esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Traveling Salesman Problems  
It is well known that the combinatorial problems are not new to heuristic algorithms and have fascinated many scientists and engineers over the past few decades. As a matter of fact, they are considered as standard benchmarks for heuristic algorithms. For example, TSP is a well-known and ancient combinatorial problem since being defined in the 1800s by the Irish mathematician [PERSON] and by the British mathematician [PERSON] [31]. To summarize, there are three major reasons that the TSP has become a standard benchmark for soft computing algorithms [32]. First, the TSP is very intuitive and quite easy to state in terms of the complete graph on \(N\) vertices, and it is similar to many practical problems such as automatic assembly and configuration planning, sensor selection and power allocation over wireless networks, robotic path planning, and many others. Second, the TSP can easily be modified to become a multi-objective problem and solving multi-objective problems is a practical challenge but perhaps not insurmountable in many areas of engineering and industry. Third, an optimal TSP solution is extremely hard to obtain by using analytical methods. Even using numerical methods, it is still quite a challenge on a large number of real problem instances. The running time for brute-force search approach lies within a polynomial factor of O(\(n!\)). For instance, for a problem with 20 cities by brute force searching, it would be \((20-1)!/2\) possible tours, so this solution becomes impractical within limited computing time and memory space.  
Because of the reasons above, many TSP problems with different diversities (even over 80,000) are formed to challenge the performance of existing heuristic algorithms, and many new algorithms inspired by nature and biological processes are specially proposed to conquer the TSP more efficiently. It is believed that our work in this paper is a useful attempt following this way, though we have not yet found the optimal solutions for symmetric TSP.
## 4 Design of Proposed Scheme: FAPR
### Reactive Part
#### 4.3.2 Backup Path Generation Module  
The backup path generation module is a very important part of FAPR, as it calculates the backup path for each link in the data plane based on the information gathered from the data collection module. Firstly, this module obtains all the link information and generates the network topology. The link information obtained is standardized, which means that the controller can find out the source and target switches of each link. Secondly, this module calculates multiple backup paths for each link, and the algorithm used here is based on the \(K\)-shortest path algorithm. In the example shown in Figure 3, we take \(K=3\), that is, we calculate in total three backup paths for each link. Note that not all links have three backup paths. In a network topology, when compared with the links in the center, the links on the edge usually correspond to lower degrees and fewer backup paths, even fewer than three backup paths.  
Finally, when all the backup paths are generated, this module stores all the calculated backup paths in the controller. When a link failure occurs, subsequent modules analyse the characteristics of the affected flow, choose the most suitable backup path and install the proper flow rules on the corresponding switches.
# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 5 Discussion
### Discussion and Analysis Based on Performance Metrics
#### 5.2.7 Analysis Based on the Return Rate  
In this part, the values of the return rate are discussed. Table 21 shows the details of the papers' return rates. In this table, papers [14,16,18,21,33,70,71,78,79,84,87,89] used the CNN-based model; paper [33] used the LSTM-based model; papers [70,71] used the DNN-based model; papers [78,79] used the reinforcement learning based model; and papers [84,87,88,91] used other deep learning methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sharpe Ratio Range** & **Reference No.** \\ \hline Sharpe ratio & [14,18,71,77,82,84,88,90,91] \\ \hline
0.1–1 & [14,18,82,88,90] \\
1–2 & [71] \\
2–5 & [77] \\
5–10 & [75,84,91] \\ \hline \hline \end{tabular}
\end{table}
Table 20: Analysis based on Sharpe ratio.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Return Rate Range** & **Reference No.** \\ \hline Return rate & [14,16,18,21,33,70,71,78,79,84,87,89] \\ \hline
1.0–1.2 & [33,70,71,79,87,88,91] \\
1.2–1.4 & [14,16,18,21] \\
1.4–1.6 & [84,90] \\
1.6–1.8 & [89] \\
1.8–2.0 & [78] \\ \hline \hline \end{tabular}
\end{table}
Table 21: Analysis based on the return rate.  
