esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation Techniques of TiO\({}_{2}\) Nanofluids and Challenges: A Review
## 2 Preparation Methods
### Two-Step Method
#### 2.2.1 Preparation of Nanoparticles  
There are several routes that are commonly used for the preparation of TiO\({}_{2}\) nanopowder.  
Sol-gel method is mostly used for the synthesis of nanoparticles because of its versatility in producing particles with a high surface area. [PERSON] et al. [47] explained the sol-gel method very precisely and this is shown in Figure 8. They completed the whole process in four different steps; in the first step the precursor titanium is dissolved in the solvent and after that the mixture is sonicated with the help of an ultrasonic bath. Hydrolysis process is then carried out by adding the deionized water drop by drop into the prepared mixture of precursor titanium and solvent under magnetic stirring or reflux. In the final step, the obtained product, which looks like a gel, is dried and calcinated to get a crystalline powder.  
[PERSON] et al. [48] prepared a nanocrystalline powder of TiO\({}_{2}\) using the sol-gel technique while using titanium tetra-isopropyloxide [Ti(OCH(CH\({}_{3}\))\({}_{2}\))]\({}_{4}\) as a precursor material. The used precursor material was added into a solution of deionized water and isopropanol at a temperature of 80 \({}^{\circ}\)C, after 1 h the solution of deionized water and concentrated [HNO\({}_{3}\)] was mixed with the solution containing nanocrystalline powder in a round flask and stirred for 6 h. For proper mixing, the stirring process was not stopped during the whole process. The prepared sol-gel was heated up at 300 \({}^{\circ}\)C for 1 h to obtain dry nanopowder in crystalline form.  
[PERSON] et al. [49] used TITP (titanium tetra-isopropyloxide) as the starting material and observed the final phase of fabricated nanocrystalline TiO\({}_{2}\). Experimental results showed that temperature variation has a significant effect at the intermediate state on the phase of Ti(OH)\({}_{4}\), TiO\({}_{2}\) structure changed from anatase to rutile as the temperature was increased during the calcination process. The temperature variation during the calcination process also increased the crystalline size. [PERSON] and [PERSON] [50] adopted the sol-gel method and prepared TiO\({}_{2}\) nanoparticle by dissolving titanium (IV) isopropoxide (TTIP) in absolute ethanol. Nitric acid was used during this approach to restrain the hydrolysis process and to adjust the pH value of the solution. Distilled water was also added to the molar ratio of Ti:H\({}_{2}\)O = 1:4.  
Figure 8: Steps involved in the preparation of TiO\({}_{2}\) nanoparticles via sol-gel [47].  
The sol-gel technique has an additional advantage of controlling the reaction rate by controlling the condensation and hydrolysis using chemical means and usage of alk-oxides, as compared to the other nanoparticle fabrication techniques.
###### Abstract
This work offers a roadmap for researchers and practitioners, bridging current trends with future innovations, including autonomous systems poised to boost throughput by 20%, safety by 15%, and energy efficiency by 25% [47; 63; 64] (values reported relative to the conventional baselines described in the cited sectoral studies).
## 3 Results and Discussion
### Adsorption Thermodynamics  
We evaluated the standard thermodynamic parameters (the standard Gibbs-free-energy change (\(\Delta G^{\circ}\)), standard enthalpy change (\(\Delta H^{\circ}\)), and entropy change (\(\Delta S^{\circ}\))) for the adsorption of Cu(II) on the modified zeolite at a temperature range from 25 to 55 \({}^{\circ}\)C.  
\(\Delta G^{\circ}\), \(\Delta H^{\circ}\), and \(\Delta S^{\circ}\) are derived from Equations (5) and (6):  
\[\Delta G^{\circ}=-RTlnK_{c} \tag{5}\]  
\[lnK_{c}=(\Delta H^{\circ}/-RT)+(\Delta S^{\circ}/R) \tag{6}\]  
where \(K_{c}\) is the equilibrium constant (\(K_{c}=C_{Ae}/C_{e}\)); \(C_{Ae}\) is the concentration of adsorbed Cu(II) (mg/L); \(C_{e}\) is the equilibrium concentration of Cu(II) (mg/L); \(T\) is the temperature (K); \(R\) is the universal gas constant (8.314 J/mol\(\cdot\)K).  
