esa-sceva/satcom-chunk-collection huggingface dataset:
# Mueller Matrix Decomposition and Image for Non-Destructive Testing of UAVs Skin
## 2 Principle
From Equations (10) and (11), the depolarization matrix is transformed into:  
\[M_{d}=\frac{d}{2}diag(2,\eta_{2},\eta_{3},\eta_{4}), \tag{12}\]  
where \(\eta_{i}(i=2,3,4)\) represent the relative depolarization as well as the relative error. From Equations (11) and (12), \(\eta_{i}(i=2,3,4)\) reveal the depolarization differences under the different polarized states induced by the scattering properties of the sample. Thus, in Equation (10), \(\lambda_{i}-\frac{d}{2}(i=2,3,4)\) denote the magnitude of depolarization caused by non-uniform multiple scattering. The entropy is a useful tool for the quantification of the total depolarization caused by multiple scattering. From Equation (11), the polarization entropy is given by:  
\[E=-\sum_{i=1}^{4}\lambda_{i}\log_{4}p_{i}-\frac{d}{4M_{11}}\sum_{i=2}^{4} \left(1+\eta_{i}\right)\log_{4}(1+\eta_{i}), \tag{13}\]  
where \(p_{i}=\lambda_{i}/trT\). For one optical system, its entropy varies from 0 to 1. When the entropy is equal to 0, the Mueller matrix is non-depolarizing, and the system has a single scattering mechanism. When the entropy amounts to 1, the Mueller matrix is completely depolarizing, and the scattering is random with the form \(diag(1,0,0,0)\). The entropy measures the disorder degree of multiple scattering. A very important application of UMMMD is used to decompose the diagonal Mueller matrices of scattering materials. The diagonal Mueller matrix of multiple scattering materials pointed out by [PERSON] [25] based on the maximum entropy principle is decomposed as:  
\[M_{A}=\frac{a+b+c}{3}diag(1,1,1,1)+ \begin{bmatrix}\frac{3-a-b-c}{3}&0&0&0\\ 0&\frac{2a-b-c}{3}&0&0\\ 0&0&\frac{2b-a-c}{3}&0\\ 0&0&0&\frac{2c-a-b}{3}\end{bmatrix} , \tag{14}\]  
where \(a\), \(b\), and \(c\) are functions of the scattering events. The decomposition form is unique since the decomposition coefficients are proportional to the polarization and depolarization coefficients. The diagonal Mueller matrices with anisotropic depolarization have been reported in the previous optical literature [24]. Some traditional methods cannot be used for the decomposition of such Mueller matrices because these methods are valid only if the ideal condition \(\lambda_{2}=\lambda_{3}=\lambda_{4}\) is fulfilled. However, our method is not subject to this condition, and the Mueller matrix is completely decomposed in this work. Clearly, the proposed method is suitable for both isotropic and anisotropic depolarization related to the non-uniform multiple scattering.
# Applications of Magnesium and Its Alloys: A Review
## 4 Magnesium and Its Alloys as a Biomaterial
### Biological Competencies  
There is growing literature on magnesium and its alloys as a biomaterial, especially as a temporary implant. The strong interest stems from magnesium's remarkable engineering properties and its natural occurrence in the human body. Magnesium is the second most abundant and essential intracellular cation [6]. It is an essential mineral that supports physiological functions in the human body, including the structural stabilization of proteins, nucleic acids, cell membranes, and the promotion of specific structural or catalytic activities of proteins, enzymes, or ribozymes [1]. Moreover, magnesium is also non-toxic and non-iritant. It promotes tissue healing in the human body and can be excreted through urine or feces [57]. Magnesium is also primarily found inside cells or bones, where it is known to support osteoinductivity [4]. Hence, with these factors combined, magnesium is naturally biocompatible to the human body.  
