esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Calculation of Ultimate Support Pressure for Shield Tunnel Face
### Functional Equation  
According to the upper bound theorem of limit analysis, the failure mechanism reaches a critical state when the external load power equals the internal energy dissipation power. At this point, the corresponding tunnel face support pressure is the ultimate support pressure. The external loads include gravity, seepage force, buoyancy, and tunnel face support pressure. The effects of seepage force and buoyancy can be transformed into the influence of pore water pressure on the failure mechanism. Therefore, the functional balance equation can be expressed as  
\[\dot{E}_{\sigma}+\dot{E}_{w}+\dot{E}_{\gamma}=\dot{E}_{e} \tag{10}\]  
Figure 10: Generation of failure mechanism using spatial discretization technique.  
Figure 9: Sketch of the cross-section and longitudinal section of the discrete failure mechanism.  
where \(\dot{E}_{\sigma}\), \(\dot{E}_{w}\), \(\dot{E}_{\gamma}\) and \(\dot{E}_{e}\) represent the work power and energy dissipation power of the tunnel face support pressure, pore water pressure, soil gravity, and external loads, respectively. These can be calculated using Equation (12):  
\[\left\{\begin{array}{l}\dot{E}_{\sigma}=\omega c_{\tau}\underset{j}{\sum}\left( R_{j}\dot{\Sigma}_{j}\cos\beta_{j}\right)\\ \dot{E}_{\tau}=\omega\underset{i,j}{\sum}\left(\gamma_{sat}R_{i,j}V_{i,j}\sin \beta_{i,j}\right)\\ \dot{E}_{e}=\omega\cos\varphi\underset{i}{\sum}\underset{j}{\sum}\left(cR_{i,j}S_{i,j}\right)\\ \dot{E}_{w}=\omega\underset{i}{\sum}\underset{j}{\sum}\left(u_{i,j}R_{j}\sin \varphi_{i,j}S_{i,j}\right)+\underset{j}{\sum}\left(u_{j}R_{j}\Sigma_{j}\cos \beta_{j}\right)\end{array}\right. \tag{11}\]  
where \(\omega\) is the angular velocity of the mechanism rotating around point O, \(\gamma_{sat}\) and \(c\) denote the saturated unit weight and cohesion of the soil, \(V_{i,j}\) represents the volume of the prismatic discrete element in the local coordinate system (Figure 10b), \(R_{i,j}\) is the rotation radius of the prismatic element around point O, \(\beta_{i,j}\) is the angle between the rotation radius and the negative \(Y\)-axis direction, \(S_{i,j}\) and \(\Sigma_{i,j}\), respectively, refer to triangular and rectangular discrete elements (Figure 10b), \(R_{j}\) and \(\beta_{j}\) are the rotation radius and the angle between the rotation radius and the negative \(Y\)-axis direction for triangular and rectangular elements [15].  
The ultimate support pressure of the tunnel face can be expressed as  
\[\sigma_{t}=(\dot{E}_{\gamma}+\dot{E}_{w}-\dot{E}_{e})/\underset{j}{\sum}\left( \Sigma_{j}R_{j}\cos\beta_{j}\right) \tag{12}\]
## 2 Materials and Methods
### Prostate Segmentation  
A segmentation model was created for prostate region extraction using deeplabv3+, a CNN for semantic segmentation with T2 WI, and published labeled images indicating the prostate regions in the same database. Table 1 shows the division of the training dataset into five subsets, labeled as subsets (a-e), for training purposes. The evaluation of the segmentation model was conducted through five-fold cross-validation, wherein the training dataset was further split into training and test data subsets (Figure 1).  
To enhance the training process, data augmentation was applied to the training images. This involved rotating the images by \(\pm\)10 degrees with increments of 5 degrees, resulting in an expanded dataset containing five times the original number of images.  
The software for the deep learning technique was developed in-house using the MATLAB software version 2022b (The MathWorks, Inc., Natick, MA, USA) and a desktop computer with two NVIDIA RTX A6000 graphics cards (Nvidia Corporation, Santa Clara, CA, USA), with 38.7 TFlops of single-precision performance, 768 GB of memory bandwidth, and 48 GB of memory per board. A deeplabv3+ was used as a CNN for prostate segmentation. The input was 384 \(\times\) 384, and the parameters were trained by loading the Neuroimaging Informatics Technology Initiative files. The optimizer was stochastic  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Number of Subjects (Images) per Subset**} \\ \hline
**a** & **b** & **c** & **d** & **e** & **Total** \\ \hline
12 & 12 & 12 & 12 & 12 & 60 \\ (1505) & (1393) & (1417) & (1243) & (1413) & (1876) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Subset data of segmentation for the five-fold cross-validation.  
