esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Complex Terrain Recognition
#### 2.3.1 Image Local Infilling for Terrain with Obstacles  
Information on terrain with obstacles collected by Kinect is used as input data for the terrain classifier. It was found that large-volume obstacles cause low accuracy of final identification, because acquired features of terrain information are severely affected, since SURF-BRISK and SURF have the same points of interest. The distributions of points in different terrains with and without obstacles are shown in Figure 6. The obstacles greatly influence local feature extraction. In order to improve the accuracy of recognizing terrain with obstacles, a method of image local infilling (ILI) is presented. The errors for terrain with obstacles in the first round of recognition are shown in Table 2.  
The obstacle area of pixel matrix \(I(m,n)\) is obtained using the obstacle detection method, as are the central pixel coordinates (\(u,v\)). Here, three infilling examples are illustrated for comparison. The obstacle area with a pixel value of \(I=255\) is presented as a white area in Figure 7b. The obstacle area with a pixel value of \(I=0\) is presented as a black area in Figure 7c. The obstacle area spliced by the no-obstacle sides of the background terrain image is presented in Figure 7d. Due to the use of both left and right sides of the terrain image for infilling, it only needs to compare the abscissa \(u\) of the obstacle area center and the abscissa \(u_{c}\) of the color image center. At the same time, according to the dimensions of \(I(m,n)\), the size and orientation of obstacles are determined. If the width of the obstacle area, i.e., the number \(n\) of matrix \(I(m,n)\), is too large, the image needs to be processed by multiple infilling. The  
Figure 6: Distributions of feature points in terrain with and without obstacles.  
classification and statistical results of the terrain classifier after ILI are also shown in Table 2. The first two methods do not improve the accuracy of image recognition, since white and black features do not contribute to the main feature points. However, the background terrain-based image infilling shows satisfactory results.
## 4 Application of Moisture Identification and Algorithm Comparison
### Results Discussion  
The overall identification rate was 94.7%, indicating that the parameters selected by the two-step method can well reflect the change effect of the moisture content. PCA maps the parameters to the new space, and the new parameter matrix can be obtained by principal component extraction. The Pearson correlation coefficient method takes the correlation between variables as its basis. The label information can be made available by the two-step method; thus, an accuracy advantage exists in rock moisture identification. Moreover, the signal variability problem in actual engineering application was also handled through the capture of the initial AE signal.  
The two-step method was designed to provide more precise and differentiated input vectors for moisture identification in low-permeability sandstone. The intended usage scenario is the preprocessing of AE parameters before low-permeability sandstone moisture identification model training. The quality of sandstone AE input parameters has a great influence on the final identification result during moisture pattern identification. Redundant sandstone AE parameters will increase the computational pressure and slow down training time. Overfitting also easily occurs. On the contrary, using too few sandstone AE parameters may mean the loss of effective moisture information and lead to incorrect identification.
## 5 Results and Analysis
### SpMV Performance
#### 5.3.1 Throughput and Speedup  
Figure 7 illustrates the double precision performance for the 26 benchmark matrices on an Nvidia K20 GPU. Each matrix was executed 1000 times using each sparse storage technique and their average was used to calculate throughout (GFLOP/s) using Equation (2). These throughput values for all the eight storage schemes are depicted as a bar chart in Figure 7.
# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 3 Method  
For action recognition, temporal information is important, as well as spatial information. Motivated by work [27] using temporal information, we proposed an architecture of two-stream convolutional networks for distracted detection as shown in Figure 1. The network is composed of three sub-networks: spatial stream ConvNet, temporal stream ConvNet, and a fusion network. Spatial stream ConvNet and temporal stream ConvNet are used to extract the spatial and temporal features, respectively, and the different features are integrated in the fusion network. By benefitting from transfer learning, the spatial stream ConvNet was designed based on the famous network configuration, VGG-16, and the pre-trained model on the ImageNet dataset can be applied. The average pooling in the temporal dimension is firstly performed on 10 consecutive RGB images, and the result is passed to the following layers of the spatial stream ConvNet. Via convolution operators performed in the convolutional layers, the spatial features are obtained from the feature map in the last convolutional layer. On the other hand, instead of using the famous 3D ConvNet that would suffer from large parameters and overfitting when there is lack of a large training dataset [27], the temporal features are obtained by extracting the motion information via the TVL\({}^{1}\) optical flow [28] from video frames and then a stack of consecutive flow images capturing vertical and horizontal motion information are input to the temporal stream ConvNet. Then, the temporal features are obtained from the feature map in the last convolution layer of temporal stream ConvNet. In the end, a fusion network, consisting of two convolutional layers and two fully connected layers, was designed to integrate the spatial and temporal features to classify 10 distracted behaviors.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 3 Materials and Methods
### MODIS Data Products
#### 3.3.3 MCD19A2 Product  
The MCD19A2 product, derived using the Multi-Angle Implementation of Atmospheric Correction MAIAC algorithm, is a level-2 dataset that offers gridded aerosol optical thickness (AOT) measurements over land surfaces [30]. The radiance measured by the satellite sensor for each pixel on the Earth's surface results from a combination of surface reflection and scattering caused by atmospheric gases and particles. This allows for the estimation of aerosol optical depth (AOD) by considering factors such as derived surface reflectivity, aerosol characteristics (e.g., single scattering albedo and phase function), and the angular distribution of scattered light, which is primarily influenced by particle size [21]. The MODIS sensor is specifically designed for aerosol retrievals and utilizes data from seven bands spanning wavelengths from 0.47 to 2.13 um. In addition to AOD, these bands provide some information about aerosol particle size. The MAIAC algorithm takes into account surface reflectivity, assumed aerosol and atmospheric properties, and assigns an aerosol type based on seasonally and spatially varying aerosol characteristics derived from ground AERONET-based measurements conducted at various sites. The AOD products are derived by aggregating the observed mean radiance over a 10 km\({}^{2}\) area, which is formed from 400 subpixels at a resolution of 500 m, excluding the brightest 50% and darkest 20% of pixels. Currently, the retrieval of AOT is limited to altitudes below 4.2 km. These AOT data serve as inputs for estimating the mass concentrations of PM2.5 and PM10 using a regression method.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior
#### 7.3.5 Weld Type  
Welding and its type may influence stress concentration and crack initiation. The weld type (manual, orbital, or bias) showed moderate to low importance, indicating that while welding affects fatigue behavior, its impact depends on specific weld geometry and residual stresses. It is known that welding discontinuities and heat-affected zones introduce localized stress concentrations, making them potential fatigue initiation points. However, if the welds are well executed and post-treated, their impact may be minimized. ML models acknowledge welds as a factor, but not a primary driver, of fatigue, suggesting that well-controlled welding processes can mitigate their influence.
# Federated Learning for Surface Roughness
## 3 Methods  
### Experimental Setup  
This study employed two AccuteX AP6040 (Accutex Technologies Co., Ltd., Taichung, Taiwan) wire electrical discharge machining (WEDM) systems for surface roughness experimentation. Each machine supports a maximum workpiece size of 800 \(\times\) 500 \(\times\) 245 mm, with a peak discharge voltage of 140 V, maximum pulse duration of 1200 ns, and a maximum off-time of 50 \(\upmu\)s. The selection of machining parameters, including pulse duration (8, 10, 12 \(\upmu\)s), off-time (8, 10, 12 \(\upmu\)s), wire tension (10, 12, 14 kg\(\cdot\)m/s\({}^{2}\)), and spark gap (0.733, 0.772, 0.811 \(\upmu\)m), was based on prior studies that demonstrated their critical influence on machining outcomes such as surface roughness, dimensional accuracy, and hardness [16; 17]. These parameter ranges were chosen to ensure experimental coverage across practical operating conditions reported the in literature. As shown in Figure 3, each workpiece measured 150 \(\times\) 150 mm and was divided into two parameter zones, with three repeated square cuts per zone, yielding six processed areas per workpiece. All machining operations were simultaneously conducted on both machines to simulate a heterogeneous equipment environment and assess the model's generalization and adaptability under a federated learning framework. Surface roughness was measured using a PORA FBT-650 roughness tester (JIN-MAO Science Technology Instrument Enterprise CO., LTD., Kaohsiung, Taiwan), featuring a repeatability of 0.005 \(\upmu\)m and a measurement range of 0.02-160 \(\upmu\)m, ensuring reliable ground truth data for model training. This well-controlled and reproducible experimental setup forms the foundation for subsequent federated model development and performance evaluation.
