esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles
## 2 Technological Infrastructure  
In many countries of the world, electric vehicles are becoming progressively more popular. However, the absence of charging stations limits the widespread acceptance of EVs by users worldwide. As EV usage grows, EV charging stations are installed in more public spaces [23; 24]. By contrast, if the EVs are charged through an existing fossil-fuel-powered system, they will negatively affect the distribution system and the environment [25]. With solar energy from photovoltaic (PV) panels and wind sources having great potential to produce electricity, charging would be an immense solution. It would also represent a sustainable advancement towards a clean environment [23]. Depending on the sources of energy available (e.g., solar radiation and wind speed), the electricity output of the charging facility can be either inferior (less than the needed power) or very high (over the power consumption). Most of the literature indicated that the installation of PV solar systems and wind energy conversion systems with the power grid is advanced and technically viable [26].  
However, the promising approach for balancing the generation of electricity from renewable energy sources can be achieved using configurable dispatch loads or energy storage systems, as it can provide electricity in low power generation [27]. The energy storage system's utilization to stabilize the power grid is no longer a new technology. Other energy sources, such as concentrated solar energy, flywheel, dedicated battery, and hydro-pumped storage systems, are some of the technologies that have been utilized. Smart meters, wireless sensors, advanced communication, and power converters are some of emerging technologies in the industry [28; 29; 30; 31; 32].
## 3 Uncertainty-Controlled Remaining Useful Life Prediction with a Data Augmentation Strategy
### Wiener-LSTM Bearing RUL Prediction Model
#### 3.2.2 The Wiener-LSTM Model with Joint Optimization Loss Function
The parameters of the LSTM network include \(\Theta\) and \(c\) now. \(\Theta\) are the parameters of the LSTM network. \(c\) is the parameter of the Wiener process which can control the uncertainty of the predicted RUL. However, the parameter \(c\) cannot be updated by the iteration of the LSTM network. It is a parameter out of the structure of the LSTM network. The \(c\) and the \(\Theta\) can not be updated simultaneously by the iteration of the LSTM network. Therefore, the optimal value of \(c\) should be calculated first. The variable \(\Theta\) is assumed as the constant value in the process of solving the optimal valve \(c\)  
\[c^{*}=\frac{n+1}{\sum_{n=1}^{N}\left[\frac{  
loss function can be updated by iteration of the LSTM network. The proposed loss function of the LSTM network is the logarithmic function as  
\[\log(\text{Loss}_{new}(\mathbf{x}_{0:\tau}^{n},\mathbf{r}_{0:\tau}^{n};\Theta))= \frac{1}{N}\sum_{n=1}^{N}\Bigg{\{}3\log(2\pi)-\log(c^{*})+\log(r_{0 }^{n})+\frac{c^{*}\big{(}r_{0}^{n}-\phi^{n}(x_{0},r_{0};\Theta)\big{)}^{2}}{r_{ 0}^{i}}+\] \[+c^{*}\sum_{i=1}^{\tau}(\phi^{n}(\mathbf{x}_{0:i},\mathbf{r}_{0:i};\Theta) -\phi^{n}(\mathbf{x}_{0:i-1},\mathbf{r}_{0:i-1};\Theta)+1)^{2}+ \tag{23}\] \[-2\log(\phi^{n}(\mathbf{x}_{0:\tau},\mathbf{r}_{0:\tau};\Theta))+3\log(r_{ \tau}^{n})+\frac{c^{*}(r_{\tau}^{n}-\phi^{n}(\mathbf{x}_{0:\tau},\mathbf{r}_{0:\tau}; \Theta))^{2}}{r_{\tau}^{n}}\Bigg{\}}\]  
The log-likelihood loss function, as shown in Equation (23) is convenient for calculating. The new loss function is the training object of the Wiener-LSTM model. During backpropagation, the parameter \(\Theta\) is updated using the Adam algorithm. The Adam algorithm is a first-order optimization technique that, like a conventional stochastic gradient descent procedure, iteratively adjusts the weights of the neural network based on training data [40]. The process \(\Theta^{*}=\arg\min_{\Theta}Loss_{new}(\mathbf{x}_{0:\tau}^{n},\mathbf{r}_{0:\tau}^{n} ;\Theta)\) can be acquired by the Adam algorithm.