Figure 1: Dataset for training and testing.  
gradient descent and momentum optimization. Regarding the training parameters, the batch size for the number of training samples was 128, the number of epochs was 10, and the initial learning rate was 0.001. The learning rate drop factor was 0.3, the learning rate drop period was 1, the L2 regularization was 0.005, and the momentum was 0.9.  
The calculation of the Dice Similarity Coefficient (DSC) was performed using the following formula when the supervised images were designated as A and the predicted images were designated as B. The DSC serves as an index to assess the level of agreement between the images, with a value approaching 1 indicating a higher degree of agreement. The highest DSC model was used to extract the prostate after the next section.  
\[DSC=\frac{2\times|A\cap B|}{|A|+|B|}\]
## 2 Background and Methods
### Machine Learning Methods for 3D BM Prediction
#### 2.2.2 XGBoost  
XGBoost is built based on gradient boosting but has more accurate approximations by enabling the regularization, and second-order gradients [32]. XGBoost is also an ensemble model similar to RF which employs gradient boosting to group multiple results from the decision tree-based models as the final result. However, it uses the hybrid of shrinkage and feature sub-sampling to reduce the impact of overfitting. XGBoost is suitable for applications requiring parallelization, distributed computing, out-of-core computing, and cache optimization, which is proper for real-world applications with high-computation costs and massive storage requirements. The training procedure of XGBoost is depicted in Figure 4. It can be seen from the figure that XGBoost is based on boosting ensemble while RF uses the bagging ensemble. More specifically, new models (decision trees) are built to predict the errors (residuals) of prior models (from Tree 1 to the current model). Once all the models are obtained, they are integrated together to make the final prediction. The objective function of XGBoost is following:  
\[L^{t}=\sum_{i=1}^{n}l(\hat{y}_{i},y_{t})+\sum_{k}\Omega(f_{k}) \tag{1}\]  
\[\Omega(f)=\gamma T+\frac{1}{2}\lambda\parallel w\parallel^{2} \tag{2}\]  
where \(l\) is the loss function that measures the difference between the prediction \(\hat{y}_{i}\); and the real value \(y_{i}\). The second term \(\Omega\) penalizes the complexity of the model (i.e., the regression tree) which helps to smooth the final learning weights to avoid the overfitting issue [32]. Parameters \(\gamma\), \(T\) and \(w\) are the coefficient, the number of leaves and leaf weights for the corresponding tree, respectively.  
Figure 3: Generic representations of Random Forest (RF) algorithm.
## 3 Inversion Results and Stability
### Electrical Conductivity Model
The average conductivity of Anomaly A in the  
Figure 5: Plots of variations in the inversion parameters with iterations. (**a**) Root–mean–squares (RMS) of data misfit; (**b**) regularization parameter \(\lambda\); (**c**) model roughness; (**d**) penalty function. lower MTZ appears to be almost twice as resistive as the global average model (Figure 8). In the deep upper mantle and the upper MTZ, only a slight anomaly is present beneath Greece. In addition to the aforementioned anomalies, reduced conductivity zones also appear beneath stations in Africa. Considering the relatively large distance between the stations, these anomalies will not be discussed here. Figure 6: Fitting curves of the inverted responses and observed data. Figure 7: Electrical conductivity model of the mantle beneath North Africa and Southern Europe. \(\sigma\) is the conductivity of the inverted model, and \(\sigma_{0}\) is the conductivity of the initial model. (**a–d**) are the electrical conductivity model of depth 250–410 km, 410–520 km, 520–670 km, 670–900 km, respectively. Black arrows and the nearby CAL and HEL are the subducted slabs imaged by seismic tomography [23]. A and B represent Anomaly A and Anomaly B, respectively.
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Section Shape of the Retained Entry  
Due to the influence of the geological structure of coal seams, entry-side construction equipment and techniques, and supporting techniques, the rectangular section shape is generally used in coal mining roadways in China. However, when the rock self-stability of the roadway roof is poor, the coal seam inclination angle is large, or the horizontal stress perpendicular to the roadway axis is high, nonrectangular section shapes such as trapezoidal and semicircular can also be used. In 587 recorded cases of GSER engineering projects, 307 cases provide the data on cross-sectional shapes of the retained entry, mainly including rectangular, trapezoidal, inverted trapezoidal, oblique trapezoidal, arched, and other forms. Figure 10 shows the application of GSER engineering projects with different cross-sectional shapes.  