# Biogas Production Enhancement through Chicken Manure Co-Digestion with Pig Fat
## 3 Results and Discussion  
### Raw Materials' Characteristics  
The main characteristics of materials used for anaerobic digestion and also found in similar studies [3; 26; 34; 35; 36] prescribe a content of total solids (TS), volatile (VS), carbon and nitrogen ratio (C/N), total ammonium nitrogen (TAN), total sulphur (S), and others, depending on study objectives. The composition of chicken manure (RM) and pig fat (FAT)exposed with anaerobic digestion in our experiments are given in Table 2. The features of the material investigated in our study were: TS and vs. content, total carbon, total nitrogen, total sulphur, and pH value. The TS content of the chicken manure we took away from a farm and investigated was 37.75% of raw material mass basis (RM); the VS Content--26.46% of RM; the total carbon--26.80% of RM; the total nitrogen--2.36% of RM; the TAN--0.55% of RM. According to the literature, chicken manure contains a high nitrogen concentration contributing to further TAN formation in a digested substrate [35; 37; 38]. Subsequently, the increase of TAN in digestate can inhibit biogas production. Sometimes, this inhibition leads to a complete stop of biogas production, as described in studies where nitrogen-rich materials were used [35; 39; 40]. The pH of RM was 7.80, indicating a slight move to alkaline substances. It is predictable because the higher TAN in the material influences its pH [38; 41]. The chemical element of total sulphur was 0.39% of RM. Sulphur contributes to unwanted and harmful pollutant formation in biogas production--hydrogen sulphur (H\({}_{2}\)S). In our case, the total sulphur wasn't as high as in the cases where protein-rich materials were used for biogas production [42]. Pig fat mainly consisted of VS content (99.96% of RM), which was constituted by high carbon amount (86.0% of RM). Such a high number of VS concentrations allowed us to use pig fat in low amounts for OLR increases. Furthermore, the high amount of carbon helps to improve the C/N ratio; thus, biogas production increases [24; 26; 27; 43]. The analysis of fat properties shows that fat consists of two times more sulphur than the chicken manure we used, but such an amount of sulphur slightly changes the overall sulphur percentage in the feeding dose because of a small fraction of fat added.
# Design and Experiment of Discharge Control Methods for Three-Stage Coil Gun Experiments
## 3 Charging Electric Circuit of Gun System  
The electric circuit to charge the capacitor requires a booster converter step-up circuit to apply high voltage according to the capacitor's capacitance. Moreover, for the multi-stage system, each stage has as many capacitors as the number of the stage, and the capacitors in each stage must operate independently once charged. In this study, a capacitor with a charging voltage of 400 V was used with a circuit for charging the capacitors in a three-stage coil gun system. We fabricated a charging device using a relay and a booster converter designed to simultaneously charge all capacitors through the capacitors connected prior to charging. The aim of this design was to reduce the difference in charge and to make charging control easier. The relay in the charging device was designed to physically bind the capacitors in each stage together when charging. Figure 4 illustrates the charging device used in the experiment.  
The operational sequence of the charging device is as follows: connecting the capacitors of the one-, two-, and three-stage coil guns by operating the relay, applying 400 V DC power to the booster converter to charge the capacitors, and turning off the relay power to complete the charging procedure once the capacitors are fully charged. The grade of the relay should be selected after considering the capacitor charging voltage to prevent damage to the relay device. The booster converter should output a higher voltage than the target charging voltage. The circuit in Figure 4 can also be used to configure a charging device for a coil gun capacitor. Figure 5 shows a prototype of the charging circuit used in our experiment.  
Figure 4: The charging device of a three-stage coil gun.
