esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Motivation
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Protocol** & **Frequency Band** & **Data Rate** & **Range** & **Energy Consumption** & **Usage Area** \\ \hline Wi-Fi [28] & 2.4/5.0 GHz & 54 Mb/s & 30 m & Medium & Home entertainment, Industrial \\ Bluetooth [2] & 2.4 GHz & 1 Mb/s & 10 m & Low & Industrial, Traffic management \\ IEEE 802.15.4 [23] & 868\(-\)915 MHz/2.4 GHz & 250 kb/s & [10, 100] m & Low & Industrial, Traffic management, Smart \\  & & & & & home, vehicle monitoring \\ LTE [29] & 2.6 GHz & 10 Mb/s & \(\leq\)15 km & High & Mobil telecommunications, Smart Cities \\ Wireless HART [24] & 2.4 \(-\)25 MHz & 250 kb/s & [1, 100] m & Low & Healthcare, Industry automation \\ BLE [31] & 2.4 MHz & 1 Mb/s & 200 m & Very Low & Healthcare, Home entertainment \\ Z-WAVE [26] & 1 GHz & 40 kb/s & 30 m & Low & Home automation applications \\ WAVENS [22] & 865\(-\)916 GHz & 100 kb/s & \(\leq\)4 km & Very Low & Chemical and healthcare applications \\ LoRaWAN [32] & 868\(-\)900 GHz & 50 kb/s & \(\leq\)15 km & Very Low & Smart City, industrial Monitoring, Agriculture \\ NB-IoT [30] & 180 kHz & 234.7 kb/s & \(\leq\)35 km & Low & Industrial Monitoring, Smart City \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of characteristics of different communicatioTn interfaces.
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 3 Initial Resource Allocation Multi-Objective Optimization Model
\begin{array}{ll}1,&\sum\limits_{t\in T_{c}^{k}}\sum \limits_{k}x_{c,t}^{f,k}\geq 1\\ 0,&\sum\limits_{t\in T_{c}^{k}}\sum\limits_{k}x_{c,t}^{f,k}=0\end{array} \right. \tag{8}\]  
\[\sum_{c\in C}\omega_{c}^{f}=1,\forall f\in F \tag{9}\]  
\[\sum_{f\in F}x_{c,t}^{f,k}\leq 1,\forall c\in C,t\in T_{c}^{k} \tag{10}\]  
\[x_{c,t}^{f,k}-x_{c,t-1}^{f,k}\geq 0,\forall f\in F,c\in C,t\in T_{c}^{k} \tag{11}\]  
\[x_{c,t+\delta_{k,k+1}}^{f,k}-x_{c,t}^{f,k}\leq 0,\forall f\in F,c\in C,t\in T _{c}^{k} \tag{12}\]  
\[\sum_{t\in T_{c}^{k}}\sum_{f\in F}\left(x_{c,t}^{f,k}-x_{c,t-1}^{f,k}\right) \leq M_{c}^{k},\forall c\in C \tag{13}\]  
Equations (3) and (4) serve as objective functions, representing the minimization of total delay costs (including ground and airborne delay costs) and the minimization of the disparity in delay allocation among different airlines (minimizing fairness loss), respectively. Equations (5)-(7) serve as allocation feasibility constraints, indicating that for any given flight \(f\), a specific route can only be assigned to the corresponding flight if and only if the airline has submitted the affected flight and has the option to choose that particular route. Furthermore, the assignment of a route to flight f must occur no earlier than the time it was submitted. Equations (8)-(10) represent uniqueness constraints for allocation. Specifically, they indicate whether flight \(f\) is assigned to the route \(c\). Each flight can only be assigned to one slot on one route, and it must be assigned to a slot, ensuring that each slot can be assigned to at most one flight. Equation (11) represents the time continuity constraint, ensuring the temporal continuity of flights through various FCAs. Equation (12) serves as the resource continuity constraint, guaranteeing the continuity of resources between FCAs. Equation (13) represents the capacity allocation constraint, ensuring that the number of flights assigned to each route does not exceed the capacity of the corresponding airway.
