esa-sceva/satcom-chunk-collection huggingface dataset:
# Evolution of Water-in-Oil Droplets in T-Junction Microchannel by Micro-PIV
## 4 Conclusions  
The present work aims at studying the behaviours of distilled water droplet formation suspended in food-grade palm olein at the interfacial surface in offset T-junction microchannels, having radiuses of 400 \(\upmu\)m, 500 \(\upmu\)m, 750 \(\upmu\)m and 1000 \(\upmu\)m, by means of micro-PIV software. The different sizes of inlet and outlet radiuses of microfluidic channels were affecting the flow behaviours of the generated water droplets. The experimental results show that increasing the radius of the offset T-junction microchannel leads to an increase in the cross-sectional area and the decrease in the distilled water phase's velocity. The microchannel with a radius of 400 \(\upmu\)m has a higher distilled water phase velocity than the channel with a radius of 750 \(\upmu\)m. Furthermore, the experimental velocity of the distilled water phase is in agreement with theoretical values, i.e., radiuses of 400 \(\upmu\)m, 500 \(\upmu\)m and 750 \(\upmu\)m microchannels have a minimal difference of 0.008 mm/s, 0.06 mm/s and 0.004 mm/s, respectively. For the radius of the offset T-junction microchannel = 1000 \(\upmu\)m, the experimental velocity could not be determined due to the fact that the droplet was forming outside of the field of view as the image was captured at the junction. The size of the droplets increased as the radius of the microchannel increased, and they are nearly equal to the microchannel's width. At t = 1000 ms, the average velocity of the channel with a radius of 400 \(\upmu\)m decreased slightly to 0.12 mm/s, which may be due to the roughness on the channel's surface, while the other channels showed a good and maintained average velocity. This concludes that the vector and velocity magnitude data are in agreement with the Hagen-Poiseuille flow equation, meaning that a small increase in the channel's size yields a significant increase in the overall flow of a liquid.  
Conceptualization and methodology, K.F.T. and H.R.; formal analysis and investigation, H.R.; data curation H.R.; writing--original draft preparation, H.R. and N.A.S.; writing--review and editing, K.F.T., N.A.S., S.M.; supervision and funding acquisition, K.F.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Malaysia Sarawak, grant number F02/PGRG/2043/2020 and UNIMAS Zamalah Postgraduate Scholarship.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 5 Proposed Path Planning Algorithm
### Artificial Potential Field Considering Road Constraints and Multiple Obstacles
\(k_{1}\) is related to the gradient of the potential field, \(U_{att_{rep}}\). If \(k_{1}\) is high, the shape of \(U_{att_{rep}}\) will be sharpened. On the other hand, \(k_{2}\) is related to real-time performance and if \(k_{2}\) has a high value, it converges quickly toward the global optima. These are related to the gradient of the potential field. The larger the gradient toward the end of the multiple goals, the better the real-time performance. However, excessive gradient increase for rapid convergence causes the collapse of the potential fields. \(U_{att_{rep}}\) and \(U_{att_{stop}}\) are the 6-order and 1-order equations, respectively. Therefore, in a general situation, the differential value of \(U_{att_{rep}}\) is larger than \(U_{att_{stop}}\). However, if we set a high \(k_{2}\), the result is reversed. In the combined potential field of \(U_{att_{rep}}\) and \(U_{att_{stop}}\), the effect of \(U_{att_{rep}}\) will be weak while the effect of \(U_{att_{stop}}\) will be improved. This collapsed potential field is not able to generate a proper path. Therefore, appropriate \(k_{1}\) and \(k_{2}\) are required. \(k_{3}\) is associated with the size of the region needed to avoid obstacles. If the value of \(k_{3}\) is large, the avoidance path for the obstacle will be created further away from the obstacle. On the other hand, if the \(k_{3}\) value is small, a path will be created with a small distance from the obstacle, resulting in a collision.
