esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
These findings point to the possible use of chitosan-PVP hydrogels in chronic wound-management [15]. Chitosan is a derivative of deacetylated chitin and has excellent hemostatic properties due to its ability to form cationic clusters that can interact with the anions of red blood cells, thus inducing platelet aggregation [16]. Gelatin has excellent characteristics such as low antigenicity and good biocompatibility and biodegradability. Gelatin can activate platelet aggregation and is also used as an absorbable hemostatic agent [17]. Gelatin has previously been mixed with many types of biopolymers to enhance its chemical stability and mechanical properties. Chitosan has amido groups and can coordinate and cross-link with gelatin. Chitosan/gelatin composite has been shown to have better hemostatic effects in rabbit artery bleeding and liver model tests, liquid absorption, and platelet aggregation than chitosan and gelatin alone [18]. Egg-yolk oil (EYO) is extracted by refining cooked egg yolks. EYO contains unsaturated fatty acids, saturated fatty acids, fat-soluble vitamins, phospholipids, and cholesterol. It has been used as a traditional medicine in China for more than a thousand years. The first record of EYO treatment for burns and scalds was found in \"Set Prescription\", datedaround 500 AD, and it was also recorded in the famous \"Compendium of Materia Medica\", written during the Ming Dynasty [19]. Currently, EYO is widely used to treat all kinds of burn wounds. [PERSON] et al., applied EYO in the treatment of third-degree burns in rats and showed abundant re-epithelialization without tissue scarring in comparison with silver sulfadiazine treatment groups [20]. [PERSON] et al., identified the hemostatic function of EYO. Coagulation assays suggested that EYO stimulates the intrinsic blood coagulation system and activates the fibrinogen system [21]. Moreover, lecithin in EYO is negatively charged and binds with positively-charged polymers; while electrostatic repulsion stabilizes an emulsion and prevents the emulsified oil droplets from coalescing and attaining oil-water separation [22]. Therefore, lecithin can be used in the emulsification and blending of hydrophobic drugs and hydrophilic polymers and is often blended with chitosan. [PERSON] et al., applied chitosan-based gels mixed with EYO to enhance the local treatment of dermal burns and found that these mixtures had excellent restorative effects [23]. [PERSON] et al., found that EYO is helpful for 3T3 fibroblast adhesion and growth [24]. The purpose of this study is to create a hydrogel-like film by combining EYO with PGC composite film so as to evaluate its properties, biocompatibility, and the ability of EYO in repairing radiation dermatitis wounds through in vivo experiments.
## 3 Results and Discussion
### Method Validation of the Developed HPLC Analytical Method
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Analyte**} & \multicolumn{2}{c}{**Intra-Day (\(n=5\))**} & \multicolumn{4}{c}{**Inter-Day (\(n=5\))**} & \multicolumn{4}{c}{**Repeatability (\(n=6\))**} \\ \cline{2-9}  & \multirow{2}{*}{**Conc.**} & **Observed** & **Precision** & **Accuracy** & **Observed** & **Precision** & **Accuracy** & **RSD (\%) of** & **RSD (\%)** \\  & **Conc.** & **(\(\mu\)g/mL)** & **(\%)** & **(\%)** & **Conc.** & **(\%)** & **Conc.** & **(\%)** & **Retention** & **of Peak** \\ \hline \hline \multirow{4}{*}{**1**} & 12.50 & 12.73 & 0.91 & 101.84 & 12.80 & 0.88 & 102.39 & \multirow{4}{*}{0.05} & \multirow{4}{*}{0.16} \\  & 25.00 & 25.91 & 1.40 & 103.64 & 25.99 & 1.24 & 103.96 & & \\  & 50.00 & 51.13 & 0.36 & 102.25 & 51.25 & 0.68 & 102.51 & & \\  & 12.50 & 12.56 & 0.75 & 100.44 & 12.95 & 2.67 & 103.58 & & \\
