esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**MCI Group**} & \multicolumn{2}{c}{**NL Group**} \\ \cline{2-5}  & \multirow{2}{*}{**Level**} & \multirow{2}{*}{**Total (n, \%)**} & \multirow{2}{*}{**MCI Group (n, \%)**} & \multirow{2}{*}{**NL Group (n, \%)**} & \multirow{2}{*}{**Chi-Squared**} \\  & & & & & \(p\)**-Value** \\ \hline \multirow{6}{*}{Auditory Verbial Learning Test Performance (RAVLT)} & Immediate Response & 39 (31, 48) & 34 (28, 42) & 45 (38, 52) & 2.00 \(\times\) 10\({}^{-16}\) \\  & Learning & 5 (3, 7) & 4 (2, 6) & 6 (4, 8) & 2.35 \(\times\) 10\({}^{-6}\) \\  & Forgetting & 4 (2, 6) & 5 (3, 6) & 4 (2, 5) & 2.00 \(\times\) 10\({}^{-06}\) \\  & Perception of forgetting & 44 (23, 72) & 57 (31, 86) & 31 (15, 50) & 2.00 \(\times\) 10\({}^{-06}\) \\  & Male & 3690 (56) & 2373 (60) & 1317 (49) & 0.11226 \\  & Female & 2910 (44) & 1599 (40) & 1351 (51) & \\  & Age 0 to 60 & 195 (3.6) & 167 (5.3) & 28 (1.2) & \\ Age & Age 61 to 70 & 1652 (31) & 1144 (36) & 508 (23) & 7.04 \(\times\) 10\({}^{-07}\) \\  & Age 71 or older & 3542 (66) & 1827 (58) & 1715 (76) & 2.00 \(\times\) 10\({}^{-16}\) \\  & White & 6128 (93) & 3665 (94) & 2443 (92) & 0.01202 \\  & Black/ African American & 271 (4.1) & 116 (3.0) & 155 (5.8) & 0.003855 \\  & Hawaiian & 5 (-0.1) & 5 (0.1) & 0 & 9.976919 \\  & Asian & 114 (1.7) & 73 (1.9) & 41 (1.5) & 0.126461 \\  & Unknown & 12 (0.2) & 12 (0.3) & 0 & 0.944922 \\  & More than one & 56 (0.8) & 32 (0.8) & 24 (0.9) & 0.000166 \\  & Hispanic/Latino & 199 (3.0) & 124 (3.2) & 75 (2.8) & 0.756096 \\ Ethnicity & Not Hispanic/Latino & 6370 (97) & 3794 (96) & 2576 (97) & \\ Unknown & 31 (0.5) & 14 (0.4) & 17 (0.6) & 0.023799 \\  & Married & 4914 (74) & 3016 (77) & 1898 (71) & 0.887499 \\  & Never Married & 222 (3.4) & 81 (2.1) & 141 (5.3) & 3.10 \(\times\) 10\({}^{-10}\) \\  & Unknown & 36 (0.5) & 31 (0.8) & 5 (0.2) & 0.007498 \\  & Widowed & 824 (12) & 446 (11) & 378 (14) & 0.816086 \\  & Elementary & 0 & 0 & 0 & \\  & High School & 2 (\(<\)0.1) & 2 (\(<\)0.1) & 0 & \\ Education & Occupational Program & 11 (0.2) & 1 (\(<\)0.1) & 10 (0.4) & 0.981215 \\  & College & 1240 (19) & 859 (92) & 381 (14) & 0.983097 \\  & Post-Collage & 5347 (81) & 3070 (78) & 2277 (85) & 0.983097 \\  & 0 & 4077 (62) & 2130 (54) & 1947 (73) & \\ APOE4 alleles & 1 & 2102 (32) & 1429 (36) & 673 (25) & \\  & 2 & 417 (6.3) & 370 (9.4) & 47 (1.8) & \\ Hippocampus & Hippocampus & 7177 (6838, 7817) & 6958 (6129, 7687) & 7431 (6787, 7918) & 2.00 \(\times\) 10\({}^{-16}\) \\ Wholebrain & Wholebrain & 1,033,000 (661,397, 1,104,800) & 1,034,980 (962,600,1,113,680) & 1,033,180 (959,755,1,096,880) & 3.35 \(\times\) 10\({}^{-09}\) \\ Ventricles & Ventricles & 32,311 (21,694, 46,110) & 33,924 (23,127, 4959) & 29,415 (20,203, 40,820) & 0.054315 \\ ICV & ICV & 1524520 (1,422,607, 1,640,600) & 1534150 (1,436,660, 1,647,330) & 1505460 (1,401,292, 1,630,490) & 0.045143 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Demographic characteristics of the study population per RAVLT test subgroup.
