esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GAN & Generative Adversarial Networks \\ OFDM & Orthogonal Frequency-Division Multiplexing \\ CSI & Channel State Information \\ LS & Least Squares \\ MMSE & Minimum Mean Square Error \\ CNN & Convolutional Neural Networks \\ MIMO & Multiple-Input-Multiple-Output \\ SNR & Signal-to-Noise \\ NLOS & Non-line of Sight \\ NMSE & Normalized Mean Square Error \\ \end{tabular}
## 1 Introduction
used a nickel-titanium memory alloy temperature-controlled QZS isolation system, in which the nickel-titanium alloy was used to fabricate a negative stiffness element of a cosine beam, whose dynamics could be controlled by changing the temperature through electrical heating [34]. [PERSON] et al. designed a cell that could satisfy the optimal QZS performance and the smallest size at the same time through the combination of a positively stiffened cardioid beam with a negatively stiffened cosine beam, and the vibration isolator made up of the cell can isolate vibrations from 3.2 to 60 Hz [35]. [PERSON] et al. conducted vibration isolation tests using a 3D-printed QZS system, employing fixed-end compressed buckling plastic beams as the negative stiffness elements, confirming the negative stiffness effect of this structure [36]. Similarly, [PERSON] et al. used 3D-printed structures for experiments and derived a complete formula for calculating negative stiffness. However, the results showed significant discrepancies between the theoretical negative stiffness values and experimental results [37]. If the material is changed from plastic to spring steel, the discrepancy increases with the rise in elastic modulus. The reason for the error is that the relationship between preload force and compression after beam buckling was not derived, resulting in inaccurate preload force values during calculations, with the preload force error being positively correlated with the amount of compression. This paper primarily addresses the vertical vibration isolation issues of the DCM. Based on a quasi-zero stiffness isolation system, it designs a vibration isolation system suitable for ultra-high stability DCMs. The system integrates positive stiffness helical springs in parallel with negative stiffness compressed buckling LSs. This study involves in-depth research into quasi-zero stiffness theory and enhances the calculation method for the negative stiffness values of compressed buckling Euler beams fixed at both ends. Compared to traditional methods, this approach significantly improves the accuracy of negative stiffness value calculations. In this paper, we conduct thorough analyses of its static and dynamic characteristics through theoretical calculations and finite element simulations, culminating in vibration transmission and random vibration response analyses.
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Gaussian Curvature
In Figure 10f (snowy conditions), the Gaussian curvature map shows strong fluctuations, particularly more pronounced at the image edges, with regions of both positive and negative curvature peaks. The wavefront distortion is significant. The Gaussian curvature map reveals an uneven distribution, with changes in snowflakes causing numerous disturbances in the optical path, affecting wavefront smoothness. Overall, by observing the bright spots in Figure 10a-f, it is found that, in all weather conditions, the areas with the most significant local distortion are located in the center or at the edges. In Figure 10b (cloudy), the central bright spot is the darkest, with the lowest curvature value, indicating minimal distortion. As weather conditions worsen (sunny, light rain, heavy rain, snowy, and dust storm), the central peak value increases, expands, and becomes more irregular, indicating increased wavefront distortion. Although Figure 10c (light rain) represents a precipitation condition, the Gaussian curvature distribution remains relatively uniform, with no significant wavefront distortion, indicating that light rain has a minimal impact on the wavefront. The Gaussian curvature distributions in Figure 10d (heavy rain), Figure 10e (dust storm), and Figure 10f (snowy) show significant fluctuations, especially in the central and edge regions, with markedly increased wavefront distortion. Heavy rain, dust storms, and snowy conditions have a substantial impact on the wavefront distortion.
