esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
Additionally, it may be valuable to test polygenic risk models that include hundreds of thousands of SNPs (including alleles of the _APOE_ gene) and demonstrated an AUC of 0.72-0.84 in the original studies [12; 13]. However, these data require further validation in independent cohorts. According to numerous data, the concentrations of amyloid and tau proteins in the CSF signify neurodegenerative changes linked to AD, and these parameters form the foundation of the ATN classification system (amyloid, tau, and neurodegeneration) for identifying dementia types [34]. Studies have demonstrated decreased levels of A\(\beta\)42 and elevated amounts of tau protein in the CSF for patients with AD [29; 35]. For amyloid, the A\(\beta\)42/A\(\beta\)40 ratio is a more informative measure of amyloid plaque burden in the brain than the absolute value of A\(\beta\)42 in the CSF [36; 37]. We analyzed A\(\beta\)40, A\(\beta\)42, A\(\beta\)42/A\(\beta\)40, tTau, and pTau181 for 57 dementia patients. The PRS showed a positive correlation with tTau and pTau181 and an inverse correlation with A\(\beta\)42/A\(\beta\)40 (Table 3). Therefore, a higher PRS was associated with CSF biomarker levels that are more indicative of AD. Similar associations between polygenic risk and CSF biomarkers have been demonstrated in other studies [22; 29; 30], although this was not always the case for all biomarkers. In our study, carriers of _APOE-e4_ had higher levels of tTau and pTau181 and lower levels of A\(\beta\)42 and A\(\beta\)42/A\(\beta\)40 ratio in the CSF (Table 3), which also reflects an AD-linked pattern. Conversely, the _APOE-e2_ allele exhibited an association with elevated levels of the A\(\beta\)42/A\(\beta\)40 ratio. This study has some limitations. Firstly, the group of studied patients with dementia did not only include patients with AD. Since polygenic risks and _APOE_ alleles significantly correlate with AD biomarkers, it can be assumed that the predictive value of genetic factors for AD may be higher. Secondly, this was a case-control study and not a cohort study. Therefore, some social characteristics could depend on the composition of groups of patients and healthy volunteers. Thus, we did not include sex and the presence of children as predictors in the models since they were not representative of the group of CN volunteers. Also, the age of onset of the disease was unknown for most patients; only the age of inclusion was noted. Future directions lie in prospective cohort studies, which include patients with MCI. The influence of genetic factors needs to be evaluated while considering clinical, social, and biochemical data. These data will help us assess the impact of PRS on MCI patients, with the hydrogel microarray-based technique acting as an auxiliary tool for identifying high-risk individuals.
## 4 Performance of Waste-Plastic-Modified Bitumen Binder and Mixture
### Characteristics of Plastic-Modified Bituminous Mixtures
#### 4.2.3 Hybrid and Composite Systems  
A study by [58] illustrated the synergistic advantages of blending PET with crumb rubber, revealing that an optimal composition of 2% PET and 10% rubber attained superior Marshall stability, stiffness, and indirect tensile strength compared to each material individually. Similar improvements in crack resistance and fracture energy have been noted with the addition of cross-linked polyethylene (Si-XLPE) waste, which enhanced toughness under Mode I, II, and mixed loading conditions [61].  
Studies by [59; 62] discovered that the use of crumb rubber with polyethylene in dry-mixed composite particles improved rutting resistance while maintaining fatigue life.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**References** & **Plastic Type** & **Dosage** & **Incorporation Method** & **Performance Outcomes** & **Notable Insights** \\ \hline
[57] & Waste plastic granules + RAP & 0.3–0.5\% granules & Hybrid (mixed method & \(\uparrow\) sustainability & No compromise or voluntrics or workability \\ \hline
[58] & PET + crumb rubber & 2\% PET + 10\% rubber & Dry method & \(\uparrow\) Marshall stability, stiffness, ITS & Synergistic effects observed (PET enhances stiffness and temperature resistance; crumb rubber improves elasticity and fatigue life) \\ \hline
[59] & PE + rubber & Not specified & Dry–mixed composite & \(\uparrow\) Rutting resistance balanced fatigue life & Twin-screw extrusion improved integration \\ \hline
[60] & Multilayered Plastic & High dose (unspecified) & Dry method & \(\uparrow\) Air voids, \(\downarrow\) moisture resistance & High content increases compaction challenges \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._Rubber-plastic composite particles, produced using twin-screw extrusion, resulted in mixes exhibiting enhanced deformation resistance and satisfactory moisture sensitivity.
