esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Summary and Conclusions  
The end damage of bridge PC girders due to the deterioration of concrete material under the influence of deicing material and freeze-thaw cycles jeopardizes the shear resistance of the girders. Due to its high strength-to-weight ratio, FRP laminate has been utilized to repair structurally deficient bridges. The effectiveness of the FRP laminate to restore the shear capacity of the PC girder with the distressed end region under short shear span was explored in this study. Three-point bending tests were conducted on full-scale PC girders and results from the control, damaged, mortar repair and CFRP repair cases were compared. In addition, a strut-and-tie model was proposed to analyze \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tested Girder** & **Experiments** & **Strut-and-Tie Model** & **Comparison** \\ \hline \multirow{7}{*}{Small-scale PC beam [8]} & Condition & \(P_{test}\), kN & \(\beta_{re}\) & \(P_{STM}\), kN & \(\frac{P_{test}}{P_{STM}}\) \\ \cline{2-6}  & Control & 283 & & 299 & 0.95 \\ \cline{2-6}  & Damaged & 206 & n/a & 218 & 0.95 \\ \cline{2-6}  & Mortar repair & 230 & & 220 & 1.04 \\ \cline{2-6}  & CFRP repair & 338 & 0.96 & 338 & n/a \\ \hline \multirow{3}{*}{Full-scale PC girder} & Control & 543.1 & & 609 & 0.89 \\ \cline{2-6}  & Damaged & 536.4 & n/a & 486 & 1.10 \\ \cline{1-1} \cline{2-6}  & Mortar repair & 496.4 & & 486 & 1.02 \\ \cline{1-1} \cline{2-6}  & CFRP repair & 557.3 & 0.26 & 557 & n/a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results between experiments and strut-and-tie model (STM). and predict the shear capacity of the PC girder with the repaired end region using the FRP laminate. Based on the test results, the following conclusions could be drawn:  
* A mortar repair alone is not sufficient to recover the shear strength and ductility of the girder with damaged end regions. * Longitudinal FRP anchors proved to be effective in preventing the overall debonding of FPR laminates at the end regions. * The CFRP shear reinforcement repair showed the effectiveness in improving the shear behavior of the girder with the damaged end region. Even under an a/d ratio of 1.25, CFRP laminates were able to recover both peak force and ductility of the girder with a 2.6% and 9.6% improvement, respectively. * For PC girders with a similar damage pattern to that was considered in this study, the proposed STM could estimate with reasonable accuracy the shear capacity of the girders repaired with FRP laminates. **Author Contributions:** Formal analysis, H.Z. ; investigation, H.Z. ; data curation, H.Z. ; writing--original draft preparation, H.Z. ; visualization, H.Z. ; conceptualization, B.A. ; investigation, B.A. ; writing--review and editing, B.A. ; supervision, B.A. ; project administration, B.A. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors acknowledge the funding provided for this research from the Illinois Department of Transportation (IDOT) and Illinois Center for Transportation (ICT) under project No. R27-156. **Conflicts of Interest:** The authors declare no conflict of interest.
# Numerical Analysis via Mixed Inverse Hydrodynamic
## 2 Mathematical Model
### Fluid Mechanics
#### 2.2.2 Boosting Zone  
In the IHL analysis, the position of the inflection point and the pressure gradient at that point are important factors that determine the oil film thickness. However, in the FE analysis, the contact pressure at both ends of the rubber seal, the inlet and outlet, decreased very rapidly, making it difficult to describe the smooth pressure change that occurs in fluid lubrication. Accordingly, this problem was solved by introducing a boosting zone in the IHL analysis. Figure 3 shows the FE contact pressure at the inlet and outlet of the contact area and the oil film pressure distribution with the added boosting area. If the reference coordinate system is set at the point where the FE contact pressure reaches a maximum near the region where the fluid enters, the position where the FE contact pressure becomes 0 on the \(x\)-axis is \(x_{c}\), and the position where the oil film pressure starts is \(x_{a}\). The \(x\)-axis position \(x_{I}\) of the inflection point exists between \(x_{c}\) and \(x_{m}\); \(x_{m}\) is the point where the pressure \(p\) and the pressure gradient \(\partial p/\partial x\) in both the FE and IHL analyses coincide. Assuming that \(\left(\partial p/\partial x\right)_{max}\) at the point where the pressure gradient is continuous, the \(x_{m}\) and the \(x_{I}\) points are at the same position. The oil film distribution in the boosting region can be expressed as follows [9]:  
\[h=1.5h_{0}+h_{FE}\left(x_{a}\leq x\leq x_{I}\right) \tag{11}\]  
\[h=1.5h_{0}\left(x_{I}\leq x\leq x_{m}\right) \tag{12}\]  
where \(h_{FE}\) is the gap between the seal and rod for a small area just before contact, after the FE static analysis. Since the \(h_{FE}\) is directly related to the deformation of rubber seal, it depends on the stroke direction and operating conditions, ambient temperature, and sealed pressure.  