# Non-Destructive Testing Applications for Steel Bridges
## 3 NDT Methods with Potential Application to Steel Bridges
### Test Methods Applicable to Internal/Subsurface Defects
#### 3.2.1 Ultrasonic Testing (UT)
This type of ultrasonic method is not only faster and more convenient than conventional monolithic technology, but it also possesses better accuracy. Further capabilities include easier accessibility to out-of-reach components, shorter inspection time, and the detection and measurement of small stress corrosion cracks. Sometimes, the limitation in orientation for the path of the ultrasonic pulse makes it difficult for PAUT to fully obtain an image of cracks and defects. It also requires costlier equipment compared to conventional UT. Time of Flight Diffraction (ToFD)-- To address the issues with orientation when using PAUT, ToFD was invented, which uses diffraction instead of reflection. A ToFD transducer generates pulsed ultrasonic waves [32; 35; 36; 37], which are diffracted at a certain degree due to the irregularities in the inspected component. After the diffraction, waves are then collected by the receiver on the other side of the component. The image is then generated, containing the size and location of the cracks and defects, using the difference in the flight time of the ultrasonic waves that traveled from the transducer to the receiver (Figure 13). This type of ultrasonic technology is preferred over PAUT when the objective is to find defects and flaws, regardless of their orientation. ToFD can also be used to inspect welded overlays and heat-affected zones. Furthermore, it is considered to be one of the fastest non-destructive testing methods as it only requires one scan for detection. An example of the reading of weld cracks with ToFD can be seen in Figure 14[36; 37; 38]. However, the ToFD method has shortcomings, including the possibility of presenting lateral wave dead zones, timing errors, off-axis errors, or resolution errors on the image if only a single scan has been performed on the component. This issue can be solved by implementing the PAUT to vary the wave angle, width, and entry point. Combining ToFD with PAUT gives the inspectors a more reliable and efficient scanner [39]. Figure 11: Phased-array ultrasonic technology (PAUT). Figure 12: Comparison between conventional UT and phased-array UT, where PAUT allows for the shaping and steering of the ultrasonic beam angles, better depth focus, and enhanced coverage [34].
## 3 Results and Discussion
### Application of the Pd-Based Photocatalyst in the Degradation Process of Phenol
#### 3.2.4 Mechanism of Phenol Degradation  
Figure 11 presents the kinetic plot (C/C\({}_{0}\) at \(\lambda\) = 270 nm vs. time) of phenol photodegradation. At different times of contact, the UV-vis absorption profiles of solutions together with the measured TOC concentrations were presented. The UV-vis absorption profiles of solutions allow making qualitative analysis for identification of the intermediate compounds of phenol oxidation.  
From the kinetic study and the absorption profiles at different time intervals, as well as by the correlation of our results with results reported in the literature, a mechani  
Figure 11: Degradation kinetics (C/C\({}_{0}\) vs. time) of phenol photodegradation process, UV-vis absorption profiles and total organic carbon (TOC) measurements of solutions at certain time intervals.  
Figure 10: BFTEM image and EDX spectrum of the reused photocatalyst.  
was proposed [32; 33]. At the same time, the TOC was measured in order to determine the degree of mineralization, not only the photodegradation degree of phenol. It can be observed that during the period of establishing the adsorption-desorption equilibrium between the phenol and the surface of the photocatalyst, it is adsorbed about 10% of the phenol present in the solution. At the start of the UV lamp, a change in the sample absorbance profile is observed due to the hyperchromic effect. This effect suggests that phenol passes into degradation intermediates: catechol, hydroquinone and p-benzoquinone [32; 33].  
Up to a 30 min irradiation time, there is no mineralization of these intermediate compounds. This can be seen from the TOC analysis, which shows that even after 30 min of irradiation, the TOC concentration is approximately equal to the initial TOC. Or it is possible that the TOC concentration to remain constant because a part of the IL is detached and degraded from the surface of the Florisil. After this time, in the sample spectrum appear a decrease in absorbance (hypochromic effect), so we can conclude that the process of mineralization has begun, besides the photodegradation process, also proven by the TOC concentration which after 60 min has reached a value of 2.69 mg/L (Figure 11).  
By further irradiating the solution in the presence of the photocatalyst, the aromatic ring breaks with the formation of aliphatic organic acids with the short molecule (maleic acid, oxalic acid, formic acid) [32; 33]. Its formation is indicated by the hyposochromic effect (a blue shift) by displacing the maximum adsorption from 270 nm to 265 nm. After 180 min, the TOC is 1.1 mg/L, which means a 93.75% efficiency of mineralization (Figure 11).  