## 4 Results and Discussions
### Radiation Pattern  
Figure 12 presents the E-field and H-field patterns of the UWB antenna array obtained at 4.5, 6.85, 7.5, 8.5, and 9.5 GHz, respectively. At lower frequencies, the E-field pattern with MC structure has 8-shape patterns with little variation at the higher frequencies. The E-field pattern without the decoupling structure does not have a regular shape. Looking at the H-field pattern, with the introduction of the MBEG decoupling structure, an omni-directional shape is observed for all the frequencies considered. A different pattern is observed when the decoupling structure was not introduced. At lower frequencies, the H-field tends to a point and exhibits an \"8\" shape only at 8.5 GHz.
## 2 Materials and Methods
### Parameter Matching of DICPS
#### 2.3.1 Motor Parameter Matching  
A 40 hp tractor was the subject of this study, and its rated power was 29.8 kW. In order to evaluate the economic performance of the DICPS, a single-motor two-speed powertrain system (SMPS) was proposed as the benchmark, which mainly included a motor EM_T and a two-speed mechanical transmission; its main parameters are listed in Table 2. The maximum draft force at 10 km/h was determined to be 12,731.3 N through numerical calculation, and the maximum speed was about 31.25 km/h, which can meet the dynamic performance indexes of tractors.  
To make a fair comparison between the proposed DICPS and SMPS, the summed power of EM_S and EM_R was equal to the power of EM_T. However, the power ratio between the two motors in the summed power, defined as PDR using Equation (12), was uncertain. To study the effect of different power ratios on the dynamic and economic performance of the DICPS, we set five PDRs here: 0.2, 035, 0.5, 0.65, and 0.8.  
\[PDR=\frac{P_{\text{max1}}}{P_{\text{max1}}+P_{\text{max2}}} \tag{12}\]  
where \(P_{\text{max1}}\) and \(P_{\text{max2}}\) denote the maximum power of EM_S and EM_R, respectively.  
To simplify the model, the efficiency MAPs of EM_S and EM_R were defined as having the same shape as EM_T. The speed range of the two motors was the same as that of EM_T, but the torques were proportional to EM_T, i.e., \(T_{\text{max1}}\) = \(PDR\cdot T_{\text{max,t}}\), \(T_{\text{max2}}\) = (1-\(PDR\))\(\cdot T_{\text{max,t}}\). The efficiency map of EM_T is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Parameters** & **Value** \\ \hline Motor & Rated/peak power (kW) & 30/49 \\  & Rated/maximum torque (Nm) & 100/200 \\  & Rated/maximum speed (rpm) & 2300/7500 \\ Transmission & 1 gear ratio & 3.3 \\  & 2 gear ratio & 2.294 \\  & Main reduction gear \(i_{m}\) & 2.67 \\  & Final reduction gear \(i_{f}\) & 5.0714 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Powertrain specifications of SMPS.
