esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Angiopoietin Treatment with Hypoxia and
## 4 Discussion
### Ang-1/2 and Its Effect on DNA Measurement in Normoxic and Hypoxic Conditions
Concerning the amount of DNA, we detected either no effect in a dose-dependent manner of angiopoieitin or specific variation in the use of Ang-1 or Ang-2 in normoxia and hypoxia for the three tested populations. [PERSON] et al. [1] demonstrated selective enhancement of the proliferation of Tie2+ GD2- CD24- (i.e., Tie2 single-cell sorted positive cells (T/sp)) and of Tie2\({}^{+}\) GD2\({}^{+}\) CD24\({}^{-}\) (i.e., Tie2 single-positive cells (TG/dp)) by using a co-culture with a cells feeder layer ( murine stromal cells overexpressing human Ang-1 (AHESS-5). A marked Ang-1 dependent growth resulted in increased proliferation of the T/sp population by 3.1 \(\pm\) 0.4 times and for the TG/dp population by 3.2 \(\pm\) 0.4 times. Unfortunately, no information about the administrated Ang-1 dose or incubation time was published. Nevertheless, these results could not be confirmed with the NP cells Tie2\({}^{+}\) population in this study. A possible explanation could be that the Tie2 receptor was not expressed anymore over in vitro culture time, as cells needed expansion before starting the experiment. Summing up, at the time of incubation with Ang-1 and Ang-2, the cells were supposedly not Tie2\({}^{+}\) progenitor cells anymore, and therefore could not react to any stimulation, as the receptor for our ligands was not present anymore. Ligand-deficient Ang1\({}^{-/-}\) or receptor-deficient Tie2\({}^{-/-}\) is embryonically lethal for transgenic mice. Both deletions showed common features in their phenotype, and both failed to develop a fully functional cardiovascular system [4]. Vasculogenesis proceeds normally, but remodeling and maturation of the vessels are defective [24]. Ang-2-deficient mice seem phenotypically normal at birth but die within 14 days or develop normally throughout adulthood. On the other hand, systemic embryonic Ang-2 overexpression results in an embryonic lethal phenotype of Ang-2 transgenic mice [18]. Furthermore, [PERSON] et al. [37] determined the role of Ang-2 in the pathology of IVDD. They verified the expression of Ang-2 in human degenerative NPC and showed that Ang-2 expression was considerably higher in severe IVDD than in mild IVDD. Additionally, they demonstrated that NP cells treated with exogeneous Ang-2 resulted in marked downregulation of type II collagen and aggrecan and a significant increase in the expression of MMP13, ADAMTS4, and the pro-inflammatory cytokine IL-1\(\beta\), both on protein and mRNA levels. The resulting dysregulation in ECM degradation in NPCs upon Ang-2 exposure contributed to the pathogenesis of IVDD. Conclusively, the study reveals Ang-2 inhibition as a potential novel therapeutic target for IVDD treatment.
## 3 Results and Discussion
### Model Selection and Prediction  
During the model optimization and selection procedure, grid search tunes hyperparameters, which vary depending on the model algorithms as shown in Table 2. The hyper-parameters include the number of estimators, max depth, subsampling size, learning rate, kernels, regularization factors, activation functions, max iterations, etc. The ten tuned models are compared using MAE and RMSE scores, which are obtained dozens of times by the cross-validation method to select the model of the best performance. As shown in Figure 2, the RF model has the smallest MAE and RMSE average, and the standard deviation of RF records good scores among the ten models. The KNN model shows the second-best performance with the training and test datasets. In terms of validation and prediction scores, the RANSAC and MLP models are ranked at the bottom. Other models, which are SVR, LightGBM, GPR, XGBoost, AdaBoost, and Lasso, have similar validation and prediction performances for estimating EDS yield with the combined fabrication data. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Preprocessed** & \multicolumn{3}{c}{**Target Data**} \\ \cline{2-3}  & **Training Data** & **Test Data** \\ \hline Count & 261 & 66 \\ Average & 0.012 & (\(-\)) 0.048 \\ Standard deviation & 1.021 & 0.926 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 2: Distribution of the 20 validated (**a**) MAE and (**b**) RMSE values for the RF, KNN, LightGBM, SVR, GPR, XGBoost, AdaBoost, Lasso, RANSAC, and MLP models using cross-validation method and statistical tables of the average (Avg) and standard deviation (StdDev) values for the models. The prediction of EDS yield with the tuned models is executed using the test dataset. The MAE and RMSE values of each model are compared, as shown in Figure 3. The prediction results reveal that the performance of the RF model is the best among all the models in both metrics, with MAE and RMSE values of 0.520 and 0.648, respectively. The result shows a similar order to earlier cross-validated scores in the model selection phase. RMSE values show constantly larger values than MAE because RMSE uses squared differences, and larger differences enhance RMSE more. As reference scores, a statistical estimator predicts target values as an average value of the training dataset. The scores of the estimator are 0.744 for MAE and 0.919 for RMSE, and they are inevitably poorer than the scores of machine learning models, as presented in Table 2.  
