esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Verification  
### The Groove Effect on the Multipactor Threshold  
For the purpose of quantifying the groove effect on multipactor, we investigate the variation tendency of multipactor threshold by constructing groove structures on the dielectric sheets. Along the transmission direction of the microwave, the alumina sheets are divided into several cells by every 0.5 mm, a rectangular groove with sizes of 0.25 mm width and 0.2 mm depth is created in each cell, as shown in Figure 5a. Here, we define the ratio of the groove projected area to the total cell area as the groove ratio, which can be expressed as \(d/w\) in Figure 5c. According to this definition, the groove ratio in Figure 5a is 50%. Similar model construction of grooves is also carried out on PTFE sheets. Via the CST simulation, we discover that the groove structure is able to increase the electric field since the grooves provide several right-angle structures, and further cause local enhancement of the electric field due to the complex non-uniform structure. To make the process of multipactor less affected by the variation of the electric field, here, we shortened the groove length to 7.6 mm and left a 0.2 mm margin on two sides. Figure 5b,d show the shape of the grooves after modification, and it can be seen that the groove ratio becomes 47.5%.  
Figure 4: The simulated evolution of electron number for the SCLPFs with (**a**) flat alumina loaded and (**b**) flat PTFE loaded.  
Figure 5: _Cont_.  
Figure 6 shows the simulation results of the electron number evolution when the grooved dielectric (alumina and PTFE) sheets are loaded in the SCLPFs. By analyzing the results, the multipactor threshold of 240.23 W for the SCLPF with grooved alumina loaded is acquired, and 727 W for the SCLPF with grooved PTFE loaded is obtained. Compared to the original multipactor threshold values in Figure 4, 137.69 W for alumina-loaded device and 347.63 W for PTFE-loaded device, it can be concluded that the grooved sheets are able to improve the multipactor threshold remarkably despite the material type.
## 3 Adaptive FNN-FDS Hybrid Model
### Adaptive Neuro-Fuzzy Inference System  
An artificial neural network is a promising tool due to its self-learning ability, but it lacks a good way to describe the reasoning function. Conversely, the fuzzy system shows good reasoning function with no self-adaptive ability. To resolve this issue, [PERSON] et al (1993) initially developed an adaptive neuro-fuzzy inference system (ANFIS) [8] by integrating the self-learning mechanism of a neural network with the good reasoning ability of the fuzzy system. Moreover, the proposed fuzzy system is converted into an adaptive artificial neural network to carry out the self-learning process. The generalized schematic flowchart of ANFIS that includes five layers is shown in Figure 4. Both the first and the fourth nodes are self-adaptive, but the others are fixed. To make adaptive predictions of EOR performance by N\({}_{2}\) huff-n-puff in a fractured-cavity carbonate reservoir, the ANFIS with only two inputs is utilized.  
The adaptive network is a multilayer feedforward network in which nodes correspond to learning parameters. The significance of each layer will be introduced for an ANFIS model with \(n\) inputs [16; 26].  
Layer 1 is composed of the input variable membership function, which is selected to fuzzify the input signal. Layer 2 is used to release the regular strength, in which the input signals will be multiplied at the node. Layer 3 is established for normalization, and the fuzzy rule is described as follows:  
\[O_{i}^{3}=\overline{\omega}_{i}=\frac{\omega_{i}}{\omega_{1}+\omega_{2}+ \cdots+\omega_{n}},\ i=1,2,\ldots n \tag{1}\]  
Each node in layer 4 is self-adaptive, and the output is expressed as  
\[O_{i}^{4}=\overline{\omega}_{i}f_{i}=\overline{\omega}_{i}(p_{i}x+q_{i}x+r_{ i}),\ i=1,2,\ldots n \tag{2}\]  
where \(\overline{\omega}_{i}\) is the output of node, and \(\{p_{i},q_{i},r_{i}\}\) acts as post-piece parameter set.  
Figure 4: Schematic architecture of the ANFIS model.  
