esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Disease Spreading Behavior Oriented to Three-Layer Complex Networks
## 3 Construction of Three-Layer Complex Networks
### The Degree Distribution Attribute of Three-Layer Complex Networks  
The degree distribution properties of the network are analyzed under three different three-layer complex network construction algorithms described above. The number of network nodes is set as follows: \(N_{\alpha}=500\), \(N_{\beta}=400\), and \(N_{\gamma}=300\). The whole structural properties of the network are analyzed by varying the intra-layer and inter-layer link probabilities. Due to the large number of parameters involved in the experiments, only a few special degree distribution are listed below. The specific experimental parameters are shown in Table 2, and Figure 2 shows the degree distributions of the three-layer EEE, three-layer EWE, and three-layer EBE networks, respectively.  
It seems that the three-layer networks have different degree distribution properties, such as the complexity of the three-layer complex network model construction algorithm, the difference in the size of the network nodes, the variation in the probability of connecting edges of intra-layer and inter-layer, and the different structural properties of each single layer. To summarize the above experimental results, the differences in the average degree values of the single layers on a three-layer network lead to multiple peaks in the degree distribution of the network, and the overall degree distribution of the three complex three-layer networks are all normally distributed uniform networks. However, it does not mean that the power-law property is masked. If the intermediary layer is denser and the other layers are relatively sparse and less randomly connected, the degree distribution of the three-layer networks may also tend to be distributed in power-law.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Type** \\ **Para** \\ **(\(m_{0}\) = 10, \(m\) = 3)** \\ \end{tabular} & \begin{tabular}{c} \(p_{\alpha\beta}\) \\ **P** \\ **(\(m_{0}\) = 10, \(m\) = 3)** \\ \end{tabular} & \begin{tabular}{c} \(p_{\alpha\beta}\) \\ **P** \\ **(\(k_{\alpha}\))** \\ \end{tabular} & \begin{tabular}{c} \(\langle k_{\beta}\rangle\) \\ **(\(k_{\gamma}\))** \\ \end{tabular} & \begin{tabular}{c} \(\langle k\rangle\) \\ **(\(k\))** \\ \end{tabular} & \
\begin{tabular}{c} \(\langle k\rangle\) \\ **(\(k\))** \\ \end{tabular} &
\begin{tabular}{c} **Degree Distribution** \\ **(\(k\))** \\ \end{tabular} \\ \hline three-layer network EEE & 0.1 & 0.1 & 0.1 & 172.904 & 164.565 & 108.415 & 157.5983 & single-peak \\  & 0.1 & 0.3 & 0.3 & 73.614 & 82.305 & 48.446 & 73.77 & bi-peak \\ \hline three-layer network EWE & 0.1 & 0.1 & 0.1 & 73.904 & 46.7945 & 48.3158 & 62.0367 & single-peak \\  & 0.3 & 0.1 & 0.1 & 174.312 & 46.6815 & 108.630 & 118.905 & tri-peak \\ \hline three-layer network EBE & 0.1 & 0.1 & 0.1 & 73.872 & 48.7855 & 49.2558 & 62.915 & single-peak \\  & 0.3 & 0.1 & 0.1 & 173.318 & 49.074 & 108.405 & 118.905 & tri-peak \\ \hline \hline \end{tabular}
\end{table}
Table 2: The degree distribution characteristics of the three-layer complex networks.
