esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Installation Process of Screw Piles Based on the FEM-SPH Coupling Method
## 3 Numerical Simulations
### Comparison between the FEM-SPH Method and FEM  
In this section, the installation of NT2 was simulated using the conventional FEM method and the FEM-SPH method, respectively, and a comparison between the field test data and two simulation results was made, including the ground soil deformation and the maximum installation torque. All two numerical models used the same settings and material parameters as the experiments. At the end of the installation, the ground soil deformation of two numerical models and the field test is presented in Figure 10. As in the field test (Figure 10a), it can be seen that the ground soil deformation was observed in both the FEM-SPH (Figure 10b) and FEM simulation (Figure 10c,d), with ground uplift occurring in both cases. In terms of the ground soil deformation, both the FEM-SPH coupling method and FE method can reflect the consistency with the field test.  
The maximum installation torque obtained from the two different numerical simulations and field test is presented in Figure 11. The results show that the max torque in the FEM model and the FEM-SPH model is 418.4 N m and 497.8 N m, meanwhile, the experimental data are 454.7 N m. The errors between the two different numerical simulations and the experimental results are 7.9% and 8.65%, respectively. In terms of the max installation torque, the FEM-SPH coupling method is more consistent with field tests than the finite element method. For the FEM and FEM-SPH numerical models, the CPU calculation time to complete the simulation of the screw pile installation is shown in Figure 11. It can be found that the calculation time for the simulation of the FEM method is greater than that  
Figure 9: Comparison of test results with numerical simulation results: (**a**) screw piles with different diameter; (**b**) screw piles with different pitch; (**c**) the Max installation torque of four screw pile.  
of the FEM-SPH method. In terms of computational efficiency, the FEM-SPH coupling method is also greater than the finite element method.  
Figure 12 shows the variation in soil stresses in the two numerical simulations at the end of the installation. It can be seen that the region of soil stress variation in both numerical models is in the vicinity of the pile body and the spiral blades. In practice, as the screw pile is screwed into the soil for installation, this will have as little effect as possible on the deep soil. However, in the finite element simulation, the soil below the screw pile showed a significant stress concentration, which is not consistent with the actual situation. From the above analysis, it can be seen that using the FEM-SPH method to simulate the installation process of screw piles is a good choice and has good applicability for dealing with large installation deformations.  
Figure 10: Comparison of the soil deformation between numerical simulation and field test: (**a**) field test; (**b**) FEM-SPH; (**c**,**d**) FEM.
## 5 Application Case
### Finite Element Analysis  
#### 5.2.1 Finite Element Model  
The seepage process at the base of the gravity dam was simulated by the multi-physics field simulation software COMSOL Multiphysics. Four-node convention/diffusion quadrilateral elements are applied in this study. Figure 4 shows the element meshes of the model. The model contains 4157 quadrilateral meshes. The curtain grouting thickness is 2 m, reaching a slightly weathered rock of 3 m. The maximum cell width is 2.5 m, and the minimum cell width is 0.1 m. A right-angle coordinate system was constructed, with the x-positive direction pointing downstream and the z-positive direction pointing vertically upwards. Two times the maximum height of the dam was extended at both the upstream and downstream in the x-direction. The depth of the foundation was taken as twice the dam height. The 2D finite element model is shown in Figure 4.  
Figure 4: 2D finite element mesh of Shimantan dam.  
Figure 3: The location of the Shimantan gravity dam.