To conclude the building yield model part, the case study shows how the multi-fabrication data are delivered as input data and how the optimization and comparison of models work. Through the suggested framework, the EDS yield of the device is predicted more accurately by 30.1% in the MAE score compared with the simple statistical prediction. The RF algorithm-based model ranks first for the case and is explained with SHAP values in
## 3 Formulation of the Satellite Module Layout Problems
### Constraint Functions  
The following geometrical and functional constraints are considered:  
* Overlapping constraints between components: No overlapping between components is allowed on each surface. As in [19; 20; 108], the plates are supposed to be sufficiently spaced to avoid any overlapping between components positioned on the surfaces \(S_{2}\) and \(S_{3}\). The overlapping constraint between components is formulated as follows: \[h_{overlap}^{C}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d},\mathbf{p}_{s}^{ d})=\sum_{k=1}^{N_{k}}\sum_{i=1}^{N_{k}-1}\sum_{j=1}^{N_{k}}\Delta A_{ijk}^{C}( \mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d},\mathbf{p}_{s}^{d})\] (9) where \(N_{k}=4\) is the number of surfaces, \(N_{k}\) is the number of components on surface \(k\) and \(\Delta A_{ijk,k}^{C}\) corresponds to the 2-dimensional projections (along the \(z\) axis) of components \(i\) and \(j\) on the surface \(k\) and is a function of the centers of inertia and orientations of the components. * Overlapping constraints between the components and the exclusion zones: No overlapping between the components and the exclusion zones is allowed on each surface. The overlapping constraint is expressed as \[h_{overlap}^{E}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d},\mathbf{p}_{s}^{ d})=\sum_{k=1}^{N_{k}}\sum_{i=1}^{N}\Delta A_{ijk,k}^{E}(\mathbf{p}_{I}^{c}, \mathbf{p}_{\alpha}^{d},\mathbf{p}_{s}^{d})\] (10) where \(\Delta A_{ijk,k}^{E}\) is the area of intersection between the two-dimensional projection (along the \(z\) axis) of a component and the exclusion zone on the surface \(k\) and is a function of the centers of inertia and orientations of the components (as well as the positions and shapes of the exclusion zones). * Balancing constraints: The center of gravity (CG) of the whole laid out module must be positioned near the center of gravity of the empty module within a given tolerance along the \(x\) and \(y\) axes. Then, the balancing constraint can be formulated as follows: \[g_{CG}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d},\mathbf{p}_{s}^{d})=\sqrt{(x_ {c}-x_{sat})^{2}+(y_{c}-y_{sat})^{2}}-\delta_{CG}\] (11) where \((x_{c},y_{c},z_{c})\) are the coordinates of the current CG of the whole module, also referred to as \(O^{\prime}\) in the system \(Oxyz\); \((x_{sat},y_{sat},z_{sat})\) are the coordinates of the empty satellite module in the \(Oxyz\) system; and \(\delta_{CG}\) represents a tolerance that corresponds to a sphere centered at the empty satellite module center of gravity and is set to the numerical value of 3.0, without loss of generality. * Constraints relative to the angles-of-inertia: In the multi-container configuration, a geometrical constraint corresponding to the angles of inertia is added, with a corresponding tolerance \(\delta_{AI}\).
## 4 Discussion
### Strengths and Limitations  
A strength of the present study is the inclusion of specialist IFs in the study material. According to our knowledge, there are few studies on the mineral content of Polish specialist IFs. In addition, few authors have comprehensively evaluated Polish IFs in terms of the compliance of mineral components against specific standards. On the other hand, a limitation of the study is that the analysis of the mineral content was only carried out on one batch of the product. To verify the mineral incompatibilities noted, it would be necessary to repeat the tests using a different batch of individual IFs.  