# A Combined High and Low Cycle Fatigue Life Prediction Model for Wind Turbine Blades
## 6 Conclusions  
This article proposes a life prediction model for CCF under cyclic loading that is used for predicting the CCF life of wind turbine blades. The main advantage of this model is that  
Figure 11: Three-dimensional damage evolution process.  
it considers the influence of load sequence and load interaction, as well as load coupling damage, on CCF damage. The main conclusions can be summarized as follows:  
1. Using damage curves to analyze the damage evolution process under CCF load, this paper proposes a CCF life prediction model that primarily takes into account the impacts of load sequence, load interaction, and load coupling.
2. Existing CCF experiments were used to evaluate the model's robustness and prediction accuracy. The majority of our model's results of prediction fall within the life factor \(\pm\) 1.5 range when compared to [PERSON]'s rule, the M-H model, and the T-K model. It is evident that the model's anticipated life agrees well with the experimental life fitting curve, suggesting that our model is better suited to forecasting CCF life than other models due to its greater prediction accuracy and more accurate prediction error.
3. Based on [PERSON]'s rule, this study introduces composite material damage components to present a new CCF life prediction model that can be used to estimate the CCF fatigue life of composite materials.
4. The proposed model, which integrates load interaction effects and nonlinear damage accumulation, provides highly accurate fatigue life predictions. Despite its increased computational complexity compared to traditional methods, it remains feasible for large-scale applications, such as evaluating the fatigue life of complex structures under variable amplitude loading. Future work will focus on further optimizing the model's computational efficiency, including the use of high-performance computing and simplified versions for specific cases, ensuring its applicability to real-time and large-scale engineering problems.  
Methodology, conceptualization, investigation, writing--original draft, data curation, M.L.; formal analysis, writing--review and editing, funding acquisition, validation, project administration, J.G.; software, investigation, writing--review and editing, supervision, resources, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (52465019), Natural Science Foundation of Xinjiang Uygur Autonomous Region (2024D01C27), Xinjiang Uygur Autonomous Region Central Guidance Local Science and Technology Development Fund Project (ZYZD025 JD07), and Changji Hui Autonomous Prefecture Science and Technology Plan Project (2023Z02).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 4 Biochemical Properties, Generation and Degradation of RAC1B
### Regulation of RAC1B Protein Stability  
Although proteasomalamy activity is discussed for RAC1B turnover [15], the poor ubiquitination suggests reduced proteolysis in contrast to RAC1-Q61L, a constitutively active RAC1 mutant, which undergoes polyubiquitination and subsequent proteasomalomal degradation in HEK293 cells. This feature of RAC1B may be of importance for the expression and tumorigenic capacity of RAC1B. Mutational analysis of all lysine residues in RAC1 revealed that the major target site for RAC1 ubiquitination is Lys147, a solvent-accessible residue that has a similar conformation in RAC1B. Like RAC1-Q61L, RAC1B was found to be largely associated with the plasma membrane, a known prerequisite for RAC1 ubiquitination. Ubiquitination of activated RAC1 occurs through a jun-N-terminal kinase (JNK)-activated process. The defective ubiquitination of RAC1B may thus be explained by RAC1B's inability to activate JNK (see Section 6.1). Interestingly, RAC1B ubiquitination could be stimulated by coexpression of RAC1-Q61L [16], suggesting positive regulation of RAC1B ubiquitination by RAC1 downstream signaling.  
Enhanced stability of RAC1B protein is under negative control by nuclear Wnt signaling and RAC1B levels are regulated by being targeted for degradation through a negative feedback loop initiated by Wnt signaling. This is possible because RAC1B itself can activate the Wnt pathway [15] (see Section 6.4).