It could be found that paper [78] performed the best, which used the reinforcement learning model, and the paper's return rate was in the range of 1.4-1.8. Papers [84; 89] had good performance, but papers [33; 70; 71; 79; 87; 88; 91] did not have high return rates.  
Among the papers that used return rate as a performance measurement, all the CNN papers were in the range of 1.2-1.4, the only LSTM paper was in the range of 1.0-1.2, all the DNN papers were in the range of 1.0-1.2, 50% of the reinforcement learning papers were in the range of 1.0-1.2, and 50% of the reinforcement learning papers were in the range of 1.8-2. The other deep learning method papers performed well, with 17% of papers in the range of 1.0-1.2, 33% of papers in the range of 1.4-1.6, and 50% of papers in the range of 1.6-1.8.
# Low-Profile Dual-Band Reflector Antenna for High-Frequency Applications
## 3 Measurement Verifications
The performance of the fabricated dual-band reflector antenna is sensitive to the distance between SRA and the conical coaxial horn. The copper clad is attached to the surface of the support substrate, serving as the higher-band SR, and an air gap appears between the surface of the support dielectric and the dielectric in the E-band feed. The difference between the measured and simulated results is probably caused by the air gap. Nevertheless, the agreement of the measurement and the simulation indicates that the proposed dual-band reflector antenna is designed properly. Figure 12: Simulated and measured (**a**) gains and (**b**) aperture efficiency of the reflector antenna within the lower band. Figure 13: Simulated and measured radiation patterns at 15.4 GHz in normalized (**a**) H-plane and (**b**) E-plane. To clearly demonstrate the good radiation performance of the designed reflector antenna, Table 3 presents a comparison between the proposed dual-band reflector antenna and several reported works on dual-band MMW/THz reflector antennas. Compared to other dual-band MMW/THz antennas, the proposed antenna achieves high gain and high aperture efficiency at a low cost. Meanwhile, the proposed dual-band reflector antenna features a lower profile than that of most reported dual-band reflector antennas.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 2 Previous Work
Although the performance of the inertial sensors was relatively good, the authors concluded that, despite the tangible advantages from the usage of IMU, inertial sensors still could not be considered as a primary backup to GNSS due to their fast position drift even when high-priced navigation sensors are employed. This statement is corroborated by our recent work [24], where the performance of a hybrid IMU/GNSS system in maritime applications was also evaluated, including modern affordable tactical grade MEMS inertial sensors. Another sensor typically used in maritime applications is the Doppler velocity log (DVL). When mounted on a vessel, the sensor faces the sea floor and can provide very accurate velocity information of the vessel with respect to the sea floor [25] for scenarios up to a certain depth. The working principle of a DVL is rather simple. Firstly, an oscillating acoustic signal is sent out along each of the transducer axes. From the frequency shift between the emitted and the returned signal, the velocity along each of the transducer axis is determined. Despite being commonly mounted on marine vehicles ranging from large ships to small autonomous underwater vehicles (AUVs), there is a scarcity of academic resources concerning the specifics and functionality of DVLs [26]. The combined usage of DVL and IMU have been reported in a number of works [27; 28; 29] for the navigation AUVs, although only in few works has it been applied to merchant vessels [30], and none have reported the performance of a hybrid IMU/GNSS/DVL navigation system. In a previous work [31], the performance of the navigation solution fusing IMU, GNSS, and DVL was demonstrated on a maritime scenario. However, the performance characterization under actual GNSS jamming conditions has not yet been realized.
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.12 H2 Magallanes Project--Magallanes Region  
The H2 Magallanes on-shore wind farm is a facility that will be located in the northeast of Punta Arenas, in the Magallanes region. It will have 533 wind turbines, the owner of which will be Total Energies H2. Furthermore, the height of the towers from the ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 5000 MW, with an estimated capacity factor of 0.59. Figure 21 shows a panoramic image of the landscape of the project site, and Table 14 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Northeast of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Total Energies H2 & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 833 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 5000 & MW \\ \hline Capacity Factor & 0.59 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 4,550,000 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 3500 & MW \\ \hline GH2 Production & 402,500 & Tons/year \\ \hline NH\({}_{3}\) Production & 2,257,500 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 4,025,000 & Tons/year \\ \hline \end{tabular}
\end{table}
Table 14: H2 Magallanes GH2 plant main specifications.  
