esa-sceva/satcom-chunk-collection huggingface dataset:
# The Objective Space and the Formulation of Design Requirement in Natural Laminar Flow Optimization
## 2 CFD Solver and Its Validation
### Aerodynamic Performance Prediction vs. Experiment  
There are some criteria for judging the laminar length over airfoil (e.g., distribution of intermittency, turbulence intensity over airfoil surface and near wall region) [30]. This paper selects to observe the distribution of intermittency over the surface above the airfoil wall by \(5\times 10^{-5}\) m (approximately 10 grids away from the wall) from the leading edge: the first position that bears intermittency factor bigger than 0.5 is referred to as the transition point which determines the laminar length. This operation is selected also for the convenience in editing automatic script applied later in optimization. Figure 3 shows that the simulated laminar transition position over the two airfoils; quantitative comparison of laminar transition position is displayed in Table 2, which shows that the two aerodynamic performances that are the concerns of this paper can be obtained with confidence. Figure 3 also shows that the pressure distribution of the first airfoil are simulated well compared with the wind-tunnel experiment. For determining aerodynamic performance under fixed lift force value, 18 CPU (Central Processing Unit)'s are used with time cost at approximately 40 min for an airfoil sample.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Mesh Type** & **Lift Coefficient \%** & **Drag Coefficient \%** & **Laminar Transition Position \%** \\ \hline Coarse (\(201\times 101\)) & 0.10278 & 0.007801 & 20.1 \\ Adopted (\(501\times 150\)) & 0.11907 & 0.007427 & 23.0 \\ Fine (\(701\times 160\)) & 0.11906 & 0.007427 & 22.9 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mesh parameters and their CFD simulation.  
Figure 2: An illustration of the adopted mesh topology.  
Proof-of-Concept Study 1: A Differential Evolution Optimization with Conventional and Improved Design Requirement Formulation
# Correlation Between Sunlight Exposure Time and Cataract Prevalence in Korean Adults
## 1 Introduction
A study by [PERSON] et al. analyzed the correlation between sunlight and cataracts through a meta-analysis and showed that sunlight exposure was associated with a slightly higher risk of cortical cataracts in three types of cataract [15]. [PERSON] et al. examined the relationship between cataract prevalence, altitude, and sunlight hours in a large, national probability sample survey conducted in 105 districts of the Himalayan Kingdom of Nepal from December 1980 to April 1981. Age- and gender-standardized cataract prevalence was 2.7 times higher in areas below 185 m than in areas above 1000 m. Cataract prevalence showed a negative correlation with altitude (r = \(-\)0.533, \(p\) < 0.0001). However, there was a significant correlation between cataract prevalence and sunlight. A positive correlation was observed between the two (r = 0.563, \(p\) < 0.0001). Areas with an average of 12 h of sunlight had 3.8-times more cataracts than areas with an average of 7 h of sunlight [26]. As mentioned earlier, various factors forming cataracts have been reported in previous studies. In addition, based on the results of previous studies that sunlight is significantly associated with cataract prevalence, this study aimed to analyze various demographic factors such as gender, age, educational background, and region, focusing on the sunlight exposure time and cataract prevalence in Korean adults. As mentioned earlier, various factors that form cataracts have been reported in previous studies. Many people are well aware that sunlight causes skin problems. As previous studies dealing with sunlight exposure and cataract prevalence reported, it can be seen that the eye is an organ that is vulnerable to damage caused by sunlight. However, only about 7% of the organization understands the association between ultraviolet rays and eye problems [27]. Considering these points, this study aimed to analyze the analysis by cataract type (cortical, nuclear, anterior subcapsular, posterior subcapsular) and various demographic factors such as gender, age, educational background, and region, focusing on the sunlight exposure time and cataract prevalence of Korean adults. While the latest technology is a study using optical technology, the purpose of our study was to assess a large national population group. In addition, we hope that it will provide data for reviewing the role of the optical industry to prepare for UV protection to protect the eyes from sunlight, and we will use these basic data for epidemiological research on cataract prevention when examining the eyes using top-notch technology in the ophthalmology field.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 4 UE Receiver PHY for CS
### DMRS Search  
The DMRS search block identifies the \(SS_{i}\in\{0,1,\ldots,7\}\) by correlating the 144 samples of received DMRS with eight candidate reference DMRS generated using the PCI detected by PSS and SSS search blocks. There are two ways to realize the DMRS search architecture: (1) Use the DMRS generator in Figure 5 to generate reference DMRS, and (2) Store all possible DMRS, i.e., 1008*8 = 8064 sequences each consisting of 144 QPSK modulated samples which demand 9.3 MegaBytes of on-chip memory. Due to memory constraints on ZSoC, we have selected the first option and the corresponding architecture is shown in Figure 9. Similar to the SSS search, we can explore serial-parallel architecture by using multiple DMRS generators and correlation blocks in parallel.  