## 5 Experiments and Analysis of Results  
This section presents the experimental setup and comprehensive analysis of the results achieved with the proposed model. Section 5.1 outlines the experimental environment and implementation details. Section 5.2 introduces the evaluation metrics used to assess model performance. Section 5.3 discusses the detection results of the proposed DOA-BPNN model, including its effectiveness in identifying various types of attacks. Furthermore, a comparative analysis is performed against several baseline techniques to demonstrate the model's advantages.
## 3 Results and Discussion
### Ideal CO2 Separation Performance
These results suggest that although the total transport of CO\({}_{2}\) molecules was solubility controlled, the stronger N\({}_{2}\) solubility in **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)** and slower N\({}_{2}\) diffusion in **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)** affected the overall permeability performances of the composite materials. The permeability properties of the pure PIL are closer to the properties of typical glassy polymers. This is visible in the He/CO\({}_{2}\) selectivity, since He permeates more than CO\({}_{2}\) in the **P\({}_{9}\)IL\({}_{0}\)Zn\({}_{0}\)**-based membranes, showing a permeability selectivity of 1.5. In the membranes prepared from PIL/IL/Zn\({}^{2+}\) composite materials, the He/CO\({}_{2}\) selectivity is reversed, since He permeates less than CO\({}_{2}\). This indicates that the composite materials are similar to rubbery polymers, wherein the transport is \"solubility controlled.\"  
Figure 6 compares the ideal separation performances of investigated materials with the reported data for three gas pairs: CO\({}_{2}\)/N\({}_{2}\), CO\({}_{2}\)/CH\({}_{4}\) and He/CO\({}_{2}\)[78]. The composite materials containing [Pyrr\({}_{14}\)][Tf\({}_{2}\)N] and Zn[Tf\({}_{2}\)N]\({}_{2}\) salt are positioned in the central part of the plot. **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{1}\)** and **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{9}\)** reveal moderate separation efficiency, while **P\({}_{9}\)IL\({}_{6}\)Zn\({}_{0}\)** shows the best performance enhanced by the presence of low viscosity IL segments. Figure 6: Positions of the composite PIL/IL/Zn\({}^{2+}\) materials on the CO\({}_{2}\)/N\({}_{2}\) (**a**), CO\({}_{2}\)/CH\({}_{4}\) (**b**) and He/CO\({}_{2}\) (**c**) [PERSON]’s plots. The ideal separation performances of reported membrane materials are presented for comparison and are freely available from the Membrane Society of Australasia [78]. Blue solid lines are for guiding the eyes.
## 4 Results and Discussion
### Evaluation of Model Robustness Under Synthetic Data and Adversarial Samples  
The primary objective of this experiment is to evaluate the robustness of the proposed sliding-window computation framework when confronted with data perturbations and non-ideal inputs, with particular emphasis on its stability and generalization under synthetic data and adversarial sample conditions. To achieve this, four comparative settings--\"None\" (no perturbation), \"Synthetic Data\" (augmented data), \"Adversarial Sam  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **Accuracy** & **F1-Score** & **FPS** \\ \hline Time-Series—Cross-Entropy Loss & 0.69 & 0.65 & 0.67 & 0.67 & 27 \\ Time-Series—Focal Loss & 0.87 & 0.82 & 0.84 & 0.83 & 34 \\ Time-Series—Proposed Method & 0.95 & 0.91 & 0.93 & 0.93 & 46 \\ Spatial Data—Cross-Entropy Loss & 0.66 & 0.63 & 0.65 & 0.64 & 30 \\ Spatial Data—Focal Loss & 0.84 & 0.80 & 0.82 & 0.82 & 37 \\ Spatial Data—Proposed Method & 0.93 & 0.90 & 0.92 & 0.91 & 49 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on different loss functionsples\" (perturbed inputs), and the \"Proposed Method\"--were established across two types of multimodal tasks. For both time-series and spatial image data, the model was assessed using five metrics: precision, recall, accuracy, F1-score, and FPS. This experimental setup aims to simulate realistic scenarios involving data drift, adversarial attacks, or incomplete observations, thereby offering a more practical understanding of the model's reliability in deployment contexts.  