## 3 UAV AG Communication Channel Modeling and Characterization
### Typical Statistical Properties of Channel Model
#### 3.2.3 Temporal ACF and Spatial CCF  
The temporal ACF, which illustrates the non-stationarity of the channel in the time domain, and the Spatial CCF, which illustrates the non-stationarity of the channel in the space domain, can be deduced from the STCF [27]. The STCF is defined as  
\[R_{pq,\bar{pq}}(t;\Delta d,\Delta t)=E\Big{\{}h_{pq}(t)h^{*}_{pq}(t-\Delta(t) )\Big{\}}. \tag{24}\]  
It contains an LoS part and an NLoS part:  
\[R_{pq,p\bar{q}}(t;\Delta d,\Delta t)=\frac{K_{R}}{K_{R}+1}R_{pq,\bar{pq}}^{LoS }(t;\Delta d,\Delta t)+\frac{1}{K_{R}+1}\sum_{n=1}^{N_{pq}(t)}R_{pq,\bar{pq}} ^{NLoS}(t;\Delta d,\Delta t) \tag{25}\]  
\[R_{pq,\bar{pq}}^{LoS}(t;\Delta d,\Delta t)=\Big{[}P_{pq}^{LoS}(t)P_{\bar{pq} }^{LoS}(t)\Big{]}^{\frac{1}{2}}\cdot e^{j\frac{2\pi}{\lambda}\big{[}d_{pq}^{ LoS}(t)-d_{pq}^{LoS}(t-\Delta t)\big{]}} \tag{26}\]  
\[R_{pq,\bar{pq}}^{NLoS}(t;\Delta d,\Delta t)=P_{s}\cdot E\Bigg{\{}\sum_{k=1}^{ L_{k}}\Big{[}P_{pq,l_{k}(t)}(t)P_{\bar{pq},l_{k}(t)}(t-\Delta t)\Big{]}^{\frac{1}{2}} \cdot e^{j\frac{2\pi}{\lambda}\big{[}d_{pq,l_{k}(t)}(t)-d_{pq}l_{k}(t-\Delta t )\big{]}}\Bigg{\}} \tag{27}\]  
where \(\Delta t\) is the time interval, \(\Delta d\) is the distance of the antenna, and \(P_{s}\) is the joint probability of cluster survival.
## 4 Training and Performance Evaluation of the CCBA Model
### Two-Stage Training Strategy
#### 4.2.3 Training Hyperparameters  
The model underwent training for up to 200 epochs using the Adam optimizer, with a batch size of 256 and an initial learning rate of \(1\times 10^{-3}\). A lower initial learning rate of \(1\times 10^{-4}\) for the Multi-head Attention module ensures stable convergence. A weight decay of \(1\times 10^{-4}\) was applied to prevent overfitting and encourage generalization, which penalizes large weight magnitudes and helps keep the model complexity in check. After each epoch, the model's performance was evaluated on the validation set, and the model was saved if the validation accuracy was increased. If there was no improvement in training accuracy over ten consecutive epochs, the training process reverted to the last saved model, and the learning rate was reduced by a factor of ten to mitigate overfitting risks. Early stopping was implemented if no improvement in validation accuracy was observed after five successive learning rate reductions or if the learning rate dropped below \(1\times 10^{-12}\). To maximize the utilization of available data, the final model was trained for one additional epoch using the combined training and validation sets. All training and testing were conducted on an NVIDIA A100 GPU with 80 GB of memory.
# Automated Fillet Weld Inspection Based on Deep Learning from 2D Images
## 2 Materials and Methods
### Methodology  
The experiments in this study were conducted in accordance with a series of steps, as illustrated in Figure 6. These steps include data acquisition (DA), feature extraction (FE), data preprocessing (DP), data augmentation (DAU), hyper-parameter selection (HS), deep learning model (DLM), and finally, performance metrics (PM). Each of these steps is explained in detail below.  
Figure 6: Diagram illustrates the methodological framework employed in this study. The process initiates with the fabrication of the welds necessary for the experimental studies, followed by the acquisition of images of these welds. Subsequently, a series of image transformations are performed to train three models, one for each experiment, capable of detecting the manufactured weld seams.
# Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms
## 1 Introduction
[40] reported that artificial neural networks (ANNs) are the most widely used method and emphasized that artificial intelligence methods successfully predict complex relationships in geotechnical engineering, particularly in areas such as frozen soils, rock mechanics, and slope stability. [PERSON] et al. [41] analyzed the geotechnical properties of soils in Erbil using geographic information systems (GISs) and artificial neural networks (ANNs), successfully estimating parameters such as SPT-N and the bearing capacity of soils, which were predominantly in the silty clay (CL) class. Their study demonstrated that these techniques provide valuable information for soil analysis. Additionally, [PERSON] and [PERSON] [42] examined the breakout dimensions caused by drilling in a rock environment using the finite element method. They then developed a model that estimated the breakout dimensions using artificial neural networks, finding that the internal friction angle has the greatest influence on the breakout dimensions. The primary aim of this work is to conduct a comparative evaluation of various machine learning algorithms for predicting the compression index (\(C_{c}\)) of soils, using soil properties such as LL, PL, \(w\), PI, \(G_{s}\), and \(e_{0}\) as the inputs. The main objective is to identify the most effective ML algorithms that accurately predict the \(C_{c}\) value, thereby enhancing our understanding of soil behavior under a load. To achieve this goal, we employed various machine learning approaches in this study, including random forest, gradient boosting, and AdaBoost regressors. Each algorithm was rigorously evaluated using regression metrics such as the coefficient of determination (R\({}^{2}\)) and error metrics including the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). Additionally, this study aims to investigate the impact of each input parameter on the predictive capability of the algorithms and provide insights into their relative effects on soil compressibility. The ultimate objective is to develop a reliable and efficient prediction tool that can assist geotechnical engineers in assessing soil behavior for a wide range of engineering applications, thereby reducing the necessity for extensive and costly laboratory testing.
## 3 Grading Creep Experiment of Gangue-Roof Combination
### Discussion  
1. Under identical load stages, the instantaneous strain of gangue decreases with increasing particle size, with the magnitude of reduction diminishing as particle size grows. As load increases, larger-sized gangue experiences intensified fragmentation, leading to increased instantaneous deformation--though the incremental deformation per load stage gradually decreases. Instantaneous strain primarily results from elastic deformation and localized crushing of gangue particles. Smaller particles exhibit higher specific surface area and more inter-particle contact points, facilitating particle slippage and fragmentation under instantaneous loading, thereby generating larger strains. Conversely, larger particles maintain relatively intact structures with minimal initial breakage, resulting in smaller instantaneous strains.
2. Under identical load stages, creep deformation decreases with increasing particle size. At lower loads, the reduction magnitude is modest, whereas it amplifies significantly at higher loads. Increasing loads intensify gangue fragmentation, elevating creep deformation--though incremental deformation per load stage progressively diminishes. During steady-state creep, frictional resistance between particles gradually equilibrates, stabilizing strain growth rates. Finer particles exhibit more pronounced particle rearrangement and plastic deformation under sustained loading, accelerating creep strain accumulation. Larger particles form skeletal structures post-fragmentation that effectively suppress creep development. Under high stress, structural stability disparities magnify with particle size, exacerbating the reduction in creep strain as particle size increases.
3. In Figure 13b, during the second-stage loading, the creep strain of 20 mm particles exceeds that of 30 mm particles. This anomaly arises from uneven void distribution within the gangue backfill during initial loading stages, which does not compromise overall experimental reliability.
4. Figure 19 reveals that under identical load stages, the instantaneous strain of the gangue-roof composite increases with higher moisture content. This occurs because lower-moisture gangue experiences less water-induced softening, retaining greater strength and thus resisting fragmentation under instantaneous pressure. Conversely, higher-moisture gangue exhibits enhanced fluidity and greater susceptibility to softening-induced fragmentation, resulting in larger deformations. As loads increase, total deformation rises, while incremental instantaneous deformation per load stage slightly decreases.
5. Figure 19 demonstrates that under identical load stages, gangue creep strain exhibits nonlinear amplification with increasing moisture content. During graded loading, moisture exerts the most pronounced influence on deformation at the first load stage, with its impact progressively diminishing at subsequent stages. Increasing loads elevate cumulative creep deformation, while incremental creep deformation per load stage moderately decreases.