esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### First Model  
The model of Equation (20) was fitted with the data on the first 10 experiments from Table 1, to each of the indices \(\gamma_{l}\) (\(l=1,2,\,\ldots,\,6\)). Moreover, the coefficient of determination \(R^{2}\) was calculated, which is the percentage variability of each index explained by the model; in other words, it is a measure of the adherence of the fitted model to the true values. \(R^{2}\) is respectively equal to 46.6 and 60.0% for the variables \(\gamma_{com}\)-n and \(\gamma_{pow}\)-n, such that the synergic model is not sufficient to reproduce those two indices. \(R^{2}\) varied between 95.0 and 99.6% in the fitted models for the four remaining variables that are shown in Table 2. The value \(s\) is also noted, which is the square root of the value of the Equation (14) for each model. It expresses the minimum value of the normalized quadratic loss function reached for each index.  
When these models are applied to the six-point test, it is possible to decide whether the predictions of the model are compatible with the experimental results. Each value calculated with the model \(\hat{\gamma}(x)\) has a mean uncertainty, as a consequence of not using the true model derived from Equation (12) by the following expression:  
\[s(\hat{\gamma}(x))=\sqrt{1+d(x)}\;s. \tag{21}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \# & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(\gamma_{com}\)-d & \(\gamma_{switch}\)-d & \(\gamma_{pow}\)-d & \(\gamma_{com}\)-n & \(\gamma_{switch}\)-n & \(\gamma_{pow}\)-n \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Experimental results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\gamma_{com}\)-d & \(\gamma_{switch}\)-d & \(\gamma_{pow}\)-d & \(\gamma_{switch}\)-n \\ \hline R\({}^{2}\)(\%) & 96.6 & 95.4 & 95.0 & 99.6 \\ s & 0.016 & 0.023 & 65.05 & 0.001 \\ b\({}_{1}\) & 0.261 & 0.411 & 13,663.22 & 0.022 \\ b\({}_{2}\) & 0.319 & 0.353 & 13,927.23 & 0.039 \\ b\({}_{3}\) & 0.318 & 0.330 & 13,773.61 & 0.104 \\ b\({}_{12}\) & \(-\)0.963 & \(-\)1.514 & 2665.42 & \(-\)0.013 \\ b\({}_{13}\) & \(-\)1.404 & \(-\)1.872 & 5189.75 & \(-\)0.  
Thus, the difference \(\hat{\gamma}(\mathbf{x})-\gamma(\mathbf{x})\) in each test point \(\mathbf{x}\) may be expressed as \({\it dif}_{n}=\frac{\hat{\gamma}(\mathbf{x})-\gamma(\mathbf{x})}{s(\hat{\gamma}(\mathbf{x}))}\), a normalized difference to take into account the position of the point in the experimental domain and the uncertainty caused by using a model that does not exactly reproduce the physical phenomenon.  
Table 3 shows the 24 values of \({\it dif}_{n}\) (six-point test by four models). Only three of these exceed the value of 3.8 such that \(\hat{\gamma}(\mathbf{x})\) and \(\gamma(\mathbf{x})\) may be considered different. They correspond to experiment 11 for variable \(\gamma_{con}\)-d, experiment 13 for \(\gamma_{pow}\)-d, and experiment 16 for \(\gamma_{switch}\)-n. On the basis of these results, it may be accepted that the models are valid and a second model will be fitted.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Radiation Pattern in Azimuth and Elevation  
The first antenna was connected to a spectrum analyzer via a sufficiently long coaxial cable, while the second antenna was connected to a signal generator. Both antennas were positioned at the same height, 7.5 m apart from each other, using a tripod and a laser for precise alignment. The first antenna was mounted on a rotating table. Radiation absorbers (black cones) were placed between the two tripods to minimize reflections, as shown in Figure 7. During testing, the signal generator drove the transmitting antenna with a continuous-wave tone at the design frequency, while the receiving antenna on its motorized turntable was rotated in fixed azimuth steps in 5\({}^{\circ}\) increments. At each angular position, the spectrum analyzer logged the received power level, producing a high-resolution radiation pattern. By keeping both antennas at identical heights and using the absorbers to suppress stray reflections, we isolated the direct line-of-sight signal, ensuring that the recorded pattern truly represented the antenna's intrinsic radiation characteristics rather than room echoes or multipath artifacts.  