## 2 Related Works
### Artificial Neural Network
The output of a neuron \(j\) in layer \(l\) can be calculated as:  
\[z_{j}^{l}=f(a_{j}^{l})=f(\sum_{i=1}^{n_{l-1}}W_{ji}^{l}a_{i}^{l-1}+b_{j}^{l}) \tag{10}\]  
where \(z_{j}^{l}\) is the weighted sum of inputs to neuron \(j\) in layer \(l\), \(a_{j}^{l}\) is the activation of neuron \(j\) in layer \(l\), \(W_{ji}^{l}\) is the weight of the connection between neuron \(i\) in layer \(l-1\) and neuron \(j\) in layer \(l\), \(a_{i}^{l-1}\) is the activation of neuron \(i\) in layer \(l-1\), and \(b_{j}^{l}\) is the bias term for neuron \(j\) in layer \(l\). The prediction error for a single training example can be measured using a suitable loss function, such as mean squared error:  
\[E=\frac{1}{2}\sum_{k=1}^{n_{\text{output}}}(y_{k}-a_{k}^{L})^{2} \tag{11}\]where \(n_{\rm output}\) is the number of neurons in the output layer, \(y_{k}\) is the desired output for neuron \(k\) in the output layer, and \(a_{k}^{L}\) is the actual output of neuron \(k\) in the output layer. The weight update rule for the connection weights can be expressed as:  
\[\Delta W_{ji}^{l}=-\eta\frac{\partial E}{\partial W_{ji}^{l}} \tag{12}\]  
where \(\eta\) is the learning rate, and \(\frac{\partial E}{\partial W_{ji}^{l}}\) represents the derivative of the cost function with respect to the weight \(W_{ji}^{l}\). The back-propagation algorithm computes the partial derivatives of the cost function with respect to the weights in a layer-by-layer fashion, starting from the output layer and moving backward to the input layer. This process is achieved by applying the chain rule to calculate the gradients. The weight update rule can be further refined using variations of gradient descent algorithms, such as stochastic gradient descent (SGD) or mini-batch gradient descent, which update the weights based on subsets of training examples rather than the entire dataset. These variations aim to improve the convergence speed and overcome issues related to large datasets. The learning process continues iteratively, with multiple passes through the training data, until the network converges to a state where the prediction error is minimized. At this point, the network has learned the underlying patterns and relationships in the training data and can make accurate predictions for new, unseen data. In summary, the learning process of feed-forward neural networks involves forward propagation of data, calculation of prediction error, back-propagation of the error to update the weights using the gradient descent algorithm, and repetition of this process until convergence. Through this iterative process, the network learns to make accurate predictions and generalize to unseen data. Please note that the above explanation provides a high-level overview of feedforward neural network learning and the associated mathematical concepts. There are various additional aspects, techniques, and variations that can be explored in greater detail for a comprehensive understanding of neural network learning.
## 5 Outer and Inner Loop Optimization Algorithm with Online Learning
### Outer Loop Optimization: Novel Dec-MPMAB Algorithm
#### 5.1.2 Decentralized-UCB (D-UCB) Algorithm
Subsequently, each player i plays the selected arm \(a^{*}\) and receives a reward \(Ri,a^{*}(t)\). In Algorithm 1, the initialization step involves setting up arrays for each agent and each arm. The time complexity of this step is O(N * A), where N is the number of agents and A is the number of arms. The outer loop runs for T time steps. Therefore, the time complexity of the outer loop is O(T). There is a nested loop for each agent. The time complexity of the agent loop is O(\(N*\ldots\)), where...represents the complexity of the operations inside the agent loop, which is presented in the inner loop in the following section. The time complexity of selecting an arm depends on the rules from Equation (20). If the selection process involves constant time operations, the time complexity is O(1). Executing the selected arm and observing the reward involves constant time operations, so the time complexity is O(1). Updating the estimated mean reward involves constant time operations, resulting in time complexity of O(1). Updating the selection number involves constant time operations, resulting in time complexity of O(1). Calculating the D-UCB index involves constant time operations, resulting in time complexity of O(1). ```
1:Input: CSI: \(\{h_{i},f_{i},g_{i}\}\), \(\gamma\), \(\tau\), \(T_{d}\), replay buffer capacity \(D\), batch size \(B\)
2:Output: Optimal phase shifting of RIS \(\theta\) and power allocation matrix \(\mathbf{W}\)
3:Initialization: Initialize the following variables: Actor network: \(\pi(s|\theta^{\pi})\) with weight \(\theta^{\pi}\)  Critic networks: \(Q_{i,\pi}(s,a|\theta^{\sharp i}),i=1,2\), with weights \(\theta^{\sharp i}\)  Corresponding target networks \(Q^{\prime}_{i,\pi^{\prime}}\) and \(\pi^{\prime}\) with weights \(\theta^{\pi^{\prime}}\leftarrow\theta^{\pi}\), \(\theta^{\pi^{\prime}}\leftarrow\theta^{\pi}\)
4:for\(i=1\) to \(N\) (num of D2D pairs) do