Currently, rectangular sections are predominantly utilized in GSER engineering projects in China, encompassing 242 engineering cases and constituting 78.83% of the total. Choosing rectangular sections for entry retaining is also more conducive to the  
Figure 10: Statistical chart of the GSER engineering projects application with different section shapes.  
construction and stability of the entry-side filling body. The trapezoid sections are also used in GSER engineering projects, with 32 cases accounting for 10.42%. There are a total of 33 cases using other section shapes, accounting for 10.75% of the total sample size.
# Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal
## 3 Data and Methods
### Poisson-Based Regression Models
All models were investigated using the GWR4.09 software, in which the Local-To-Global algorithm was used to iteratively select global variables for the S-GWPR model [59; 67]. The estimation of the parameters \(\beta_{k}(u_{i},\ v_{i})\) was performed through a spatial weighting function that can be fixed or variable (i.e., based on a fixed or adaptive kernel). This function is based on distance so that municipalities closest to municipality \(i\) have more influence on the estimation of these parameters. In general, when the polygons that define the spatial units (i.e., the municipalities) have different sizes, it is preferable to use an adaptive kernel so that the bandwidth parameter of the spatial weighting function can vary according to the density of the data. Consequently, the number of neighboring municipalities to consider in each local regression (Equations (5) and (6)) is optimized. The bi-square kernel with adaptive bandwidth is one of the most frequently used weighting functions, and it was used in this study:  
\[w_{ij}= \begin{cases}\left(1-\frac{d_{ij}^{\,2}}{\beta_{(k)}}\right)^{2},&d_{ ij}<\theta_{i(k)}\\ 0\,&d_{ij}>\theta_{i(k)}\end{cases}  \tag{7}\]  
where \(w_{ij}\) corresponds to the weight assigned to the observation of municipality \(j\) for estimating the coefficient at the municipality \(i\) location, \(d_{ij}\) represents the Euclidean distance between the centroids of municipalities \(i\) and \(j\), and \(\theta_{i(k)}\) is the adaptive bandwidth size defined as the \(k\)-th nearest neighbor distance to location \(i\)[59]. The optimal bandwidth size was determined by means of comparison of the AICc (small sample bias-corrected Akaike Information Criterion) of models with different bandwidth sizes. This strategy is recommended for local regression models to account for small degrees of freedom [66]. The comparison between Poisson models was made considering the AICc formulation proposed by [PERSON] et al. [66]. The lower the AICc, the better the performance of the model. We also considered the Percent of Deviance Explained as a model performance criterion. This measure in Poisson regression is analogous to the R\({}^{2}\) value in classical linear regression; thus, the higher the Percent of Deviance Explained, the better the model fits to the data.
## 3 Results and Discussion
### Lipid Production, Biomass, and FAME Composition in ADSW with Optimized Glucose Supplementation
The fatty acid composition determines the quality of biodiesel derived from microalgae. Lipids extracted from FACHB-8 were esterified and analyzed via gas chromatography (Table 6). C16-C18 fatty acids accounted for \(\sim\)80% of the total fatty acids, similar to prior studies (80\(\sim\)90%) [68; 69]. The primary fatty acids in FACHB-8 included palmitic acid (C16:0, 26.99%), linoleic acid (C18:2, 25.51%), oleic acid (C18:1, 15.97%), and arachidonic acid (C20:0, 13.00%). Saturated (SFA, 50.77%) and unsaturated fatty acids (UFA, 49.23%) were nearly balanced. [PERSON] et al. [70] also found that the FAME composition of _C. pyrenoidosis_ cultured in ADSW supplemented with 0.8% glucose was 51.14% SFA and 48.86% UFA, which is consistent with this study. Microalgae with balanced SFA/UFA ratios are generally regarded as ideal for biodiesel due to their oxidative stability and cold flow properties [71]. Thus, FACHB-8 lipids are suitable for biodiesel production. \begin{table}
\begin{tabular}{c c} \hline \hline
**Fatty Acid** & **Percentage (\%)** \\ \hline C14:0 & 4.51 \(\pm\) 0.29 \\ C14:1 & 2.96 \(\pm\) 0.16 \\ C16:0 & 26.99 \(\pm\) 0.72 \\ C16:1 & 4.16 \(\pm\) 0.63 \\ C18:0 & 6.26 \(\pm\) 0.15 \\ C18:1 n-9 & 15.97 \(\pm\) 1.11 \\ C18:2 n-6 & 25.51 \(\pm\) 1.09 \\ C20:0 & 13.00 \(\pm\) 0.42 \\ C20:5 n-3 & 0.64 \(\pm\) 0.11 \\ SFA a  & 50.77 \(\pm\) 0.97 \\ UFA b  & 49.23 \(\pm\) 0.97 \\ MUFA c  & 23.09 \(\pm\) 1.63 \\ PUFA d  & 26.14 \(\pm\) 0.98 \\ \hline \hline \end{tabular} Note: a, saturated fatty acids; b, monounsaturated fatty acids; c, unsaturated fatty acids; and d, polyunsaturated fatty acids.  