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## 2 Problem Formulation and Preliminaries
### Preliminaries on PPC  
In prescribed performance control (PPC) [33], the primary aim is to achieve a specific output tracking performance, ensuring the convergence of the error to a predetermined residual set with a predefined minimum convergence rate. The approximation-free framework, introduced in [33], focuses on the difference between the measured output \(y(t)\) and the desired reference trajectory \(y_{d}(t)\), referred to as the tracking error \(e(t)\). This performance is quantified by exploiting a decaying exponential function \(\rho(t)=(\rho(0)-\rho_{\infty})\exp{(-\lambda t)}+\rho_{\infty}\), characterized by parameters \(\rho(0)\), \(\rho_{\infty}\), and \(\lambda\). The parameters \(\rho_{\infty}\) and \(\lambda\) represent the maximum allowable steady-state error and the convergence rate, respectively. The prescribed performance requirement demands that the tracking error \(e(t)\) remains bounded within the performance envelope defined by \(\rho(t)\) and \(-\rho(t)\) for all \(t\geq 0\) (i.e., \(-\rho(t)<e(t)<\rho(t),\ \forall t\geq 0\)). Additionally, the initial tracking error \(e(0)\) is considered within the performance envelope, adhering to the condition \(\rho(0)>|e(0)|\) at \(t=0\).
# IOTA-BT: A P2P File-Sharing System Based on IOTA
## 5 Conclusions and Future Work  
In this paper, we have successfully developed an IOTA-based BitTorrent system based on distributed ledger technology. IOTA-BT enables the original BT framework to be executable on the IOTA mainnet to support a P2P file-sharing service with higher security, availability, and public access. The experimental result shows that the client takes only 6-8 s to release a seed file in the IOTA mainnet, and then other clients can query the detail of the seed file through any IOTA mainnet node within one second. Besides, a node takes only 1.4 and 0.7 s to register its heartbeat and obtain the peer list, respectively. It is almost negligible for users because the time interval of issuing a heartbeat in the BT network is 10 min. Therefore, it is recommended that the time interval length of issuing a heartbeat can be further shortened to increase the frequency of information exchange and reduce the time of file downloading. On the other hand, the experiment result also shows that IOTA-BT can indeed effectively reduce the number of unnecessary transaction searches and decoding. Finally, the efficiency of IOTA-BT is indeed acceptable for P2P file sharing.  
IOTA recently has released the Coordicide white paper, and it is ready to move towards version 2.0. Its goal is to achieve true decentralization, abolish the PoW mechanism, and realize smart contracts with more application value. We will exploit IOTA 2.0 smart contracts to make IOTA-BT evolve into a P2P file trading system in the future. Additionally, service discovery is a critical issue for fog computing. Since the resources of fog computing are distributed to different network levels or organizations, a central service discovery mechanism is not practical and realistic. We will develop a distributed and public service discovery mechanism for fog computing by referencing the framework of IOTA-BT.  
Figure 15: Cost of getting a peer list in the BT network.  
**Author Contributions:** Conceptualization, T.-Y.L.; Data curation, L.-Y.H.; Funding acquisition, T.-Y.L.; Investigation, T.-Y.L.; Methodology, T.-Y.T. and T.-Y.L.; Project administration, T.-Y.L.; Resources, L.-Y.H.; Software, L.-Y.H. and T.-Y.T.; Supervision, T.-Y.L.; Validation, L.-Y.H.; Visualization, L.-Y.H.; Writing--review & editing, T.-Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology in Taiwan grant number [MOST 108-2221-E-992-030].  