Magnesium's greatest strength lies beyond biocompatibility. It excels in biodegradability and bioavailability [46]. It is also bioresorbable and a bioactive material [4]. Often, the body only requires the temporary presence of an implant or device [33] to support its tissue regeneration and restoration of impaired physical functions [58]. However, if the implant is not removed promptly, it may lead to undesirable responses like chronic physical irritation and inflammatory response, or worse, introduce biotoxicity [59].  
When evaluating for use as a temporary implant, biodegradability is of the utmost importance. Ideally, the implant should biodegradable and completely dematerialize after the tissue has been fully recovered. As the host tissue gradually heals and recovers its load-bearing capacity and mechanical integrity, the strength and stiffness of the implant should degrade at an inversely proportionate rate [59]. Therefore, the closer the host tissue is to a complete recovery, the lesser degree of support and reliance it should receive from an implant, which corresponds to the gradually reduced presence of the implant. This inverse relationship is illustrated in Figure 3.  
Figure 3: A Stiffness–Time inverse relationship between degradable implants and healing bone. Reproduced with permission from [PERSON], et al., Magnesium (Mg) corrosion: a challenging concept for degradable implants; published by Woodhead Publishing, 2011 [60].  
Additionally, the biodegradation process should also occur without any adverse effects on the human body. Unlike traditional metallic implant materials, such as stainless steel, cobalt-chromium alloys, and titanium alloys, which are bio-inert, magnesium is biodegradable and bioabsorbable. This eliminates the need for follow-up surgery to remove the implant after the impaired physical functions have been restored [46].
## 3 Results and Discussion
### Flow Stress Behavior Modeling Experiments
#### 3.3.2 ANN Model  
Due to its simplicity and quick response, the ANN was commonly used to model and simulate metallic materials' flow behavior. Accordingly, the main objective here was to examine the predictability of the investigated alloy's flow behavior via the developed ANN and the classical method based on the ACE approach. A backpropagation algorithm with a three-layer network was utilized to model the investigated alloy's flow behavior in the present work. The input layer (strain and deformation strain rate and temperature), the output layer (flow stress), and the hidden layer were the three selected layers used here. The graph in Figure 8 schematically demonstrates the ANN architecture. The function of TrainLM with a Tan-sigmoid hidden-transfer function and a Purelin output-transfer function were utilized for the ANN training due to its speed and effectiveness [80; 81].  
With one neuron in the hidden layer, the trial-and-error process was initiated and further executed with more neurons. A suitable number of hidden layers should be  
Figure 8: Artificial neural network (ANN) architecture schematic illustration for the used network.  
identified. Figure 9 illustrates the reliance on the network efficiency of the number of neurons in the hidden layer. To verify the output of the used ANN, the mean squared error (MSE) was used. It was found that at 20 neurons, the MSE reached the minimum value. The relationship between the fitted stresses and the experimental stresses derived from the ANN model is shown in Figure 10. At the studied temperature and strain rate ranges, the fitted and approximated stresses were in excellent compliance with the experimental stresses.  
Figure 10: Experimental and predicted stress vs. strain via the ANN model at different strain rates and temperatures of (a) 420 \({}^{\circ}\)C, (b) 460 \({}^{\circ}\)C, (c) 500 \({}^{\circ}\)C, and (d) 540 \({}^{\circ}\)C.  
Figure 9: Dependence of the mean squared error (MSE) in the hidden layer vs. the number of neurons.
# PD Detection and Analysis Triggered by Metal Protrusion in GIS Through Various Methods
## 2 Experimental Setup
### Defect Design  
We designed a metal protrusion on the conductor and the enclosure. The defect of protrusion on the conductor was fixed on the shield of GIS and the protrusion on the enclosure was fixed inside of the enclosure, over against the location of the defect on the conductor.  
The inside radius of the enclosure was 406 mm, and the diameter of the shield was 196 mm. The defect was a cylindrical steel needle with a diameter of 0.8 mm, and the tip radius was 0.4 mm.  
Photographs of the metal protrusion defects are shown in Figure 3. The lengths of the defects on the conductor were 3.5, 6 and 12 mm, and that on the enclosure was 12 mm.  
