esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Expression Analysis of BnRSH Genes  
Total RNA was extracted from the _B. napus_ organs using TRI Reagent (Sigma-Aldrich, Poznan, Poland), according to the manufacturer's protocol. RNA was analysed by spectrophotometric measurement and gel electrophoresis in 1% agarose gel in 1x TAE (Tris-Acetate-EDTA) buffer stained with ethidium bromide. Prior cDNA synthesis from 1 ug of RNA genomic DNA was removed using RNAse free DNase I (Thermo Fisher Scientific, Waltham, MA, USA). Further oligo(dT)\({}_{18}\) primer and RevertAid reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA) were used for cDNA synthesis, in accordance with the protocol described in [84].  
Semi-quantitative RT-PCR (sqRT-PCR) assays were performed to evaluate the effects of NaCl and/or the presence of PGPR on mRNA level of \(RSH\) genes. For each pair of primers, the PCR conditions, including the concentration of primers, DNA polymerase, and Mg\({}^{2+}\), annealing temperature, and the number of cycles, were optimised according to [85]. The relative expression level of \(BnRSH1\), \(BnRSH2\), \(BnRSH3\), and \(CRSH\) genes, was expressed as a ratio of the amount of PCR product for analysed gene to the amount of PCR product for the reference gene. \(B\). _nupus_ actin-7 (\(BnAc\), NCBI GenBank accession no. XM_013858992.2) was used as a reference gene. The PCR reaction mixture contained: 1.25 U of Opti_Ta_ DNA polymerase (EURx, Gdarsk, Poland), 1.5 \(\mu\)L of cDNA as the template, 0.15 \(\mu\)M of each primer, and 1.5 mM MgCl\({}_{2}\), in a total reaction volume of 20 \(\mu\)L. Primers are listed in Table 2. The thermal cycling conditions were as follows: 95 \({}^{\circ}\)C for 30 s, 54 \({}^{\circ}\)C (\(BnRSH2\), \(BnRSH3\)), 52 \({}^{\circ}\)C (\(BnCRSH\)), or 58 \({}^{\circ}\)C (\(BnRSH1\)) for 40 s, and 72 \({}^{\circ}\)C for 40 s for 26 cycles (\(BnAc\)), 39 cycles (\(BnRSH1\)), 33 cycles (\(BnRSH2\), \(BnRSH3\)), and 37 cycles (\(BnCRSH\)). Products of sqRT-PCR were separated on a 1.5% agarose gel with EtBr in TAE buffer and quantified by intensity using the ImageGauge 3.46. software (FujiFilm, Tokyo, Japan). Each reaction was repeated three times.
# SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
## 1 Introduction
The InSAR phase-filtering method in the wavelet domain was first proposed by [PERSON] et al. [20]. Modeling and reduction of SAR interferometric phase-noise in the wavelet domain and a phase-filtering method using wavelet packet were introduced [21]. [PERSON] et al. [22] proposed an improved method of the adaptive switching median filter, which takes into account the corresponding coherence map values. [PERSON] et al. [23] presented a sparse regulation approach, which performs phase noise reduction and despeckling. The wavelet transform allows maintaining of the spatial resolution in the filtered phase interferogram. However, these methods estimate the interference phase noise from wavelet coefficients, and do not take into account the statistical properties of the InSAR interferometric noise phase. In addition, the traditional wavelet transform is isotropic, which cannot properly deal with complex multidimensional image data. It has been proven that the traditional wavelet transform can efficiently approximate signals containing point singularities [24], but cannot accurately represent the multidimensional data that contains a large number of edges and line elements, which restricts the application of image processing. Because traditional threshold filtering methods for SAR interferograms based on wavelet transform neither express information of edges and stripes, nor incorporate the statistical properties of the SAR interferometrc phase, a novel filtering method is proposed in this paper, which combines the shearlet transform with the phase standard deviation for InSAR interferograms. In the proposed method, the filtering intensity depends on the coherence and look number of the phase noise. In addition, this method does not require any windowing process, so it is not affected by the window size and can preserve more fringe details.
