esa-sceva/satcom-chunk-collection huggingface dataset:
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Instances**}} & \multicolumn{8}{c}{CPLEX} & \multicolumn{8}{c}{**C\&W Savings Heuristic**} & \multicolumn{8}{c}{**VNSred**} & \multicolumn{8}{c}{**VNSfull**} \\ \multicolumn{1}{c}{**Name**} & \(n\
u_{1}\) & \(n\
u_{2}\) & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Gap(\%)**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} \\ \hline C101\_C10 & 1 & 4 & 1 & 4 & **538.31** & 0 & 3021.74 & 1 & 5 & 568.85 & 0.00017 & 1 & 3 & **538.31** & 538.74 & 0.568 & 1 & 4 & **538.31** & **538.31** & 13.233 \\ C104\_C10 & 1 & 3 & 1 & 2 & **484.32** & 0 & 5309.78 & 1 & 4 & 663.74 & 0.00024 & 1 & 2 & **484.32** & **484.32** & 0.979 & 1 & 2 & **484.32** & **484.32** & 7.119 \\ C202\_C10 & 1 & 3 & 1 & 2 & **425.53** & 0 & 152.011 & 1 & 5 & 625.02 & 0.00021 & 1 & 2 & **425.53** & **425.53** & 0.030 & 1 & 2 & **425.53** & **425.53** & 2.018 \\ C205\_C10 & 1 & 3 & 1 & 3 & **415.48** & 0 & 157.97 & 1 & 3 & 435.37 & 0.00024 & 1 & 2 & **415.48** & 419.64 & 0.005 & 1 & 3 & **415.48** & **415.48** & 1.006 \\ R102\_C10 & 1 & 4 & 1 & 3 & **505.50** & 0 & 6150.84 & 1 & 4 & 648.65 & 0.00019 & 1 & 3 & **505.50** & **505.50** & 2.477 & 1 & 3 & **505.50** & 524.59 & 22.556 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Computational results for small-sized instances with 10 customers. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Instances**}} & \multicolumn{8}{c}{**CPLEX**} & \multicolumn{8}{c}{**C\&W Savings Heuristic**} & \multicolumn{8}{c}{**VNSred**} & \multicolumn{8}{c}{**VNSfull**} \\ \multicolumn{1}{c}{**Name**} & \(n\
u_{1}\) & \(n\
u_{2}\) & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Gap(\%)**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**n**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} & \multicolumn{1}{c}{**m**} & \multicolumn{1}{c}{**Dist**} & \multicolumn{1}{c}{**Avg**} & \multicolumn{1}{c}{**\(\overline{t(s)}\)**} \\ \hline C101\_C5 & 1 & 2 & 1 & 2 & **385.49** & 0 & 1.67 & 1 & 3 & 442.19 & 0.00021 & 1 & 2 & **385.49** & **385.49** & 0.989 & 1 & 3 & **385.49** & **385.49** & 12.509 \\ C103\_C5 & 1 & 1 & 1 & 1 & **341.33** & 0 & 0.09 & 1 & 2 & 360.94 & 0.00011 & 1 & 1 &
# _photonics_
## 3 FSO Channel Modeling
### Free-Space Loss  
The free-space loss of the optical signal is due to the optical wave propagation from the transmitter to the receiver and is calculated as [32]:  
\[L_{fsl}=\bigg{(}\frac{\lambda}{4\pi\,d(\theta)}\bigg{)}^{2}, \tag{3}\]  
where \(\lambda\) (m) is the wavelength of the signal and \(d(\theta)\) is the distance between the satellite and the OGS, which depends on the elevation angle of the satellite and can be given by [45]:  
\[d(\theta)=R_{e}\big{(}\sqrt{\bigg{(}\frac{H+R_{e}}{R_{e}}\bigg{)}^{2}\cos^{2 }\theta}-\sin\theta\ \big{)}, \tag{4}\]  
where \(H\) (m) is the satellite attitude above Earth's surface, \(R_{e}\) (m) is the Earth's radius, and \(\theta\) (rad) is the elevation angle.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
## 4 Simulation Results and Analysis
### Comparisons of Discharge Pulse Width
#### 4.2.1 Comparison Results of Aerodynamic Forces and Moments  
The plasma actuator at the trailing edge position has a weak influence on the drag coefficient. As shown in Figure 10, the influences of several different discharge pulse widths on the drag coefficient are compared. On the whole, in all conditions, the plasma actuators have a certain drag reduction effect, but all of them are weak. Among them, the discharge with the 100 ns pulse width has the maximum drag reduction when the angles of attack are 4 and 6\({}^{\circ}\) without large-scale flow separation. When the pulse width is 200 ns, there is the most obvious drag reduction effect at 8 and 10\({}^{\circ}\) angles of attack where flow separation has taken place, which is up to 15.49%.  