## 4 Discussion
### Limitations
This article does not attempt to refine the learning base of the LLM for the narration of key life events, as the broad range of potential response types for individuals was desirable as the starting point for this effort. However, future avenues of research require assessing the validity of narratives within their respective domains, such as for births and Death events, and within a larger context, such as refugee camps, natural disaster response, etc. 6. **Underspecification hinders narrative generation. ** Increased specificity in the prompting of desired narratives from ChatGPT has been beneficial in reducing the number of iterations for generating and assessing the correctness of the narratives. Similar to ML pipeline problems with underspecification, where underspecification in training leads to problems in reliability and validity [93], underspecification of narrative requests from ChatGPT led to many more erroneous responses and the expansion of the structure provided in this article. 7. **Tweet comparison sets. ** Tweets were not categorized per event type like the generated narratives. The tweet set is utilized assuming that it represents a general sample of the population. As such, the generalizability of the sentiment findings should not be extended to other sample populations without proper supporting justifications about the reasonableness of the extension. 8. **Not Correcting for Multiple Comparisons. ** For tests involving multiple comparisons, it is common practice to apply a correction to adjust the significance level of the applied hypothesis tests. However, no correction is advised if: (1) the study is restricted to a small number of planned comparisons; (2) the study is exploratory using post hoc testing of unplanned comparisons for further investigation; (3) multiple simple tests are envisaged and it is the results of the individual tests that are important; (4) it is imperative to avoid a type II error [94]. Meanwhile, a Bonferroni correction is advised if: (A) a single test of the universal null hypothesis that all tests are not significant is required; (B) it is imperative to avoid a type I error; (C) a large number of tests are carried out without preplanned hypotheses in an attempt to establish that any result may be significant [94]. The design of this study fits with all four points for when the correction is not necessary and it does not fit any of the three points pertaining to when to utilize the Bonferroni correction. Additionally, since we are interested in the individual comparisons for each PANAS category, not applying the Bonferroni correction is more appropriate for the design of this study as we are interested in identifying interesting patterns or potential relationships that may warrant further investigation. For this study, we are accepting a higher risk of Type I errors (false positives) to reduce the chance of Type II errors (false negatives). As such, this study is more willing to identify something as significant even if it might not be, in order to not miss any potential findings.
# Research Progress on the Influence of Thermo-Chemical Effects on the Swelling Pressure of Bentonite
## 4 Effect Mechanism of Swelling Properties
### Intergranular Stress
\(\Omega^{l}\) is expressed as:  
\[n^{l}p^{l}\Omega^{l}(T,n^{l})= n^{l}_{0}p^{l}\Omega^{l}(T,n^{l}_{0})+\int_{n^{l}}^{n^{l}_{0}} \Bigl{[}s_{\mathrm{M}}(T,n^{l})-\Pi_{\mathrm{D}}(T,n^{l})\Bigr{]}dn^{l} \tag{8}\]  
where \(n^{l}\) is the volume fraction of water, defined as the volume of water in the pore as a proportion of the total volume, \(p^{l}\) is the density of water, for dilute solutions, \(\rho^{l}\approx\rho^{l_{\mathrm{H_{2}O}}}_{\oplus}\), \(s_{\mathrm{M}}\) is the measured (or controlled) matrix suction, \(p^{l}_{W}\) is the measured pore pressure, \(n^{l}_{0}\) is the volume fraction of pore water at saturation, equal to the porosity \(n\). The intergranular stress expression can consider the physicochemical effects between particles and unitize the infiltration, capillary, and adsorption effects caused by physicochemical interactions, which can better describe the behavior of bentonite in terms of strength and deformation. Moreover, the capillary and physicochemical pressure at the microscopic scale can be better applied to the intergranular stress expression at the macroscopic scale, in unsaturated soils. [PERSON] et al. [92] experimentally researched the effect of pore solution on the swelling pressure of expansive soils, pointed out that the variation of the swelling pressure is determined by the intergranular stress, described the swelling pressure variation from the perspective of macro-mechanics, and proposed a model of swelling force prediction. The superiority of this model for swelling pressure prediction was confirmed by validatingit on several bentonites with different properties (Figure 11). Overall, studies of the effect of intergranular stress on the swelling pressure of bentonite under thermo-chemical effects are still relatively rare and lack extensive experimental validation; therefore, further investigation is necessary.
