esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Method
### Research Method
#### 3.2.2 Coupling Coordination Degree Model  
Calculate the ratio coefficient P\({}_{\rm ij}\) of the index \(x_{\rm ij}\):  
\[P_{\rm ij}=\frac{x_{\rm ij}}{\sum_{i=1}^{\rm m}x_{\rm ij}} \tag{4}\]  
Calculate the entropy value \(e_{\rm j}\) of the index:  
\[e_{\rm j}=-k\sum_{i=1}^{\rm m}P_{\rm ij}\ln\Bigl{(}P_{\rm ij}\Bigr{)},\ k= \frac{1}{\ln(\rm m)} \tag{5}\]  
Calculate the information entropy redundancy \(d_{\rm j}\):  
\[d_{\rm j}=1-e_{\rm j} \tag{6}\]  
Calculate the weight of the index:  
\[w_{\rm j}=\frac{d_{\rm j}}{\sum_{j=1}^{\rm n}d_{\rm j}} \tag{7}\]  
Calculate the comprehensive assessment index of carbon emissions, economic development, and environmental protection, respectively [58; 59]. The formula is as follows:  
\[U_{\rm i}=\sum_{j=1}^{\rm n}w_{\rm j}x_{\rm ij}{}^{\prime} \tag{8}\]  
In this formula: w\({}_{\rm j}\) is the weight of each index, \(x_{\rm ij}{}^{\prime}\) is the standardized value of each index, and U\({}_{\rm i}\) represents the comprehensive assessment index of carbon emissions, economic development, and environmental protection, respectively.  
The coupling degree and coordination degree are quantified, respectively, using the following formula [60]:  
\[C=\Bigl{\{}(X\times Y\times Z)/[(X+Y+Z)/3)]^{3}\Bigr{\}}^{1/3} \tag{9}\]  
\[D=\sqrt{C\times T},\ \text{where}\ T=\alpha X+\beta Y+\gamma Z \tag{10}\]  
In this formula: D is the coordination degree between carbon emissions, economic development, and environmental protection. C is the coupling degree between the three factors. X, Y, and Z represent the comprehensive assessment index of carbon emissions, economic development, and environmental protection, respectively. T is the comprehensive assessment index. \(\alpha,\ \beta,\ \text{and}\ \gamma\) are undetermined coefficients. Based on the existing research literature, carbon emissions, economic development, and environmental protection are equally important [51; 61; 62]. Given that carbon emissions, economic development, and environmental protection are of equal importance, the value is set at 1/3 here for each undetermined coefficient.  
In reference to the classification method of the coordination degree in the relevant literature, we divide the coordination degree into ten levels [63] (Table 3).
# Order in Chaos: Lesser-Conserved and Repeat Structures in Dehydrins
## 4 Discussion
### K Segments Bind Membranes and Cryoprotect Proteins  
In a pair of studies on a maize K2 Dhn, [PERSON] et al. demonstrated that the protein binds anionic phospholipid vesicles, accompanied by a gain in helicity attributed to the K segment, which adopts an amphipathic \(\alpha\)-helix structure in nonpolar environments [29]. Both the native protein and a modified version with one K segment deleted bind to liposomes containing anionic phospholipids, while no binding occurred either in proteins modified by deletion of both segments or in lipids lacking anionic phospholipids [10]. Similarly, a _Vitis_ K2 Dhn binds acidic liposomes via K segment Lys residues and prevents freezing-induced fusion [30]. This suggests that membrane binding is a primary function of the K segment.  
Studies of Lti30, a K6 Arabidopsis Dhn with paired His residues flanking the K segments, confirmed binding to membranes with a negative surface potential, with K segments adopting an \(\alpha\)-helical structure [31] and showed that phosphorylation of Thr residues and reversible protonation of the flanking His residues regulate binding behavior [11]. Further work with Lti30 demonstrated that the protein stabilizes multilamellar membrane systems via the electrostatic bonding of positively charged Lys and protonated His residues due to negative charges on phospholipid headgroups, thereby preventing the transition to non-lamellar structures and preserving membrane integrity [32].  
