esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Deep Learning-Based Models for Real-Time Super-Resolution  
In recent years, we have seen swift progress in the field of real-time SR thanks to enhancements in computational power and the emergence of deep learning. The purpose of deep learning is to discern the characteristic distribution of data by learning a layered representation of fundamental features. In more detail, deep learning progressively fine-tunes the SR algorithm methodology via an array of learning tactics. These include aspects like deep network architecture, optimization tools, and the design of loss functions. Concurrently, it also addresses the inherent challenges associated with SR. In the last years, many deep learning-based real-time SR models were introduced and made significant progress. In this section, we will in detail observe the most famous deep learning models such as CNNs, recurrent neural networks (RNNs), generative adversarial networks (GANs), and sparse representation models.
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 3 Damage Detection in Wind Turbine Blades Based on the CBNLM-BLS  
In order to solve the problems of low operational efficiency of NLMs and the complex structure of deep neural networks [27], an improved model based on CBNLMs to accelerate the BLS was proposed in this paper. It was applied to the field of damage detection in wind turbine blades. To demonstrate the effectiveness of the proposed network for wind turbine blade damage detection and the simplicity of the model, this chapter compared it with existing mainstream methods in terms of classification capability as well as model structure.
## 5 Evaluation Results
### Results of Usability Analysis
Additionally, we have taken into consideration the availability of open learning resources on the Internet. Through the analysis, we have discovered that Google Cloud run service has the most detailed and user-friendly official documentation, along with several third-party resources readily available online. AWS App Runner service, on the other hand, has documentation that may be challenging for beginners to follow due to the abundance of information on the cloud console dashboard. However, there are still resources available online for application deployment on the service. Azure has a more straightforward cloud console and clear documentation compared to AWS, but, due to its recent launch, there are fewer online resources available as compared to the other two services. It should be noted that these ratings were solely based on the authors' experience during the study with the three cloud services. They may not necessarily reflect the objective views or opinions of others. As they are subjective, the evaluation results may be not very significant and representative. Scale to zero is another factor that is considered for the usability analysis of the services. This feature allows a service to shut down the container if it does not receive any requests for a specified amount of time. This is particularly beneficial in terms of cost savings when the end user is not utilizing the application. While this capability is only available on GCP and Azure, it proves to be useful when scaling based on requests instead of CPU and memory usage. Container Apps support scaling to zero when the scaling setting is not based on CPU utilization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ease of Use 1** & **Google Cloud Run** & **AWS App Runner** & \begin{tabular}{c} **Microsoft Azure** \\ **Container Apps** \\ \end{tabular} \\ \hline Documentation & Excellent 4.5/5 & Good 3/5 & Good 3.5/5 \\ \begin{tabular}{c} The learning curve for \\ a beginner \\ \end{tabular} & \begin{tabular}{c} The availability of a lot of \\ getting-started templates \\ makes learning faster. \\ \end{tabular} & \begin{tabular}{c} Pretty steep. Too much \\ information to grasp. \\ \end{tabular} & \begin{tabular}{c} Gradual learning curve. \\ External resources were less \\ as it is relatively new. \\ \end{tabular} \\ \
\begin{tabular}{c} Scale to zero \\ \end{tabular} &
\begin{tabular}{c} Yes 2 \\ \end{tabular} & No & Yes 2 \\ \hline \hline \end{tabular}  
* The ratings of the documentation and learning curve presented were solely based on the authors’ experience during the study with the three cloud services. They may not necessarily reflect the objective views or opinions of others. 2 Applications that scale on CPU or memory load cannot scale to zero.  
\end{table} 
Table 3: Comparison results on ease-of-use usability.
