esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmentation of River Scenes Based on Water Surface Reflection Mechanism
## 3 Results and Discussion
### Discussion of Patch Size  
The patch size, that is, the size of each detection window in the image, was the basic unit in the feature extraction operation in our algorithm. Theoretically, using a smaller patch size is faster in feature extraction, but the total times of feature calculation will increase, while the larger patch size made each patch contain more pixels, which might include negative samples (non-water pixels) that damaged the judgment of segmentation algorithm. Figure 15 shows the segmentation results using different patch sizes in our algorithm.  
The \(F_{\beta}\) and speed under different patch sizes were shown in Figure 16. With the patch size increased, \(F_{\beta}\) (\(\beta=1.5\), see Equation (20)) reduced and the time cost was lower. After comprehensive consideration of the segmentation performance and time consumed, a 6 \(\times\) 6 patch size is usually adopted in practice.
## 5 Simulation and Numeric Results  
### Simulation Setup and Environment Parameters  
This section demonstrates the performance of the proposed algorithm through large-scale numerical simulation. Unless otherwise stated, the values are set as follows (Table 4).  
UAVs are allocated to serve for \(N\) SDs, which are randomly and evenly distributed over a \(400\times 400\) m\({}^{2}\) area. To simplify the analysis, the acceleration and deceleration process of the UAV is ignored when it leaves and arrives at the hovering point. The depot's coordinate is \(h_{0}=(200\) m, \(200\) m), and the cruising speed of a UAV is \(V_{e}=10\) m/s. Moreover, when the UAV takes off from the depot, it flies at a fixed altitude \(H=20\) m with a constant speed. The main simulation parameters are given in Table 4. Since more than two objectives can be optimized at the same time, NSGAIII and MOEA/D were selected for a comparison algorithm. Each scenario was run 50 times independently, and the average value was statistically analyzed.  
In addition, we used the three criteria described below.  
1. _UAV's trajectory_. This indicator reflects the influence of the number of UAVs on the trajectory length.
2. _UAV's energy efficiency_. It represents the proportion of the energy received by SDs in the total energy consumption of UAVs, which is affected by different network sizes and the number of UAVs.
3. _Average time delay of information collection_. This index reflects the timeliness of gathering information perceived by SDs.
# Propagation Characteristics of a Twisted Cosine-Gaussian Correlated Radially Polarized Beam
###### Abstract  
Recently, partially coherent beams with twist phases have attracted growing interest due to their nontrivial dynamic characteristics. In this work, the propagation characteristics of a twisted cosine-Gaussian correlated radially polarized beam such as the spectral intensity, the spectral degree of coherence, the degree of polarization, the state of polarization, and the spectral change are investigated in detail. Due to the presence of the twisted phase, the beam spot, the degree of coherence, and the state of polarization experience rotation during transmission, but the degree of polarization is not twisted. Meanwhile, although their rotation speeds closely depend on the value of the twist factor, they all undergo a rotation of \(\pi/2\) when they reach the focal plane. Furthermore, the effect of the twist phase on the spectral change is similar to the coherence, which is achieved by modulating the spectral density distribution during transmission. The twist phase opens up a useful guideline for manipulation of novel vector structure beams and enriches potential applications in the field of beam shaping, optical tweezers, optical imaging, and free space optical communications.  
partially coherent; radially polarized; twist phase; state of polarization +
Footnote †: journal: Applied Sciences
## 3 Electromagnetic Performances Comparison and Analysis
### Torque Performance  
The curve of torque production and torque density per volume with different current densities under the rated speed is shown in Figure 10. At the rated current density, the 12/11 design has the highest torque density, which is 38.4 kNm/m\({}^{3}\). The torque density of the 12/7 design is 32.1 kNm/m\({}^{3}\), which is 16% lower than the 12/11 design. However,  
Figure 8: The optimization flowchart using FEA and NSGA-II.  