**Acknowledgments:** We would like to thank the support of the Ministry of Science and Technology in Taiwan on this study under the project number: MOST 108-2221-E-992-030.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Algorithms for Optimal Power Flow Extended to Controllable Renewable Systems and Loads
## 4 Results: Optimal Power Flow Solution Considering Heuristic and Analytical Algorithms
### Fifty Seven-Node IEEE System
#### 4.2.3 Discussion of Obtained Results in the 57 Buses System  
The results obtained are presented in Table 13, where it is observed that the optimal power flow solution could be found using all optimization techniques. It is also observed that, for the heuristic methods, 17 decision variables are being taken, namely, active and reactive powers in all generators, except for the slack and the voltage of the slack node. Again the interior point method has superiority with respect to the speed of solving the problem, since the metaheuristic techniques took more than 10 min to solve the problem.  
Figure 5: 57 buses system.  
From the results shown in Table 13, it is possible to see that, as the number of variables in a problem grows, analytical methods have a greater advantage over the heuristics in terms of the speed with which they solve the optimal flow of power and due to the fact that they are not subject to random variation computation of the first iteration of the variable decisions and the computationally high costs for large systems.  
It is also observed that the analytical optimum presents a value of the objective function that is lower than that presented by heuristic methods. Furthermore, in this case it is observed that the DEEPSO algorithm does not have such good behavior as to determine the highest value of the objective function, as is observed in the 57 buses system. Additionally, it is also observed that the genetic algorithm has a lower value of the objective function and takes less time than the DEEPSO algorithm. In this way, the appropriate optimization technique for solving the OPF was determined. The inclusion of the cost functions of renewable systems was also considered.
## 3 Method
### Model Improvement and Optimization
#### 3.3.3 Improvement of Loss Function  
In YOLOv5, complete intersection over union loss (CIOU_Loss) was used as the frame loss function. The formula is as follows:  
\[CIOU\_Loss=1-IOU+\frac{\rho^{2}\big{(}b^{st},b\big{)}}{c^{2}}+\alpha v \tag{9}\]  
\[IOU\ =\ \frac{A\cap B}{A\cup B} \tag{10}\]  
\[\alpha=\frac{v}{(1-IOU)+v} \tag{11}\]  
\[v=\frac{4}{\pi^{2}}\bigg{(}arctan\frac{w^{st}}{h^{st}}-arctan\frac{w}{h} \bigg{)} \tag{12}\]  
where \(IOU\) represents the intersection ratio of the predicted box and the real box area. \(\frac{\rho^{2}\big{(}b^{st},b\big{)}}{c^{2}}\) represents the distance between the predicted frame and the actual frame center point, \(\alpha\) is the weight function used to balance the scale, and \(v\) is used to measure the consistency of the aspect ratio.  
The width and height of the predicted frame cannot be increased or decreased at the same time during regression because av is not the real difference between the width and height and its confidence; as a result, once it converges to the line-to-line ratio between the width and height of the predicted frame and the real frame, it sometimes prevents the model from effectively optimizing for similarity. The loss function \(EIOU\_Loss\)[41] splits the aspect ratio influence factor \(\alpha v\) on the basis of \(CIOU\_Loss\) and calculates the length and width of the predicted frame and the real frame to solve the problem of \(CIOU\_Loss\). \(EIOU\_Loss\) is calculated by Equation (13).  
\[EIOU\_Loss=1-IOU+\frac{\rho^{2}\big{(}b^{st},b\big{)}}{c^{2}}+\frac{\rho^{2} \big{(}h^{st},h\big{)}}{c^{2}_{h}}+\frac{\rho^{2}\big{(}w^{st},w\big{)}}{c^{2 }_{w}} \tag{13}\]  
where \(\frac{\rho^{2}\big{(}h^{st},h\big{)}}{c^{2}_{h}}\) represents the difference between the height of the predicted box and the real box; \(\frac{\rho^{2}\big{(}w^{st},w\big{)}}{c^{2}_{w}}\) represents the difference between the width of the predicted box and the real box.  
When there is an edge that is far away, only \(EIOU\_Loss\) computation will become slower without early convergence. This research suggests a novel enhanced loss function, \(ECIOU\), which can increase the prediction frame adjustment and accelerate the frame regression rate.  