**2** & 25.00 & 24.99 & 1.23 & 99.98 & 25.41 & 2.21 & 101.64 & 0.05 & 0.16 \\  & 50.00 & 51.03 & 2.81 & 102.05 & 50.98 & 2.69 & 101.97 & & \\  & 12.50 & 12.97 & 1.49 & 103.79 & 13.33 & 2.54 & 106.62 & & \\
**3** & 25.00 & 26.05 & 1.65 & 104.18 & 26.57 & 2.56 & 106.28 & 0.04 & 0.52 \\  & 50.00 & 51.50 & 1.51 & 103.00 & 51.72 & 1.79 & 103.44 & & \\  & 20.00 & 20.72 & 1.28 & 103.62 & 21.30 & 2.65 & 106.52 & & \\
**4** & 40.00 & 41.64 & 1.67 & 104.11 & 42.25 & 2.10 & 105.63 & 0.03 & 0.35 \\  & 80.00 & 82.13 & 1.11 & 102.66 & 82.43 & 1.40 & 103.03 & & \\  & 12.50 & 13.09 & 2.00 & 104.74 & 13.41 & 2.70 & 107.26 & & \\
**5** & 25.00 & 26.27 & 2.10 & 105.09 & 26.63 & 2.27 & 106.53 & 0.03 & 0.46 \\  & 50.00 & 51.73 & 1.50 & 103.47 & 51.85 & 1.46 & 103.70 & & \\  & 12.50 & 13.06 & 1.82 & 104.48 & 13.34 & 2.52 & 106.69 & & \\
**6** & 25.00 & 26.27 & 1.96 & 105.06 & 26.59 & 2.01 & 106.34 & 0.03 & 0.41 \\  & 50.00 & 51.67 & 1.31 & 103.34 & 51.73 & 1.20 & 103.46 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision validation of compounds **1-18** in the developed analytical assay.
## 3 Methodology
### Overview of the Model  
Represented in Figure 1, initially, we convert the absolute positions \(\left(x_{i}^{t},y_{i}^{t}\right)\) in the trajectory data into relative positions and input them into an LSTM encoder. Simultaneously,the pedestrian's historical trajectory is fed into both the Scene Potential Module and the pedestrian Transition Intention Module, constructing the Scene Tensor and the Transition Tensor, respectively. Subsequently, a feature fusion module combines the environmental features, pedestrian intention features, and encoded trajectory features corresponding to pedestrian coordinates. The present module utilizes an attention method to combine the previously described characteristics, which are subsequently inputted into a graph attention layer to capture the interaction aspects among pedestrians. Finally, the intermediate states processed by the GAT are sent into a decoder to predict the future trajectory of the pedestrian. Further details will be elaborated in the next section.
## 5 Results
### Time and Space Complexity  
As already shown in Figure 6, the Q-gram filter and the FRMS together perform a real-time fuzzy matching run. The speed is affected by the alpha threshold parameter, which affects the size of the pool of potential candidates in the second stage of FRMS. Testing was performed to compare relative time complexity on a single-core Intel i7 11370H device with a maximum turbo frequency of 4.80 GHz and 16 GB of RAM. The results of these tests are presented in Tables 3 and 5. The results of the tests are only meant to demonstrate the  
Figure 8: Comparison of the relative performance of Q-gram filter+FRMS and hybrid similarity functions.  
Figure 7: Relative performance of selected similarity functions from the group of hybrid, edit, and Q-gram similarities.  
Figure 9: Comparison of the relative performance of Q-gram filter+FRMS in an ablation study.  
real-time capacity of the system architecture we proposed, which was used in comparison to the trivial configuration of individual similarity functions. The achieved overall result of 220 ms for the Q-gram filter and FRMS is measured for all datasets, and the system architecture shown in Figure 5 is used. Furthermore, we deal separately with the analysis of the time complexity of the Q-gram filter and FRMS.
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 3 Document Image Degradation Methods
### Background Texture Problem  
Methods for processing document images' background texture can be divided into two kinds.  