In the present study, a limitation could be the lack of information on the current and habitual intake of lactating women concerning the mineral content of HM. The studies on dietary intake's influence on breast milk's mineral composition are inconclusive. Available studies show that a mother's dietary intake does not affect the Ca, Zn, and Fe content of her milk [45; 48; 67]. On the other hand, [PERSON] et al. [68] report that following a Mediterranean diet during the first month of lactation resulted in higher levels of Ca and Zn in milk. [PERSON] et al. [69], in a systematic review of intervention studies, indicated that there are inconclusive studies on the effect of supplementation on Zn content. In addition, they reported that Fe supplementation did not affect its levels in the milk but increased lactoferrin levels in the milk. It is indicated that the type of food consumed and the supplementation used only affect the levels of iodine, selenium, and mercury in breast milk [4; 30], which were not analyzed in this study.  
It should also be emphasized that the present study only quantifies the mineral content of IFs and HM; however, its bioavailability was not investigated. As stated earlier during the discussion of results, the bioavailability of minerals from IFs is significantly lower than that from HM. Therefore, future studies would be required to consider the bioavailability of each mineral and investigate the extent to which they are absorbed by the infant's body.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 5 Simulation Results and Analysis
### Simulation Results and Analysis
#### 5.2.1 Impact of Simulation Time
The EARR protocol selected edge nodes within the \(R/2\) radius, ensuring a one-hop distance. As the time progressed, the gap in the hop counts became significantly pronounced in the graph. This is because the EARR protocol guarantees a consistently high packet arrival rate and throughput, thereby resulting in busy communication on the routing paths. Additionally, the protocol considers the network's available bandwidth, which means some nodes might not be chosen for a period due to busy communication. This limitation restricts the characteristic of EARR forming the shortest path. Figure 13 displays the standardized delays at different simulation time points. It can be observed that there is little difference in delays among the various protocols; however, our protocol consistently maintained the lowest standardized delays. The standardized delays of each protocol varied with changes in the vehicle environment during different simulation times. As the nodes gradually approached, the delays decreased, but as the distance increased, the delays also increased. Utilizing standardized delays allows for a better evaluation of latency performance under different environmental changes. In Figure 11, it is evident that numerous packets were accepted when they were closer to the destination, thereby leading to higher throughput. However, once the distance increased, the throughput decreased. Therefore, packets closer to the destination contributed significantly to the calculated performance metrics, and our protocol consistently ensured high throughput. This is why we standardize packet delays based on the routing length. The EARR protocol demonstrated the highest packet delivery rate and required only one second beacon intervals to establish routes. Therefore, EARR stands out as the most efficient routing protocol, thereby boasting low communication overhead. The relationship between the overhead ratio and simulation time is depicted in Figure 14. The overhead ratio varied with the movement of vehicles: the closer the distance, the faster the route establishment, thereby resulting in a lower overhead ratio. The EARR protocol ensures rapid route establishment in any environment, coupled with a higher message delivery rate, which minimizes the number of discarded messages, thereby consequently reducing the overhead ratio. Messages that are discarded consume network resources during transmission but do not achieve the intended goal of delivering useful information, thereby increasing communication overhead.
# Simulation of Wind Speeds with Spatio-Temporal Correlation
## 2 Methodology
### Simulation
#### 2.2.2 Obtaining Correlations from Random Normal (0,1) Series  
The correlated sequences can be obtained from the randomly generated ones. To achieve this, it is only necessary to solve a linear system, such as the one represented in Equation (16), due to the nature of the distributions used, i.e., standardized Normal [20]:  
\[\mathbf{Y}=\mathbf{L}\mathbf{X} \tag{16}\]  
where \(\mathbf{L}\) is a unique lower triangular matrix obtained from a decomposition of the correlation matrix, \(\mathbf{X}\) is the matrix of uncorrelated random variables with Normal (0,1) distribution, and \(\mathbf{Y}\) is the matrix of correlated random variables with Normal (0,1) distribution.  
Figure 5: Cross-correlation between the series \(\mathbf{X}\) (continuous line) and a shifted version of the series \(\mathbf{Y}\) (dotted line): (**a**) lag 0, (**b**) lag 1, (**c**) lag 2, (**d**) lag 3.  