Figure 9: The Pareto fronts of the GA. (**a**) 12/7; (**b**) 12/8; (**c**) 12/10; (**d**) 12/11.  
it is worth mentioning that although the 12/7 design torque density is the lowest among the four cases, its torque density is still considered high compared to conventional PM machines, which are generally under 20 kNm/m\({}^{3}\).
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 4 The Proposed Framework for Travel Time Prediction  
Accurate travel time prediction in urban environments requires both high-quality input data and robust modeling techniques. The proposed framework integrates advanced spatiotemporal data refinement techniques with a deep learning model to address the challenges of capturing complex traffic dynamics. This section introduces the preprocessing methods, feature categorization strategies, and the model architecture that collectively form the foundation of our predictive framework applied to the NYC TLC dataset.
# Application of Absolute Nodal Coordinate Formulation in Calculation of Space Elevator System
## 3 Numerical Simulation  
In this section, a study of rope axial stress profile of the elevator systems will be conducted, consisting only of a tether and counterweight and without any payload. A comparison of the three tether models(lumped mass tether model (LM), finite element method (FEM) and absolute nodal coordinate (ANCF)) is presented to verify the efficiency and accuracy of the above approaches.
## 3 Multi-Body Dynamics Analysis Model of Disc Brake Coupling in High-Speed Trains
### Finite Element Model of Disc Brake Braking Process  
#### 3.2.1 Assumptions  
To facilitate the multi-physical field coupling simulation of disc brakes, corresponding simplifications were performed in the modeling process, and the braking process of disc brakes was simulated as realistically as possible. The following assumptions were made when the finite element model was established:  
1. The brake disc and brake pads are comprised of isotropic materials.
2. The initial temperature of the brake disc, brake pads, and ambient temperature are both 20 \({}^{\circ}\)C.
3. The phenomenon of friction pair wear and material damage during braking is ignored.
## 2 Materials and Methods
### Datasets
#### 2.1.5 Mapping-XCO2 Dataset  
The global 1\({}^{\circ}\) land mapping XCO2 dataset (Mapping-XCO2) was derived from satellite XCO2 retrievals of GOSAT and OCO-2 spanning the period of April 2009 to December 2020. The dataset adjusted and integrated XCO2 retrievals from GOSAT and OCO-2, and then, utilizing spatiotemporal Kriging method to interpolate XCO2 values in data-sparse regions, resulting in a comprehensive global terrestrial XCO2 dataset [36]. The spatial resolution is 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) and the temporal resolution is 3 days or 1 month. The overall bias of Mapping-XCO2 dataset is 0.01 ppm, and a standard deviation of the difference of 1.22 ppm compared with TCCON observations, indicating high quality and reliability. We collected monthly Mapping-XCO2 from January 2015 to December 2020 and extracted for the China region.  
In general, in our experiment, the target variable is OCO-2 XCO2, while the auxiliary variables comprise fourteen meteorological features from ERA5 dataset (blh, u10, v10, si10, msl, t2m, e, skt, ssr, sp, tco3, tcov, tp, totalx), seven flux features from EGG4 dataset (aco2 gpp, aco2 nee, aco2 rec, fco2 gpp, fco2 nee, fco2 rec, tcch4), and four additional geographic covariates (nighttime light dataset, DEM, latitude and longitude), totaling 25. The tcco2 variable, representing CO\({}_{2}\) column concentrations from the EGG4 dataset, along with ground-based XCO2 observations from the TCCON network and the Mapping-XCO2 dataset, were used to assess the accuracy and uncertainty of the experimentally generated dataset across multiple dimensions.
# Using IoT for Cistern and Water Tank Level Monitoring
## 3 Monitoring of Water Architecture
### Water Tank and Cistern System Description
#### 3.5.3 Flows and Dashboard in Node-RED  
Figure 11 shows the Node-RED programming code used for the dashboard. In this program, all elements are integrated. These include the following:  
* Distance or water level in the water tank.
* Distance or water level in the cistern.
