of activation exercises used, the timing of post-activation performance, and individual physiological differences among athletes.  
Furthermore, protocols using isometric exercises also showed promise. Studies demonstrated that isometric squats could effectively enhance jump performance, highlighting that different types of muscle contractions might be useful for inducing the PAPE effect.  
In conclusion, while PAPE protocols generally enhance vertical jump performance, their effectiveness can be highly individualized. These findings underscore the importance of tailoring PAPE protocols to the specific needs and characteristics of athletes to maximize performance improvements. Future research should aim to refine these protocols, considering variables such as load intensity, type of exercise, and recovery periods to optimize the application of PAPE in athletic training.
## 1 Introduction  
Semiconducting polymers have been widely used in various organic electronic devices such as field-effect transistors (OFETs), photovoltaics (OPVs), or light-emitting diodes (OLEDs), due to their good electrical and optical properties and solution processability [1; 2; 3; 4; 5]. Through studies on polymer-based OFETs in recent decades, the community has seen significant improvements in the charge carrier mobility and has come to better understand the charge transport in thin films. In particular, the development of vast numbers of semiconducting polymers has greatly contributed to this advance in understanding [6; 7; 8; 9; 10]. However, the number of semiconducting polymers that enable electron transport (of \(n\)-type and ambipolar polymers) is still limited compared to the hole transporting (\(p\)-type) semiconducting polymers, despite the fact that they are indispensable to realizing organic logic circuits. One plausible reason is that strong electron-deficient building units, which can lower the lowest unoccupied molecular orbital (LUMO) energy level of the semiconducting polymers and thereby ensure electron transportation, are not in abundance.  
Thiazolothiazole (TzTz) (Figure 1a) is an electron-deficient unit that was introduced in semiconducting polymers as well as molecules in the earlier stage, and has provided high-performance materials for OFETs as well as OPVs [11; 12; 13; 14; 15; 16; 17; 18]. For example, we have reported a series of TzTz-thiopophene copolymers (PTzBTs) [16; 18; 19]. PTzBTs showed relatively high hole mobilities of 0.4 cm\({}^{2}\) V\({}^{-1}\) s\({}^{-1}\) and relatively high power conversion efficiencies of more than 7% [19]. However, although some TzTz-based small molecules have been reported to be n-type semiconductors [20], TzTz-based polymers are mostly \(p\)-type semiconductors, and, to the best of our knowledge, none of them have been reported to show \(n\)-channel or even ambipolar characteristics. This is most likely because the electron deficiency of TzTz is relatively weak [21], and thus TzTz-based polymers tend to show shallow LUMO energy levels (\(E_{\text{LUMO}}\)).  
Recently, we have developed dithienothienothiophenebisimide (TBI) (Figure 1b) as an electron-deficient building unit for semiconducting polymers [22]. With two imide moieties bridging thienothiophene and the two neighboring thiophene rings, TBI has relatively strong electron deficiency and thus offers deeper \(E_{\text{LUMO}}\). Here, we combined TBI with Tz Tz Tz to develop a semiconducting polymer having an electron transporting property. In this paper, we report the synthesis, electronic properties, ordering structure in the thin film, and OFET characteristics of a new Tz Tz Tz-TBI semiconducting polymer.
# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 4 Results
### Experimental Results of Simulated Images  
Table 1 presents the quantitative results of different denoising techniques on simulated images, with the best results highlighted in bold. All methods achieved very high performance metrics; however, the proposed method demonstrated effective improvements. Compared to the weakest baseline method, UNet, the proposed method reduced the MAE from 3.7986 to 2.5834, a decrease of 1.2152. Additionally, the SSIM improved from 0.9942 to 0.9973, an increase of 0.0031, and the PSNR rose from 60.6075 to 64.0642, an improvement of 3.4567. Moreover, the proposed method slightly outperformed DRANet in terms of SSIM, with an increase of 0.0004, and was 0.1763 lower than AST in MAE, with PSNR also showing an improvement of 0.3721. However, it is important to note that these differences are relatively small, and since the evaluation metrics are nearing saturation, further validation of the experimental results will be conducted through qualitative evaluations of denoising on simulated images using different networks, to provide supplementary insights to these quantitative findings.  