Layer 5 denotes a fixed node, indicating the output of signals, which can be defined as a linear combination of the post-piece parameters:  
\[O^{5}=\sum\limits_{i=1}^{n}\overline{\omega}_{i}f=\sum\limits_{i=1}^{n}(( \overline{\omega}_{i}x)p_{1}+(\overline{\omega}_{i}y)q_{1}+\overline{\omega}_{ i}r_{1}) \tag{3}\]  
where \(O^{5}\) denotes the prediction data of production by N\({}_{2}\) injection in a fractured-cavity carbonate reservoir.
## 2 Algorithms And Methods
### Related Mathematical Model  
The received signal strength (\(RSS\)) of both the networks is calculated using the following equation:  
\[RSS(dBm)=Pt(dBm)+K(dB)-10\times n\times\log 10\times[\frac{d}{d_{0}}] \tag{1}\]  
\[K(dB)=20\log 10\times[\frac{\lambda}{4}\times\pi\times d_{0}] \tag{2}\]  
False handoff initiation probability is calculated using the following equation:  
\[Pa=1-[\frac{1}{\pi}\times(a\tan\frac{a}{2d})] \tag{3}\]  
False handoff failure probability is calculated using the following equation:  
\[P_{f}=\frac{a\cos[\frac{d}{(v\times handoff_{delay})}]}{a\sin[\frac{a}{2\times d }]} \tag{4}\]  
where \(Pt=\) transmitted power of system; \(d=\) distance between MS and BT of system; \(d_{0}=\) reference distance from BT; \(n=\) path loss exponent; \(v=\) velocity of MS; and \(a=\) coverage area radius of the system.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis
### Analysis of the Supporting Performance of Self-Drilling Anchor Bolts
#### 4.3.2 Selection of a Support Scheme  
The design of anchor rod parameters requires a dynamic balance between safety and economy. For a safety-prioritized solution, it is recommended to use long anchor rods, a moderate angle, and small spacing. For example, the design in experiment No. 23, with a 10-m anchor rod length, 35-degree angle, and 1.5-m spacing, yields a corrected safety factor of 1.430. However, the number of anchor rods is relatively high, reaching 113.3, making it suitable for high-risk slope projects. For an economy-prioritized solution, a combination of short anchor rods, a large angle, and wide spacing may be chosen, such as in experiment No. 5, with a 6-m anchor rod length, 45-degree angle, and 3-m spacing. This results in a lower safety factor of 1.280 but with a significantly reduced anchor rod usage of only 34.0, making it appropriate for low-risk temporary projects.  
Considering both the anchor rod usage and safety, we selected the design with a 7-m anchor rod length, 30-degree angle, and 2-m spacing, corresponding to experiment No. 7. This solution achieves a safety factor of 1.303 and an anchor rod usage of 59.5, balancing reasonable safety with cost control.  
Stability Analysis of the Reinforced Slope with Self-Drilling Anchors: Displacement Results Analysis
## 4 Applicability of the Practical Criterion
### Influence of Low-Voltage Protection  
In the above discussion, the low-voltage protection link of the motor was not considered. Most industrial motors are equipped with a low-voltage protection. The low-voltage protection is usually achieved by electromechanical AC contactors. If the bus voltage is lower than a certain threshold, such contactors trip immediately and cut off the power supply of the motor. The tripping voltage \(U_{trip}\) of the contactor was about 0.3-0.7 pu. When the bus voltage is lower than \(U_{trip}\), the motor is cut off to prevent transient voltage instability. Therefore, the unstable region of the motor will change after considering the low voltage protection, as Figure 6 shows. If the low-voltage protection is activated, the induction motor is removed, and the motor will not stall. Therefore, in the following discussion, \(U_{sqg}\) was chosen to be greater than \(U_{trip}\).  
Figure 5: Transient voltage stability boundaries: (**a**) transient voltage stability boundaries for different values of stator reactance; (**b**) transient voltage stability boundaries for different values of rotor reactance; (**c**) transient voltage stability boundaries for different values of rotor resistance; (**d**) Transient voltage stability boundaries for different load rates.  
Figure 6: Unstable region considering low-voltage protection.
