esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### MK Model Approach for Biological Effect Prediction  
In the MK model approach [23; 24; 25], employing the formalism as presented in previous work, e.g., [22], we can predict the SF based on the linear-quadratic (LQ) model:  
\[S(D)=e^{-\alpha D-\beta D^{2}}, \tag{1}\]  
\[\alpha=\alpha_{0}+\frac{\beta}{\rho\pi r_{d}^{2}}\overline{y_{D}}, \tag{2}\]  
where \(S\) is the SF; \(\alpha\) and \(\beta\) are the parameters of the LQ model; \(D\) is the measurable physical dose; \(\alpha_{0}\) is the beginning slope of the survival curve when LET = 0; \(r_{d}\) and \(\rho\) are the radius and density of the domain; \(\overline{y_{D}}\) is the dose-mean lineal energy defined by \(\overline{y_{D}}=\int~{}yd(y)dy\), where \(d(y)\) is the probability density of the absorbed dose with regard to lineal energy \(y\). Inorder for the MK model to represent the saturation effect on the cell survival, [PERSON] et al. [8] suggested the saturation-corrected dose-mean lineal energy \(y^{*}\):  
\[\alpha=\alpha_{0}+\frac{\beta}{\rho\pi r_{d}^{2}}y^{*}, \tag{3}\]  
\[y^{*}=\frac{y_{0}^{2}\int\left[1-e^{-(y/y_{0})^{2}}\right]f(y)dy}{\int\ yf(y)dy}, \tag{4}\]  
\[y_{0}=\frac{\rho\pi r_{d}R_{n}^{2}}{\sqrt{\beta\big{(}r_{d}^{2}+R_{n}^{2}} \big{)}}, \tag{5}\]  
where \(f(y)\), \(y_{0}\), and \(R_{n}\) are the probability density of the \(y\), saturation parameter, and radius of the cell nucleus, respectively. As discussed in Section 4, \(\overline{y_{D}}\) or \(y^{*}\) is dependent on the size of the measured site. However, at a tissue-equivalent diameter ranging within 0.1-6 \(\mu\)m, the difference in \(y^{*}\) was almost within the systematic and stochastic errors as discussed by [PERSON] et al. [8].  
Applying the predicted SF based on Equations (1) and (3)-(5), we can calculate the RBE-weighted dose, namely, biological dose \(D_{Bio}\), as [10]:  
\[D_{Bio}=RBE\times D=\frac{-\alpha_{X}+\sqrt{\alpha_{X}{}^{2}-4\beta_{X}ln(S)} }{2\beta_{X}}, \tag{6}\]  
where \(\alpha_{X}\) = 0.252 Gy\({}^{-1}\) and \(\beta_{X}\) = 0.0474 Gy\({}^{-2}\) are the LQ model parameters for the HSGc-C5 cells determined by 200 kVp X-ray exposure (published by [15]), which were adopted in the RBE calculation in this study. The RBE was then calculated as \(RBE=D_{Bio}/D\).
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 2 Related Works
### Research Status
Reference [26] proposes an M-OLSR routing protocol based on the SL-MPR selection algorithm, considering node mobility and link variations to tackle issues in current UANETs, such as topology changes and control message redundancy. While many scholars have addressed the selection of MPR nodes considering factors like link status, network load, and node energy conditions, utilizing neural network optimization algorithms and leveraging multiple parameters for decision-making, there remains a gap in proposing a comprehensive evaluation algorithm grounded in multi-dimensional perception. Such an algorithm would integrate various indicator parameters from different aspects of the same evaluation object to derive a holistic evaluation metric. Therefore, it is imperative to propose a multi-attribute comprehensive evaluation routing algorithm based on multi-dimensional state perception for UANETs operating in highly dynamic and rapidly changing topology scenarios.