# Reliable and Energy-Efficient LEO Satellite Communication with IR-HARQ via Power Allocation
###### Abstract  
This paper examines reliable and energy-efficient transmission in low earth orbit (LEO) satellite communication systems. In particular, we analyze the link transmission characteristics of the LEO satellite to the ground user and model the channel as a combination of large-scale fading and small-scale fading. Based on this, we consider an incremental redundancy hybrid automatic repeat request (IR-HARQ) technique with a variable-power allocation method, and we call it the IR-HARQ-VPA scheme. In this method, the outage probability after each IR-HARQ round can be obtained through numerical integration based on the fast Fourier transform (NI-FFT). This method is suitable for any number of HARQ transmission rounds and can improve the accuracy compared with previous approximation methods. In addition, variable-power allocation based on the genetic algorithm (VPA-GA) is introduced to reduce the energy consumption. The simulation results show that the proposed IR-HARQ-VPA scheme cannot only meet the requirements of transmission reliability but also achieves higher energy efficiency than IR-HARQ with equal power (IR-HARQ-EP) transmission and a previously proposed variable-power allocation method. Moreover, the simulation results in a LEO satellite communication window also confirm the effectiveness of the proposed IR-HARQ-VPA scheme.  
LEO satellite communication; energy efficiency; IR-HARQ protocol; power allocation +
Footnote †: journal: Sensors
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 6 Design and Implementation of the Autonomic Feature Management Controller
### Design of the Control Loop  
Kubernetes uses a declarative management approach to manage cluster resources. The user specifies the desired state of cluster resources through the API server. A control loop monitors the actual state of the resources and compares it with the user-specified desired state. If there is any difference between them, the control loop takes actions to bring the actual state to the desired. The controller in our approach adopts a similar idea to implement the control loop (Figure 7) for feature compatibility management in a Kubefed cluster federation:  
* The user specifies the desired features that each member cluster should support through a declarative API;
* The controller monitors actual supported features and compares them with the desired features for each member cluster;
* If a desired feature is not supported in the cluster, the controller performs the vendor-agnostic feature reconfiguration tactics to activate the missing feature.  
The control loop is event-driven, and it blocks after an iteration finishes. When desired features change, the control loop continues with the next iteration to maintain feature compatibility. Regularly, the controller restarts the control loop for every installed feature to see if the feature is still working.  
Figure 7: Feature compatibility controller’s control loop.
## 3 Results and Discussion
### Effects of Airfoil on Fan Performance
#### 3.1.2 Aerodynamic Performance of Different Airfoil Fans  
After comparing and analyzing the airfoil's intrinsic performance, we designed three-dimensional blades and applied them to the fan model for simulation. The airfoil blade construction method involves bending the centerline of the airfoil according to the design method of the single-arc blade. Figure 14 shows the blades designed with four airfoils. A three-dimensional impeller model is established for the four airfoils, and meshing and numerical calculations are carried out following the numerical calculation method of the single-arc model. The values for the constants in the Realizable k-\(\varepsilon\) model are typically taken as follows: C\({}_{1\varepsilon}=1.44\); C\({}_{2\varepsilon}=1.92\); C\({}_{u}=0.09\); \(\sigma_{K}=1.0\); and \(\sigma_{\varepsilon}=1.3\).  
Five operating points are selected for numerical simulation as shown in Table 4. The flow rate range of 200 m\({}^{3}\)/h to 450 m\({}^{3}\)/h is the common operating condition. Therefore, the operating points Case 2, Case 3, and Case 4 within this range are selected as representatives to analyze and discuss the internal flow field of the fan.  
