esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Flight Control System Health Assessment under Uncertainty
### Probability Distribution Based on the Maximum Entropy  
The maximum entropy is a criterion for selecting the statistical characteristics of random variables that are most in line with the objective situation. When the information of unknown parameters is partly obtained, such as the mean and variance, the maximum entropy selects the probability distribution that conforms to this information and has the largest entropy value as the statistical characteristic of a parameter. The essence of entropy is the uncertainty of random variables; the largest entropy value indicates the largest uncertainty. In this sense, the probability distribution based on the maximum entropy is the most uncertain inference in accordance with the known information. Therefore, for the probability distribution of the assessment output in this paper, the maximum entropy can be considered as a choice that meets the objective situationbest.  
Assuming that \(x\) is a one-dimensional continuous random variable, and its probability density function exists, the entropy of \(x\) can be expressed as:  
\[H(x)=-\int_{\Omega}f(x_{i})\text{ln}(f(x_{i}))dx=\int_{\Omega}f(x_{i})\frac{1} {\text{ln}(f(x_{i}))}dx, \tag{20}\]  
where \(\Omega\) is the integration space and \(f(x)\) is the probability density function of.  
Take the first \(l\)-order center moment of \(x\) as a constraint. The maximum entropy can be expressed as:  
\[\begin{array}{l}maxH(x)\\ s.t.\left\{\begin{array}{l}\int_{\Omega}f_{x}(x)dx=1\\ D_{i}=\int_{\Omega}\left(x-\mu\right)^{i}f_{x}(x)dx,i=1,2,\ldots,l\end{array}\right. \end{array} \tag{21}\]  
where \(\mu\) is the mean value of the random variable and \(D_{i}\) is the central moment of the random variable.  
Introduce the Lagrangian multiplier and define the function:  
\[\begin{array}{l}L(x)=-\int\limits_{\Omega}f_{x}(x)\text{ln}f_{x}(x)dx+( \lambda_{0}+1)\left(\int\limits_{\Omega}f_{x}(x)dx-1\right)\\ +\sum\limits_{i=1}^{l}\lambda_{i}\left(\int\limits_{\Omega}(x-\mu)^{i}f_{x}(x) dx-D_{i}\right)\end{array} \tag{22}\]  
where \(\lambda_{i}\) is the pending multiplier, \(i=1,2,...,l\).  
Calculate the derivative of \(L(x)\) with respect to \(f_{x}(x)\) when the partial derivative is 0. The maximum entropy PDF can be obtained as:  
\[f_{x}(x)=\exp\left(-\sum_{i=0}^{l}\lambda_{i}(x-\mu)^{i}\right), \tag{23}\]  
Substitute Equation (23) into Equation (21) and the undetermined multiplier can be solved. In order to ensure the accuracy of \(f_{x}(x)\), the first four central moments of the variable are usually selected as the constraints, and as the integral of the probability density function in its integral space is always 1, there are five constraints to solve for five undetermined multipliers from \(\lambda_{0}\) to \(\lambda_{5}\); theoretically, there is an optimal solution.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 4 Animal Models of \(\beta\)-Thalassemia
### Mouse Models
To address this issue, researchers have developed several humanized mouse models derived from Hbbth3 mice. These novel models incorporate mutations of the human \(\beta\)-globin gene into the mouse genome, thereby providing a more accurate representation of human diseases. Hbbth4 mice represent the first animal model induced by a known human splice mutation. [PERSON] et al. [46] incorporated a human \(\beta\)-globin gene carrying the IVS-2-654 mutation into the mouse locus to replace the deleted adult globin gene in Hbbth3 mice. Following successful integration, this mouse model produced incorrectly spliced transcripts, resulting in heterozygotes exhibiting characteristics of intermediate \(\beta\)-thalasemia, while homozygous mice were unable to survive. The proportions of the lung, liver, kidney, and heart relative to the body weight of Hbbth4 mice are approximately equal. However, these mice exhibit splenomegaly and extramedullary hematopoiesis in the liver, resembling the symptoms observed in patients with \(\beta\)-thalasemia. Furthermore, at 5 months of age, substantial iron deposits were observed in the renal tubules of the mice [46]. Currently, two methods have been validated to ameliorate \(\beta\)-thalasemia caused by the IVS-2-654 mutation in this model. First, [PERSON] et al. [47] successfully inhibited the abnormal splicing sites in precursor mRNA through the intravenous administration of splice-switching oligonucleotides (SSOs), thereby prompting the splicing mechanism to reselect the correct splice sites. Secondly, [PERSON] et al. [48] successfully demonstrated the application of the CRISPR/Cas9 genome editing method to target the CT mutation at IVS-2-654 and the recessive splice site at IVS-2-579, resulting in the elimination of an additional 73 nucleotides and the correction of abnormal splicing in \(\beta\)-globin RNA. Furthermore, bacterial recombination techniques were employed to introduce specific \(\beta\)-thalassemia mutations into BACs containing human genomic fragments, resulting in the creation of transgenic animals and the establishment of a more comprehensive model. The simplicity and high fidelity of this recombination method facilitate the generation of various genomic constructs that harbor common \(\beta\)-thalassemia mutations, including humanized mice that carry the HBe, IVS1-110, and CD 41/42 variants [49; 50; 51; 52].