# Onboard Processing in Satellite Communications Using AI Accelerators
## 2 Onboard AI Processing
### AI Gain  
Existing solutions for the proposed use cases often have inherent limitations in translating theory to practice when handling the computational complexity and/or latency required for results, especially when dealing with an ample search space or high degrees of freedom. This has been the typical motivation for using AI, as it has shown a strong potential to overcome this challenge through data-driven solutions. This paper will con  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**KPI**} & \multicolumn{6}{c}{**Scores**} \\ \cline{2-7}  & **0** & **1** & **2** & **3** & **4** & **5** \\ \hline Onboard & Not & & Network of & & & & \\ Applicable & & regenerative & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{4}{*}{AI Gain} & \multirow{4}{*}{No gain} & Human & The previous & The previous & The previous & The previous \\  & & intervention & score + & score + & score + & score + \\  & & reduction & & & & & processing \\  & & & reduction & & & & time reduction & gain \\ \hline \multirow{4}{*}{Complexity} & \multirow{4}{*}{Unfeasible} & Extensive & Computational & Computational & & & \\  & payload & level + & level + & Computational & & \\  & changes & memory-intensive & memory-intensive & level + & & \\  & & required & + power and time & + power & memory-intensive & \\ \cline{1-1}  & & & constraints & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: KPI analysis for the applicability of onboard AI implementation use cases.  
Figure 1: AI chipset implementation in the space segment for onboard processing. The training of the ML algorithm is performed offline in the ground segment with a training database, thus saving the obtained model, and the AI chipset can go on board the satellite using the model for inference.  
sider these criteria and evaluate the expected latency, complexity, and throughput gains achieved with AI-based techniques in each considered use case.
## 2 Results and Discussion
### Optimization of the Magnetic Solid-Phase Extraction (MSPE) Procedure
#### 2.4.3 Optimization of Elution Procedure  
For the MSPE method, it was crucial to choose an effective desorption solvent for target compounds. Hence, in this study, the elution of nine fungicides was investigated carefully using several types of elution solvents, including pure acetonitrile and a series of acetonitrile solutions containing different proportions of ammonium hydroxide (\(v/v\)). The obtained results in Figure 4d indicated that pure acetonitrile was not very suitable for the desorption of nine fungicides, owing to the terrible desorption efficiency of the target fungicides (19.8% to 31.5%). It could be concluded that some other interactions took place, except a \(\pi\)-\(\pi\) interaction between fungicides and DETA-MPs-GO. The amino groups of DETA-MPs-GO were able to form stronger interactions with fungicides, for instance, hydrogen bonds. As shown in Figure 4d, alkaline acetonitrile performed better desorption recoveries than pure acetonitrile. This is because the hydrogen bond was easy to destroy under alkaline conditions. Considering the cost and convenience, acetonitrile containing 1% ammonium hydroxide showed the highest recovery; therefore it was chosen as the desorption solvent in subsequent tests.  
The effects of the elution time and elution solution volume were also investigated. This revealed that 1.0 mL of acetonitrile containing 1% ammonium hydroxide was sufficient to desorb all nine fungicides, and 5 min was enough for the elution process to obtain the desired elution efficiency.
## 3 Fault Diagnosis Experiment and Analysis
### Experimental Data Preparation  
In this paper, the sliding time window method was used to perform overlapping sampling on the original vibration signals to reduce the risk of overfitting in the model.  
The accuracy of the model will increase with the increase of time window length, but a long time window length will make the calculation more complex and have little impact on the improvement of accuracy. Currently, the commonly used time window length is 1024, and this length had been proven to be a suitable time window length [36; 37]. According to reference [25; 38], the optimal input image size for the TLVIT model is 224 \(\times\) 224. Therefore, in order to ensure that the pre-trained position encoding can continue to be used, this paper set the input image size of the model to 224 \(\times\) 224. According to the calculation method of GAF, when the sliding time window length is 1120, the size of the generated GAF image is 1120 \(\times\) 1120, which is exactly an integer multiple of the input image size selected in this article. Therefore, while ensuring the accuracy of the model, in order to facilitate the compression of image size by using the Python programming language, this article ultimately chose 1120 as the length of the time window. The sliding window length selected in the reference literature is 1024, with a sliding step size of 341 [37]. In this paper, the sliding step size is adjusted to 350 accordingly, referencing its window overlap rate. As shown in Figure 3, the blue box indicates the nth data sample, while the red box represents the (n+1)th data sample.  