The K segment may also be involved in binding and protecting proteins. Both native Dhns and isolated K segments prevent freezing-induced aggregation, inactivation, and denaturation of lactate dehydrogenase (LDH), an enzyme that is commonly used to assess cryoprotective activity [33]. In the same study, experimental replacement of hydrophobic residues in isolated K segments with Thr, Lys, or Glu abolished cryoprotective activity, while the replacement of charged residues with Thr had little effect. This supports a transient hydrophobic interaction model, where the hydrophobic residues in the K segment bind or interact with the hydrophobic regions of the enzyme to prevent freeze-dehydration-induced damage [33].
## 2 Mission Description
### Mission Requirement  
The occulter must maintain a configuration with a nominal separation of 40 m from the observation target, the Sun, because the focal length of the optical camera is designed to be 40 m. Alignment must be performed during daylight, which has less than one orbital period. Therefore, the relative position, \(\mathbf{r}\), must be controlled to the desired trajectory, \(r_{d}\), during the shaded daylight period (Figure 3). The control accuracy requirement is located within 3 m for each axis at the nominal position. This requirement was designed considering the size of the solar corona and the performance of the detector. Since the thrust system has only two degrees of freedom, it is impossible to fire the thrust in the direction of the occulter, making it impossible to control the relative distance. The requirements are divided into three groups: fine requirement, coarse requirement (1), and coarse requirement (2), as shown in Table 1. The most stringent requirement for fine alignment must be met to obtain an ideal depiction of the solar corona. Even if the relative distance offset is substantial, the solar corona can still be detected if the alignment angle is less than 7.5\({}^{\circ}\). Coarse requirements (1) and (2) are defined as relative distance errors of 5 and 10 m, respectively, with an alignment angle under 7.5\({}^{\circ}\). If these requirements are accomplished, the mission will be successful and the solar corona will be able to be obtained. The more detailed derivation of the mission requirement associated with the celestial object and payload is suggested in [35].  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Requirement** \\ \hline Nominal distance & 40.0 m \\ Fine requirement & \(<\) 3.0 m for each axis \\ Coarse requirement (1) & \(\left\|\mathbf{r}-\mathbf{r}_{d}\right\|_{2}\)\(<\) 5.0 m \\ Coarse requirement (2) & \(\left\|\mathbf{r}-\mathbf{r}_{d}\right\|_{2}\)\(<\) 10.0 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: IAH requirements for CANYVAL-C mission [35].  
Figure 2: Relative orbit control scenario of CANYVAL-C mission.
## 4 Experimental Design and Setup
### Prediction Accuracy Measures
#### 4.3.3 Grid Search  
Parameter tuning is performed on RF using a grid search with ten-fold cross-validation. Grid search is the process of exploring the search space of the hyperparameter values with the specification of parameter pairs and evaluation measurement (e.g., AUC). This process is iterated via ten-fold cross-validation until the optimal hyperparameters are determined, which results in the highest prediction accuracy [56]. Grid search is created using the tunegrid function in R, along with the randomForest, mlbench, and caret packages to build the RF model. RF depends on four parameters: the number of trees to construct, the sub-sample size common to each tree, the tree depth, and the number of variables randomly sampled for splitting [26]. However, only the last parameter was tuned (i.e., the number of variables randomly sampled for splitting), using Mtry variable in R. The rationale for focusing on just the Mtry parameter is because there is no clear indication or theory of which value of this parameter is more appropriate under most circumstances [26]. Therefore, the grid search used in Section 5.3.7 is considered a linear search, in which the vector of candidate values ranges from 1 to 15. These values were initialized because there is no recommendation to select the number Mtry parameter [26].
## 3 General Framework and Methodology
### The Self-Adaptive-Attention-Prompt-BERT-RCNN Model  
#### 3.3.1 Self-Adaptive Attention  
The self-adaptive attention mechanism automatically assigns a weight value to each keyword by calculating the correlation between the feature vectors of the input keywords. The weight value reflects the importance of the keyword in the current text, and the higher the weight value, the greater the impact of the keyword on the text representation.  
Suppose the feature matrix of the input keywords is \(X\in\text{Rh}\times\text{d}\), where n is the number of keywords and d is the feature dimension.  
