esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analyses  
To demonstrate the accuracy of the proposed expression for speed-time characteristics determination of an IM during a direct startup, we studied an IM whose parameters are given in Table 1 in the Appendix B. We have also considered the Matlab/Simulink model for speed-time curve determination during direct startup described in [20,24].  
For the analyzed machine, the simulation speed-time curve during direct startup, determined for different bearing loss values (\(B=0.1\) and \(B=0.2\)), is explored in Figure 1. The results obtained using the proposed expressions are compared with the model results in the same figure. As can be seen, the proposed expression for the speed-time curve during no-load direct startup based on the Thevenin theorem is very close to the results obtained by the Simulink model. Furthermore, for the mentioned curves, the settling time and the rise time are almost identical.  
On the other side, the Kloss equation's proposed expression demonstrates some mismatching with the previously mentioned results. The impact of the variation of the supply voltage on the speed-time curve during the IM's no-load direct startup is presented in Figure 2. The results obtained using the earlier expressions in [8,20,24] for IM speed-time curve determination are also shown in Figure 2.  
It can be seen that the bearing loss has an impact on the speed-time characteristics, as demonstrated in Figure 3. As the bearing loss's value increases, its effect on the mentioned curves will be considerable and visible. Furthermore, the higher values of bearing loss led to a rise in the machine's take-off time.  
Figure 1: Speed-time curve of the IM (\(J\) = 4.9 kgm\({}^{2}\), \(B\) = 0.1, \(U\) = 200 V) during no-load direct start-up: (**a**) \(B\) = 0.1, and (**b**) \(B\) = 0.2.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.22 Maxduration-Minangle GPSR (MM-GPSR)  
GPSR presents some drawbacks because of the frequent communication with vehicles that could be out of range or construct a path with redundancy. To solve the above problems, MM-GPSR routing protocol was proposed in [46]. The enhancement in greedy forwarding first includes determining the allowed communication area, and then computing and comparing the cumulative communication duration of neighbor vehicles; finally, the neighbor with maximum duration is selected as the next hop. When greedy forwarding fails, the perimeter forwarding process is used. Improvements in this case include calculating and comparing angles from source to neighbor nodes and selecting the neighbor with minimum angle as the next hop to forward data.  
In order to improve the greedy-forwarding scheme, the cumulative communication duration between the neighbor nodes in the communication range of the source is calculated by using Equation (25).  
\[T_{i}=Ti-1+t_{i}-t_{i-1}, \tag{25}\]  
where \(T_{i}\) is the current cumulative communication duration, \(Ti-1\) is the last cumulative communication duration, \(t_{i}\) is the current time of receiving _hello_, \(t_{i-1}\) is the time of receiving the last _hello_. The \(T_{i}\) value is compared with nodes in the communication range of the _source_, and the node with maximum \(T_{i}\) is steady and close to the destination, and is selected as the next hop.  
Finally, to solve routing redundancy, enhanced perimeter forwarding takes the positional connection between neighbor and destination nodes into consideration. So, the proposal includes finding a next-hop node that does not deviate from _source_ to _destination_. To do this, if needed, it draws a line from _source_ to _destination_, and then draws lines from _source_ to any neighbor node. Each line through a neighbor node forms an angle with the line through the _destination_, and this angle is named as \(\theta\). By analyzing and comparing the corresponding \(\theta\) of all neighbor nodes of the _source_, an optimal next hop is selected.
# A Benchmark for Water Surface Jet Segmentation with MobileHDC Method
## 3 Benchmark Description
### Metrics  
#### 3.3.1 Accuracy Evaluation Metrics  
The objective of a jet segmentation model is to separate the jet regions and the background, i.e., pixel-wise image classification. Herein, five common evaluation metrics, including _precision, recall,_\(F1\) score, mean intersection over union (_mloU_), and the Hausdorff distance, are used to comprehensively assess the performance of the different models.  