## 2 Experimental Methodology
### Audio Pre-Processing and Choice of Features  
To ensure the quality of our model's training, we aim to restrict the use of data solely to the relevant subject. Hence, all audio associated with the interviewer (including when it overlapped with the subject) was removed while retaining all other audio segments (such as silence and filler words), as these non-speech segments could still contain useful cues. To achieve this, segments corresponding to the interviewer were manually removed using Adobe Audition [46]. Various training features were then extracted in both audio and text domains, at both frame-level or file-level (detailed in Table 1). Text features were extracted from transcriptions of the recorded speech generated using Otter.ai [47], a commercial speech-to-text transcription service.  
As speech cues can occur at different levels (individual phonated utterances or the entire string of utterances across the interview), we analyze the data on two levels: frame-level ('granular' or 'frame-by-frame' descriptors) and file-level (across the entire interview recording). Specifically, audio features at the frame-level were obtained by segmenting the audio signal into shorter frames (\(\sim\)10-25 ms), whereas the audio features at the file-level were computed by utilizing the audio recording of the complete interview (minus the interviewer). Likewise, text features at the frame-level were extracted from words and short phrases (i.e., below the sentence level) of the transcribed speech (relying on a speech-to-text service), rather than employing the complete transcription of each subject's interview, where the latter was utilized in the generation of text features at the file-level. This approach facilitated insight into distinctive and efficacious features from both audio and text data at different levels of granularity.
###### Abstract  
In this paper, an instantaneous correlation coefficient and simplified coherent averaging method for single-channel foetal ECG (FECG) extraction is proposed. The instantaneous correlation coefficient is used to determine the position of the R peak of the measured ECG signal, and the simplified coherent averaging method is used to extract the main information of the ECG signal. The loss of the nonlinear and nonstationary characteristics by coherent averaging is recovered by threshold processing of the residual signal. The FECG signal extraction is performed in three steps. In the first step, the main information of the maternal electrocardiogram (MECG) is extracted from the abdomen electrocardiogram (AECG) signal by means of the instantaneous correlation coefficient and simplified coherent averaging method, and then the noisy FECG is obtained by subtracting the MECG obtained by simplified coherent averaging from the AECG. The second step is to extract the main information of the FECG by applying the instantaneous correlation coefficient and simplified coherent averaging method to the noisy FECG. The remaining signal is obtained by subtracting the simplified coherent averaging FECG from the noisy FECG. Thirdly, the threshold method is utilised to remove MECG residual noise and random gross value noise from the remaining signal to extract the nonlinear and nonstationary information, and the final FECG extraction is obtained by adding the nonlinear and nonstationary information to the simplified coherent averaging FECG. The validity of the proposed method is verified by experiments using synthetic data and real database data. FECG extracted by the method has the advantages of clear QRS complex wave, reasonable enhancement of P wave and T wave morphology, and no loss of nonlinear and nonstationary characteristics.  
correlation coefficient; coherent averaging; R wave detection; FECG extraction; nonstationary; nonlinear  
[PERSON] and [PERSON] (2020) 10 5634 11 August 2020 14 August 2020 14 August 2020 10 5634 10.3390/app10165634
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
## 2 Regulatory Steps in LPS Biosynthesis  
Until recent discoveries, it was presumed that LPS biosynthesis occurs in a constitutive manner. However, recent studies have revealed regulatory controls exerted right from early steps in LPS biosynthesis till its final delivery in the OM as highlighted below: (a) Regulation of GlnS expression for the synthesis of UDP-GlcNAc, an essential metabolic precursor for LPS and peptidoglycan, by recruiting GlnZ/Y noncoding small regulatory RNAs (sRNAs). (b) Balanced synthesis of phospholipids and LPS by regulated turnover of LpxC by FtsH/LapB proteins, since they use \(R\)-3-hydroxymyristoyl-ACP as a common precursor [9]. (c) Bacteria also ensure that only completely synthesized LPS is delivered to the Lpt translocation system by preferential selectivity of hexaacylated LPS by the MsbA transporter and by recruiting LapA and LapB proteins as a scaffold for various LPS biosynthetic enzymes in the IM. (d) The transcriptional control by RfaH of the large _waaQ_ operon encoding various LPS core biosynthetic enzymes and the \(\eta\)_fb_ operon whose products are required for O-antigen biosynthesis [10; 11; 12]. (e) Regulation of the incorporation of non-stoichiometric modifications of LPS by the induction of genes, whose expression is controlled by Bass/R, PhoP/Q and PhoB/R two-component systems (TCSs) (Figure 1). The expression of some of these genes is regulated either at a transcriptional or post-transcriptional level or both [13]. (f) Some of the genes, whose products are involved in either lipid A biosynthesis, LPS translocation or phospholipid biosynthesis, are transcriptionally regulated by the RpoE sigma factor and the CpxA/R TCS [14]. (g) The LPS assembly in the OM requires a correctly folded outer membrane protein LptD. Its correct folding is SurA- and DsbA-dependent, which can be further fine-tuned by BepA for the removal of misfolded LptD.
