esa-sceva/satcom-chunk-collection huggingface dataset:
# MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization
## 2 Topology Optimization
### Problem Formulation  
The definition of the structural optimization problem requires introducing: a function (known as objective function) that refers to the criterion to be optimized, design unknowns (known as design variables), and state quantities. The criterion \(F\) that is to be optimized represents an objective metric that could either be maximized or minimized. Such an objective could be the material volume or the stiffness of the structural system. Moreover, the structural design domain and the state quantities associated to the criterion need to be defined. The design variable \(x\) represents unknown parameters that are required in order to describe the design of the structure; it could represent, for example, the geometry. The state quantity \(y\) represents the structural response quantities, which may represent strain, stress or displacement.  
\[\begin{split}\min_{x\in S}F(x,y(x))\\ \text{subject}\quad\text{to}:\\ \text{state}\quad\text{constraints}\quad\text{on}\quad y(x)\end{split} \tag{1}\]  
where the objective function \(F(x)\) (e.g., stiffness of the structure, and material volume) is to be optimized, and \(S\) is the design space of the unknowns' vector \(x\). The state quantities depend on the design variable \(y(x)\). The objective function is subjected to the design variables and state quantities constraints to lead the optimization process to the desired solution. A so-called state function \(g(y)\) that corresponds to a certain state quantity can be devised, for example, as a deformation quantity along a certain degree of freedom (DoF). This state function \(g(y)\) can be integrated as a constraint function to the structural optimization problem, where it is commonly expressed such that \(g(y)\leq 0\), for example, the case where \(g(y)\) is expressed by the displacement quantities vector \(g(U(x))\) in a discrete finite element problem. In STO problems, the design variable \(x\) represents the presence or absence of material in a specific part of the design domain. It might involve features such as the size and/or the number of holes in the design domain. The general formulation of a STO problem is summarized below:  
\[\begin{split}\min_{x\in[0,1]}F(x)\\ subject\quad to:\\ K(x)\cdot U(x)=P\\ g(x)\leq 0\end{split} \tag{2}\]  
where \(x\) refers the vector of unknowns, i.e., the density values of the finite elements, \(K\) denotes the global stiffness matrix of the structural system, vectors \(P\) and \(U\) contain the loads and displacements, respectively, and \(g(x)\) refers to the vector of constraint functions (volume fraction, etc.).
## 4 The IDRR Traffic Control Algorithm
### IDRR Model Layout  
The layout discretization proposed in [16] for IDRR uses WP stations and TX crossings as nodes, as in the top-right plot of Figure 1. The movement of an AGV can be characterized as transitioning from node to node through aisles, entering each node by the entry CP, and leaving it by the exit CP.  
This type of discretization becomes essential in the formulation of the traffic rules of IDRR, because TX nodes are used as conflict resolution areas. Hence, denser discretizations require a reformulation of IDRR by considering that conflict resolutions are only possible in TX-nodes, not in the additional intermediate ones eventually introduced in aisles, as in the bottom-right plot of Figure 1. IDRR's current traffic rules would be valid under denser discretizations just when these new nodes also have room enough for conflict resolution, but this is not generic; in fact, it could be considered as a change of layout.
# Parameterized Optimization in Uncertain Graphs--A Survey and Some Results
## 2 Distribution Models for Uncertain Graphs
### Linear Reliable Ordering (LRO) Model  
The case when the partition of \(E\) consists of only one set, the SBD model is called the Linear Reliability Ordering (LRO) model introduced by [PERSON] et al. [11]. Let \(p(e_{m})<p(e_{m-1})<\ldots<p(e_{2})<p(e_{1})\). Then, under the LRO model it follows that for each \(j>i\), \(\Pr(e_{j}\text{ fails }\mid e_{i}\text{ fails })=1\). Further, the possible worlds is the set of \(m+1\) graphs \(\{G_{0},\ldots,G_{m}\}\) where \(E(G_{0})=\emptyset\), \(E(G_{m})=E\), and for each \(0\leq i\leq m-1\), \(E(G_{i+1})=E(G_{i})\cup\{e_{i+1}\}\). The following lemma shown by [PERSON] et al. [11] regarding the probability distribution on the possible worlds uniquely defined by the LRO model.  
