esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Analysis of Numerical Simulation Results
#### 3.4.3 Interface Shear Stress Result Analysis  
Figure 18 illustrates the stress distribution curve along the anchor-soil interface, extending along the length of the anchor rod. These values cannot be directly read but must be back-calculated using Equation (6) from earlier in the text. The curve demonstrates that the interface stress (or bond strength) is not uniformly distributed and, as the load increases, the maximum bond strength shifts deeper into the soil. Additionally, the maximum bond strength gradually increases until it reaches its peak value, which, for the RS6-01 anchor parameters, is 0.151 MPa. After the peak, the stress also increases with increasing displacement or ultimate pullout capacity of the anchor. As seen from the figure, the pre-peak stress reaches a plateau, with the displacement exceeding 16 mm before becoming nearly constant. This linear segment indicates that the bond effect at the front of the anchor has failed, and the stress present at the interface is now residual stress.  
By analyzing the interface stress, it can be concluded that the equivalent bond strength parameter used in traditional homogenization theories cannot accurately describe the actual stress distribution along the anchor-soil interface. Systematic analysis of the pullout test results shows that the interface bond strength decays exponentially along the axial direction of the anchor. As the anchorage length increases, the proportion of the effective bearing section decreases non-linearly. Once the critical anchorage length is exceeded, the distal section becomes ineffective in transmitting stress, and the discontinuity at the interface leads to a reduction in the overall bond strength parameter.  
Since pile elements are used for simulation, it is not possible to directly observe the interface stress between the anchor and the surrounding soil. However, we can indirectly derive the stress variation trend at the interface by analyzing the soil's shear stress increments and the stress in the YY direction. Figure 19 presents the maximum shear stress increment and the stress in the YY direction for the surrounding soil when the self-drilling anchor reaches its ultimate pullout capacity. Upon examining the contour maps, it is evident that the peak soil stress and maximum shear stress increment are mainly concentrated at the front end of the anchor. This result aligns with the curve comparison, confirming its accuracy. Additionally, it can be observed in the contour map that the soil stress in the area of maximum axial force at the anchor's front end is not high. This is because, when the ultimate pullout capacity is reached, the front end of the anchor undergoes failure and  
Figure 18: Stress distribution curve of the anchor-soil interface.  
slip. Consequently, in this segment, it is assumed that only residual stress and the stress transmitted from the rear soil influence the region.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
###### Abstract  
Chitosan is a natural biopolymer that can be suitable for a wide range of applications due to its biocompatibility, rigid structure, and biodegradability. Moreover, it has been proven to have an antibacterial effect against several bacteria strains by incorporating the advantages of the electrospinning technique, with which tailored nanofibrous scaffolds can be produced. A literature search is conducted in this review regarding the antibacterial effectiveness of chitosan-based nanofibers in the filtration, biomedicine, and food protection industries. The results are promising in terms of research into sustainable materials. This review focuses on the electrospinning of chitosan for antibacterial applications and shows current trends in this field. In addition, various aspects such as the parameters affecting the antibacterial properties of chitosan are presented, and the application areas of electrospun chitosan nanofibers in the fields of air and water filtration, food storage, wound treatment, and tissue engineering are discussed in more detail.  
electrospinning; chitosan; natural biodegradable polymers; _Pleurotus ostreatus_; antibacterial; water filtration; medical textiles; nanocomposites; food storage; tissue engineering; sustainability +
Footnote †: journal: Medical  
combination with other polymers in composites. Finally, this review focuses on the various possible applications, depending on their compatibility and appropriateness.
# Embedding Equality Constraints of Optimization Problems into a Quantum Annealer +
## 7 Improved Embeddings for the Case \(k=1\)
### Increasing the Size of the Constraint  
In the previous subsections, we only showed how to embed constraints using \(n+2\) Chimera graph cells, almost three times fewer than with the general approach. However, since our design uses a single row of cells, it means that on a \((n\times n)\)-grid Chimera graph we could only embed a constraint with \(n-2\) variables, or 10 variables in case of D-Wave 2X and 14 variables in case of D-Wave 2000Q. We now show that we can easily adapt the above approach to use all the unit cells of the Chimera graph, allowing us to be able to embed constraint with up to \(n^{2}-2\) variables.  
The reason we were restricted to a single row in the previous design was that we used only horizontal edges (couplers) to connect to the neighboring cells (Figure 7). Hence, in order to allow such sequences to occupy more than one row, we need to define \"corner\" tiles that have combinations of horizontal and vertical connecting edges and thereby allow \"turns\", and use them together with the tiles defined for the single-row case to produce snake-like configurations of the type illustrated on Figure 10.  