As reflected in Table 7, under the \"None\" condition for time-series data, the model exhibited relatively poor performance, with a precision of 0.63 and recall of only 0.60, indicating weak adaptability to data perturbations and limited predictive capability. After introducing synthetic data, all the performance metrics improved, with the F1-score increasing to 0.73, demonstrating that representative augmented samples effectively enhance the model's ability to adapt to data distribution variations. The inclusion of adversarial samples further improved the performance, with the F1-score reaching 0.84, implying a certain degree of robustness, although still constrained by the complexity of adversarial perturbation construction. In contrast, the proposed method achieved the highest performance, with an F1-score of 0.93 and FPS of 46, significantly outperforming all the other approaches. This result validates the effectiveness of the designed sliding-window attention mechanism and sliding loss function in improving both predictive power and computational efficiency. A similar trend was observed in spatial data tasks, where the performance under the \"None\" condition was the lowest, gradually improved with synthetic and adversarial samples, and peaked under the proposed method, achieving a precision of 0.93 and an F1-score of 0.91. These outcomes collectively confirm the generalizable robustness of the proposed framework across multimodal tasks.
# On the Use of Transformer-Based Models for Intent Detection Using Clustering Algorithms
## 3 Materials and Methods
### Clustering  
Clustering algorithms refer to a category of unsupervised machine learning algorithms that enable the grouping of similar data points, usually based on their similarity or distance. The primary objective of clustering is to divide a dataset into clusters to ensure that data points within the same cluster exhibit a high level of similarity, while those in different clusters are dissimilar. Numerous clustering algorithms have been proposed as state-of-the-art to cluster sentence embeddings. Among them, the K-Means [40] is still one of the most used since it is highly scalable and converges quickly. This algorithm divides the data points into \(k\) clusters (a user-specified parameter) such that each point is assigned to exactly one cluster.  
Agglomerative clustering is a common alternative to K-Means. This algorithm takes a bottom-up approach, where data points initiate their own cluster [41]. Then, iteratively, the closest cluster pairs are merged until all points belong to a single cluster.  
Both K-Means and agglomerative clustering were examined in this work. HDBSCAN, a clustering algorithm based on density that builds a hierarchy of clusters using a tree structure, was also studied to determine if noise detection could improve performance. Regarding HDBSCAN, two cluster extraction methods were explored: leaf and Excess of Mass (EOM). The latter calculates the cluster stability from the condensed cluster tree, while the former choose leaf nodes that belong to a condensed tree hierarchy.
## 2 Structure Prototype and Finite Element Model
### Structural Dynamic Properties  
Modal analysis is performed in this study to better understand and identify the dynamic behaviour of a mechanical structure. Modal analysis is the determination of natural frequencies, mode shapes and modal damping of a system using Frequency Response Function (FRF) measurements. However, the study will focus on the mode shapes and natural frequencies of the telecommunication steel tower. This is because the mode shapes and natural frequencies of any mechanical system are likely the single most essential attribute. As a result, the designer can well understand all the forms of vibrations that are feasible by knowing each mode shape of the structure.  
Mode shape is defined as the structure deforming or vibrating in a given shape when it is excited at a particular natural frequency. The transmission steel tower has many mode shapes, which means that each mode shape has its own natural frequency. In any case, the first three modes of the tower will be discussed. The first three mode shapes of the tower are shown in Figure 11 and the natural frequency for the first three mode shape is shown in Table 7. The tower is excited at its first frequency with 0.942 Hz, it deforms and translates along the \(Y\)-axis as shown in Figure 11a. The second mode shape of the tower, as shown in Figure 11b, translate along \(X\)-axis at 1.267 Hz. The tower is excited at the third frequency with 1.536 Hz, and the third mode shape is on contrary with the first two mode shape, which is rotated about the \(Z\)-axis as shown in Figure 11c. The natural frequency of the tower increases as the mode number increases from the observation. The result also demonstrates that as the number of modes increases, so will the node points.  
Figure 10: Transmission tower assessment under the effect of three seismic scenarios ((**1**) far-field, (2) near-field, and (3) repeated earthquakes).  
The natural frequency of the tower should never coincide with the excitation frequency. This is to avoid the tower from exhibiting extremely high amplitude vibrations, which could cause the tower to collapse and fail if the excitation frequency coincides with a natural frequency of the tower. The tower can be redesigned or modified to move the natural frequency of the tower away from the excitation frequency to avoid this issue, for example, by enhancing the stiffness of the frame members. As a result, making sure that the tower's natural frequencies must be greater than any excitation frequency.