Comparing the performance of the studied materials with other photocatalysts reported in the literature (Table 3) could be concluded that the studied materials, which actually consist of a waste resulted from other processes, presents good photocatalytic activity in the degradation process of phenol from aqueous solutions.
## 3 Results and Discussions
### Ultrasonic Velocity Studies
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Mode Fraction** \\ **of Ethanol** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Density (Experimental Values) kg/m3** \\ \end{tabular} } & \multicolumn{3}{c}{**[PERSON]–[PERSON]-**} & \multicolumn{1}{c}{**Hankinson and Thomson Model (\(\rho_{\text{HT}}\))**} & \multicolumn{1}{c}{**[PERSON] and**} & \multirow{3}{*}{
\begin{tabular}{c} **[PERSON] et al.** \\ **Model (\(\rho_{\text{R}}\))** \\ \end{tabular} } \\ \cline{3-7}  & & **Model (\(\rho_{\text{MNM}}\))** & & **(\(\rho_{\text{HT}}\))** & **(\(\rho_{\text{YG}}\))** & **Model (\(\rho_{\text{R}}\))** \\ \hline
0.00 & 817.2 & 659.5 & 862.7 & 858.1 & 855.2 \\ \hline
0.13 & 815.9 & 661.6 & 864.3 & 859.7 & 856.9 \\ \hline
0.24 & 814.6 & 663.3 & 865.2 & 860.7 & 857.9 \\ \hline
0.33 & 813.4 & 664.4 & 865.6 & 861.2 & 858.5 \\ \hline
0.55 & 808.6 & 666.3 & 865.2 & 860.9 & 858.5 \\ \hline
0.70 & 801.2 & 666.9 & 863.5 & 859.3 & 857.2 \\ \hline
0.81 & 794.1 & 666.9 & 861.4 & 857.2 & 855.5 \\ \hline
0.89 & 788.8 & 666.5 & 859.3 & 855.1 & 853.7 \\ \hline
0.96 & 785.6 & 666.0 & 857.3 & 853.0 & 851.9 \\ \hline
1.00 & 783.9 & 665.6 & 855.9 & 851.6 & 850.7 \\ \hline
**APD from experimental values** & **17.13** & **–7.46** & **–6.92** & **–6.66** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental and theoretical density of binary liquid system of Ethanol + 1-Octanol at 303.15 K.  
For the Ethanol + 1-hexanol binary liquid system, the following trend was noticed: \(\Delta\)U\({}_{\text{R}}\) \(>\)\(\Delta\)U\({}_{\text{N}}\) \(>\)\(\Delta\)U\({}_{\text{IMR}}\) \(>\)\(\Delta\)U\({}_{\text{IR}}\) \(>\)\(\Delta\)U\({}_{\text{J}}\). The calculations reveal that the APD of ultrasonic velocity is the most when using [PERSON]'s specific velocity relation [38] and is the least in [PERSON]'s relation [39]. This leads to the conclusion that [PERSON]'s relation is the most suitable theoretical method for estimating the ultrasonic velocity for the Ethanol + 1-hexanol binary liquid system. In the case of the Ethanol + 1-octanol binary liquid system, it was observed that deviation takes the order \(\Delta\)U\({}_{\text{IMR}}\) \(>\)\(\Delta\)U\({}_{\text{R}}\) \(>\)\(\Delta\)U\({}_{\text{N}}\) \(>\)\(\Delta\)U\({}_{\text{IR}}\) \(>\)\(\Delta\)U\({}_{\text{J}}\). The APD of ultrasonic velocity values as calculated from the Van [PERSON] and [PERSON] [36] relation were relatively higher than those predicted by [PERSON]'s relation [39]. Thus, it can be concluded that [PERSON]'s relation [39] is the most suitable theoretical method for estimating the ultrasonic velocity for Ethanol + 1-octanol binary liquid system.