5: Collect current system state \(s^{(1)}\)
6:for\(t=1,2,\ldots,T(timesteps)\)do
7: Select action \(a^{(t)}=\pi(s^{(t)}|\theta^{\pi}+e_{1},\epsilon\sim\mathcal{N}(0,\sigma^{2})\)
8: Execute action \(a^{(t)}\) to obtain instant reward \(r^{(t)}\) and next state \(s^{(t+1)}\)
9: Store \((s^{(t)},a^{(t)},r^{(t)},s^{(t+1)})\) in the replay buffer \(\mathcal{D}\)
10: Sample mini-batch \(\mathcal{B}\) from replay buffer
11:for j=1,2,...,\(\mathcal{B}\)do
12: Compute target action from Equation (24)
13: Compute the target Q value according to Equation (25)
14:endfor
15: Update the critic network by minimizing the loss function defined in Equation (26)
16:if t mod \(T_{d}\)then
17: Update the actor policy by using the sampled policy gradient of Equation (27), i.e.,
18: Update the target networks by Equation (29)
19:endif
20:endfor
```  
**Algorithm 2** TD3-based RIS phase shifting and power allocation algorithm
# Microalgae: Potential for Bioeconomy in Food Systems
## 6 Agriculture  
Crops can be greatly affected by the lack of phosphate. Novel alternative sources have needed to be developed due to the limited availability of this mineral and the negative impact that the application of inorganic phosphate compounds has on soils [107; 108]. Phosphate is ubiquitously found in biomolecules and physiological processes in living organisms [109; 110]. A viable option is the use of biomass for phosphate recycling [107]. In particular, microalgal biomass can provide an additional benefit because the biomass obtained from wastewater treatment can be used as an environmentally friendly fertilizer [16]. Microalgae are a feasible sustainable alternative and their use as fertilizers offers an opportunity to reduce the current dependence on agrochemicals that in most cases has secondary or detrimental effects [111]. Microalgae benefit plants by increasing nutrient availability, producing phytohormones, forming associations with roots, and protecting against pathogens and pests [16].  
Some researchers have proposed that the mechanisms through which microalgae promote plant growth are associated with signaling and nutrition, such as the uptake of substances excreted by microalgae. However, these mechanisms remain unclear and further studies should be conducted in this regard [111; 112]. Microalgae have been shown to be a feasible alternative to the use of chemical fertilizers because of their ability to accumulate nutrients in the form of polyphosphate granules [111]. The effects of microalgae on plant growth have been studied using species such as _Chlorella_ sp., _Gloecapsa_ sp., _Oscillatoria amphibia_, _Microcytsi aeruginosa_, _Synechococcus rubescens_, and _Cyanobium gracile_[111; 113; 114]. _C. vulgaris_ and _C. sorokiniana_ promoted the growth of _Triticum aestivum_, releasing phosphate gradually and progressively over time [107; 111; 112]. Likewise, fertilization with _Microcytsi aeruginosa_ MKR0105, _Anabaena_ sp. PCC7120, and _Chlorella_ sp. enhanced plant growth and physiological performance by increasing cell membrane stability, chlorophyll synthesis, photosynthetic rate, stomatal conductance, and decreasing intracellular CO\({}_{2}\) concentration [115]. Finally, the biomass of _Scenedesmus_ sp. was proven to be effective as a biofertilizer and can be applied in combination with chemical fertilizers, which may help to reduce excessive use of the latter [116].
# Metagenomic Analysis of Bacterial Diversity on Reusable Tourniquets in Hospital Environments
###### Abstract  
**Background**: Reusable tourniquets are commonly used to aid venipuncture and blood collection. However, inadequate disinfection may lead to bacterial contamination and increase the risk of healthcare-associated infections (HAIs). Tourniquets can function as fomites, facilitating the spread of pathogenic bacteria. This study assessed microbial contamination of reusable tourniquets in the emergency department and operating theatre, focusing on clinically relevant HAI-associated pathogens. **Methods**: Tourniquets from four hospital departments (emergency: adult observation and resuscitation; surgical theatres: pediatric and adult general surgery) were sampled at three time points (n = 12). DNA was extracted and analyzed via 165 rRNA sequencing using NGS technology to identify microbial contamination and taxonomic composition. **Results**: Sequencing revealed 131 bacterial species across the 12 tourniquets, including clinically important pathogens. Among the top ten HAI-associated groups, _Klebsiella_ spp. were detected in 5/12 samples, _Enterococcus_ spp. in 9/12, _Staphylococcus aureus_ in 1/12, _Pseudomonas aeruginosa_ in 9/12, and _Acinetobacter_ spp. in 10/12. No _Escherichia coli_, _Clostridium difficile_, coagulase-negative staphylococci, _Proteus_ spp., or _Enterobacter_ spp. were found. Emergency department tourniquets showed higher bacterial loads; operating theatres had greater species diversity. **Conclusions**: Reusable tourniquets harbor significant bacterial contamination. Considering disinfection challenges and the lack of guidelines, single-use tourniquets should be considered to reduce HAI risk.  