The simulated results for the distribution diagrams of the electric field intensity of different defects under voltage of 100 kV are shown in Figure 4. As shown in the figure, the electric field at the defect is very high due to either the protrusion on the conductor or enclosure, which makes the field uniform in GIS distortion.  
Figure 3: Pictures of metal protrusion defects: (**a**) protrusion on the conductor; (**b**) protrusion on the enclosure.  
Figure 2: Scheme of the detection system.  
Figure 5 shows the distribution curves of the electric field intensity under different sizes and defect locations. In the figure, D denotes the distance to the surface of the shield. It can be seen that the electric field changes more severely with longer protrusions. The maximum electric field under the defect of protrusion on the conductor is higher than that of the enclosure. The maximum field intensity for a 12 mm protrusion on the conductor is about 7 kV/mm, which is 5 times higher than that with no defect. Also, the maximum field for protrusion on the conductor is 3.68 times higher than that of the enclosure when the defects are both 12 mm.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 3 Simulations
### Transfer Response Analysis  
The main cause for the deterioration in noise reduction performance when the head rotates is the variation of the transfer responses, including the transfer responses \(\mathbf{S}_{\mathrm{p}}\) for physical plants, the transfer responses \(\mathbf{S}_{\mathrm{v}}\) for virtual plants and the transfer responses \(\mathbf{G}\) between the virtual microphone locations and the physical microphone locations in the primary field. However, the transfer responses \(\mathbf{S}_{\mathrm{p}}\) almost remain unchanged during head rotation, so only the variation curves of the other two transfer responses of a single channel system are shown in Figure 5. Since the primary diffuse field contains multiple sound field samples, the variation curves of the transfer response G are given in Figure 5c,d for each of the five sound field samples, which are chosen from 30 primary sound field samples. In Figure 5c, Sample 1 displays the maximum variation of magnitude among 30 primary field samples while Sample 2 displays the minimum variation of magnitude. In Figure 5d, Sample 3 displays the minimum variation of phase among 30 primary field samples while Sample 4 displays the maximum variation of phase. It can be concluded that the transfer response \(\mathbf{S}_{\mathrm{v}}\) varies more with the head rotation than the transfer response G, thus only these kind of transfer responses are analyzed and optimized later. Figure 6 demonstrates the variation curves of transfer responses \(\mathbf{S}_{\mathrm{v}}\) for single channel system and dual-channel system with two layouts when head rotates, where the subscript \(i\) and \(j\) (\(i,j=1,2\)) of \(S_{\mathrm{v}\overline{i}}\) indicate that the transfer response is between the \(i\)th secondary source and the \(j\)th virtual microphone. The dual-channel system with layout A has the maximum variation of both magnitude and phase, with a maximum variation of 6.2 dB in amplitude and 1.0 rad in phase. As for the dual-channel system with layout B, similar to the single channel system, the variation curves are also symmetrical about the rotation angle of 0. Its maximum difference in amplitude is 4.1 dB, less than the 5.5 dB of the single channel system, while the phase variation curve is very similar to that of the single channel system and varies very little, with a maximum difference of only 0.6 rad.  
Figure 5: Variation of the transfer responses for virtual plants with human head rotation: (**a**) magnitude; (**b**) phase. The transfer responses between the virtual microphone locations and the physical microphone locations in the primary field: (**c**) magnitude; (**d**) phase.
