esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Example
### Motion-Based Design of Passive Damping Devices under Uncertainty Conditions
#### 4.3.1 Viscous Damper  
First, the motion-based design of the viscous damper under uncertainty conditions was performed. The design problem of this viscous damper may be formulated as follows:  
\[\begin{split}\text{find}\ \theta=c_{d,v}\ \text{to minimize}\ f(\theta)=c_{d,v}\\ \text{subject to}\Bigg{\{}\begin{array}{l}c_{\min}<c_{d,v}<c_{ \max}\\ \beta_{s}(\theta)\geq\beta_{t}=1.35.\end{array}\end{split} \tag{21}\]  
As result of the optimization process, the damping coefficient, \(c_{d,v}\), was obtained. The optimum value obtained was \(c_{d,v}=1.06\times 10^{5}\) sN/m. The reliability index for this solution was, \(\beta_{s}(\theta)=1.37\), which met the design requirements. Figure 6 shows the maximum displacement at the mid-span of the cable damped by the viscous damper for the different elements of the sample.
# A Novel Adaptive Sparrow Search Algorithm Based on Chaotic Mapping and T-Distribution Mutation
## 4 Numerical Experiments and Analysis
### Experimental Results and Analysis
#### 4.3.1 SSA with Different Strategies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Functions** & **Algorithms** & **Optimal Value** & **Mean Value** & **Std.Dev** \\ \hline \multirow{8}{*}{\(f_{5}\)} & SSA & 2.3569E\(-\)07 & 1.0000E+00 & 1.5551E\(-\)04 \\  & CSSA & 6.5956E\(-\)09 & 1.0000E+00 & 1.4077E\(-\)05 \\  & WSSA & 9.1926E\(-\)08 & 1.0000E+00 & 4.3810E\(-\)05 \\  & TSSA & 9.6663E\(-\)10 & 1.0000E+00 & 2.7537E\(-\)06 \\  & CWSSA & 4.9007E\(-\)10 & 1.0000E+00 & 7.7839E\(-\)06 \\  & CTSSA & 3.7843E\(-\)09 & 9.9999E\(-\)01 & 1.7175E\(-\)05 \\  & WTSSA & 1.0968E\(-\)07 & 1.0000E+00 & 8.3877E\(-\)05 \\  & CWTSSA & 1.9196E\(-\)10 & 1.0000E+00 & 8.1964E\(-\)07 \\ \hline \multirow{8}{*}{\(f_{6}\)} & SSA & 0.0000E+00 & \(-\)8.2921E\(-\)85 & 1.5647E\(-\)04 \\  & CSSA & 0.0000E+00 & \(-\)1.8452E\(-\)12 & 2.5029E\(-\)12 \\  & WSSA & 0.0000E+00 & \(-\)1.5465E\(-\)09 & 2.1184E\(-\)09 \\  & TSSA & 0.0000E+00 & \(-\)1.7196E\(-\)14 & 8.7606E\(-\)14 \\  & CWSSA & 3.4106E\(-\)13 & \(-\)4.0845E\(-\)10 & 9.8165E\(-\)09 \\  & CTSSA & 0. As can be seen from Table 3, for unimodal functions \(f_{1}-f_{4}\), whether the single strategy or combination strategy is selected, it can achieve higher convergence accuracy than the basic SSA, which has different degrees of improvement, indicating that the three used strategies for SSA are all effective. Especially for \(f_{1},f_{2},f_{4}\), when three strategies are used at the same time for SSA (CWTSSA), the average and standard deviation are both theoretically optimal solutions. It is not difficult to find that the convergence accuracy of using the adaptive weighting strategy has been decreased, but the effect of the t-distribution mutation strategy has been improved because the t-distribution mutation operator can make full use of the current population information on the original basis. The t-distribution mutation operator gives full play to the disturbance ability, and the global optimization performance of the algorithm can be improved. Among the multimodal functions \(f_{5}-f_{8}\), most of the functions perform well, and have good performance in terms of convergence accuracy and stability. Especially for the function \(f_{6}\), the combination of adaptive weighting strategy and t-distribution mutation operator shows a clear advantage. The mean value has increased by more than two hundred orders of magnitude. Moreover, it converges to the global optimal solution, which demonstrates the search flexibility of the CWTSSA and stable optimization performance. In addition, in the relatively special test function \(f_{9}-f_{12}\), the overall improvement effect is mediocre. However, the SSA with different strategies has dif
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 2 Method: The Optical Efficiency Calculation Model and Optimization Method for Combined Disk System
### Optimization Method for Combined Dish System  
The net thermal energy is equal to the absorbed optical energy minus heat loss of the receiver. The heat loss rate depends on the receiver structure, operation temperature, thermal properties of the material used, and so on. If the operation temperature is fixed, the heat loss rate per unit glass or open window surface area can be considered as a constant. The optimization process is described below.  