## 3 Simulation and Analysis  
Design and optimization for the proposed dual-band LP-to-CP converter was carried out using standard electromagnetic software, High Frequency Structure Simulator (HFSS). HFSS is based on the finite element mesh (FEM) solver. The design was simulated using master-slave boundary conditions, and Floquet ports at the input and output of the unit cell were applied to realize the periodic array structure. In order to validate the performance of the proposed structure, an optimized unit cell was re-simulated in Computer Simulation Technology (CST) software (2015 Version, Dassault Systemes SE, Velizy-Villacoublay, France). The finite difference time domain (FDTD) solver was selected for CST. Scanning time was set to 200 ns to get accurate results. Close agreement between FEM results and FDTD results validate the performance of the proposed structure. Total transmission in the X direction can be computed by \(\mathrm{T_{all}}=\left|\mathrm{t_{yX}}\right|^{2}+\left|\mathrm{t_{ox}}\right|^ {2}\)[46], and for transmission in the Y direction by \(\mathrm{T_{all}}=\left|\mathrm{t_{yy}}\right|^{2}+\left|\mathrm{t_{xy}}\right| ^{2}\)[46]. Figure 3a,b depict transmission characteristics for the incident X polarized and Y polarized wave travelling in the -Z direction in CST and HFSS. It is pertinent to mention here that transmission spectra with incident X and Y polarizations are not exactly equal due to the absence  
Figure 1: Schematic of the design: (**a**) Two-dimensional (2D) periodic array structure; (**b**) top view; (**c**) 3D perspective view.  
Figure 2: Scheme for dual-wide-band LP-to-CP converter with outgoing Left Handed Circular Polarization (LHCP) and Right Handed Circular Polarization (RHCP) waves.  
of symmetry in X and Y planes. Figure 4 shows the phase difference in degrees with incident X polarization. Further discussion on Figures 3 and 4 is carried out in Section 4.
## 2 Materials and Methods
### Multi-Temporal SPOT5 Pan-Sharpened Surface Reflectance
#### 2.1.1 Ortho-Rectification  
A geographic baseline image was created from SPOT5 panchromatic imagery captured in 2005. It was registered to a state-wide dataset of ground control points using a digital elevation model (DEM) and a satellite orbital model. In eastern NSW the DEM was based on topographic mapping data, and was supplied by Land and Property Information, the state government's surveying agency. In western NSW the Shuttle Radar Topography Mission (SRTM) DEM was used. The baseline image was rectified to the ground control points with a root mean square error (RMSE) of 5 m. All other images were rectified to the baseline with an average RMSE of less than 3.75 m.
# A Novel QoS Provisioning Algorithm for Optimal Multicast Routing in WMNs
## 1 Introduction
The main contributions made in this paper are summarized as follows:  
* We present a model for the MCOMR problem from the approximate perspective. This model allows multiple QoS metrics to be considered in WMNs with guaranteed multicast service performance. * We formulate the problem of the MCOMR problem and develop a novel multicast heuristic approximation (NMHA) algorithm based on the technique of auxiliary graph construction, scaling, rounding and the MPH algorithm to solve the problem. * We analyze the theoretical properties of the proposed algorithm. Analytical results show that our algorithm is effective and achieves lower complexity and the approximate optimal solution for WMNs. * We conduct experiments to evaluate the performance of the proposed algorithm and compare the algorithm against variations of current best known algorithms. Obtained numerical results indicate that the proposed algorithm is efficient and accurate for multicast service in WMNs. The rest of this paper is organized as follows: Section 2 formally formulates the problem to be studied, along with notations, and describes the previous algorithms that will be used in later sections. Section 3 presents the approximation algorithm for the optimal multicast routing with multiple QoS constraints and its theoretical analysis. Simulations obtained from special network are reported to verify the algorithm in Section 4. Finally, Section 5 summarizes this paper.
# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 3 System Model
### Task Execution Model
#### 3.3.1 Platoon Execution  
The application can be initiated by any member in the platoon. The platoon execution includes local execution and platoon members execution. Every member in the platoon has the opportunity to participate in the execution of the tasks. If the task \(k\) is offloaded to \(v_{2}\), the computing time of the task \(k\) is expressed by  
\[t_{k}^{v_{2}}=\frac{\omega_{k}}{f_{v_{2}}} \tag{5}\]  
and the cost of offloading the task \(k\) is  
\[b_{k}^{v_{2}}=\alpha_{v}f_{v_{2}} \tag{6}\]  
where \(f_{v_{2}}\) is the computation resource that the vehicle \(v_{2}\) can provide and \(\alpha_{v}\) is the computation resource cost of each unit provided by the platoon members. Suppose that the unit price of resources provided by each member is the same, \(f_{v_{2}}\) is constant during the task execution.