## 3 Numerical Verification
### Time-Invariant System  
The six-story shear building under consideration is characterized by modal damping ratios (\(\xi_{i}\)) of 5%, \(m_{1}=4\) ton, \(m_{2}=3\) ton, \(m_{i}\) (\(i=3\sim 6\)) = 2 ton, \(k_{1}=1800\) kN/m, \(k_{2}=1200\) kN/m, and \(k_{i}\) (\(i=3\sim 6\))\(=600\) kN/m, where \(m_{i}\) and \(k_{i}\) represent the mass and stiffness of the \(i\)th story, respectively. The modal frequencies of the structure are 0.801, 2.14, 3.15, 4.25, 5.04, and 5.37 Hz. Notably, the frequency difference between the fifth and sixth modes is less than 10%, indicating a significant degree of mode interference between these two modes.  
In Figure 6, the base excitations and acceleration responses with added noise for the first and sixth floors are illustrated. The first mode scalogram of the responses of the sixth floor, presented in Figure 7a, indicates a time-invariant system. The scalogram was computed using the Morlet wavelet transform.  
The data within the time interval t = 10 to 20 s, as depicted in Figure 6, underwent processing using the proposed approach. Modal parameters for the first and second modes were identified using responses in subspaces \(U_{6}^{(1)}\) and \(U_{6}^{(2)}\), respectively. The third and fourth modes were identified through responses in subspace \(U_{6}^{(4)}\), while the fifth and sixth modes were obtained using responses in subspace \(U_{6}^{(3)}\). Stabilization diagrams for the identified frequencies and modal damping ratios, as shown in Figure 8, reveal very few spurious modes denoted by blue symbols in the preserved frequency ranges of these subspaces.  
Figure 6: Time histories and Fourier spectra of the simulated acceleration responses of the six-story shear building.  
Figure 7: Scalograms of the responses of the six floors of shear buildings: (**a**) time-invariant system; (**b**) periodically varying system; (**c**) sharply varying system.  
Table 1 summarizes the identified modal parameters, with parenthesized data representing analytical (true) results. The identified results demonstrate excellent agreement with the true values. Frequencies and modal damping ratios exhibit maximum differences of 0.9% and 5%, respectively. The values of MAC exceed 0.97 for all identified mode shapes in comparison to theoretical mode shapes.
## 1 Introduction  
Since the first liquid crystal displays (LCDs) at the beginning of the seventies--based on the twisted-nematic cell configuration [1]--LC-based devices [2] have shown a great potential not only as a display technology, but also for spatial light modulation applications.  
Among the different LC-based technologies, liquid crystal on silicon (LCoS) has become one of the most widespread technologies for spatial light modulation in optics and photonics' applications [3; 4; 5; 6]. These reflective microdisplays are composed of a high-performance silicon complementary metal oxide semiconductor (CMOS) backplane, which controls the liquid crystal layer's light modulating properties. State-of-the-art LCoS microdisplays may exhibit a very small pixel pitch (below 4 \(\upmu\)m), a very large number of pixels (resolutions larger than 4 K), and high fill factors (larger than 90%). They modulate illumination sources covering the UV, visible, and IR.  
LCoS technologies are used as displays as well as polarization, amplitude, and phase-only spatial light modulators, where they achieve full phase modulation. Due to their excellent modulating properties and high degree of flexibility, they are found in all sorts of spatial light modulation applications, such as in LCoS-based display systems for augmented and virtual reality, head-up display, head-mounted display, projector, true holographic displays, digital holography, optical storage, adaptive optics, diffractive optical elements, super-resolution optical systems, optical metrology techniques, reconfigurable interconnects, beam-steering devices, wavelength selective switches in optical telecommunications, wave-front sensing of structured light beams, holographic optical traps, or quantum optical computing.  
In order to fulfill the requirements in this extensive range of applications, specific models and characterization techniques are proposed. These devices may exhibit a number of degradation effects such as limited modulation range for high spatial frequency image content, interpixel cross-talk and fringing field, and time flicker, which may also depend on the analog or digital backplane of the corresponding LCoS device. Appropriate characterization and compensation techniques are then necessary.