## 2 Materials and Methods
### Statistical Analysis  
The basic descriptive parameters (Table 1) were calculated for all the observed variables. Normality of distribution was tested by using the Shapiro-Wilk test, and homogeneity of variances was tested by using Levene's test. The data did not meet the criteria of normal distribution and homogeneity of variances, and based on the mentioned, non-parametric statistics were used to analyse the data. The Median test was used to determine the differences in obtained variables between one- and three-step run-ups. Effect size (n2) calculations (eta squared; small (n2 \(\geq\) 0.01), medium (n2 \(\geq\) 0.06), and large (n2 \(\geq\) 0.14)) were used to estimate the magnitude of the results (differences between 2 run-ups techniques) and were reported as a measure of practical significance. A significance level was considered at the 95% confidence level for all statistical parameters. Moreover, [PERSON]'s chi-squared test was used to determine the differences in jump shot precision parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **N** & \(\bar{\mathbf{x}}\) & **Min** & **Max** & **SD** \\ \hline
**Age** & 27 & 16.77 & 14.72 & 18.43 & 1.10 \\
**H** & 27 & 170.29 & 101.00 & 193.60 & 15.62 \\
**W** & 27 & 64.62 & 53.80 & 86.70 & 6.77 \\
**\% BF** & 27 & 21.68 & 14.40 & 27.30 & 3.29 \\
**BMI** & 27 & 21.53 & 18.90 & 24.30 & 1.79 \\ \hline \hline \end{tabular} Legend: H—body height; W—body weight; % BF—percentage of body fat; BMI—body mass index; N—number of respondents; \(\bar{\mathbf{x}}\)—mean value; Min—minimum value; Max—maximum value; SD—deviation of results.  
\end{table}
Table 1: Descriptive statistics of the basic anthropometric and morphological characteristics of the players.  
Figure 1: Illustration of target setting on the upper goal post.
## 5 Simulation Results
### Residual Energy  
This performance metric is the sum of the remaining energy of the surviving nodes in each round. For the length of the simulation, a fixed initial energy of 0.5 J has been used. The ESO-GJO technique presented in this research is compared with PSO-LSA, EPOA-CHS, LEACH-IACA, and LEACH-ANT residual energy for a scenario with 100 nodes. The results are displayed in Figure 3a. It is noted that LEACH-IACA and LEACH-ANT lost their remaining energy after 1500 cycles. On the other hand, PSO-LSA,EPOA-CHS, and ESO-GJO managed to live with 15.29 J, 16.41 J, and 18.13 J, respectively. All nodes of PSO-LSA and EPOA-CHS die after the 2400 th round. However, the proposed ESO-GJO still has 0.08 J of energy remaining. Figure 3b shows the results of comparing the residual energy of ESO-GJO with other protocols for the scenario of 200 nodes. It is observed that at the end of 1000 rounds, all the energies of LEACH-IACA and LEACH-ANT are exhausted. However, PSO-LSA, EPOA-CHS, and ESO-GJO have 55.30 J, 55.01 J, and 56.79 J remaining, respectively. With PSO-LSA, all nodes die at round 2500. However, the proposed ESO-GJO still has 2.67 J of energy remaining. Figure 3c shows the results of comparing the residual energy of ESO-GJO with other protocols for the scenario of 300 nodes. It is observed that at the end of 800 rounds, all the energies of LEACH-IACA and LEACH-ANT are exhausted. However, PSO-LSA,EPOA-CHS, and ESO-GJO have 94.14 J, 94.95 J, and 95.40 J remaining, respectively. With PSO-LSA and EPOA-CHS, all nodes die at round 2350. However, the proposed ESO-GJO still has 0.05 J of energy remaining. Figure 3a-c show that the proposed ESO-GJO increases network residual energy over the compared LSA, LEACH-IACA, and LEACH-ANT protocols.
## 4 Experiments
### Ablation Studies  
#### 4.4.1 Effect of Dynamics Information  
For human motion prediction involving forecasting future joint coordinates, it is intuitive to use only kinematic information such as joint positions and velocity vectors as inputs. To validate the effectiveness of incorporating dynamic information, we conducted experiments utilizing both kinematic and dynamic data. Table 4 presents the experimental results for models using solely kinematic information (Kinematics only), fusing dynamic and kinematic information (KD-fused). The results indicate that the KD-fused model exhibits slightly better performance than that of the Kinematics-only model in short-term predictions. In addition, in long-term prediction tasks, the performance margin between them increases, especially at 560 ms.  