Figure 8: SSS Search Architecture.  
Figure 7: PSS Search Architecture.
## 5 EI-RNN Optimization and Analysis
### EI-RNN Training
#### 5.1.2 Train-Debug Cycle  
We chose a total of 8 datasets, which were grouped into 2 to experiment with two methods, namely, the train-val-test split method and the cross-validation method. Usually cross-validation is applied. To the four datasets in Table 3, we applied the train-val-test split hold out method, whereas to the four datasets in Table 4, we applied the cross-validation method (see Section 5.1). In the train-debug cycle phase of modeling, we observed the loss and accuracy plots across the training and validation datasets. We used six different recurrent units, namely, Vanilla RNN, LSTM, GRU, FGRNN, FRNN, and UGRNN. We addressed the under-fitting case (see Figure 9(a)) to improve the model performance on the training set by increasing the number of hidden units of the RNN. We addressed the over-fitting case (see Figure 9(b)) to improve model performance on the validation set using regularization methods. The implementation included the adaptive learning rate during training with the help of TensorFlow placeholders (tf.compat.v1.placeholder ('float', name = 'lr')). Dropout regularization (by setting _input_keep_prob_ and _output_keep_prob_ hyperparameters) and regularization through early stopping criteria were incorporated. The RNN underwent unstacking (with the help of tf.unstack (x, timeSteps, 1) Application Programming Interface (API)), and the Dropout wrapper (available in the TensorFlow library) was applied whenever we encountered over-fitting. A compute graph was built in TensorFlow v1.15 to find the final hidden states, logits (output of the classifier), and softmax predictions.
# Rao and Wald Tests for Moving Target Detection in Forward Scatter Radar
## 5 Numerical Evaluation
### Performance Analysis of the Proposed Detectors without Secondary Data  
In this part, the detection performance of the Rao-FSR and Wald-FSR detectors when the secondary data are not available is analyzed.  
Figure 4: Probability of detection versus \(K\) (**a**) \(R=4000\), \(DSNR=42\) dB; (**b**) \(R=600\), \(DSNR=35\) dB.  
Figure 3: ROC of the detectors (**a**) \(R=4000\), \(DSNR=35\) dB; (**b**) \(R=600\), \(DSNR=30\) dB.  
Figure 5 shows \(P_{d}\) versus DNSR for \(R=4000\) and \(R=600\). The probabilities of detection of the Rao-FSR and Wald-FSR are obtained by the test statistics (53) and (56). For comparison, probabilities of detection of the GLRT-FSR and the proposed Rao-SD-FSR and Wald-SD-FSR detectors are also given. From Figure 5, we can see that the Rao-FSR, GLRT-FSR, and Wald-FSR detectors coincide, which is consistent with the theoretical analysis. Meanwhile, the Rao-FSR and Wald-FSR detectors achieve about 3 dB and 8 dB detection performance improvement with respect to the CVD. Compared with the proposed Rao-SD-FSR and Wald-SD-FSR, the Rao-FSR and Wald-FSR detectors suffer detection performance degradation if the secondary data are not available. The detection performance losses are about 0.5 dB and 1.5 dB for \(P_{d}=0.9,R=4000\) and \(P_{d}=0.9,R=600\).  