1. Compute the hidden representation of the features by a linear transformation and an activation function (e.g., tanh): \[H=\text{tanh}(W\times X)+\text{b}\] (4) where \(W\in\text{Rh}\times\text{d}\) and b \(\in\text{Rh}\) are the learnable weight matrix and bias terms, and h is the dimension of the hidden layer.
2. Calculate the attention score for each keyword: \[\alpha_{\text{i}}=\text{softmax}(\text{v}\times H_{\text{i}})\] (5) where \(\text{v}\in\text{Rh}\) is the learnable attention vector and \(\text{H}_{\text{i}}\) is the hidden representation of the i-th keyword.
3. Weigh the keyword features based on attention scores: \[X_{weighted}=\sum_{i=1}^{n}\alpha_{i}X_{i}\] (6) where \(\alpha_{i}\) is the attention weight of the i-th keyword and \(\text{X}_{\text{i}}\) is the feature vector of the i-th keyword.
## 4 Experimental Studies
### Simulation Results and Discussion  
The experimental results of these four algorithms, i.e., NSGA-II, MO-MFEA, EMT-EGT, and Tr-EGT, on two tasks under 10 operational conditions are listed in Tables 6 and 7, respectively. The average HV values over 20 independent runs of all algorithms are shown, and we highlight the best result for each task in boldface. The best run result for each test case is highlighted. The Wilcoxon rank sum test at the significance level of 0.05 is employed, where the symbol \"\(+\)/ \(-\) / \(\approx\)\" indicates that the result of the corresponding algorithm is significantly better, significantly worse, and comparable to that Tr-EGT, respectively. It can be observed that Tr-EGT performs better than NSGA-II, MO-MFEA, and EMT-EGT, which indicates that the strategy of transfer learning-based explicit genetic transfer can accelerate the convergence speed of the population. EMT-EGT only obtains the best results on T1 of 10 operational conditions using an evolutionary solver with NSGA-II, while NSGA-II performs well in all tasks, but may not take full advantage of different solvers (i.e., NSGA-II and SPEA2), leading in the occurrence of negative transfer. From the MO-MFEA experiments, we can see that it does not always help to solve a strong correlation e.g., PPCFP model, and harnessing the inductive bias may hurt, thereby leading to the possibility of negative knowledge transfer. To visualize the performance of all algorithms during execution, Figure 4 shows the average HV numerical curves over 20 independent runs on task\({}_{1}\) under all conditions. \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Algorithm** & **mp** & **SBX \(\eta_{c}\)** & **PM \(\eta_{m}\)** & **N** & **G** & **Maxgen** & **Runs** \\ \hline NSGA-II & - & 20 & 20 & 100 & - & 200 & 20 \\ MOMFEA & 0.3 & 20 & 20 & 200 & - & 200 & 20 \\ EMT- & - & 20 & 20 & 200 & 10 & 200 & 20 \\ EGT & - & 20 & 20 & 200 & 5 & 200 & 20 \\ Tr-EGT & - & 20 & 20 & 200 & 5 & 200 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameter setting for the multitasking multiobjective experiment.
## 2 Overview European XFEL
### European XFEL Governance and Organization  
The operation of the overall European XFEL facility is entrusted to the European XFEL GmbH, based on an intergovernmental agreement between the participating countries Denmark, France, Germany, Hungary, Italy, Poland, Russia, Slovakia, Spain, Sweden, Switzerland, and the United Kingdom. The largest contributors are Germany with 58% and Russia with 27% of the total construction cost. Each participating country determines a legal entity to hold their shares and to represent the country in the Council, the superior governance board of European XFEL. The construction costs of the facility can be sub-divided into the three major areas: civil construction, accelerator complex, and X-ray systems with shares of roughly 30:50:20. About 50% of the construction cost was provided through in-kind contributions by the participating countries. The annual costs for operation are shared amongst the participating countries initially according to the participation in the construction period. Starting in 2023, 50% of the annual operation costs will be distributed according to real usage of the facility by research groups from the participating countries, calculated using a three-year average.  