The first method is based on binarization. Image binarization converts the acquired image into a binary image, and the converted image performs better in text recognition. Binarization can be divided into threshold-based and numerical-based binarization. Threshold-based binarization methods, such as the Otsu algorithm [19], separate the foreground from the background of an image by finding a threshold value that maximizes the variance between the classes. It has the advantage of a fast computation speed for dealing with background texture problems, but the processing effect is still not ideal. [PERSON] [20], Niblack [21], and other local threshold algorithms have better separation effects and applicability performance than their global counterparts. However, some problems exist because the thresholding performance depends heavily on the sliding window size. In addition, the parameter tuning method [22], the mixed-threshold algorithm of the clustering method [23], and the advantages of the global threshold and local threshold algorithms are integrated to better segment the foreground and background and reduce the impact of the original image background texture on the document image. Numerical theory-based methods such as the conditional random field (CRF)-based Howe algorithm, [24], which uses the Laplace operator to minimize the target energy function, can have relatively stable results in most cases but has high requirements for image quality. Ref. [25] estimated local threshold values using high-contrast image pixels to binarize the document image.  
The second method is based on pixel classification. For example, [PERSON] et al. proposed a model [26] based on U-Net. They combined it with the structural advantages of CNN for historical document images to improve the ability of the model to obtain context information. However, manual parameter adjustment and data enhancement models are needed, and the model's generalization ability needs to be improved. In 2021, [PERSON] et al. proposed an FD-Net model [27], as shown in Figure 3. It is a full dilation convolutional network, replacing all the downsampled and upsampled layers with dilation convolution. It can enhance the receptive field and segmentation accuracy. However, a grid effect, i.e., a discontinuity in the convolution kernel, may arise, which is unfavorable for the pixel-by-pixel prediction task.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 7 Results and Discussion  
Based on the experiments described above, this section evaluates the performance and accuracy of the proposed DRAMBR in identifying false MRs under various scenarios.  
First, we present the results obtained from applying DBSCAN. The outputs from this stage are essential for the subsequent stage, where we implement the iForest methodology to evaluate the DBSCAN findings. Following this, we analyze the aggregated results using GMM to finalize the reporter's behaviors. After that, we implement the combined Random Forest and XGBoost to estimate the effectiveness and the accuracy of the results before moving to the final stage of updating the reputation.  
Our findings provide insights into the system's ability to differentiate between honest and malicious behaviors, showcasing the impact of reputation-based decision-making compared with the existing certificate-based system.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 3 Problem Statements  
In this paper, the MCS is assumed to be organized in a cluster topology. In every cluster, to investigate the spatial correlation of data samples, the sensing region is divided into grids of equal size based on the two-dimension coordinate, and users are densely distributed in these grids. A participant selection scheme will be built merely for those grids whose owner cluster consists of \(M\) grids. A fundamental assumption of the participation selection scheme is that the sensing data sampled by neighboring grids are likely to be correlated. Nevertheless, how to model and exploit such spatial correlation still needs further investigation.
# A Brief Review on Challenges in Design and Development of Nanorobots for Medical Applications
## 2 Challenges in the Design and Development of Nanorobots
### Monitoring
#### 2.5.3 X-ray Imaging  
X-ray is a well-established imaging technique. There are many techniques based on X-ray imaging, including CT Scans, radiography and fluoroscopy. Among these techniques, fluoroscopy seems to be the best approach to be used for the localization of nanorobots. It has been used by [PERSON] et al. [122] for visual servoing and control of the orientation of needles in animal studies. There is a good review paper by [PERSON] et al. on this topic [123]. Some advantages of X-ray fluoroscopy are real-time imaging, high sensitivity, it can distinguish material densities very well, and it can penetrate into the human body in depth. The main disadvantage of X-rays is the radiation that causes cancer.
# Research on Shore-Based River Flow Velocity Inversion Model Using GNSS-R Raw Data
## 4 River Flow Velocity Inversion Result and Analysis
### Influence of Elevation Change Rate  
It has been shown in Section 2.3 that the velocity of satellite movement has a direct influence on the elevation change rate of the satellite. Theoretically, the larger the elevation change rate is, the larger the \(\Delta f_{m}(t)=f_{rm}(t)-f_{dm}(t)\) will be; \(\Delta f_{m}(t)\) will superimpose with the low frequency \(f_{flow}\) generated by the river flow and affect the inversion accuracy of river flow velocity. Figure 15 shows the sensitivity of river flow inversion accuracy to satellite elevation and elevation change rate, respectively.  