In Figure 6, the impact of the observation time on the performance of the Rao-FSR and Wald-FSR detectors is analyzed. Figure 6 shows that the probabilities of detection of the Rao-FSR and Wald-FSR detectors approach the ideal optimum detector as the observation time increases. The impact of the observation time on the detection performance of the Rao-FSR and Wald-FSR is in agreement with the proposed Rao-SD-FSR and Wald-SD-FSR.  
Figure 5: Probability of detection versus DNSR: (**a**) \(R=4000\); (**b**) \(R=600\).  
Figure 6: ROC of the detectors: (**a**) \(R=4000\), \(DSNR=35\) dB; (**b**) \(R=600\), \(DSNR=30\) dB.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 3 Algorithm Framework and Modules
### Feature Filtering Network  
The feature filtering network mainly consists of the homogeneous feature filter \(F_{h}\) and the isolated feature filter \(F_{i}\). A common approach for extracting the same features from images of different modalities is weight sharing or processing the superimposed image pairs. Based on the assumptions, the images of different modalities may not be registered, leading to unexpected results. Different modalities will jointly use a locally normalized Lite Transformer [20], which is the module as the homogeneous feature filter \(F_{h}\) for each modality to enhance the robustness of the feature extraction module. This module processes global features through a gating matrix, while local normalization reduces the differences between images of different modalities, thereby improving the quality of feature extraction.  
Edge and texture information is crucial for image fusion tasks, and the isolated feature filter \(F_{i}\) should extract much independent detailed information from each modality. Therefore, this filter can losslessly extract features and excel at extracting texture information. Applying the bottleneck residual block from MobileNetV2 [21] in an invertible neural network [22] is the isolated feature filter, balancing computational cost and feature extraction capability.  
Figure 3: Structure of the attention convolutional sparse codec.
## 12 New Advancements in Combating Thermal Necrosis Caused by PMMA Polymerization  
Several new advancements have been made in the field of mitigating the extent of thermal necrosis caused by the polymerization of PMMA bone cement. [PERSON] et al. have found that using BODBB as a catalyst has a significant effect on dampening the temperature values reached during the polymerization reaction. This catalyst regulates the polymerization temperature to a maximum of 36.05 \({}^{\circ}\)C, a value well below the threshold of thermal damage occurring in the bone tissue; thereby, they concluded that it helps to enhance the cement biocompatibility and its osseointegration [58; 59].  
Novel composite cements also present a new avenue for research. As mentioned above, introducing certain PCMs lowers the maximum temperature values reached during polymerization. Due to this fact, procedures like hip or knee replacement, vertebplasty, and balloon kyrophoplasty have a significantly lower risk of leading to thermal necrosis [43].  
The use of alternative monomers (glycidyl methacrylate) and nanoparticles (magnesium oxide) in PMMA bone cement has been shown to lower the exothermic temperature of the polymerization reaction [23].  
Adding highly reduced graphene oxide nanoplatelets to PMMA bone cement has a delaying effect on polymerization, reducing the temperature released during polymerization, thus reducing the risk of thermal necrosis without having a significant effect on the mechanical properties of the cement [22].  
Keeping both the cement and the metallic components of the prosthesis at room temperature prior to mixing, using advanced mixing techniques such as vacuum mixing, and keeping the cement mantle to the recommended minimum are all effective strategies for combatting the risk of thermal necrosis [23].  
A study by [PERSON] et al. demonstrated that a novel composite PMMA-based bone cement incorporating microcapsules containing a phase-change material (paraffin) significantly reduced the maximum exothermic temperature and the thermal necrosis zone compared to conventional PMMA cement types. This suggests that such formulations may be suitable for use in procedures like vertebplasty and balloon kyrophoplasty, where thermal necrosis is a concern [23].  
Additionally, [PERSON] et al. reviewed clinical trials involving thermosensitive nanomaterials, highlighting their potential in various medical applications, including reducing thermal damage during procedures. Although the focus was broader, the review underscores the growing interest and ongoing research in this area [86].  
These findings indicate that novel PMMA formulations with nanoparticles or phase-change materials are being actively investigated and show promise in mitigating thermal necrosis during clinical applications.