The annual input of solar energy to a paraboloid dish is  
\[E_{0}=\pi R^{2}\int_{0}^{365}24n(t)g(t)DNI(t)dt. \tag{12}\]  
The annual net thermal energy to a paraboloid dish is  
\[E_{heat}=\pi R^{2}\eta_{opt}\int_{0}^{365}24n(t)DNI(t)dt-\pi r^{2}q\int_{0}^{36 5}24n(t)g(t)dt, \tag{13}\]  
where \(\eta_{opt}\) is the optical efficiency of solar system, and \(n\) (\(t\)) is correlated with local weather, i.e., \(n\) (\(t\)) = 0 when it is cloudy or rainy, and \(n\) (\(t\)) = 1 when it is sunny. \(DNI\) (\(t\)) refers to the direct normal incident solar radiation, \(q\) is the heat loss rate, \(r\) is the radius of the receiver, and \(g\) (\(t\)) represents the operating condition function; it is assumed that the radiation is strong enough when the solar elevation is equal to or more than \(15^{\circ}\).  
According to Equations (12) and (13), the annual net heat efficiency of the system is calculated as follows:  
\[\eta\text{heat}=\frac{Eheat}{E0}=\eta opt-\frac{r^{2}}{R^{2}}\times\frac{q \int_{0}^{365}24n(t)g(t)dt}{\int_{0}^{365}24n(t)g(t)DNI(t)dt} \tag{14}\]Considering the heat loss of the receiver, Equation (14) can be simplified as  
\[\eta_{heat}=\eta_{opt}-\frac{(r/f)^{2}(1+\cos\varphi_{rim})}{4(1-\cos\varphi_{rim })}\times\frac{q}{DNI}, \tag{15}\]  
where DNI is the average direct normal irradiation on a sunny day, and q/DNI is defined as the heat loss coefficient \(\xi_{v}\) which represents the ratio of the heat loss rate of the receiver to the annual average solar radiation when the system works. \(\xi\) is about 18.177 and 168.25 for 1.67 \(\times\) 10\({}^{3}\) W/m\({}^{2}\) and 1.63 \(\times\) 10\({}^{4}\) W/m\({}^{2}\) heat loss rates of the two typical receivers with or without glass windows, respectively [22], as shown in Table 1.  
Although some optimization algorithms can be applied including genetic, pattern search, and quasi-Newton algorithms, the annual average efficiency of the combined solar dish system under various designs is calculated to reduce the error of the optimized results.
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 4 Technical Discussion
### 4.1. Note Specifications and Applications
#### 4.1.4 Communication Capabilities  
As can be observed in Table 2, several different types of transceivers have been utilized by the different sensor modes. The LoRa and ZigBee modules are the most commonly used transceivers by most of the recent sensor nodes. Sensor nodes such those by [14; 24; 33; 35] and the Waspmote Plug and Sense! are capable of integrating different transceivers for communication. In order to minimize the overall sensor more size, devices such as the Nordic nRF24E1 and ARM Cortex-M4 MCU-CC3200 integrate both the microcontroller and Wi-Fi (for internet connectivity) onto a single chip. Most of the radio modules operate at 2.4 GHz and are IEEE 802.15.4-compliant for interoperability purposes. The IEEE 802.15.4 communication protocol is suitable for low data rate WSN applications. The ZigBee protocol is chosen for its low power consumption. The Waspmote Plug & Sense! utilizes technologies such as Xbee-PRO and cellular networks to achieve wide coverage. However, in cellular networks there are much higher energy consumption and costs. LoRa transceivers have better energy efficiency and are also utilized for applications requiring long-range coverage. Introducing additional functionalities in a sensor node comes with an extra power consumption cost. In underground WSNs, network failure may be caused by high levels of signal attenuation. Appropriate and energy-efficient routing protocols should continue to be proposed to prevent premature overall network failure due to the nodes' failure. Long-range and low-power communication modules should be developed to achieve the long network lifetime of WSNs.