## 3 Feature Selection Algorithms
### Multi-Objective NSGA3 and NSGA2  
[PERSON] et al. [27] proposed that the NSGA2 was an improvement over the NSGA because it lacks elitism, sharing parameters and speed. The NSGA II has been the most frequently used multi-objective algorithm since its appearance in the literature. However, since the inception of NSGA3, it has been playing the same role as the NSGA2 for performance evaluation [28]. Subsequently, the NSGA3 begin operations using a set of reference points with a description. The offspring of the population is created using the current population of the parent at a particular generation using genetic operators. The combination of the population is sorted into non-domination at different level. The population members that cannot be accommodated in the last front are saved and the remaining members are rejected. The next set of members that are selected for subsequent generation and the other members are selected for the last front. The selection is conducted by performing systematic analysis of the set members with respect to the reference points as supplied, unlike the case in NSGA2 where the front members are chosen based on the large crowd distance value. To obtain an identical range for objective values and supplied reference points, they are first normalized. As such, zero vector becomes the normal point of the set. The members of the set are associated with the reference point based on proximity to the reference line attained by linking the normal point with the point of reference. It is found that this procedure typically assists in determining the number and the population indices for the members associated with the reference point for each supply. As the result, the niching procedure is applied for the selection of the population members not represented in the set using the result of the association procedure as described in the preceding section. The reference point with the least number of associations in the population searches for an associated point. The members are continuously added one-by-one until the population is full [28]. The performance of the NSGA2 and NSGA3 is inconsistent because the performance depends on the datasets or the target problem to be solved. A study indicates that NSGA2 outperform NSGA3 on the knapsack problems, whereas NSGA3 outperforms NSGA2 for distance minimization problems [28].
# A Grey Wolf-Based Method for Mammographic Mass Classification
## 4 Experimental Results
### Comparison with Related Feature Selection Methods  
For further evaluation of our proposed methods, we compared it with 4 other feature selection methods, namely Chi-Squared [32], Information Gain [33], Significant attributes [34] and Correlation attributes [35]. Table 9 highlights the results of this comparison. This comparison was conducted using the same features used in the results discussed in Section 4.3. From this table, two remarks would be drawn. Firstly, in case of normal and abnormal detection, the proposed method is outperforming the compared methods. This means that the BGWO-based selection method would help in detecting abnormal cases with a fewer number of features while achieving high classification measures. Secondly, for detecting malignant case, although there is no big improvement, the proposed method is still better than the other feature selection methods, in terms of accuracy, precision and recall measures. However, Chi-Squared, Information Gain and Significant attributes methods are better than the proposed method in terms of the number of features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Selected Features** & **Number of Features** & **Accuracy** & **Precision** & **Recall** & **ROC Area** \\ \hline Feat. set 1 & 365 & 73.0\% & 73.0\% & 73.0\% & 0.856 \\ Feat. set 2 & 319 & 75.0\% & 75.1\% & 75.0\% & 0.87 \\ Feat. set 3 & 409 & 72.0\% & 72.0\% & 72.0\% & 0.85 \\ Feat. set 4 & 354 & 72.5\% & 72.6\% & 72.5\% & 0.85 \\ Feat. set 5 & 367 & 76.5\% & 76.6\% & 76.5\% & 0.86 \\
**Feat. set 6** & **374** & **78.0\%** & **78.1\%** & **78.0\%** & **0.871** \\ Feat. set 7 & 304 & 74.5\% & 74.6\% & 74.5\% & 0.871 \\ Feat. set 8 & 374 & 74.5\% & 74.6\% & 74.5\% & 0.865 \\ Feat. set 9 & 418 & 77.5\% & 77.6\% & 77.5\% & 0.88 \\ Feat. set 10 & 396 & 72.5\% & 72.7\% & 72.5\% & 0.859 \\ Feat. set 11 & 388 & 77.5\% & 76.6\% & 76.5\% & 0.86 \\ Feat. set 12 & 305 & 74.5\% & 74.5\% & 74.5\% & 0.865 \\ Feat. set 13 & 351 & 73.5\% & 73.8\% & 73.5\% & 0.864 \\ Feat. set 14 & 312 & 73.0\% & 73.5\% & 73.0\% & 0.871 \\ Feat. set 15 & 303 & 72.5\% & 72.7\% & 72.5\% & 0.867 \\ Feat. set 16 & 345 & 70.0\% & 70.2\% & 70.0\% & 0.851 \\ Feat. set 17 & 405 & 72.5\% & 72.6\% & 72.5\% & 0.86 \\ Feat. set 18 & 356 & 72.0\% & 72.2\% & 72.0\% & 0.865 \\ Feat. set 19 & 365 & 73.0\% & 73.2\% & 73.0\% & 0.861 \\ Feat. set 20 & 340 & 74.0\% & 74.5\% & 74.0\% & 0.872 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Detection of benign or malignant using BGWO with fusion features of curvelet and wavelet.  