# Evaluation of Antioxidant and Anticorrosive Activities of _Ceriops_ _tagal_ Plant Extract
## 3 Results and Discussion
### Corrosion Inhibition
#### 3.3.1 Weight Loss Measurement  
Effect of Concentration  
The inhibition competency of different concentrations (100, 200, 300, 400, 500, and 600 ppm) of _C. tagal_ extract was tested against mild steel in 1 M HCl at 303 \(\pm\) 1 K. Maximum \(\eta\)% (95) was established by 600 ppm, from there the inhibition tended to decrease (49%) until 100 ppm, as presented in Table 1.  
Several previous reports have reported the influence of inhibitor concentration in decreasing corrosion rate. Studies had emphasized the correlation between the surface coverage (\(\theta\)), concentration, and inhibition efficiency (\(\eta\%\)) which perfectly suits the action of _C. tagal_ extract, as in Table 1[47; 48; 49]. Here, the surface coverage (\(\theta\)) values increased with an increase in concentration, as the higher concentration increased the strength of the protective layer, thereby, lifting the inhibition efficiency of the extract at 303 \(\pm\) 1 K [50; 51].  
_C. tagal_ extract was tested against 1 M HCl with different temperatures (313, 323, 333, and 343 \(\pm\) 1 K) and concentrations (100, 200, 300, 400, 500, and 600 ppm) for 3 h. Table 2 reveals the effect of change in concentration and temperature affecting the \(\eta\%\) at a large scale.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Conc. (ppm)** & **W (mg\(\cdot\)cm\({}^{-2}\))** & \(\theta\) & \(\eta\%\) & \(\sigma\)a  \\ \hline Blank & 0.0989 & - & - & - \\
100 & 0.0501 & 0.49 & 49 & 0.03 \\
200 & 0.0432 & 0.56 & 56 & 0.02 \\
300 & 0.0388 & 0.61 & 61 & 0.01 \\
400 & 0.0298 & 0.70 & 70 & 0.02 \\
500 & 0.0102 & 0.90 & 90 & 0.03 \\
600 & 0.0055 & 0.95 & 95 & 0.01 \\ \hline \end{tabular}
\end{table}
Table 1: % IE for various concentrations (Conc.) of the _C. tagal_ extracts for corrosion of mild steel in 1 M HCl obtained by weight loss measurements at room temperature (303 \(\pm\) 1 K).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**T (\(\pm\)1 K)** & **Conc. (ppm)** & **W (mg\(\cdot\)cm\({}^{-2}\))** & \(\theta\) & \(\eta\%\) & \(\sigma\)a ** \\ \hline Blank & 0.1251 & - & - & - \\  & 100 & 0.0702 & 0.44 & 44 & 0.02 \\  & 200 & 0.0633 & 0.49 & 49 & 0.04 \\
313 & 300 & 0.0504 & 0.60 & 60 & 0.03 \\  & 400 & 0.0  
This test shows the inability of a high concentration (600 ppm) in inhibiting corrosion (54%) at 343 \(\pm\) 1 K. Although concentration helped to increase the inhibition efficiency and to decrease corrosion rate, a gradual fall in surface coverage (\(\theta\)) and \(\eta\)% were observed at 313, 323, 333, and 343 \(\pm\) 1 K. A change in \(\eta\)% according to the rise in temperature has been observed in plants such as _Mentha pulegium, Zenthoxylum alatum_, Jojoba oil, _Aster koraiensis_, and _Cryptoseiga grandiflora_ due to decreased adsorption and increased desorption of inhibitor along with an increase in hydrogen evolution [23; 25].