## 3 Experimental Results and Discussion
### Results and Analyses
Nonetheless, the numerical results still suggest that the proposed method has the best image quality, i.e., the SAM, ERGAS, and UIQI are 3.84, 4.56, and 0.9485, respectively, which are obviously superior to the others. In order to give an intuitive comparison of spatial detail aspects, Figures 6 and 7 show some local enlargements of the fused images. Of course, compared with the simulated LR HS image in Figures 6d and 7d, the fused images present abundant spatial details and clearer texture characteristics. From Figure 6, it can be seen that the conventional methods always suffer from apparent noise, which is caused by the simulated noisy MS image. This can also be inferred from Table 2; e.g., the SAMs and ERGASs of CNMF and DPLM are 9.89, 11.81, 4.49, and 4.75, respectively. By contrast, the CNN-based methods can effectively overcome this issue. From Figure 7, we can see that the detail-injection CNN methods have relatively better overall appearances, corresponding to the lower SAMs (4.17 and 3.84) and higher UIQIs (0.9415 and 0.9485), whereas the TCNNF method presents obscure edges and textures compared with the other methods. In sum, the CNN-based hyper-sharpening methods are much preferable to the conventional matrix factorization-based methods, and our proposed method indeed surpasses the other CNN-based methods in both quantity and visual aspects. Figure 4: Fused images of Washington DC Mall. (**a**) CNMF; (**b**) JNMF; (**c**) DPLM; (**d**) TCNNNF; (**e**) DiCNN; (**f**) CpCNN. Figure 5: Fused images of San Francisco. (**a**) CNMF; (**b**) JNMF; (**c**) DPLM; (**d**) TCNNNF; (**e**) DiCNN; (**f**) CpCNN. Figure 6: Local enlargements of fusion result of Pavia City center. (**a**) CNMF; (**b**) JNMF; (**c**) DPLM; (**d**) LR HS image; (**e**) TCNNNF; (**f**) DiCNN; (**g**) CpCNN; (**h**) Reference high-resolution, hyperspectral (HR HS) image.
## 3 Proposed Method
### Age Estimation
#### 3.1.2 Regression through Classification  
Age estimation is basically a regression problem, as age is a continuous value rather than a set of discrete classes. The deployed pre-trained model VGG-16 architecture is applied for the ImageNet classification task where the output layer consists of 1000 neurons normalized using the Softmax function, one for each of the object classes. In practice, we replace the last layer with only one output neuron and employ a Euclidean loss function for the regression task. It is unfortunate that, if we train a CNN solely for any regression task the model experienced a high error because of the instability when handling outliers. As a result, the network is facing the difficulty of poor convergence due to high gradients and predictions become unstable.  
Under these circumstances, we handle the age estimation task through a classification approach by discretizing the ages into \(K\) categories. Following this procedure, we learn our CNN model for age classification and quantify the regression value from the expected value formulated using the Softmax-probabilities that belong to the \(K\) neurons, as shown in Equation (1).  
\[E=\sum_{i=0}^{|k-1|}y_{i}.p_{i} \tag{1}\]  
where \(k\) stands for 101 age categories, \(y_{i}\in[0,100]\), and \(0\leq i\leq 100\). \(p_{i}\) denotes the Softmax-normalized output probability of neuron \(i\). The experimental results show that this formula increases the robustness during training and the prediction accuracy during testing.
## 5 Active Control: Self-Adjustment  
The main point of an active control of smart structures is the possibility of adjusting their parameters by using their inherent properties. In the case of tensegrity systems, it is the ability of self-adjustment that allows one to actively control the properties of these structures.  
The following example proves that tensegrities are prone to structural control through an adjustment of self-stress within both the whole structure and its selected part.  
In the analysis, the plate-like structure presented in Figure 8 was considered, with the same materials, dimensions of the cross-sections, values of self-stress and loading as in the previous examples. The calculations were performed in SOFiSTiK software (educational license), using the finite element method, according to the second order theory.  
The aim of the analysis was to determine how the self-stress in a single module influences the displacement \(\delta_{A}\) of the structure, which was loaded with concentrated forces \(P=10\) kN. Pre-stressing forces \(S_{0}\) were introduced successively into Modules 1-7 (Figure 12) and, for comparison purposes, into the whole system. When the considered module was pre-stressed, the others had no self-stress.  
