esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Ablation Study  
To verify the effects of multi-level feature fusion module and attention mechanism on the performance of MFFA-GAN, this study constructed a comparative analysis framework based on structural variants. Three sets of controlled experiments were designed through an incremental component stacking strategy: the first phase of experiments used the general U-net module (Without ELA + DRB); the second phase improved the model performance by targeting the encoder network improvement and using only the ELA attention mechanism to enhance the feature representations of the local regions (Without DRB); and in the third phase, three dense residual blocks were added to the encoding paths to validate the effectiveness of deep-level feature extraction (Without ELA). Other network parameters  
Figure 11: The OCT retinal image from Data-3 processed by different denoising methods: (**a**) ground-truth image, (**b**) NLM, (**c**) BM3D, (**d**) DrCNN, (**e**) SDSR-OCT, (**f**) DudeNet, (**g**)MDGAN, (**h**) MDRGAN, (**i**) DCANet, (**j**) DRANet, and (**k**) MFFA-GAN. The blue square represents the background area, and the red square represents the signal area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **ENL (\(\uparrow\))** & **CNR (\(\uparrow\))** \\ \hline NLM & 26.389 & 2.762 \\ BM3D & 54.281 & 3.401 \\ DrCNN & 378.078 & 4.799 \\ SDSR-OCT & 191.811 & 4.619 \\ DudeNet & 446.660 & 4.767 \\ MDGAN & 503.134 & 4.669 \\ MDRGAN & 343.154 & 4.623 \\ DCANet & 595.731 & 5.526 \\ DRANet & 222.046 & 5.141 \\ MFFA-GAN & 674.836 & 5.208 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison with different methods on Data-3.  
were unchanged in each experimental stage, and the effectiveness of each component in improving the quality of OCT image denoising was systematically evaluated through qualitative visual comparisons and quantitative metrics. The visual effect of the ablation experiment is presented in Figure 12.  
The quantitative results of the ablation experiments are shown in Table 4, where MFFA-GAN demonstrates comprehensive advantages in OCT retinal image noise reduction. Compared with other ablation configurations, the full model showed improvements in PSNR (29.117) over the \"Without ELA + DRB\" (28.741), \"Without DRB\" (28.508), and \"Without ELA\" (28.752) by 1.3%, 2.1%, and 1.2%, respectively. It also showed improvements in SSIM (0.679) by 1.5%, 8.1%, and 3.8% over the comparison group (0.669, 0.628, and 0.654). It especially performed well in the key noise suppression metric ENL. The ENL value of MFFA-GAN reached 1234.055, which was 64.1%, 452.2%, and 61.6% higher than that of \"Without ELA + DRB\" (751.870), \"Without DRB\" (223.460), and \"Without ELA\" (763.653), which fully verifies the synergistic effect of the joint optimization on image uniformity. In addition, the CNR was increased to 4.425, which was higher than the other configurations (4.112, 3.9236, and 4.269), indicating that this method can effectively retain the peripapillary structure of the retina images while suppressing the speckle noise, which provides a higher-fidelity image base for clinical diagnosis.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 3 Incorporation of By-Products
### Culinary Quality  
The effects of incorporating fruit and vegetable waste ingredients into pasta will be determined by their components. Some of these components will impart a certain color to the final product, while others will have a greater impact on its culinary characteristics. In general, these by-products are high in fiber, primarily cellulose.  
Studies on fiber enrichment in pasta have mainly focused on in vitro digestibility analysis and fibers such as resistant starch, inulin, or oat bran [101; 102; 103; 104]. Soluble fibers strengthen the gluten network or the starch-gluten-fiber matrix, reducing solid losses during cooking and slowing sugar release during in vitro starch digestion. In contrast, insoluble fibers like cellulose interfere with the formation of the gluten network and increase sugar release during digestion [102; 104]. The results showing a reduction in the predicted glycemic index with soluble fibers align with those found by [PERSON] et al. [103], although these researchers also identified antagonistic effects when different types of fibers were combined. Therefore, predicting the effects of incorporating fiber-rich by-products is challenging, as the outcome depends on both the fiber content and the type of fiber and its combinations.  
It should also be noted that there are few studies on the effects of cellulose, one of the main fibers present in fruit and vegetable waste, or its impact on pasta quality. The only study analyzing the inclusion of a cellulose-rich fiber, such as pea fiber, found minimal changes in the texture or culinary quality of pasta (spaghetti) at inclusion levels up to 12.5%, but it did not assess factors such as the form or size of this type of fiber [104]. On the other hand, a fiber with a high water absorption capacity, such as guar gum, results in lower cooking losses, pasta with higher moisture content, a softer and stickier texture, and less elasticity, although these effects depend on the amount added.