To ensure consistency in the number of channels for the ViT model, the sampled data were processed by using GASF, GADF, and CWT. These methods generated three pseudo-color images, which can be used as a control group in the study. The three pseudo-color images were converted to grayscale images, and an IFI was created by concatenating these grayscale feature images in the order of the RGB channels. It is as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Motor Load (HP)** & **Motor Speed (RPM)** \\ \hline
1 & 1772 \\
2 & 1750 \\
3 & 1730 \\ \hline \end{tabular}
\end{table}
Table 1: Motor loads and motor speeds.
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation
### Methods for Comparison
#### 5.2.1 SH-MUSIC  
The SH-MUSIC algorithm is the MUSIC method conducted in the spherical harmonic domain. A spectrum is computed for all the possible candidates of DOAs, and the peak is found to give the corresponding DOA of the source  
\[(\hat{\theta}_{d},\hat{\phi}_{d})=\arg\max_{(\phi_{d})\in\mathfrak{L}}\frac{1} {\mathbf{y}^{H}(\theta,\phi)\mathbf{U}_{n}\mathbf{U}_{n}^{H}\mathbf{y}(\theta,\phi)}, \tag{21}\]  
where \(\mathfrak{L}\) is the DOA search grid set containing all the possible DOA candidates, \(\mathbf{U}_{n}\) is the noise sub-space decomposed from the covariance matrix of the array signals using eigenvalue decomposition [45]. The covariance matrix can be computed from (14).
## 2 Material and Methods
### Performance Assessment of the Models and Statistical Analyses  
The performance of models was evaluated with the use of correlation coefficient (\(R\)), root mean square error (\(RMSE\)), mean absolute error (\(MAE\)), and mean absolute percentage error (\(MAPE\)), as shown below:  
\[R=\frac{1}{n-1}\sum_{i=1}^{n}\frac{\left(M_{i}-\dot{M}\right)\left(E_{i}-\dot {E}\right)}{S_{M}\ S_{E}} \tag{4}\]  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(E_{i}-M_{i}\right)^{2}}{n}} \tag{5}\]  
\[MAE=\sum_{i=1}^{n}\frac{\mid E_{i}-M_{i}\mid}{n} \tag{6}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{E_{i}-M_{i}}{E_{i}}\right|\times 100 \tag{7}\]  
where \(n\) is the number of data instances, \(M_{i}\) measured values, \(E_{i}\) predicted values, \(M\) mean measured values, \(\dot{E}\) mean predicted value, \(S_{M}\) measured target values sum, and \(S_{E}\) predicted target values sum. The \(R\) was analyzed to assess the reliability of model prediction [45].
## 4 Result and Discussion
### Verification of the Mixing Temperature Prediction Model  
To validate the mixing temperature prediction model, we compared the AHU mixing temperature sensor data, the thermocouple data, and the mixing temperature prediction model results. Based on the data obtained from the field experiment of the automatically controlled HVAC system in the laboratory of Yeungnam University, the mixing temperature prediction model expression was substituted into Equation (10). Table 6 summarizes the cases where the difference between the thermocouple data and the mixing temperature prediction was smaller than that between the thermocouple data and the mixing temperature sensor measurements. Thus, the prediction model was validated. The absolute error between the existing mixing temperature sensor and the mixing temperature prediction model was found to be as low as 0.008 \({}^{\circ}\)C and as high as 0.422 \({}^{\circ}\)C, demonstrating its performance with regard to predicting the mixing temperature.  
Figure 8: CFD simulation boxplot result.