## 4 Model Analysis and Results
Figure 10: Experimental measurements and model predictions, i.e., Equation (6), for 16 November 2019. Figure 9: Experimental measurements and model predictions, i.e., Equation (6), for 6 April 2020. In order to further validate the accuracy of the model, we applied it in days that were not included in the data of the regression process. We chose days with a wide variety of weather conditions, in order to observe the models behavior, and the results are presented in Figures 9-11. From the experimental measurements and the predictions of the empirical model of Equation (6), of Figures 9-11, it can be seen that they follow the same behavior as those of Figures 5-8. Specifically, the model is very accurate in cases of both low wind speedand relative humidity, without precipitations and diverges in more severe weather changes. The values of \(R^{2}\) for the days that were not used in the regression method for the extraction of the model are presented in Table 6, as follows:  
Figure 3:0 Experimental measurements and model predictions, i.e., Equation (6), for 17 November 2019. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Day** & **Time** & \(R^{2}\) \\ \hline \multirow{2}{*}{6 February 2020 (Figure 5)} & 00:00–01:00 & 0.632 \\ \cline{2-3}  & 20:00–23:59 & \textless{}0.5 \\ \hline \multirow{2}{*}{16 February 2020 (Figure 6)} & 00:00–05:00 & 0.741 \\ \cline{2-3}  & 20:00–23:59 & 0.584 \\ \hline \multirow{2}{*}{15 February 2020 (Figure 7)} & 00:00–05:00 & \textless{}0.5 \\ \cline{2-3}  & 20:00–23:59 & 0.776 \\ \hline \multirow{2}{*}{30 January 2020 (Figure 8)} & 00:00–05:00 & 0.701 \\ \cline{2-3}  & 20:00–23:59 & 0.784 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: \(R^{2}\) values for several days. According to Table 7, it is clear that the attenuation coefficient mostly depended on temperature, wind speed, and relative humidity, as the standard deviation in cases of almost same values of these parameters was very low. On the other hand, the model might become more accurate if more atmospheric and weather parameters, i.e., atmospheric pressure, are investigated.
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
## 3 MOEA/D with Optimal DE Schemes
### The Combined Decomposition Method  
As shown in Section 2.2, three decomposition approaches are introduced, on account of the friendly expansibility of the weighted sum approach and the Tchebycheff approach. We combine them in advance and propose a combined decomposition method, named the weighted sum \(\delta\) Tchebycheff (WST) approach. Here, we refer to a concept from [31]: for subproblem \(i\), let \(x^{i}\) be the current solution in the decision space; the improvement region of \(x^{i}\) can be defined as: \(\{F(x)|x\) is better than \(x^{i}\) for subproblem \(i\}\). As illustrated in Figure 1, for two objective optimization as an example, \(\lambda_{i}=\{0.5,0.5\},i=1,\ldots,N\), the improvement regions of two commonly-used decomposition approaches, (a) the weighted sum approach and (b) weighted Tchebycheff approach, are shown. In each sub-figure, the grey area is the improvement region. The square point is the current solution of the subproblem \(i\) with the direction vector and the triangle point being its optimal solution. Thereafter, the optimal target of the square point is to find its corresponding triangle point. As described in Equations (2) and (3) and from Figure 1, we inform the reader that the weighted Tchebycheff approach barely gives priority to the salient objective, while on the contrary, the weighted sum approach attaches importance to all of the objectives. Therefore, we would like to combine these two approaches together by considering them on both a salient field and an entire field. A combined decomposition method, named the WST method, is introduced. The optimization problem of the WST approach is defined as:\[\begin{split}\text{minimize}& g^{\text{\emph{west}}}(x|\lambda,z^{*})= \frac{(m-1)^{2}}{m^{2}}\max_{1\leq i\leq m}\{\lambda_{i}|f_{i}(x)-z_{i}^{*}| \}+\\ &[1-\frac{(m-1)^{2}}{m^{2}}]\sum_{i=1}^{m}\lambda_{i}f_{i}(x)\\ \text{subject}&\text{to}\quad x\in\Omega\end{split} \tag{11}\]  
where \(m\) is the number of objectives, \(\lambda\) is the weight vector as defined in (2) and \(z^{*}=(z_{1}^{*},...,z_{m}^{*})\) is the reference points as defined in (3). For each \(i=1,\ldots,m\), \(z_{i}^{*}=\min\{f_{i}(x)|x\in\Omega\}\).  