**Lemma 1** ([PERSON] et al.[11]).: _For \(0\leq i\leq m\), the probability of the possible world \(G_{i}\) is given by_  
\[P(G_{i})=\begin{cases}1-p(e_{1})&\text{if }i=0\\ p(e_{i})-p(e_{i+1})&\text{if }1\leq i<m\\ p(e_{m})&\text{if }i=m.\end{cases}\]  
Coming back to the SBD model, on the uncertain graph \(\mathcal{G}=(V,E,p)\) and \(E\) partitioned into \(\{E_{1},E_{2},\ldots E_{\ell}\}\) for some \(\ell\geq 1\), the probability of an outcome \(H\) under the SBD model naturally follows from Lemma 1. The idea is to consider an outcome of the SBD model as \(\ell\) independent samples from the LRO model on the uncertain graphs \((V,E_{1},p_{1}),(V,E_{2},p_{2}),\ldots,(V,E_{\ell},p_{\ell})\), where for each \(1\leq i\leq\ell\), \(p_{i}\) is the function \(p\) restricted to \(E_{i}\).  
This completes our description of the distribution models known in the literature. We next present two dynamic programming algorithms on the input uncertain graph \(\mathcal{G}=(V,E,p)\), when the graph \(G=(V,E)\) is presented as a nice tree decomposition. The algorithms compute an expectation under the RF model and have worse running times than that of corresponding algorithms for computing the expectation under the LRO model.
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Complete Process of the Proposed Algorithm  
The mixed noise filtering algorithm with salt and pepper + Gaussian noise proposed in this paper is composed of the above three improved algorithms. The specific process of the algorithm is shown below:  
Step 1. Input the original image and add mixed noise to the image: Density (salt and pepper noise)/Variance (Gaussian noise) = \(\rho/\sigma\);  
Step 2. Use the improved median filter algorithm to filter the noise image and obtain the preliminary denoised image;  
Step 3. Perform wavelet decomposition with \(n\)-scale on the preliminary denoised image; the selection of decomposition scale \(n\) will be explained in the experiment part.  
Step 4. Use the new threshold function and the new threshold to process the high-frequency components obtained from each layer of wavelet decomposition.  
Step 5. Reconstruct the high-frequency components after threshold processing and the unprocessed low-frequency components to obtain a reconstructed image1.  
Step 6. Perform wavelet decomposition with 1-scale on the reconstructed image1, and then process the low-frequency components with the improved NLM algorithm to filter out the Gaussian noise remaining in the low-frequency components. At the same time, continue to perform the new wavelet threshold function to process the high frequency components.  
Step 7. Reconstruct the low-frequency components and the high-frequency components that have been processed individually to obtain the reconstructed image2, which is the final denoised image.  
The whole process of the proposed algorithm in this paper is shown in Figure 5.  
Figure 4: Comparison of the weight function.
# Dual Clustering-Based Method for Geospatial Knowledge Graph Partitioning
## 2 Methods
### Geographic Node Partitioning Based on Density Peak Clustering  
To improve the efficiency of a spatial query, it is crucial to assign spatially adjacent nodes to the same partition. Therefore, GKGP-DC uses spatial clustering to pre-analyze the spatial structure of data. To find the natural spatial clusters from geographic nodes, we use a well-established spatial clustering algorithm, i.e., density peak clustering (DPC) [52], to cluster geographic nodes. DPC can naturally identify the clustering structure and automatically determine the number of clusters. The DPC algorithm is based on two fundamental observations: (i) the local density of the cluster center is higher than that of the surrounding samples;  
Figure 1: Comparison of partitioning methods when ignoring and considering spatial information.  
Figure 2: Flowchart of GKGP-DC.  
(ii) the distance between cluster centers is relatively large. DPC determines the cluster centers by identifying the density peaks in the dataset, and then defines the cluster boundaries based on density and relative distance. Inspired by DPC-KNN [53] and an adaptive cluster center selection method proposed by [PERSON] et al. [54], we partition the geographical nodes in the following two steps:
# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
## 4 Discussion  
The results reported in the previous paragraph show excellent performances of the MLMVN-based classifier both in training and in validation.  
During the training procedure, the neural classifier with 75 neurons in the hidden layer allows a classification rate of 92% in the learning phase and 91.66% in the test phase. Comparable results can be obtained by increasing the number of neurons in the hidden layer, but this produces a greater difference between the two phases. Therefore, the generalization capability of the neural network decreases by using more than 75 neurons, which means that the CR, obtained by processing new measurements during validation, decreases. Figure 11 summarizes the heuristic procedure used to select the best number of neurons.  
As regards the validations, it can be stated that the MLMVN confirms a classification rate higher than 90% using the same conditions as the training, while in the second validation, there is a reduction of up to 86.66%. These results show the possibility of obtaining good performances even without introducing numerous environmental conditions into the dataset used during the training phase.  