The effects of plasma actuators with different pulse widths on the lift coefficient are shown in Figure 11. The flow field under the plasma excitation has the effect of moving the stall point backward and has a certain effect of lift increase. The maximum lift coefficient occurs under the 50 ns pulse width discharge (up to 10.89% at a 14\({}^{\circ}\) angle of attack). From the curve trend, the 500 and 50 ns discharges have similar effects to delay stall, but the effects of the 100 and 200 ns discharges are more obvious. The 100 ns discharge significantly delays the maximum lift coefficient point from 10 to 12\({}^{\circ}\), and the effect of lift increase is close to that of the 50 ns discharge (10.91% increasement at a 14\({}^{\circ}\) angle of attack). The lift coefficient of the airfoil under the 200 ns discharge does not decrease rapidly when the angle of attack reaches 14\({}^{\circ}\), indicating that the stall delay effect is obvious.  
Figure 10: Drag coefficient curve of variable pulse width.  
The effects of plasma actuators with different pulse widths on the lift-drag ratio are shown in Figure 12. Under the attack angles of 4 and 6\({}^{\circ}\) without flow separation, the 100 ns discharge has the best increase of the lift-drag ratio, up to 10.75%. When the flow field has separated at 8 to 14\({}^{\circ}\) angles of attack, the 200 ns discharge has the best increase of lift-drag, up to 21.55%.  
In conclusion, the pulse actuator working at 50 ns pulse width has a better effect on lift, but the 100 and 200 ns pulse widths have better comprehensive control ability.
# Feature Augmentation Based on Pixel-Wise Attention for Rail Defect Detection
## 3 Materials and Methods  
### Dataset  
The rail defect dataset is a series of high resolution (2048 \(\times\) 2000 pixel of 96 dpi) rail surface images with annotations. We collected 9039 images from the 9 km railway test loop built by the National Academy of Railway Sciences Test Center. The images are taken by CMOS line scan cameras with laser light source and preprocessed by an image processor to eliminate specular reflections. Only 400 of the 9039 images contain objects and are used to build the dataset. The objects in the images can be categorized into four main classes: _damage_, _gap_, _dirt_, and _unknown_. _Damage_ class can be further divided into five classes: _general damage_ (defects that cannot be categorized into other classes), _dent_, _crush_, _scratch_, and _slant_. A detailed division of the dataset is listed in Table 1.  
The dataset is annotated by following the YOLO's annotation format with five numbers. The first number is object type and the following four numbers are object coordinates. Object types are recorded as integers starting from zero, while object coordinates are recorded as four float numbers with six significant digits: \(x\), \(y\), \(w\), and \(h\). \(x\) and \(y\) are are normalized center coordinates of the bounding box, while \(w\) and \(h\) are the normalized width and height of the bounding box. The YOLO format can be easily converted to COCO format or PASCA VOC format. Annotated images as examples are illustrated in Figure 1, where blue bounding boxes refer to non-damage features while red boxes refer to damages.  