**Keywords:** tourniquets; fomites; cross infection; microbiota; metagenomics; operating rooms; emergency service; hospitals
## 5 Results and Discussion
### Performance of YOLO
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Mean\_P [\%]** & **Mean\_R [\%]** & **Mean\_mAP@.5** & **Parameter** & **Size** & **Epoch** & **InferenceTime** \\ \hline YOLOv3 & 63.7 & 48.1 & 49.1 & 63.0 M & 125.8 MB & 192 & 95.2 ms \\ YOLOv4 & 65.1 & 49.1 & 50.1 & 63.9 M & 246 MB & 183 & 92.6 ms \\ YOLOv4-csp & 66.4 & 50.6 & 51.2 & 61.1 M & 202 MB & 197 & 91.0 ms \\ YOLOv5m & 68.8 & 52.0 & 53.0 & 22.0 M & 42.4 MB & 161 & 79.8 ms \\ YOLOv5l & 70.6 & 53.7 & 54.3 & 47.9 M & 91.9 MB & 179 & 83.9 ms \\ YOLOv6m & 72.1 & 54.4 & 54.8 & 34.9 M & 72.6 MB & 142 & 81.7 ms \\ YOLOv6l & 72.4 & 55.0 & 55.0 & 59.6 M & 114.0 MB & 158 & 84.3 ms \\ YOLOv7 & 73.2 & 56.3 & 55.6 & 36.9 M & 35.5 MB & 155 & 78.3 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of YOLO models under multiple indicators. Model size and inference time: YOLOv7 model has the smallest size of 35.5 MB, which is nearly 1/7 of the largest YOLOv4 model and over 1/3 of the YOLOv3 model, with sizes of 246 MB and 125.8 MB, respectively. Moreover, YOLOv7 achieved the fastest inference time of 78.3 ms, with a 12.9% improvement compared to the slowest inference time of 95.2 ms of YOLOv3. The YOLOv5m and YOLOv7 models have shown particularly impressive results in this aspect, with 42.4 MB and 35.5 MB in size, respectively, and 79.8 ms and 78.3 ms in inference time. * Parameters: YOLOv5m model has the smallest number of parameters of 22.0 M, with a 65.5% reduction compared to YOLOv4, which is the model with the largest number of parameters (63.9 M). This is followed by YOLOv7 with a 42.2% parameter reduction compared to YOLOv4. For further \"compression\", the Tiny or S size type of models can be utilized. However, this will often come at the cost of reduced precision. * Performance: By comparing models' mean detection performance across all different data subsets, we can see how well they function under adverse circumstances. Overall, YOLOv7 performed best, with mean precision of 73.2%, mean recall of 56.3%, and mean mAP@5 of 55.6%. Moreover, YOLOv7 shows the best inference time compared to other YOLO models. The YOLOv61 and YOLOv7 models have shown particularly good overall results with 72.4% and 73.2% mean precision and 55.0% and 56.3% mean recall, respectively. * Epoch: YOLOv6m model has the shortest training time at 142 epochs, with a 28% reduction compared to the longest training time of 197 epochs of YOLOv4-csp. The YOLOv7 shows a 21% reduction in the number of epochs compared to YOLOv4-csp. The YOLOv7 model has the best performance, the smallest size, and the fastest inference time. YOLOv7 is suitable for applications with lower performance requirements or computational constraints. This model has shown significant improvements in all aspects compared to other models, making it a strong contender for various object detection applications. It is important to note that the YOLOv5m and YOLOv6m models also showed impressive results, particularly in the areas of accuracy, model size, and inference time.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
###### Abstract  
Accurately classifying degraded images is a challenging task that relies on domain expertise to devise effective image processing techniques for various levels of degradation. Genetic Programming (GP) has been proven to be an excellent approach for solving image classification tasks. However, the program structures designed in current GP-based methods are not effective in classifying images with quality degradation. During the iterative process of GP algorithms, the high similarity between individuals often results in convergence to local optima, hindering the discovery of the best solutions. Moreover, the varied degrees of image quality degradation often lead to overfitting in the solutions derived by GP. Therefore, this research introduces an innovative program structure, distinct from the traditional program structure, which automates the creation of new features by transmitting information learned across multiple nodes, thus improving GP individual ability in constructing discriminative features. An accompanying evolution strategy addresses high similarity among GP individuals by retaining promising ones, thereby refining the algorithm's development of more effective GP solutions. To counter the potential overfitting issue of the best GP individual, a multi-generational individual ensemble strategy is proposed, focusing on constructing an ensemble GP individual with an enhanced generalization capability. The new method evaluates performance in original, blurry, low contrast, noisy, and occlusion scenarios for six different types of datasets. It compares with a multitude of effective methods. The results show that the new method achieves better classification performance on degraded images compared with the comparative methods.  