There are different possibilities to obtain the unique lower triangular decomposition of the correlation matrix \(\mathbf{P}\), that make it possible to apply the method, i.e., QR or Gaussian methods. However, \(\mathbf{P}\) has some particularities. It is symmetric and positive semidefinite, so the method to be used is the Cholesky decomposition, where the calculations of terms of matrix \(\mathbf{L}\) are given by Equation (17). This is a less computationally expensive method than the previous ones [32]:  
\[\left\{\begin{array}{c}l_{ii}=\sqrt{\rho_{ii}-\sum_{k=1}^{i-1}l_{ik}^{2}}\\ l_{ii}=\frac{1}{l_{ii}}\Big{(}\rho_{ii}-\sum_{k=1}^{i-1}l_{jk}l_{ik}\Big{)},\;j= i+1,\;i+2,\;\ldots,m\end{array}\right. \tag{17}\]  
where \(j\;=\;1,\;2,\;\ldots,\;m\) and \(l\) correspond to each coefficient of the matrix \(\mathbf{L}\).  
By denoting the number of locations that are taken into account as \(m\) and by considering the number of sums, subtractions, products, divisions, and roots necessary to determine each of the aforementioned decompositions, the Gaussian method needs \(\frac{2m^{3}}{3}\) operations, the QR dependent on Householder decomposition needs \(\frac{4m^{3}}{3}\), and the Cholesky one uses \(\frac{m^{3}}{3}\)[32].  
Furthermore, the fact of applying this decomposition to the correlation matrix also offers the possibility of quickly and easily getting a solution of the correlated random series closer to the real ones, as long as the distribution is a standardized Normal, with which it can be ensured that the solution also has a standardized Normal distribution. Otherwise, keeping the desired distribution cannot be ensured [18].
## 5 Research Methodology
### Data Collection Parameters
#### 5.3.1 Network Latency  
Network latency is the term used to describe the delay in transmitting data within a blockchain network. It represents the amount of time it takes for blocks to be delivered across the network. Blockchain networks that have longer delays will experience higher latency [36]. This increased latency can result in failure for low-powered sensor networks that are utilized in the Internet of Things (IoT), which in turn affects the overall efficiency of the network's performance [36]. As a result, assessing network latency is a crucial factor in determining the scalability of a network.
## 4 Simulation Results and Analyses  
Simulations are conducted to illustrate the performance of the proposed scheme. The data bits are encoded with a 1/2 rate binary convolutional code with generator polynomial [171; 133], and the length of the generated coded bits is 2048. The coded bits are then interleaved with a random interleaver and modulated with QPSK. The training sequence with a length of 300 is inserted into the front of blocks, the total number of blocks is 100, and the total repetitions of the Monte Carlo simulation is 100. First, the CSI in the receiver is estimated through sparse iterative channel estimation. Second, the IFDTE processes the received signals with five self-iterations, and the damping factor \(\beta_{d}\) is set as \(0.7\times 0.9^{s}\)[13], where \(s\) is the \(s\)-th time of self-iteration. Finally, the log-domain maximum a posteriori probability (Log-MAP) is used for the decoder. An additive white Gaussian  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Equalizer Vector** & **Symbol Estimation** & **A Posteriori Moments** & **Conditional Moments** \\ \hline \hline FDDF-FDTE [11] & \(8 MK\) & \(2 MK+\) & - & \(\frac{2Q+1}{2}\) \\ \hline Proposed IFDTE & \(5 MK\) & \((2 MK+\) & \(K\log_{2}K/2)S\) & \(2^{Q+1}S\) & - \\ \hline \multirow{3}{*}{SDFE [6]} & \((M^{2}N_{s}^{3}/2+MN_{s}+\) & \multirow{3}{*}{\((N_{s}+N_{2}+L)MK\)} & \multirow{3}{*}{-} & \(\frac{2Q+1}{2}\) \\  & \((M+M^{2})(N_{s}+L-2)^{2}K\) & & & \\ \hline \multirow{3}{*}{LE [20]} & \((M^{3}N_{s}^{3}/2+\) & \multirow{3}{*}{\(M(N_{s}+L)K\)} & \multirow{3}{*}{-} & \(\frac{2Q+1}{2}\) \\  & \((M+M^{2})(N_{s}+L)K\) & & & \\ \cline{1-1}  & \(+L)^{2}\))\(K\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity of equalizing symbols for different turbo equalizers.  
noise with _i.i.d._ zero-mean real and imaginary components is used in the simulations and is completely described by its variance.