Figure 21: Panoramic view of the landscape in the H2 Magallanes project.  
Considering Figure 21, it is evident that the H2 Magallanes on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 14, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 4,550,000 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 3500 MW of installed capacity. In this sense, the GH2 production plant will generate 402,500 tons/year of GH2, and 2,257,500 tons/year of NH3. Finally, this facility will avoid 4,025,000 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
# Monitoring Rainfed Alfafa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery
## 2 Material and Methods
### Data Analysis
Eight plots (195, 199, 221, 11, 257, 59, 63, and 347) were used to check the correlation obtained between the in situ FVC measurements and VI values (Table 5; Figure 3A). At the pixel level, the correlation between FVC and NDVI data derived from the Sentinel-2 satellite multispectral images was calculated considering the data for each year (i.e., 2017 and 2018) separately and both years together (Figure 3B). The accuracy of each index was specifically analyzed with linear (f(x) = ax + b), polynomial of second order (f(x) = ax2 + bx + c), and exponential fitting (f(x) = a \(\times\) exp(bx)). At both the parcel and pixel levels, the coefficient of determination (\(R^{2}\)) was selected as the indicator of the accuracy of the statistical estimation models. The main steps of the procedure for the assessment and in situ analysis of remote sensing vegetation indices are provided in Supplementary Materials Figure S1. Figure 3: Second-order polynomial correlations between FVC and NDVI at the parcel level (**A**). The second-order polynomial correlations between FVC and NDVI derived from the Sentinel-2 images at the pixel level in 2017 (blue), 2018 (red), and 2017–2018 (green) (**B**). These data belong to Experiment 1 in which plots 195, 199, 221, 11, 257, 59, 63, and 347 were analyzed. The maximum gap between the in situ sampling and the Sentinel-2 images was 5 days. During the second experiment (E2), the temporal evolution of the average NDVI values was assessed between July 2016 and April 2019 for the three different areas (I, II, and III) (Figure 4A) and from January 2017 to May 2019 for the 29 alfalfa plots (28 rainfed; 1 irrigated) in the Bardenas Reales area (Figure 4B). Figure 4: (**A**) Evolution of the NDVI alfalfa plots in different areas of Bardenas Reales: zone I included 195 and 199 high-soil-quality plots in a low-altitude area; zone II comprised 11, 221, and 257 plots, described as regular-quality areas in a medium altitude territory; zone III consisted of 59, 63, and 347 good soil quality plots in a high-elevation area. Finally, the average NDVI evolution of the 28 rainfed alfalfa in the territory is also shown. The data are from Experiment 2, which was conducted between July 2016 and April 2019. (**B**) The NDVI evolution between January 2017 and May 2019 for the 28 rainfed alfalfa plots compared to an irrigated alfalfa plot in the Bardenas Reales area from Experiment 2.
# The Shared Bicycle and Its Network--Internet of Shared Bicycle (IoSB): A Review and Survey
## 3 Key Technologies of IoSB
### Localization  
In fact, IoSB is an application of Mobile Wireless Sensor Networks (MWSNs) [59]. Different from the localization of static nodes in WSN, the nodes in MWSNs will move irregularly, which makes it harder to get location. Meanwhile, multipath and Non-Line-of-Sight (NLoS) propagation are always underlying causes to affect the performance of location [60]. The commonly used mobile localization methods include Global Positioning System (GPS), Cellular Positioning System (CPS) and Wireless Local Area Network (WLAN) positioning system [61].  
As one of the earliest application of positioning technology, GPS positioning performs quite well. At present, most of shared bicycles are equipped with a GPS module for positioning. However, with GPS alone, positioning accuracy is not enough. To further improve the bit rate of the navigation data encoded in the signals transmitted by the satellites (one of the main disadvantages of GPS), Assisted GPS (A-GPS) is designed to reduce the startup time by fetching the navigation data over the Internet, commonly by connecting via a cellular network [62]. By combining with the cellular positioning system (CPS), this system (A-GPS) can locate the caller to within 50 m for 67% of calls and to 150 m for 95% of calls [63]. CPS location accuracy depends solely on the cell size, but this can be enhanced with support of other techniques, such as sector division by directional base station antennas and Received Signal Strength (RSS). With more dense base stations distributed in the upcoming 5 th generation wireless systems, the combination of GPS and CPS will greatly improve the accuracy of mobile positioning, which is expected to be the trend towards shared bicycle positioning.  