The dataset contains 148 general damages, 180 dirt, 87 unknown, 51 gaps, 35 dent defects, 94 crush defects, 129 scratch defects, and 43 slant defects. The total number of the annotated objects is 767. These annotations are provided by railway maintenance engineers, but still include some mislabeled and unlabeled patterns. We confirmed 11 errors (including wrong labeled and mislabeled objects) within the 767 annotations after double check. The error ratio is approximately 1.43% and is relatively lower than many other widely used datasets [36]. Therefore, we believe the dataset is acceptable for training and detection. Experiments were conducted concerning both 8 and 4 classes (regarding all defect types as one class).  
\begin{table}
\begin{tabular}{l c} \hline
**4 Class Division** & **8 Class Division** \\ \hline _gap_: gaps left between successive rails on a railway track \\ _dirt_: paint, or mud that covers the surface of the rail \\ _unknown_: unrecognized features \\ \hline \multirow{4}{*}{damage} & _general damage_: displacement of parent metal from the rail surface \\  & _dent_: tear of the lateral planes of the rail surface \\  & _crush_: big/severe wear of the lateral planes of the rail surface \\  & _scratch_: small/ mild wear of the lateral planes of the rail surface \\  & _slant_: tear of the lateral planes of the rail surface \\ \hline \end{tabular}
\end{table}
Table 1: Two divisions of the rail defect dataset.
## 3 Experiment Verification
### Translator Replacement Validation Experiment and Result Analysis  
In order to further assess the performance of the proposed model and gain deeper insights into the impact of the TAU translator module on spatiotemporal prediction, five groups of translator replacement validation experiments were carried out using representative spatiotemporal prediction modules. To achieve this, sample results were visualized, as depicted in Figure 15, and line graphs and box plots of the CSI, FAR, and POD metrics at 30 dBZ were generated, as showcased in Figures 16-18. In these figures, Translator-TAU represents our model, Translator-Swin denotes the model utilizing the Swin module based on Transformers, Translator-VIT corresponds to the model using the vision-transformer-based VIT module, Translator-ConvNext refers to the model employing the modern convolution-based ConvNext module, and Translator-VAN indicates the model utilizing the visual-attention-network-based VAN module. Additionally, Translator-GSTA represents the model utilizing a gated spatiotemporal attention mechanism based on spatial attention and multilayer perceptions. The conclusions are summarized as follows:  
Figure 16: CSI metric line chart and box plot of 10-frame prediction images for translator replacement experiment at 30 dBZ: (**a**) CSI metric line chart of 10-frame prediction images; (**b**) CSI metric box plot of 10-frame prediction images.  
Figure 15: Translator replacement comparison validation experiment model prediction visualization sample.  
A comparative analysis of the visual representations generated by our model and those produced by other module-based models, as illustrated in Figure 15, reveals that our model demonstrates a heightened sensitivity to a diverse array of echo regions. Unlike the authentic images presented in the first row, our model effectively predicts and maintains the integrity of various echo areas, including smaller, discrete echo regions. This capability significantly contributes to the overall clarity of the predicted images. Based on Figures 16-18, our model shows slightly lower performance compared to the other models in the initial frames. However, starting from the sixth frame, both the CSI and POD metrics of our model consistently outperform those of the other models. This suggests that our model maintains strong predictive performance for long time sequence predictions, demonstrating greater stability in long-term forecasting capability.
## 3 Properties and Characteristics of LoRaWAN Technologies
### Adaptive Data Rate (ADR)  
Adaptive Data Rate (ADR) is also an important LoRaWAN approach that maximizes battery life for static end devices and improves overcommunication efficiency. This method is based on real-time channel conditions, and therefore it dynamically modifies three crucial parameters, which are SF, CR, and Transmit Power (TX). ADR becomes essential when channel requirements are dynamic and are faced by variable interference, signal quality, and propagation conditions [20].  
Since ADR regularly checks the integrity of the transmission link, its adaptive nature makes it useful for devices that are mobile in nature and navigate in dynamic settings. The network server can initiate downlink messages that prompt data rate adjustments to correspond to the present conditions. The network server uses metadata such as SNR, Received Signal Strength Indicator (RSSI), timestamp, and signal quality to decide which SF configuration is best for each device. ADR considers key factors such as the link budget of a device to carefully adjust SF configurations according to the signal strength that is available. There is a difference between network-managed ADR and Blind ADR. Network-managed ADR is implemented by a server for fixed positioned end devices, while Blind ADR autonomously implements the mobile end devices, as they already have knowledge of the current link state [18].