* Flow rate or water pressure coming from the tap water.
* Water quality.
* Submersible water pump.  
Appendix A includes more programming code used in the system.  
Figure 10: Tank sensor node electronic circuit diagram.  
3.5.4. Mathematical Models of the Water Level, Flow, and Quality  
We use the following Formulas (1), (2), and (3) to calculate the water level in the cistern and tank, the water flow rate, and the water quality.  
Ultrasonic sensors measure the distance to the water in the cistern and water tank, which allows the calculation of the water level using the following formula:  
\[h=H-d \tag{1}\]  
where  
* \(h\) is the height of water in the tank,
* \(H\) is the total height of the tank,
* \(d\) is the distance the sensor measures from the top of the tank to the water surface.  
This model provides a direct relationship between the detected distance and the water level, allowing an accurate and continuous conversion of the ultrasonic signal into water volume.  
The flow sensor measures the flow rate (Q) in liters per minute (L/min) by the generated pulse frequency. This flow rate is calculated through  
\[Q=k\cdot f \tag{2}\]  
where  
* \(Q\) is the instantaneous flow rate,
* \(f\) denotes the frequency of the pulses generated by the water flow,
* \(k\) is a sensor constant given in the sensor manual.  
This model directly converts the signal frequency into a volumetric flow rate, which can calculate the amount of water entering and leaving the system over time.  
For the TDS (Total Dissolved Solids) sensor, a standard conversion formula can be used that measures electrical conductivity (C) and converts it to dissolved solids concentration:  
\[TDS=k\cdot C \tag{3}\]  
where  
* _TDS_ is the dissolved solids concentration in parts per million (ppm),
* \(C\) is the conductivity measured in \(\mu\)S/cm,
* \(k\) is a conversion constant.  
We can quantify water quality regarding dissolved solids concentration; thus, we continuously assess the potability.
# Optimum Geometries for Wireless Power Transfer Between an Oblong Coil and a Rectangular Coil
###### Abstract  
The geometry of the coils in a magnetic link and their relative position are crucial for increasing their mutual inductance, which is important for obtaining a higher induced voltage, transferred power, and electrical efficiency. General design guidelines found in the literature point to an increase in mutual inductance by making the coils similar in shape, positioning them as close as possible, and using high-permeability soft-cores to concentrate the flux between them. But these recommendations are often difficult to follow in dynamic inductive wireless power transfer (DIWPT) configurations for vehicular applications. This is mostly due to the necessity of a mechanical clearance between the lane and the vehicle assembly, which creates an \"air gap\". Also, unless tracks are used, the lateral movement of a vehicle over a primary coil potentially causes a variation in the induced voltage, which is not adequate to energize the powertrain. Considering these intrinsic problems of DIWPT applications, we developed a few theorems that might facilitate some optimum designs, in the case where rectangular secondary coils are used over oblong primary coils, for two different design targets: (i) maximum induced voltage on the secondary coil and (ii) better insensitivity to the vehicle lateral misalignment on the inductive lane.  
wireless power transfer; dynamic inductive wireless power transfer; optimum coil geometry design; oblong coils; maximum induced voltage; maximally flat induced voltage +
Footnote †: journal: Applied Sciences  
Article
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 5 Performance Evaluation
### Results and Discussions
In the proposed scheme, it is assumed that only one packet will be transmitted per round because it is an event-driven routing scheme that senses the event, and the nodes located near the event forward their data via multi-hop communication. It is evident that the proposed scheme has the highest number of packets transmitted to the BS when compared with the other three routing schemes. This is owing to the fact that the nodes die early in the other schemes and dissipate large amounts of energy which may cause the node isolation problem in some areas of the network, and the packets cannot reach the final node, which is CH in this study. Additionally, ESRAD shows better performance compared to FML and SPFL, where the latter shows the lowest packet reception at the BS. The SPFL scheme does not take into account the residual energies of the intermediate nodes causing packet loss owing to inefficient network energy consumption. Overall, the proposed scheme performs better owing to the consideration of neighbor node's residual energy and distance to the CH, which improves the load balancing of the whole network and prolongs the average lifetime of each cluster node. Figure 9: Cont.