Figure 3: Film pressures vs. contact pressures and assumption for the film profile in the boosting zone.
# Geometric Spherical Surfaces Emulating the Geostationary Orbit at Any Latitude with Zenith Links
## 2 Geocentric Spherical Surfaces Emulating the Geostationary Orbit
Because the satellite cannot be at the zenith all the time when crossing diagonally the service area, although it is very close to it, the link budget will lose 3 dBs on the side of the receiver, and 3 dBs on the side of transmitter when the satellite is entering/leaving the service area. When the sites are really at the equator, with the above assumptions there is no overlapping of the \(-3\) dBs footprint of each satellite. For all different latitudes, there will be overlapping, the larger the closer to the poles. The overcrowding of the satellites at the high latitudes is a drawback of the [PERSON] star constellation, but this effect can be partially taken care of if the orbit planes are inclined just a little bit more (or less) than \(90^{\circ}\)[1]. In any case, as for all constellations, a careful frequency planning and frequency reuse must be considered. The [PERSON] star constellation, as noted in Section 1, makes easier the use of inter-satellite links both within the same orbit plane (intra links) and with adjacent orbit planes (inter links). Let us now explore, in the next section, the geometrical and radio electrical parameters of the GeoSurf design.
###### Abstract  
Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to single classifiers. Therefore, in this study, we propose a customer churn prediction (CCP) based on ensemble system fully incorporating clustering and classification learning techniques. The proposed churn prediction model uses an ensemble of clustering and classification algorithms to improve CCP model performance. Initially, few clustering algorithms such as k-means, k-medoids, and Random are employed to test churn prediction datasets. Next, to enhance the results hybridization technique is applied using different ensemble algorithms to evaluate the performance of the proposed system. Above mentioned clustering algorithms integrated with different classifiers including Gradient Boosted Tree (GBT), Decision Tree (DT), Random Forest (RF), Deep Learning (DL), and Naive Bayes (NB) are evaluated on two standard telecom datasets which were acquired from Orange and Cell2 Cell. The experimental result reveals that compared to the bagging ensemble technique, the stacking-based hybrid model (k-medoids-GBT-DT-DL) achieve the top accuracies of 96%, and 93.6% on the Orange and Cell2 Cell dataset, respectively. The proposed method outperforms conventional state-of-the-art churn prediction algorithms.  
churn prediction; machine learning; clustering algorithm; classification algorithm; ensemble model +
Footnote †: journal: Medical Informatics
# Efficient Inorganic Stabilization Materials for Chromium and Arsenic Pollution in Water and Soil
## 3 Mechanism of Chromium and Arsenic Stabilization
### Ligand Exchange and Surface Complexation Mechanism  
There are usually some oxygen-containing functional groups on the surface of chromium and arsenic stabilized materials, such as hydroxyl (-OH), carboxyl (-COOH), etc. These functional groups can act as ligands to interact with chromium or arsenic ions. For example, pollutant ions replace hydrogen atoms in hydroxyl groups to form coordination bonds with oxygen atoms, thereby being fixed on the surface of materials. [PERSON] et al. [93] used Mn salt to connect cellulose nanocrystal (CNC) and polyethyleneimine (PEI) to prepare CNC-Mn-PeEI. Mn was introduced as a \"bridge junction\" to form O-Mn-O bonds with cellulose nanocrystals (CNC) and polyethyleneimine (PEI), to enhance the stability of the material and the adsorption capacity of As(III) by successfully connecting CNC and PEI. In the adsorption process, As(III) was attracted to the CNC-Mn-PEI surface through electrostatic attraction and interacted with oxygen-containing groups on the material surface to form stable Mn-O-As bonds, thus achieving removal. The adsorption capacity for As(III) is 78.026 mg/g.  
