esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Deployable Antenna
#### 3.1.4 Summary of Deployable Antennas  
Framework-based, perimeter truss-based, and radial rib-based deployable antennas each offer unique advantages for different applications.  
(1) Framework deployable antennas utilize a modular truss design, forming a spatial grid through interconnected rods and nodes. They offer a high stowage ratio, precision, and deployment stability with strong structural rigidity, making them suitable for large aperture and high-bandwidth applications. However, their complex assembly and uneven weight distribution may impact performance.  
(2) Perimeter truss deployable antennas use a ring truss and cable-net reflector, resulting in a lightweight, high-stowage-ratio structure with uniform weight distribution and simplified deployment mechanisms. They effectively balance the deployment ratio, structural stability, and mass distribution, making them ideal for next-generation SAR satellites.  
(3) Radial rib deployable antennas rely on a central deployment mechanism with outward-extending ribs, offering simplicity, reliability, and lightweight characteristics. They are particularly suited for large spaceborne antennas and high-resolution SAR missions.  
Table 2 summarizes the SAR satellite missions that employ different types of deployable antennas.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
## 3 Challenges and Results
### Flicker Issue and the Digital Signal Processing (DSP)-Based Solution
Figure 9b also provides a zoom-in view of \(r(t)\) and \(e(t)\). It can be observed that the flicker \(e(t)\) has been successfully extracted from \(r(t)\) and it corresponds to the fluctuating DC level. Once subtracted from \(r(t)\), the flicker can be removed, and a stable DC level at around zero is achieved, as shown in Figure 9c,d. The improvement in BER performance can be clearly observed from the decoded constellation diagrams at the 1 st carrier before and after flicker removal, as shown in Figure 10. We also find that only the 1 st carrier was affected, which confirms our analysis that the flicker interferes with the sub-band while using the same spectrum for data transmission. Figure 8: Digital signal processing (DSP)-based solution to remove the flicker in the received signal. \(r(t)\) is the signal captured directly by the oscilloscope (OSC) at a sample rate of \(f_{R}\) and \(e(t)\) is the estimated low-frequency “envelope” signal for the flicker. \(y(t)\) is flicker-free after the removal of \(e(t)\) from \(r(t)\).
## 4 Experimental Results
### Quantile Comparisons Using Peak, Average, and Bad-Case Performances

Table 4: Experimental results of ACO algorithms for the DTSP with weight changes. Figure 6: Box plots of \(\mathcal{M}\)MAS and P-ACO drawn between the first (\(Q_{0.25}\)) and third quartile (\(Q_{0.75}\)) of the distribution on rat575 with node (**left**) and weight (**right**) changes. Another issue that the use of quantiles may address is when the distribution of executing ACO algorithms multiple times is asymmetrical (i.e., having several outliers such as the distribution of \(\mathcal{MM}\)AS in Figure 5 for kroA100 with weight changes, and P-ACO in Figure 6 for rat575 with weight changes), affecting the mean comparisons [17]. Therefore, the quantile \(Q_{0.50}\), that defines the median of the distribution, may be a more adequate choice to measure the average performance of ACO algorithms than the mean values shown in Section 4.2. This effect is demonstrated when comparing the mean results of ACO algorithms in Table 1 (and Table 2) with the corresponding median results in Table 3 (and Table 4). Specifically, it can be observed that the comparisons of the DTSP with weight changes are consistent, whereas the comparisons of the DTSP with node changes have several inconsistencies as follows. P-ACO has better mean values than \(\mathcal{MM}\)AS in most DTSP cases of pr1002, while \(\mathcal{MM}\)AS has better median values than P-ACO on the same DTSP cases. The difference regarding the distribution of P-ACO and \(\mathcal{MM}\)AS can be observed in Figure 7, in which P-ACO has a more symmetrical and concentrated distribution than \(\mathcal{MM}\)AS. From the quantile comparisons in Tables 3 and 4, it can also be observed that: first, the peak performances of both ACO algorithms are better, as expected, than the average performance or bad-case performance, and second, the average performance (i.e., \(Q_{0.50}\)) comparisons in Table 4 for the DTSP with weight changes are consistent with the peak (i.e., \(Q_{0.10}\)) and bad-case (i.e., \(Q_{0.90}\)) performance comparisons since \(\mathcal{MM}\)AS outperforms P-ACO in all cases. In contrast, when comparing the average performance results with the peak performance results for the DTSP with node changes in Table 3, there are some inconsistencies. For example, \(\mathcal{MM}\)AS has a better peak performance than P-ACO on the pr1002 test cases, whereas P-ACO has a better average performance than \(\mathcal{MM}\)AS on the same test cases. In summary, \(\mathcal{MM}\)AS may be a better choice for DTSP with weight changes when the independent execution is performed in parallel. This is because it has a better peak performance than P-ACO, and hence, it is expected to obtain a solution at least as good as \(Q_{0.10}\). In contrast, P-ACO may be a better choice when a single execution is performed because there are more chances in obtaining a solution as good as \(Q_{0.50}\).