## 4 Results  
### Remotely-Sensed LAI Estimates  
In this study, 16 images with a high overall quality between green-up and maturity in 2014 were acquired, and the inversion of LAI was carried out by the PROSAIL radiation model based on the LUT method. The LAI estimated maps were validated by comparing the LAI values measured in the 55 sample plots at seven phenological stages with the corresponding values estimated from the LAI maps. The results (Table 5) showed that the estimated LAI at seven key phenological stages have high  
Figure 3: Flowchart of the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4 DVar) strategy with the CERES-Wheat crop model.  
correlation with the measured LAI, with an R\({}^{2}\) value of \(>\)0.54. A \(t\)-test [82] was used to determine whether the fitting curves between the measured LAI and the estimated LAI were the best fit. The linear regression formulas all passed the \(t\)-test (\(p<0.05\)) with no significant difference. The accuracies of the estimated LAI were also all high, with the relative error (RE) values of \(<\)12.18% and the RMSE values of \(<\)0.51. Additionally, the total accuracy of the estimated LAI is also high, with an RE value of 9.44% and an RMSE value of 0.39 (Figure 4). The validation results showed that the remotely-sensed LAI maps accurately represented the LAI values during the given time periods and the temporal changes in LAI and could be employed in crop model data assimilations.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 4 Experimental Evaluation
### Experimental Methods and Environment
To evaluate the efficiency of SPinDP, we consider three Storms: Ethernet-based, IPoIB-based, and RDMA-based Storms. In the experiments, we rapidly generate input data consisting of random integers, real numbers, and text combined in 1 KB-sized virtual streams without latency to verify the distributed processing performance of SPinDP. For each Storm, we run the purification algorithms and compare the stream processing throughput and latency of each. In order to obtain the maximum performance in SPinDP, we apply RDMA-based Storm and locality-aware dynamic grouping together for experimentation. As a performance comparison target, the existing Ethernet-based and IPoIB-based Storms use the same hardware environment and shuffle grouping. In each environment, experiments are performed for the purification algorithms KSample, systematic sampling, query filtering, and Bloom filtering, and each algorithm is implemented in a Storm Topology form like that shown in Figure 2. The stream processing throughput and latency are measured for five minutes.
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
###### Abstract  
In the construction of deep foundation pits, early warning measures are essential to reduce construction risks and prevent personnel injuries. In underground structure and pressure analysis, soil layer and support structure data are indispensable. Therefore, soil layer reconstruction serves as a critical step, while sparse borehole data limit the accuracy of traditional reconstruction methods. This paper proposes a machine learning-based soil layer reconstruction method to address this issue. First, various types of borehole and soil layer data are generated by simulating the formation process of Earth's soil layers, thereby providing sufficient training data. Subsequently, a coding algorithm is designed to extract soil layer features as inputs for the convolutional neural network. Finally, 3D meshing is performed on the soil layer generated from real boreholes, and soil model rendering is achieved through a voxel clustering algorithm. The algorithm achieved an accuracy rate of over 90% in tests and demonstrated excellent robustness. By applying this algorithm, we successfully reconstructed the soil layers at a typical foundation pit site in a Chinese city, validating its effectiveness in real-world scenarios and its potential for large-scale engineering applications.  