## 4 Intelligent Computing Methods: Brief Descriptions and Implementations
### Multivariate Adaptive Regression Spline (MARS)  
In the early 1990s, an adaptive regression technique was introduced, based on spline conception, to build a non-parametric and stepwise regression equation; this model was called Multivariate Adaptive Regression Spline (MARS). This regression model appears to benefit immensely from the high potential of improving, over conventional regression-based-equations, the feeding number of observations between 50 and 1000. Between the principles of the MARS technique, the number of observations to develop a formulation is in the range of 50-1000. In the current study, the available 38 experimental observations are not well-suited in the applicability of the MARS technique, leading to an overfitting process. To eradicate this problem, K-folds (dataset partitioning) are considered within the performance of training and testing stages. In fact, 10 folds are set to fix the regression equation by MARS. Furthermore, the MARS technique has the capability to be a crucial enhancement in dealing with datasets with high dimensionality between 3 and 20. It provides a hierarchical structure using a set of basis functions (BFs), which are selected in a stepwise manner. Basically, the MARS model is capable of simulating a relationship between input variables and target values by an approximation function of the type ([PERSON] [37]):  
\[\Omega(x_{1},x_{2},x_{3},\ldots,x_{\rm O})=\omega_{0}+\big{[}\omega_{1}\cdot BF _{1}(X)+\omega_{2}\cdot BF_{2}(X)+\omega_{3}\cdot BF_{3}(X)+\ldots+\omega_{Q} \cdot BF_{Q}(X)\big{]} \tag{11}\]  
where \(\Omega\), \(\omega_{0}\), O, and \(Q\) are the approximation function, constant coefficient, number of input variables, and number of BFs, respectively. In addition, \(\omega=\big{(}\omega_{1},\omega_{2},\ldots,\omega_{Q}\big{)}\) is the set of weighting coefficients associated with the basis functions and \(X=(x_{1},x_{2},\ldots,x_{O})\) is the vector of input variables.  
The programming computer codes of the MARS technique have been provided in MATLAB2015 software. In this research, the MARS models (i.e., Equations (A7)-(A9) in Appendix B) were developed for the prediction of \(V_{H}^{*}\), \(V_{L}^{*}\), and \(V_{R}^{*}\). The numbers of BFs were set to 6, 7, and 4 for \(V_{H}^{*}\), \(V_{L}^{*}\), and \(V_{R}^{*}\), respectively, depending on the standard deviation of each BF and the total influential number of parameters associated with the BFs.
# Dynamic Divide Grouping Non-Orthogonal Multiple Access in Terrestrial-Satellite Integrated Network
## 7 Conclusions  
To improve the fairness and resource utilization of user access, a NOMA method based on dynamic divide grouping is proposed. By constructing an optimization model of joint user grouping, power control and resource allocation, the interference problem caused by the integration of satellite network and terrestrial network is solved. Aiming at the time-varying channel gain of satellite-terrestrial link, the relationship model between user elevation angle, beam angle and distance is established, and the dynamic user pairing algorithm is proposed to ensure the effective pairing of users. In order to obtain the optimal resource allocation, the power control factor is derived according to the instantaneous channel gain of users. To solve the non-convex problem, the original problem is transformed into a convex problem by variable substitution, and the optimal solution is obtained by dual algorithm. Simulation results show that the proposed method can effectively improve the system throughput and outage probability performance.  
Conceptualization, Y.Y. and H.X.; Data curation, Y.Y. and H.X.; Formal analysis, G.H. and M.L.; Funding acquisition, N.Z.; Investigation, Y.Y. ; Methodology, Y.Y. and H.X.; Project administration, H.X.; Resources, N.Z.; Software, Y.Y.; Validation,Y.Y. and H.X.; Writing--original draft, Y.Y. and H.X.; Writing--review and editing, N.Z., G.H. and M.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by China Postdoctoral Science Foundation under Grant 2020M672412.  