# A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
## 4 Case Study
### Robustness against Variable Operating Environment  
In the engineering application of fault diagnosis, the variable operating environment greatly influences the system's performance. For example, wind turbines operate under continuous variable speed and variable loads. In the same wind farm, the operating conditions of wind turbines differ by local geographical environment. In addition, there are more difficult diagnostic scenarios, such as the same type of planetary gearboxes mounting in different wind farms. In these cases, diagnostic systems require high robustness against environmental changes. In this sub section, we sequentially take a subset belonging to one wind turbine from the original dataset as the robustness test data and use the remaining data to train the two-stage framework as described in Section 4.1. The performance metrics  
Figure 7: Comparison of performance and time consumption between different methods.  
and fault classification accuracy of the trained model on the robustness test dataset are shown in Figure 10.  
We can observe that the segmentation and classification performance in each healthy state is affected by the variable operating environment in some way. The proposed two-stage framework achieves an average F1 score of 0.747 and a classification accuracy of 83.1% on the robustness test dataset. The normal state gives the best segmentation and classification performance on the robustness test dataset, reaching an average F1 score of 0.91 and a classification accuracy of 99.18%. Fitting-misalignment concurrent faults gives the worst robustness. A possible reason is that more complex fault causes make less similarity between different machines. In addition, the experimental results also show that there is no dependence of the classification accuracy on a certain segmentation performance metric when surveying four health states, which indicates that the influence of errors in the analytical stage on the geometric moments is uncertain. To sum up, the pro  
Figure 8: Classification confusion matrix.  
Figure 9: Trained decision tree in the experiment.  
posed framework presents satisfactory robustness against variable operating environments. Therefore, it is suitable for application in the actual industrial environment with variable operating environments.
## 5 Experiments and Results  
To evaluate the classification performance of the proposed UN15 urban noise dataset and to verify the effectiveness and generalization capability of the ResNet-TF model in urban environmental sound classification tasks, a two-stage experimental framework was designed in this study. In the first stage, experiments were conducted using the widely adopted UrbanSound8K public dataset to assess the baseline performance of various deep learning models under standard environmental sound classification scenarios. In the second stage, the same models were deployed on the newly constructed UN15 dataset to comprehensively examine their classification accuracy and robustness in complex, multi-source real-world urban acoustic environments. This experimental workflow not only provides quantitative evidence for the usability of the UN15 dataset but also offers practical guidance for algorithm selection in future intelligent urban noise monitoring systems.
# Software Defined Wireless Mesh Network Flat Distribution Control Plane
## 4 Topology Discovery
### Aggregated Topology Discovery Mechanism
#### 4.1.2 Entire Network Topology Aggregation  
Using the coordinator module each local controller can discover other neighboring domain's controllers and maintain a control channel to them, then the connectivity agents can exchange and aggregate topology discovery information of the surrounding domain in each controller and aggregate this information of entire network topology in its aggregated-DB. By using this hierarchically aggregated mechanism over FD-SDWMN architecture, the heavy topology discovery computation burden on a controller can reduce and the slow convergence time problem of the distributed nodes of high scale WMNs can be addressed. Figure 5 depicts the control message flow of the aggregated topology discovery mechanism.  
Figure 5: Messages flow for the aggregated topology discovery mechanism.
## 2 Methodology of the Study
### Database Creation  
To create the database, a benchmark URM building was selected; this was followed by the development of a numeric model of the building using a simplified finite-element modelling approach, using layered shell elements to model the masonry. This methodology was chosen to minimize the computational time required for database development. Compared to other detailed finite-element modelling techniques that employ solid elements, this  
Figure 1: Methodology of the study.  
shell-based three-dimensional modelling approach demonstrates enhanced computational efficiency while maintaining an acceptable level of accuracy. Then, the material properties of unreinforced brick masonry reported in the literature were collected. To consider the uncertainties associated with material properties, 'N' random samples were assembled using the Latin Hypercube Sampling (LHS) technique. This methodology was employed to minimize the number of simulations, while encompassing a broader spectrum of material properties. The random samples were created by partitioning each material property into an equal number of sub-intervals according to their probability distribution and selecting a sample from each interval. To incorporate the uncertainties associated with seismic events, 'N' compatible ground-motion records were identified and randomly paired with the material property samples.  