Therefore, we have to introduce \"corner\" analogues of the \"straight\" tile types from Figure 7. The only difference between the new and old tiles is the positions of the _type_ variables, which for the new types should be on one row and one column of the Chimera graph cell. On Figure 7a, the top red and the side blue edges illustrate the positions of the interface variables for a corner tile allowing a clockwise turn.  
To find the \(\mathbf{a}\), \(\mathbf{b}\), and \(\mathbf{c}\) coefficients for the new gadgets, we have to solve a similar but different optimization problem compared to the one described in Section 6, for the new gadgets only. We map the variables as illustrated on Figure 11. Implementing the optimization problem in AMPL and solving it with the Gurobi solver, we find \(\gamma=4\) and \(\mu=-13\).  
Figure 10: This example illustrates the connections between the tiles in a solution for the multi-row case for a Chimera graph of size \(4\times 4\) cells, each cell representing a tile with one problem variable, with the exception of first and last tiles that contain no problem variables.  
Please note that we do note need to compute different sets of gadgets for clockwise and counterclockwise turns because of the symmetries in the Chimera graph cell. Although the values of \(\mathbf{a}\), \(\mathbf{b}\), and \(\mathbf{c}\) are generally different for clockwise and counterclockwise turns, they can be converted from one into the other by suitable permutations of the variable positions.
## 3 Results and Discussions  
### Analysis of Changes in the Intensity of Building Damage and Mining Impacts over Time  
Tables 3 and 4 and Figures 3-6 present the changes over time of the average values of the technical condition parameters (\(s_{z},w_{u}\)) and indicators of the development hazard from mining impact (\(\varepsilon_{max}\), \(PGA_{H}\) i \(a_{sg}\)).  
It was found that with the passage of time not only the degree of wear \(s_{z}\) of the studied buildings increases, but also the extent of their damage represented by \(w_{u}\). Despite the increase in natural wear associated with the passage of time, as a result of repairs and maintenance of the studied buildings, a decrease in the contribution of damage in technical wear (\(w_{u}/s_{z}\)) is observed from 22% in 2002 to 20% in 2012 for large-block buildings and from 33% to 31% for large-panel buildings. This may also indicate a decrease in the intensity of impacts that are potential causes of damage. The results of the dependency analyses in the studied groups of buildings are certainly influenced by construction-material differentiation and age difference. For example, large-block buildings are on average more than nine years older than the buildings built with large-panel technology, and this allows us to think that natural wear directly related to the age of the building plays a bigger role in the technical wear of large-block buildings.  
The analysis of trends in changes of average values of mining influence indicators, i.e., \(\varepsilon_{max}\) and \(PGA_{H}\), indicates that in recent years there has been a decrease in both the values of horizontal ground deformation and the intensity of mining tremors.  
With lower parameters of the most intense tremors (decrease in the average value of the \(PGA_{H}\) index), there is a smaller increase in the value of the \(a_{sg}\) index, which also takes into account the repetition of tremors affecting the building.
## 6 Case Study  
We apply our method using two different models on four different datasets that are often used in literature for crowd-counting applications. These two models are MCNN [62] and CSRNet [63], as shown in Figures 10 and 11, respectively. We use four different datasets, namely UCF-QNRF, UCF-CC-50 [96], World Expo datasets [97], and ShanghaiTech B [62]. We use the same data augmentation and training routine for the floating point models as presented in [98].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & _Top-1 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline VBMF & 75.09\% & 0.015 & 70.96\(\times\) \\ Sensitivity & 81.89\% & 0.034 & 32.16\(\times\) \\ Random & 78.17\% & 0.040 & 26.89\(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Impact of rank selection on the ResNet-20 architecture on CIFAR-10.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & _Top-1 (\%)_ & _Param Size (MB)_ & _Compression_ \\ \hline \multicolumn{3}{c}{ResNet-20} \\ \hline Resnet20-FP [71] & 92.60\% & 1.1 \\ Mobile-net [93] & 90.18\% & 12.4 \\ Mobile-netv2 [94] & 91.29\% & 9.0 \\ Efficient-net [95] & 91.330\% & 11.4 \\ ResNet20-Xnor [10] & 83.93\% & 0.047 \\ \hline Ours & 81.89\% & 0.034 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of different architectures on CIFAR-10.  