### UAVs: Roots and Advancements
#### 1.2.3 Advanced Technologies and UAVs Advancements  
UAVs range vastly in size, design, and capabilities but generally contain advanced sensors, navigation systems, remote communication links, and autopilot technology, enabling increasingly autonomous flight- and data-capture results [31]. While initially constrained by high costs, short flight times, and limited payloads, UAVs have benefited immensely from numerous technological advancements, such as improved battery technology, lightweight composite materials, satellite navigation integration, and computing power supporting real-time data processing [32]. The expanding availability of affordable consumer-grade UAVs has further fueled applications by providing customizable airborne platforms for environmental sensing.  
A key innovation spurring drone adoption has been the development of flexible, lightweight battery technology that can store the electrical charge needed to power rotors, sensors, and navigation systems while minimizing the overall aircraft weight [33]. Moreover, lithium polymer batteries allow for necessary power storage versus payload ratios for 30 min or more of sustained flight times for many UAV platforms [34]. Charging technologies have also improved to enable rapid recharging between missions [35]. Advanced composite materials, such as carbon fiber, also provide high strength-to-weight ratios for drone airframes and components. The hardening of microprocessors and sensors has further enabled drones to operate in harsh environmental conditions related to factors, such as temperature, humidity, and vibration [2]. Satellite-based navigation integration has been pivotal to developing UAVs, facilitating automated flight control and precise positioning capabilities [36]. Global positioning systems (GPS) and inertial measurement units (IMUs) allow drones to stabilize, calculate altitude, and follow pre-programmed routes or dynamic flight paths [37]. Onboard processing capacity leveraging graphics processing units (GPUs) supports the analysis of incoming sensor data as well as computer vision techniques, such as simultaneous localization and mapping (SLAM) for situational awareness during flight. Advanced onboard artificial intelligence can even optimize missions based on the variables encountered mid-flight or integrate the findings from previous flights [38]. Control and feedback are provided to users via radio links over a few kilometers, with some systems enabling a first-person-view (FPV) flight through a real-time video transmission [39].
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model
#### 3.3.8 Parameter Configuration for the TD3 Deep Reinforcement Learning Strategy Model
Total Reward Function: The objective of the optimization process is to maximize the total reward function, denoted as \(\left(R_{\text{total}}\right)\), which is composed of the turning angle cost, the velocity decay mechanism (implicitly through its effect on travel time), and the path length reward. \[R_{\text{total}}=-\alpha\cdot\text{C}_{\text{angle}}+\beta\cdot\sum_{k=i}^{j}v _{k}+\gamma\cdot R_{\text{length}}, \tag{15}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) represent the weighting coefficients for each component, respectively, used to balance the influence of turning angle, velocity, and path length. 5. Optimization Process: Employing the TD3 algorithm, we train a policy network (Actor) to generate optimized path points that maximize the total reward function \(R_{\text{total}}\). Specifically, the input to the policy network is the current path point along with its preceding and succeeding points, and the output is the position of the next path point. The Critic network is employed to evaluate the reward value of the generated path points and update the policy network parameters via gradient descent. 6. Smooth Transition: The optimized path segment \(\left(P^{\prime}\right)\) is smoothly connected to the original path at the connection points \(\left(p_{i-1}\right)\) and \(\left(p_{j+1}\right)\), ensuring that the turning angles \(\left(\theta_{i-1}\right)\) and \(\left(\theta_{j}\right)\) do not exceed a predefined threshold, thereby preventing abrupt changes in the trajectory. Through the aforementioned method, we can achieve local partial optimization on the initial path generated by BIT*, generating a path that is smoother, shorter, and more conducive to Unmanned Surface Vehicle (USV) execution.