To analyze the radiation pattern in the azimuthal direction, both antennas were positioned vertically, maintaining the same polarization. The rotating table completed a full rotation from 0\({}^{\circ}\) to 360\({}^{\circ}\) in 5\({}^{\circ}\) steps. During the rotation, a 1.2 GHz signal was transmitted using the signal generator. The purpose of this setup was to observe and analyze in detail how the antennas radiate the signal depending on the azimuth angle.  
To ensure the accuracy of the measured values, the received power was recorded over an extended range from -10\({}^{\circ}\) to 370\({}^{\circ}\), allowing for a comparison between measurements at 0\({}^{\circ}\) and 360\({}^{\circ}\), as well as \(-5^{\circ}\) and 355\({}^{\circ}\). The results confirmed this symmetry, indicating good repeatability of the experiment.  
Adjustments were made to the antenna positioning to measure the radiation pattern in the elevation plane. The transmitting antenna (Tx) remained fixed and directed toward the receiving antenna (Rx), while the receiving antenna varied its reception angle from -90\({}^{\circ}\) to 90\({}^{\circ}\).
# A Convex Optimization Algorithm for Electricity Pricing of Charging Stations
## 4 Case Studies  
### Data Generation  
The case uses an IEEE 33-bus system [25], and the urban area is divided into residential area, commercial area, and industry area [26]. The total load is 3715 kVA and there are three types of EVs in the distribution network. The structure of the distribution network and the location of the charging station are shown in Figure 3 and the electricity price of DSO is shown in Figure 4. The data of electric vehicles can be obtained in [20]. The battery capacity of EVs is assumed to be 42 kWh, and the initial state of charge (SOC) is 30%. There are 60 charging piles in the CS and the maximum charging power of each charging pile is 30 kW. In order to simplify the problem, this paper assumes that there are three types of EVs in distribution network and their parking time is shown in Figure 5. Additionally, the number of three types of electric vehicles is 200, 150, and 50 respectively. All simulations are implemented on a laptop with Intel(r) Core(TM) i5-7300 HQ [EMAIL_ADDRESS] GHz, 8 GB memory, and the algorithms are compiled by MATLAB. YALMIP tools [23] are used to model and CPLEX [27] is called to solve the MILP model by branch and bound method. Branch and bound method is a common method, whose process only briefly described in this article can be seen in [28].  
Figure 3: IEEE 33-bus system.
## 4 Experiments and Results
### Ablation Study  
Ablation experiments are conducted to verify the effectiveness of each module in the EBLCS model, including ERNIE, channel attention, and spatial attention. In this paper, four models are constructed: BLCS, EBLC, EBLS, and EBLCS. Here, BLCS adds channel attention and spatial attention to BiLSTM; EBLC adds channel attention to ERNIE-BiLSTM; EBLS adds spatial attention to ERNIE-BiLSTM; and EBLCSD combines both channel and spatial attention with ERNIE-BiLSTM. The ablation experiments are performed on the HSD dataset, and the results are shown in Table 5.  
From the results in Table 5, it is evident that adding ERNIE, channel attention, and spatial attention improves the model's performance. Furthermore, the combination of all three provides even greater enhancements, indicating that the proposed model performs better overall. The BLCS model shows a 5.24% accuracy improvement over the BiLSTM model, demonstrating that channel attention and spatial attention effectively enhance the feature extraction capabilities of BiLSTM. The ERNIE-BiLSTM model improves accuracy by 8.26% compared to BiLSTM, highlighting the superior text characterization ability of the ERNIE model.  
The EBLC model achieves a 1.62% accuracy increase over the ERNIE-BiLSTM model. This improvement results from channel attention utilizing average and maximum pooling to capture the importance of different word embedding dimensions, combined with a multilayer perceptron that learns the complex interactions between these dimensions. This process enhances the model's ability to capture important features by weighting different dimensions and reducing the influence of less significant features.  
The EBLS model improves accuracy by 1.42% compared to the ERNIE-BiLSTM model, confirming that spatial attention is effective at capturing important elements in the utterance structure. The convolution operation strengthens the model's ability to identify keywords, allowing it to focus on key feature information and improving classification performance.