## 6 Conclusions  
In this paper, a VMD and improved KELM-based rolling bearing state identification method is proposed. To address inconspicuous fault feature signals of rolling bearings in early fault stages and the challenge of feature extraction, the VMD method and \(ApEn\) are combined to extract fault features, and a mode number fluctuation method is proposed to determine the mode decomposition number for signal feature extraction. A simulation test shows that this method is superior to conventional EMD and LMD in terms of mode anti-aliasing and anti-noise performance. When fault diagnosis is based on the KELM method, the penalty coefficient \(C\) and kernel function parameter \(\sigma\) have significant impacts on the KELM performance; employing optimal parameters is the key to improving the KELM method's state forecast accuracy. Therefore, a PSO optimization-based KELM bearing state identification method is proposed in this paper; this method optimizes the KELM parameters using a PSO optimization algorithm to obtain a KELM forecast model with optimal parameters. An analysis of rotor test bed data reveals that the proposed rolling bearing fault diagnosis method based on combination of VMD, \(ApEn\), and PSO-KELM is effective for bearing state mode identification in various states. Compared with the BP neural network and conventional ELM and SVM algorithms, the fault diagnosis method proposed in this paper has higher diagnosis accuracy and can achieve a more accurate identification of bearing fault states. In addition, state identification of the bearing in various operating conditions is also performed by using the methods proposed in this paper. The diagnostic results show that the diagnostic accuracy of the PSO-KELM algorithm is improved with an increase in rotating speed and fault dimension, the overall accuracy of PSO-KELM is greater than 80%, and the highest accuracy can reach 95.02%. The results further demonstrate the effectiveness of the methods proposed in this paper.  
The authors would like to acknowledge the National Natural Science Foundation of China (51775243, 51675035), Key Project of Industry Foresight and Common Key Technologies of Science and Technology Department of Jiangsu Province (BE2017002-2), and Fundamental Research Funds for the Central Universities (Grant No. JUSRP51732B).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# MSGCN: Multi-Subgraph Based Heterogeneous Graph Convolution Network Embedding
## 4 MSGCN Framework
### Original Graph Decomposition  
Considering many graph embedding models comprehensively, those models that use node feature information tend to get stronger embedding representations. In order to use the graph neural network to obtain the embeddings, the data must be preprocessed firstly. All methods need to ensure that the data on each node has the same dimension. Real data often have different content information. For example, some nodes may have text information, image information, or attribute information, while others may have multiple types of information. Using this content information directly is inconvenient because the dimensions of each type of content information are different. Therefore, it is necessary to unify all types of information into the same space.  
For different types of information, different methods are used to map them to a common space with the same dimension. For example, a convolutional neural network is used for image information and word2 vec [42] is used for text information. Then we can use average pooling to get the feature information \(X\) that will be input into the model. However, if the nodes have the same dimension of content information, or even if they have only one type of content information, then this step will be much easier.  
\[X=Aggregate(X_{1},X_{2},...X_{n}), \tag{1}\]  
where \(X_{i}\) represents the content information of \(i\)-th type information. \(X\) represents the aggregated node content information matrix, which will be used as the input of MSGCN model.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbols** & **Descriptions** \\ \hline \(G,G_{i}\) & Original homogeneous graph and the i-th subgraph \\ \(V\) & Node set \\ \(E\) & Edge set \\ \(E_{i}\) & The edge set of the i-th subgraph \\ \(X\) & Node content information matrix \\ \(A_{i}\) & The adjacency matrix of the i-th subgraph \\ \(W\) & Weight matrix \\ \(Z_{i}\) & The vector representation of the i-th subgraph \\ \(D_{i}\) & The degree matrix of the i-th subgraph \\ \(H\) & The final output embeddings \\ \(V_{new}\) & The new node set \\ \(A_{sample}\) & Sampled adjacency matrix \\ \(H_{new}\) & Vector representation of the new nodes \\ \hline \hline \end{tabular}
\end{table}
Table 2: The notations in this paper.  
For partial datasets with no content information, we choose to randomly initialize a content matrix \(X\). Note the content matrix \(X\) is shared in the following steps.  
