esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Results and Verification
#### 3.5.2 Model Validation  
Table 2 illustrates the computational results of the AMIGOS dataset. In the second method, after comparison, it is concluded that the accuracy obtained by using the XGBoost algorithm is 81.8%, which is higher than other algorithms because XGBoost uses multimassifier stacking, which can achieve a better classification effect.  
From the Table 2, it can be concluded that, use of deep neural networks takes longer than machine learning methods, but due to its model characteristics, the accuracy is better than machine learning. Using the computing framework proposed in this paper has obvious advantages in reducing the running time of the model and determining the response delay rate, decentralization, and rational use of edge resources.
## 7 Challenges and Limitations  
The integration of AI into communication networks brings numerous advantages but also presents several challenges and limitations. This section highlights the main obstacles faced when deploying AI in modern communication systems, particularly concerning data privacy, scalability, model interpretability, and ethical concerns [136].
# Three-Dimensional Modeling of the Xichang Crust in Sichuan, China by Machine Learning
## 5 Seismotectonic Structure
### Density Calculation and Isosurface Extraction  
The distribution of equivalent seismic particles in the seismic energy clusters is not uniform. The more concentrated the distribution is, the greater the density is, the more concentrated the energy release is, and the greater the rupture degree of the crustal medium. The seismic energy cluster density isosurface can better describe the seismic source structure's morphological characteristics. Therefore, in this study, the kernel density of each seismic energy cluster is calculated through Gaussian kernels. Then the isosurface is extracted to obtain more natural geometric characteristics of the source. The clusters in Figure 5c are divided into three levels according to the density marked with blue, yellow, and red, respectively. The blue delineated area is mainly the seismic cluster at the lower part of several fault zones, and the long axis direction is consistent with the fault direction. The crustal medium is relatively broken due to fault crisscross in the Shimian area. The  
Figure 5: Feature extraction of seismotectonic structure. The optimal clustering parameters were obtained by evaluating different cluster parameters based on the Silhouette Coefficient (**a**), analyzing results based on DBSCAN (\(\varepsilon\) = 0.58, _MinPts_ = 70) (**b**), results of equivalent seismic particle analysis, and kernel density calculation (**c**), seismotectonic structure extraction based on seismic kernel density isosurface (**d**).  
yellow delineated area is mainly the intersection of the Daliangshan fault, Xianshuie fault, Anninghe fault, and the corner position near Qinghe fault and Mopanshan fault. The area delineated in red is mainly the intersection axis of the Daliangshan fault, Xianshuie fault, and Anninghe fault, which is the location of active seismicity (Figure 5d). Therefore, the seismic source structure is not only impacted by the fault's direction, but also by the bending shape of fault and the cutting depth of crustal rock mass.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
## 2 ECM Components That Contribute to Tissue Engineering Strategies
### Collagen  
Collagens are the predominant fibrous proteins in the ECM of the majority of tissues. Functionally, they provide structural integrity and support to the ECM. Additionally, they promote tensile strength through the presence of a triple helix structure (Figure 1), and other cellular processes such as adhesion [55], which is facilitated by the presence of adhesion motifs such as the peptide sequences GFOGER and DGEA. In total there are 28 different collagen types [56] and within any tissue a heterogenous combination will exist, although one type will commonly dominate [55]. Collagen contains three polypeptide chains that form a characteristic triple helical structure [58], which can further assemble into complexes of a higher-order arrangement, such as fibrils or structural networks, depending on the type of collagen [55]. Collagens have been classified into many groups, but there are three primary classifications (Table 1): fibril-forming collagens (collagen types I, II and III), network forming collagens (collagen type IV) and fibril-associated collagens (collagen types IX, XII) [56].  
Figure 1: The characteristic structural feature of collagen is its triple helix- a tight right- handed helix of three \(\alpha\)-chains. Each chain contains one or more regions encompassing the repeating amino acid motif Gly-X-Y, where X and Y can be any amino acid, but are commonly hydroxyproline and proline, respectively. Triple helix formation is initiated intracellularly by the C-terminal propeptide. Procollagen is secreted from the cell and converted to tropocollagen in the extracellular space by the removal of N and C terminal propeptides via enzyme cleavage. Crosslink formation between tropocollagen fibrils generates collagen fibre. Created with BioRender.com.