# Shapley Values as a Strategy for Ensemble Weights Estimation
## 1 Introduction
To test the new ensemble weighting approach, we compare it to other weighting strategies: random weights, equal weights, simple performance-based and Shapley voting-based [19] weighting by using two large datasets for machine learning classification tasks from the banking domain. The proposed weighting strategy could be used for general ensembling purposes but especially in the knowledge transfer architecture domain to help in constructing a teacher model with strong privacy guarantees after the student model is learned as a weighted ensemble. We envision the medical, financial, and scientific domains as the primary areas of application when participants are few, but their proprietary data is large and sensitive. We are planning to use the proposed weighting strategy as an incentive mechanism in blockchain-powered PPML systems by continuing the work on previously presented research in this field [20; 21]. Our proposed approach introduces two novel performance-based weight selection strategies. This study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value. The proposed weighting strategy is applied to two large banking datasets using logistic regression and decision tree classifiers and has been benchmarked against other known approaches. The study explores the applicability of the proposed strategy not only with homogeneous but also with heterogeneous ensembles. The rest of this paper is structured as follows. Section 2 provides an overview of existing weighting strategies and related research on using the Shapley value in the context of machine learning. Section 3 describes the methods used to experimentally test the proposed solution. In Section 4, the settings and parameters used in the experiment are introduced. The experiment results for two tested ensemble creation scenarios are presented in Section 5 as well as discussion on the advantages and drawbacks of the proposed strategy. Section 6 concludes the investigation.
# RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
## 5 Results and Discussions
### Nonlinearity Compensation
Moreover, that of the conventional approach [14] is also illustrated and compared here. It can be seen that the latter constellation in Figure 12b,c which are beneficial for symbol discrimination, are clear and easy to distinguish. However, the constellation in Figure 12c suffers less distortion and is obviously tighter than the former one in Figure 12b, showing the performance  
Figure 11: Algorithm training (**a**) for different split slots (**b**) for different step factors (**c**) for different LUT sizes (**d**) corresponding LUT gain value. enhancement is obtained in resistance to the RCLED nonlinearity. Furthermore, the corresponding BER are calculated as 0.00293, 0.00096 and 0.00013. Compared with the previous two, more than up to 12 and 8 dB BER performance improvements are achieved by the proposed LUT pre-distorter. It is a fact that the proposed digital LUT pre-distorter can obviously increase the system's robustness to the nonlinearity and significantly improve the transmission performance. It should be pointed out that the BER degradation in the conventional approach is because of the output instability in the LUT prediction accuracy, thus the system linearity would be affected and the corresponding BER may be degraded. Based on the previous study on pilot overheads, computational complexities, parameter optimization and the BERs, the performance comparisons between the conventional and the improved pre-distorter are finally summarized in Table 2. As compared in the Table 2, the pilot overheads and the convergence time of the proposed approach have been greatly reduced to a certain extent, which are more appreciated in the practical POF transmissions. Therefore, the proposed method saves hardware resources and its overall performance outperforms that of the conventional one. \begin{table}
\begin{tabular}{c c c c c} \hline
**Pre-distorter** & **Pilot Overheads** & **Convergence Time (s)** & **BER Reduction (dB)** & **Parameters Adaptive** & **Accuracy** \\ \hline The conventional & 79 & 12 & 4 & No & Middle \\ The proposed & 21 & 3 & 12 & Yes & High \\ \hline \end{tabular}
\end{table} 
Table 2: Performance comparison. Figure 12: Received constellations of DMT signals operated at 22 mA point (**a**) without pre-distorter (**b**) with the conventional approach (**c**) with the proposed pre-distorter. With the proposed LUT pre-distorter, the RCLED is allowed to work at the high operating point since the peaks can be amended up to the saturation level, and the RCLED efficiency is also improved, indicating a potential solution to the issue of RCLED nonlinearity mitigation in a high-speed POF communication system.
## 4 Simulation Verification and Analysis
### Simulation Verification of Inertia Coordination Response Control Strategy
After providing inertia support, the system power should smoothly transition back to a steady-state level without excessive oscillations or secondary frequency dips. A recovery that is too fast might cause a power overshoot, while a recovery that is too slow could interfere with other frequency regulation mechanisms (e.g., primary frequency control).