## 3 Results  
### Experiments  
#### 3.1.1 Water Absorption and Solubility  
Figure 3 and Table 2 show significant differences in water absorption between the groups. The post-hoc analysis demonstrated that significance was attributed to differences between the conventional manual with two polyjets, SLA, and milling methods. The water absorption of SLA, PJ-1, PJ-2, and milling were 52.31, 35.00, 31.60, and 30.69 \(\upmu\)g/mm\({}^{3}\), respectively, while that of manual was 18.51 \(\upmu\)g/mm\({}^{3}\). There was no significant difference between the two polyjet groups. The water absorption, in order of highest to lowest, was SLA, PJ-1, PJ-2, milling, FDM, SLS, manual, and DLP. Only the SLA group, which showed the highest absorption rate of 52 \(\upmu\)g/mm\({}^{3}\), did not meet the requirements of ISO 10477 (50 \(\upmu\)g/mm\({}^{3}\) or less). The DLP group demonstrated the lowest water absorption rate of 17 \(\upmu\)g/mm\({}^{3}\).  
Figure 3: Comparison of water absorption of each group. * Denotes \(p<0.05/28\) for comparison between the manual and experimental groups; when 50 \(\upmu\)g/mm\({}^{3}\) or less, ISO10477 is satisfied (based on the red line).  
Figure 4 and Table 2 show significant differences in water solubility between the groups. The post-hoc analysis demonstrated that significance was attributed to differences of the SLA method with the other methods. The water solubility of the SLA method was 15.60 \(\upmu\)g/mm\({}^{3}\), while those of other methods ranged from \(-\)1.55 to 5.96 \(\upmu\)g/mm\({}^{3}\). Only the SLA group did not meet the requirements of ISO 10477 (7.5 \(\upmu\)g/mm\({}^{3}\) or less).  
The FDM, SLS, and DLP methods exhibited water absorption and solubility comparable with the conventional manual method and acceptable values based on the ISO standard.
# Micro-Nanobubble Technology in Surface Cleaning and Defouling
## 2 Properties and Mechanisms of MNs
### Surface Interactions and Attachment Mechanisms  
NBs are essential in surface contacts and attachment mechanisms, especially when they affect particle-bubble dynamics and aggregation processes [24]. Applications such as mineral flotation, water treatment, and surface cleaning are significantly affected by their capacity to create capillary forces and produce stable surfaces. To maximize their effectiveness in practical applications, it is essential to understand how NBs and surfaces interact by considering elements such as surface roughness, wettability, and bubble size [34]. These interactions have been clarified in recent research using both experimental and computational techniques, offering an important new understanding of the physics and mechanics of attachment events caused by NBs.  
[PERSON] et al. [35] investigated the effect of NBs on hydrophilic glass bead particles with different surface roughness (Figure 3). Glass beads were altered to produce varying degreesof surface roughness, and NBs were created using the temperature increase technique. The findings demonstrated that surface roughness had a considerable impact on bubble attachment, with rougher glass beads attaching to bubbles more frequently than smooth ones. Nevertheless, the addition of NBs only slightly enhanced the hydrophilic particles' attachment to bubbles, increasing the attachment area by less than 1%. This study found that although NBs had a negligible effect on this connection, increasing the particle roughness improved the bubble-particle attachment because it created a wider contact area.  
[PERSON] et al. [36] used molecular dynamics (MD) simulations to study the dynamics of fine particle aggregation in liquids caused by NBs (NBIA). The impact of contact line pinning, surface roughness, surface wettability, and NB size on NBIA was analyzed. The findings demonstrated that particle aggregation was caused by attractive NB capillary forces that resulted from both concave and convex NB bridges between hydrophobic and hydrophilic surfaces. An enhanced capillary force model could reliably estimate the equilibrium spacing between fully aggregated particles. Additionally, this study discovered that there is a threshold contact angle below which the merged NBs separate from the surface, inhibiting aggregation, and that contact line pinning at the sharp edges of particles slows down the aggregation process. MD simulations supported these predictions, offering insights into the microscopic features of NBIA dynamics that are currently not accessible by experimental methods.