In short, the BGWO used in the proposed method has improved the results in terms of accuracy, precision and recall which are the most important measures for breast cancer detection systems. This would also mean that BGWO algorithm as a features selection has the capability to select the most discriminative features to either detect the abnormal or malignant cases from mammogram images.
# Adaptive Emergency Call Service for Disaster Management
## 1 Introduction
* The performance of the proposed adaptive protocol is shown to be significant when compared with competitive existing protocols such as M-HELP [9] and FINDER [10] in terms of transmission success rate, network traffic control, and energy management under extreme emergency scenarios. The remainder of this paper is organized as follows, Section 2 provides an overview of other related works, while Sections 3 and 4 explain the system model and proposed design of the multi-hop emergency calls service protocol, respectively. Section 5 provides the performance analysis and comparison with existing protocols and Section 6 concludes the paper.
## 4 Results
### Prediction with Machine Learning Models
#### 4.5.2. Un-Supervised Learning with K-Means Clustering  
The data, already standardized, required no further processing before K-Means clustering. Using the elbow method, the optimal number of clusters was determined to be four (k = 4), as the graph of Within-Cluster-Sum-of-Squares (WCSS) flattened after this point. Cluster centroids were computed to analyze average feature values and the distribution of treatment and control group participants. Four distinct clusters emerged, each with unique characteristics in emotional expressions and engagement metrics, see Table 6.  
Cluster 0 (\"Reserved and Steady\") exhibited consistent emotional responses and moderate engagement. Cluster 1 (\"Expressive and Dynamic\") displayed greater emotional variability and higher engagement. Cluster 2 (\"Neutral and Bright\") showed neutral emotional behavior and significant environmental brightness. Cluster 3 (\"Low Engagement\") was characterized by lower brightness and reduced emotional and interaction dynamics. Cluster 1 had the highest mean (0.4652) and median (0.1445) \"Performance Slope\", suggesting a link between expressiveness and dynamic engagement with performance.
## 6 Conclusion
(**A**) Schematic of the structure of a photonic crystal (PC) surface biosensor with a surface-attached nanoparticle, inset: photo of a PC biosensor fabricated on a glass slide; (**B**) Instrument schematic of the modern Photonic Crystal Enhanced Microscopy (PCEM); (**C**) Scanning electron micrograph of the photonic crystal surface, inset: zoomed-in image on the edge of the PC biosensor; (**D**) Normalized spectrograph (surface plot) measured with PCEM. Inset: PCEM-acquired 3D spectrum data; (**E**) AFM images of a tDPN-printed 3 \(\times\) 3 array of nano-dots (each with dimension of 540\({}^{2}\)\(\times\) 40 nm\({}^{3}\)), inset: zoomed-in AFM image of one tDPN-printed dot; (**F**) PWV image of the tDPN-printed dots (displayed in a 3D surface plot) within a 20\({}^{2}\)\(\upmu\)m\({}^{2}\) field of view, inset: 2D PWV image; (**G**) Normalized spectra of a representative tDPN-printed dot and a background pixel, inset: zoomed-in image of the spectra with 2D polynomial fitting (Reprinted in part with permission from [48], © 2014 RSC Publishing.). nearly 100%) on dielectric surfaces of PC biosensors enable measurement of resonant wavelength shifts with high spectral resolution. The PC resonant mode can be measured in a noncontact detection instrument configuration, in which normal incident-angle illumination results in simple integration with a standard microscope. The resonant wavelength can be selected on a PC surface by tuning its geometry (e.g., grating period) or the incident angle of illumination. Thus, the sensing and imaging can be realized in many spectral ranges, including ultraviolet, visible, and infrared (IR). Although PC surfaces have been fabricated by expensive and time-consuming approaches (such as electron-beam (e-beam) or nano-imprint lithography), recent developments in high-throughput and large-area polymer-based techniques (such as nanoreplica molding at room temperature) have led to the commercial introduction of single-use disposable PC sensors that can be manufactured in a roll-to-roll fashion. These PC sensors can be subsequently integrated with standard format microplates, microscope slides, and microfluidic devices for high-throughput drug or cytotoxicity screening of biomolecule or cell assays. The goal of this review is to summarize the genesis, development, and recent advances of PCEM.