## 2 System Model
### Communications Model  
The vehicle offloads the computation task to the MEC server via RSU, and the server computes the results and then feeds them back to the vehicle via RSU. This process ignores channel interference [17]. According to the Shannon formula, the rate at which the vehicle transmits data to the RSU is defined as:  
\[R_{m}=B_{m}log_{2}(1+\frac{L_{m}P_{m}}{B_{0}}) \tag{1}\]  
where, \(B_{m}\) denotes the subchannel bandwidth between the vehicle and the MEC server, \(L_{m}\) denotes the channel gain between the vehicle and the RSU, \(P_{m}\) denotes the vehicle's transmitter power, and \(B_{0}\) denotes the power of the Gaussian channel white noise.  
Figure 1: Network model.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 5 Future Research Issues  
We introduced the characteristics of routing protocols in recent years through classification: topology aware routing, position aware routing, cluster-based routing and beaconless opportunistic routing. In each protocol category, we list the latest research and introduce the characteristics of each protocol category in detail. The problems to be solved for each kind of protocol are also expounded. In addition, we need to point out that most of the routing protocols we discussed before have a common premise that the network is fully connected. However, in the real environment, due to the large distance between UAV nodes, communication links are likely to be interrupted, and many routing protocols will fail [54]. What is more notable is that in many routing protocols, UAV nodes are assumed to move in two-dimensional space, so as to facilitate experiments and simulation. However, in the real UAV movement in the three-dimensional environment, some of the environmental conditions are even very bad [55], and the communication links between UAVs are extremely vulnerable to the interference of environmental factors such as weather, terrain and height. Therefore, we must find a way to build a more suitable movement model for UAV nodes to overcome these problems.  
In this section, we summarize five challenging research directions in future and we hope to provide interested research direction for other researchers, as Table 6 shows.
## 3 Results
### Correlation of ARFI Values with Metavir Score and Treatment Duration  
In the treatment group (Table 3), 19 of the 34 patients (55.9%) received segmentectomy for hepatocellular carcinoma (HCC). All of them had been receiving anti-HBV therapy at the time of histology analysis and ARFI study. Twenty-two patients (64.7%) received Entecavir, eight (23.5%) received Tenofovir, two (5.9%) received Telbivudine, one (2.9%) received pegylated interferon 2a, and one (2.9%) received Adefovir/Telbivudine combination therapy.  
Figure 4: Correlation of Mean ARFI with FibroScan in 27 cases without anti-HBV therapy. The Pearson correlation is 0.794 with a \(p\) < 0.001 (2-tailed).  
Figure 3: Mean ARFI values of each METAVIR fibrosis score. A significant correlation was found between mean ARFI values with Metavir grading in the non-treatment group ((**a**) [PERSON]’s rho correlation coefficients 0.597, \(p\) < 0.001) and treatment group ((**b**) [PERSON]’s rho correlation coefficients 0.587, \(p\) < 0.001).  
The mean ARFI was negatively correlated with treatment duration ([PERSON]'s rho correlation coefficients \(-\)0.428, \(p\) < 0.012). According to our unpublished experiences for those patients with chronic hepatitis B with an ALT level lower than 180 IU/L, the cut-off values for prediction of Metavir fibrosis scores 0, 1, 2, 3, 4 are 1.105, 1.218, 1.343, and 1.478 m/sec, respectively. When ARFI predicted that Metavir fibrosis scores were correlated with histology Metavoir score and duration of therapy, the ARFI value tended to be equal (3/5, 60%) or higher (2/5, 40%) than the corresponding Metavir score for treatment duration within 12 months, equal between 13 and 31 months (9/12, 75%), and frequently lower (12/17, 71.4%) for treatment duration greater or equal to 32 months (\(p\) = 0.0012, Table 3).