Some atoms or groups on the surface of materials can form surface coordination sites through chemical reactions or physical adsorption, and form surface complexes with chromium or arsenic ions through coordination bonds. During this process, heavy metal ions provide empty orbitals, and the coordinating atoms on the surface coordination sites provide lone pair electrons, forming a stable coordination structure. Chitosan magnetic graphene oxide (CMGO) nanocomposites were synthesized by [PERSON] et al. [94] to form surface complexes through the interaction of surface functional groups (such as -NH2 and -OH) with As(III). CaSO4-H2O is encapsulated around ferric arsenate to further stabilize the arsenic. [PERSON] et al. [95] reported that when steel slag is added as a stabilizer, As forms complexes with Fe and Ca elements in the steel slag.
## 3 Proposed Algorithm for Energy-Efficient Node Mobility
### Scenarios Based on Mobile Node Movement
#### 3.3.2 Case of Parent Node Information Being Renewed during the Motion of the Mobile Node  
In this section, we describe the case where the parent node information is renewed while the mobile node is moving. The same process as described in Section 3.3.1 is used to receive the information of neighboring router nodes and to store the information in the PP column of the parent list table upon receiving DIO messages followed by DIS message transmission. Afterward, the OF values of a PP node selected from the parent list table and the previous parent node are compared.  
During this process, if there is a router node with the same ID as the parent node selected at \(t_{n-1}\), then the OF value of this node is examined. If the OF value of this node is higher than a threshold, then the router node selected at \(t_{n-1}\) is left as the parent node in the next step. If not, the router node with the highest OF value in the PP column of the parent list table is selected as the new parent node. Here, the threshold represents the signal strength that allows for smooth communication between the mobile and parent nodes.  
In the example shown in Figure 6, while the router node ID with the greatest OF value in the PP column is 2, because the OF value of the previous parent node is higher than the threshold, router node ID 4 continues to be used as the parent node. The method for the dynamic allocation of \(I_{MTP}\) after parent node selection is that of Section 3.3.1.  
Figure 5: Update state in the parent list table when mobile node first participates in low power and lossy networks (LLN).  
Figure 6: Update state in the parent list table when the parent node information is renewed during the movement of the mobile node.
## 7 Results
### Data Cleaning and Standardization  
To ensure the integrity of the dataset, a rigorous data preprocessing pipeline was implemented:  
1. Missing Data Handling: Missing entries, particularly in early Olympic records (before 1920), were addressed using imputation techniques based on historical trends or exclusion where imputation was infeasible. For instance, incomplete athlete participation records were supplemented with average participation rates for the respective country and era.
2. Data Merging for Temporarily Divided Nations: Political divisions, such as those of East and West Germany (1949-1990), required merging medal counts and participation records to treat them as a single entity (Germany) for consistent analysis. Similar unifications were applied to other temporarily divided nations, such as the Soviet Union and its successor states, ensuring continuity in longitudinal trends.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Country** & **Year** & **Nu.** & **C.E** & **Type (\(s_{k}\))** & **Projection** \\ \hline Korea & 2028 & 25 & 1.0 & 0.4 & 4.997 \\ Korea & 2028 & 25 & 0.5 & 0.4 & 2.986 \\ \hline China & 2028 & 27 & 1.0 & 0.2 & 5.115 \\ China & 2028 & 27 & 0.5 & 0.2 & 3.104 \\ \hline \end{tabular}
\end{table}
Table 11: 2028 Olympic projections.  
Outlier Detection and Correction: Outliers, such as anomalous medal counts due to boycotts (e.g., the 1980 and 1984 Olympics), were identified using statistical methods (e.g., Z-scores) and adjusted by referencing alternative data sources or contextual historical context to prevent skewing model predictions.
* Standardization of Variables: All numerical variables, including participation counts and medal growth rates, were standardized to a common scale (mean = 0, standard deviation = 1) to facilitate factor analysis and machine learning model training. Categorical variables, such as event types, were encoded using one-hot encoding to ensure compatibility with the K-means clustering and neural network models.