The following can be concluded from the results: (1) satellite elevation (Figure 15a) has little influence on inversion accuracy; (2) on account of the low elevation change rate of the BDS satellite during the experimental period, the BDS satellite overall has the lowest  
Figure 14: BDS B11 inversion results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Satellites System**} & \multirow{2}{*}{**PRN**} & \multicolumn{2}{c}{**Close Points Rate**} & \multirow{2}{*}{**MAE(m/s)**} & \multirow{2}{*}{**RMSE(m/s)**} & \multirow{2}{*}{\(R\)} \\  & & **(Close Points/Total Points)** & & & \\ \hline GPS L1 & PRN 4 (1 st period) & 100\% & 0.028 & 0.036 & 0.855 \\ GPS L1 & PRN 4 (2 nd period) & 13\% & 0.080 & 0.090 & 0.401 \\ GPS L1 & PRN 9 & 0\% & 0.103 & 0.14 & 0.378 \\ BDS B1I & GEO 2 & 100\% & 0.048 & 0.063 & 0.806 \\ BDS B1I & IGSO 10 & 100\% & 0.061 & 0.073 & 0.763 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of satellite inversion results.  
elevation change rate and the more stable inversion precision (Figure 15b); and (3) the elevation change rate of satellites plays a significant role in the inversion precision of river flow velocity. The experimental inversion results are consistent with the theoretical hypothesis. It is worth mentioning that it can be seen in Figure 15 that over a relatively long period, the elevation angle change rate has a negative impact on the inversion accuracy. On the contrary, the GPS PRN 4 inversion results show an abnormal phenomenon for a short time when the elevation angle change rate is greater than 2.5 degree/min. This may be due to the velocity recorded by the current meter being incidentally close to that at the specular reflection point of THE GPS PRN 4; because of this, a deviation exists between the water flow velocity at the specular point and the water flow velocity detected by the river current meter.  
Table 3 shows the correlation coefficient between inversion accuracy and elevation change rate. \(R\) of GPS 4 and GPS 9 are 0.652 and 0.876, respectively. The comprehensive correlation coefficient reaches up to 0.727, proving a strong correlation between the inversion accuracy error and the elevation angle change rate.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 4 Derivation of Analytical Expression for the Error Probability of the Quasi-Bell State  
In this section, we derive an analytical expression for the error probability of the AQC system when using the quasi-Bell state. As an example of the quasi-Bell state, we consider the case where the maximum quasi-Bell state is used. To calculate the error probability of given by Equation (22), it is necessary to obtain eigenvalues for the difference between the two received quantum states \(\Psi^{(0)}_{\rm SA}-\Psi^{(1)}_{\rm SA}\). The coherent state is represented using either an infinite dimensional vector or an infinite matrix (i.e., the density operator). The maximum quasi-Bell state used here is constructed from the coherent states, and its density operator is thus infinite. The eigenvalues of \(\Psi^{(0)}_{\rm SA}-\Psi^{(1)}_{\rm SA}\) also take the form of an infinite matrix and are generally difficult to calculate. However, in this paper, we find that the received quantum states \(\Psi^{(0)}_{\rm SA}\) and \(\Psi^{(1)}_{\rm SA}\) can be represented by an \(8\times 8\) matrix when a special orthonormal basis is used. Additionally, by deriving the eigenvalues for the \(8\times 8\) matrix of \(\Psi^{(0)}_{\rm SA}-\Psi^{(1)}_{\rm SA}\), we can also derive an analytical expression for the error probability of the AQC system using the quasi-Bell state.  
There are four steps that must be considered in the derivation of this analytical expression. Each step is explained individually below.
## 5 Experimental Results
### Results without Pathologies
#### 5.2.7. Results for XGBoost with Evolutionary Feature Selection  
Results obtained with the XGBoost using the evolutionary GA were as follows.  