_Precision_ is the proportion of pixels that are correctly predicted as a jet, defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{1}\]  
_Recall_ is the proportion of pixels predicted to be correct, which is the predicted proportion of pixels with a jet. It is defined as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{2}\]  
\(F1\) score is the harmonic mean of _precision_ and _recall_, providing a balance between the two. It is defined as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{3}\]  
Intersection over union (_loU_) is the ratio of the intersection area of two rectangular boxes to their union area. In a segmentation network, it is the ratio of the intersection area of the predicted result and the ground truth label to their union area. Mean intersection over union (_mloU_) is the average value of the IoU of all images. It is defined as follows:  
\[mIoU=mean(\frac{TP}{TP+FP+FN}) \tag{4}\]  
wherein true positive (_TP_) means that the true class of the pixel is a jet and that it is correctly predicted as a jet. False positive (_FP_) means that the true class of the pixel is the background and it is incorrectly predicted as a jet. True negative (_TN_) means that the true class of the pixel is the background and it is correctly predicted as the background. False negative (_FN_) means that the true class of the pixel is a jet and it is incorrectly predicted as the background.  
Hausdorff distance is a metric used to quantify the similarity between two sets of points, which primarily focuses on the boundary accuracy in segmentation tasks. It is a specific definition of the distance between two point sets: given two sets \(A\left\{a_{1},\ldots,a_{p}\right\}\) and \(B\left\{b_{1},\ldots,b_{p}\right\}\), the Hausdorff distance between these two point sets is defined as follows:  
\[D_{H}(A,B)=max\left(\underset{a\in A}{max\;min}\;\|a-b\|,\underset{b\in B}{max \;min}\;\|a-b\|\right) \tag{5}\]  
where \(\|a-b\|\) represents the distance between points \(a\) and \(b\) (typically the Euclidean distance). In this case, the 95% Hausdorff distance is used to exclude unreasonable distances caused by outliers, thereby maintaining the overall stability of the measure.
# Recent Advances in Efficient Dynamic Graph Processing
## 4 Algorithms
### Graph Coloring  
Graph coloring is a fundamental graph problem that is widely applied in a variety of applications such as nucleic acid sequence design in biochemical networks, air traffic flow management, channel assignment in wireless networks, and community detection in social networks. Classic graph coloring includes _vertex coloring_ and _edge coloring_.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### C-V2X Resource Management
To ensure adequate data transfer, the radio system must calculate the minimum SINR required for the chosen number of physical resource blocks (\(N_{PRB}\)). The \(I_{\text{\tiny MCS}}\) and TB sizes have been determined to meet the specifications outlined in the 3 GPP reference table. The minimum SINR or SINR threshold (\(SINR_{min}\)) may be expressed differently depending on the TB size and can be formulated as presented in [23]:  
\[SINR_{min}=10\text{log}_{10}\Big{(}2^{\frac{\pi}{\mathcal{D}}}-1\Big{)}, \tag{1}\]  
where \(\eta_{c}\) is the channel spectral efficiency and \(\mathcal{D}=0.4\) is a constant taken from 3 GPP standard [22]. As the value of \(I_{\text{\tiny MCS}}\) increases, there is a corresponding increase in \(SINR_{min}\). This may be attributed to the increasing significance of the beacon (TB) capacity, resulting in a decrease in \(N_{PRB}\).
# Blind Detection for Serial Relays in Free Space Optical Communication Systems
## 5 Conclusions  
A novel ABER performance analysis of a dual-hop blind detection FSO system was derived in this paper over a log-normal channel. Our Monte Carlo simulation results verified the validity of the theoretical predictions. Blind detection results were close to the CSI results while using a small window of length 32. Simulation results also showed that the proposed relay technique achieves better performance as compared to the results of a direct link, without a relay. It is worth to mention that as per the simulations, the equidistant relay is the optimum relay location. Additionally, the simulation results showed that even a non-equidistant relay would have a better performance than a direct link.  