Given a original heterogeneous graph \(G=(V,E,A,R)\), and there are \(n\) edge types \(R\), i.e., \(|R|=n\). Supposing the type of each edge is already known, the original heterogeneous graph \(G\) is decomposed into \(n\) subgraphs:  
\[G\to G_{1}+G_{2}+...+G_{|R|} \tag{2}\]  
where each subgraphs \(G_{1}=(V,E_{1}),G_{2}=(V,E_{2})...G_{|R|}=(V,E_{|R|})\) can be viewed as a homogeneous graph. In this way, we can successfully solve the heterogeneity of nodes and edges.  
For each \(E_{i}\) in \(G_{i}\), \(E=E_{1}+E_{2}+...+E_{|R|}\).
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Quantum Deep Learning Techniques with Copyright Detection  
#### 2.2.1 Introduction to Quantum Neural Networks (QNNs)  
Quantum neural networks (QNNs) represent an innovative fusion of quantum computing principles and classical neural network architectures, aiming to harness quantum phenomena like preposition and entanglement for enhanced machine learning capabilities [40; 41; 42; 43]. These networks typically consist of quantum circuits with parameterized gates that can be trained using variational methods, allowing for classical and quantum data processing. QNNs offer potential advantages in certain computational tasks due to quantum parallelism and interference effects but also face challenges such as limited qubit coherence times and the need for error correction in current quantum hardware. As the field of quantum computing advances, QNNs are merging as a promising avenue for exploring new approaches to machine learning problems, particularly in areas where quantum effects could provide a significant computational edge over classical methods.
# Remote Sensing Image Segmentation of Marculture Cage Using Ensemble Learning Strategy
## 3 Results
### Experimental Settings  
#### 3.2.1 Experimental Environment and Parameter Settings  
In this paper, the experimental environment is ubuntu20, the GPU is Nvidia GeForce RTX 3080, Pytorch and python3.6. For the annotation of the dataset, the labelme [22] annotation software is used to obtain mask images, and then they are converted into black and white binary maps in uint8 format as label images [23]. The Swin Transformer is available in four different sizes, namely Swin Tiny (Swin-T), Swin Small (Swin-S), Swin Base (Swin-B) and Swin Large (Swin-L). Given the performance limitations of the system, we choose Swin-T, using pretrained weights on the Ade20K dataset, which has over 25,000,000 images and 150 categories. The embedding dimension of the first stage is set to \(C=96\), the window size is set to 7, and the number of blocks and heads in each stage is set to {2, 2, 6, 2} and {3, 6, 12, 24}, respectively. We used AdamW as the optimisation function with a learning rate of \(1\,\times\,10^{-5}\)[24].
## 3 Model Validation and Discussion of the Obtained Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No** & **Section** & **Qi** & **AB** & **BCWS** & **BCWP** & **ACWP** & **\% of Execution** & **MRI** & **Cost of Remaining** & **NMC** \\ \hline
1 & R-418 & 0.736 & 234,934.8 & 117,467.4 & 17,620.1 & 30,442.4625 & 15 & 0.58 & 199,694,5578 & 345,014.575 \\ \hline
2 & R-425 & 0.519 & 165,868.0 & 82,934.0 & 8293.4 & 10,077.415 & 10 & 0.82 & 149,281.2362 & 181,393.47 \\ \hline
3 & R-435 & 0.507 & 161,973.0 & 80,986.5 & 20,246.6 & 22,028.8125 & 25 & 0.92 & \multicolumn{3}{c}{The end of iteration} \\ \hline
4 & R-425a & 0.451 & 144,169.3 & 72,084.6 & 27,031.7 & 30,164.85 & 37.5 & 0.90 & \multicolumn{3}{c}{The end of iteration} \\ \hline