# Ranking Authors in an Academic Network Using Social Network Measures
## 4 Results and Discussion
### Finding Top Authors Based on Centrality Measures  
The results of the top 20 authors based on degree centrality, betweenness centrality and closeness centrality were calculated with the help of the co-authorship network and are shown in Table 3. The authors appearing consecutively in three centrality measures are shown prominently in bold font, and authors appearing in two centrality measures are marked with bold and italic font. According to the results, a few authors were consecutively highly ranked using all three centrality measures. The results are presented in the form of the triple of degree centrality, betweenness centrality and closeness centrality, respectively in parenthesis, of an author; for instance, for [PERSON] (2-3-16), [PERSON] (3-1-12), [PERSON] (8-6-14), [PERSON] (15-4-13), [PERSON] (16-11-19), [PERSON] (19-9-17). Degree centrality values for all six authors represented their frequent collaboration; betweenness centrality values for each author showed the flow of knowledge between other authors; closeness centrality values for all six authors represented the independence of the individual author.  
Figure 2: Power law analysis of (**a**) betweenness, (**b**) closeness, (**c**) degree, (**d**) PageRank and (**e**) Eigenvector.  
Similarly, a few authors were consecutively highly ranked using two centrality measures. This means that some top authors according to the degree and betweenness centrality measures had a low closeness centrality value. For instance, [PERSON] had a high degree centrality, indicating that he had collaborated with many authors (147 authors), but his closeness centrality was relatively low, which ranked 25 out of 6441, which is why the author was not shown in the top 20 in Table 3. The reason behind the low closeness centrality was that a few of his co-authors ([PERSON], [PERSON], [PERSON], etc.) were located in Israel; thus, he was close to Israeli authors, whereas distant from the authors of other regions.
# Simulating Slosh Induced Damping, with Application to Aircraft Wing-like Structures
###### Abstract  
The added damping generated by liquid sloshing in a tank has been utilized in a number of civil applications, including aviation, to reduce the vibration of the system. As part of a wider EU H2020 project called SLOWD (**Sloshing Wing Dynamics**), the presented study performed numerical simulations on the slosh-induced damping of liquid in tanks that were under free decay oscillations and embedded in an aircraft wing-like structure. A new open-source partitioned fluid-structure interaction software framework is presented and employed for the numerical simulations. Periodic sloshing waves and violent vertical fluid motions are observed in the study. These demonstrate the effects of slosh-induced damping under different excitation amplitudes of the structure and a varying number of baffled regions within the tank. Various sloshing patterns caused by different combinations of the excitation amplitude and compartment numbers lead to different induced dampings of the free decay motion. We observed a distinctly non-monotonic function on the slosh damping when the initial excitation amplitude is small (i.e., 0.25), with a 59% reduction when we increase the number of baffled compartments from one to four, and a 153% increase when moving from one to eight compartments. This is due to the change in the sloshing wave frequency, resulting in a significant change in the impact of the fluid between the tank ceiling and the wave crests. When the initial excitation amplitude is large (i.e., 1.0), there is no significant change in the slosh-induced damping when changing the number of compartments in the tank, for the range of parameters considered, due to the highly turbulent fluid motion. This work is expected to form the basis of further, more detailed studies within the context of the SLOWD project and its ever-expanding experimental data output.  
sloshing; fluid-structure interaction; multiphase flow Article
# Multi-Scale Indoor Scene Geometry Modeling Algorithm Based on Segmentation Results
## 3 Methods
### Determining the Type of Planar or Curved Surface
#### 3.1.1 Selection of Search Neighborhood  
Currently, the neighborhood of a given three-dimensional point can be divided into a spherical neighborhood, a cylindrical neighborhood, and a fixed-point neighborhood. Spherical neighborhood and cylindrical neighborhood refer to searching for points within the corresponding shape around the given point as neighbors. The neighborhood shape is simple and symmetric. On the other hand, fixed-point neighborhood refers to finding the specified number of points closest to the given point as neighbors, so the obtained neighborhood shape is not fixed. For different point cloud data, neighborhood selection  
Figure 1: Algorithm process.  
is generally carried out through empirical or heuristic methods. In addition, considering the three-dimensional structure of the point cloud and the local point density, some single-scale and multi-scale neighborhood search methods have been proposed to meet the demand for accurately extracting geometric features. For single-scale neighborhood search, geometric features calculated using points within a smaller radius range lack stability and are susceptible to noise and outliers. Points within a larger radius range lead to over-smoothing of calculated geometric features, making them unable to reflect the true shape [16].  