## 3 Results and Discussion  
### pH and WHC of Set Yoghurt  
Figure 1a presents the pH of fermented set yoghurt with different RE additions. All set yoghurt samples showed similar pH values without significant differences. The pH could affect the microbial activity of the final product significantly [24]. Our results demonstrated that the addition of RE had no effect on the change in the activity of the yoghurt starter bacteria, which was consistent with previous reports [20; 25].  
WHC is a critical parameter in the texture and flavour of set yoghurt [19]; i.e., the higher the value, the better the curd stability [25]. As shown in Figure 1b, the WHC values of set yoghurt with 0.05%, 0.10%, and 0.15% RE were 44.09 \(\pm\) 2.90%, 46.74 \(\pm\) 1.90%, and 47.48 \(\pm\) 2.53% after 24 h of low-temperature storage, respectively. The results indicated that an increasing trend was observed with an increase in RE concentrations, although no significant difference was found among the three samples. However, compared with the control (40.54 \(\pm\) 1.19%), the WHC values of the set yoghurt with 0.10% (46.74 \(\pm\) 1.90%) and 0.15% (47.48 \(\pm\) 2.53%) RE showed a significant difference. Several studies reported that the addition of polysaccharides significantly increased the WHC values in the set yoghurt after 24 h of low-temperature storage [19; 20; 22]. Based on previous investigations, the improvement of WHC of set yoghurt after the addition of polysaccharides might be attributed to the strong hydrophilicity of polysaccharide molecules, which resulted in the rigidity of the protein gel network by absorbing water [26], and the interaction between proteins and polysaccharides, which led to the formation of stable complexes to improve the structure of protein gels [25]. The honeycomb-like porous structure with spike-like expansions around the pores of RE [8] might be beneficial for the increase in WHC during the low-temperature storage of set yoghurt in this study.
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 4 Application of Biochar in Soil Remediation
### Amelioration of Soil  
The improvement of soil by biochar is mainly reflected in the improvement of soil organic matter content, the increase of nitrogen, potassium, and other nutrients contents and utilization rate, and the improvement of soil erosion and acid soil.  
The application of biochar can significantly increase the content of soil organic matter, alkali-hydrolyzed nitrogen, ammonium nitrogen, and available potassium, but the more biochar added is not better. Excessive application of biochar can inhibit the content of nutrients. [PERSON] et al. [125] mixed acacia, croton, and eucalyptus charcoal into the soil in a basin of the Ethiopian plateau, and found through laboratory and field experiments that it had a good improvement on the water conservancy characteristics of degraded soil, so as to reduce runoff and erosion. Biochar is mostly alkaline, which can improve the utilization and absorption of nutrients in rice by increasing the pH value of acid soil [126]. In addition, the effect of biochar on soil cation exchange capacity was significant. [PERSON] et al. [127] found that biochar, compost, and their compounds significantly improved the availability and use of plant nutrients: Soil organic carbon, moisture content, CEC, and peanut yield all increased, and greenhouse gas emissions decreased.  
Nitrogen is an essential nutrient for plant growth. The application of nitrogen fertilizer could replenish soil nitrogen and maintain land productivity. However, over-application will cause a large loss of soil nitrogen, reduce the efficiency and utilization of nitrogen fertilizer, and aggravate the eutrophication pollution degree of surrounding water environment such as rivers and lakes. The inhibition of nitrogen and phosphorus leaching by biochar is considered as follows: Biochar changed the microbial-mediated reactions in soil nitrogen and phosphorus cycles, namely N\({}_{2}\) fixation, nitrogen and phosphorus mineralization, nitrification, ammonia volatilization, and denitrification. At the same time, biochar provided a reactive surface in which nitrogen and phosphorus ions remain in the soil microbial biomass and exchange sites, both of which regulate crop nitrogen and phosphorus availability [128].
## 4 Simulation Results and Performance Analysis  
In this paper, the orbital parameters of the FM13 satellite in the ORBCOMM constellation of the S-IoT are selected for simulation, and the user access capacity of the MHSTFC-UD under the condition of limited frequency resources is compared with the fixed resource allocation of MHSTFC, UFFR [17] and BH[26] methods. The specific simulation parameter settings are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of Variables & 38 \\ Crossover Fraction & 0.8 \\ Generations & 3800 \\ Population Size & 200 \\ Migration Fraction & 0.2 \\ Migration Interval & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for the genetic algorithm.