For every random sample, static push-over analyses were carried out to record the lateral load-carrying capacity of the URM building. Incremental dynamic analyses were then carried out to identify the seismic response of the URM building for every input sample. The maximum IDR values were then identified through numerical analyses. Finally, the database for the regression-based ML models was developed, with the predictor variables being material properties and ground-motion intensity and the response variable being IDR. The definitions of damage levels associated with URM buildings, obtained from the literature, were then used to classify the building damage based on the values obtained for the engineering demand parameter (IDR). Using the damage class identification, the database for the classification-based ML models was developed with the predictor variables (material properties, ground-motion intensity, and IDR) and categorical response variables (damage states).
# Multimodal Classification of Anxiety Based on Physiological Signals
###### Abstract  
Multiple studies show an association between anxiety disorders and dysregulation in the Autonomic Nervous System (ANS). Thus, understanding how informative the physiological signals are would contribute to effectively detecting anxiety. This study targets the classification of anxiety as an imbalanced binary classification problem using physiological signals collected from a sample of healthy subjects under a neutral condition. For this purpose, the Electrocardiogram (ECG), Electrodermal Activity (EDA), and Electromyogram (EMG) signals from the WESAD publicly available dataset were used. The neutral condition was collected for around 20 min on 15 participants, and anxiety scores were assessed through the shortened 6-item STAL. To achieve the described goal, the subsequent steps were followed: signal pre-processing; feature extraction, analysis, and selection; and classification of anxiety. The findings of this study allowed us to classify anxiety with discriminatory class features based on physiological signals. Moreover, feature selection revealed that ECG features play a relevant role in anxiety classification. Supervised feature selection and data balancing techniques, especially Borderline SMOTE 2, increased the performance of most classifiers. In particular, the combination of feature selection and Borderline SMOTE 2 achieved the best ROC-AUC with the Random Forest classifier.  
anxiety; classification; wearable sensors; multimodal dataset; machine learning; physiological signals; self-reports Article
# A Review of Degradation and Life Prediction of Polyethylene
## 5 Prediction Techniques for Polyethylene Materials  
### Thermogravimetric Analysis for Kinetic Modeling  
In recent decades, predicting the lifetime of polymeric materials such as polyethylene has become a significant research issue [94; 95; 96]. The mass decomposition of materials that are linearly dependent on time and temperature can be determined using thermogravimetric analysis (TGA) [97], which is frequently used to research the mass decomposition and kinetics of polyethylene materials. It is challenging to study each stage of the polyethylene breakdown process separately using a straightforward kinetic model because of how complex it is [98; 99]. Approaches based on single-step approximations, either model-free or model-fitting methods, are typically employed to explain polyethylene dynamics [100].  
The degree of conversion that changes with time or temperature is referred to as the reaction rate in thermogravimetric analysis research, and the conversion rate \(\alpha\) is determined using Equation (12) in terms of mass loss.  
\[\alpha=\frac{\omega_{o}-\omega_{t}}{\omega_{o}-\omega_{\infty}}=\frac{\Delta \omega}{\Delta\omega_{o}} \tag{12}\]where \(\omega_{o}\), \(\omega_{t}\), and \(\omega_{\infty}\) stand for the initial mass, the mass at temperature \(t\), and the final mass, which is the mass at which the mass loss is practically constant, respectively. The product of two functions, one of which is dependent on temperature \(T\) and the other on the rate of the reaction, is typically used to indicate the conversion rate of a kinetic process. The general kinetic model of degradation is defined by Equation (13) [101]:  
\[\frac{d_{\alpha}}{d_{t}}=K(T)f(\alpha) \tag{13}\]  
where \(f(\alpha)\) is the transformation function, and \(K(T)\) is the temperature-dependent function given by the Arrhenius Equation (14) [102].  
\[K(T)=Ae^{-E_{a}/RT} \tag{14}\]  
Thus, Equation (13) can be further written as:  
\[\frac{d_{\alpha}}{d_{t}}=Aexp\left(-\frac{E_{a}}{RT}\right)f(\alpha) \tag{15}\]  
where \(A\) is the pre-exponential factor, \(E_{a}\) is the activation energy, and \(R\) is the gas constant. The reaction model has various forms, some of which are shown in Table 2.