error (MAE from 365.2 to 416 and RMSE from 577.2 to 621.5). For CSRNet, our method leads to a compression ratio of 23\(\times\), reducing the model size from 65 MB to 2.74 MB, with only a slight increase in MAE (from 111.4 to 121.2) and RMSE (from 199.4 to 198.3). These results demonstrate that our approach effectively compresses models while keeping the accuracy close to the floating-point counterparts, making such models more suitable for deployment on edge devices with limited storage and computational resources.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Update of Pheromone  
The update of the pheromone means that, after each ant successfully finds the destination node or the data is transmitted on the path, the pheromone is deposited on the path. As time goes by, the pheromone will periodically evaporate, which helps to find the optimal path and accelerate the convergence of the algorithm. The residual energy and the distance of the node are considered in the update of the pheromone, and the reward and punishment mechanism is introduced [26]. When the forward ant finds the path successfully, the backward ant generated by the destination node returns along the original path and updates the pheromone (reward). When the ant fails to find a path, the last node generates a backward ant to return along the original path and update the pheromone (punishment). The update rules are as follows:  
\[\tau(t+1)=(1-\rho)\cdot\tau(t)+\rho\cdot\Delta\tau, \tag{13}\]  
and  
\[\Delta\tau=I_{RS}\cdot\frac{E_{min}}{L_{path}}, \tag{14}\]  
where \(I_{RS}\) indicates the routing result. If the ant routes successfully, \(I_{RS}=1\); otherwise, \(I_{RS}=0\). If a path does not pass ants for a long time, on the one hand, it indicates that the path may have a low pheromone, and, on the other hand, the path may have failed. Therefore, the pheromone needs to be evaporated periodically.  
\[\tau(t+t_{0})=\rho\cdot\tau(t). \tag{15}\]  
Here, \(t_{0}\) is the evaporation period. Whenever the node completes the data transmission, it triggers the timer and starts to evaporate the pheromone. All the symbols used in this algorithm are described in Table 1.
## 2 Materials and Methods
### Proposed Methodology for Signal Processing
#### 2.3.3 Proposed Method for Extracting Characteristics Using ICA Together with r\({}^{2}\)  
The proposed method for extracting features from EEG signals is based on the method Independent Component Analysis (ICA) and the coefficient of determination r\({}^{2}\) and involves the following steps:  
* one recording made during training is imported;
* the signals corresponding to the motor imagery movement of the right hand, the left hand and relaxation are extracted;
* each signal (relaxation or motor imagery movement of the right or left hand) is converted into the frequency domain using the power spectral density;
* filter each signal (motor imagery of the movement of the hand right/left and relaxation) with a wavelet UWT;
* the coefficient of determination (according to the method proposed in the previous paragraph) is calculated for the filtered signal corresponding to the motor imagery load of the right/left hand versus the filtered signal corresponding to relaxation;
* graphical display of results according to frequency and channels;
* the ICA algorithm is applied, which is based on the Second-Order Blind Identification (SOBI) algorithm, for all three signals, in order to estimate the mixing matrix and the sources;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject S**} & **Imagery** & \multirow{2}{*}{**Frequency (Hz)**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Max r\({}^{2}\) Channel**} \\  & **/Motion** & & & & \\ \hline St 1 & Right/Left & 15-24/15–20 Hz & CP3, P3, C3/CP4, C4 & 0.321 (CP3)/0.499 (CP4) \\ St 2 & Right/Left & 18–22/20–22 Hz & CP3, P3/C4 & 0.256 (CP3)/0.218 (CP4) \\ St 3 & Right/Left & 12–22/14–20 Hz & CP3, C3, P3/CP4, C4 & 0.332 (CP3)/0.360 (P4) \\ St 4 & Right/Left & 18–22/12–20 Hz & P3, P2, CP3/CP4, C4 & 0.450 (P3)/0.360 (CP4) \\ St 5 & Right/Left & 8–10/18–20 Hz & P3, P4/CP4 & 0.231 (P4)/0.268 (CP4) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information about the subject in different repetitive stages.  
the columns of the source matrix are normalized;  
the norms of the columns of the mixing matrix are calculated;  
these columns are ordered in descending order according to the corresponding norms;  
the first three columns of the mixing matrix are retained and the rest are equal to zero;  
the signal is reconstructed after the first three sources;  
the coefficient of determination is calculated for the reconstructed signals after the application of the ICA corresponding to the imagery tasks of the right/left hand movement versus the reconstructed signal corresponding to the relaxation;  
graphical display of results according to frequency and channels.  
The method was implemented in the LabVIEW software environment.
## 5 Analytical Techniques for Detection and Quantification of PFAS
### Discussion  
Analytical techniques for the detection of PFAS have come a long way and continue to develop. Currently, modern PFAS analysis can use various methods, including the combination of various hyphenated chromatography techniques such as liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). These techniques allow for accurate and sensitive detection of individual PFAS and sometimes even identifying specific PFAS precursors. So far, the main innovations rely on in situ and high throughput techniques which are necessary to answer the monitoring requests. In parallel, the implementation of non-targeted screening methods will allow the understanding of the novel classes of PFAS introduced in the industry products and, also, the characterization of their presence in environment and food. The development of even more accurate and sensitive in situ and screening techniques will constitute the main strategy to fight PFAS contamination and to provide reliable inputs to regulatory bodies and lawmakers.