The construction, commissioning and operation of the superconducting accelerator and its ancillary systems depend on the expertise residing at DESY, a major accelerator and photon science laboratory located in Hamburg, Germany. During the construction phase DESY led the international Accelerator Consortium that designed, built, and commissioned the accelerator. European XFEL staff has been responsible for the X-ray systems and ancillary instrumentation, including the undulators. For the operation phase European XFEL and DESY have concluded an agreement according to which DESY provides the personnel and expertise to operate and further develop the accelerator. European XFEL takes the responsibility for the X-ray systems and the user program of the facility.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
###### Abstract
In this method, the antenna array is physically rotated around its center, and a second radar measurement is performed. The two data sets are then subtracted to remove undesired signals. In this paper, a modified and compact flexible Vivaldi antenna with a size of \(25\times 20\times 0.1\) mm\({}^{3}\) is proposed and studied. Two versions of antennas, one with two resonant frequencies at 4 and 9.4 GHz and the other with one resonant frequency at 4.4 GHz, are discussed. Both are of the same size, and the improved antenna is fabricated on Polyimide flexible material with a thickness of 0.1 mm and a dielectric constant of 3.5 using a standard PCB manufacturing process. The reflection coefficients, current distribution, and radiation patterns are discussed and evaluated. An array of nine identical antennas of the second version, i.e., with one resonant frequency at 4.4 GHz, is used as a microwave breast imaging model to detect and locate the tumor inside the proposed multilayer phantom model using Microwave Radar-Based Imaging toolbox (MERIT). This simulation setup successfully identifies and locates tumors of different sizes and positions inside the phantom model. The imaging results are very promising for the practical use of the proposed microwave imaging as a good candidate for breast cancer detection. The novelty of the work is the use of the proposed modified flexible antenna and the proposed breast phantom for breast imaging applications.
Using the Flow Duration Curve (FDC), their results indicated that the location and size of bioretention and the soil texture together have the most significant impact on the benefits of LIDs, yet their 1D or 2D approaches failed to capture complex 3D flow dynamics. To address this gap, Computational Fluid Dynamics (CFDs) provides a robust framework for resolving 3D flow fields through Navier-Stokes equations and turbulence models, enabling precise visualization of infiltration, drainage, and overflow processes. However, CFD models are sensitive to empirical parameters (e.g., soil porosity, slope), necessitating systematic sensitivity analysis. Here, the local Morris parameter sensitivity analysis method is introduced to quantify the influence weight of structural parameters (such as matrix layer thickness and slope) on the overflow and peak flow [17]; furthermore, the TOPSIS multi-objective decision model is used to prioritize the parameter combination schemes according to the positive and negative ideal solution distance [18; 19]. This method not only makes up for the deficiency of the traditional model in the analysis accuracy of single facilities but also overcomes the empirical dependence of parameter optimization through sensitivity-benefit collaborative analysis. The present study first utilized numerical simulation at the 3D level and, combined with local Morris parameter sensitivity, TOPSIS methods to adjust the structural indicators of concave herbaceous fields, which are typical LIDs. In this research, the precipitation process and volume were calculated by the rainfall characteristics of Tianjin and the Chicago precipitation pattern formula. Then, the reduction of overflow volume and peak flow were analyzed through LID schemes with different parameters, utilizing the continuity equation, the momentum equation, and Darcy's law, calculated in a numerical model. The sensitivity discriminant factors and the priority values of each plan were subsequently obtained by analyzing the flow decrease and applying the local Morris parameter sensitivity analysis and TOPSIS. After obtaining the most significant parameter and the optimal scheme, three storm events were used to simulate the runoff control effect of the concave herbaceous field under the best plan. Meanwhile, its stormwater control process, benefits, and application methods under actual working conditions were acquired. The proposed model and analysis framework could help to simulate the practical performance of sponge cities under actual precipitation conditions and provide technical references for buildings and the optimization of LID facility structures in the subsequent sponge city construction.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 4 Software-Based Dynamic Information Flow Tracking  
In this section, we will introduce common software-based DIFT designs. We classify the software-based DIFT designs into two types: (i) system-level DIFT design; and (ii) program level DIFT design. For system-level DIFT design, the security analysis are operated on real scenarios, e.g., Operating System and CMP platforms. Furthermore, the inconsistencies between the original data and metadata (tag) need to be solved efficiently. The program-level DIFT design uses target software through binary instrumentation and source code modifications to support DIFT operations.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis
### Results of Numerical Analysis
#### 2.4.3 Effect of Cavitation on Pressure Coefficient  
According to research above, the texture dimples can induce disturbance to velocity and pressure distribution in the flow field near dimples. The oil velocity without texture disturbance is chosen as the reference velocity \(u\infty\) for critical pressure coefficient \(C_{pmit}\), the oil velocity with texture disturbance is chosen as the local velocity \(u_{x}\) for the pressure coefficient \(C_{p}(x)\), and the pressure value with texture disturbance is chosen as the local pressure \(p_{x}\) for the pressure coefficient.  