## 2 Calibration Status of Landsat 8 Operational Land Imager (OLI)
### On-Orbit Radiometric Calibration of OLI  
Radiometric calibration and characterization of the OLI instrument is accomplished by processing all data through the Landsat Product Generation System (LPGS) or the Image Assessment System (IAS) at the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. To show the radiometric gain trend through time, the average response from each of the on-board calibrators (working lamp, pristine lamp, backup lamp, working solar diffuser and pristine diffuser) were normalized to a point in time shortly after launch. Figure 2 shows the radiometric gain trend over time. Only the coastal aerosol (\(\sim\)1.5%) and blue (\(\sim\)0.3%) bands show any significant change in gain over almost five years on-orbit.  
The on-board calibrators provide a very precise trend of the changes in the instrument response, but any connection to the absolute radiometric accuracy is tied back to a NIST reference through pre-launch calibration. In order to get on-orbit validation of this pre-launch calibration, vicarious methods are used [7] as provided by the University of Arizona and South Dakota State University (SDSU). While the vicarious results are not precise enough (\(\sim\)3-5%) to identify small errors or short-term trends, the results provide a measure of the absolute radiometric accuracy and enable long-term gain adjustments [8]. The University of Arizona developed an automated radiometric calibration test site (RadCatS), deployed in Railroad Valley, Nevada, that provides surface measurements continuously throughout the day [9].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Term**} & \multicolumn{8}{c}{**Uncertainty (1\(\sigma\))**} \\ \cline{2-10}  & **CA** & **Blue** & **Green** & **Red** & **NIR** & **SWIR1** & **SWIR 2** & **Pan** & **Cirrus** \\ \hline Initial Diffuser BRDF & 1.4\% & 1.3\% & 1.1\% & 1.0\% & 1.0\% & 1.7\% & 1.4\% & 1.1\% & 1.7\% \\ Diffuser Geometry & 0.9\% & 0.9\% & 0.9\% & 0.9\% & 0.9\% & 0.9\% & 0.9\% & 0.9\% & 0.9\% \\ Diffuser Stray Light & 0.7\% & 0.7\% & 0.7\% & 0.7\% & 0.7\% & 0.7\% & 0.7\% & 0.7\% & 0.7\% \\ Pristine, Wkg Non-Linearity & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% & 0\% \\ Wkg, Scene Non-Linearity & 1.0\% & 1.0\% & 1.0\% & 1.0\% & 1.0\% & 1.0\% & 1.0\% & 1.0\% & 1.0\% \\ FFOV Non-Uniformity & 0.3\% & 0.2\% & 0.3\% & 0.3\% & 0.3\% & 0.3\% & 0.4\% & 0.3\% & 0.3\% \\ Long Term Stability & 0.1\% & 0.1\% & 0.1\% & 0.0\% & 0.0\% & 0.3\% & 0.2\% & 0.1\% & 0.6\% \\ Total & 2.1\% & 1.9\% & 1.7\% & 1.7\% & 1.7\% & 2.2\% & 2.0\% & 1.7\% & 2.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: OLI reflectance-based calibration uncertainty (modified Ball Aerospace table).  
Figure 2: OLI on-orbit gain trends.
# Valkyrie--Design and Development of Gaits for Quadruped Robot Using Particle Swarm Optimization
## 4 Gait Development Using PSO
### PSO Algorithm Implementation and its Significance  
Inverse kinematics is a key issue in robotics, especially for problems such as path planning, motion generation, or trajectories optimization. It becomes very tedious to calculate with many degrees of freedom, and also, the results are not optimal. Since the focus of this paper is to develop robust gaits for the quadruped robot and, it is necessary to determine an optimal value of joint angles to achieve efficient locomotion. Due to this non-linearity of the system, rather than using a conventional approach, PSO is a very efficient method. As all the legs are symmetric to each other, it is sufficient to run the algorithm for one leg and utilize the results obtained for the other legs. Thus, through the PSO algorithm, we are accelerating each particle, i.e., joint variables towards the \(P_{\text{best}}\) and \(G_{\text{best}}\) through repetitive iterations to obtain the optimal joint variables. The pseudo code of the developed PSO algorithms is given as Algorithm 1 below.  