## 3 Results and Discussions
### Effects of TP Height on the Interaction Force and FH at the HDI  
The adhesion forces of the HDI without TP and that with TP having different heights when the slider is at the flying height are compared in Figure 8. It should be noted that the \(FH\) is equal to the nominal flying height \(z_{\text{r}}\) for the case of HDI without using the TFC technology. It is observed that the adhesion force at the HDI without TP is larger than that at the HDI with TP, and this discrepancy is more remarkable when the \(FH\) becomes smaller, demonstrating the necessity of using the TFC technology at ultra-low flying heights. The effects of the TP height on the adhesion force, contact force and \(FH\) at a fixed nominal flying height are presented in Figure 9. Transitions of the slider operation  
Figure 7: Pressure and distance distributions in the section of \(y=0\) at different operation states under \(l_{\text{c}}\) = 10 nm, \(t_{\text{hub}}=t_{\text{DLC-disk}}=t_{\text{DLC-slider}}\) = 2 nm: (**a**) Pressure \(P\) and (**b**) Height \(h\).  
state from the flying state (the left grey region) to the TP-lube contact state (the middle yellow region) and then to the TP-DLC contact state (the right pink region) with the increase in the TP height are observed. During the flying state, the adhesion force increases and the \(FH\) decreases if the TP height is increased. When the TP comes into contact with the lubricant, the increased TP height results in a decreased \(FH\) and an almost unchangeable adhesion force. If the slider is at the TP-DLC contact state, the adhesion force and contact force increase but the \(FH\) cannot be obviously reduced by increasing the TP height on one hand, and on the other hand, the contact zone and contact pressure increase, as depicted in Figure 10. The increase in the contact pressure and contact zone can affect the friction and wear of the HDI and, as a result, may influence the read/write accuracy. Therefore, excessive TP height should be avoided.  
Figure 8: Effects of TP on the adhesion force under different flying height \(FH\).  
Figure 9: Force and flying height versus TP height under \(t_{\text{lub}}=t_{\text{DLC-disk}}=t_{\text{DLC-slider}}=2\) nm, \(z_{\text{r}}=10\) nm: (**a**) Adhesion force \(F_{\text{a}}\), Contact force \(F_{\text{c}}\) and (**b**) Flying height \(FH\).
# A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis
## 4 Discussion  
### Ablation Study  
To test the effect of the MK-MMD distance, a fault signal with SNR = \(-4\) is used for experiments, and compare the performance of the original adversarial model and the model combined with different distance losses in cross-domain diagnosis. Each model is trained for 100 iterations. In each iteration, the training set of the source domain and the target domain is used for training, and then the model is applied to the test set from the source domain and the target domain to obtain the classification result. The test is repeated 10 times and obtains the average classification accuracy of the optimal accuracy from each time on the target domain as the classification result. As shown in Table 5, indices 0, 1, 2, and 3 represent the working conditions of the bearing under different loads, respectively, and the arrows indicate the direction of the cross-working condition.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Domain**} & \multirow{2}{*}{**Average**} \\ \cline{2-2} \cline{4-6}  & **0** & **1** & **2** & **3** & \\ \hline Multi DAN & 95.51 & **93.40** & 96.37 & 93.21 & 94.62 \\ Multi DANN & 98.45 & 92.15 & 93.66 & 95.48 & 94.93 \\ MCDANN & **98.49** & 92.02 & **98.51** & **95.81** & **96.20** \\ \hline \hline \end{tabular} Note: The bold number mean it is the largest in this column.  
\end{table}
Table 4: DIRG classification accuracy of different models on unknown working conditions without labels. (SNR = \(-4\), static load = 1000 N)  
Figure 10: DIRG classification accuracy comparison of three multi-source domain adaptation models on unknown working conditions without labels. (SNR = \(-4\), static load = 1000 N).  
As can be seen from Table 5, compared with the original adversarial network, the adversarial network that adds various distance metrics to correct the domain adversarial loss has a certain accuracy improvement, and the adversarial network that combines the MK-MMD distance has the best overall performance. However, the accuracy of adding MK-MMD distance is not so high. Due to the problem of domain mismatch, there are still different degrees of errors in cross-domain diagnosis.  
