esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning-Powered GRU Model for Flight Ticket Fare Forecasting
## 2 Related Work  
With advancements in machine learning, price forecasting techniques have made significant strides in diverse industries [28], such as agriculture, stock, bitcoin, and oil [29; 30; 31]. Recent studies on flight fare prediction have utilized mainly machine learning approaches. A study discussing the challenge of air ticket fare prediction shows that its variability and dependence on various factors lead to revenue losses and customer dissatisfaction [32]. This research proposed an ensemble model using neural networks, XGBoost regressor, and light gradient boosting machine regression to estimate flight ticket fares with the least mean absolute error. The dataset contained flight ticket data from March to June 2019 for various airlines. Airlines can plan their strategies of operations and allocate necessary resources according to predicted ticket prices so as to impact specific market segments. Another study compares the prediction performances of flight ticket prices based on eight regression models, including multilayer perceptrons, generalized regression neural networks, extreme learning machines, random forest regression trees, and so on [33]. This study aimed to identify the factors that impact airfare, such as departure and arrival times, the amount of free luggage allowed, the days before departure, and so on. The models were trained on a dataset containing 1814 flights for a single international route, and the results indicated that the model \"bagging regression tree\" achieved the highest accuracy, followed by the random forest model. Additionally, in another study, random forest and multilayer perceptron models were trained individually and combined with assigned weights to form a stackedprediction model [34], which was shown to outperform the individual models. The data used to train the models consisted of 51,000 records for a 7-day round trip nonstop flight with three domestic airlines. Twelve features were extracted from the data, including the airline, flight number, date of purchase, departure date and time, and so on. The stacked prediction model outperformed the random forest and multilayer perceptron models with 4.4% and 7.7% improvement, respectively, based on the R\({}^{2}\) metric.  
Furthermore, a new model was proposed to predict the minimum available price for a particular itinerary, i.e., a specific flight on a given route for a specific departure date [26]. To achieve this, a modified ensemble-based learning algorithm was proposed to learn from past patterns of price changes and make forecasts for future prices. This study utilized a recursive approach to predict future prices sequentially. The model used various features, including prices of the same itinerary, recent itineraries, and itineraries for the same day of the week or month. The experimental results demonstrated that the model performed best on diverse routes, where different flights have independent pricing behaviors. A comparison of the algorithms showed that the proposed model based on ensemble learning was superior to KNN. Table 1 shows the summary of related works.
## 5 Co-Located Atmospheric Disturbances  
In this section, we examine the evolution of the meteorological parameters, namely the humidity, latent heat flux, and aerosol load, over the same area surrounding the epicenter as sampled for the Swarm satellite passages. We extracted climatological data for January 2024 from the MERRA-2 reanalysis [59]. The 3-h and daily values of the selected parameters are available via a web-based system, NASA Geospatial Interactive Online Visualization and Analysis Infrastructure (GIOVANNI), developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC), with a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.625\({}^{\circ}\).  
For the local tropospheric parameters, the situation with the reference point is not as clear as that for the ionospheric _Ne_ (for which the background is removed due to subtraction of the data collected by the paired satellites). While the approach is not strict, we define the meteorological anomaly as a deviation from the level established 10 days before and 10 days after the EQ under consideration. Another possibility is to construct the long-term composite for a particular day. According to climatological practice, the 30-year  
Figure 10: Stack plot of \(\Delta\)Ne (in 10\({}^{4}\) cm\({}^{-3}\)) for four consecutive passages (descending orbit) closest to the passage along the 81\({}^{\circ}\)E meridian (00 UT, 22 January) from the west and east sides.  
period should be used. However, the natural interannual variability of the meteorological parameters does not allow for the construction of a reliable long-term composite with a small enough standard deviation. Thus, the use of the 20-day window centered on the EQ day was considered a preferable method to establish the reference point.