5 & R-426 & 0.431 & 137,677.0 & 68,838.5 & 27,535.4 & 17,853.5 & 40 & 1.54 & 82,606.17736 & 53,560.5 \\ \hline
6 & R-428 & 0.428 & 136,767.9 & 68,384.0 & 34,192.0 & 37,423.075 & 50 & 0.91 & \multicolumn{3}{c}{The end of iteration} \\ \hline
7 & R-427 & 0.407 & 129,852.1 & 64,926.0 & 8115.8 & 9680.6875 & 12.5 & 0.84 & 113,620.5612 & 135,529.625 \\ \hline
8 & R-418b & 0.349 & 111,580.9 & 55,790.4 & 4184.3 & 2231.57025 & 7.5 & 1.88 & 103,212.2948 & 55,045.3995 \\ \hline
9 & R-419 & 0.327 & 104,564.9 & 52,282.5 & 18,296.9 & 107,256.5778 & 35 & 0.17 & 67,967.2017 & 398,381.5745 \\ \hline
10 & R-437 & 0.224 & 71,488.7 & 35,744.4 & 16,085.0 & 13,298.76 & 45 & 1.21 & \multicolumn{3}{c}{The end of iteration} \\ \hline
11 & R-426 & 0.202 & 64,657.5 & 32,328.8 & 4011.1 & 10,596.85938 & 12.5 & 0.38 & 56,575.34299 & 148,356.0313 \\ \hline
12 & R-435a & 0.114 & 36,465.9 & 18,233.0 & 2734.9 & 2139.9 & 15 & 1.28 & 30,996.0286 & 24,252.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of monitoring and control process—Iteration 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Alternative** & **Label** & **Si+** & **Si–** & **Qi** & **Rank** \\ \hline A1 & R-418 & 0.141 & 0.832 & 0.855 & 2 \\ \hline A2 & R-418b & 0.870 & 0.162 & 0.157 & 6 \\ \hline A3 & R-419 & 0.144 & 0.947 & 0.868 & 1 \\ \hline A4 & R-425 & 0.558 & 0.421 & 0.430 & 3 \\ \hline A6 & R-426 & 0.880 & 0.136 & 0.134 & 7 \\ \hline A7 & R-
## 3 Cross-Layer Design
* _Retransmission-based error recovery_: Hop-by-hop retransmission can provide loss recovery with low impact in the end-to-end delay. The type and relevance of the encoded data may be used to guide the possible number of retransmissions. * _In-network multimedia compression_: This is a cross-layer optimization in essence. When all or part of raw multimedia data is compressed by nodes other than the source, routing and transport protocols have to be employed to deliver the tasks among the nodes and coordinate the distributed compression. Note that some of these design objectives may interrelate in some works. For example, transmission rate adjustment may directly impact the energy preservation, but reduction in energy consumption may be achieved without changes in the current transmission rate. The surveyed works in next section bring contributions in one or more of the presented design objectives.
## 3 Results and Discussion
### Results for Time-Frequency-Domain
#### 3.1.2 Water-Filled Pipe in Finite Porous Medium
The relatively low-frequency part of the low-order single guided wave mode is widely used for pipeline detection. This is because the low-order guided wave modes are easily excited and reduce the complexity of signal processing. Therefore, it is particularly important to study the propagation characteristics of low-order modes. The following research work focuses on the low-order guided wave modes in a water-filled pipe embedded in porous medium, namely \(\alpha\), L(0,1), L(0,1)\({}_{E}\), and L(0,1)\({}_{P}\).
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## Appendix B  
In this appendix, the controllers and proof of Theorem 1 are presented. **Kinematic controller design**. The Lyapunov function candidate (LFC) is assumed according the idea [43] that was presented in [22],  
\[V_{K}=\frac{1}{2}x_{e}^{2}+\frac{1}{2}y_{e}^{2}+\frac{1}{2}z_{e}^{2}+(1-c \theta_{e})+(1-c\psi_{e}), \tag{104}\]  
in order to design the kinematic control algorithm. Calculating the time derivative of (104) one obtains  