## 5 Case Study
### Analysis of Control Effects
K-fold cross-validation: Applying 5-fold cross-validation to assess the stability of the proposed model, the RMSE variance across folds is only 1.8%, ensuring the general applicability of the proposed model in different operational scenarios. 3. Ablation Studies: Removal of the key model component temporal pattern attention mechanism increases RMSE by 14.8%, confirming its key role in capturing temporal dependencies. Taking a particular day to analyze the control effect, the RMSE between the actual output of the wind farm and the planned value on that day is 8.61%, and the whole day's output is 662.17 MWh. Figure 3 shows the wind power output and control deviation curves under the five control strategies. From Figure 3 and Table 7, all five control strategies can effectively track the planned power output. However, the wind storage joint control strategy based on TPA-BiGRU has the best control effect and the highest control accuracy, and the proportion of the assessed electric quantity (KDB) is only 0.33%. It shows that the wind storage joint control strategy based on TPA-BiGRU has a solid ability to adapt to the uncertainty of the wind farm output and can track and regulate energy storage well even when the wind power output changes suddenly. Table 8 shows the regulation of the energy storage systems with five control strategies. The wind storage joint control strategy based on TPA-BiGRU has the best energy storage regulation performance. In the case that the system can effectively track the planned output, the control strategy proposed in this paper has the least total regulated electricity quantity (TD), the least proportion of the regulated electricity quantity (TDB), and the least number of energy storage operations. It can effectively avoid equipment aging and loss accelerated \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Control Strategy** & **RMSE** & **ABS\_MAX (MWh)** & **KD (MWh)** & **KDB** & **KS (min) \({}^{\star}\)** & **KSH** & **K1\%** \\ \hline Before control & 8.61\% & 17.94 & 178.78 & 17.71\% & 90 & 6.25\% & 25\% \\ TPA-BiGRU & 0.88\% & 3.37 & 0 & 0\% & 0 & 100\% & 100\% \\ TPA-BiLSTM & 0.94\% & 4.53 & 3.16 & 0.47\% & 4 & 95.83\% & 95.83\% \\ BiGRU & 1.01\% & 6.64 & 3.61 & 0.55\% & 10 & 89.58\% & 89.58\% \\ AROCS & 2.63\% & 16.35 & 8.78 & 1.32\% & 13 & 86.46\% & 86.46\% \\ PI & 3.05\% & 8.34 & 21.66 & 3.27\% & 55 & 42.71\% & 42.71\% \\ \hline \hline \end{tabular} \({}^{\star}\) Every 15 min is an assessment period.  
\end{table} 
Table 7: Comparison of control effects of wind storage systems. Figure 3: Wind farm output under five control strategies. (**a**) Output curves of various control strategies; (**b**) output deviation curves of various control strategies. by frequent actions, and too much regulation power leads to the overuse of energy storage equipment, which affects the equipment's life.
## 6 Results and Discussions
### Diagnosis of Combined Two BRB and Different ITSC Faults  
The experimental tests are executed on SC-IM with a combined two BRB and different ITSC faults under different loading conditions. The first test is implemented in the case of  
Figure 19: The Index of fault for combined different BRB and ITSC faults.  
Figure 18: FLC Model to diagnose a combination of different BRB and ITSC faults.  
SC-IM under 10% of load with a combined two BRB, 2%, 5%, 7%, and 10% of ITSC faults. The second test is performed in the case of SC-IM under 20% of load with a combined two BRB, 2%, ITSC, 5% ITSC, 7% ITSC, and 10% ITSC faults. Then, the other tests are performed in the case of SC-IM under 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of load. Figure 20 shows the index of faults to diagnose combined two BRB and different ITSC faults under different loading conditions.  
Figure 20: The index of fault for combined different BRB and ITSC faults under different loading conditions: **(a)** 10% of the load; **(b)** 20% of the load; **(c)** 30% of the load; **(d)** 40% of the load; **(e)** 50% of the load; **(f)** 60% of the load; **(g)** 70% of the load; **(h)** 80% of the load; **(i)** 90% of the load; **(j)** 100% of the load.  
From Table 7, the training and testing RMSE are acquired. It is proven that the ANFIS based DWT achieves high performance for diagnosis of a combined two BRB and different ITSC faults in SC-IM under different loading conditions.  