# Edge-Event-Triggered Synchronization for Multi-Agent Systems with Nonlinear Controller Outputs
## 6 Conclusions  
This paper investigates the synchronization problem of nonlinear multi-agent systems with controller output nonlinearities based on edge-event- and edge-self-triggered policies. The combined edge state is utilized by all controllers, and it is affected by the controllers' inherent nonlinear characteristics and the quantization transmission of communication networks. The event-triggered strategy ensures the intermittent update of all controllers instead of continuous execution, while the self-triggered strategy further prevents sensors from continuously capturing information, thereby saving sensor resources. Not only are the proposed policies effective, but also the trade-off of different policies for different system resources is effective. Our future work on multi-agent systems will involve the performance design, network security problem, and switching topologies.  
Conceptualization, M.-Z.D. and C.Z.; methodology, J.L.; software, C.Z.; validation, J.L., J.W. and C.Z.; formal analysis, J.L. and M.-Z.D.; investigation, J.L. and M.-Z.D.; resources, J.W.; data curation, C.Z.; writing--original draft preparation, J.L.; writing--review and editing, J.L., M.-Z.D. and J.W.; visualization, C.Z.; supervision, J.W.; project administration, M.-Z.D.; funding acquisition, M.-Z.D. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Agents** & \(\mathbf{v_{1}}\) & \(\mathbf{v_{2}}\) & \(\mathbf{v_{3}}\) & \(\mathbf{v_{4}}\) & **Total Numbers** & **Average Time Intervals** \\ \hline The edge-event-triggered policy & 35 & 35 & 23 & 20 & 113 & 0.354 \\ The edge-self-triggered policy & 71 & 97 & 145 & 48 & 361 & 0.111 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the triggering numbers of each agent in the time interval \(t\in[0,10]\).  
Figure 4: Simulation results of edge-self-triggered control. (**a**) State trajectories of all individuals. (**b**) Update times of all controllers. (**c**) Update values of all controllers.  
Figure 3: Simulation results of edge-event-triggered control. (**a**) State trajectories of all individuals. (**b**) Update times of all controllers. (**c**) Update values of all controllers.  
**Funding:** This research received no external funding.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 4 Cooperative Relative Positioning
### GNSS-Based Relative Localization  
With the introduction of a comprehensive and standardized communication technology for ITS, the idea of performing cooperative positioning in vehicular ad hoc networks becomes a reality. Unlike the previous techniques, GNSS-based relative localization relies on the information sent over this communication link. In V2V communication, CAMs and BSMs are beacon messages that include the information about the vehicle's absolute position in Earth coordinates, its speed and heading along with their associated uncertainty. ETSI's standard for CAM messages does not specify the technique used for computing this information in each vehicle [2]. Most certainly, GNSS will be used as the primary source for position information. Therefore, in this section, we review the different techniques related to GNSS to localize a vehicle. Moreover, we will differentiate between absolute positioning, where a vehicle aims at localizing itself on the Earth, and relative positioning, where the aim is to know the relative position between two vehicles.
## 5 IRSA for Solving Classification Problems
### Statistical Indicators for Regression  
In order to compare the predictive capabilities of the selected models, we used several statistical measures, such as root mean square error (_RMSE_), relative error (_RE_), and the coefficient of determination (\(R^{2}\)) [38]. These indicators are described by the following equations:  
\[RMSE=\sqrt{\frac{\sum_{j=1}^{n}\left(y_{j}-P_{j}\right)^{2}}{n}} \tag{32}\]  
\[RE=\sum_{j=1}^{n}\left|\frac{y_{j}-P_{j}}{y_{j}}\right| \tag{33}\]  
\[R^{2}=1-\frac{\sum_{j=1}^{n}\left(y_{j}-P_{j}\right)^{2}}{\sum_{j=1}^{n}\left( y_{j}-\overline{y_{j}}\right)^{2}} \tag{34}\]  
where \(y_{j}\) and \(p_{j}\) are the true and predicted values, respectively. \(\overline{y_{j}}\) is the mean of the value, and n represents the total number of data samples.  