Combining the two loss functions of \(CIOU\) and \(EIOU\) is the foundation of \(ECIOU\). The prediction frame's aspect ratio is first changed by \(CIOU\) until it converges to a suitable range, and then each edge is carefully tweaked by \(EIOU\) until it converges to the right value. _ECIOU_Loss_ is calculated by Equation (14). Figure 7 depicts the loss comparison curve for _CIOUL_Loss_, _EIOU_Loss_, and _ECIOU_Loss_ throughout the training procedure.  
\[ECIOU\_Loss=1-IOU+\alpha v+\frac{\rho^{2}(b^{st},b)}{c^{2}}+\frac{\rho^{2}(h^{ st},h)}{c_{h}^{2}}+\frac{\rho^{2}(w^{st},w)}{c_{w}^{2}} \tag{14}\]  
Figure 7 shows that _ECIOU_Loss_ converges the quickest, and the loss value drops to less than 0.03 after 50 rounds. After 300 rounds, the loss values for _ECIOU_Loss_, _EIOU_Loss_, and _CIOUL_Loss_ are around 0.015, 0.017, and 0.020, respectively. Both _CIOUL_Loss_ and _EIOU_Loss_ are inferior to _ECIOU_Loss_.
## 3 Discussion
coli_ systems capable of synthesizing 4 HC (1) from the inexpensive precursors (e.g., glucose, sodium salicate) to act as the ATaPT's substrate along with enhancing the flux of isoprenoid precursors would be beneficial for large-scale production of the two farnesylated-4 HC analogs reported herein. Although we demonstrated that the _E. coli_ BL21(DE3)pLysS strain could be engineered to produce ferulenol (2) and its isomeric structure (3), further optimizations are needed to improve the yields of final product, which seems to be limited by the supply of isoprenoid precursors [7; 8; 9]. Several studies have demonstrated that overexpression of the multiple enzymes regulating the flux of isoprenoid precursors led to greatly improved terpenoid-derived natural products in _E. coli_[19; 20; 21; 22; 23; 24]. Future pathway engineering via overexpression of the other rate-controlling steps in the DXP pathway, such as DXR, IDI, and farnesyl pyrophosphate synthase (FPPS), will be carried out to improve the yields of the two prenylated-4 HC derivatives reported herein. Since a large amount of 4 HC (1) was also found to remain in the culture medium, optimizing substrate consumptions, i.e., a time course production is needed to enhance the yield of those prenyl derivatives. There are several factors affect the heterologous production of natural products in bacterial systems, such as temperature, codon usage, and plasmid copy number [62; 63; 64]. Thus, optimizing these parameters will be examined. Our results illustrated the in vivo functional expression of AtaPT and CstDXS in the _E. coli_ system, which could be seen from the formation of ferulenol (2) and its isomeric structure (3) secreted into the culture medium. These findings also shed new light on the use of the engineered _E. coli_ system as the prenylation system to produce valuable secondary metabolites instead of isolation from plants and chemical synthesis. Since the AtaPT used in this study exhibits broad substrate specificity towards many types of aromatic acceptors, e.g., benzophenones, xanthones, stilbenes, and chalcones [46], the future impact of clones carrying pCDFDuet-AtaPT-CstDXS on synthesizing the prenylated products might not be restricted to 4 HC (1) but could be applied to the other types of aromatic acceptors.
# Investigating the Applications of Machine Learning Techniques to Predict the Rock Brittleness Index
## 3 Modelling Process and Results
### Conqueror Models
#### 3.2.3 KNN Model  
In developing KNN model, several assumptions and parameters were considered. The KNN model was developed to establish the balance between speed and accuracy. Therefore, the model automatically selected the best number of neighbors, within a small range. In the present study, we used k number between the values 3-5 by implementing a trial-and-error method of the system. In addition, the distance computation was based on Euclidean metric. Predicted \(BI\) values by KNN, along with their actual values for training and testing datasets, are displayed in Figure 11. With \(R^{2}\) of 0.81 and 0.84 for train and test stages, respectively, in fact, the KNN model is able to offer a balance range for these stages compared to RF and ANN. Figure 12 shows a suggested structure of the KNN predictive model in predicting \(BI\).  
