esa-sceva/satcom-chunk-collection huggingface dataset:
# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 6 Selected MT-InSAR Techniques Using HPC or Cloud-Based Platforms  
This section presents the processing strategies for multi-temporal interferometric SAR (MT-InSAR) analysis characterized by different computational approaches, emphasizing their potentialities and limitations. Indeed, in recent years, several research groups have attempted to investigate the potential of using parallel computing in retrieving InSAR large-scale deformation information for wide areas and long time series. Indeed, both the PS and SB methods suffer from long processing times (see Section 2.6).  
In the following, we provide an overview of the current approaches for MT-InSAR processing by distinguishing (i) high performance computing approaches for efficient processing and (ii) the available cloud-based services for Earth observation (EO) data exploitation. In the former case, the focus is on the parallel performance; in the latter case, the emphasis is on data-centric logic for handling large geospatial databases.
# CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction
###### Abstract
The unfolding-deep-learning-based approach is of great interest to the mathematical community and for scientific research since, by exploiting the physical model of the CT inversion and the regularization properties imposed on the solution by the prior, it maintains coherence between the reconstruction and the sinogram; therefore, it is more explainable and exhibits higher stability properties than other deep learning methods. **Contributions**. The aim of this work is to propose an accurate and robust unfolded approach, named CTprintNet in the following, for the reconstruction of CT images from sparse views. The main ingredients of the CTprintNet architecture are those of the iRestNet approach, proposed in [21] for image deblurring, which unrolls the proximal interior point algorithm for the solution of a regularized minimization problem and learns the regularization parameter of the model, as well as two more proper parameters of the algorithm. We consider as prior a smooth generalization of the TV regularizer, which is very effective in medical imaging, in particular in CT, at both reducing the noise and preserving the edges of objects, such as masses or fibers, which is useful for diagnosis [28; 29; 30]. We conducted tests of CTprintNet in the presence of different noise intensities with respect to the noise used for training to check the stability property of the framework. Our network proves to be very stable with respect to the noise on the input sinogram; when compared to an image-to-image method, it shows greater robustness with respect to noise. **Organization of the paper**. This paper is organized as follows. In Section 2, we briefly recall the CT imaging problem and reformulate it as an inverse problem. The optimization method exploited to address its solution is detailed in Section 3, while the CTprintNet architecture built by unfolding the iterations of this method is illustrated in Section 4, together with some implementation choices made for the numerical tests. The experimental results are presented in Section 5. Finally, some concluding remarks and considerations are given in Section 6.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters
### Chemical Spraying  
Chemical spraying using quadcopters has been increasingly adopted in smart agriculture by researchers and commercial markets. Being equipped with various sensors, quadcopters can reduce the use of fertilizers and pesticides and increase efficiency.  
Two carbon fiber quadcopters were built in [103] to evaluate the spray quality of close formation spraying by applying indoor and outdoor trials in Guangzhou and Changji, China. The two quadcopters were used to collaboratively evaluate the droplet amount, density, and distribution form over different overlapping agricultural areas.  
A machine-learning system was applied in [48] with the aid of the commercial quadcopter DJI Phantom 3 Pro, equipped with a 4K camera, to capture images and distinguish between spray and non-spray areas in agricultural croplands and orchards.  
The commercial 3 DR Iris+ quadcopter was equipped with a Raspberry Pi NoIR camera in [106] to detect areas deficient in chemicals, using image processing, and identify where spraying is needed.  
The authors in [99] designed and built a quadcopter for pesticide spraying in agriculture areas in a semi-autonomous way.  
Figure 9: Different custom-built quadcopters used in various agricultural missions: (**a**) furrow irrigation management ([PERSON] et al., 2016 [96]); (**b**) weed detection and herbicide spraying ([PERSON] et al., 2021 [102]); (**c**) pneumatic planting system ([PERSON] et al., 2022 [104]); (**d**) precision agriculture in a rice field ([PERSON] et al., 2023 [105]).
## 2 FEM-SPH Conversion Coupling Algorithm
### FEM-SPH Conversion Principle  
The FEM-SPH conversion algorithm was based on equal effect variation; that is, specifically, when the finite element mesh reached the set equal effect variation threshold, it replaced the Lagrange finite element mesh in the form of the SPH integral to participate in the subsequent calculation [21]. The FEM-SPH conversion process is shown in Figure 1. The whole object was initially a finite element mesh. During the analysis process, some meshes were excessively distorted and converted into SPH particles. C and D elements associated with node N1 reached the equivalent strain conversion threshold, met the conversion conditions, and the element failed; however, A and B elements did not meet the conditions, and the element did not fail. Class I SPH particles were generated at the interface node between the failed element and the non-failed element, and these particles were fixed at the interface node. The units associated with node N2 failed, and class II SPH particles were generated at this node, which could move freely.  