Additionally, we would like to point out that the WST method combines the advantages of the weighted sum and the weighted Tchebycheff approach. It not only emphasizes one single aspect, but also pays attention to the entire direction of the objects. Our experimental results have proven its validity in the end.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 2 Unmanned Ship-Wind and Wave Coupling CFD Model
### Analysis of the Berthing Characteristics of Unmanned Ship
#### 2.5.1 The Influence of Speed  
The law describing the relationship between water or air resistance and speed with regard to an unmanned ship is shown in Figure 4, and the law describing the relationship between viscous resistance or differential pressure resistance and speed is shown in Figure 5.  
It can be seen from Figures 4 and 5 that the maximum resistance is 67.05 N and the minimum resistance is 1.16 N. The total resistance was analyzed; and showed a non-linear relationship with the flow velocity. The air resistance kept increasing at a high speed within 0-2 m/s, but the total value was relatively small.  
Figure 4: Relationship between water or air resistance and ship speed.  
Figure 5: Relationship between differential pressure or viscous resistance and ship speed.  
For value above 3 m/s, changes were reduced with the increase in wind speed, and it basically remained between 5 and 6 N. Water flow resistance is the main source of resistance for unmanned ships, accounting for more than 80% of the total resistance, and is related to two factors, i.e., water density and water dynamic viscosity.  
It is worth mentioning that the relationship between the resistance and the speed of an unmanned ship navigating on water is not a simple linear relationship, but a non-linear exponential relationship, which may be related to the presence of free liquid surfaces.
## 5 Simulation
### Performance of the Online Causal-Factor-A3C-Based Algorithm for Optimal Resource Allocation  
We use the Rayleigh distribution [36] to simulate fading conditions in wireless channels, especially in scenarios where there is no line-of-sight path. Additionally, we adopt the Monte Carlo method to simulate the dynamic behavior of channels in real-world scenarios, as it can account for various uncertainty factors and generate random samples that follow the expected distribution.  
1. Spectral Efficiency and Energy Efficiency with Optimal Resource Allocation vs. number of BS antennas and RIS units  
The developed algorithm will optimize the control of RIS phase shifting and allocation of transmit power to stimulate the full potential of multi-RIS-assisted wireless networks. The performance of the developed reinforcement learning algorithm based on CF-A3C is illustrated below. Figure 7 compares the different learning process numbers of RIS units (\(M=2,4,8,12\)). As shown in Figure 8, increasing the number of RIS units can improve energy efficiency, and the proposed CF-A3C algorithm significantly outperforms other benchmark algorithms.  
2. Evaluation of Online Learning Performance  
Subsequently, an assessment was conducted to observe the learning dynamics of the average reward of the network across successive time steps. As depicted in Figure 9, there  
Figure 8: Average EE vs. num. of RISx with different methods.  
Figure 7: Average rewards vs. num. of RIS units with M = 2 \(\times\) 2, 4 \(\times\) 4, 8 \(\times\) 8, 12 \(\times\) 12.  
is a noticeable upward trend in the average reward as time progresses. Moreover, the newly devised resource allocation algorithm, leveraging CF-A3C reinforcement learning, showcases its ability to approach the optimal solution efficiently within a set timeframe. This contrasts with the baseline algorithm, as our approach maintains convergence even amidst the variability of wireless channels.
## 4 Results and Discussion
### Rotor Optimization Results
#### 4.2.1 Meridional Optimization Results  
Figures 17 and 18 show the change of meridional contours after the rotor optimization. It can be seen that both optimized rotors have a thinner backplate and a larger trailing edge height which has a backward axial sweep. The thinner backplate contributes the most to reduce the rotor weight. The lower hub contour benefits the rotor weight also, but at the risk of higher stress. The backward axial swept trailing edge also reduces the rotor weight a little, but most importantly it lowers the centrifugal force concentrating on the outlet hub region. Figure 19 shows the comparison of meridional contours between T-100 baseline and T-100 re optimized design. It can be seen that T-100 re optimized design is much more compact.  