Conceptualization, M.A.; Data curation, M.A.; Formal analysis, M.E. and M.H.A.; Investigation, M.E.; Supervision, M.A. and M.H.A.; Validation, M.E., M.A. and M.H.A.; Writing-original draft, M.E.; Writing-review & editing, M.A. and M.H.A.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Results of MSCR Test on Unaged and Short-Term Aged Binders
However, once the SIS content reaches 10%, even though the percentage of recovery remains high, the rate of improvement starts to decrease after aging. This trend indicates that there is an ideal level of SIS content, beyond which further SIS does not continue to improve performance in a linear manner. Excessive SIS can hinder the flow and self-healing properties of the binder, resulting in a slight decline in its performance enhancements. In summary, the findings from the MSCR tests emphasize the crucial importance of SIS in improving the performance of binders, specifically by increasing the percentage of recovery and decreasing \(J_{nr}\) values. The enhancements render SIS-modified binders highly suitable for application in pavement engineering, particularly in high-traffic environments where resistance to cutting is of utmost importance. Nevertheless, the noticeable shift in pattern at increased SIS contents emphasizes the significance of fine-tuning the composi  
Figure 5: % Recovery and \(J_{nr}\) at 64 \({}^{\circ}\)C of the modified asphalt binders in original and RTFO conditions. tion of binder modifiers to attain the desired equilibrium between rigidity and pliability, guaranteeing optimal pavement functionality.
## 5 Final Remarks  
### Discussion of Experiment Results  
The proposed algorithm achieved competitive levels across all datasets since it combined the strengths of two advanced solving methods [14; 17]. Additionally, the proposed algorithm showed significant improvements over the baseline relation-flow algorithm. This improvement is attributed to the incorporation of LLMs, enabling the algorithm to access external knowledge to better understand and decompose complex scenes in AWPs, thereby facilitating relation extraction and reasoning.  
Further comparative experiments confirmed that the proposed algorithm outperforms LLM-based baseline algorithms under the condition of using the same LLM. This superiority is mainly due to the proposed algorithm converting scenes generated by LLMs into relation sets for reasoning, avoiding errors that may arise from LLMs directly using natural language to reason about mathematical logic. Furthermore, the proposed algorithm integrates the Scene-Aware \(S^{2}\) method and symbolic solver, achieving high-performance relation extraction and reasoning, resulting in more accurate and stable solutions compared to using LLMs alone.  
Additionally, Table 4 also demonstrates the improvements brought by pre-training. By incorporating pre-trained BERT as the encoder, **JieAlg** achieved an 8.8% improvement in solving accuracy compared to **ZhangAlg**. Furthermore, **YasuAlg**, which leverages LLMs, demonstrated an additional 7% improvement over **JieAlg**. These improvements indicate that the models can acquire external knowledge through pre-training. Such knowledge helps the solving algorithms understand more complex scenes in AWPs, and as the model size increases, it significantly enhances solving performance.  
The case study results provide an alternative explanation for the improvement in solving accuracy. All baseline algorithms can correctly solve simple AWPs because they only require the algorithm to convert the semantic information of the problem statement into expressions or answers. However, complex AWPs require extensive world knowledge. For example, in Case 2, the algorithm must know that \"bananas and apples are fruits, but rice is not\" to solve the problem correctly. Acquiring such knowledge typically requires the model to undergo unsupervised and reinforcement learning on large world datasets, which is an advantage of LLMs. For more complex AWPs, besides world knowledge, longer reasoning steps and the generation of multiple equations are involved. The accumulation of errors makes LLMs more prone to generating invalid logic and equations. By introducing the relation set as a stable intermediate state, this paper reduces the impact of LLM-generated errors on problem-solving and thereby achieves a more reliable AWP-solving algorithm.