\end{table} 
Table 6: Fatty acid methyl ester (FAME) composition (% total fatty acids) of FACHB-8 after carbon source optimization.
## 2 Related Works
### Motivation  
The fundamental ideology of the existing research on the MSIMPLTS problem is to decompose the large-scale problem into multiple smaller sub-problems for solving from the spatial dimension, i.e., divide the objective task into multiple subsets based on the decision vector of the search space and optimize multiple objective functions simultaneously according to the characteristics of the global search to achieve a reduction in the objective space in the scenario of large-scale objective tasks [20; 41]. The global optimization of the MSIMPLTS planning scheme relies solely on the optimization algorithm, ignoring the correlation between satellite performance conditions and target tasks. Figure 2 shows a schematic diagram of observation time window conflicts in the mission planning scheme under the classical global optimization method, including three types of satellite observation time window conflicts for multiple target missions: unilateral intersection, bilateral intersection, and intrinsic conflicts.  
Figure 1: MSIMP basic mission scenarios: (**a**) The sequence of multi-satellite imaging mission assignments. (**b**) The visible time window allocation scheme for each satellite and the target task.  
It can be seen that the observation time windows overlap highly in the mission planning cycle, and the combined characteristics of the MSIMPLTS problems can be seen more clearly. The MSIMPLTS scheme faces several challenging issues, including overlapping observation time windows, abnormal target task allocation, satellite resource conflicts, and poor task execution timeliness. These factors make it difficult to obtain high-quality planning schemes within a limited time. This motivated us to build the MSIMPLTS model based on multi-layer optimization and a progressive optimization mechanism.  
To address the aforementioned issues, a research approach based on the MSIMPLTS model based on multi-layer optimization and the progressive optimization mechanism is proposed. The different optimization objectives at various layers of this model can complement each other, reducing the risk of potential solution loss and enhancing the globality and diversity of the solutions. At the same time, the progressive optimization mechanism refines and adjusts the optimization process step by step, more effectively balancing the efficiency and quality of the solution. By employing staged optimization (including task allocation, time window optimization, and global optimization phases), the process gradually approaches the optimal solution, overcoming the limitations of a single-layer global optimization method, ultimately solving the multi-satellite imaging mission planning problem in large-scale target scenarios.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 4 Spear: Neighbor Discovery Framework  
### Framework Overview  
As shown in Figure 3, Spear consists of four modules: **energy module** is in charge of the node's energy management; **application module** collects various latency constraints from the applications; **algorithmic module** generates a neighbor discovery schedule by invoking the discovery protocols; and **communication module** is responsible for the communication with neighbors.  
Spear accepts bounded latency constraints and remaining energy as the inputs, and the neighbor discovery algorithms (such as Hedis, Hello, Searchlight, and U-Connect in the figure) are plugged into the framework to generate the discovery schedule. Spear outputs the discovered neighbors and the corresponding discovery latency, which both are needed for constructing the network and achieving other functions.
# Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images
## 2 Methodology  
The following section details our methodology for dicentric chromosome detection. Briefly, the detection system was divided into different steps. Figure 1 depicts a scheme of the entire detection process. An image of chromosomes in individual metaphase cells was first supplied as an input to the system. Then, the image was preprocessed by applying different binarization operations and morphological filters separately. In this way, several distinct images were generated from the original one in this phase. After that, each generated image was transferred to a trained deep neural network. Once the predictions of all generated images were released by each model, they were combined using a consensus function to produce the final output prediction.