## 4 Results of the Experimental Studies
### Examining the Impact of Freight Vehicles on Roundabout Capacity  
The aim of the study was to determine whether an increase in the number of trucks significantly affects roundabout capacity. The first stage of the study was examining the roundabout capacity without trucks. In total 500 cars crossed the roundabout on average in 375 iterations of the cellular automaton, applying the most effective traffic organisation--Scenario 4. In the subsequent tests, the percentage of trucks was increased by 1%, which resulted in an increase in the time needed for the constant number of vehicles to cross the roundabout. The distribution of the received results is presented in Figure 17.  
It is visible that the more trucks are involved, the longer the average duration of the experiment, which means that the roundabout capacity is getting smaller. A more accurate analysis has shown that the more trucks there are, the more unstable the traffic becomes. The differences between the maximum and minimum values start to increase.
# Typhoon Track Prediction Based on Deep Learning
## 1 Introduction
[24] used Generative Adversarial Networks (GAN) for typhoon path prediction using satellite cloud images as input, which provides a visual complement to the typhoon path prediction method, but the prediction model constructed by this method converges slowly. However, the convergence speed of the prediction model constructed by this method is slow, and the prediction effect for typhoons with inconspicuous wind eyes is poor. In summary, it is feasible to predict typhoon paths based on deep learning, and it has the advantages of being adaptable and overcoming the interference of data from typhoons with more specific paths on model parameters. Therefore, this paper chooses to build a model based on deep learning to predict typhoon paths. In addition, the timely and accurate prediction of typhoon path has a certain guiding role for the prevention and control of natural disasters in Hainan. At the same time, the study of Hainan as the object of improving typhoon prediction capability, strengthening disaster prevention and mitigation construction, can accumulate some practical experience for the national disaster prevention and mitigation construction, and improve the national corresponding construction capacity. Therefore, the research on this topic has important practical significance. To achieve the goal of predicting the path of typhoons generated in the South China Sea based on deep learning, this paper focuses on three main aspects. * Data Pre-processing Because of the long period of the dataset and complex data sources, there are inevitably problems such as uneven data quality, so the original dataset was first screened and mergedto construct the best path data set of typhoons in the South China Sea. In addition, some of the typhoon path data in the dataset, such as wind speed, central pressure, radius, and other important data, are missing, so it is necessary to fill in such missing meteorological data to lay the foundation for the subsequent improvement of prediction accuracy. * Multi-feature selection  
Typhoon paths are affected by some variables, such as the atmospheric environment, and the performance of the prediction model is highly dependent on the data set input to it. Therefore, in this paper, before inputting the data set into the prediction model, Granger Causality Test will be performed on the data set to select the variables with greater influence on typhoon paths, remove irrelevant noise, and realize data dimensionality reduction. * Construction of prediction model  
After the selection of multiple features, this paper will combine the CNN network, which has a stronger ability to extract special features, and the LSTM network, which has a stronger ability to deal with long time series, to build a formation C-LSTM prediction model to predict the path of typhoons forming with the South China Sea. At the same time, the experimental results are compared with the prediction results of the LSTM network model for experimental analysis.
# End-to-End Light Field Image Compression with Multi-Domain Feature Learning
## 4 Experiments
### Complexity Analysis  
In order to assess the computational complexity of MFLFIC-Net, MFLFIC-Net and the end-to-end method SADN [19] are tested to record their encoding and decoding times. The encoding and decoding times for SADN [19] are 449.441 s and 1107.301 s, respectively, while those for the proposed MFLFIC-Net are 996.422 s and 1482.784 s. Compared with SADN [19], MFLFIC-Net has a higher complexity. The increase in time can be attributed to the computational complexity of the mutual attention mechanism applied in the network, which is acceptable compared to the improvement in compression efficiency. In addition, it can be observed that the increase in encoding time is larger than the increase in decoding time. The reason is that, compared with the decoding time, the increase in the encoding time is more reliant on exploring the large disparity angle information contained in the EPI domain.