Table 6 shows the result comparison of single-source and multi-source methods. In Table 6, for each domain, only one source domain is used for cross-domain diagnosis. We choose the optimal source domain cross-domain diagnosis result as the benchmark, which is compared with the result of dual-source domain MCDANN and triple-source domain MCDANN.  
In this table, the accuracy improvement of MCDANN from dual-source domain to triple-source domain indicates that, as the number of source domains involved in training increases, the diagnostic accuracy of MCDANN will also improve.
## 3 Some Key Issues on the Use of SLNs and NLCs as Oral Nanovehicles for Nutraceuticals or Bioactives
### Lipid Digestion and Bioactive Solubilization
[PERSON] and colleagues [54] further indicated that the release of encapsulated bioactives (e.g., curcumin) in the NLCs could be controlled by modulating the lipid type and composition, and by using a lipase inhibitor. A more recent work, interestingly, confirmed that the bioaccessibility of curcumin encapsulated in NLCs with glyceryl trtriglyceride (MCT) as the solid and liquid lipids was closely correlated with the relative liquid lipid ratio in the total lipids, and a maximum value of bioaccessibility was obtained at the relative liquid ratio of 20% [55].
## 4 Experiments
### Data Synchronization  
Navigation GPS maximal output frequency was declared as 5 Hz, which means that the GPS position (\(p_{GPS}\)) was reported a maximum of five times per second (Figure 7a). To compare both the registered tracks, i.e., RTK and GPS, the data rate for both RTK and GPS should be the same (Figure 7b). However, the RTK system reported position (\(p_{RTK}\)) with a maximum of 50 points every second and in fact both systems registered tracks at different rates.  
To align the track rates a linear interpolation was applied to the lower rate track, i.e., the GPS track. All GPS positions (\(p_{GPS}\)) were interpolated with the maximum number of points equal to that for the RTK (\(p_{RTK}\)).  
Assuming a data set consisting of independent data values \(x_{i}\) and dependent data values \(y_{i}\), where \(x=1,\ldots,n\), we can find an interpolation function \(\hat{y}(x)\) such that \(\hat{y}(x_{i})=y_{i}\) for every point in our data set. This means that the interpolation function goes through the given data points. Given a new \(x*\), we can interpolate its function value using \(\hat{y}(x*)\)[43]. In the present study, a linear interpolation was used.  
The estimated positions are assumed to lie on the line joining the nearest registered positions of the estimated track with \(n_{\text{GPS}}\) registered positions. It is assumed, without loss of generality, that the  
Figure 6: Survey pattern plans.  
Figure 7: Recorded GPS and RTK tracks (**a**) different frequency (**b**) interpolated GPS positions.  
GPS coordinates transformed to PL-2000 (\(X_{G}\) and \(Y_{G}\)) are in ascending order, then new interpolated position coordinates \(\hat{X}_{\text{G}_{\text{i}}}\) and \(\hat{Y}_{\text{G}_{\text{i}}}\) are calculated according to:  
\[\hat{X}_{\text{G}_{\text{i}}}=X_{\text{G}_{\text{i}}}+\frac{\Big{(}X_{G_{i+1}}- X_{\text{G}_{\text{i}}}\Big{)}(n_{R}-n_{\text{G}})}{(n_{G+1}-n_{G})} \tag{9}\]  
\[\hat{Y}_{\text{G}_{\text{i}}}=Y_{\text{G}_{\text{i}}}+\frac{\Big{(}Y_{G_{i+1}} -Y_{\text{G}_{\text{i}}}\Big{)}(n_{R}-n_{\text{G}})}{(n_{G+1}-n_{G})} \tag{10}\]  
where: \(n_{R}\) represents the RTK measurement number, that is, \(n_{R}=1,\ldots,n_{RTK}\), \(n_{G}\) represents the GPS measurement number, that is, \(n_{G}=1,1+r,\ldots,n_{GPS}\), where \(r\) is a RTK to GPS measurement ratio calculated according to the following equation:  
\[r=\frac{n_{RTK}}{n_{GPS}} \tag{11}\]  
An example of a result for the GPS position interpolation is presented in Figure 6. In every case the number of GPS measurements was increased and equals the number of RTK measurements.