Figure 1: Conversion of FEM to a SPH particle.  
After the conversion of FEM to SPH particles, the newborn SPH particles will inherit the physical quantities of the failed nodes and participate in the subsequent calculations, and the assignment process is as follows:  
\[x_{p}=x_{n} \tag{1}\]  
\[m_{p}=m_{n}=\sum_{i=1}^{N_{e}}\frac{1}{N_{n}}\rho_{ei}V_{ei} \tag{2}\]  
\[v_{p}=v_{n} \tag{3}\]  
\[\sigma_{p}=\sigma_{n}=\frac{1}{N_{e}}\sum_{i=1}^{N_{e}}\sigma_{ei}=\frac{1}{N_ {e}}\sum_{i=1}^{N_{e}}\sum_{j=1}^{N_{g}}\omega_{gj}\sigma_{gj} \tag{4}\]  
where, \(x\), \(m\), \(\rho\), \(V\), \(v\) and \(\sigma\) are the position, mass, density, volume, velocity, and stress tensor, respectively. Subscripts \(p\) denote SPH particles, subscripts \(n\) denote finite element nodes, subscripts \(e\) denote finite cells, and subscripts \(g\) denote Gaussian integration points within the finite element. \(N_{e}\) is the number of finite cells associated with a node; \(N_{n}\) is the number of individual finite cell nodes. \(\sigma_{ei}\) is the unit stress tensor. \(\sigma_{gj}\) and \(\omega_{gj}\) is the stress tensor and weighting factor at the Gaussian integration point \(g\) inside the unit \(i\). \(N_{g}\) is the number of Gaussian integration points within a single finite element.  
Since the concept of SPH smooth length does not exist in Lagrange finite elements, the newly generated SPH particles cannot be directly inherited from the finite element nodes, instead, they must be calculated using the law of mass conservation:  
\[h_{p}=\frac{\alpha}{N_{e}}\sum_{i}^{N_{e}}r_{0i}(\frac{\rho_{0i}}{\rho_{i}})^{ \frac{1}{2}} \tag{5}\]  
where, \(\rho_{0}\) and \(\rho\) are the initial and current densities, respectively. \(r_{0}\) is the initial cell size. \(a\) is the smooth length to cell size ratio factor. \(d\) is the dimensionality.  
Finite Element Model of Bank Slope-Bridge Pile Foundation Interaction under Reservoir Water Level Cycling Conditions
## 4 Simulation and Result  
In this section, the effectiveness of the HDE algorithm is proved by simulating the jamming results of fake targets and scenes, and the algorithm's computational complexity is analyzed. In the simulation process, the jamming signal is first generated by the jammer according to the received SAR signal, and then the SAR radar performs motion compensation imaging. The jamming object is an X-band nonlinear-trajectory SAR operating in strip mode. Table 1 shows the main parameters of the simulation. It is assumed that the radar trajectory has a trajectory deviation relative to the nominal straight trajectory. The function of the trajectory deviation in the horizontal direction is \(y(x)=0.1 sin(pi/200x)+0.1 cos(pi/600x)+0.1 rand\), and the function of the trajectory deviation in the vertical direction is \(z(x)=0.15 sin(pi/200x)+0.15 cos(pi/600x)+0.1 rand\), where \(rand\) denotes the uniformly distributed random variable, the maximum trajectory deviation is 0.5 m, as shown in Figure 7. The settings of the above parameters all satisfy the validity constraints of the HDE algorithm in Section 3.2.