The diagrams (Figure 15) show the influence of self-stress in single modules and in the whole structure on the considered displacement. It should be noticed that prestressing of a single module did not reduce the displacements to the same degree as self-stress applied to the whole structure did. However, it eliminated the infinitesimal mechanism and decreased the value of the considered displacement significantly. Depending on the module, the influence of self-stress on structural stiffness varies. The biggest reduction of the displacement \(\delta_{\mathrm{A}}\) was obtained for the prestressing of Module No. 5, and the least effective was the pre-stressing of Module No. 1.  
The results presented in Figure 15 were obtained with the use of the second order theory. This theory is sufficient to show the differences in the influence of self-stress in single modules and in all modules together. The results within the third order theory show the same tendency of the results.
To address this research gap, Tongluo Mountain Benggang in Yangkeng Village, Longmen Town, Anxi County, was utilized as a case study area. UAV images were obtained at various flight proximities; then, the optimal distance was determined in terms of positioning accuracy, point cloud data, and DSM data direction. Control-free nap-of-the-object photogrammetry image technology was combined with this optimal flight proximity to adjust the photogrammetric route and lens head of the point cloud model in the study area. The shooting plane of the route parallel to the Benggang was fitted in order to obtain fine image data. The feasibility of this approach was verified by comparing aerial survey data with image control points and ground real-time kinematic (RTK) base station measurement data. The findings may provide technical support for the efficient and accurate monitoring of collapse walls.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 2 Related Work
### Attention Mechanisms in Efficient Neural Architectures  
Attention mechanisms have been widely adopted in both Transformer-based and CNN-based architectures to enhance feature representation. In CNNs, channel and spatial attention modules such as SE [22], CBAM [23], and ECA [24] recalibrate feature maps to emphasize informative regions. Meanwhile, Transformer-based models rely on multi-head self-attention (MHSA) to model long-range dependencies. However, MHSA introduces quadratic complexity with respect to sequence length and multiple heads, motivating the development of lightweight variants such as grouped or factorized multi-head attention [25; 26] and Sparse Attention [27; 28].  
Unlike these methods, which are primarily designed to reduce the cost of handling many attention heads in Transformer-based architectures, our proposed Multi-Head Large Kernel Attention (MLKA) focuses on incorporating multi-head attention into a CNN-based framework. MLKA extends the Large Kernel Attention (LKA) mechanism [12] by embedding multiple attention heads and introducing a Channel Shuffle operation to promote interaction between them, enhancing spatial representation rather than compressing existing attention structures.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method  
As illustrated in Figure 1, we discuss the VANET protocol that is boosted by the ACO in this section. VANETs are adaptive intersection-based routing systems that employ the suggested method to find the suitable tracking path of the QoS. Through leveraging the node throughput, the QoS is quantified from the side of the connection likelihood, the ratio of the packet delivery, and the latency. Prior to sending data packets to the shortest path, each firefighting UGV sends a request for routing to the nearby fire sensor terminal and receives throughput for the available routing information. If such routing information exists, an affirmative answer is delivered back to the firefighting UGV, which instantly commences the data packet forwarding and subsequently directs all firefighting UGVs into the fire region through the designated route. Otherwise, the fire sensor terminal transmits a negative message to the firefighting UGV, and subsequently, depending on the ACO protocol, all firefighting UGVs quit this route. The novel ACO for AVNETs is made up of essentially two components; the optimal route establishment and the terminal intersection selection.  
The ACO's advantages make it appropriate for the dynamic applications where positive feedback leads to the rapid identification of good solutions. It might also be a distributed computation, which avoids the premature convergence.
## 9 Algorithms, AI Models and Digital Twins
### Digital Twins in Agricultural UGVs
In this approach, certain components in the virtual environment are connected to real-world hardware objects, such as camera sensors or Electronic Control Units (ECUs) for cars. The responses from these real hardware components are used as if they were produced by their virtual counterparts, contributing to a more realistic simulation and system test. This solution allows for the testing of critical components of a real system in the most accurate, non-destructive, and repeatable way. The great impact and recent diffusion of DTs has been made possible by the lowering cost of IoT-embedded platforms, the increased bandwidth for wireless communication, and the availability of computational power with advanced AI models. Thanks to the ever-increasing technological availability and the desire to automate and streamline as many aspects of agricultural production as possible, rovers and drones have found widespread use in both the monitoring and field intervention phases. In order to successfully use these tools, it is necessary to go through the design, prototyping, and validation phases of the robotic solutions. Despite their advantages, the implementation of DTs for agricultural UGVs faces several challenges related to (i) data management and processing with limited UGV resources, (ii) the integration of existing agricultural systems and technologies with the virtual platforms, and (iii) the specialized knowledge required for developing and maintaining these advanced technologies. DT represents a cutting-edge technology and its usage in agriculture offers unprecedented simulation, real-time monitoring, and prediction capabilities. Even though most studies on robot navigation and path planning were initially conducted in simulated environments [95], there are not many research works explicitly related to the use of agricultural UGVs for plant monitoring, due to the previously reported limitations. As reported by [118], the use of DT in agriculture is still in its infancy; therefore, many challenges remain, which must be solved in order to achieve the complete adoption of the DT paradigm.