**GA with _Stud selection_ for XGBoost (XGB-S):** As shown in Table 2, a mean accuracy rate of 0.6989 and a maximum of 0.7424 using 12 variables were achieved. In this case, among the variables selected, we found some of the most common ones, such as sex, age, edu, or soda. In addition, there are variables such as vegetation or those related to alcoholic beverages.  
**GA with tournament selection for XGBoost (XGB-T):** Using the 11 selected variables, an average precision ratio of 0.6948 and a maximum of 0.7288 were obtained, as can be seen in Table 2. In addition to the most common variables, such as sex, age, education, or soda, we can find variables related to alcoholic beverages, both distilled and fermented, vegetable intake, and earning.  
Table 4 gives a sample of time in seconds taken by the GA for the different algorithms and datasets. One run of each type was carried out individually (no other program was running in parallel) on the same machine to obtain these values. The time is strongly related to the algorithm chosen.  
Figure 11. Heat map representation of the frequency of different variables obtained using FS with GA, dataset without pathologies.  
Distributed according to a \(\chi^{2}\) distribution with \(k-1\) degrees (\(k=19\)) of freedom [33]. As usual, we can define the \(p\)-value as the probability of obtaining a result as extreme or even more extreme than the observed one, provided that the null hypothesis is true. As usual, we have chosen a \(p\)-value of 0.05. For this \(p\)-value, the critical value according to the distribution of \(\chi^{2}\) with 18 degrees of freedom is 28.8693. To reject H0, it is necessary to exceed this value. Applying the formula of [PERSON]'s statistic, Equation (7), a value of 34.6421 is obtained, so the critical value is exceeded and the hypothesis H0 is rejected, confirming that there are significant differences between the models.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 1 Introduction
[26] believe that NS-DBD will generate random local thermal disturbance near the wall in a relatively long-time scale (\(>100\) us) after discharge, and will form compression wave in a very short time (1-10 us). [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] et al. [14; 19; 20; 27; 28] suggest that shock wave, which is the dominant factor of flow control, is generated after NS-DBD discharge due to thermal effect. This paper focuses on the research of NS-DBD plasma actuator on the control of increasing lift and reducing drag of two-section airfoil. The researches of NS-DBD plasma actuator on the control of flow separation of airfoil have been carried out extensively [29; 30], but the research on multi-section airfoil is few. The control of flow separation, lift improvement, and drag reduction for multi-section airfoil is of great significance and value in practical engineering applications, especially in the takeoff and landing stages of aircraft. In this paper, NS-DBD plasma actuators are arranged near the gap between the main wing and the flap, and the influence on the aerodynamic performance of the flap lift device is analysed by adjusting the discharge parameters of the actuator. This method has the advantages of simplicity, energy savings, and small size, and it is applicable to the case in which the space for mounting the actuator around the flap is relatively limited. To some extent, it can replace the traditional multi-element flap lift device, which has a complicated mechanism structure and additional weight. It can be referred to as a plasma flap.
## 3 The Experimental Results
### Packet Delivery Ratio
It is worth noting here that rather low values of the PDR were reported for the higher traffic intensities. Moreover, the LTE-D2D mode achieved only around 70% of the PDR, even in the case of the highest perimeter and with higher numbers of generated messages.
## 4 Results
### Application of Landslide Susceptibility Models
The results of landslide susceptibility zoning using the RFFT model show that these percentages are 37.62% (very low), 21.41% (low), 7.79% (moderate), 12.25% (high), and 20.93% (very high), respectively. For the DFT model, the percentages of very low, low, moderate, high, and very high classes are 19.70%, 30.59%, 23.72%, 16.50%, and 9.49%, respectively.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline
### Superscalar Pipeline Ping-Pong Buffer  
In the SAR imaging system, the read data need to be buffered into the on-chip RAM first, and then sent to the calculation engine such as FFT for processing after an entire row or column of data has been accessed. Therefore, a data buffer is a relatively important data exchange method in SAR imaging systems. Superscalar pipelining can dramatically improve the efficiency of data exchange compared to traditional non-pipelined buffering and most current scalar pipeline processing.