## 2 Results and Discussion
### Optimization of Extraction Parameters
#### 2.1.1 Liquid-Liquid Extraction  
In the initial stage, procedures based on liquid-liquid extraction with ethyl acetate as the organic phase were carried out. To select the most optimal extraction conditions, the influence of the matrix pH on extraction efficiency was evaluated. To acidify or alkaline the test sample, 0.1 M HCl or 25% ammonia solution was used. The best results were obtained when the pH of the sample matrix was between 5.5 and 5.8. This was probably due to the pKa values of the tested analytes, indicating that DHPG (\(-\)3, 9.21) and MHPG (\(-\)3, 9.91) are weak acids. For VMA, the pKa values (\(-\)4.1 and 3.11) indicate that it is a stronger acid (Table 1). Considering the log P values (\(-\)0.72 for DHPG, 0.11 for MHPG, and 0.43 for  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Analyte** & **Chemical Structure** & **pKa Values** & **Log P** & **Water Solubility** \\  & & & **(Experimental)** & **[g/L]** \\ \hline \multicolumn{5}{c}{**Zwitterion compounds**} \\ \hline DHPG & HO & OH & \(-\)3 & \(-\)0.72 & 16.7 \\  & HO & & 9.21 & \(-\)0.72 & 16.7 \\ \hline MHPG & OH & \(-\)3 & \(-\)3 & \(-\)0.11 & 8.99 \\  & HO & & 9.91 & 0.11 & 8.99 \\ \hline \multicolumn{5}{c}{**Compound with the acidic nature**} \\ \hline VMA & OH & \(-\)4.1 & 0.43 & 5.16 \\  & HO & & 3.11 & 0.94 & 5.16 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The analytes and their physicochemical parameters.  
VMA), DHPG shows the most polar properties among the investigated compounds, while VMA is the least polar.  
Based on the extraction efficiency of LLE procedures (see Table 2), it was determined that the optimal pH range for DHPG, VMA, and MHPG is between 5.5 and 5.8. For single LLE extractions, the best results were obtained at pH 4.6 for DHPG, pH 5.5 for MHPG, and pH 6.0 for VMA. The R values were 159.4% (\(\pm\)10.5), 125.2% (\(\pm\)5.9), and 197.8% (\(\pm\)5.7), respectively, which allowed for signal enhancement by 1.6 times for DHPG and 1.2 times for MHPG, and nearly a two-fold concentration increase for VMA. However, for double LLE, the highest R values for all analytes were obtained at pH 5.8. These results were more favorable than those from single LLE, enabling signal amplification by up to 2.2 times for DHPG and 1.6 times for VMA. In contrast, neutral and alkaline conditions proved unfavorable for MHPG extraction. A similar extraction method was used by [PERSON] et al. for the analysis of plasma levels of HVA, free MHPG, total MHPG, and 5-HIAA in healthy individuals [30]. Ethyl acetate was applied for analyte extraction, with the optimal pH of 5.5 achieved by adding 1 mL of 0.5 M acetate buffer. However, it should be noted that LLE-based procedures are not highly efficient, do not allow for significant preconcentration of analytes, and require relatively large volumes of organic solvents. For these reasons, LLE is rarely used for the analysis of DHPG, MHPG, or VMA.
# High-Quality Classroom Dialogue Automatic Analysis System
## 2 Related Work
### Artificial Intelligence in Classroom Analysis  
Recent advancements in classroom analysis can be categorized into three key domains: natural language processing (NLP), computer vision (CV), and large language models (LLMs), each offering unique contributions to improving teaching and learning environments. In the realm of NLP, [PERSON] et al. [17] introduced a hybrid CNN-BiLSTM-Attention model that effectively captures both local and global features within a seven-class encoding framework for classroom dialogue. Similarly, [PERSON] et al. [26] developed an automated teaching analysis system that uses a 3D-CNN and Conv2 DLSTM to enhance real-time dialogue classification. These works primarily focus on assigning labels to sentences in large-scale dialogue, often neglecting the importance of speaker information. However, recent studies [27; 28; 29] have shown that integrating speaker transitions significantly boosts the accuracy of dialogue classification. For example, [PERSON] et al. [30] enhanced performance by combining speaker, semantic, and contextual information within a hierarchical label dependency framework. In the field of CV, gesture and behavior detection are gaining prominence. [PERSON] et al. [31] introduced GestureDet, a real-time gesture detection model based on MobileNetV2-SSD, which employs multidimensional attention mechanisms to achieve high accuracy in recognizing classroom gestures, even in resource-limited environments. Similarly, [PERSON] et al. [32] proposed SBD-Net, a lightweight model for detecting student behavior, integrating advanced feature fusion and attention mechanisms to maintain low computational complexity while improving detection accuracy in smart classrooms. Shifting to the use of LLMs, recent innovations have explored their role in classroom simulations and dialogue analysis. For instance, SimClass [33] introduced a multi-agent classroom simulation framework powered by LLMs, allowing for real user participation and replicating classroom dynamics. Moreover, [PERSON] et al. [34] demonstrated the effectiveness of GPT-4 in analyzing classroom dialogue, highlighting substantial time savings and strong alignment with human coders, thereby illustrating LLMs' potential in facilitating teaching evaluation and improving educational outcomes. Together, these works demonstrate the broad applicability of NLP, CV, and LLMs in enhancing classroom analysis, offering more personalized, real-time, and efficient educational support.