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
## 1 Introduction
The usefulness of the existing models is limited because they use obsolete semiautomated tracking and segmentation algorithms that still require tedious and time-consuming demarcation, no better than visual inspection in terms of efficiency [15; 16]. Recently, advances in deep learning technology have dramatically improved the accuracy of image classification by computers to a level exceeding that of human eyes [17]. Moreover, deep learning models can classify objects in images without human intervention if appropriately trained on a sufficient amount of data. Due to the accuracy and capability of automation, deep learning is expected to reduce errors in reading medical images, including conventional radiography, MRI, and CT images [18; 19; 20]. VFSS analysis may be one of the best candidates for the application of deep learning models, considering its vulnerability to human fatigue and errors. Although deep learning models may provide a novel alternative to semiautomated methods that have been used till date with limited success, the applicability of deep learning in VFSS analysis has rarely been studied. Only two studies have been published in which fully automatic detection of the hyoid bone and pharyngeal phase has been attempted using a deep learning model [16; 21], whereas the identification of abnormal findings in VFSS images using deep learning models has not previously been attempted. The present study focuses on the detection of airway invasion (including the laryngeal penetration and aspiration) that has the utmost clinical importance in the analysis of VFSS images. Airway invasion is the main pathologic process that can cause pneumonia or asphyxia and should be clinically considered. To the best of the authors' knowledge, machine-based identification of airway invasion from the VFSS images has not been tried or published yet, whether automatic or not. The aim of the present study was to develop a deep learning model that can detect airway invasion in a completely automated manner, without human intervention. If successfully developed, the model will be able to reduce the effort and fatigue of the clinicians, and thereby improve the reliability of the analysis of the VFSS, by providing screening information about the existence of airway invasion. Based on this model, the authors expect to develop a more comprehensive automatic analysis model for VFSS analysis.
## 1 Introduction  
Radiochromic films change colors upon exposures to radiation doses as a result of solid-state polymerization (SSP) [1], where energy is transferred to the receptive part of the monomers [2]. Commercially available radiochromic films are primarily designed for, and have become widely used in, clinical X-ray dosimetry [2]. Application of radiochromic films for clinical X-ray dosimetry has, in essence, relied on dose-response curves (response in terms of film coloration) obtained through calibration. However, many intriguing properties of radiochromic films are not yet fully understood, including their responses to fluorescent light [3, 4] and ultraviolet (UV) radiation [5, 6, 7, 8]; the energy dependence of their responses [9, 10, 11, 12, 13]; the effects of polarization and scanning orientation [14, 15, 16]; and the effects of mechanical pressure [17] and auto-development [18], etc. In order to understand these intriguing properties, development of a theoretical model at both atomic and macroscopic scales is pertinent. To the best of our knowledge, there are at present no theoretical models in the literature that can reproduce experimentally obtained dose-response curves of radiochromic films. The present work was devoted to developing such a model. Despite the fact that radiochromic films were primarily designed for clinical X-ray dosimetry and that most dose-response curves were understandably obtained for X-rays, development of a theoretical model in the atomic scale using X-ray data would involve added complications of ionization, non-uniform energy deposition, as well as dispersed doses [19] caused by secondary electrons set in motion by the indirectly ionizing X-ray photons. Such complications will inevitably introduce added uncertainties to the model and might overshadow the basic SSP processes. Fortunately, dose-response curves for the Gafchromic EBT3 film have also been obtained for UV radiation [8], for which complications caused by secondary electrons described above are insignificant. These data would provide reference information for the development of our model. A future paper will cover further investigations that add processes involving secondary electrons created by X-ray photons into the model developed here.  
The present task was divided into two parts, namely, (1) the atomic scale model and (2) the macroscopic scale model, in which molecular dynamics (MD) and finite element method (FEM) simulation techniques were coupled. The active layer in the EBT3 film consisted of diacetylene (DA) pentacosa-10,12-diynoate monomers [20] (hereafter referred to as DA monomers), which were modelled using the MD technique. The degrees of SSP in the atomic scale upon different UV exposures were obtained in order to determine the absorption coefficients of the active layer, which were then input into the FEM model. The classical steady-state Helmholtz equation was engaged to model the re-emittance (i.e., reflection) from and transmittance through the active layer using the FEM technique. The multifrontal massively parallel sparse direct solver (MUMPS) [21] was employed to solve the present numerical problem.