Figure 20 shows the total-to-static efficiency alone with the relative trailing edge height. It indicates that the larger trailing edge height could improve the aerodynamic performance of the radial inflow turbine, but most individuals violate the stress constraint. Figure 21 shows the equivalent stress with respect to the sweep direction. It can be seen that backward axial sweep benefits the stress for both T-100 and T-100 re rotor, which reveals that the centrifugal force concentrating on the hub region decreases due to the axial sweep of the trailing edge at the tip. The conflic  
Figure 19: Meridional contour of T-100 baseline and T-100 re optimized design.  
Figure 17: Meridional contour of the baseline and the optimized design (T-100).  
Figure 18: Meridional contour of baseline and optimized design (T-100 re).  
aerodynamic performance and centrifugal force is especially true in this area, so higher trailing edge height with backward axial sweep becomes a trade-off solution between these two aspects.
# Development of Screen-Printable Nafion Dispersion for Electrochemical Sensor
## 1 Introduction
The dispersion is not very compatible with screen printing, due to its low viscosity and low boiling point. The inks for screen printing need to possess high viscosity at a low shear rate and a shear-thinning behavior in which the viscosity decreases with increase in shear rate [19]. In addition, the solvent should have a moderately high boiling point to prevent the sample from drying during the printing process. Meanwhile, commercial Nafion solution is in the acidic form, meaning the sulfonic acid groups on the stable PTFE backbone have a pKa value of about \(-6\)[20]. This strong acid property limits the use of commercial Nafion dispersions with some printing methods and substrates. In this work, a novel approach was developed to prepare a sodium-formed Nafion dispersion with tunable viscosity for screen printing and stencil coating. Nafion dispersion as a copolymer of polytetrafluoroethylene and perfluorosulfonic acid was neutralized to its sodium form by titration with a base, and the original low boiling point and low viscosity solvent was replaced by a high boiling point solvent by rotor evaporation. Rheology properties of the dispersion were characterized by a rotational rheometer. The elemental composition and degree of crystallinity of the fabricated film were measured. The modified Nafion dispersion was also screen printed and stencil coated onto commercial electrochemical sensors. The electrochemical performance of the modified sensors was evaluated by detection of Pb(II) using square-wave anodic stripping voltammetry (SWASV). To our knowledge, this is the first time a screen-printable Nafion dispersion has been developed and characterized for the application of electrochemical sensors.
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 4 Polymer-Based Nanomaterials
### Polymer Nanoparticles  
Emulsion polymerization has been known to be a conventional method for the preparation of polymer particles in the size range of 100-1000 nm. It has been shown that by applying mini- and micro-emulsion techniques, particles with size ranges of 50-200 and 20-50 nm, respectively, could be prepared. It is known that in micro-emulsion polymerization, a large amount of surfactant is needed to make nano-sized particles. However, the removal of surfactant from the resultant dispersion is extremely difficult. Emulsions are not thermodynamically stable systems and the stability of emulsion during the course of the irradiation period must be checked. The developments on radiation-induced emulsion polymerization have been discussed in a recent review by [PERSON] et al. [27].  
It has been suggested that block copolymers can self-assemble into nanoparticles in a selective solvent in which only one block is soluble [28]. Not only block copolymers but also ionomers have been shown to self-assemble in a selective solvent to form surfactant-free nanoparticles. The self-assembly can be induced by chemical reaction, polymer-polymer complexation and microphase inversion. In this respect, micro-wave radiation has been used to prepare uniform surfactant-free nanoparticles [29].  
Using monomers as the building blocks, polymer nanoparticles can be directly synthesized by radiation-induced dispersion and emulsion polymerization techniques. Dispersion polymerization is a simple single-step process leading to the formation of micron to sub-micron size microbeads as shown in the case of synthesis of highly monodisperse PMMA particles by irradiation of MMA in aqueous alcohol solution [30]. One of the most interesting examples of preparation of polymer-based nanoparticles by radiation-induced dispersion polymerization is the synthesis of PTFE nanoparticles with \(\sim\)200 nm. Whenthe dispersion of tetrafluoroethylene in acetone was gamma irradiated at \(-78\)\({}^{\circ}\)C, 100% conversion was achieved at 4 kGy with the formation highly monodisperse nanoparticles. The PTFE nanopowder obtained has excellent properties of friction and abrasion [31]. Chitosan nanoparticles with average size of 15 nm were synthesized by gamma irradiation of its dilute solutions. These particles were tested for their antioxidant properties as well as a reducing agent for the preparation of gold nanoparticles [32].