```
Inputs:\(t_{max}\) (Maximum Iterations), S (Swarm Size), D (Dimension of particle), C1 and C2 (Learning rates), \(\omega\) (Inertial Weight), [\(\theta_{1 min}\), \(\theta_{2 min}\)] (Lower bounds), [\(\theta_{1 max}\), \(\theta_{2 max}\)] (Upper bounds) Outputs:\(G_{\text{best}}\) (Global / Best Cost)
1: Randomly generate the Initial Swarm S within the range of 0 to 20 for each particle of dimension D
2: Initialize the Swarm Parameters
3: Check whether the CoM/CoG lies within the support polygon for the generated particles
4: Calculate the current position using Equation (3)
5: Determine the Fitness value using Equation (15)
6: Evaluate the fitness of the swarm and obtain the \(P_{best}\) and \(G_{best}\)
7: Update the velocity and position of particles using Equations (12) and (13)
8:while\(t\leq t_{max}\)do
9: Evaluate the fitness of new particles generated.
10:for each particle in the swarm
11:if current fitness < \(P_{best}\). assign \(P_{best}\) = current fitness
12:endif
13:endfor
14: Repeat steps 2, 3, 4 and 5
15: Increase the iteration number \(t\) by 1
16:endwhile
```  
**Algorithm 1** PSO Algorithm Implementation
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Functional Validation
#### 4.3.3 Implementation of the IPTV Service Functions  
We have prototyped the different components of the IPTV service. In particular, all the VNFs have been implemented using Linux virtual machines (Ubuntu 16.04.5 LTS). The multicast router VNFs have been built using an implementation of Protocol Independent Multicast--Sparse Mode (PIM-SM) [32], i.e., the Linux _pimd_ multicast routing daemon. The IPTV server VNF is based on the _VLC_ media player, which can be used to stream a video content to a specific multicast host group. The laptop and the virtual machine representing the IPTV subscriber equipment also use the _VLC_ tool to consume the multicast video streams. In this respect, _VLC_ implements the Internet Group Management Protocol (IGMP) [33], which can be used to join and leave any multicast host group corresponding to a TV channel.
# Transformer Fault Diagnosis Method Based on SCA-VMD and Improved GoogLeNet
## 4 Tests and Analysis of Results
### Network Structure and Hyperparameter Settings  
During the construction of the experimental model, the input images were first weighted using a spatial-based attention mechanism module. The spatial attention mechanism module was composed of two convolutional layers and sigmoid activation function, where the first convolutional layer had an input channel number of 2, which represented the result after averaging and maximising the input feature maps, and the second convolutional layer had an output channel number of 1, which represented the result after attentional weighting of the input feature maps; in forward propagation, the input feature maps were averaged along the channel dimensions and maximum value, and then spliced together as input; after two convolutional layers and the sigmoid activation function, the attention weight was obtained, and finally, the original feature map was multiplied using the attention weight and fed into the GoogLeNet network. The convolutional layer in the GoogLeNet network used 64 7 \(\times\) 7 convolutional kernels (step 2, padding 3) and 3 \(\times\) 3 pooling kernels (step 2, padding 1), the Inception module performed convolutional operations on the inputs using convolutional kernels of different sizes, as shown in Figure 3, and spliced the outputs of multiple different sizes in the channel dimensions, using the LRN layer for local response normalisation, and then performed channel attention weighting on the spliced feature maps, and finally, multiplied the original feature maps by the attention weights and performed dimensionality reduction on the feature maps using global average pooling. The use of two fully connected layers, as well as the use of ReLU and softmax activation functions, had good nonlinearity while the network output a probability distribution.  
In the training phase, the loss function was set to the cross-entropy loss, which is most widely used in classification problems. A training mode with a batch size of 64 was used to speed up the network convergence and save computer memory. In the selection of the  
Figure 7: Periodic map spectra and corresponding feature maps.  
optimisation algorithms, this study used the gradient-based SGD optimiser method and the learning rate decay strategy, which adjusted the learning rate after every certain epoch. In addition, L2 regularisation and 0.4 dropout were introduced during the training process to avoid the risk of overfitting and enhance the generalisation ability of the model.