The authors declare no conflicts of interest.
# Multi-Objective Collaborative Control Method for Multi-Axle Distributed Vehicle Assisted Driving
###### Abstract  
For human-machine collaborative driving conditions, a hierarchical chassis multi-objective cooperative control method is proposed in this paper. Firstly, based on the phase plane theory, vehicle dynamics analysis is carried out to complete the definition of vehicle stability region. Secondly, based on the linear time-varying (LTV) system model, a cooperative control strategy combining fuzzy control with model predictive control (MPC) is proposed in the upper layer. In this strategy, the assisted driving weight adjustment coefficient and the stability weight adjustment coefficient are obtained by fuzzy mapping combining human-machine cooperation index and the vehicle stability region, respectively, and the optimization objectives of MPC are designed based on the above coefficients. In the lower layer torque allocation strategy, the stability weight adjustment coefficient is introduced to achieve multi-objective optimization of tire load rate and energy efficiency. For energy efficiency optimization, an optimal energy efficiency point-based tracking method is proposed to avoid nonlinearity caused by the introduction of motor loss models. Simulation analysis results show that the proposed strategy can effectively alleviate human-machine conflicts and improve vehicle handing stability. It also can achieve smaller tire load rate optimization through torque allocation and can reduce energy consumption by approximately 8% compared with the inter-axle torque allocation strategy. This study helps to promote the improvement of the comprehensive performance of assisted driving vehicles in human-machine cooperation, handling stability, and energy-saving torque distribution.  
human-machine collaborative driving; phase plane theory; fuzzy control; model predictive control (MPC); the comprehensive performance
## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Energy Potential Analysis Using Transformer Models
Transformer-based model use for renewable energy forecasting has been investigated recently. Transformers have demonstrated their efficacy in solar irradiance prediction, with a maximum worst-case mean absolute percentage error of 3.45% achieved for forecasts made one day in advance [42]. A hybrid CNN-LSTM-Transformer model was suggested for forecasting solar energy production, and it performed more accurately than previous models [43]. SolarFormer, a multi-scale Transformer model that meets or outperforms cutting-edge models in the field of solar panel identification from aerial data, was introduced by [44]. Expanding the use to wind energy, [45] created a ChatGPT-inspired Transformer-based model that surpassed conventional forecasting models in accuracy. Together, these studies show how Transformer-based models can enhance forecasting and mapping of renewable energy, thereby advancing the field of sustainable energy. Table 2 lists the numerous ways Transformer models are used in energy forecasting, especially in relation to renewable energy sources [46; 47; 48; 49; 50]. Using Transformers can lead to increased integration of renewable energy sources into the grid, better resource management, and more accuracy in estimating energy outputs. These models present a viable method for maximizing renewable energy systems and guaranteeing a steady supply of electricity in an environmentally friendly way. When compared to more conventional models like Autoregressive Integrated Moving Average (ARIMA) models and Long Short-Term Memory (LSTM) networks, Transformer models have shown to be a potent tool for energy prediction. Since each of these models has advantages and disadvantages, they can be used for various energy forecasting jobs. In order to anticipate energy usage, recent research has compared deep learning techniques--specifically, LSTM networks--with conventional time series forecasting techniques like ARIMA. Although ARIMA has proven to be a strong statistical analytic tool, LSTM models have shown to be more accurate in predicting energy use [51]. According to empirical research, LSTM performs better than ARIMA, reducing error rates on average by 84-87% [52]. Strong seasonal patterns in the data, however, may be necessary for these models to function well [53]. Other deep learning architectures, such as RNN, GRU, and Time Series Transformers, have been studied more recently for forecasting home energy usage. Despite possible overfitting problems, results imply that Transformer designs function better with fewer samples. Additionally, a 23% improvement in hourly granularity forecasting has been demonstrated by optimizing a voting ensemble using the Simulated Annealing metaheuristic [54].