In the complex urban environment, indoor positioning technology has always been a pain point for the existing shared bicycle. Currently WLAN positioning system is the dominant indoor positioning due to the IEEE 802.11 standard's wide application and can make up for the defects of positioning technology in existing IoSB. Historically, from the position computation perspective of radio-based signaling systems, the known approaches for WLAN positioning can be divided into three main types [64]: (1) Angle of Arrival (AoA) and related Direction of Arrival (DoA) methods; (2) Time of Arrival (ToA) and related Time Difference of Arrival (TDoA) techniques; (3) RSS exploitation methods (fingerprinting). In terms of positioning performance, the accuracy of typical WLAN positioning systems using RSS is approximately 3 to 30 m, with an update rate in the range of a few seconds [65]. However, it is still a little difficult to apply WLAN positioning systems to shared bicycle, which involves power consumption, positioning chip design technology and so on.
# Analytical Model for Angular Distortion in Multilayer Welding under Constraints
## 3 Results and Discussion
### Effect of Bead Size and Initial Thickness on Angular Distortion  
We investigated the multilayer welding characteristics while using the proposed model. Prediction curves of angular distortion with varying initial thickness and bead thickness were obtained. The target thickness was 50 mm, whereas the root gap and root face were set to 4 and 0 mm, respectively. The groove angle was 50\({}^{\circ}\) and the initial angular distortion was set to zero in all cases. Figure 16 shows the corresponding results, where the initial thickness corresponds to the origin of each curve. The bead height is related to the bead size, and it is given by each interval between consecutive marks on the curves. When the initial thickness changes from 4 to 10 mm, the angular distortion was substantially reduced to 61% (bead height: 5 mm) and 45% (bed height: 2 mm), respectively. When the initial thickness was the same, the angular distortion was smaller as the bead height (bead size) decreased, and the angular distortion was notably reduced to 91% (initial height: 10 mm) and 80% (initial height: 4 mm).  
Higher heat input increases the bead size (height) and reduces the final angular distortion due to the increase in initial thickness for a fixed initial angular distortion under the same number of layers. On the other hand, if the initial thickness is the same, reducing the heat input to reduce the bead height should reduce the angular distortion. According to our previous study [10], the angular distortion decreases as the heat input increases during multilayer welding. In this case, the difference in initial thickness has occurred, because the same welding conditions were applied to all passes. In other words, when the initial thickness was twice the bead height (triangular and circular marks in Figure 16),  
Figure 16: Prediction of angular distortion at different bead heights and initial thicknesses.  
Figure 15: Comparison of experimental and predicted values of 28\(t\) X butt welding.  
the final angular distortion decreases by the influence of the initial thickness. Overall, increasing the constraints reduces angular distortion. Alternatively, applying high heat input at welding onset for at least two layers for high initial thickness and then decreasing the heat input for small bead height can also reduce angular distortion during multilayer welding. This corresponds to the case where the initial angular distortion is zero. The initial angular distortion is difficult to predict, but it is desirable to minimize deformation during fitting, setting, and welding to the second layer through increased constraints.