# Probabilistic Modeling of Congested Traffic Scenarios on Long-Span Bridges
## 2 Review of Probabilistic Model of Extreme Response Scenarios in Long-Span Bridges
### Heavy Vehicle Weights and Correlations  
Since the distribution levels of heavy vehicle weights vary across different lanes, parameter fitting and simulation were conducted separately for the outer lanes and the inner lanes during the extreme scenario modeling process. The vehicle weight distribution shows clear multimodal characteristics that cannot be described with typical unimodal probability distribution models [27; 34]. To better fit the multimodal characteristics of the weights, the Gaussian Mixture Model (GMM), which can accurately capture multiple peak attributes, was applied in this study [35].  
The clustering of consecutive heavy vehicles in the same lane is a key factor contributing to extreme load effects, posing a threat to the structural safety of bridges. To simulate this clustering effect, it was assumed that there was a certain correlation between the weights of heavy vehicles in adjacent cells. The Nataf transformation method, combined with the GMM distribution, was introduced to simulate this correlation [36]. The Nataf transformation utilizes the marginal probability density functions of the variables, the correlation coefficient matrix, and the multivariate standard normal distribution to construct a joint probability density function through the Gaussian Copula function. This process converts correlated non-normal variables into independent standard normal variables. In this method, the weight correlation over a certain length is characterized by the Pearson correlation coefficient:  
\[\rho_{j,k}=\frac{\alpha_{j,k}}{\lambda_{j}\cdot\lambda_{k}} \tag{1}\]  
where \(\rho_{j,k}\) represents the correlation coefficient of the total weights of heavy vehicles at positions \(j\) and \(k\), while \(\alpha_{j,k}\) corresponds to the covariance between the total weights of the two heavy vehicles. \(\lambda_{j}\) and \(\lambda_{k}\) denote the standard deviations of the total weights of heavy vehicles at positions \(j\) and \(k\), respectively. It is evident that the correlation coefficient \(\rho_{j,k}\) between the total weights of two heavy vehicles weakens as the distance between them increases.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants  
### Carthamus tinctorius L  
_Carthamus tinctorius_ L., commonly known as safflower or false saffron, is an herbaceous annual plant that belongs to the _Asteraceae_ family [7; 8]. The safflower is a plant with extensive branching, growing up to a meter in height. Its leaves are lanceloate and stalkless, measuring between 4 and 10.5 cm in length and 1 and 2.8 cm in width. Its bright yellow-orange or red flowers are large and sit within a cluster of leafy bracts, forming an involucre around 3 cm across. The fruits comprise achenes, with four deformed ribs and a truncated top. The seeds, which may be hairy or hairless, are encased in a thick pericarp [7; 9]. _C. tinctorius_, one of the earliest cultivated crops, has been documented in commercial use for over 2200 years. The flowers and seeds of _C. tinctorius_ have been used in traditional herbal medicine throughout China, Korea, Japan, and other Asian countries to treat various health problems [9]. Table 1 presents a comparison of the potential pro-health properties of _Carthamus tinctorius_ L., _Camelina sativa_ L., and _Nigella sativa_ L. based on the analyzed recent data. The phytochemistry of safflower has been extensively studied, with over 200 isolated compounds. They include, among others, flavonoids, alkaloids, organic acids, and polyacetylenes. Flavonoids, which have a wide range of biological activity, are considered the characteristic, active components of safflower. Substances such as quinochalcones, which comprise almost all yellow and red pigments in flowers, C-glycos, O-glycos, and kaempfer derivatives have been isolated from this plant. In recent years, a major component of the yellow pigment, hydroxyasafflor yellow A (HSYA), has garnered significant attention from researchers owing to its promising therapeutic potential. Widely distributed in safflower seeds and leaves, alkaloids, which are serotonin derivatives, can be used as natural antioxidants. _C. tinctorius_ seeds are rich in fatty acids, including linoleic acid (LA) (40-80%), oleic acid (20-50%), palmitic acid (6-10%), and stearic acid (2-3%). In flowers, roots, and immature seeds, polyacetylene compounds have been found. However, their phytochemistry and biological activity have not been given sufficient attention in the available literature [8; 9; 10].