## 5 Conclusions  
Aiming to reduce the manpower and material resources required to manually spray trees in the north of China and to address the problems of existing tree-spraying devices, an automatic tree-spraying device based on the collaborative operation of multiple nozzles was proposed and researched, and its working process is to improve spraying efficiency and quality by imitating the ring structure of trees and to realize the high-efficiency spraying of trees. The mechanism is mainly composed of a spraying device, lifting mechanism, cleaning mechanism, and motion structure. The adaptability of the spraying device to different working environments was studied using a finite element simulation. The results show that the spraying device works well in different environments. In terms of the design and overall structure of the control system of the tree-whitening machine, Delta PLC is used as the controller in the hardware design and sensor selection. The control system software design, measured via a test of the machine efficiency of 300 trees/h/hour, can adapt to trunk circumferences in the range of 500-1250 mm, has a spraying angle adjustment range between 80 and 140\({}^{\circ}\), and achieves an average spraying coverage of up to 96.5%. Compared with the traditional tree-whitening machine, the working efficiency is increased by 7.1%, the range of the trunk circumference can be adapted to expand by 20%, the adjustment of the spraying angle can be increased, and the average spraying coverage is increased by 6.7%, which is more advantageous.  
Conceptualization, S.T. and Y.C.; methodology, Y.Z. and D.W.; investigation, L.Q. and X.Z.; writing--original draft preparation, S.T.; writing--review and editing, L.Q., X.Z. and P.Y.; software, Y.C. and L.Q.; validation, S.T., Y.C., and P.Y.; formal analysis, Y.Z. and X.Z.; project management, S.T., Y.C., and Y.Z.; supervision, S.T. and L.Q.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the National Key Research and Development Program, and the subject number is 2022 YFD2001905.  
Not applicable.  
The data presented in this study are available in article.  
Author [PERSON] was employed by the CIMIINO Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Results
### Impacts of Cultivated Land Change on GPP
#### 3.3.4 The Spatial Pattern of Sensitivity of GPP to Cultivated Land Change  
As shown in Figure 11, the number of insensitive counties was relatively stable between 1990 and 2000 and 20014, while the number of low-sensitivity and highly sensitive counties decreased slightly and the number of moderately sensitive counties increased in the later period. In terms of land types, the grain production potential was not very sensitive to the area change in paddy fields and dryland in various periods. The number of insensitive and low-sensitivity counties accounted for approximately 90%. In contrast, the number of highly sensitive and moderately sensitive counties showed the following characteristics: cultivated land \(>\) paddy field \(>\) dryland. It was also found that the number of highly sensitive and moderately sensitive counties in the two periods was lower than that from 1990 to 2014, regardless of the overall cultivated land perspective or paddy fields and dryland perspectives. This result indicates that the longer the period and the more complex the land type structure, the higher the sensitivity of the grain production potential to the land area, while the land-use policies in different periods played a key role in mitigating the changes in GPP caused by land-use conversion.  
Figure 10: The movement of the barycenter of (**a**) cultivated land area and (**b**) grain production in Hunan Province from 1990 to 2014.  
From a spatial perspective, the spatial patterns of the sensitivity of cultivated land and paddy fields were more similar. As time progressed, the highly sensitive and insensitive levels of paddy fields and drylands tended to move westerly, and the concentration of highly sensitive, moderately sensitive, and insensitive levels increased. In general, the GPP of the contiguous region of Changde-Yiyang-Xiangtan-Loudi-Xiangxi was more sensitive to changes in cultivated land area, especially the change in paddy fields, while the GPP of eastern and southern Hunan was less sensitive to changes in cultivated land area. There were two possible reasons for this phenomenon. First, compared with eastern Hunan and southern Hunan, the natural production conditions in these areas were slightly worse, and the stability of land quantity had a more obvious role in ensuring the balance of the GPP. Second, because the proportion of paddy field area decreased in these regions, the production potential of paddy fields per unit area was larger than that of dryland; thus, changes in paddy field area had a greater impact on the regional GPP.  
Figure 11: The sensitivity of grain potential productivity changes to arable land in Hunan Province.