###### Abstract  
Uninary volatile compounds (VCs) have been recently assessed for disease diagnoses. They belong to very diverse chemical classes, and they are characterized by different volatilities, polarities and concentrations, complicating their analysis via a single analytical procedure. There remains a need for better, lower-cost methods for VC biomarker discovery. Thus, there is a strong need for alternative methods, enabling the detection of a broader range of VCs. Therefore, the main aim of this study was to optimize a simple and reliable liquid-liquid extraction (LLE) procedure for the analysis of VCs in urine using gas chromatography-mass spectrometry (GC-MS), in order to obtain the maximum number of responses. Extraction parameters such as pH, type of solvent and ionic strength were optimized. Moreover, the same extracts were analyzed using Proton Nuclear Magnetic Resonance Spectroscopy (\({}^{1}\)H-NMR), to evaluate the applicability of a single urine extraction for multiplatform purposes. After the evaluation of experimental conditions, an LLE protocol using 2 mL of urine in the presence of 2 mL of 1 M sulfuric acid and sodium sulphate extracted with dichloromethane was found to be optimal. The optimized method was validated with the external standards and was found to be precise and linear, and allowed for detection of \(>\)400 peaks in a single run present in at least 50% of six samples--considerably more than the number of peaks detected by solid-phase microextracton fiber pre-concentration-GC-MS (328 \(\pm\) 6 vs. 234 \(\pm\) 4). \({}^{1}\)H-NMR spectroscopy of the polar and non-polar extracts extended the range to \(>\)40 more (mainly low volatility compounds) metabolites (non-destructively), the majority of which were different from GC-MS. The more peaks detectable, the greater the opportunity of assessing a fingerprint of several compounds to aid biomarker discovery. In summary, we have successfully demonstrated the potential of LLE as a cheap and simple alternative for the analysis of VCs in urine, and for the first time the applicability of a single urine solvent extraction procedure for detecting a wide range of analytes using both GC-MS and \({}^{1}\)H-NMR analysis to enhance putative biomarker detection. The proposedmethod will simplify the transport between laboratories and storage of samples, as compared to intact urine samples.  
Keywords:liquid-liquid extraction; volatile compounds; urine; method optimization; GC-MS; \({}^{1}\)H-NMR +
Footnote †: journal: Journal of the American Chemical Society
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 3 Multi-Level Trust Model and Problem Statement
### Multi-Level Trust Representation Model and Problem Statement  
In this work, a social network is modeled as a probabilistic directed graph \(G=(V,\ E)\), where \(V\) is nodes set and \(E\) is direct links set. All edges in E have corresponding probability values. Suppose \(p\) and \(q\) are two nodes in \(V\) and there is a direct link \(A\) between these two nodes. The trust information of \(A\) has \(k+1\) levels from trust Level 0 (totally distrust) to trust Level \(\mathbf{k}-1\) and an uncertainty level \(\mathbf{u}\), as demonstrated in Figure 1.  
Trust information vector of link \(A\) is indicated as \(\text{dir}(A)\ =\ \left(t_{A,0},\ t_{A,1}\ldots,t_{A,k-1},\ u_{A}\right)\), here \(0\ \leq\ t_{A,0}\), \(t_{A,1}\ldots,t_{A,k-1}\), \(u_{A}\ \leq 1\) and \(\sum_{i=0}^{k-1}t_{A,i}+u_{A}=1\). \(t_{A,i}\) represents the trust probability of \(A\) occupying level \(i\). Particularly, \(t_{A,0}\) represents the probability of totally distrust. \(u_{A}\) is the probability of uncertainty (meaning that party \(p\) has no idea or is confused about party \(q\)).  
MDD is one of the reliability models. It consists of a set of decision branches and two sink nodes, which represents the system is in a certain state or not [36]. Usually a system includes many multi-state components. Suppose \(A\) is one of the multi-state components and \(x_{A}\) is corresponding multivalued variable. The MDD for \(A\) has \(k\) outgoing edges. The \(j\)-edge (\(0\leq j\leq k-1\)) represents the \(j\)th value of \(x_{A}\). Each non-sink node in the MDD encodes a multi-valued function using the case format as Equation (1):  
\[\begin{split} F=A_{0}*F_{x_{A}=0}+A_{1}*F_{x_{A}=1}+\ldots+A_{k-1 }*F_{x_{A}=k-1}\\ =\textit{case}&\big{(}A,F_{x_{A}=0},\ F_{x_{A}=1}, \ldots,F_{x_{A}=k-1}\big{)}\\ =\textit{case}&(A,\ F_{0},\ F_{1},\ldots,F_{k-1}) \end{split} \tag{1}\]  
The operating rules of MDD can be referred to in [37].  