Figure 7: **Left: probability distribution of correlation coefficients of joint velocity and torque; right: repeatability across samples. Note, IQR is interquartile range.**  
From a mechanical perspective, this phenomenon can be attributed to the fact that, on very short time-steps, the influence of applied forces or torques on displacement and velocity is not significant (i.e., displacement and velocity do not undergo abrupt changes), and such effects are delayed over time. Therefore, in short-term predictions, dynamic information may be perceived by the model as noise, increasing the difficulty of learning and leading to decreased prediction accuracy. In contrast, dynamic information is propagated to long-term predictions through the mechanisms of the update and reset gates, thereby improving the model's prediction accuracy. This further substantiates the superiority of integrating dynamic information in long-term motion prediction.
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 5 Teak Allelopathy  
A possible release process of phytotoxic substances with allelopathic activity is discussed in this section. In agriculture and forestry systems, phytotoxic substances in plants can be released into the soil, either by the exudates from living plant tissues or by the decomposition of plant residues. Some of those substances have allelopathic effects [25; 26; 69]. Annual litter fall from teak trees onto forest floors varies, from 1.7 to 6.4 t/ha, which will depend on the tree's age. More than 90% of the litter originates from fallen leaves because teak is a deciduous tree species. About 25% of teak litter on the forest floor decomposes during the initial 60 days after accumulation [70], and 54% of the accumulated litter decomposes in the first six months [36]. Compared with other tropical plant species, such as _Paraserianthes falcataria_ (Mig.) [PERSON] and _Eucalyptus tereticornis_ [PERSON], the decomposition rate of teak litter was faster than that of their litters in tropical humid conditions [71; 72; 73].  
The phytotoxic substances in the leaves may be liberated into forest soil by the decomposition process of the leaves, and act as allelopathic substances. Allelopathic substances are able to inhibit germination, seedling establishment, and plant growth of other plant species [25; 26; 69]. In fact, a substantial number of phytotoxic substances with allelopathic activity were identified in teak leaves (Table 2). It was also reported that the soil under teak trees had inhibitory characteristics [40; 41]. Therefore, teak trees may be able to inhibit the germination and growth of understory plant species through their allelopathy.
## 3 Results and Discussion
### Matric Suction Determined Using the Filter Paper Method
#### 3.1.3 Accuracy of the Filter Paper Method in Measuring the Matric Suction  
Several studies, which includes the studies of [PERSON] et al. [45] and [PERSON] et al. [46], have also utilized the filter paper method in determining the relationship of the moisture content of a compacted soil with a wide range of matric suctions, since the method is easy and accessible. However, there are some limitations in utilizing the filter paper method. This  
Figure 20: Predicted vs. actual matric suction of CPCL and CL.  
includes the precision in conducting the experiment due to the sensitivity of the filter paper to air when being weighed to determine its moisture content and the limited accuracy of the filter paper method in the high suction range. According to [PERSON] et al. [47] and [PERSON] et al. [32], the contact filter paper method wherein the set of filter papers is in between two compacted specimens may be inaccurate in the high suction range, because water transport in that range is mostly due to vapor transport rather than capillary transfer. [PERSON] et al. [32] also inferred that, beyond the low suction range, even if the set of filter paper is in contact or non-contact with the specimen, the filter paper method measures only the total suction, or the sum of the matric suction and osmotic suction, due to the water transport, which is mostly vapor transport. Osmotic suction is due to the variation in salt content in the soil pore water [32], which has a minimal contribution on the total suction of the compacted specimen in the study, since distilled water is utilized in the soil preparation and re-saturation; furthermore, the clay mineral present in the specimen is kaolinite, which has a low osmotic suction. It was also inferred that the osmotic suction, which is the difference between the total and matric suction, is almost constant [32]. However, the matric suction must still be verified using other methods that may directly measure it. Thus, in the study of [PERSON] et al. [48], the matric suction in the high suction range was measured using the vapor equilibrium technique, in which the experimentation period is 1 to 2 months, while, for the lower matric suction range, the filter paper method was utilized.  
The limitations of the filter paper method in accurately measuring the matric suction of the specimen as it dries may result in errors in predicting the water retention curve and the corresponding unsaturated hydraulic conductivity. To quantitatively assess the accuracy of the filter paper method in this study, a sensitivity analysis of measurement errors could be conducted in future work. Comparing results with direct measurement methods, such as tensiometers or the pressure plate apparatus, would provide a more comprehensive evaluation of its reliability in various suction ranges.