From the previous discussion, it is clear that ANFIS based on DWT provides higher performance than all other techniques; the testing RMSE is 5.5547 \(\times\) 10\({}^{-7}\) in diagnosis of combined BRB and 2% of ITSC faults; while the worst output is obtained by ANFIS without data pre-processing, where the testing RMSE is 2.1271. The result of the analysis proves that DWT improved the ANFIS performance. It plays a very important role in extracting the features of input data. The proposed method provides better generalization capability, faster learning speed and easier implementation when it is compared to other techniques. The proposed method can accurately detect and classify two different SC-IM faults at the same time.
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 4 Results and Discussion
### Flow Field  
This section describes the effect of various Reynolds numbers such as 2, 4, 6, 8, 10, and 12 on the mixing performance of microfluidics micromixers. Figure 16 describes the graphical representations of the variation of the mixing index by different Reynolds numbers. It can be observed that the mixing index decreased linearly with the Reynolds number, but non-linear behaviour has been noted for the case with no micropillars; at first, it increased, and then it decreased. The maximum mixing performance is observed at Reynolds number 2, while the minimum mixing performance is examined at Reynolds number 12. Similar behaviour has been observed in [51], where they investigated the  
Figure 14: Variation of the mixing index with different micropillar gaps.  
Figure 15: Distribution of species concentration with different micropillar gaps: (**a**) 0.150 mm, (**b**) 0.175 mm, (**c**) 0.200 mm, (**d**) 0.225 mm, (**e**) 0.25 mm.  
maximum mixing performance index at Reynolds number 1 \(\times\) 10\({}^{-3}\), corresponding to a flow velocity of 0.05 mm/s, and the lowest mixing performance index at 2 \(\times\) 10\({}^{-3}\), corresponding to a flow velocity 0.1 mm/s. The main reason behind this linear decrease in mixing performance is the inclusion of micropillars, which help to properly homogenise the heterogeneous fluids in the microchannel. Moreover, the non-linear behaviour in the case of no micropillars is also observed due to improper mixing in the microchannel. Furthermore, the contours of distribution of flow velocity distribution at different Reynolds numbers of microfluidic micromixers are also presented in Figure 17. It can be observed that the maximum flow velocity is obtained at a Reynold number of 12, while the lowest flow velocity is observed at a Reynolds number of 2. The maximum and minimum magnitudes of flow velocity are 0.00146 m/s and 2.58 \(\times\) 10\({}^{-4}\) m/s, respectively.  
Figure 16: Graphical representations of the mixing index at different Reynolds numbers.  
Figure 17: Distribution of the species concentration in different Reynolds numbers.  
Moreover, streamlines in the case of the 12 Reynolds number is stronger compared to other values that cause a lower contact area and lower contact time for the mixing of different fluids, while lower Reynolds number values provide more contact area and contact time for the proper mixing of fluids. Finally, it is investigated that better mixing is observed at lower Reynolds numbers with fewer micropillar gaps for blade-shaped micropillars.  
In addition, a comparison study has also been conducted in order to compare the mixing performance of the present work with the current literature on various microfluidic micromixers listed in Table 4. It is evident that people conducted performance analysis on various types of microfluidics micromixer designs, but the present proposed micromixer design has a better mixing index of 0.97. Furthermore, the mixing index can be improved based on these findings.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 3 The Proposed Framework
### The Dual-Stream Framework  
Feature Pyramid Network (FPN) is a classical method to fuse pyramid features at all scales, which is not accommodated for dual-stream framework. Inspired by FPN, we propose Multiple Feature Pyramid Network (MFPN) for the two streams feature fusion, as shown in Figure 3. The MFPN has three pathways, namely the bottom-up pathway on the left, the bottom-up pathway on the right and the top-down pathway in the middle. Since the feature map of first layer has a larger resolution, the feature maps of second to fifth layers in image and illumination streams are applied. For the top-down pathway in the middle, the feature maps are fused by the image and illumination, recorded as \(\{K_{2},K_{3},K_{4},K_{5},K_{6}\}\). The feature map of MFPN \(K_{i}\) is expressed as (3):  
\[K_{i}=\text{Conv}(C_{i})+\text{Conv}(P_{i})+\text{Up}(K_{i+1}) \tag{3}\]  
where Conv () is the \(1\times 1\) convolution function to adjust dimension and connect horizontal, Up () is the up-sampling function with the factor of 2.