# Alginate Silver Nanoparticles and Their Effect on Sperm Parameters of the Domestic Rabbit
## 3 Results and Discussion
### Alkaline Comet Assay
As with the first parameter, the semen of rabbits after exposure to AgNP AlgB 24 h showed the lowest level of tail moment damage, at 0.05 \(\pm\) 0.00 TM. The value for this group showed significant differences compared to Alg 24 h (_p_ < 0.05). Due to the compactness of the cell nucleus and the highly condensed structure of nuclear chromatin in mammalian spermatozoa, including rabbits, it is difficult to determine changes in DNA integrity. The condensed structure of chromatin makes it impossible to adequately unwind the DNA strand to make it possible to visualize its breaks and, following electrophoresis, to measure them [18; 68]. Therefore, the results obtained do not indicate a clear level of damage to nuclear chromatin generated by silver nanoparticles, or the additional effect of the used reductants. The identified damage to DNA strands after exposure to the tested solutions was minimal, less than 1% DNA loss, a value lower than the physiological changes occurring in rabbit reproductive cells, which were at the level of 2.19% of DNA in the comet tail. The results indicate a protective effect of silver nanoparticles, in both solutions, on the stability of rabbit sperm. The effect of AgNP on spermatozoids in the rat epididymis was evaluated by [PERSON] et al. [69]. The studied doses of AgNP (5 or 10 mg/kg) were injected into the tail vein of rats by a single injection and their effects were analyzed 24, 7 and 28 days after administration. In this team's study, morphologically abnormal spermatozoids were analyzed after maceration of the epididymis, as well as DNA damage using a comet assay. After 24 h exposure, abnormal germ cells were found at 20%, and after 7 days between 35-45%. Analysis of the comet assay showed a significant increase in damage, in terms of % of DNA in the comet tail, after 24 h exposure to the tested doses of AgNP (damage amounting to 12-15% tail DNA, control 7% tail DNA), and a decrease in fragmentation after 7 and 28 days [69].
## 7 Attack Strategies
### Secure Communication Protocols  
FL, with its decentralized design, promises new horizons in innovation and collaboration, but it also exposes systems to vulnerabilities, especially from eavesdropping and MitM threats. To counteract these vulnerabilities, the importance of secure communication protocols cannot be overstated. End-to-end encryption forms the foundation of these protocols, promising foundational privacy. By encrypting data directly at their source and only decrypting them at their destination, this mechanism ensures that intercepted communications remain unintelligible. Furthermore, the dynamic nature of modern encryption algorithms ensures that they can adapt to and combat emerging threats, providing a continually evolving protective barrier. However, encryption alone is not sufficient. This is where the Public Key Infrastructure (PKI) becomes relevant, introducing a robust framework of trust. PKI operates based on the validation provided by certificate authorities (CAs) to verify the authenticity of participants. This added layer of trustworthiness means that MitM attackers find it exceptionally difficult to impersonate genuine nodes, thanks to the rigorous verification steps that PKI entails [107].  
Another crucial aspect is secure aggregation. Instead of transmitting raw model updates, which could inadvertently reveal patterns to attackers, nodes employ cryptographic techniques. They send aggregated and encrypted summaries, which remain indecipherable until they reach the central server. There, the processes of aggregation and decryption unfold. This approach ensures that individual node updates remain shielded from potential eavesdroppers. However, the pursuit of security does not end here. Continuous authentication protocols introduce dynamic trust verification. Regular authentication checks throughout communication sessions improve the security by discovering anomalies early on and preventing man-in-the-middle (MitM) attacks. Using virtual private networks (VPNs) or secure tunnels enhances the data security by encrypting and encapsulating packets, lowering the risk of eavesdropping. The human factor is also important: participants can become vulnerabilities if they are not educated about secure communication and threat recognition. Regular training and an emphasis on cyber-hygiene can transform participants into watchful defenders, preventing them from becoming vulnerabilities. A complete methodology that combines modern technology with proactive human involvement maintains the integrity and security of federated learning systems.