## 3 Results and Discussion
### In Vivo Toxicity Test  
The _C. elegans_ nematode model was used to evaluate in vivo toxicity of the GNPs. The use of this type of in vivo model is very useful and cost-effective for toxicity testing [34]. Furthermore, this nematode shares human proteins, lipids, genes, and signaling ways, and its digestive system possesses many aspects that are similar to those of mammals [17; 35; 36; 37; 38]. Additionally, _C. elegans_' genomics is used for the study of human development and disease [17]. Besides, it presents not many ethical difficulties. In contrast to cytotoxicity assays, in vivo toxicity tests performed with _C. elegans_ provide data from a complete animal [18]. Thus, the survival rate of the worms was analyzed after an exposure of 24 h (acute toxicity) and 72 h (chronic toxicity) to several concentrations of GNPs ranging from 12.5 to 500 \(\upmu\)g/mL (see Figure 7).  
Figure 6: Gene expression in human keratinocyte HaCaT cells exposed at two non-cytotoxic concentrations (0.005 and 0.01 \(\upmu\)g/mL) of graphene nanoplatelets (GNPs) for 24 h. Data are shown as mean \(\pm\) standard deviation from three replicates. Thirteen genes (_SODI_, _CAT_, _MMP1_, _TGFB1_, _GPX1_, _FN1_, _HAS2_, _LAMBI_, _LUIM_, _CDH1_, _COLA41_, _FBN_, and _VCAN_) are analyzed. The results of the statistical analysis are presented as fold-change of control and relative expression to actin beta (ACTB). * \(p\) < 0.05; **_p_ < 0.01; ***_p_ < 0.001, ***_p_ < 0.0001, n.s: not significant.  
GNPs can be used in vivo at higher non-toxic concentrations as expected. Thus, exposure of _C. elegans_ to 25 and 12.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for 24 h showed no significant in vivo toxicity. However, at a higher exposure time of 72 h (chronic toxicity), only a concentration of 12.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of GNPs showed non-cytotoxic effects. Since the concentrations of GNPs at 500, 400, 250, 200, 150, and 100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of GNPs show toxic effects at 24 h, the chronic toxicity of these concentrations was not evaluated at 72 h of exposure.  
According to the current studies carried out in this emerging research field, the generation of reactive oxygen species (ROS) in target cells is proposed as the most important cytotoxic mechanism of graphene materials [28]. The capacity of cell proliferation and up-regulation of the six genes of human keratinocytes (_SOD1_, _CAT_, _TGFB1_, _FN1_, _CDH1_, and _FBN_) can also be attributed to the ROS generation of the GNPs. However, further studies are necessary to better elucidate the toxicity pathways on a molecular level.  
Therefore, GNPs used at non-cytotoxic concentrations are very promising functional two-dimensional materials for biomedical applications.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results
### Comparing Algorithms
#### 4.1.1 Linear Regression Analysis Results  
Linear Regression analysis is a fundamental statistical technique to model the relationship between a dependent variable and one or more independent variables. This algorithm serves as a baseline for comparing the performance of more complex models.  
The scatter plot (Figure 4a) illustrates the relationship between the actual and predicted 'Time Factor' values during the training phase. Each point represents an observation, with the red dashed line indicating a perfect fit. Similar to the training performance, Figure 4b scatter plot shows the testing phase results, comparing actual and predicted 'Time Factor' values. Figure 4c visualizes the validation phase results, highlighting how closely the model predictions match the actual values.  
The scatter plot generated by the Linear Regression model visualizes the relationship between the actual and predicted 'Time Factor' values. Each point represents an observation, with the x-axis displaying the actual 'TF' values and the y-axis displaying the predicted 'TF' values.  
The red dashed line is a reference line representing a perfect fit. Points lying on this line have predictions that match the actual values perfectly.  