We found the \(\Delta H\) and \(\Delta S^{\circ}\) from the slope and intercept of the linear plot (\(lnK_{c}\) versus \(1/T\)). We summarize the results in Table 5.  
We determined the values of the \(\Delta G^{\circ}\) in the temperature range of 298 to 328 K to be between \(-16.976\) and \(-22.259\) kJ/mol. According to the negative values of the \(\Delta G^{\circ}\), the adsorption of Cu(II) onto the modified is zeolite-favored and spontaneous. The increase in the negative values of the \(\Delta G^{\circ}\) with an increase in the adsorption temperature indicates that the process was more spontaneous at higher temperatures.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{\(C_{0}\) (mg/L)} & \multirow{2}{*}{\(q_{e}\) (exp)} & \multicolumn{2}{c}{**Pseudo First Order**} & \multicolumn{2}{c}{**Pseudo Second Order**} & \multicolumn{2}{c}{**Intraparticle Diffusion**} \\ \cline{3-10}  & & & **K\({}_{1}\)\(\cdot\)10\({}^{-2}\)** & \(q_{e}\) (cal) & \(R^{2}\) & \(K_{2}\) & \(q_{e}\) (cal) & \(R^{2}\) & \(K_{id}\) & \(R^{2}\) \\  & (min\({}^{-1}\)) & (mg/g) & & (g/mg\(\cdot\)min) & (mg/g) & & (mg/g\(\cdot\)min\({}^{0.5}\)) & & (mg/g\(\cdot\)min\({}^{0.5}\)) & \\ \hline
50 & 4.501 & 2.213 & 1.459 & 0.9244 & 0.405 & 4.610 & 0.9997 & 0.411 & 0.8048 \\
100 & 6.364 & 0.876 & 0.870 & 0.8122 & 0.257 & 6.452 & 0.9997 & 0.427 & 0.7805 \\
200 & 6.945 & 2.412 & 2.454 & 0.8595 & 0.191 & 7.199 & 0.9994 & 0.749 & 0.7286 \\
400 & 20.656 & 4.269 & 19.51 & 0.9246 & 0.033 & 22.173 & 0.9981 & 3.450 & 0.7934 \\ \hline \end{tabular}
\end{table}
Table 4: The kinetic parameters of the adsorption.  
The standard enthalpy change \(\Delta H^{\circ}\) (40.914 kJ/mol) value is positive, a characteristic of an endothermic process. Moreover, the positive value of the standard entropy change \(\Delta S^{\circ}\) indicates the increased randomness at the solid-liquid interface.
## 4 Test Case: A Two-Link Flexible Robot Arm
### Numerical Assessment  
#### 4.2.1 Test Case 1: Increasing Damping of the Second Vibrational Mode  
The state-feedback controller is designed such that the second pole pair is assigned to \(-5.000\pm 38.177j\), i.e., damping is increased to \(\xi_{3,4}=0.130\) while the damped natural frequency is kept unchanged. The maximum peak of the sensitivity function is imposed to \(M_{s}=1.75\) to ensure adequate robustness.  
Pole placement is performed by computing the control gains \(\mathbf{k_{0}}\) through the receptance method proposed in Section 3.1, leading to \(\mathbf{f_{0}}=[-0.1346\quad-0.0129]^{T}\) and \(\mathbf{g_{0}}=[0.0060\quad-0.0422]^{T}\). The closed-loop poles are computed through the collection of Matlab functions released under the package DDE-Biftool [36] and are shown in Figure 3. The dominant closed-loop poles are summarized in Table 2, while the latent roots are omitted for brevity. The desired closed-loop poles are correctly assigned. Conversely, the closed-loop system Nyquist curve for the Loop Gain function \(L(j\omega)\) shown in Figure 4 highlights that \(\mathbf{k_{0}}\) does not satisfy the prescribed robustness condition, since the Nyquist curve belongs to the \(M_{s}\) circle.  