# Research on Pore Pressure Detection While Drilling Based on Mechanical Specific Energy
## 2 Theory
### Normal Trendline  
During drilling, excessive pressure overbalance conditions will increase the resistance of rocks and cuttings to the bit, which may result in a reduction in ROP and increase the MSE [32]. Therefore, the MSE was corrected with the regional normal pore pressure ratio on equivalent circulation density (Equation (33)).  
\[MSE=\left(\frac{WOB}{A_{\mathrm{b}}}+\frac{120\pi\times N_{\mathrm{t}}\times T_ {\mathrm{t}}}{A_{\mathrm{b}}\times ROP}\right)\times\frac{NPP}{ECD} \tag{33}\]  
We plot the MSE with depth on a semi-log. The interval with clean shale and reliable drilling parameters data is selected as the target interval for establishing the normal trendline. For the abnormal points on the curve of the target interval, the values of MSE are smoothed out by moving the average filtering of the five-point bell-shaped function (Equation (34)) [44]. The coefficients \(\beta\), \(\gamma\) and \(\varepsilon\) take the values 0.11, 0.24 and 0.3, respectively.  
\[MSE_{i}=\beta(MSE_{i-2}+MSE_{i+2})+\gamma(MSE_{i-1}+MSE_{i+1})+\varepsilon MSE _{i} \tag{34}\]
## 2 GTSP Solution for UAV Material Drop Paths
### Based on a Hybrid Whale-Swarm Solution
(2) Calculate the difference in path lengths before and after optimization: let \(L_{original}\) be the length of the original path and \(L_{new}\) be the length of the new path after performing the swap. The new path is formed by disconnecting the two selected pairs of vertices and reconnecting \(V_{a}\) to \(V_{b}\) and \(V_{a+1}\) to \(V_{b+1}\). The new path is the length of the original path. (3) Determine whether to accept the new path: if the total length \(L_{new}\) of the new path is less than the total length \(L_{original}\) of the original path, the new path is accepted. The calculation of path length before and after optimization is expressed by the following formula:  
\[L_{\text{original}}\ =\sum_{i=1}^{n-1}d\left(V_{i},V_{i+1}\right)+d\left(V_{n},V_{1}\right) \tag{18}\]  
\[L_{\text{new}}\ =L_{\text{original}}\ -\left(d\left(V_{a},V_{a+1}\right)+d\left(V_{b },V_{b+1}\right)\right)+\left(d\left(V_{a},V_{b}\right)+d\left(V_{a+1},V_{b+1} \right)\right) \tag{19}\]  
where \(d\left(V_{i},V_{j}\right)\) denotes the distance between vertices \(V_{i}\) and \(V_{j}\). Step 6: Dynamically adjust the weights of the IWOA and ABC Algorithm strategies. The adjustment factor is set to \(\lambda=0.1\). The IWOA has a solid global search capability; when \(fit_{best}(X_{i})(t+1)<fit_{best}(X_{i})(t)\) this indicates that the global search is working well, the weight \(w_{IWOA}\) is augmented, and the formula is calculated as follows:  
\[\left\{ \begin{array}{l}w_{ABC}=w_{ABC}-\lambda\times w_{ABC}\\ w_{IWOA}=w_{IWOA}+\lambda\times w_{IWOA}\end{array} \right. \tag{20}\]  
Conversely when \(fit_{best}(X_{i})(t+1)>fit_{best}(X_{i})(t)\), the formula is as follows:  
\[\left\{ \begin{array}{l}w_{ABC}=w_{ABC}+\lambda\times w_{ABC}\\ w_{IWOA}=w_{IWOA}-\lambda\times w_{IWOA}\end{array} \right. \tag{21}\]  
Additionally, ensure that the following equation holds:  
\[\left\{ \begin{array}{l}w_{IWOA}+w_{ABC}=1\\ 0.1\leq w_{IWOA}\leq 0.9\\ 0.1\leq w_{ABC}\leq 0.9\end{array} \right. \tag{22}\]  
Step 7: Return to Step 2 to resume execution if the maximum number of iterations has not been reached. Once the maximum number of iterations is reached, the hybrid algorithm will showcase the ideal method for addressing the GTSP. Through the above process, the proposed Whale-Swarm Hybrid Algorithm can achieve a good balance between the global and local search and improve the performance of solving GTSPs. This hybrid algorithm can be effectively applied to various complex optimization problems, providing an efficient and reliable solution for engineering practice. The solution flowchart is shown in Figure 1. The pseudocode of the Whale-Swarm Hybrid Algorithm is shown in Algorithm 1. Figure 1: Flowchart of Whale-Swarm Hybrid Algorithm.