## 3 The Digital Twin in Cycling
### Design of the Digital Twin in Cycling  
Indeed, the proposed digital twin in cycling consists of the following three components (Figure 2):  
* network connection;
* a feedback system;
* predicting model.  
Each athlete in the implementation of a sport training session follows the demands of a training plan that determines a framework for a digital twin model.  
The training plan can be transferred to the mobile device using a network connection through a wireless Internet, such as WiMAX, 4G, 5G, or Wi-Fi. Although many formats exist for data transfer, a special DSL EasyTrain is applied in [26] in order to enable real, as well as artificial sport trainers (such as AST [27]) to transfer training plans in an easy to use and universal way. The detailed description of the DSL is omitted due to the paper length limitation.  
A feedback system has a closed-loop characteristic [28]. This means that the output affects the control on the one hand and induces sequence of cause-and-effects that exist between the input and output variables, on the other. Data (the input for the model) are obtained from the IoT sensors, with which a cycle or an athlete is equipped. The model transforms raw data into training load indicators, on which basis new predictions are made. The results of the prediction are presented on the display of the mobile device together with the sensed data, and both are subjects of the athlete's final decision making.  
Many modern ML methods could be used for predicting the digital twin's decision. However, the selection of the proper one relies on the speed of the digital twin's response. Obviously, the speed is connected strongly to the limited hardware on mobile devices.
## 3 Laboratory Experiment and Measurement
### Sensor Experiment
#### 3.1.4 Comparison among Some Commercial Sensors and the Sensor in This Work  
The NDIR sensors in intelligent agriculture own some advantages compared with other kinds of sensors, such as semiconductor and electrochemical ones. As for CO\({}_{2}\) sensors, some commercial NDIR sensors are designed and implemented by Senseair and ELT Sensor Companies. Compared with typical commercial sensors, the developed sensor has more satisfying precision and targets special application in solar greenhouses in the north of China in CO\({}_{2}\) fertilization. Firstly, commercial sensors, such as K30 (Senseair Company) and S300 (ELT Sensor Company) [26], process the received data depending on the software algorithm to realize its miniaturization. The stability (3% of reading \(\pm\)50 ppm) and LoD (nearly 50 ppm measured, even the measurement range in the datasheet is 0-5000 ppm), are insufficient compared with our fabricated NDIR sensor. Secondly, in our design, a two-channel pyroelectric detector was used to replace the commercial single-channel detector. After differential operation, environmental noises were suppressed, generating a high SNR and good long-term stability. Thirdly, both temperature and humidity sensors were integrated in the fabricated sensor node. The temperature of the monitored optical part was used to decrease temperature drift. Finally, in consideration of CO\({}_{2}\) concentration control, a related higher sampling frequency can improve the control precision. However, the slow response time caused by difficult diffusion and slow modulation frequency of typical commercial detectors significantly limits the control precision.  
However, even the fabricated first-prototype sensor has a satisfying performance in the application in greenhouse with CO\({}_{2}\) fertilization; a further miniaturization of sensor node size is expected that the current node size is 14 cm \(\times\) 15 cm \(\times\) 12 cm. Under 4 Hz modulation mode, the max average power consumption is nearly up to 0.98 W which is not acceptable to a self-powered sensor node. And the main power consumption comes from the light source driving. However, environmental information sampling ratio is adjustable according to the terminal commands for reasonable energy distribution. For example, at night, without CO\({}_{2}\) fertilization, the sampling ratio could be set to a very low speed to save the power instead of the fixed sampling ration of typical commercial sensors. It ensured that a large capacity lithium battery is feasible to be used as a power supply.