genetic programming; degraded image classification; evolutionary computation; program structure; information transmission +
Footnote †: journal: Medical Image Analysis  
1
Footnote 1: [[https://doi.org/10.3390/appl4041613](https://doi.org/10.3390/appl4041613)]([https://doi.org/10.3390/appl4041613](https://doi.org/10.3390/appl4041613))
# European Geostationary Navigation Overlay Service Performance Prediction +
## 3 EGNOS Performance Prediction
### The Use of Macro Models  
Several macro models are in use to predict EGNOS performance. We used a proprietary macro model provided to us by the ESA to compare our results against. The European Satellite Services Provider (ESSP) also employs a (proprietary) macro model [2], which possibly builds on the model from [1]. Finally, our partner Iguassu employs a macro-model in their SBAS simulator [7]. These models mainly rely on satellite and ground segment geometry, and a simple relation (e.g., in the form of tables) with the UDREI and GIVEI to the set of RIMS stations tracking the satellites and the set of ionospheric piercing points (IPPs) around the ionospheric grid point (IGP) to which the GIVEI relates. Due to their simplicity, these models are easy to implement and fast to run. Their simplicity is expected to limit their performance, and we would expect that machine learning can improve prediction performance considerably.
# Relationship Between Ball Speed and Spin in Elite Youth Table Tennis Players Using Optical Sensors
## 3 Results  
### Correlation Between Ball Linear Speed and Rotation  
The correlation analysis revealed a strong positive linear relationship between the ball's speed and spin (\(r\) = 0.96, \(R^{2}\) = 0.93, \(p\) < 0.001) (Figure 8). A linear regression is used to describe the model and is summarized here in Equation (6).  
\[\omega=2v \tag{6}\]  
with \(\omega\) as ball spin (RPS) and \(v\) as ball speed (m.s\({}^{-1}\)).
## 5 Discussion
### Distribution of Mixed Sedimentation and the MSR Reservoirs
#### 5.3.2 Distribution of the MSR Reservoirs  
Changes in provenance supply provide varying mineral compositions and particle sizes in sedimentary rocks, significantly influencing the distribution of MSRs reservoirs. As shown in Figure 7, the four types of MSRs reservoirs differ in quality. Sandstone and sandstone-bearing dolomites have the highest porosity and permeability among these MSRs, followed by dolostone, sandy dolostone, and dolomitic sandstone. In particular, sandy dolostone exhibits the poorest reservoir quality and often cannot serve as a conventional reservoir.  
The distribution of sandstone reservoir is directly related to regions with abundant provenance supply, where distributary channels or mouth bar developed (Figure 11). On both sides of the channel beds, sand particles are reworked by tidal currents, resulting in sand particles mixing with dolomite crystals, thereby forming sandstone-bearing dolomite and dolomitic sandstone reservoirs (Figure 11). Dolostone-bearing sand reservoirs are distributed in the transitional zone between sandy flat and dolomite flat (Figure 11). In regions without direct provenance supply, thinner and isolated sandy dolostone deposits formed in the subtidal zone (Figure 11), controlled primarily by storm-induced tidal currents. The worst physical properties of sandy dolostone are mainly due to poor particle sorting and small pore throats, which are caused by density flow deposition under storm conditions.