## 3 Methodology
### Focal Loss Function
By integrating the weighting factor \(\alpha\) and the modulating factor \((1-p_{t})^{\gamma}\), the model can effectively tackle the challenge of imbalanced distribution between positive and negative samples while alleviating the impact of easy samples on model performance. Focal loss demonstrates unique advantages when addressing class imbalance issues without the need for introducing additional data operations. It simply involves adjusting the weighting factor and modulating factor within the loss function, thus avoiding potential noise or information loss introduced by additional data operations. Furthermore, it maintains relatively low computational costs. In contrast, oversampling and undersampling typically require the addition or removal of a significant number of data samples, which can lead to alterations in data distribution, the loss of crucial information, and a substantial increase in computational overhead [15; 29]. When compared to other cost-sensitive learning methods, focal loss does not necessitate the introduction of an extra cost matrix or a multitude of redundant parameters. This simplifies the model's training and tuning process. Some cost-sensitive learning techniques, such as cost-sensitive support vector machines (SVMs), often require researchers to predefine a cost matrix before training the model. This matrix includes cost weights between different classes, demanding prior knowledge of different classes and introducing numerous extra parameters for managing the cost matrix and weights [52; 53]. The proposed method integrates the aforementioned components, CNN, BiLSTM, and the focal loss function, to construct a comprehensive network intrusion detection model. Within this model, the CNN-BiLSTM structure comprehensively captures the features of network traffic data. Specifically, the CNN is responsible for extracting spatial features, with its primary objective being the capture of local patterns and crucial features within the data. On the other hand, BiLSTM focuses on modeling temporal features, effectively capturing the data's temporal dependencies. The focal loss function plays a pivotal role in the model, addressing class imbalance issues by adjusting the weighting factor and modulating factor within the loss function, all without introducing additional data operations or a cost matrix. This integration endows the model with a more powerful feature representation capability, effectively addressing the challenges posed by imbalanced classes. As a result, the model can more accurately detect network intrusion behavior, enhancing the performance of network intrusion detection.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
###### Abstract  
In this paper, a fault location method based on divide-and-conquer (DAC) is proposed to solve the inadequacy problem that arises when using the traditional fault section location method based on the optimization model of logic operation. The problem is that it is difficult to balance speed and accuracy after the scale of the distribution network is expanded. First, the causal link between fault information and the faulty device was described using the road vector, the equivalent transformation of the logical operations in the traditional model was implemented with the properties of the road vector, and the numerical computational model of the fault location was constructed. Based on this, the optimization-seeking variable \"approximation gain\" was introduced to prove that the proposed model conforms to the recursive structure of DAC, and the method of applying DAC to locate faults is proposed. The method applies the \"Divide-Conquer-Combine\" recursive mode to locate faults, and each level of recursion contains only linear-time \"approximation gain\" operations and constant-time decomposition and combination operations. The efficiency analysis and simulation results show that the proposed method has linear-time complexity and can accurately locate faults in milliseconds, providing a reference for solving the fault location problem in large distribution networks.  
distribution network; fault location; divide-and-conquer; road vector
## 3 Discussion
### Roles of Photosynthesis Genes in Resin Yield
Previous works revealed a significant correlation between chlorophyll contents and resin yield in masson pine, and the chlorophyll contents of high resin yield were significantly higher than those of common resin yield [64]. Consistently, in our study, the chlorophyll contents were dramatically increased in high resin yield in comparison with that in the common one. High chlorophyll contents would be beneficial for harvesting light energy and its conversion to chemical energy [65]. The variations in chlorophyll contents between high and common resin yield demonstrated that there might be great differences in photosynthetic capacity between them. Chl b is a prerequisite for the stable