\[\dot{V}_{K}=x_{e}\dot{x}_{e}+y_{e}\dot{y}_{e}+z_{e}\dot{z}_{e}+\dot{\theta}_{e }s\theta_{e}+\dot{\psi}_{e}s\psi_{e}. \tag{105}\]  
Applying Equations (18)-(22), grouping the terms, and denoting \(s\theta_{e}=s(\theta-\theta_{d})\), this time derivative can be given as follows:  
\[\dot{V}_{K} =x_{e}\big{(}u-u_{d}(c\psi_{e}c\theta_{e}+s\theta s\theta_{d}) \big{)}+vy_{e}+z_{e}\big{(}w+u_{d}(1-c\psi_{e})c\theta s\theta_{d}\big{)}\] \[\quad+(q-q_{d}-u_{d}z_{e}c\psi_{e})s\theta_{e}+(r/c\theta-r_{d}/ c\theta_{d}+u_{d}y_{e}c\theta_{d})s\psi_{e}. \tag{106}\]  
To satisfy the condition that \(\dot{V}_{K}<0\), the speeds \(u,q,r\) are defined as virtual control inputs. In turn, the desired velocities are assumed in the form [22]  
\[\mu_{dt} =u_{d}(c\psi_{e}c\theta c\theta_{d}+s\theta s\theta_{d})-k_{x}x_{e}, \tag{107}\] \[\mu_{dq} =q_{d}+u_{d}z_{e}c\psi_{e}-k_{\theta}s\theta_{e},\] (108) \[\mu_{dr} =(r_{d}/c\theta_{d}-u_{d}y_{e}c\theta_{d}-k_{\psi}s\psi_{e})c\theta, \tag{109}\]  
where \(k_{x}>0\), \(k_{\theta}>0\), \(k_{\theta}>0\) are some positive numbers. If the virtual speeds \(u,q,r\) are equivalent to the corresponding desired speeds, then applying the Equations (107)-(109) into (106) one has  
\[\dot{V}_{K}=-k_{x}x_{e}^{2}-k_{\theta}\,s^{2}\theta_{e}-k_{\psi}\,s^{2}\psi_{ e}+vy_{e}+z_{e}\big{(}w+u_{d}(1-c\psi_{e})c\theta s\theta_{d}\big{)}. \tag{110}\]Because for any cosine function \(|\cos(\cdot)|\leq 1\), thus \(z_{e}\big{(}w+u_{d}(1-c\psi_{e})c\theta s\theta_{d}\big{)}\leq|z_{e}(w+2u_{d})| \leq|z_{e}w|+2|z_{e}u_{d}|\). This implies that  
\[\dot{V}_{K}\leq-k_{x}x_{e}^{2}-k_{\theta}\,s^{2}\theta_{e}-k_{\psi}\,s^{2}\psi_ {e}+|vy_{e}|+|z_{e}w|+2|z_{e}u_{d}|. \tag{111}\]  
As direct control in the directions of movement are not guaranteed, the two lemmas are proposed. These are analogous but not the same as the lemma in [22]. **Lemma A1** (based on [44]). : _Assuming a limited disturbance function \(f_{v}\), it is possible to limit the speed of \(v\), which makes the rate of \(\zeta_{2}\) also bounded._  
Proof.
# Antisense-Mediated Down-Regulation of Factor V-Short Splicing in a Liver Cell Line Model
## 2 Materials and Methods
### F5 Transcript Analysis
#### 2.5.3 Quantification of Alternatively Spliced \(F5\) Transcripts by qPCR  
The alternatively spliced \(F5\) transcripts (full-length FV and FV-short) were quantified by real-time quantitative PCR (qPCR) with SYBR-Green detection using a LightCycler 480 Real-Time PCR instrument (Roche Applied Science). Separate amplification reactions for the full-length FV and FV-short transcripts were carried out using the second set of primers (spanning exons 13-14) described above. Each qPCR reaction was performed in a total volume of 10 \(\upmu\)L including 5 \(\upmu\)L LightCycler(r) 480 SYBR Green I Master mix (Roche), 0.5 \(\upmu\)M of each primer and 10 ng of cDNA. The qPCR conditions were as follows: Initial denaturation at 95 \({}^{\circ}\)C for 5 min, 45 cycles of 20 s of denaturation at 95 \({}^{\circ}\)C, 15 s of annealing at 60 \({}^{\circ}\)C, and 15 s of extension at 72 \({}^{\circ}\)C. The specificity of the amplified products was verified by melting curve analysis (65-95 \({}^{\circ}\)C) and by agarose gel electrophoresis. qPCR data were analysed using LC480 Conversion (Roche) and LinRegPCR [36] software. Data analysis was carried out by relative quantification using the modified \(\Delta\Delta\)C\({}_{t}\) method, described by [PERSON] et al. [36], which takes amplification efficiency into account. All qPCR reactions were carried out in duplicate.