However, one consideration is needed: observing the results obtained for each class of failure, it can be stated that the main problem is to correctly classify the presence of malfunctions on \(C_{2}\). This aspect is not particularly important when the environmental conditions are similar to those used in training but becomes relevant otherwise. In fact, two false negatives are presented in Figure 10b, and this could be a problem for practical applications. Therefore, even if the classification rate does not decrease significantly, it is advisable to use a dataset with various environmental conditions during the training phase.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Hyperparameters** & **Learning Phase** & **Test Phase** & **Validation 1** & **Validation 2** \\ \hline MLMVN & 75 Neurons & 92\% & 91.66\% & 96.66\% & 86.66\% \\ SVM & 13 Support Vectors & 88.7\% & - & 93.33\% & 83.33\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Simulation Results.  
Figure 10: Comparison between MLMVN and SVM; **(a)** performance obtained by processing measurements under the same conditions of the training phase; **(b)** performance obtained by processing measurements with different irradiance and temperature values.
## 1 Introduction  
Microalgae comprise a vast group of both marine and freshwater habitats organisms. Based on DNA sequence data, they have been classified into ten major phyla which are Glaucophyta, Euglenophyta, Cryptopbyta, Haptophyta, Dinophyta, Heterocontophyta (including diatoms), Rhodophyta, Chlorophyta, and the prokaryotic Cyanophyta (cyanobacteria) [1]. In recent years, a focus has been shifted towards these organisms due to their diverse metabolic contents. Indeed, they synthesize a large number of bioactive compounds, including pigments, sterols, polyphenols, fatty acids, proteins, vitamins, alkaloids, and sulfated polysaccharides with various chemical structures and biological activities [2,3]. These biomolecules are useful for microalgal-based feed and food, nutraceuticaltical, pharmaceutical, and cosmetic industries through their antioxidant, antifungal, antibacterial,antiviral, antienzymatic, anti-cancer, or anti-inflammatory activities [4; 5; 6; 7]. Several studies have reported the therapeutic properties of _Spirulina_ and _Nostoc_ species and their ability to scavenge superoxide and hydroxyl radicals and inhibit lipid peroxidation [8; 9]. Besides, due to their rich nutritional properties, some Chlorophyceae and diatoms genera have been widely used as dietary supplements for both humans and animals [10; 11].  
Nowadays, there is a focus on using microalgae in renewable energy sources as well as environmental applications. Several studies reported the conversion of microalgae biomass into biofuels [12; 13]. Also, microalgae environmental applications are mainly represented in CO\({}_{2}\) sequestration and wastewater treatment [14]. Recent studies have investigated microalgae capacities for the final polishing of municipal wastewater, or the direct treatment of a variety of industrial wastewaters [14; 15]. They demonstrate that several microalgae species can uptake heavy metals with high efficiency [16]. Moreover, multiple metal-biosorption mechanisms have been reported that differ according to the species used, biomass kind, and adopted procedure [15]. These include ion exchange, chelation, complexation, and microprecipitation [16]. The goal of the present work was to isolate, identify, and characterize native microalgae strains from Tunisian coasts to study their biotechnological potentialities for future industrial-scale valorization in biotechnological fields.
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 3 Simulation and Result Analysis
### Simulation of Two-Wheel Slip with Different Axles  
The initial state of the simulation was the front right and rear left drive wheels slipping after inter-axle torque control, and the adjustment process was to perform slipping wheels braking based on the initial inter-axle torque control so that some drive torque on the slipping wheels was transferred to the well-attached drive wheels, and if they slipped again, further engine output torque control was applied. Finally adjusted to no drive wheels slipping. The simulation results are shown in Figure 10a,b.  
From the simulation results, when no control was applied to the harvester, within 1.7 s, the average speed of the slip wheels first rose sharply to the maximum value due to slippage and then fell sharply to below 3 km/h due to the increase in attachment, during which the speed of the harvester showed a decreasing trend due to wheels slippage. After 1.7 s, the harvester speed increased with the average speed of the slip wheels and finally reached the uniform speed at the same time. The harvester speed and the speed of the slip wheel fluctuated greatly during the whole process, and the speed of the slip wheel was overshoot, the maximum overshoot was 17.14%; after the control was applied, as shown in the solid line in Figure 10, the speed of the harvester and the average speed of the slip wheels increased steadily during the whole adjustment process, and the adjustment time to reach the uniform speed was 41.4% shorter than that when the control was not applied.  
In summary, active torque control distribution could effectively eliminate two-wheel slip with different axles, and improve the passing ability as well as the driving stability of the harvester during operation.  
Figure 10: Simulation results of the harvester with two wheels slipping on the different axles: (**a**) harvester speed, (**b**) average speed of slipping wheels.