## 2 Theoretical Background
### Hyperparameter Selection  
The selection of a kernel and its hyperparameters is referred to as model training and determines the properties of the Gaussian process function, such as stationarity and smoothness. The selected SE kernel is an example of a stationary and smooth function with unknown parameters \((l,\sigma_{f}^{2},\sigma_{n}^{2})\) to be identified. In particular, the characteristic length scale \(l\) represents the length along which data points are strongly correlated [19], while \(\sigma_{f}^{2}\) and \(\sigma_{n}^{2}\) are the magnitude of the covariance function and the magnitude of the noise term, respectively. As in [3], all the unknown parameters are considered hyperparameters. A possible method of parameter identification uses the definition of logarithmic marginal likelihood \(log\ p(\mathbf{y}|\mathbf{Z})\)  
\[log\ p(\mathbf{y}|\mathbf{Z})=\] \[=-\frac{1}{2}\mathbf{y}^{T}[\mathbf{K}+\sigma_{n}^{2}\mathbb{I}] ^{-1}\mathbf{y}-\frac{1}{2}log(|\mathbf{K}+\sigma_{n}^{2}\mathbb{I}|)-\frac{N }{2}log(2\pi)\]where \(\mathbf{K}+\sigma_{n}^{2}\mathbf{I}\) is the covariance matrix for noisy targets \(\mathbf{y}\), the term \(log(|\mathbf{K}+\sigma_{n}^{2}\mathbb{I}|)\) is a penalty depending on covariance and inputs and \(\frac{N}{2}log(2\pi)\) is a normalization constant depending on \(N\) the number of observations.  
The value of the hyperparameters is found by maximizing the log function. This optimization problem is non-convex, requiring the use of algorithms, such as gradient descent or Broyden-Fletcher-Goldfarb-Shanno (BFGS) [20].
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 3 Methodology
### Slot Filling and Intent Detection
#### 3.2.2 Intent Detection  
In this study, we adopted the SVM multi-class classification method [65] for intent detection. The algorithm constructs \(k\) SVM models, where \(k\) is the number of classes. All the examples in the \(m\)th class with positive labels are used to train the \(m\)th SVM and all the other examples with negative labels. Formally, given training data \((x_{1},y_{1})\),..., \((x_{l},y_{l})\), where \(x_{i}\)\(\in\)\(R^{n}\), \(i=1,\)..., \(l\), and \(y\)\
# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Feature Selection Methods
#### 2.3.4 Identification and Validation of Gene Signature  
IG and ReliefF were performed on the DEGs' expression profile in WEKA [21], with the threshold set to \(-1.7976\), which is the default value used to identify significant features by filtering out those with weights below this value. In each case, 20 top genes were selected for further discriminant analysis (Section 2.4). BioVenn ([[https://www.biovenn.nl/](https://www.biovenn.nl/)]([https://www.biovenn.nl/](https://www.biovenn.nl/)) (accessed on 25 June 2022)), a web application for comparing and visualizing biological lists, was used to identify and visualize the distribution and intersected genes among feature selection methods.  
Common genes among the three methods were selected as potential biomarkers to demonstrate the efficacy of feature selection methods in identifying important genes involved in biotic and abiotic stresses in Arabidopsis. A 10-fold cross-validation was performed using RandomForest and XGBoost packages in R. The _ROCR_ package was employed to determine accuracy and receiver operating characteristics (ROC). Ultimately, the values of the area under the ROC curve (AUC) were considered to assess the efficiency of the selected key predictive genes [27]. Out-of-bag error, also called OOB estimate, was reported for measuring the prediction error of random forests using bootstrap aggregating (bagging). Bagging creates training samples by subsampling with replacement, allowing the model to learn from different data combinations. OOB error is the mean prediction error on each training sample \(x_{i}\), using only the trees that did not have \(x_{i}\) in their bootstrap sample [28]. Mean decrease accuracy, mean decrease gini, and the Boruta algorithm by Boruta package [29] were also adopted to rank feature importance. In the Borutaanalysis, each feature is labeled as either confirmed, tentative, or rejected. These labels indicate whether a gene was considered important, unclear, or not essential, respectively, for the classification.