###### Abstract  
The dewatering of foundation pits with a suspended waterproof curtain causes different groundwater drawdowns inside and outside the pit, resulting in the drawdown difference between the inside and outside the pit. Maintaining a groundwater drawdown difference between the inside and outside of a foundation pit can eliminate the adverse effects of dewatering on the surrounding environment. According to previous studies on unsteady flow, an analytical solution of the groundwater drawdown with a suspended waterproof curtain under unsteady flow has been proposed. The analytical solution of unsteady flow and the formula of groundwater drawdown difference with a suspended waterproof curtain were validated by comparing pumping tests and finite-element method (FEM), in which a good agreement was observed. The magnitude of the drawdown difference generally represents the extent of surrounding groundwater affected by groundwater drawdown inside the pit. This paper also investigated the effects of sensitivity parameters on the drawdown difference for minimizing the effect of surrounding environment. During the process of dewatering with a suspended waterproof curtain, the groundwater drawdown (\(S_{h}\)) should not exceed the length of the waterproof curtain (\(L\)), and the optimal radius of foundation pit (\(R_{w}\)) and length of waterproof curtain (\(L\)) were found, i.e., \(R_{w}/H_{0}=0.781\) and \(L/H_{0}=0.813\) (\(H_{0}\) is 32 m). Beyond these values, the drawdown difference tends to be stable. The drawdown difference is also significantly affected by the dewatering time. When \(t<48\) h, the groundwater drawdown difference decreases rapidly; when \(t>48\) h, the groundwater drawdown difference stabilizes.  
Keywords:foundation pit dewatering; suspended waterproof curtain; phreatic aquifer; analytical method; drawdown difference +
Footnote †: journal: Applied sciences
## 4 Experimental Validation
Figure 6: \(Acc_{y}\) analysis of HCOA-SACDC approach using dissimilar TR/TS dataset sizes. A detailed precision-recall inspection of the HCOA-SACDC method on various forms of datasets is described in Figure 7. It can be observed that the HCOA-SACDC approach obtained maximal precision-recall performance with all datasets. Next, a comprehensive ROC study of the HCOA-SACDC method using the distinct datasets is described in Figure 8. The results indicate that the HCOA-SACDC approach successfully categorized two different classes, namely, insult and normal, within the test dataset. Figure 8: ROC analysis of HCOA-SACDC approach using dissimilar TR/TS dataset sizes. Figure 7: Precision-recall analysis of HCOA-SACDC method using dissimilar TR/TS dataset sizes. Figure 9 demonstrates the training and validation accuracy examination of the HCOA-SACDC algorithm using dissimilar TR/TS dataset sizes. The figure shows that the HCOA-SACDC system has maximum training/validation accuracy in the classification of the test dataset. It also shows that the HCOA-SACDC system has low training/accuracy loss in the classification of the test dataset. To highlight the enhanced outcomes of the HCOA-SACDC method, a brief accuracy analysis with recent methods was conducted, and the results are presented in Table 2 and  
Figure 9: Accuracy and loss graphs of HCOA-SACDC technique using different TR/TS dataset sizes. Figure 10[23]. The results indicate that the LSTM and RNN models obtained low accuracies of 81.66% and 81.84%, respectively. This is followed by the B-LSTM and GRU models, which had moderately improved accuracies of 81.66% and 83.36%, respectively. Additionally, the BiGRNN and ODLCDC models accomplished reasonable accuracies of 93.33% and 93.76%, respectively. However, the HCOA-SACDC model achieved the maximum value with an accuracy of 94.94%. Therefore, the experimental results show that the HCOA-SACDC method has effectual outcomes in comparison to the other methods. The enhanced performance of the proposed method is mainly due to the inclusion of the HCO algorithm, which can optimally select SAE parameters. This helps to considerably reduce computation complexity and to improve the performance of the classification. Thus, the proposed method can be employed for the classification of sarcasm and to ensure security in the OSN environment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Testing Accuracy** \\ \hline B-LSTM Model & 83.89 \\ \hline Bi-GRNN Model & 93.33 \\ \hline LSTM Model & 81.66 \\ \hline RNN Model & 81.84 \\ \hline ODLCDC Model & 93.76 \\ \hline GRU Model & 83.36 \\ \hline HCOA-SACDC & 94.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Testing accuracy analysis of HCOA-SACDC technique with recent algorithms. Figure 10: Testing accuracy analysis of HCOA-SACDC technique with recent algorithms.