The small values of _RMSE_ and _RE_ and the higher values of \(R^{2}\) give clear indications of the improved accuracy of the proposed model. These values are represented in Table 11. According to this table, the IRSA-RBFNN provides the highest values of \(R^{2}\) and lowest values of RMSE for both wind and solar prediction, proving that the performance of IRSA is best when compared to other prediction models. The PSO-RBFNN model provides a slightly lower prediction efficiency. BMO-RBFN and RSA-RBFN are ranked third and fourth in terms of prediction performance [62; 63].  
Figure 11: Solar Power Prediction.
## 4 Discussion
### Learning Experience: Motivation, Satisfaction, Engagement and Control over the Learning Process  
As expected, both groups reported an overall positive learning experience. In line with previous findings, this study highlights the positive impact of the VRER on learners' engagement [24; 31; 34; 44], motivation [27; 28; 29; 30; 32; 33] and satisfaction [29; 44], but also on learners' control over the learning process. When it comes to comparing the two groups, pairs exhibited higher levels in all aspects of the learning experience; however, only results on engagement showed a statistically significant difference. Therefore, we can conclude that learners who worked collaboratively felt more engaged and active in the learning process. Results are further enhanced by the qualitative data, indicating that learners who worked collaboratively reported on the experience with more enthusiasm, highlighting the positive outcomes of working in a pair. Even though there were also a few participants stating that they preferred working alone, their quantitative results on learning performance and experience were not better than those of learners working in pairs, suggesting, perhaps, that the benefits of collaboration in this context outweigh individual preferences and may provide a more effective learning environment overall. In addition to that, the mode of collaboration could potentially impact the learning experience, as quantitative data showed that pairs working remotely exhibited slightly higher motivation compared to pairs sharing the same computer, but their satisfaction, engagement and control levels were lower.  
Although the sample size of the current research is too small to be able to draw definitive conclusions, the results indicate a clear trend: learners who work collaboratively in an EFL VR escape room experience tend to exhibit better learning outcomes and higher levels of engagement, motivation and satisfaction, as well as experience a positive effect on their perceived cognitive benefits, perceived learning outcomes and a greater sense of control over the learning process.
# Light-Controlled Microbots in Biomedical Application: A Review
## 2 Prospective Applications  
Due to material selection choices and control flexibilities, the use of focused light beams to control microbots is gaining traction. There are multiple factors to consider to get the most out of this technology, such as the size and shape of the microbots, the maneuverability method, the functionalities of the microbot, the selection of materials based on the purpose of use, etc. The microbot's tailoring and maneuverability are both controlled by concentrated high-powered light beams. The wavelength of the projected beam should be chosen on the basis of the intended usage. Near-infrared light (NIR) is utilized for trapping, which is undetectable to the naked eye, and the wavelength is usually between 1060 nm and 1090 nm. NIR is used because it can provide relatively stable microparticle entrapment while also decreasing photodamage to the trapped particles. Optical control allows for long-range, fast, durable, precise, and dextrous microbot actuation. Microorganisms respond to environmental cues to display impressive autonomous behaviors such as motility and taxis. As a result, microbots should be able to implement a minimum level of intelligence. Active particles have fundamental collective and autonomous behaviors, but they lack the behavioral complexity of microbes. Studies also presented data about the autonomous light actuation of microbots, although the biological application they are still in the preliminary stage of development.
## 2 Principle
It is shown that our system could complete high-quality frequency conversion without any spurs because the mixing spurs with the frequency of \(f_{1}\) (\(\geq\)20.2 GHz for upconversion, \(\geq\)22.8 GHz for downconversion), \(f_{2}\) (\(\geq\)21 GHz), \(f_{1}+f_{2}\), \(2f_{1}\), \(2f_{2}\), \(2f_{2}-f_{1}\), and \(2f_{1}-f_{2}\) are outside the system frequency range of 0.8-18 GHz.