Figure 2 shows the MDD representation of link \(A\) with \(k+1\) trust levels.  
Problem Statement  
Given a social network \(G=(V,\ E)\), \(p\), \(q\in\mathrm{V},\ \exists\) for at least one path from \(p\) to \(q\). The problem is to calculate the probability of each trust level toward user \(q\) from user \(p\)'s perspective. The notations used in this paper are shown as Table 1.
## 4 Results and Discussion
[29], who used a custom CNN which performed an accuracy of 0.9280 and F1 score of 0.9430, and [PERSON] et al. [22], who also employed a custom CNN but did not report an F1 score. Similarly, our results outperform [PERSON] et al. [46], who combined a CNN with SVM and achieved an accuracy of 0.9200 without reporting an F1 score. Compared to ensemble methods, such as [PERSON] and [PERSON] [24], our approach demonstrated a higher accuracy of 0.9642 compared to 0.9503 and similar F1 scores of 0.9640 compared to 0.9604. This suggests that our model is more streamlined while maintaining comparable or better performance than more complex architectures. Instead of developing custom CNNs, which demand extensive hyperparameter tuning and large training datasets for effective generalization, our approach leverages transfer learning with a pre-trained DenseNet201 model. The utilization of features pre-trained on the extensive ImageNet dataset facilitates efficient training and robust performance, even  
Figure 4: Grad-CAM heatmaps demonstrating model focus in pediatric pneumonia diagnosis. with limited medical data, representing a substantial advantage in medical imaging where the acquisition of large, annotated datasets is often both challenging and costly. While custom CNNs trained on limited data are susceptible to overfitting and poor generalization, transfer learning with DenseNet201 provides a strong inductive bias, promoting better generalization and faster convergence. The incorporation of multi-scale convolutions and the Mish activation function further refine feature extraction from chest X-rays, surpassing standard transfer learning performance. This combined strategy of transfer learning with targeted architectural modifications offers a more efficient and effective alternative to building custom CNNs from scratch. A clear progression in performance metrics can be observed over recent years as models have evolved to leverage larger datasets and more advanced architectures. For instance, early approaches, such as [PERSON] et al. [47] using ResNet50 in 2021, achieved an accuracy of 0.9306 and an F1 score of 0.9271 on 5232 images. Similarly, [PERSON] et al. [44] in 2022 achieved an accuracy of 0.9391 and an F1 score of 0.9343 using ensemble methods. However, more recent studies, such as [PERSON] and [PERSON] [42] in 2022, who reported an accuracy of 0.9626 accuracy and an F1 score of 0.974, and [PERSON] [28] in 2024, who reported an accuracy of 0.9503 accuracy and an F1 score of 0.9603, reflect a steady improvement in results, primarily due to advancements in model architectures and training strategies. Hybrid models, such as the one proposed by [PERSON] et al. [46], which integrate CNN with SVM, leverage the improved classification boundaries provided by SVM. However, their relatively lower accuracy of 0.9200 and omission of F1 scores raise concerns about their ability to manage class imbalances effectively. Similarly, ensemble methods such as those employed by [PERSON] and [PERSON] [24] and [PERSON] et al. [44] leverage the diversity of models like InceptionResNetV2, DenseNet201, and Vision Transformers to
# Multiple Thermal Parameter Inversion for Concrete Dams Using an Integrated Surrogate Model
## 3 Case Study
### Predictive Performance Analysis of Different Surrogate Models
continuously increasing the sample size, but this implies higher FEM computational costs, contrary to the original intention of improving the efficiency for parameter inversion. In this study, the parameters to be inverted for the two types of concrete are the same, but the differences in thermal properties and construction conditions will cause differences in the inversion problem model. Therefore, the predictive performance of the individual models can be compared from two aspects: problem model and sample size. In the same problem model, as shown in Figure 7a, when the sample size is 60 or less, the predictive accuracy of the SVR model reaches its peak. While the sample size is larger than 60, the Kriging model has the highest accuracy. In Figure 7b, when the sample size is 40 or less, the Kriging model has the highest predictive accuracy; while the sample size is larger than 40, the SVR model performs the best. With the same sample size, it is equally difficult to determine which model to use when dealing with different problem models. For example, when