The prescribed robustness is, in contrast, achieved by the method described in Section 3.2, that has led to the gain vector \(\mathbf{k}=\mathbf{k_{0}}+\mathbf{Vk_{r}}\) with \(\mathbf{f}=[-0.0388\quad-0.0071]^{T}\) and \(\mathbf{g}=[0.2311\quad-0.3180]^{T}\). The resulting closed-loop system features a Nyquist curve that does not belong to the \(M_{s}\) circle for any complex value, as evidenced by Figure 4, and the desired poles are correctly assigned.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Open-Loop Poles** & **Desired Poles** & **Poles with \(\mathbf{k_{0}}\)** & **Poles with \(\mathbf{k}\)** \\ \hline \(-1.197\pm 7.122j\) & - & \(-2.461\pm 36.167j\) & \(-13.635\pm 11.480j\) \\ \(-1.037\pm 38.177j\) & \(-5.000\pm 38.177j\) & \(-5.000\pm 38.177j\) & \(-5.000\pm 38.177j\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test case 1: open-loop and closed-loop poles.  
Figure 4: Test case 1: Nyquist curve of \(L(j\omega)\).  
Figure 3: Test case 1: closed-loop poles.  
Note that neglecting the time delay in the synthesis of the controller, i.e., setting \(\tau_{f}=\tau_{g}=0\) in Equation (8) and requiring the same two pairs of the primary closed-loop poles obtained through \(\mathbf{k}\), leads to a control gain vector that destabilize the closed-loop system if these gains are applied to the time delayed system, due to the primary roots crossing the imaginary axis.
## 4 Empirical Assessment Using Real-World Data
### Experimental Design
#### 4.2.1 Classification Stage
* True negatives (TN): Correctly predicted negative observations. * False negatives (FN): Incorrectly predicted negative observations (type II error). The accuracy indicates the proportion of true results (both true positives and true negatives) to the total number of cases examined. It is defined as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{20}\]  
Precision reflects the ratio of true positive observations to the total predicted positives, and it is given as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{21}\]  
Recall, or sensitivity, measures the proportion of actual positives correctly identified by the model, emphasizing the model's ability to detect positive instances. It is formulated as follows:  
\[\mathrm{P}(Y=1|X)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{m}x_{ m})}} \tag{13}\]0  
These metrics are essential not only for understanding the overall effectiveness of the model but also for providing insight into the balance between the model's sensitivity (recall) and precision. Additionally, the F1 score, which is the harmonic mean of precision and recall, is introduced to provide a single metric that balances both concerns. The F1 score is beneficial when the class distribution is imbalanced, as it considers both false positives and false negatives. It is defined as follows:  
\[\mathrm{P}(Y=1|X)=\frac{1}{1+e^{-(\beta_{0}+\beta_{1}x_{1}+\cdots+\beta_{m}x_{ m})}} \tag{13}\]1  
The F1 score provides a more comprehensive measure of a model's performance, especially in cases where precision and recall are not evenly balanced. It serves as an overall indicator of the model's accuracy and robustness in classification tasks.
# Cyclic Consistent Image Style Transformation: From Model to System
## 2 Related Work
### Unstable Training Dynamics  
The instability in GAN training primarily arises from the adversarial game between the generator and the discriminator. During training, both models continuously adjust their strategies, making it difficult to maintain a stable training process. Specifically, issues such as gradient vanishing or exploding occur due to the adversarial relationship between the generator and discriminator. Gradient vanishing happens when the discriminator is too powerful, causing the generator's gradient signals to become very weak and impeding its learning. Conversely, if the generator is too powerful, it can lead to gradient explosion in the discriminator, resulting in training instability [5; 6]. Mode collapse is another common issue, where the generator tends to produce a limited number of sample variations, lacking diversity [7; 8; 9]. To address these problems, several improvements have been proposed. Wasserstein GAN (WGAN) [23; 24] replaces the traditional JS divergence with Wasserstein distance, improving training stability. Additionally, spectral normalization [25] normalizes the weights of the discriminator to control the network's Lipschitz constant, thus enhancing training stability. Gradient penalty, introduced in WGAN-GP [26], helps avoid gradient explosion and vanishing issues. Moreover, techniques such as progressive training and dynamic learning rate adjustments have been employed to improve training stability.  