In [43], the decision tree method was used to analyze NDVI data, based on Landsat images, aimed at predicting land degradation in Brazil. The validation of the decision tree achieved Kappa 0.82. As can be seen from the performed analysis, there are many studies dealing with management of pastures and meadows. They are aimed at assessing the pasture biomass and dry matter, identifying the vegetation species, monitoring the regenerative grazing, large-scale categorization of pasture lands, etc. There are also many studies specifically aimed at assessing and monitoring degradation, though most of them rely on vegetation indices and multispectral data. So far, very few authors have used the machine learning approach in a combination with visual spectrum images for remote assessment of pasture degradation. Considering also the broad understanding of the term degradation by the different stakeholders, a visible research gap can be observed. The main objective of this work is to investigate the efficacy of different supervised pixel-based and object-based machine learning algorithms for remote assessment of the degradation of a pasture near the village of Obichnik, Bulgaria. To achieve this, appropriate degradation classes should be defined, corresponding to the specifics and problems of the investigated location. This study relies on UAV-obtained data from visual spectrum sensors, though satellite-obtained data are also applicable. The obtained results should provide new knowledge about the application of remote sensing and machine learning for evaluation of pasture and meadow degradation. The rest of the paper is organized as follows. Section 2 describes the object and means of the investigation, as well as the methodology for data collection and data analysis. Section 3 presents the obtained row data and their processing, application, and evaluation by three machine learning algorithms--maximum likelihood (MxL), random trees (RT), and SVM. Furthermore, the optimal models are compared in terms of Kappa, precision, and recall in order to gain an in-depth understanding of their performance. The obtained accuracies are also compared with those obtained in other studies, in order to evaluate the effectiveness of the proposed approach. Finally, Section 4 presents the conclusions from the study.
## 2 Data and Methods
### Methodology
#### 2.2.3 Neural Network Modelling  
This article uses the generalized regression neural network (GRNN) to learn and memorize the relationship between the EEI posterior model and the petrophysical properties log at the well location in order to enhance and ensure the accuracy of the petrophysical properties prediction given the seismic data volume where no well log data is available. GRNN is a radial basis neural network that consists of four layers, i.e., input, pattern, summation and output layers [20]. The GRNN is trained with the posterior model \(\mathbf{EEI}_{\text{po}}\) as training input and the petrophysical log \(\text{P}_{\text{log}}\) as training target with the following scheme:  
\[\overline{\text{P}_{\text{log}}}\big{(}\mathbf{EEI}_{\text{po}}\big{)}=\frac{ \sum_{i=1}^{n}\text{P}_{\text{log}}\text{e}^{\big{(}\frac{\text{P}_{\text{log }}^{2}}{2\sigma^{2}}\big{)}}}{\sum_{i=1}^{n}\text{e}^{\big{(}\frac{\text{P}_{ \text{log}}^{2}}{2\sigma^{2}}\big{)}}}, \tag{3}\]  
where \(\overline{\text{P}_{\text{log}}}\) is the estimated petrophysical log for given posterior model \(\mathbf{EEI}_{\text{po}}\). i is sample index, n is number of samples, \(\sigma\) is sample smoothing factor or spread that is optimized by the trial-and-error process. \(\text{e}^{\big{(}-\frac{\text{D}_{\text{i}}^{2}}{2\sigma^{2}}\big{)}}\) has a role of the GRNN activation function. \(\text{D}_{\text{i}}^{2}\) is the squared distance between the samples and the point of prediction.  
The training is conducted iteratively until the mean-squared error between estimated petrophysical, \(\overline{\text{P}_{\text{log}}}\), and targeted petrophysical log, \(\text{P}_{\text{log}}\), is minimum. The smoothing factor \(\delta\) is the only adjustable factor to control the performance of the simulation. It has to be defined (trial and error) before training. A smoothing factor value that gives the highest correlation and smallest error between the actual and predicted value should be obtained to optimize the GRNN model. Theoretically, when the smoothing factor value is much bigger than zero, smooth interpolation values between the actual values will be produced. The smoothing factor can be allowed to be very small (approaches zero) in order to fit the actual data and allows for high-order curves [20].  