The efficiency and total pressure curve of four airfoil fans as shown in Figure 15. The A airfoil blades have excellent fan efficiency under all operating conditions. Except for Case 2, which is lower than the B airfoil blade, the A airfoil blade is the most efficient at all other flow points. When comparing the A, C, and D airfoil blades, combined with the lift-to-drag ratio curve in Figure 9, the curve change rules of the A airfoil, C airfoil, and D airfoil are consistent with the trend of the efficiency curve. This result indicates that the lift-to-drag ratio of the airfoil has a great influence on the efficiency performance of the multi-blade centrifugal fan. However, from the efficiency curve of the B airfoil with the worst lift-to-drag ratio, it has the highest efficiency value in Case 2. Only in a specific  
Figure 14: Four airfoil blades: (**a**) A airfoil blade, (**b**) B airfoil blade, (**c**) C airfoil blade, and (**d**) D airfoil blade.  
Figure 13: Streamline distribution of 5\({}^{\circ}\)and 10\({}^{\circ}\) attack angles for four airfoils: (**a**) A airfoil, (**b**) B airfoil, (**c**) C airfoil, and (**d**) D airfoil.  
working condition interval does the efficiency value of the fan have a positive relationship with the lift-to-drag ratio of the airfoil blade.  
Except for the fact that the total pressure of the D airfoil is significantly lower than that of other airfoils in the large flow range, other airfoils show different total pressure performance under different working conditions. Although it is difficult to judge the influence of the lift-to-drag ratio of the four airfoils on the total pressure performance of the fan, the total pressure curve of the A airfoil has the highest stability, and there is no fluctuation in the other three airfoils.
## 3 Results and Analysis
### SVM-Based Prediction Model of Improved Loess Permeability
#### 3.3.2 Selection of Parameters  
The SVM regression algorithm can be implemented with a dedicated toolbox, and a large number of research results have shown [23; 24; 25] that Radial Basis Function (RBF) kernel functions have better applicability because, in most cases, SVR will be able to produce more accurate fitting results when using RBF kernel functions; therefore, in this paper, the RBF kernel function is used.  
Before training the data, the best \(c\) and gamma parameters are found by the cross-validation method. \(c\) can be understood as the weight of preference of two indicators (interval size, classification accuracy) in regulating the optimization direction, i.e., error tolerance. The higher the \(c\) is, the more intolerant the error is, and the easier it is to overfit. The smaller the \(c\) is, the easier it is to underfit. The generalization ability becomes poor when \(c\) is too large or too small. gamma is a parameter that comes with the RBF function after choosing it as the kernel. It implicitly determines the distribution of the data after mapping to the new feature space. The larger the gamma is, the fewer support vectors there are; the smaller the gamma is, the more support vectors there are. The number of support vectors affects the speed of training and prediction. The best \(c=1\) and the best gamma = 8 for this training.
## 3 Proposed Method
### Enhancing Feature Extraction with AKConv
The yellow outlines represent the predefined initial convolution shape (using example (3) from Figure 2(b)), while the blue outlines depict the transformed convolution shape, with green indicating overlapping parts (simplified for demonstration purposes). Visually, defect areas present as randomly distributed linear flaws and dark spots. Similarly, AKConv adaptively adjusts both the cardinality and topology of convolutional kernel parameters based on the characteristics of the migration feature map. This allows the convolutional kernel to deviate from traditional regular sampling grids, actively aligning with the spatial structures of asymmetric defects like cracks and spots. Concurrently, its dynamic parameter control capability enables the kernel to flexibly allocate parameters and convolution points based on the importance of different regions, which improves the model's adaptability to local features and its responsiveness to complex geometric shapes and varying targets. Furthermore, by learning saliency weights for each sampling position, AKConv effectively suppresses spurious responses caused by background textures. These integrated characteristics enhance the model's discriminative capability and robustness when processing images with intricate textures and varied defect morphologies, thereby further resolving the issue of low mAP in YOLOv8's original convolutions for crack-like defect detection. Figure 3: Initial sampling shape; (**a**) illustrates the flexibility of kernel size customization, while (**b**) provides an example of the kernel shape with a fixed size of \(5\times 5\).