This figure shows the relationship between the predictors and K selection. In the horizontal axis of the chart, the numbers of the nearest neighbor are displayed. Sums of square errors are shown in the vertical axis. As shown by the figure, the errors for k = 3, 4, and 5 were determined as 372.31, 363.70, and 365.92 respectively. The results revealed that k = 4 is the best value of the nearest neighbor numbers for the developed KNN model. The KNN model also identified the importance of the input  
Figure 7: Input variable’s importance to predict the \(BI\) derived from the RF model.  
variables (Figure 13). As can be seen, the \(R_{n}\) was identified as the most important variable and followed by \(V_{p}\), \(D\), and \(Is_{50}\), respectively. It should be noted that \(R_{n}\) was introduced by all RF, ANN and KNN model as the most influential factor on rock \(BI\).  
Figure 8: ANN network for predicting the \(BI\).  
Figure 10: Input variable’s importance to predict the \(BI\) derived from the ANN model.  
Figure 9: Testing and training results of ANN model to predict the \(BI\).
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 3 Results and Discussion
### Characterization of CA Beads and CA-SCGs Beads by SEM-EDX Spectra
The CA-SCGs beads showed a Cd\({}^{2+}\) content four times larger than the CA beads. The difference is less for Ni\({}^{2+}\), possibly because as it will be discussed later in Section 3.6, Ni\({}^{2+}\) has a lesser affinity to alginate and lignin versus Cd\({}^{2+}\). Another possible reason could be the swelling of the bead after adsorption of Ni\({}^{2+}\). The depth of EDX analysis ranges from 100 nm to 1 \(\upmu\)m, depending on the type of material and the acceleration voltage chosen [30]. The depth of 1 \(\upmu\)m represents a small portion of a bead with an approximate diameter of 1 mm. The analyzed area is close to the surface of the bead. Metal adsorbed at a depth of more than 1 \(\upmu\)m will not be detected. Therefore, the metal seen in the spectra was adsorbed at a depth of less than 1 \(\upmu\)m. The lower levels of Ni\({}^{2+}\) and Cd\({}^{2+}\) detected in the CA beads after adsorption could be due to uniform adsorption throughout the CA bead because of their homogeneous composition of sugar acids with the polar groups responsible for the adsorption, which are equally distributed through the bead. The composition of the CA-SCGs beads is not homogeneous because of hydrophobic groups, such as lipids. These hydrophobic groups will not adsorb metals but will stay away from the water, occupying positions further inside the bead. Therefore, the adsorption of metals will be lower in deeper regions of the bead and greater at the first depth micrometer. Adsorption sites that are more accessible allow for more contact between the metal and the adsorption sites. If the adsorption sites are deeper in the structure of the bead, these sites will be less accessible and part of the metal could stay dissolved, no matter the concentration of metal. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CA** & **CA-SCGs** & **CA After Adsorption** & **CA-SCGs** & **After Adsorption** \\ \cline{2-6}  & **Ca\({}^{2+}\)** & **Ca\({}^{2+}\)** & **Ca\({}^{2+}\)/Ni\({}^{2+}\)** & **Ca\({}^{2+}\)/Cd\({}^{2+}\)** & **Ca\({}^{2+}\)/Ni\({}^{2+}\)** & **Ca\({}^{2+}\)/Cd\({}^{2+}\)** \\ \hline
1 & 12.66 & 12.21 & 1.76/2.22 & 2.73/0.76 & 5.06/4.07 & 8.65/5.21 \\
2 & 22.97 & 10.56 & 4.22/2.45 & 9.35/2.74 & 16.49/3.37 & 9.66/5.28 \\
3 & 19.58 & 19.11 & 1.27/2.32 & 4.60/1.37 & 1.03/1.87 & 6.22/4.37 \\