## 4 Performance Evaluation and Analysis
### Performance Analysis of GreenRP
Adjustments to the weight coefficients directly determine the algorithm's focus, with an increase in a particular metric's weight leading to prioritized improvements in that area, potentially at the expense of other metrics. Hence, the GreenRP algorithm can meet specific performance demands by adjusting the weight ratios. From the perspective of network lifetime (Figure 8b), GreenRP shows significant improvements across all weight configurations compared to SP, indicating that extending network lifetime by putting satellite payloads to sleep is a relatively achievable goal, consistently attainable under various weight configurations. In the packet loss analysis (Figure 9a), we observe that increasing the weight on network lifetime significantly raises the packet loss rate compared to other weight distribution schemes. This is due to the reduction in network capacity from more payloads being put to sleep, consequently increasing packet loss rates. However, increasing the weight on average service delay slightly raises the packet loss rate, reflecting the network's congestion state where shorter paths facilitate successful transmissions, and longer paths tend to drop data. Nevertheless, the packet loss rate remains close to SP's baseline in most cases, demonstrating that optimizing network topology and payload sleeping strategies effectively supports current service demands while extending network life. When the weight corresponding to packet loss increases, packet loss can be lower than SP, suggesting that trimming the topology might better adapt to traffic demands, reducing packet loss compared to earlier configurations. Finally, regarding average service delay (Figure 9b), increasing the delay weight coefficient significantly reduces delays. However, increasing weights for packet loss and network lifetime results in higher delays, indicating that reducing service delays under a topology planning scheme requires careful consideration of weight ratios. Collectively, these analyses necessitate a comprehensive consideration of the weight configurations across various performance metrics to achieve an energy-saving topology plan best suited for current network operations. Figure 9: (**a**) Packet loss rate metrics in relation to weight coefficients; (**b**) average service delay metrics in relation to weight coefficients.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 5 Conclusions  
Currently, most of the solutions presented and developed to solve the routing and lifetime problems of wireless sensor networks are based on clustering. However, these techniques are not always optimized according to the topology and architecture of the network as well as its evolution and dynamics. The hypothesis that clustering is the best approach for extending the lifetime of WSNs is not always correct. In this paper, we demonstrated that for a 100 \(\times\) 100 m topology with 100 nodes and a BS located at the center (50, 50), it is more effective for nodes to send their data individually. However, for a topology with the same characteristics but with the BS located at (100, 100), our findings suggest that the area should be divided into three zones for optimal performance. In the first zone, where nodes are within 83 m of the BS, nodes should send data individually. For nodes located between 83 and 123 m from the BS, nodes should collaborate in pairs. Finally, in the third zone, clusters of three nodes are more efficient where they are beyond 123 m from the BS. These findings demonstrate that clustering is not always the best method for enhancing WSN lifetime. Also, the assumption that dense clusters are always better should be applied considering the distance to the BS.  
As future work, we aim to complete this study for the Internet of Things, where thousands of nodes are distributed in a large area.  
Conceptualisation, K.K. and D.D.; methodology, D.D.; software, A.C.; validation, I.B.O. formal analysis, K.K.; investigation, D.D. resources, K.K.; data curation, K.K.; writing--original draft preparation, D.D.; writing--review and editing, A.C.; visualization, D.D.; supervision, A.C.; funding acquisition, I.B.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 Concept Development
### Data Collection Method  
With the apparatus fabricated, the tensile load application method was designed. Earlier, it was stated that the tensile force would be developed by tightening a nut on the threaded rod that would react with the Looney Bin plate wall. A load cell would then need to be constructed to measure the load applied from tightening the nut and connecting the ball joint to the threaded rod. This was facilitated by constructing a load cell using  
Figure 3: (a) Fabricated plinths and (b) plinths bolted to the Looney Bin walls. The plinths are used as supports for the cube specimen.  
a standard 50-mm-long coupling nut with strain gauges attached to measure the tensile force. Two strain gauges were attached on diametrically opposite sides of the coupling nuts along their centerline to provide an average strain occurring at the coupling nut. The strain gauges and their exposed wires were covered with RTV silicone to help protect them from accidental contact. The strain gauge wires were zip-tied to the coupling nut approximately 13 mm from the strain gauge for strain relief.  