Figure 1: Evolution of GAN technology.
# Evaluation of the Robustness for Integrated Production Scheduling and Maintenance Planning Problem
## 2 Modeling and Assumptions
### Preventive Maintenance Formulation  
Unexpected failure usually leads to instability of the production system and loss of scheduling performance. Therefore, PM is carried out in actual production to improve the condition of the machine and reduce the risk of an unexpected failure of the machine. This paper considers two maintenance activities: preventive maintenance and corrective maintenance. We assume that the PM activity can restore the machine to be \"as good as new\" and does not allow interruption of workpiece processing. The average duration of PM activity is denoted by \(tp\). If a failure occurs, a CM activity is executed to restore the machine to the operating state without changing the age of the machine. The average duration of CM activity is denoted by \(tc\). Since corrective maintenance activities cannot be planned in advance, it is reasonable to assume that \(tc>tp\). To maximize the utilization of the machine, differentiation and algebraic analysis result in an optimal PM interval of \(T_{j}=\theta_{j}\big{\{}tp_{j}/\big{[}tc_{j}(\beta_{j}-1)\big{]}\big{\}}^{1/ \beta_{j}}\)[25].
## 2 Proposed Method
### Multi-Objective Instance-Weighting Strategy  
The main objective of the instance weighting strategy is to reduce distribution dissimilarity between the source and target domain instances. Therefore, it is important to decide how to effectively evaluate the dissimilarity between instances from different domains. Two indicators, KLD and MMD, measure the distribution dissimilarity between domain instances using different theoretical methods. These two indicators are suitable to be used to achieve the objective of instance weighting. However, instance weighting using a specific indicator may not be consistently effective. Their performance varies according to the type or number of domain data used for model training. Even if the data are collected in the same environment, suitability of the indicator in the process varies inevitably depending on the number of training data. Therefore, we use multi-objective instance weighting to minimize the two metrics concurrently. Domain optimization using multi-objective instance weights is conducted to achieve two objectives: (1) to make the target and source domain instances have similar distributions and (2) to transfer useful information to the target domain task.
## 2 Results and Discussion
### Impact of Cluster-Zone Leaf Removal on Grape Phenolic Profiles
#### 2.2.2 Flavonols  
A total of 15 flavonols were identified in the grape skins (Table 3), with notable variations in concentration observed between the two years. The total concentration of flavonols in 2022 Cabernet Sauvignon grapes was approximately 45% higher than that in 2021 grapes, which could be attributed to the differences in irradiance between the two years [18]. Furthermore, the proportions of flavonols differed between the two varieties, with Cabernet Sauvignon grapes having a higher proportion of kaempfer- and quercetin-type flavonoids, while Marselan grapes exhibited higher proportions of myricetin, laricitrin, and syringetin-type flavonoids. These findings indicate that Marselan grapes tend to accumulate F3\({}^{\prime}\)5\({}^{\prime}\)H flavonols more than Cabernet Sauvignon grapes.  