existence of light-harvesting complex protein (LHCP) and the increase of Chl b was found to be beneficial for the absorption of short-wavelength light, thus effectively improving the light energy utilization efficiency of plants under low light [66]. Previous studies suggested that Chl b could switch from an active photochemical state to a photoprotective state [67]. In the present study, the Chl b content was greatly increased in high resin yield in comparison with that in the common one, which was beneficial to the stability of needle LHCP, thus enhancing the utilization of light energy under low light. Moreover, the higher Chl b content of high resin yield indicated that the photosynthetic protection mechanism was more responsive than that of common resin yield. This could also be supported by the Car content, which is involved in several photoprotection mechanisms [68], sharing the same pattern of variations. Further, we focused on the expression of chlorophyll synthesis genes in high resin yield. Consistent with the changes in chlorophyll contents, many key genes involved in chlorophyll biosynthesis were significantly up-regulated at the transcriptional or proteomic level, including _HemL_, _HemB_, _HemC_, _HemY_, _chlH_, _chlM_, _chlE_, _POR_, and _chlG_. Of which, the chlG enzyme catalyzes chlorophyllide a (chlide a) and chlorophyllide b (chlide b) to Chl a and Chl b, the reaction is the last step in chlorophyll biosynthesis, the enzyme is essential for the formation of chromoprotein complexes and stabilization of chlorophyll-binding proteins [69]. Furthermore, _chlG_ is involved in the feedback regulation of chlorophyll biosynthesis and plays an important role in the stable assembly of chlorophyll-binding proteins and other thylakoid membrane components [63; 70]. In the present study, _HemB_, _HemY_, and _ChlM_ were up-regulated at the transcriptional level; _HemL_, _HemC_, _chlH_, _chlE_, and _POR_ were up-regulated at the proteomic level. It is worth noting that _chlG_ was significantly up-regulated both at the transcriptional and proteomic levels. Therefore, the improved expression of multiple genes related to chlorophyll biosynthesis facilitated the increase of chlorophyll contents, leading to the enhancement in photosynthetic efficiency, thus, contributing to the accumulation of photosynthetic products and the increase of resin yield.
## 3 Gradient Boosting Machines
### Application of the Gradient Descent Process to the Error Function  
Before applying the gradient descent process to an error function, one should express the estimating function of a system \(F\) for an expected loss function \(g\). For a given dataset in the form of \(X_{\mathrm{i}}\), \(Y_{\mathrm{i}}\) (\(i\) = 1,... n), \(X_{i}\) is a variable with k-dimensions and \(Y_{\mathrm{i}}\) is a response with continuous or discrete values (in the case of continuous values, the problem is characterized as regression, in the case of discrete values as classification). Let us assume for reasons of simplicity that \(Y\) is univariate. From the function: \(F\): \(\mathbb{R}\mathrm{x}\)\(\rightarrow\)\(\mathbb{R}\) and expected loss \(g\), the aim is to minimize the expected loss \(\mathbb{E}[g(Y,F(X))]\), \(g(.,.):\mathbb{R}\)\(X\)\(\mathbb{R}\)+.  
\[\hat{F}(x)=\underset{F(x)}{arg}\ \underset{F(x)}{min}\ \ g(y,F(x))\]  
As mentioned earlier, \(F\) and \(g\) are the estimation and the loss function, respectively. The function \(F\) expresses the correlation of \(X\), \(Y\). To make sure that the method will work correctly, the loss function \(g\) should be smooth and convex in the second argument. The loss function depends on the domain with which we have to cope. The features of the system (e.g., if the system has outliers or it is high dimensional) pose a significant impact on the decision of how the machine learning task should be considered. In most of the cases, the loss function belongs to one of the following [9]:  
1. Gaussian L2
2. Laplace L1
3. Huber with \(\delta\) specified
4. Quantile with \(\alpha\) specified
5. Binominal
6. AdaBoost
7. Loss function for survival models
8. Loss function counts data
9. Custom loss function  
In case the response of the system is continuous, we try (a) to (d) from the above list; in case the response is categorical, we try \(\mathbf{\xi}\) or (f). To the extent of our knowledge, the three remaining loss functions are currently not supported by any open-source implementation of GBM.  
The following figure (Figure 1) illustrates two continuous loss functions:  
1. L2 squared loss function with generic format: \(\Psi(y,f)_{L_{2}}=\ \frac{1}{2}\ (y-f)^{2}\) and
2. Huber loss function with generic format: \[\Psi(y,f)_{Huber,\delta}=\left\{\begin{array}{rl}\frac{1}{2}\ (y-f)^{2}\,\ |y-f|\leq \delta\\ \frac{1}{\delta}\ \Big{(}|y-f|-\frac{\delta}{2}\ \Big{)},\ |y-f|>\delta\end{array}\right.\] (1) for the case (I) \(\delta\) = 1, (II) \(\delta\) = 0.5 and (III) \(\delta\) = 0.25.