The angle between perfect fit and best fit, as defined by the following mathematical equation, is illustrated below:  
\[\theta=\arctan\biggl{(}\frac{m_{2}-m_{1}}{1+m_{1}m_{2}}\biggr{)} \tag{25}\]  
where \(m_{1},~{}m_{2}\) are the slopes of the two lines.  
We notice that as the \"TF\" value increases, data points tend to approach a state of \"perfect fit\". However, there appears to be a significant error level for a substantial number of data records, particularly those below a \"TF\" value of 700.  
The Linear Regression model provided a straightforward benchmark for evaluating other algorithms. It demonstrated moderate accuracy with some degree of prediction error. Despite its simplicity, it highlighted the linear relationships among the variables. However, its performance was limited in capturing complex, non-linear patterns in the data, as seen in Table 3 below.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Metric** & **Training** & **Testing** & **Validation** \\ \hline Mean Squared Error (MSE) & 2963.599 & 2892.051 & 2781.079 \\ Root Mean Squared Error (RMSE) & 54.439 & 53.778 & 527.36 \\ Mean Absolute Error (MAE) & 40.530 & 38.114 & 36.905 \\ Median Absolute Error (MedAE) & 32.210 & 25.299 & 23.322 \\ Mean Squared Logarithmic Error (MSLE) & 0.008 & 0.007 & 0.007 \\ Root Mean Squared Logarithmic Error (RMSLE) & 0.087 & 0.086 & 0.085 \\ Mean Bias Deviation (MBD) & 0.010 & 0.009 & 0.008 \\ Mean Absolute Percentage Error (MAPE—Validation) & 6.30\% & 5.95\% & 5.77\% \\ Huber Loss (Validation) & 40.034 & 37.619 & 36.410 \\ Angle between Perfect Fit and Best Fit (degrees) & 0.066 & 0.048 & 0.061 \\ C-index & 0.70 & 0.70 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Linear Regression analysis.
# Prediction of Friction Resistance for Slurry Pipe Jacking
## 4 The Verification of the Effectiveness of the Proposed New Approach
### Comparison between the Predicted Friction Resistances and the Numerical Simulation Result
For the result of numerical simulation, the decrease of jacking force in the initial stage of drives (before 62.5 m) is explained by authors that the weight of the pipe jacking machine (the material of which is steel, featuring a larger unit weight) directly pressing on the soil, causing a larger face resistance. After the drives of 62.5 m, the influence exerted by the machine weight decreased gradually and the increase of jacking force is caused by the accumulation of friction resistance [25]. In other words, the slope of regression from drives of 62.5 to 100 m represents the friction resistance estimated by numerical simulation (\(f_{\text{num. }}=64.4\) kN/m). It is obvious that both the predicted friction resistances of the analytical equations (\(f_{\text{cal. }}=57.7\) kN/m) and the numerical simulation (\(f_{\text{num. }}=64.4\) kN/m) are acceptable as compared to the measured data (\(f_{\text{mea. }}=41.5\) kN/m). However, better accuracy (especially towards the first 80 m of drives) is obtained by the approach of this paper. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Geology** & \(h\) **(m)** & \(D_{p}\) **(m)** & \(\Delta R\) **(mm)** & \(\gamma\) **(kN/m3)** & \(c\) **(kPa)** & \(\varphi\) **(\({}^{\circ}\))** & \(E_{s}\) **(MPa)** & \(v_{s}\) & \(e\) \\ \hline Gravel & 13.0 & 2.85 & 10 & 21 & 15 & 37 & 33 & 0.20 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The details of the slurry pipe jacking in Taichung Science Park. Figure 7: The setting of pipe-soil contact range and frictional coefficient in the numerical simulation. (**a**) 1/3 of the pipe-soil contact; (**b**) 1/2 of the pipe-soil contact; (**c**) 1 of the pipe-soil contact. The predicted accuracy of friction resistance by the numerical simulation approach is highly affected by the setting of pipe-soil contact angle. The pipe-soil contact angle of 71 degrees is that calculated by the analytical equations for this field case. Thus, it seems that the pipe-soil contact angle of 120 degrees (1/3 contact) in the simulation is set too large, resulting in the calculated result slightly larger than the measured data. From this point of view, the numerical simulation approach can accurately predict the friction resistance, but the contact angle of pipe-soil in the simulation needs to be set reasonably to obtain good prediction results. Conversely, in the approach of this paper, the pipe-soil contact angle is theoretically calculated with respect to soil property, overcut, pipe diameter, etc., while human factors or empirical factors can be eliminated as far as possible.