soil layer visualization; soil layer reconstruction; sparse borehole coding; CNN; foundation pit engineering Article
## 3 Cache Strategy Formulation Based on Stackelberg Game
As the follower, the UAV utilizes a greedy algorithm to obtain the current optimal caching decision based on the current rental prices of each content provider. Subsequently, the content provider calculates its own profit based on the current caching decision of the UAV and adjusts the rental prices of each file according to its current profit. Finally, the UAV formulates the optimal caching decision based on the new file rental prices, and this process iterates until the profits of the content providers are equal. The specific process is illustrated in Algorithm 1. For the Stackelberg-Bertrand game, assuming the amount of content is \(F\), and an initial price is set for each file during leader pricing initialization, which has a time complexity of \(O(F)\). When the UAV makes the cache decision, it sorts all files according to the cache revenue and selects the maximum value set that meets the cache capacity limit, and the corresponding time complexity is \(O(F\log F)\). In the revenue calculation process of the content provider, the revenue of cached files is calculated according to the cache decision, the price is adjusted according to the revenue feedback, and the time complexity is \(O(F)\). Assuming that the iteration t converges, the total time complexity is \(O(t\cdot F\log F)\). ```
Input: Initial pricing of content provider file \(v_{\kappa,f}^{initial}\) Output: UAV optimal cache strategy \(\mathbf{C^{*}}\), Optimal file pricing for content provider \(v_{\kappa,f}^{*}\)
1. Initialize the pricing of file \(v_{\kappa,f}^{(i)}=v_{\kappa,f}^{initial}\), UAV cache strategy \(\mathbf{C}^{(i-1)}\)\(\leftarrow\)\(\mathbf{0}\)
2. Repeat:
3. Determine the current caching strategy \(\mathbf{C}^{(i)}\) based on \(v_{\kappa,f}^{(i)}\), and calculate the current content provider revenue \(U_{cp_{\kappa}}^{(i)}\) according to (15)
4. While \(c_{\kappa,f}=0\)
5. If \(v_{\kappa,f}^{(i)}>v_{\kappa,f}^{0}v_{\kappa,f}^{(i)}=v_{\kappa,f}^{(i)}- \epsilon_{\kappa}\)
6. else \(v_{\kappa,f}^{(i)}=v_{\kappa,f}^{0}\)
7. end if
8. end while
9. Until \(\left|U_{cp_{1}}^{(i)}-U_{cp_{2}}^{(i)}\right|<\varepsilon\)
```  
**Algorithm 1** Content Provider Pricing and UAV Cache Placement Algorithm
# Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares
## 2 Materials and Methods
### Drone Network Architecture and Communication
The experimental layout, consisting of the NodeMCU Lua Wi-Fi board and the micro-SD card module mounted on a breadboard, is shown in Figure 5a. Arduino code was used to program the Wi-Fi boards. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Mission Phase** & **Mean Flight Time** & **Standard Deviation** \\ \hline Take off + climb (30 m) & 8.24 s & 0.193 \\ Cruise segment (3000 m) & 232 s & 0.187 \\ Descent + landing (30 m) & 12.12 s & 0.085 \\ Transfer data (3 points) & 120 s & - \\ Total flight on square cell & 1308 s & 0.651 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Flight test results. Figure 4: Square-shaped flight mission profile. A protective mounting case (Figure 5b) for the Wi-Fi boards was designed using the SolidWorks version 2016 software (Dassault Systemes, Waltham, MA, USA), and then 3D printed using material extrusion technology from PLA (BCN3D Technologies, Barcelona, Spain) on a BCN3D Sigma R19 printer (BCN3D Technologies, Barcelona, Spain). The Wi-Fi board, micro-SD module, SD card, connection wires, and power cable weighed a total of 21.8 g, thus representing an increase of 2.4% in the total weight of the drone.
## 1 Introduction  
The Unit Commitment Problem (UCP) has garnered substantial attention from both industrial and academic communities due to its critical role in power system operations. Fundamentally, the UCP involves determining the optimal operation schedule of generating units across multiple time periods while accommodating fluctuating load demands and adhering to various operational constraints [1]. Over the years, diverse methodologies have been developed to address this complex problem, including Lagrangian relaxation [2; 3], priority list methods [4], dynamic programming [5], and Mixed Integer Programming (MIP) approaches. The advancement of MIP theory, coupled with the enhanced efficiency of modern branch-and-cut solvers [6; 7], has significantly increased the popularity of MIP-based solutions for UCPs. Given the NP-hard (non-deterministic polynomial-time hard) nature of UCP computations [8], substantial research efforts have been directed toward improving computational efficiency through tighter problem formulations [9] and Linear Programming (LP) relaxation [10]. In recent years, the emergence of learning approaches has opened new avenues for enhancing UCP solution processes [11; 12; 13], offering promising directions for future research. Notably, Graph Neural Networks (GNNs) possess a unique structural advantage due to their inherent permutation invariance [14]. This characteristic enables GNNs to generalize more effectively in UCP tasks compared to other neural network architectures [15]. Consequently, this has led to the development of numerous methods that leverage GNNs to assist MIP formulations in recent years.
## 2 Materials and Methods
### Spectral Data Processing  
To establish a calibration model to quantify alfalfa nutritive parameters, different chemometric strategies were assayed. The software Unscrambler X version 10.4 was employed to find the linear correlation between the NIRS spectra and nutritive parameters. This software takes NIRS spectra and transforms them into a matrix with X and Y variables, defined as the wavelength and reflectance. To detect potential spectral outliers, a principal component analysis was performed on the calibration set, before regression models were constructed using partial least squares (PLS) [16]. An optimal number of PLSs factors is determined using the Unscrambler X software version 10.4 package, based on the minimum residual variance and 20 segments. Different spectrum mathematical pretreatment strategies were used for NDF, MC, and CP quantification. These approaches were performed using both the full range of equipment, from 900 to 1700 nm, and a reduced one, from 900 to 1600 nm.  