## 2 Related Work and Motivation
### Federated Learning for Intrusion Detection
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & \begin{tabular}{l} **Needs** \\ **Labels?** \\ \end{tabular} & \begin{tabular}{l} **Needs Auxiliary or** \\ **Shared Data?** \\ \end{tabular} & \begin{tabular}{l} **Clustered/Similarity Signal** \\ \end{tabular} & \begin{tabular}{l} **Extra Communication vs. FedAvg** \\ \end{tabular} & \begin{tabular}{l} **Computational Overhead** \\ \end{tabular} \\ \hline WSCC [8] & ✗ & ✗ & Full model weights; cosine/2 distance & \
\begin{tabular}{l} High–entire weight tensors \\ transferred for pairwise \\ comparison \\ \end{tabular} & Low (simple distance calc.) \\ K-FL [15] & ✗ & ✗ & Kalman-filtered weight trajectories & Moderate & Low \\ FedPVD [9] & ✗ & ✗ & rounds & Moderate & Low \\ HIC-FL [10] & ✗ & ✗ & Weight similarity + intra-cluster model & Moderate to high & Medium \\ FLS [11] & ✓ (labels on & ✓ (server keeps small \\ aux-set) & & labeled dataset) & Output logits on shared aux-data & Low (only logits) &
\begin{tabular}{l} High (server runs \\ inference K times per \\ round) \\ \end{tabular} \\
**FedSAGE** & ✗ & ✗ & Latent \(\mu\)-vector from pre-trained VAE & Low (only vector size \(1\times d\)) & Low \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Client clustering methods in federated learning. (✓: required; ✗: not required). solutions can be integrated into FL to continuously learn from new attack patterns, swiftly adapt to emerging threat trends, and strengthen IDS defenses against network threats in real time, without requiring extensive human intervention.
## 1 Introduction
Simulations results show that the BLJCAR wireless transmission algorithm embedded in coding-aware routing outperforms the traditional ONC policy in terms of jitter, packet delivery delay, packet loss ratio and network throughput in network congestion in any traffic rates. It is a simple, efficient and easy algorithm to be implemented. The remainder of this paper is organized as follows. Section 2 explores the coding opportunity analytically. Section 3 presents a unified framework for wireless network coding with random packets arrival in single rate case and multiple rate case. The threshold of a queue-length based threshold policy adopted is computed as well. Based on these results, we present the implementation details for the BLJCAR scheme in Section 4. Section 5 presents performance evaluation results. Section 6 concludes the paper.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## 1 Introduction
### Our Contribution  
Differently, in this paper, the authors propose an alternative signal constellation for data carriers in 5G/6G systems. The proposed constellation is characterized by the insertion of a zero-energy symbol that sources can use in the presence of variable information rate and discontinuous activity. The impact of this new signaling scheme is twofold. First data carriers modulated with the \"zero\" symbol to save energy. In presence of different traffic flows transported by the same bearer, the suppression of the entire frame is possible only when all flows are silent. The proposed scheme map silences with much more flexibility within the single frame producing a fine granularity in energy consumption. Low-rate sources woken up by events (i.e., sensors), face a high signaling latency unless ather re assigned dedicated continuous resources. The presence of a silent symbol in the constellation avoids the high layer activity signaling, thus dramatically reducing the service latency for impulsive low rate sources.  
In this paper, we provide the baseline of the proposed energy efficient constellation, where a zero-energy symbol is added to the classical binary phase shift keying (BPSK). This constellation maps silence states of the information source to that symbol which corresponds to a zero energy signal transmitted from the antenna. As shown below, the presence of a zero energy symbol increases the energy efficiency for low activity sources and provides an instant resume when the source leaves the silence state. The proposed signaling scheme differs from OOK (on-off keying) for the presence of a complete energy balanced set of information signals. The concept can be extended to multilevel constellation (QAM) and the transmitted signal is physically (but not semantically) backward compatible. In other words, the zero-energy symbol is one legitimate symbol of the constellation that the receiver could expect to receive from the source. This fact reaches a two-fold benefit: a) there is no need to send additional control bits to inform the receiver that a silent state is active; b) there is no need to implement a wake up of the receiver, since the zero-symbol is directly detected by the demodulator. A sleep state of the receiver can be implemented anyway to save energy in case of very long periods of inactivity. In this case, the wake-up strategy is exactly the same of any receiver, according to the protocol implemented, e.g., wake up is bond to the reaching of a specific date and time, etc.  
Section 2 provides the maximum-a-posteriori and maximum-a-priori criterion for the proposed signaling scheme for AWGN channel. In Section 3 some performance metrics are proposed for a fair comparison with the traditional constellations. Section 4 provides some commented numerical results while Section 5 provides an evaluation of the attained energy efficiency and channel capacity. In Section 6 there is a evaluation of the equivalent signaling overhead in traditional constellations. Section 7 has some concluding remarks.