In this study, in order to quickly and simply determine the type of the current face, while considering point cloud noise and uneven density, the stable characteristics of the central region of the segmented face are fully utilized, and a spherical neighborhood is defined with the center of the face as the center of the sphere. Based on this, a multi-scale spherical neighborhood search is performed to calculate subsequent covariance features, which can accurately and stably determine the type of the face.  
The schematic diagram of the multi-scale spherical neighborhood search region for a single face is shown in Figure 2. The red dots in the figure represent the centers of the face, which are used as the centers of the spherical neighborhoods. Three radius values are uniformly selected as search radii, with the minimum width of the face as the upper limit of the radius. The arrows of different colors in the figure indicate the selected radii. By organizing and managing the points within the face using KD trees, the spherical neighborhoods of the current face under different search radii can be quickly obtained for the subsequent calculation of covariance features.
## 4 Numerical Examples
### A Branched Crack in an Infinite Plate  
The example is a branched crack in an infinite plate, as shown in Figure 9. A branched cracks embedded in an infinite plate are simulated by the p-version FEM and CIM to demonstrate the accuracy and effectiveness when dealing with complex cracks. Uniform tension \(\sigma=1\,\text{MPa}\) unit is applied to the boundary of the plate. The dimensions of the model are \(W=5\) m, \(L=4\) m, \(a/W=0.05\), \(b/a=0.9\) and \(\theta=45^{\circ}\), and the plane stress condition is assumed. The material parameters are considered as [PERSON]'s modulus \(E=1000\) MPa and [PERSON]'s ratio \(\
u=0.3\). The normalized SIFs for crack tips A and C are as follows:  
\[F_{IA}=\frac{K_{IA}}{\sigma\sqrt{\pi c}},\ F_{IC}=\frac{K_{IC}}{\sigma\sqrt{ \pi c}},\ F_{IIC}=\frac{K_{IIC}}{\sigma\sqrt{\pi c}},\ c=\!\frac{a+b\!cos\! \theta}{2} \tag{16}\]  
In this example, the normalized SIFs reported in the literature ([PERSON] et al., 2019) are used as reference solutions. The numerical results obtained by the present method are shown in Table 9, which are compared with the results using the NMM in the literature ([PERSON] et al., 2019; [PERSON] et al., 2020) and the reference solutions. It is easy to see that relative error is no more than 0.21% for all the cases, which is less than the relative error in Cai ([PERSON] et al., 2019; [PERSON] et al., 2020).  
Figure 9: (**a**) A branched crack in an infinite plate; (**b**) Global and local meshes (the total number of elements is 329).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(P\) & **DOF** & **Error of Energy Norm/(\%)** & \(K_{i}(\text{MPa}\)**-m\({}^{(2)}\)** & \(K_{i}\)/\(\sigma\sqrt{\pi a}\)** & **Relative Error/(\%)** & \(K_{i}(\text{MPa}\)**-m\({}^{(2)}\)** & \(K_{i}\)/\(\sigma\sqrt{\pi a}\)** & **Relative Error/(\%)** \\ \hline
1 & 328 & 38.730 & 5.471 & 2.182613 & \(-\)22.386 & \(-\)0.069 & \(-\)0.02753 & \(-\)42.292 \\
2 & 940 & 14.790 & 6.832 & 2.725574 & \(-\)3.078 & \(-\)0.065 & \(-\)0.02593 & \(-\)45.500 \\
3 & 1584 & 8.210 & 6.980 & 2.786417 & \(-\)0.0799 & \(-\)0.110 & \(-\)0.04388 & \(-\)8.333 \\
4 & 2512 & 4.430 & 7.032 & 2.805362 & \(-\)0.241 & \(-\)0.114 & \(-\)0.04548 & \(-\)5.000 \\
5 & 3724 & 3.120 & 7.039 & 2.808155 & \(-\)0.142 & \(-\)0.119 & \(-\)0.04747 & \(-\)0.750 \\
6 & 5220 & 2.350 & 7.043 & 2.80975 & \(-\)0.085 & \(-\)0.120 & \(-\)0.04787 & \(-\)0.333 \\
7 & 7000 & 1.930 & 7.045 & 2.810548 & \(-\)0.057 & \(-\)0.120 & \(-\)0.04787 & 0.000 \\
8 & 9064 & 1.620 & 7.046 & 2.810947 & \(-\)0.043 & \(-\)0.120 & \(-\)0.04787 & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Errors of energy norm of the P-FEM solutions and relative errors of SIFs (\(L/W=3\), \(c/L=0.3\)) comparison with [PERSON] et al. (2020).