To establish a successful model, a combination of pretreatments, including scatter correction with the standard normal variate (SNV), and the first and second Savitzky-Golay derivatives (SG), were tested in this study. Six possible pre-treatments were developed for three parameters, studying two possible wavelength ranges, for both the raw and milled samples. A total of 72 mathematical pre-treatments were assayed.  
For the evaluation and selection of the most suitable chemometric model, the following statistics were calculated: the coefficient of determination for calibration (\(R^{2}\), see Equation (2)), and the standard error of calibration (_SEC_, see Equation (3)) [17].  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{2}\]  
\(y_{i}\) are the reference values obtained in the laboratory, \(\hat{y}_{i}\) is the prediction of the model, and \(n\) represents the number of samples used in the calibration set.  
\[SEC=\sqrt{\frac{1}{n}}\ \sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2} \tag{3}\]  
This parameter provides an average of the typical uncertainty for future sample prediction based on the \(\hat{y}_{i}\) and \(y_{i}\) values for the \(i\)th sample.  
For each parameter evaluated, the best mathematical pre-treatment was selected for the raw and ground NIR sample analysis. The criteria for the selection of these pre-treatments were based on the lowest values of calibration standard error (_SEC_), as well as the values closest to one for the calibration determination coefficients (\(R^{2}\)) [18].
# Learning Swimming Techniques by Means of Real-Time Monitoring with Embedded Devices
## 2 Materials and Methods
### Software Analysis
#### 2.2.3 Dynamic Time Warping (DTW)  
This algorithm is used to measure the similarity between two temporal sequences that may vary in speed or length. It allows non-linear alignments by compressing segments of the sequences to find a suitable match.  
The Dynamic Time Warping (DTW) algorithm constructs a cost matrix, where each element represents the sum of distances between corresponding points in the two sequences. It determines an optimal warping path through a dynamic implementation.  
For the Voting routine, the algorithm considers the series of mean values recorded during the Training routine, as well as the confirmed and desired input value series, which are verified by the movement detection feature.  
A software-based approach (pseudocode) to the Dynamic Time Warping is presented in Figure 5.  
Once the two series are confirmed, the execution of Dynamic Time Warping (DTW) follows. Three separate computations of DTW are performed for each axis: X, Y, and Z of the accelerometer values. The final result of the computation is the DTW distance for each axis.  
Using these values, a percentage is calculated to determine the similarity of the entire movement per device. This percentage falls within the range of 0 to 100, where 0 indicates no similarity between the two series, and 100 signifies a perfect match.  
The calculated similarity score is then sent to the feedback webpage for further analysis.  
As a mathematical view, Dynamic Time Warping can be used as shown below.  
Given two sequences:  
\[X=[x_{1},x_{2},\ldots,x_{N}] \tag{3}\]  
\[Y=[y_{1},y_{2},\ldots,y_{M}] \tag{4}\]  
where \(N\) and \(M\) indicate the lengths, \(DTW\) constructs a cost matrix \(D(i,j)\) where each elemet represents the minimum cumulative cost to align \(X_{i}\) and \(Y_{j}\).  
The DTW distance is computed as follows:  
\[D(i,j)=d\big{(}x_{i},\,y_{j}\big{)}+min\begin{cases}D(i-1,\,j)\,\,(insertion)\\ D(i,\,j-1)\,\,(deletion)\\ D(i-1,j-1)\,\,(match)\end{cases} \tag{5}\]  
where  
* \(d\big{(}x_{i},\,y_{j}\big{)}\) is the local distance between points \(x_{i}\) and \(y_{j}\) (usually Euclidean distance: \(d\big{(}x_{i},y_{j}\big{)}=\big{|}x_{i}-y_{j}\big{|}\));
* \(D(i,j)\) is the accumulated cost at each step;
* The min operation ensures the optimal alignment path is chosen. Conditions:
* \(D(0,0)=0\) (start of the alignment);
* \(D(i,0)\) and \(D(0,j)\) are initialized as infinity to prevent empty alignments.  