Figure 11 shows the distribution of pressure coefficient of the oil film without texture and with homogeneous hexahedral texture. Without textures, the pressure coefficient decreases along the radial direction, the value is greater than the critical pressure coefficient, and the cavitation inception area fraction \(S\) is 0, i.e., no cavitation is generated. With textures, the pressure coefficient distribution is generally consistent with that of the oil film  
Figure 10: Effect of texture on pressure at outer radius.  
Figure 9: Pressure distribution of nontextured and textured oil film ((**a**) is oil film with textures; (**b**) is oil film without textures).  
without textures, but in the texture region at the outer radius, the value is lower than \(C_{pmin}\), and \(S\) is 0.0049.  
Isolated rough elements and homogeneous rough elements have distinct separate turbulence on the flow field, and it needs to be confirmed whether texture has the same impact on the flow field pressure at low and high densities. Take \(N=10\) and \(M=20\) to encrypt the texture distribution. When the texture rate is increased, the pressure coefficient at the outer diameter decreases significantly and is lower than the critical pressure coefficient \(C_{pmin}\) (Figure 12), the cavitation inception area \(S\) increases to 0.102, and it is discovered that the cavitation area increases rapidly when the texture density is increased.
## 3 Case Study--The ANN Models for Load Forecasting
### Evaluation Metrics  
To evaluate the performance of ANN models the mean absolute error (_MAE_), mean square error (_MSE_), and root mean square error (_RMSE_) are used (Equations (21)-(23)) in this study.  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(|x_{i}-\hat{x}_{i}|)^{2} \tag{21}\]  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}(|x_{i}-\hat{x}_{i}|) \tag{22}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(|x_{i}-\hat{x}_{i}|)^{2}} \tag{23}\]  
where \(x_{i}\) is the \(i\)th value, \(\hat{x}_{i}\) is the corresponding predicted value, and n is the number of data points.  
As ANN models become more complex, the explanation of how the models make such predictions or decision become more ambitious. Explainable artificial intelligence has gained a lot of attention recently for explaining how ANN models perform in any given task. The Shapley value is one of the common metrics to evaluate the average marginal contribution of any feature of output value produced by the ANN model.  
\[{}_{i}(f,x)=\sum_{z^{\prime}\subseteq x^{\prime}}\frac{|z^{\prime}|!(M-|z^{ \prime}|-1)!}{M!}[f_{x}(z^{\prime})-f_{x}(z^{\prime})\backslash i] \tag{24}\]  
Figure 14: Some examples of the multivariable input sequence window [24 h], power load, temperature sine and cosine of the days and years.  
Figure 13: Some examples of the univariable input sequence window [24 h], power load only.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Date and Time** & \
\begin{tabular}{c} **Hourly Records before** \\ **Removing Outliers** \\ \end{tabular} &
\begin{tabular}{c} **Hourly Records after** \\ **Removing Outliers** \\ \end{tabular} \\ \hline
1 January 2009 00:00 to 21 & & \\ October 2022 22:00 & 121,004 & 120,882 \\ \hline
11 June 2015 07:00 to 21 & & \\ October 2022 22:00 & 61,000 & 61,000 \\ \hline
26 February 2019 15:00 to 21 & & \\ October 2022 22:00 & 32,000 & 31,898 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The data in three datasets.  
where \(i\) is the Shapley value for feature \(i\), \(f\) is the model, \(x\) is the input data point, \(z^{\prime}\) is the subset of data, \(M\) is the number of features, and \(f_{x}(z^{\prime})\) and \(f_{x}(z^{\prime})\backslash i\) are the model output without and with feature \(i\). In a nutshell, the Shapley value is calculated by computing a weighted average payoff gain that feature \(i\) provides, included in all coalitions that exclude \(i\).