## 2 State of the Art  
### Analysis in Frequency-Domain  
According to the literature [20; 21; 22], the FHR signal involves information of interest that lies in different frequency bands. A continues component corresponds to the FHR signal average. A very low frequency (VLF) band (\(\leq\)0.03 Hz) that involves slow signal dynamics is related to the nonlinear morphological behavior of FHR decelerations and accelerations. A low frequency (LF) band (0.03-0.15 Hz) is mainly associated with fetal sympathetic ANS activity. A medium frequency (MF) band (0.15-0.5 Hz) is related to the fetal movements and breathing of the mother. Finally, a high frequency (HF) band (0.5-1.0 Hz) is related to fetal breathing.  
Given the above, different approaches have been proposed to extract information from such frequency bands of interest. Most of them depend on spectral-based operations because it is assumed that variations in the frequency-domain can be related to the fetus's condition [23]. In general, these methods are based on fast Fourier transform (FFT) [20; 24; 25; 26; 27; 28; 29], and AR spectral-based analysis, which allows the extraction of quantitative spectral parameters [30; 31; 32].
## 4 Results and Discussions  
The study utilized historical landslide data from the western region of Henan Province as sample data. The information value model and frequency ratio method were employed to randomly select negative sample points. With the assistance of Random Forest (RF) and Back Propagation Neural Network (BPNN) models, landslide susceptibility assessment in the western region of Henan Province was conducted. The main process and modeling are as follows: 1. Compile landslide data in the study area and obtain relevant environmental factors. 2. According to the frequency ratio method, determine the \(FR\) values of the factors for landslide that have occurred. 3. Use the information value model to assess landslide susceptibility and then classify into five levels: very high, high, medium, low, and very low. 4. Adopt two sampling methods: (a) Positive samples are landslide points, and negative samples are chosen randomly from the entire area. (b) Landslide points constitute the positive samples, while negative samples are randomly selected from the medium-, low-, and very-low-susceptibility zones delineated by the information value model. Use \(FR\) values as input variables for the model, with landslide and non-landslide points as output variables. 5. Apply the trained model for predicting landslide susceptibility indices and generating maps. 6. Assess the model's accuracy using the Receiver Operating Characteristic (ROC) curve and compare evaluation outcomes while analyzing the influence of negative sample selection on landslide susceptibility.
## 4 Results and Discussion
### Accuracy and Loss Analysis  
The data obtained from the training method with a learning rate of 0.001 were selected, and it was found that as the training accuracy increased, the validation accuracy started to increase, and as the training loss decreased, the validation loss started to decrease. In addition, the accuracy of the validation set was found to be generally higher than that of the training set during the training process. The reason for this may be that the data set was not evenly proportioned when it was sliced, resulting in the variance within the training set being greater than that within the validation set to the point of error.  
After completing the step-by-step training and testing of the land use classification data set of four cities, the overall data set was integrated according to 14 classifications and applied to the EfficientNetV2 convolutional neural network model for testing and training. The accuracy of the overall data set classification is 84.56%, indicating that the model performs well when applied to land use classification for this data set. It can be applied to identify large-scale spatial land use types and statistical analysis of urban land use. Figure 5 shows the overall accuracy and loss graphs of the four cities, which proves that the current model has good accuracy on both the training and testing sets. In this study, it was found that too few training times do not fully realize the learning ability of the model, while too many training times may lead to unstable change curves or cause an overfitting phenomenon. With the increment of epoch, the loss values of both the training and testing sets start to show a decreasing trend, and the loss curve converges and stabilizes after 250 epochs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & \
\begin{tabular}{c} **Accuracy with Pre-Training** \\ **Weight (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy with Transfer** \\ **Learning Weight (\%)** \\ \end{tabular} \\ \hline Handan City data set & 81.44 \\ Shijiazhuang City Data set & 79.12 & 84.67 \\ Xingtai City data set & 79.83 & 86.58 \\ Tangshan City data set & 80.27 & 88.22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Testing sets accuracy comparison.  
Figure 5: Accuracy and loss of experimental results. (**a**) training set accuracy and testing set accuracy; (**b**) training set loss and testing set loss.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Data Set Name**} & \multicolumn{4}{c}{**Metric**} \\ \cline{2-5}  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Handan City data set & 0.814 & 0.815 & 0.814 & 0.812 \\ Shijiazhuang City data set & 0.847 & 0.862 & 0.862 & 0.860 \\ Xingtai City data set & 0.866 & 0.890 & 0.888 & 0.889 \\ Tangshan City data set & 0.882 & 0.892 & 0.886 & 0.886 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy, Precision, Recall, and F1-Score, which were averaged over the train and test sets.