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 2 Methodology
### Modeling of Gas Turbine
The state equation of the turbine inlet temperature can be written as follows:  
\[\frac{dT_{S}}{dt}=\frac{G_{f}\Big{(}h_{f}+HV\Big{)}+G_{c}h_{c}-G_{t}h_{t}-(h_{ t}-R_{t}T_{3})\Big{(}G_{f}+G_{c}-G_{t}\Big{)}}{\rho_{g}Vc_{p,g}} \tag{5}\]  
where \(HV\) stands for the heating value of the fuel, \(h\) is the enthalpy, \(c_{p,g}\) is heat capacity, and \(\rho_{g}\) is the gas density in the combustor. The turbine inlet pressure and temperature are both calculated through the state equations. The results are sent to the compressor and turbine model to obtain the pressure ratio for characteristics maps and to calculate the output power. **Rotating Shaft. ** A rotor is used to connect the compressor and load to the turbine. The mode is described by the following equation. \[\frac{dn}{dt}=\frac{900}{nI\pi^{2}}(P_{t}-P_{c}) \tag{6}\]  
where \(I\) is the moment of inertia, \(P_{t}\) is input power from turbine, \(n\) is the rotational speed, and \(P_{c}\) is the output power to drive the compressor. The fuel flow rate is calculated by a proportional-integral-derivative (PID) controller. The input of this controller is the deviation between the set rotational speed and the measuring rotational speed. After parameter tuning of the PID controller, this dynamic model can simulate the measurement parameters of this gas turbine, setting the power condition and ambient condition. The Block diagram for the speed control is shown in Figure 2.0
## 4 Design and Optimization of Hardware Prototype System
### Hardware Prototype Optimization
#### 4.2.1 Embedded Software Architecture  
DSP needs to respond and process GNSS data, as well as IMU data simultaneously. Therefore, DSP should respond to two external interrupts, priority interrupt (Interrupt 1), used for receiving and processing GNSS correlator output, and priority interrupt (Interrupt 2) used for receiving and processing IMU raw data. Since the correlators update every millisecond, the interval time of Interrupt 1 must be less than 1 ms to receive each integration result. Considering the processor computing power and the maximal vehicle dynamics, the interval time of Interrupt 1 is set as 0.707 ms the Interrupt 2 as 50 ms.  
When DSP enters an interrupt service routine, other interrupts are forbidden by default, whatever their priority. If the execution time of an interrupt exceeds 0.707 ms, some 0.707-ms interrupts will be missed. Therefore, interrupt priority control and interrupt nesting in the software are necessary. Interrupt 1 with a 0.2-ms execution time has the highest priority, and Interrupt 2 with a 10-ms execution time has the second highest priority. At the same time, interrupt nesting is allowed in the lower priority interrupt. Tests showed that the processor was able to handle all tasks completed within the tolerated time, even with abundant free time [26].
## 2 Methodology
### Case Study Results
On the other hand, working with high isentropic efficiencies and elevated inlet gas pressure favours the decrease in consumption. Nevertheless, it must be noted that equipment with higher efficiencies implies higher acquisition costs, so optimization is required to take into account all these variables. Finally, it must be highlighted that the most important dependency in consumption is the density of the working gas. For economic calculations a base case with 4 compressor intercooling stages has been chosen. Under this configuration compression and storage process is composed of 4 compressors, 8 heat exchangers and one high pressure gas storage tank. Table 4 show the CAPEX and variable cost description for economic calculations, and Tables 5 and 6 the main economic variable varying the number of stages. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**K**} & \multicolumn{2}{c}{**W\({}_{\text{net}}\)**} & \multirow{2}{*}{**H\({}_{\text{u}}\) (k/kg)**} & \multicolumn{2}{c}{**H\({}_{\text{nn}}\)**} & \multicolumn{2}{c}{**W\({}_{\text{net}}\)**} & \multicolumn{2}{c}{**H\({}_{\text{u}}\)**} & \multicolumn{2}{c}{**H\({}_{\text{nn}}\)**} \\  & **(k/kg)** & & **(k/kg)** & & **(k/kg)** & **(k/kg)** & **(k/kg)** & **(k/kg)** \\ \hline
1 & 15.23 & 14.60 & 1.64 & 40.72 & 38.15 & 2.52 \\
2 & 12.86 & 10.77 & 3.12 & 32.19 & 27.11 & 5.04 \\
3 & 12.13 & 8.56 & 4.58 & 29.86 & 22.26 & 7.55 \\
4-SC & 11.77 & 6.72 & 6.06 & 28.77 & 18.65 & 10.07 \\
5 & 11.56 & 5.03 & 7.54 & 28.15 & 15.51 & 12.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Net power required, and the heat used and discarded considering a storage pressure of 20 bar: CO\({}_{2}\) (left); H\({}_{2}\) (right). The results highlight the greater influence of CAPEX than OPEX in the economic calculations and its variation according the number of stages considered. The cost of the equipment is clearly higher when working with a higher number of compression stages. It also can be observed that there is a minimum in OPEX that emerges because of the difference between the scenarios in terms of the heat reused, not reused, and the work required. Therefore, an optimization study must be carried out, to obtain the right balance between consumption and costs.