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 2 System Model
### Data Detection
#### 2.2.2 For Blind Data Detection  
To estimate the channel, the two-stage block by block receiver is performed:  
\[\tau_{1}=\frac{1}{L}{\sum_{K=1}^{L}r[k]} \tag{6}\]  
where \(\tau_{1}\) is the first threshold used for the first step, and \(L\) is the observation window. Using the threshold, the first step of blind detection is:  
\[r[k]>\tau_{1};\;s^{\prime}[k]=1 \tag{7}\] \[r[k]<\tau_{1};\;s^{\prime}[k]=0 \tag{8}\]  
where \(s^{\prime}[k]\) is the first step of decision for \(s[k]\).  
The second stage is performed as in [13] with the simplified form introduced in [7]  
\[\tau_{2}=\frac{1}{M^{\prime}}{\sum_{K=1}^{L}r[k]}s^{\prime}[k] \tag{9}\]  
where  
\[M^{\prime}={\sum_{K=1}^{L}s^{\prime}[k]} \tag{10}\]  
which would result in  
\[r[k]>\tau_{2};\;s^{\prime\prime}\left[k\right]=1 \tag{11}\] \[r[k]<\tau_{2};\;s^{\prime\prime}\left[k\right]=0 \tag{12}\]  
where \(s^{\prime\prime}[k]\) is the second step of decision for \(s[k]\).  
In the proposed system, using the relay technique, stages one and two are performed at the relay and at the destination.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 3 Methods
### Gaussian Pruning and Expansion
#### 3.2.1 Clustering for 3 DGS Reduction  
Each Gaussian primitive in 3 DGS is defined by its position \(\mu\), scale \(S\), rotation \(R\), opacity \(o\), and spherical harmonics \(sh\). To enhance the quality of clustering, we first compute the intensity \(I\) for each Gaussian primitive and filter out those with low contributions. The intensity is defined as follows:  
\[I=o\times Tr(\Sigma)\times l, \tag{5}\]  
where \(o\) denotes the opacity, \(\Sigma\) is the 3D covariance matrix, and \(l\) is the luminance of the Gaussian primitive's color. The luminance \(l\) is calculated from the RGB color converted from the spherical harmonics coefficient \(sh\) by the following:  
\[l=0.2126R+0.7152G+0.0722B, \tag{6}\]  
where \(R\), \(G\), and \(B\) are the respective color channels after clamping to the range \([0,1]\).  
Gaussians with importance scores below a certain threshold are pruned to remove floating noise and redundant primitives.  
After the filtering step, we perform clustering over the remaining Gaussians to aggregate them into \(n\) clusters, aiming to obtain a compact and efficient representation.  
Considering the large number of primitives typically involved, we adopt MiniBatchKMeans instead of standard KMeans to reduce memory consumption and accelerate the clustering process, without significantly compromising the clustering quality. Each Gaussian is encoded as a feature vector that concatenates its 3D position, scaling parameters, and RGB color. Clustering is performed based on these feature vectors.  
For each resulting cluster, we compute the mean values of the positions, opacities, scaling factors, rotations, and spherical harmonics coefficients of its assigned Gaussians. This aggregation effectively preserves both the geometric structure and appearance information of the original 3D Gaussian distribution while substantially reducing the number of primitives.