## 2 Materials and Methods
### Slime Mold Algorithm (SMA)
#### 2.3.5 Optimization Process  
Initiated with a population size of 100, the SMA underwent 1000 iterations to ensure convergence to optimal solutions. A unique computational strategy was employed, where the weights in each row were multiplied by a common factor during each iteration to enhance the exploration efficiency within the solution space.  
Through the SMA, the optimization of the neural network's weights and biases was methodically conducted, aligning with the defined fitness function. This strategic approach enabled the precise tuning of UAV operational parameters, underscoring the efficacy of SMA in complex system optimizations, particularly in enhancing UAV efficiency against a backdrop of variable environmental conditions and operational constraints.
# Partially versus Purely Data-Driven Approaches in SARS-CoV-2 Prediction
## 2 Related Work
In [13], epidemiological equations and data-driven neural network model is used to predict that countries in which rapid government interventions and strict public health measures for quarantine and isolation were successful in halting the spread of infection and prevent it from exploding exponentially. In [14], the researchers proposed the SEIR model and artificial intelligence (AI) approach to predict the COVID-19 in China. They predicted that the pandemic should peak by late February, showing a gradual decline by end of April. A generalized SEIR epidemiological model in [15] is proposed to predict the COVID-19 pandemic by applying a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver. The goal of their study was to improve the reliability of predictions within 30 days. The researchers in [16] studied the use of machine learning, an artificial neural network (ANN), and a simple statistical test to identify SARS-CoV-2 positive patients from full blood counts without knowledge of symptoms or history of the individuals. The research found that with full blood counts random forest, shallow learning, and a flexible ANN model predict SARS-CoV-2 patients with high accuracy between populations on regular wards and those not admitted to hospital or in the community. Epidemiological models are used in [17] to predict the number of infected individuals and the mortality rates of the COVID-19 outbreak. However, they claimed that the available pandemic data lack essential elements and contains uncertainty, which make epidemiological models not accurate for long-term prediction. To overcome this weakness, they proposed a hybrid machine learning approach to improve the prediction. The hybrid machine learning methods combined ANFIS and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) to predict time series of infected individuals and mortality rate. The models were applied to data from Hungary and were able to predict that by late May, the outbreak and total mortality will drop substantially. In [18], an enhanced SEIR model is used to analyze the epidemic's progression of COVID-19 in eight different countries. The enhancements incorporated in the model reflect the societal feedback on pandemic and confinement features. The model with its enhancement was applied to predict the virus propagation under different conditions of confinement.
## 4 Experiment and Result Analysis  
### Experimental Platform and Dataset Construction  
A time-multiplexed polarization imaging system was selected to capture the polarization images of small scratches on the surface of photovoltaic cells, as shown in Figure 7. Images of polarized light intensity in different directions were obtained by placing a rotatable polarizer in front of the imaging detector. The whole system is mainly composed of three parts: a shortwave camera, DC power supply, polarizer turntable and laptop computer with polarizing image acquisition software. The shortwave infrared camera model is Owl 640 S, featuring a response wavelength ranging from 900 nm to 1700 nm and an internal InGaAs sensor. The polarizer is a precision line grid polarizer, model 20 LP-NIR, with an extinction ratio of 1000:1. The excitation power supply is a Goodyear 3030 DD DC power supply, capable of providing a constant current output within a voltage range of 0-30 V and a current range of 0-3 A. The experimental object is a monocrystalline silicon solar cell sheet, measuring 156.6 mm \(\times\) 156.6 mm with a thickness of approximately 0.1 mm. The scratches on the cell have a maximum length of 9 mm and a width of 1 mm. The positive and negative poles of the cell are led out with leads to facilitate connection to the DC regulated power supply.  
The experiment was conducted in a dark room, with the photovoltaic cell positioned perpendicular to the surface of the mobile platform. The distance between the camera lens and the center of the photovoltaic cell was maintained at 60 cm, and the DC power output was set at 1V-3A to make the photovoltaic cell positive. Multiple images of the photovoltaic cell were captured at different observation angles, corresponding to polarization azimuths of 0\({}^{\circ}\), 45\({}^{\circ}\), 90\({}^{\circ}\) and 120\({}^{\circ}\). The Stokes parameters and polarizati  
Figure 7: Short wave polarized infrared shooting device diagram.  
obtained by means of Equations (1) and (2). The DOP images obtained by analysis are marked with marking software, and the marking information is exported in txt form.  
The key parameters of the deep learning platform in this experiment are as follows: Operating system: Ubuntu 18.04; graphics card model: NVIDIA RTXA 2080 Ti with 11 GB memory. CPU model: Intel(R) Xeon(R) Platinum 8255C [EMAIL_ADDRESS] GHz. Deep learning framework Pytorch1.7.0; CUDA version 11.0. The defect data set was randomly divided into training set, test set and verification set according to the ratio of 7:21, with 309, 87 and 35 pictures, respectively.