GRNN modeling can be conducted in the MATLAB system using the _newgrnn_ built-in function. Model training can be conducted with syntax [21]:  
\[\text{net}=newgrnn(\text{P,T,spread}), \tag{4}\]  
where 'net' is the GRNN model being built, 'P' is input vectors, 'T' is target vectors and'spread' is the smoothing factor.  
And model testing can be conducted using syntax:  
\[\text{T}^{\prime}=\text{net}(\text{P}^{\prime}), \tag{5}\]  
where 'T' is the prediction vectors and 'P' is the new input vectors.  
The smoothing factor'spread', as well as the GRNN model 'net', can be optimized by manually simulating the value of'spread'. The optimized model is associated with the'spread' value that gives the smallest error between the predicted vectors 'T' and the target vectors 'T'.
## 2 Correlation Model of Monitoring Data
### Influence of Correlation Degree on Reconstruction Error  
In order to discuss the influences of the selection of response variables, two additional cases are discussed, which are:  
\[\begin{array}{llll}\mathbf{X}_{P2}&=\left\{\begin{array}{llll}(\mathbf{x})_{1 3}&(\mathbf{x})_{14}&(\mathbf{x})_{15}&(\mathbf{x})_{16}\end{array}\right\}\\ &=\left\{\begin{array}{llll}\sigma_{3-5-5}(t)&\sigma_{3-3-6}(t)&\sigma_{3-5-6}( t)&\sigma_{3-1-5}(t)\end{array}\right\}\end{array} \tag{45}\]  
and:  
\[\begin{array}{llll}\mathbf{X}_{P3}&=\left\{\begin{array}{llll}(\mathbf{x})_{ 30}&(\mathbf{x})_{31}&(\mathbf{x})_{32}&(\mathbf{x})_{33}\end{array}\right\}\\ &=\left\{\begin{array}{llll}\sigma_{3-5-8}(t)&\sigma_{3-5-3}(t)&\sigma_{3-6-4}( t)&\sigma_{3-2-1}(t)\end{array}\right\}\end{array} \tag{46}\]  
The correlation model using \(\mathbf{X}_{P2}\) is:  
\[\mathbf{y}_{2}(i)=0.02(\mathbf{x}(i))_{13}+0.01(\mathbf{x}(i))_{14}+0.39( \mathbf{x}(i))_{15}+0.02(\mathbf{x}(i))_{16}-23.85 \tag{47}\]  
The contrast curves between reconstructed values and actual values are shown in Figure 5. The average absolute error is 0.97 MPa and the relative error is 2.97%.  
The correlation model using \(\mathbf{X}_{P3}\) is:  
\[\mathbf{y}_{3}(i)=-0.01(\mathbf{x}(i))_{30}+0.01(\mathbf{x}(i))_{31}-0.01( \mathbf{x}(i))_{32}-0.01(\mathbf{x}(i))_{33}-32.03 \tag{48}\]  
The contrast curves between reconstructed values and actual values are shown in Figure 6. The average absolute error is 1.03 MPa and the relative error is 3.13%.  
The errors of the reconstructed information using different correlation coefficients of the response variables are summarized in Table 2.  
Figure 5: The reconstructed value and the actual value using response variable \(\mathbf{X}_{P2}\): (**a**) the curve of the reconstructed value and the actual value; and (**b**) the curve of reconstruction error.  
Figure 6: The reconstructed value and the actual value using response variable \(\mathbf{X}_{P3}\): (**a**) the curve of the reconstructed value and the actual value; and (**b**) the curve of reconstruction error.  
It can be seen from Table 2 that the errors become larger when the coefficients of response variables become smaller, in which the average absolute error and relative error are minimum when \(\mathbf{X}_{P1}\) is chosen as the response variables. It indicated that the response variables with closer correlation to the reconstructed variable should be chosen, which is better for the reconstruction of the sensor measurements.