## 4 Results and Analysis
### EV Penetration in Rural Network
#### 4.2.4 Case 8: 80% EV Penetration in Rural Network  
The last set of simulations for the rural network as in Figure 16 shows that the impact of a massive penetration of EVs is not producing too many criticalities for the transformer, even if the loading in its peak increased from 66% of the base case to 128%. This value can be reduced to 117%, implementing the PV coupled to the _ESS_. The voltage profile is not so much affected, since even in the worst case it reaches 0.94, with a voltage drop of 4% from the base case. This means that the rural network analysed is a robust and oversized grid, capable of withstanding a large amount of additional power.  
In Table 2 are summarised the loading data related to the rural transformer for the different scenarios analysed. The results are much lower with respect to the ones presented in Table 1, because of the reduced load that the transformer is feeding. In fact, the base case peak, reaches almost 50% of the transformer nominal capacity. The peak hour, in which most of the electric vehicles gets charged, corresponds to the time when people gets back home and shows that the installed 250 kVA transformer is capable of withstanding the additional load, since it is dimensioned to work up to 150% of its nominal power. Even in case of an 80% penetration of EVs, which is an unlikely scenario in 2030, the transformer can work up to 128%, so it will not be necessary to substitute it, but the application of smart measures will help relieve the load in the most congested hours. As can be seen from Figures 11-14, in  
Figure 16: Comparison of rural (**a**) transformer loadings and (**b**) most loaded feeder voltage profiles with 80% EV penetration.  
Figure 15: Comparison of rural (**a**) transformer loadings and (**b**) most loaded feeder voltage profiles with 60% EV penetration.  
fact, the implementation of PV coupled with \(ES\) is capable of reducing the power flowing through the transformer, even below the base case scenario.
## 4 Experiments without Frame Aggregations
### Packet Success Rate  
For the packet error probabilities of arriving packets, we used the National Institute of Standards and Technology (NIST) error rate model in the ns-3. For the experiments, we used two wireless nodes that operated with the IEEE 802.11g standard. Initially, the distance between the sender and receiver was set to 5 m. During the simulation time of 13 s, one node (sender) moved away from the other node (receiver) at a speed of 6 m/s while it sent CBR traffic of 54 Mbps to the receiver. Thus, the distance between the two nodes changed from 5 to 83 m. The sender used one (fixed) MCS level among the eight MCS levels for the data packet transmissions in each simulation. As a result of the movement, we can expect the SINRs of the received packets to deteriorate while the distance between the sender and receiver increases.  
In Figure 1, we show the experimental results that show the packet success rates (i.e., \(1-\) packet error rates) according to the SINRs of the received packets and distances between the sender and receiver. The experimental results in Figure 1a show that the packet success rates decreased when the SINRs of the arrived packets at the receiver decreased regardless of the MCS levels used by the sender. The experimental results in Figure 1b show that the packet success rates decreased when the distance between the sender and receiver increased, regardless of the used MCS levels. The experimental results clearly showed that packets may be more successfully delivered to the receiver when the sender adopts low MCS levels until the distance between the two nodes becomes greater than approximately 73 m.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency & 2.412 GHz \\ Channel bandwidth & 20 MHz \\ Transmit power & 100 mW \\ Receiver sensitivity & \(-101\) dBm \\ Slot time & 20 us \\ Noise floor & \(\sim-94\) dBm \\ Clear channel threshold & \(-82\) dBm \\ Data packet size (including IP/UDP header) & 1500 bytes \\ Device queue size & 500 packets \\ Guard interval & 800 ns \\ Preamble detection model threshold & \(-30\) dB \\ Preamble detection model minimum RSSI & \(-99\) dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters used for experiments.