## 2 Materials and Methods  
### Clinical Cases  
This study was approved by the Institutional Review Board of our hospital. For building and testing DLS models, 257 patients were selected with stage I NSCLC in total. The dataset with treatment planning CT images for 208 cases consists of 115 training cases from our hospital and 93 internal test cases (internal test dataset A) from the same hospital. Another dataset included 49 cases selected from NSCLC Radiogenomics [20], the Lung Adenocarcinoma Collection [21] and the Lung Squamous Cell Carcinoma Collection [22] in The Cancer Imaging Archive (TCIA) public dataset, as shown in Table 1. The 208 cases underwent SBRT. The 49 external test cases received radiation therapy or chemo-radiotherapy. The datasets included three types of tumors, i.e., 118 solid, 118 part-solid, and 21 pure GGO tumors, because DLS models have the potential to segment part-solid tumors including GGO and solid components by learning pure GGO and solid tumors as well as part-solid tumors [18]. The internal test dataset B and external test datasets shown in Table 2, which contain only part-solid cases, were employed in the prediction of CTRs. The internal test dataset B includes part-solid tumor cases out of the internal test dataset A. The patients' times to progression were obtained by a radiation oncologist (T.Y.) in a Kaplan-Meier (KM) analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & **Training Dataset** & **Internal Test Dataset A** & **External Test Dataset** \\  & **(n = 115)** & **(n = 93)** & **(n = 49)** \\ \hline \multirow{2}{*}{Institution} & Kyushu University & Kyushu University & \multirow{2}{*}{TCIA *} \\  & Hospital & Hospital & \\ \hline \multirow{2}{*}{Age (years)} & 40–92 & 44–89 & 50–85 \\  & (median: 76) & (median: 78) & (median: 72) \\ \hline \multirow{2}{*}{\begin{tabular}{c} Sex \\ Male \\ Female \\ \end{tabular} } & 69 & 61 & 24 \\  & 46 & 32 & 25 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Tumor type \\ Solid \\ Pure GGO \\ \end{tabular} } & 73 & 45 & 0 \\  & 31 & 38 & 49 \\  & 11 & 10 & 0 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Matrix size \\ \end{tabular} } & 512 \(\times\) 512 & 512 \(\times\) 512 & 512 \(\times\) 512 \\ \hline \multirow{2}{*}{\begin{tabular}{c} Number of slices \\ Female \\ \end{tabular} } & 103–235 & 103–225 & 38–515 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Pixel size (mm) \\ \end{tabular} } & 0.78–0.98 & 0.78–0.98 & 0.55–0.98 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Slice thickness \\ (mm) \\ \end{tabular} } & 2.0 & 2.0, 3.2 & 0.63–5.0 \\ \hline \hline \end{tabular}  
* TCIA: The Cancer Imaging Archive.  
\end{table} 
Table 1: Patient characteristics with imaging parameters for training, internal test A and external test datasets for building and testing deep learning segmentation models of three lung tumor types.
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Steady State Phase (SSP)
#### 3.3.1 Session_ST Beacon  
The role of the Session_ST beacon is to synchronize the network and to set communication parameters of the tree nodes in SSP. The payload of the beacon contains the following information: (1) Public channels. They are two channels and are used for communication between tree nodes. When the main channel is unavailable for some reason (such as electromagnetic interference from other wireless networks or electronics), it switches to the standby one; (2) Tree topology scale \(L\). It indicates the highest level of tree topology; (3) Session duration \(W_{s}\). It refers to the duration of a single session; (4) Parameter \(\alpha\) of sharable slot generation function. It is used to calculate the length of the sharable slot for each level. \(\alpha\) and \(W_{s}\) are updated according to changes in network traffic; (5) Beacon transmission duration. \(T_{B}=L_{SS}^{B}/R_{c}\), where \(L_{SS}^{B}\) is the length of the Session_ST beacon (bit) and \(R_{c}\) is the physical channel data rate (bps).