# PornNet: A Unified Deep Architecture for Pornographic Video Recognition
## 3 Our Proposed Methods
### Fusion of Pornographic Image and Audio Recognition Results  
In our scenario, we extract image frames and audio data from the given video with a 1 fps sampling rate and 16 kHz sampling frequency respectively. Thus, the generated \(N\) images and the audio data are delivered to our DCNet and RANet for pornographic content recognition. Furthermore, with the classification result of each image frame \(R_{m}^{i}\in\{0,1,2\}\) obtained, we aggregate the recognition results of all the images via voting strategy, leading to the aggregated result \(R_{m}\in\{0,1,2\}\). Here, 0, 1, 2 represent three image classes, i.e., normal, porn, and sex. Analogously, the recognition result of the audio data can be computed as \(R_{a}\in\{0,1\}\). Thus, the following aggregation function is pre-defined to fuse the results of the porn image and audio recognition:  
\[R=\begin{cases}1(porn)&R_{m}=1\ or\ R_{a}=1\\ 0(normal)&R_{m}=R_{a}=0\\ 2(sexy)&R_{m}=2\ and\ R_{a}=0\end{cases} \tag{9}\]  
Equation (9) can be interpreted as the following three cases: Firstly, the test video is identified as pornographic when either the image or audio data in the video is classified as pornographic. Secondly, the test video is normal when both of the two modalities are recognized as normal. Thirdly, the test video is classified as sexy when the audio data is normal whereas the image data is identified as sexy.
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 1 Introduction
Additionally, as a typical anisotropic soft rock, coal has attracted considerable academic attention regarding its pronounced time-dependent characteristics. [PERSON] et al. [15; 16; 17] investigated the creep mechanical behavior of coal-rock under varying temperatures and pore pressures, establishing a creep mechanics model that accounts for coupled temperature-pore pressure effects in deep coal-rock masses. Furthermore, [PERSON] et al. [18] examined the time-dependent creep mechanical behavior of rock masses following deep coal mining. However, critical knowledge gaps persist regarding single-stage coal-specific time-dependent fracture evolution under prolonged water immersion, particularly in CMUR operational contexts where coal pillars experience decades of saturation. The absence of acoustic emission (AE)-monitored long-term creep data for water-immersed coal impedes the accurate prediction of dam service life. In summary, while significant progress has been made in research on coal pillar dams in CMURs and single-stage long-term creep experiments of rocks, there remains a notable gap in systematic studies concerning the time-dependent mechanical behavior and acoustic emission fracture patterns of coal pillar dams under prolonged water immersion. To address this research gap, this study conducts uniaxial single-stage long-term creep tests integrated with acoustic emission (AE) monitoring on water-immersed coal-rock specimens from the Daliuta CMUR project. The investigation analyzes the influence of long-term water immersion on the mechanical behavior of coal and rock masses and their pre-failure characteristics. This research establishes a methodological framework and provides experimental data to support the long-term mechanical analysis and stability assessment of CMURs in the Shendong Mining Area.
## 7 Discussion  
The experimental results confirm the effectiveness of the proposed Hybrid Genetic Algorithm (HGA), which integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model. This combination significantly enhances the quality of route planning by improving travel time estimation, increasing optimization performance, and reducing the risk of premature convergence.
## 2 Problem Formulation and Preliminaries
### Background Subtraction  
Background subtraction involves distinguishing the foreground (moving objects) from the background in video frames. We can formulate this problem mathematically as follows. The input video sequence is represented as a series of frames: \(F_{1},F_{2},\ldots,F_{t},\ldots,F_{N}\), where \(N\) is the total number of frames and \(t\) is the current time instance. Each frame \(F_{t}\) is a 2D array of pixels denoted as \(F_{t}(i,j)\), where \(i\) and \(j\) represent the spatial coordinates of the pixel in the frame. Denote the input image at time \(t\) as \(I(x,y,t)\) which is composed of a static background \(B(x,y)\) and a moving foreground \(F(x,y,t)\), such that:  
\[I(x,y,t)=B(x,y)+F(x,y,t) \tag{1}\]  
The goal of background subtraction is to create a binary mask \(M(x,y)\) for each frame \(F_{t}(i,j)\), where pixels belonging to moving objects are labeled as 1 (foreground), and the rest are labeled as 0 (background).  
\[M(x,y)=\begin{cases}1,&\text{if }|I(x,y,t_{d})-I(x,y,t_{f})|>T_{t}\\ 0,&\text{otherwise}\end{cases} \tag{2}\]  
where \(T_{t}\) is the user-defined threshold, \(t_{d}\) and \(t_{f}\) represent the time instances of the dark and bright frames, respectively.