# Leveraging Multi-Modal Information for Cross-Lingual Entity Matching across Knowledge Graphs
## 4 Experiments
### Results and Discussions
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**DBP15K** & \multicolumn{3}{c}{**zh-en**} & \multicolumn{3}{c}{**ja-en**} & \multicolumn{3}{c}{**fr-en**} \\ \hline
**Models** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** \\ \hline CLEM-RAD & 53.45 & 80.02 & 56.50 & 51.70 & 84.19 & 55.07 & 57.13 & 71.61 & 60.40 \\ \hline CLEM-ADI & 64.45 & 83.41 & 68.20 & 74.37 & 80.38 & 77.90 & 74.25 & 80.40 & 76.20 \\ \hline CLEM-RDI & 56.46 & 78.35 & 60.80 & 62.45 & 73.62 & 65.10 & 67.85 & 72.24 & 69.30 \\ \hline CLEM-RAI & 61.87 & 80.29 & 64.70 & 69.09 & 82.64 & 74.30 & 71.99 & 78.20 & 74.50 \\ \hline
**CLEM** & **85.41** & **93.45** & **87.90** & **88.48** & **95.81** & **90.40** & **93.60** & **97.64** & **95.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison results of CLEM and its variants in the ablation experiments. Figure 3: The result of the multi-view integration experiment in terms of Hits@1. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**DBP15K** & \multicolumn{3}{c}{**zh-en**} & \multicolumn{3}{c}{**ja-en**} & \multicolumn{3}{c}{**fr-en**} \\ \hline
**Methods** & \begin{tabular}{c} **Hits@1** \\ **(Prec.)** \\ \end{tabular} & **Hits@10** & **MRR** & \
\begin{tabular}{c} **Hits@1** \\ **(Prec.)** \\ \end{tabular} & **Hits@10** & **MRR** &
\begin{tabular}{c} **Hits@1** \\ **(Prec.)** \\ \end{tabular} & **Hits@10** & **MRR** \\ \hline MTransE & 30.83 & 61.41 & 36.40 & 27.86 & 57.45 & 34.90 & 24.41 & 55.55 & 33.50 \\ \hline JAPE & 41.18 & 74.46 & 49.00 & 36.25 & 68.50 & 47.60 & 32.39 & 66.68 & 43.00 \\ \hline KDCoE & 43.42 & 75.77 & 52.70 & 39.48 & 70.75 & 50.50 & 33.59 & 69.52 & 44.70 \\ \hline MultiKE & 50.87 & 57.61 & 53.20 & 39.30 & 48.85 & 42.60 & 63.94 & 71.19 & 66.50 \\ \hline EVA & 76.10 & 90.70 & 81.40 & 76.20 & 91.30 & 81.70 & 79.30 & 94.20 & 84.70 \\ \hline DAEA & 82.80 & 92.40 & 84.30 & 87.00 & 95.10 & 88.20 & 93.60 & 97.10 & 94.80 \\ \hline
**CLEM** & **85.41** & **93.45** & **87.90** & **88.48** & **95.81** & **90.40** & **93.60** & **97.64** & **95.20** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The comparison results between CLEM and other entity matching methods. improves the results of the entity matching. In addition, we found that the CLEM-CAT is inferior to the other variants. This may be due to the fact that the concatenation method does not have the training process, and the integration after training cannot fully consider the association between different modalities.
# Link Pruning for Community Detection in Social Networks
## 3 Our Proposed Framework
### Algorithm for Clustering Using Link Pruning  
#### 3.2.1 Proposed Algorithm  
Regarding the three types of link attributes introduced in Section 3.1, it is expected that attributes of links connected in a community will be higher than those connected between different communities. A similarity between nodes or curvature-based links defines these link attributes, and their values increase when the neighborhood substructures of the two nodes are similar. We design and analyze an efficient algorithm for community detection using the characteristic of link attributes.  