Figure 11 shows the results of different denoising techniques applied to the simulated images labeled as a, b, c, and d. By comparing the regions within the red boxes in the figure, the following observations can be made: Although UNet is able to remove some noise, a significant amount of noise remains in the image. In contrast, UNet3 Plus, with full-scale  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Evaluating** & **UNet** & **UNet3 Plus** & **DRANet** & **UFormer** & **AST** & **This Study** \\
**Indicator** & 3.7986 & 3.1189 & 2.8718 & 2.8728 & 2.7597 & 2.5834 \\ SSIM & 0.9942 & 0.9958 & 0.9969 & 0.9967 & 0.9968 & 0.9973 \\ PSNR & 60.6075 & 61.5843 & 62.6493 & 62.8602 & 63.6921 & 64.0642 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation results for the simulated image experiment.  
skip connections, demonstrates better denoising performance. However, as shown in Figure 11b,c, UNet3 Plus smooths out terrain texture details during the denoising process, leading to the loss of topographical information. DRANet, while effective at noise removal, causes blurring along the edges of small pits in Figure 11a after denoising. UFormer still leaves considerable noise, especially in Figure 11b. The AST network almost eliminates all noise, but it still lacks sufficient texture detail retention capability, with very small pits or hills being reconstructed rather vaguely. In comparison, the method proposed in this study not only effectively preserves the details of the image during denoising but also removes a large amount of noise, while better restoring textures and topographical features obscured by noise, thus demonstrating the highest visual quality.
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
## 2 RCM and RBM Methods as Support for Maintenance
On the other hand, RBM provides a more detailed estimation and assessment of risks associated with functional failures when compared to RCM. The risk allows identifying which items do not meet the acceptable risk and shall be prioritized by maintenance management. Although the RBM indicates that it is necessary to determine the maintenance plans to reduce the risk to items that exceed the acceptance criteria, it does not provide further guidance. As for the RCM, the selection of the fault management policy guides the definition of cost-effective maintenance tasks for the item under analysis, which is usually supported by decision tree diagrams. Finally, it should be noted that the proposed RRCM was conceived to integrate both RCM and RBM strategies into a novel and single method that allows the definition of maintenance plans oriented to reliability, risk, and cost. Therefore, it combines their main features to take advantage of both RCM and RBM strengths, as will be presented in detail in the next section.
# A New GNSS-Derived Water Vapor Tomography Method Based on Optimized Voxel for Large GNSS Network
## 3 Experiment and Validation  
To assess the optimized tomographic algorithm, we design a comparative experiment between the conventional and proposed approaches. First, we describe the study area (Nanjing) and data processing. The experimental results and comparison between the two approaches are then presented.
## 5 Simulation Results and Analysis
### Simulation Results
Combined with Figure 4, part of the reason SINR-max scheme performs worse in system throughput is that the forced termination caused by the load balancing of LEO constellation is not considered. As the services of grade 2, 3, and 4 can connect to the GEO satellite, the probability of forced termination is smaller than that of grade 1. Comparing the simulation results in different communication scenarios, we find that when the proportion of delay-sensitive services is larger, the system throughput will decrease, and the forced termination probability will increase. This is because delay-sensitive services cannot be connected to GEO satellites, a large number of services make the LEO satellite full load, resulting in more access or handover failures, and lower throughput.