The strain gauge readings were collected using National Instruments(tm) (NI) data collection equipment coupled with the program, LabVIEW(tm). The strain gauge output was measured by attaching the wires to an NI 9236 quarter-bridge, 350 Ohm strain gauge module. The module was then inserted into an NI CompactDAQ eight-slot USB chassis, which was then directly connected to a computer using a USB cable. Since the Looney Bin was designed to be a closed system, a hole was required through a plate wall to allow the wires to be fed out of the apparatus for data collection.  
The strain gauge readings were calibrated to display load using a universal testing machine. Threaded rods were attached to loading platforms using standard nuts, with the coupling nut used to attach the top and bottom threaded rods. The standard nut was then tightened to at least five loads, and the corresponding strains from the coupling nut were recorded. A linear calibration was then fitted to the strains, and the slope of that line was used as a multiplier for the strain data to convert it to load. This process was then replicated to check the calibration factor and verify accuracy.  
The coupling nut-load cell was attached to the ball joint and threaded rod. Each fixture was only threaded 13 mm into the coupling nut load cell to ensure the threaded regions did not overlap with the attached strain gauges. A smaller nut on each of the fixtures was used as a guide to ensure the appropriate amount of thread was inserted into each coupling nut load cell. The guide nut was located 13 mm from the end of the respective fixtures, and then the fixture was threaded into the coupling nut load cell until it reached the nut. Once both fixtures were attached, the guide nuts were tightened on the coupling nut load cell (applying slight compression) to ensure the threaded rod and ball joint did not come loose during testing. Once the coupling nut load cell was calibrated, the Looney Bin fabrication was complete, and trial runs could commence.
# Optimized Polar Codes as Forward Error Correction Coding for Digital Video Broadcasting Systems
## 5 Simulation Results  
In this section, simulation results are implemented using A Fast Forward Error Correction Toolbox (AFF3 CT) simulator and a library dedicated to channel coding [42]. During the simulations, all the error performance, throughput and latency measurements have been obtained on a single core of an Intel i7-4510U Central Processing Unit (CPU), which is based on the Haswell architecture and is manufactured in 22 nm with a base clock frequency of 2 GHz and a maximum turbo frequency of 2.6 GHz. The description has been compiled on Windows with the C++ Clang compiler for AFF3 CT 2.3.5 version.  
The number of simulated frames is up to 250,000 frames with typically 100 frame errors to stop the simulation, the transmitted bits are modulated using Binary Phase Shift Keying (BPSK). The channel model is AWGN channel, with zero mean and variance \(N_{0}/2\). The simulations results have been done for different code rates \(R\).
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
###### Abstract
Besides, the power system control center should distribute more power load to the generation with low emission during dispatch periods when \(L_{\text{AQI}}\) is high, yet this has been addressed by few studies. In this paper, the impacts of F-H on photovoltaic output and power load are first analyzed, and the wavelet neural network (WNN) model is adopted to predict photovoltaic output and load under F-H, based on the idea of similar days of F-H. On that basis, the power system multi-objective optimal dispatch model containing VPPs under F-H is employed to achieve the goals of energy conservation and emission reduction. The main contents in this paper comprise the following six parts: F-H impacts on photovoltaic output and load forecasts (Section 2); the dispatch of power systems containing VPPs (Section 3); the mathematical model of dispatch under F-H weather (Section 4); solving the optimal dispatch model based on mixed integer linear programming (MILP) (Section 5); case study (Section 6); conclusions (Section 7).