Previous studies have indicated that flavonols are reliable indicators for assessing canopy structure and solar radiation [19]. Consistent with these findings, our study showed that the concentrations of most flavonol compounds, except for quercetin-3-O-rutinoside, increased in the three LR treatments of Cabernet Sauvignon grapes in 2021. Furthermore, the flavonoid concentrations in the three LR treatments of Cabernet Sauvignon grapes in 2022 were found to be significantly higher than those in the control group. It has been demonstrated that flavonols can contribute to the copignentation of anthocyanins during winemaking [20], which suggests that LR treatments may potentially enhance the quality and stability of the colour of Cabernet Sauvignon wines. In 2022 Marselan grapes, the total flavonol concentration was significantly higher in the LR1 treatment compared to the control group, while the LR2 and LR3 treatments exhibited lower flavonoid concentrations than the control group. Furthermore, the composition of flavonoids differed between the LR treatments and the control group. The LR treatments resulted in higher proportions of kaempfer and quercetin, whereas the control group exhibited higher proportions of myricetin, laricitrin, and syringetin-type flavonoids.
# Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation
## 3 Proposed Method
### Voxel Labeling
#### 3.2.2. Matching the Object Pose Estimated by Deep Learning with That in the Voxel Database
```
Input: box1_vector is box_corners1[6]-box_corners1[0] # box_vector = [ x, y, z ] box2_vector is box_corners2[6]-box_corners2[0] Used dot and cross products to obtain the rotation matrix  C is cross of box1_vector and box2_vector  D is dot of box1_vector and box2_vector  Norm_box1 is norm of box1_vector # used for scaling if\(\sim\) all (C = = 0) # check for collinearity  Z is [ [ 0, -C(3), C(2) ], [ C(3), 0, -C(1) ], [ -C(2), C(1), 0 ]]  R is eye(3) + Z + (Z + (Z^2 X (1-D) / (norm(C) 2))/Norm_box1 2 # rotation matrix else  R is sign(D) X (norm(box2) / norm(box1)) # orientation and scaling R is the rotation matrix, so that (except for round-off errors)...  R * box1_vector #... equals box2_vector inv(R) * box2_vector #... equals box1_vector
```  
**Algorithm 2** Calculated rotation matrix between two box 3D vectors  
Therefore, if the rotation value is calculated using the rotation transformation matrix, the transformed coordinates of the camera origin rotated in the voxel database can be obtained. Equation (6) represents the rotation transformation matrix with the unit vector \(u=(u_{x},u_{y},u_{z})\) as the axis. When the camera origin is transformed into a rotation transformation matrix \(R_{u}(\theta)\), the transformed camera coordinates in the voxel database are calculated. Consequently, the voxel, including the addressed coordinates configured in Section 3.2.1, can be determined by applying the proposed method. \[R_{u}(\theta)=\left[ \begin{array}{ccc}\cos\theta+u_{x}^{2}(1-\cos\theta)&u_{ x}u_{y}(1-\cos\theta)-u_{z}\sin\theta&u_{x}u_{z}(1-\cos\theta)+u_{y}\sin\theta\\ u_{y}u_{x}(1-\cos\theta)+u_{z}\sin\theta&\cos\theta+u_{y}^{2}(1-\cos\theta)&u_{ y}u_{z}(1-\cos\theta)-u_{x}\sin\theta\\ u_{z}u_{x}(1-\cos\theta)-u_{y}\sin\theta&u_{z}u_{y}(1-\cos\theta)+u_{x}\sin \theta&\cos\theta+u_{z}^{2}(1-\cos\theta)\end{array} \right] \tag{6}\]
# Model Test on the Behaviors of Deep Excavation with Lateral Confined Water
###### Abstract  
To investigate the excavation characteristics and mechanisms of a deep foundation under lateral confined water pressure, a model test was conducted with real-time monitoring of the stress and deformation of the foundation strut system. The results indicate that in stages 1 and 3 (the process of raising the lateral confined water level, O and F), the rise in lateral confined water levels caused the diaphragm wall to shift inward. However, the reduction in earth pressure due to the inward shift of the diaphragm wall exceeded the increase in water pressure from the raised confined water level, resulting in an overall decrease in lateral pressure on the diaphragm wall. During stage 2 (the excavation and supporting process, K1-Z4), as excavation and strut installation progressed, the lateral pressure on the diaphragm wall decreased, while both bending moment and horizontal displacement increased, with the most pronounced changes occurring when excavation reached the depth of the lateral confined aquifer. Upon reaching the soil layers within the depth of the lateral confined aquifer, the axial force of struts increased significantly, with the second level of strut experiencing the greatest axial force. In deep foundation design, it is essential to account for the maximum bending moment and horizontal displacement of the diaphragm wall within the depth range of the lateral confined aquifer, as well as the maximum vertical displacement in the range of 0.50%_D_-0.83%_D_ outside the pit. Due to the rapid transmission of lateral confined water pressure changes in fine sand, and the delayed transmission in clay due to their low permeability, the diaphragm wall response is most pronounced within the depth range of the lateral confined aquifer.  