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
###### Abstract  
Femtosecond (FS) laser-induced surface structuring is a robust, maskless, non-contact, and single-step process for producing micro- and nanoscale structures on a material's surface, which remarkably alters the optical, chemical, wetting, and tribological properties of that material. Wettability control, in particular, is of high significance in various applications, including self-cleaning, anti-fouling, anti-icing, anti-corrosion, and, recently, oil-water separation. Due to growing energy demands and rapid industrialization, oil spill accidents and organic industrial discharges frequently take place. This poses an imminent threat to the environment and has adverse effects on the economy and the ecosystem. Oil-water separation and oil waste management require mechanically robust, durable, low-cost, and highly efficient oil-water manipulation systems. To address this challenge superhydrophobic-superoleophilic and superhydrophobic-underwater superoleophobic membrane filters have shown promising results. However, the recyclability and durability issues of such filters are limiting factors in their industrial application, as well as in their use in oil spill accidents. In this article, we review and discuss the recent progress in the application of FS laser surface structuring in producing durable and robust oil-water separation membrane filters. The wide variety of surface structures produced by FS laser nano- and micromachining are initially presented here, while the excellent wetting characteristics shown by specific femtosecond-induced structures are demonstrated. Subsequently, the working principles of oil-water separation membranes are elaborated, and the most recent advances in the topic are analyzed and discussed.  
superhydrophobic; superoleophobic; femtosecond laser; superhydrophilic; oil-water separation +
Footnote †: journal: Applied Sciences
## 4 Discussion  
Figure 18 shows clearly that the boost converter is operating as a step-up converter and we notice from the input voltage that the PV panel delivers the maximum power. It is noticed from Figure 19 that the coil current follows the radiation shape and it reaches the maximum power point corresponding to 5 A when the radiation is almost 800 W/m\({}^{2}\).  
By inspection of Figures 13b and 20, we clearly notice that the power, in the case of the proposed topology (red curve), is greater than the power of the standard boost topology (blue curve), except during the intervals where the current that flows into both circuits is around the value that does not saturate the core. These results confirm that the load power is greater because the boost losses are minimized by avoiding magnetic core saturation in the case of the proposed topology. This outcome is further confirmed by Figures 13c and 21 where the power efficiency of both converters is reported. It is apparent that the proposed converter maintains a power efficiency as high as 92% when the solar radiation is high, i.e., when the input current saturates the core of the inductor of the conventional converter, and the efficiency of which lowers to 80%.
## 3 Results and Discussion
### Model Results
#### 3.2.1 Electric Potential  
Figure 10 shows variations of the electric potential as a function of position at times 2, 6, 10, 14, and 18 ms. The electric potential presents the highest gradient near the electrodes, which is due to electron accumulation at the anode shielding the electric field. This sheath formation has also been found in other contact-electrode plasmas, such as DBD [44; 45; 46]. Higher electric potential gradients at the electrodes gave a higher electric field in these positions. The electric field was able to reach a value of 4000 kV/m for 22 kV near the electrodes, decreasing to 1000 kV/m in the zone between them. Inside the inlet and outlet metal pipes, the potential was almost constant, which means the electric field was zero in these zones.  
When the applied HV voltage was changed, the potential oscillated between electrodes according to the voltage variation, maintaining similar spatial distribution behavior (see different times in Figure 10).