# Enhancing Reliability in Wind Turbine Power Curve Estimation
## 3 Results
### Experiment 2  
Once we have estimated the power curve of each WT, we have an estimate of the power curve of each WT that fits the data better than the curve determined from the points provided by the manufacturer, which has been used in the first filter. The question at this point is whether the filtering rule can be improved with the knowledge of this new curve. That is why we conduct this second experiment. We use the network of five hidden nodes to filter the row data and eliminate some points that in the improved rule may be considered anomalies, but that with the original rule may not.  
In Figure 14, we show graphically, for the WT80, the power curve obtained from the ANN on the filtered data. In this experiment, for all WTs, values of \(w_{off}=1.1\) and \(P_{off}=55\) had been used, which are smaller than those used in the original filtering rule and therefore are more selective. In this figure, the points considered are shown in lighter blue, and those discarded as anomalies are shown in darker blue. With this new filtering,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Hidden Layer Size:** & **5** & **7** & **10** & **15** & **20** & **25** \\ \hline WT80 & 86.07 & 86.03 & 86.02 & 85.98 & 85.98 & 85.96 \\ WT81 & 78.22 & 77.80 & 77.94 & 77.78 & 77.77 & 77.74 \\ WT82 & 82.10 & 82.15 & 81.89 & 82.01 & 81.94 & 81.76 \\ WT83 & 74.87 & 75.05 & 75.57 & 74.  
the previous experiment was repeated by training models of different sizes and evaluating them in the test. The results are again given in terms of the RMSE. In Table 4 for the training and in Table 5 for the test.  
Based on the results of this experiment, improving the filtering rule improves the results obtained in the training, but leaves the test results practically unchanged.
# Research on Bone Stick Text Recognition Method with Multi-Scale Feature Fusion
## 4 Experimental Analysis and Discussion
### Comparison Test with Classical Network Model Method  
Each classical network has a wide range of applications in Chinese character recognition. In this paper, we compared the classical AlexNet, DenseNet121, GooLeNet, Vgg16, ResNet34 network models and the MS-ResNet model of a multi-scale feature fusion network. Among the classical networks, ResNet was the best in recognition performance, with a slightly longer running time than AlexNet but excellent accuracy performance. The improved MS-ResNet neural network model in this paper based on ResNet can effectively improve the recognition rate of bone stick text with an accuracy rate of 90.3%, which is higher than AlexNet, DenseNet121, GooLeNet, Vgg16, and ResNet34, with accuracy rates of 8.6%, 5.0%, 4.2%, 2.7%, 1.7%, respectively. At the same time, the algorithm in this paper also has a higher recall, accuracy, and F1 value, and the algorithm produced the lowest loss value.  
This experiment shows that classical convolutional neural network algorithms have a lower recognition rate for bone stick text. The multi-scale feature fusion model enhances the underlying detail features of bone stick text, and the overall recognition effect on the data set of this paper is better than other neural network methods. The final recognition rate results of different methods on the data set are shown in Table 2 below.  
Figures 10 and 11 below show the test set's accuracy curve and the training set's loss value of the bone stick text dataset in the first 80 iterations of each algorithm. The figure shows that the MS-ResNet network model converges the fastest, reaches the highest accuracy rate at more than 30 rounds of training, and outperforms the other networks overall, achieving the highest accuracy rate of 90.3%. The data show that excessive network training while yielding smaller loss values, can lead to performance degradation due to the overtraining of the network. ResNet has the second-highest accuracy and convergence speed, AlexNet has the slowest convergence speed and the lowest recognition rate of 81%, and DenseNet has the fastest convergence speed. However, the recognition accuracy is not high, remaining at 84%, andthe recognition rate of Vgg16 is not much different from that of ResNet, but the model convergence speed is slower, and the training time is increased by three times.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Recall/\%** & **Precision/\%** & **FI/\%** & **Acc/\%** & **Loss** \\ \hline AlexNet & 72.2 & 78.0 & 75.0 & 81.7 & 1.093 \\ DenseNet & 75.7 & 81.0 & 78.2 & 85.3 & 0.98 \\ GooLeNet & 76.1 & 83.0 & 79.4 & 86.1 & 1.025 \\ Vgg16 & 79.5 & 82.8 & 81.1 & 87.6 & 1.257 \\ ResNet & 79.3 & 88.2 & 83.5 & 88.6 & 0.454 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recognition rate of bone stick text by different algorithms.  
Figure 10: Change in accuracy value of bone stick recognition by different algorithms.