## 3 Results  
### Intact and UKA Model Validation  
The FE model was subjected to evaluations based on experimental data available in the literature. The intact model validation was conducted in a previous study [22,23] and is briefly introduced herein. Mesh convergence was defined as the point at which cartilage pressures were within 95% of the pressure of the next two smaller mesh sizes. This criterion was met by a mesh size of 2.0 mm. If the differences between FE model simulations and experimental data were lower than two standard deviations (SDs) from the mean of the experimental data, the FE model was considered to be validated. This was explained with an in vivo laxity test for the subject identical to the FE model. The laxity test was applied to the anterior-posterior drawer under 30\({}^{\circ}\) and 90\({}^{\circ}\) of flexion with a series of stress radiographs. Its results were compared with the results from the experiment on the FE model's subject to validate the intact FE model. Under the loading condition with 30\({}^{\circ}\) flexion, the anterior tibial translation was measured as 2.83 mm in the experiment and 2.54 mm in the FE model, and the posterior tibial translation was measured as 2.12 mm in the experiment and 2.18 mm in the FE model for validation [23]. A good agreement was thus shown between the experiment results and the FE model. In addition, the results were also compared with previous FE results for validation. Under an axial load of 1150 N, average contact pressures of 3.1 and 1.53 MPa were evaluated on the medial and lateral meniscus, respectively. Both contact pressures were within 4% of the equivalent pressures of 2.9 and 1.45 MPa reported by [PERSON] et al. [27]. These minor differences might have been due to variations in the geometries, such as the thickness of the cartilage and meniscus, between different studies. However, on an overall basis, the high consistency between the results of the validation and reference substantiated the FE model's ability to produce reasonable results [27]. The UKA model validation was performed through the use of anterior and posterior translations of tibial compartments in the anterior and posterior drawer tests at 134 N for 6.1, 9.9, 8.7, and 8.5 mm; and 5.8, 4.3, 3.8, and 4.9 mm at 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) knee flexion in the UKA model, respectively (Figure 4). These findings from the simulation indicate good agreement with prior experimental data within the ranges of values under the anterior and posterior drawer loads [43].
# Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems
## 4 Simulation Results and Discussions
### Vehicle Speed Prediction: Tests and Discussion  
A realistic simulation test in the non-congested mode was conducted using the proposed fuzzy logic system. In this test, the traffic flow input parameter was set at 40% of its maximum value, and the traffic density was set at 20%. When these parameters were fed into the fuzzy logic system, the predicted vehicle speed was approximately 102 km/h, as shown in Figure 24. For comparison, the polynomial regression model in Figure 23 predicts a vehicle speed of about 103 km/h at the same traffic flow level (40%), while the regression model in Figure 21 predicts a speed of about 100 km/h at a traffic density of 20% in the non-congested mode. The simulation results confirm the accuracy of the proposed fuzzy logic system in predicting vehicle speed. The predicted speeds are within about 1% of those generated by regression analysis methods in high-fidelity traffic scenarios. This demonstrates that the proposed system provides predictions that closely approximate actual traffic conditions, validating its feasibility for vehicle speed prediction. Another simulation was performed under a \"Quite Low\" traffic status scenario, where the traffic flow was set at 34% (classified as \"Quite Low\") and the traffic density at 20% (classified as \"Low\"). Using the centroid method for defuzzification, the fuzzy logic system operating in a non-congested mode predicted a vehicle speed of 101 km/h, as shown in Figure 25. This result further confirms the system's ability to reflect realistic changes in highway traffic conditions.  
For the test in the congested mode, the input parameters were set with the traffic flow at 21% (classified as \"Low\") and the traffic density at 89% (classified as \"Very High\"). As shown in Figure 26, the fuzzy logic system produced an output vehicle speed of 23.2 km/h. This outcome indicates that under congested conditions, the predicted speed aligns well with actual traffic behavior.  
The experimental results indicate that both the fuzzy logic and regression models produce consistent and reliable outputs, confirming the practical feasibility and robustness of the proposed travel-time prediction system. Specifically, the fuzzy logic system effectively applies fuzzy rules to the input values based on their degrees of membership, accurately capturing the complex relationships among vehicle speed, traffic flow, and traffic density to generate meaningful predictions. Overall, the simulation results offer valuable insights  
Figure 24: Defuzzification of the proposed fuzzy logic system in non-congested mode with traffic flow at 40% and traffic density at 20%.  
into traffic behavior under various conditions, reaffirming the flexibility and effectiveness of the designed fuzzy logic system.