the sample sizes are 80 and 100, the Kriging model has the highest accuracy in C35 concrete, while the opposite is true in C40 concrete. This indicates that for different inversion problem models and sample sizes, the prediction accuracy of the three individual surrogate models behaves differently, with no one consistently outperforming the other. This reflects the uncertainty of the thermal parameter inversion problem. In other words, it is risky to directly choose a particular surrogate model to replace the finite element analysis. Combined with the previous analysis, these models can perform well with an ideal size of samples. However, this is relative to some defined problem models and a defined range of variables. In reality, it is problematic that their relationship lacks a priori information, so the focus should be on ensuring the robustness of the surrogate model to weaken this effect. Figure 7 shows that for different problem models and sample sizes, the predictive accuracy is always better than or close to that of the individual model with the best prediction. Moreover, the accuracy index of the ISM has the same trend as that of the individual surrogate model, indicating that its predictive performance relies on the individual model. The ISM does not have an overwhelming improvement in predictive accuracy relative to individual ones; its most significant advantage lies in mitigating the influence caused by the aforementioned lack of prior information and improving the robustness of the model. In addition, the ISM requires a smaller sample size to achieve stable accuracy, which is an advantage for a more efficient inversion of concrete thermal parameters. Figure 7: Comparison of test results based on different sample sets. (**a,b**) describe the comparison results for C35 and C40 concrete, respectively.
## 1 Introduction
### Brain MRI Images  
MRI is the most efficient and extensively used technology for diagnosing a brain tumor. MRI offers data on the human soft tissue structure, which is magnified to provide detailed views in each direction. MRI is utilized in medical imaging to show variations in soft tissues [10]. The MRI picture is used to indicate brain tumors [7].  
MRI produces images with varying properties dependent on internal anatomical structures [8]. MRI is noted for its excellent picture clarity and tumor appearance precision. MRI alone cannot detect or diagnose malignancies; the segmentation of brain tumors is required for categorization [9; 10]. When analyzing medical images, brain tumor segmentation is critical. MRI scanning is utilized for medical diagnostics [11]. The interpretation of MRI brain tumor images presents numerous obstacles [12]. Computational intelligence and machine learning advances allow earlier detection of brain cancers. Many methods for classifying brain cancers from MR images exist. Example: Artificial Neural Networks, knowledge-based approaches, expectation-maximization (EM), Fuzzy C Means (FCM), and SVM [7; 9]. These are efficient algorithms for region segmentation and data extraction from medical pictures [12]. Brain tumors can be identified and segmented using Gaussian mixture modeling, hidden Markov random fields, and Non-Negative Matrix Factorization [13; 14]. In image processing, segmentation is a challenging task due to the inefficiency of previous research and approaches, which increase overlapping and reduce feature efficiency.  
Classification of Brain MRI Images  
The literature proposes several techniques to classify brain tumors using MR imaging. The study [5] found that integrating 3D DWT (Discrete Wavelet Transformation) feature extraction with a Random Forest (RF) classifier improved brain tumor classification accuracy. Also used for MR image categorization are SVMs [15]. The author [13] investigated a GA-SVM hybrid system using a Gaussian RBF kernel. For this purpose, GA optimization is used. However, optimal feature extraction and selection procedures require specific knowledge [15]. Deep learning-based methods like classification, reconstruction [16], and even segmentation [17] have lately become popular in medical picture analysis. The feature extraction method used by [18] was the VGG-16 model. A feature map was input into an LSTM recurrent neural network to classify brain cancers. The authors claim that pre-trained CNN models for feature extraction outperform GoogleNet [5], ResNet [19], and AlexNet [20]. The 2D CNN limits for brain tumor MRI categorization have recently been investigated [8]. White matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) may all be recognized using a voxelwise residual network (VoxResNet) based on 3D CNN-based architecture [8]. Training results with XG-Boost are dependent on optimizing the structure and lowering complexity.