After the construction of the \(DTW\) matrix, the final \(DTW\) distance is the minimum cumulative distance to optimally align the two sequences:  
\[DTW(X,Y)=D(N,M) \tag{6}\]
# A Parallel Two-Stage Iteration Method for Solving Continuous Sylvester Equations
## 1 Introduction
In this paper we have proposed a parallel algorithm of two-stage iteration for solving large-scale continuous Sylvester equations with the combination of the HSS iteration method and the SYMMLQ algorithm. The main idea is to split the coefficient matrices into a symmetric and an anti-symmetric matrix, respectively. Then, the original equations are transferred into symmetric matrix equations which are solved by the SYMMLQ algorithm. Furthermore, we focus on the improvement of the parallel efficiency of the SYMMLQ algorithm by adjusting the calculation steps. The remainder of this paper is organized as follows. In Section 2, a description of the two-stage iteration method is presented based on a splitting method and the SYMMLQ algorithm for solving the continuous Sylvester Equation (1). Then the parallel implementation of the algorithm is given in Section 3. Its convergence analysis and numerical examples are mentioned in Sections 4 and 5, respectively. We end with conclusions. Notation in this paper: \(A^{\mathrm{T}}\) denotes the transpose of matrix \(A\); inner product using \([A,B]=tr(A^{\mathrm{T}}B)\); matrix norm of \(A\) induced by \(\|  
More details can be found in [24; 28; 29; 31]. Then, the continuous Sylvester Equation (1) can be written as the following matrix equation:  
\[M_{1}X+XM_{2}=N_{1}X+XN_{2}+F. \tag{3}\]  
Given an initial guess \(X^{(0)}\in\mathbb{R}^{n\times n}\), because the initial guess has an effect on the convergence speed of the algorithm, it has little influence on the calculation results. For convenience, the initial guess is taken as \(X^{(0)}=O\) in the numerical examples. For \(k=0,1,2,\cdots\), we use the following iteration scheme until \(\{X^{(k)}\}\) converges:  
\[M_{1}X^{(k+1)}+X^{(k+1)}M_{2}=N_{1}X^{(k)}+X^{(k)}N_{2}+F. \tag{4}\]  
Let \(\widetilde{F}^{(k)}=N_{1}X^{(k)}+X^{(k)}N_{2}+F\). Then, the outer iteration can be expressed as  
\[M_{1}X^{(k+1)}+X^{(k+1)}M_{2}=\widetilde{F}^{(k)}. \tag{5}\]  
We need to solve Equation (5) at each step of the iteration method so as to form the two-stage iteration method. Equation (5) is computed by the SYMMLQ algorithm, since the new coefficient matrices are symmetric.
# Weak Signal Processing Methods Based on Improved HHT and Filtering Techniques for Steel Wire Rope
## 2 Experiments
### Time and Frequency Analysis
**(a)** Signal 1 of one-wire broken defect from sample 1 with liftoff distance of 13 mm; **(b)** Signal 2 of three-wire broken defect from sample 1 with liftoff distance of 13 mm; **(c)** Defect signal 3 from sample 2 with liftoff distance of 13 mm and coil sensor; **(d)** Defect signal 4 from sample 3 with liftoff distance of 13 mm; **(e)** Defect signal 5 from sample 3 with liftoff distance of 20 mm; **(f)** Defect signal 6 from sample 2 with liftoff distance of 20 mm and coil sensor. The signals 1, 2, 4, and 5 are detected with the sensor of the inductive transducer, while signals 3 and 6 are tested with copper coils. defect signal and the interference components, especially for the signals shown in Figure 3a-c,e.f. Comparing these STFT spectrum and the typical time domain signals, it can be found that the common signal filtering technique are incapable of processing these mixed and weak signals.
## 2 Materials and Methods
### Adsorption Experiment  
To investigate the adsorption of representative chemical agents (L, VX, GB, and GD) by MGONCs, a solution of dichloromethane (200 \(\mu\)L) mixed with each chemical agent (1 \(\mu\)g) was injected into a centrifuge tube containing MGONCs (10 mg). The centrifuge tube was then placed in a 5350 oscillating heater (Eppendorf Company, Hamburg, Germany) at 1000 rpm oscillation. Different levels of adsorption time and temperature were fully optimized. The centrifuge tube was removed after the capturing process and placed in a magnetic frame (Magnetic 3700 Gauss, Dynamag-2, ThermoFisher Company, Waltham, MA, USA). The supernatant was carefully collected using a pipette. Finally, the amounts of L, VX, GB, and GD were estimated by analyzing the supernatant solution using gas chromatography-mass spectrometry (GC-MS).