# Eye Fatigue Detection through Machine Learning Based on Single Channel Electrooculography
## 4 Method
### Feature Extraction
#### 4.2.1 Slow and Fast Eye Movement Feature Extraction  
Medical researchers have proven that slow eye movements reflect the three different states of drowsiness, wakefulness, and light sleep, which are closely related to mental fatigue and eye fatigue of humans [16; 17]. There are three types of eye movements belonging to slow eye movements, which are drifting, rolling, and oscillating with frequencies between 0.1 Hz and 1 Hz [18]. The above three eye movements could be treated as one type during the processing of the EOG signal. From experiments, it has been proven that slow eye movements have the most obvious signal characteristics in the range of 0.2-0.6 Hz. For efficiently extracting slow eye movement features, this paper designed a band-pass filter. A 16-fold downsampling operation is first performed on the EOG signal. Then, the signal is transformed by a 256-point FFT to obtain the spectrum. Finally, the spectral integration values, which are frequencies in the range of 0.2-0.6 Hz, were taken as the slow eye movement features. The extraction process for fast eye movement features is the same as slow eye movement, except that the downsampling fold is 16 and the frequencies range of spectral integration values is 1-2 Hz.
# Improved Lightweight YOLOv4 Foreign Object Detection Method for Conveyor Belts Combined with CBAM
## 3 Foreign Object Detection Method Based on GhostNet-CBAM-YOLOv4 for Conveyor Belts
### Anchor Frame Optimization Method  
The GhostNet-CBAM-YOLOv4 model was trained before using the Kmeans++ [44; 45; 46] algorithm to preprocess the dataset and redesign the size of the anchor frame to obtain a suitable anchor frame for the foreign object target size.  
1. Enter the width-height set T and the cluster center K of the foreign object and coal onto the conveyor belt in the dataset.
2. Randomly select one point from the set T as the initial clustering center O\({}_{1}\).
3. Calculate the shortest distance D(x) between each sample and the existing cluster center, calculate its probability of becoming a cluster center P(x), select the new cluster center by roulette wheel method, and find where k cluster centers. \[\rm{P(x)}=\frac{D(x)}{\sum_{x\in N}D(x)}^{2}\] (10)
4. Calculate the distance D(x) of all the points in set T to each of the k cluster centers and classify the point into the category of the cluster center with the smallest distance.
5. For the clustering results, recalculate the center Ci category center Ci of each cluster category. \[\rm{Ci}=\frac{\sum_{x\in C_{i}}x}{|C_{i}|}\] (11) where x is the sample of kind Ci. Ci is the center of each clustering category.
6. Repeat step (4) and output the k cluster center results when the cluster center Ci of each cluster category no longer changes.  
According to Figure 7, the anchor frame size of the GhostNet-CBAM-YOLOv4 model was optimized to obtain nine sets of a priori frames: (12, 15), (24, 39), (26, 77), (29, 50), (31, 28), (33, 35), (38, 53), (48, 61), (64, 38), where the intersection ratio of the selected prior frames and the target to be identified reached 87.14%. It could be seen that the clustering centers of foreign objects with different sizes were evenly distributed, and each class was closely spaced from the clustering center, which effectively accelerated the convergence speed of the network and improved the accuracy.  
Figure 7: Anchor frame clustering results at K = 9.
## 2 Related Works
### Semantic Information with Synthesized Surround View Image  
The homogeneous transform or IPM relied on by surround view synthesizing techniques is a very classic image processing method [47; 48]. With the rapid development of vision systems in recent years, this technology has also been widely used, such as the reverse assistance system in cars, which usually uses this technology to enable the driver to easily observe the situation of surrounding objects and the distance from the robot. Similarly, when mobile robots complete tasks such as mapping, positioning, and navigation, researchers also focus on how to extract effective semantic information from the synthesized surround view image, such as various pavement markings, obstacles, and passable areas, and describe them in different forms of representation such as point clouds and occupied grids to help complete related tasks [49; 50].  
However, because models based on deep neural networks often require a large amount of manually labeled data for training, they are difficult to quickly scale and apply to new scenarios. Therefore, this work considered only the rough passable space segmentation results to assist in extracting the semantic information in the surround view synthesized image and obtain rich and effective semantic information such as passable area boundaries, pavement sparse feature points, and pavement marking edges, which not only improves the effective information quality of the input SLAM system, but also greatly reduces the requirements for annotated data, so it can be applied to mapping positioning and navigation tasks in new scenes more quickly than the previous methods.