Algorithm 1 shows the procedure of the proposed community detection algorithm based on link pruning. In Step 1, the proposed algorithm calculates the link attributes in a given graph \(G=(V,E)\). In Step 2, it sorts link attributes according to their values. In Step 3, it prunes links with low link attributes values. The pruning rate is the fraction of connections in the graph that are pruned. The degree of sparsification of the resulting graph is determined by the pruning rate \(\alpha\). The pruning rate makes a trade-off between the memory efficiency and the accuracy throughout the algorithm. In Section 4, we verify the performance of the proposed algorithm in a wide range of pruning rates. This process is performed to successfully remove links, which are likely to have less importance to detect community. In Step 4, the community detection algorithm \(A\) is applied to the converted sparsified graph \(G^{*}\). The proposed algorithm exhibits better performance for identifying a community \(C\) when applied to the converted sparsified graph than applied to the origin graph \(G\). Because the origin \(G\) and converted graph \(G^{*}\) have the same set of nodes, the proposed algorithm is not required to convert a community for the set of nodes in \(G^{*}\) to identify a community for the set of nodes in \(G\).  
```
input : (i) a graph \(G=(V,E)\); (ii) a link attribute attribute; (iii) a pruning rate \(\alpha\); (iv) a clustering algorithm \(A\); output :a set of clusters \(C\) of \(V\);
1 /* Step 1: Calculate the link attributes */
2 for\(\{u,v\}\in E\)do
3 Calculate \(attribute(u,v)\);
4 /* Step 2: Sort the link attributes */
5 Sort \((attribute(u,v))_{\{u,v\}\in E}\);
6 /* Step 3: Prune low-value links */
7 \(G^{*}\leftarrow\) remove the smallest 100\(\alpha\)% links from \(G\);
8 /* Step 4: Detect communities in the transformed graph */
9 \(C\leftarrow\) apply \(A\) to \(G^{*}\);
10 return\(C\);
```  
**Algorithm 1**Clustering with Link Pruning.
# Bionic Design of the Vertical Bracket of Wide Angle Auroral Imager by Additive Manufacturing
## 3 Bionic Optimization Design of the Vertical Bracket of Wide Angle Auroral Imager  
### Topology Optimization Design of the Vertical Bracket  
A preliminary optimization model is obtained by topology optimization. The topology-optimized design space is defined according to the original design of the vertical support. Moreover, the structural features of the holes connected with shafting and the satellite platform are the non-design space, as shown in Figure 2.  
The minimization of the global compliance \(C\) is chosen as the objective function in the topology optimization procedure. The volume fraction of the solid material \(\overline{V}\) is restricted, whose upper bound is set to be 0.3. In summary, the topology optimization design problem can be formulated as follows:  
Find:  
\[x=\{x_{i}\}\;i=1,2,\cdots,n \tag{1}\]  
Minimize:  
\[C=U^{T}KU \tag{2}\]  
Subject to:  
\[KU=F\]  
\[V=\sum_{i=1}^{n}x_{i}v_{i}\leqslant\overline{V} \tag{3}\]  
\[0<x_{\min}\leqslant x_{i}\leqslant 1,i=1,2,3,\cdots,n\]  
where, \(x_{i}\) denotes the pseudo-density of the \(i\)-th element and \(n\) is the number of elements in the design domain. As the objective function, \(C\) represents the overall compliance of the  
Figure 1: The original structural model of the vertical bracket. **(a)** The structural schematic diagram. **(b)** Front view of the vertical bracket. **(c)** Side view of the vertical bracket.  
Figure 2: Geometric model for topology optimization.  
structure. \(U\) represents the global displacement, \(K\) represents the global stiffness matrix, \(F\) represents the external load of the structure, \(v_{i}\) is the volume of the \(i\)-th element, \(\overline{V}\) represents the volume fraction constraint, and \(V\) is the total volume of the design domain with the upper bound \(\
abla\)[37]. Under the constraint of 30% volume fraction, classical topology optimization is performed to obtain a lightweight and high-performance initial design. The optimized structural configuration of the vertical bracket of wide angle auroral imager is illustrated in Figure 3a. Based on the optimal material distribution obtained by topology optimization, the following principles should be considered: the redesign model should be approximate to the result of topology optimization; At the same time, the assembly process should be considered. For example, bolts should ensure sufficient operating space.