4 & 23.36 & 3.15 & 0.56/2.28 & 0.96/0.23 & 4.65/2.87 & 6.75/4.25 \\
5 & 20.47 & 1.75 & 0.65/1.90 & 1.41/0.56 & 1.29/1.81 & 4.05/2.55 \\
6 & 17.91 & 6.56 & 0.80/1.95 & 3.00/0.97 & 4.86/2.73 & 2.67/1.60 \\ Ave. & 19.49 & 8.38 & 1.54/2.19 & 3.68/1.11 & 5.56/2.79 & 6.33/3.88 \\ S.D. & 3.59 & 5.05 & 1.27/0.20 & 2.80/0.81 & 5.16/0.79 & 2.42/1.36 \\ Med. & 20.03 & 8.56 & 1.04/2.25 & 2.87/0.87 & 4.76/2.80 & 6.49/4.31 \\ M.A.D. & 2.53 & 4.49 & 0.43/0.14 & 1.60/0.41 & 1.89/0.75 & 2.30/0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of wt. % for six beads.
## 2 Data Processing and Analysis Procedure
### Data Processing Methods
#### 2.2.3 Granger Causality Test  
To further investigate the causal drivers of changes in low-frequency signal strength during XRA events, this study introduces the Granger causality test. The Granger causality test is a statistical method used to determine the presence of a causal relationship between time series. In the context of studying the relationship between X-ray flux (XRA) and low-frequency signal strength during solar storms, this test helps us determine whether changes in XRA can statistically predict variations in long-wave signal strength. This is crucial for understanding the disturbance mechanisms in the ionosphere and the impact of solar storms on low-frequency signal propagation characteristics.  
The basic model for the Granger causality test is as follows:  
\[Y_{t}=\alpha_{0}+\sum_{i=1}^{p}\alpha_{i}Y_{t-i}+\sum_{j=1}^{d}\beta_{j}X_{t-j} +\varepsilon_{i} \tag{5}\]  
In this model, \(Y_{t}\) represents the long-wave signal strength, \(X_{t}\) is the X-ray flux (XRA), and \(\varepsilon_{t}\) is the residual. The Granger causality test determines whether \(X\) Granger-causes \(Y\) by comparing the goodness-of-fit of two models. One model includes only the autoregressive terms of \(Y\), while the other model incorporates lagged values of \(X\). If the inclusion of lagged \(X\) significantly improves the model's fit to \(Y\), then \(X\) is considered to Granger-cause \(Y\).  
In the context of this study, the Granger causality test is used to evaluate whether changes in XRA can significantly explain and predict fluctuations in long-wave signal strength. This approach not only quantifies the time-dependent relationship between the two variables but also reveals potential causal chains. For instance, in the case of a positive lag (i.e., when changes in XRA precede changes in signal strength), a significant test result would suggest that the enhancement of X-ray flux might directly alter the ionospheric electron density, thereby affecting the propagation path of low-frequency signals. The temporal precedence in this relationship is crucial for predicting the effects and response mechanisms of ionospheric disturbances.  
On the other hand, if the test shows a significant result in the case of a negative lag (i.e., when signal strength changes precede changes in XRA), it may suggest the existence of a complex ionospheric feedback mechanism. For example, the early arrival of high-energy particle flows may have already disturbed the ionosphere, and changes in XRA could later amplify this effect. This phenomenon implies that XRA is not the sole causal factor but rather an important yet complex variable in the ionospheric disturbance process. In brief, the Granger causality test adds a temporal dimension to our analysis, providing crucial statistical support, particularly in exploring the dynamic influence between solar flares and signal strength.