# Attention-Based Two-Branch Hybrid Fusion Network for Medical Image Segmentation
## 5 Disscusion  
In the current study, we have implemented the GLFUnet, a sophisticated deep neural network architecture, for the precise task of segmenting pathological images. This model synergistically combines the spatial information extraction prowess of the ConvNeXt branch with the adept global contextual information assimilation of the Swin Transformer branch. The efficacy of the model is further augmented by the concerted effort of the attentional feature fusion module and the decoder module. Following this integration, we meticulously evaluated the segmentation performance of our proposed network. Comprehensive experimental results from two distinct datasets, one specific to gastric cancer and the other to liver disorders, showcased varying complexities in terms of cancer type, density, and target size. These trials affirmed the commendable performance and adaptability of GLFUnet within the realm of medical image segmentation. In addition, we pitted GLFUnet against a roster of widely recognized segmentation models, including U-Net, ResUNet, FCN, ConvNeXt, MedT, TransUnet, SwinUnet, and TransFuse. Our comparative analysis revealed that GLFUnet not only outperforms these models but also significantly boosts the accuracy and efficiency of medical image segmentation tasks.  
A noteworthy advantage of GLFUnet lies in its innovative dual-branch hybrid network design, which effectively surmounts the challenges faced by Convolutional Neural Networks (CNNs) in grasping global context and the relative limitation of Transformer models in processing fine-grained local details. Furthermore, the integration of the Attentional Feature Fusion (AtFF) module represents another stride forward, as it introduces  
Figure 10: Comparison of line plots of ablation experiments on the gastric cancer dataset.  
Figure 9: Comparison of line plots of ablation experiments on the liver cancer dataset.  
an attention mechanism to sharpen the focus on critical features and selectively merges valuable information from features with corresponding resolutions across branches.  
Despite its robustness, GLFUnet encounters certain limitations inherent to its design: the autonomous encoding of each branch leads to increased computational demands and a higher number of parameters that require learning, contrasting with the more streamlined nature of single-branch networks. It is well-established that hyperparameters play a crucial role in dictating the performance of deep neural networks, including variables such as the number of hidden layer nodes, mini-batch size, learning rate, weight decay, and the number of training epochs. However, in this study, we confined ourselves to using default or moderate values for these hyperparameters without engaging in exhaustive tuning. This implies that there is substantial room for enhancing our results by means of hyperparameter optimization and the selection of more optimal values.
# High-Speed Visible Light Communications: Enabling Technologies and State of the Art
## 3 Enabling Technologies for a High-Speed VLC System  
A typical VLC system includes transmitter, transmission channel and receiver. The transmitter in a VLC system might be a LED or an array of LEDs. The current that flowing through the LEDs is controlled by a driver circuit so that the brightness of the radiated light can be modulated and coded. The signal is transmitted in the free space and then extracted from the modulated light beam by the receiver via signal processing, demodulation, and decoding. The receiver consists of an amplification circuit, optical filter, optical concentrators, photodiode, and a setup for demodulation of the data. And the photodiode can be a silicon photodiode, p-i-n photodiode (PIN) diode, avalanche photodiode (APD) or a camera sensor which converts radiation into photo-current. Both in the transmitter and receiver, equalization technology may be used to further enhance the system capacity. As a high-speed VLC system requires high signal-to-noise-ratio (SNR) values, line-of-sight (LOS) paths are required for most of the applications.  
As VLC has numerous inherent advantages and wider application areas when compared to the traditional wireless communication systems, it has drawn much attention from research institutes and companies worldwide, and great development have been achieved as a result. However, it still has some challenges for the VLC system in the practical process, such as a limited achievable data rate due  
Figure 2: Intelligent transportation scenario.  
to the slow modulation response of LEDs, limited transmission distance caused by the sharp decrease in illumination with distance, multi-path dispersion (MPD), fluorescent light interference (FLI) and so on. Among them, how to achieve high-speed data transmission is one of the hotspots as it is a key problem to be solved in the VLC system. In this section, the possible solutions to improve the data rate are discussed, including novel transmitter architectures, advanced modulation and equalization technology, and optical filters.