foundation pit; model test; lateral confined water; diaphragm wall; strut +
Footnote †: journal: Applied Sciences
###### Abstract  
This research examined the association between the risk of falls and cognitive function in older individuals at risk of mild cognitive impairment. One hundred seventy-five older adults were included in 2021. Balance confidence was scored using the Activities-Specific Balance Confidence Scale (ABC), gait speed was assessed by the 4 m test, handorigin strength by a digital dynamometer, and balance by the Timed Up-and-Go Test (TUG). The Mini-Mental State Examination (MMSE), The Controlled Oral Word Association Test, and The Boston Naming Test short-version questionnaires assessed global cognitive function, verbal fluency, and language, respectively. A bivariate correlation analysis and multivariate linear regressions were applied, adjusting for confounders (BMI, sex, age, and educational level). Shorter time in the TUG and greater educational status were independently associated with improved scores on the MMSE. Lower age and greater educational status were independently associated with increased phonological fluency. Better ABC and performance on the TUG and higher educational attainment were independently associated with enhanced semantic fluency. Higher education level and gait speed were independently associated with increased language (all \(p<0.05\)). Improved physical factors, such as gait speed, grip strength, balance, and balance confidence enhanced cognitive function, particularly global cognitive function, verbal fluency, and language, in individuals over 60, with education as a potential confounder.  
older adults; aging; accidental falls; cognitive impairment; physical fitness Article
## 1 Introduction
* FXAPL-I To calculate the coefficient update of the FXAPL-I algorithm, the filtered-x signals are arranged in a matrix, as follows: \[\textbf{X}_{ijk}(n)=\left[\textbf{x}^{\prime}_{ijk}(n)\quad\textbf{x}^{\prime}_{ ijk}(n-1)\quad\ldots\quad\textbf{x}^{\prime}_{ijk}(n-L+1)\right]\] (7) where \(\textbf{x}^{\prime}_{ijk}(n)\) represents the filtered-x signals as follows: \[\textbf{x}^{\prime}_{ijk}(n)=\left[\textbf{x}^{\prime}_{ijk}(n)\quad\textbf{x} ^{\prime}_{ijk}(n-1)\quad\ldots\quad\textbf{x}^{\prime}_{ijk}(n-N+1)\right]^{t}\] (8) Based on Equation (7), the filter update for the FXAPL-I algorithm is as follows: \[y_{j}(n)=\mathbf{w}_{ji}^{t}(n)\mathbf{x}_{i}(n) \tag{1}\]0 (9) where step size \(\mu_{ijk}\) is calculated using the following expression: \[y_{j}(n)=\mathbf{w}_{ji}^{t}(n)\mathbf{x}_{i}(n) \tag{1}\]1 (10) where \(sf\) is a scaling factor used to compensate the mismatch between \(\textbf{\delta}_{kj}(n)\) and \(\textbf{s}_{kj}(n)\). Commonly, the value of \(sf\) can be defined in the interval \([0,1]\), and the designer can choose it by trial and error. A critical parameter of the existing FXAPL-I algorithm is the scaling factor because the convergence can be affected by choosing a wrong value by the designer. Until now, there has been no established method to determine the scaling factor. The following section presents a new method to dynamically adjust this parameter to optimize the design time.