esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration-Based Fingerprint Algorithm for Structural Health Monitoring of Wind Turbine Blades
###### Abstract  
Monitoring the structural health of wind turbine blades is essential to increase energy capture and operational safety of turbines, and therewith enhance competitiveness of wind energy. With the current trends of designing blades ever longer, detailed knowledge of the vibrational characteristics at any point along the blade is desirable. In our approach, we monitor vibrations during operation of the turbine by wirelessly measuring accelerations on the outside of the blades. We propose an algorithm to extract so-called vibration-based fingerprints from those measurements, i.e., dominant vibrations such as eigenfrequencies and narrow-band noise. These fingerprints can then be used for subsequent analysis and visualisation, e.g., for comparing fingerprints across several sensor positions and for identifying vibrations as global or local properties. In this study, data were collected by sensors on two test turbines and fingerprints were successfully extracted for vibrations with both low and high operational variability. An analysis of sensors on the same blade indicates that fingerprints deviate for positions at large radial distance or at different blade sides and, hence, an evaluation with larger datasets of sensors at different positions is promising. In addition, the results show that distributed measurements on the blades are needed to gain a detailed understanding of blade vibrations and thereby reduce loads, increase energy harvesting and improve future blade design. In doing so, our method provides a tool for analysing vibrations with relation to environmental and operational variability in a comprehensive manner.  
vibration monitoring; structural health; wind turbines; wireless sensors +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 2 Materials and Methods
### Testbench Use-Case  
A use-case featuring a communication between the nano-satellite and the Mission Control Center is introduced in Figure 7. The nano-satellite is equipped with a camera and takes pictures of the Earth. For each picture, it processes the image using a Grayscale filter followed by a Sobel filter. The result is encrypted with AES-256 and stored in the internal memory. In order to secure the communication with Earth, the image is then signed using the SPHNCS\({}^{+}\) post-quantum signature scheme to allow robust data authentication [17]. This signature scheme has been chosen because it is a good candidate to secure the future communication of nanosatellite systems. Being designed to prevent attacks from near-coming quantum computers, it provides an additional level of security to authenticate information such as configuration data or firmware updates coming from the Mission Control Center.  
In Safe mode the first flexible TMR partition is used to host a TMR version of the AES-256 IP. This IP has been chosen over the Grayscale and Sobel filters because it benefits the most from the hardware acceleration. Both filters are execu  
Figure 7: Operation modes use cases.  
processor. The second flexible TMR partition is used to host three instances of a hardware implementation of the SPHINCS\({}^{+}\) algorithm [18].
## 4 System Overview--HOS-Re
### Feature Selection  
The HOS-Re extracts cumulant features from the raw CSI data. However, the recognition task with \(F_{n}\) cumulant features is time-consuming and computationally expensive. In such a case, adopting a proper feature selection method simplifies the complexity of the recognition task by reducing the total number of cumulant features to form an optimal subset of features, as explained in Section 3.3. Information theory feature selection algorithms like MIFS, mRMR, CIFE, JMI, CMIM, DISR, ICAP, and CONDRED helps in reducing the \(F_{n}\) cumulant features to \(k\) selected indices of feature subset based on mutual information. The feature selection criteria in the proposed work limit the feature size \(k\) as 30, 50 and 80 cumulant features to make it serve as well-grounded input to the multi-level SVM classifier to reduce the training time and complexity. The three values adopted in the present study are  
Figure 5: Feature extraction.  
chosen from the experience gained in conducting some experimental runs to assess the performance of the present methodology. The results proved that if the set of the selected features is less than 30, the given accuracy drops significantly. When \(k\) is higher than 80, the recognition accuracy is stable with a marginal increase in recognition accuracy beyond \(k=80\), but the designed model becomes very complex. The ML classifier measures the performance of feature selection algorithms and identifies the best algorithm based on the recognition accuracy. HOS-Re implements feature selection algorithms, using feature selection toolbox (FEAST) integrated with MATLAB [28].
# Dynamic Obstacle Avoidance and Path Planning through Reinforcement Learning
## 4 Discussion
### Propagation Model of RL  
Scholars have employed several propagation models of reinforcement learning. However, Q-network learning, continuous learning, 2D and 3D sensors, and soft actor-critics were the most adopted propagation models of reinforcement learning in obstacle avoidance and path planning algorithms [47; 53; 54]. Meanwhile, different improved propagation techniques of Q-network learning based on deep reinforcement learning have been the most prominent approaches adopted across different experimental studies. In general, acceptability and adoption may be traced to the ability to accurately extract information about the obstacle in a dynamic environment, which is used to create a navigation map for the robot to move in a collision-free path, as the real-world environment is heterogeneous, containing both static and dynamic environments. This propagation model, therefore, helps mobile robot algorithms consider unforeseen obstacles and dynamic objects. Ref. [54] uses the idea of partially directed Q-learning and the artificial potential field (APF) method to enhance the traditional Q-learning strategy. Because Q-learning and the APF approach are combined, the proposed QAPF learning algorithm [49] for path planning can speed up learning and improve performance overall.  
A propagation model of recurrent neural network with DDQN was used in [55; 56], aiming at navigation and path planning problems. In [57], the authors used a Mobile Robot Collision Avoidance Learning (MCAL) Soft Actor-Critic (SAC) based on RL. For RL as a path optimization technique, the propagation model of Q-learning and Dyna-Q algorithms was employed. A multimodal DRL with Auxiliary Task (MDRLAT) was utilized by [53] to help indoor mobile robots avoid obstacles. In [57; 58], a Deep Deterministic Policy Gradient (DDPG) propagation was employed. Refs. [59; 60] employed the Deep Neural Network (DNN) propagation technique to design a mobile robot's path autonomously.
## 3 Results
### Tensile Properties  
Young's Modulus (E), tensile strength (\(\sigma_{\text{uts}}\)), and elongation at break (\(\varepsilon_{\text{b}}\)) are the mechanical properties of the tested films which are presented in Table 2.  
As for Young's Modulus (E), it is obvious from the measurements that the addition of maleic anhydride in PS does not affect this property. The addition of NaMt in the PS matrix causes a decrease of (E) values while the addition of OrgMt causes an increase. This behaviour is enhanced by the addition of OO in both cases i.e., OO@NaMt and OO@OrgMt. However, when the polymeric matrix changes to PScoMa the addition of OO@NaMt change behaviour and causes a high increase of the (E) values.  
\begin{table}
\begin{tabular}{c c c c} \hline  &
\begin{tabular}{c} **Young’s Modulus-E** \\ **(Mean.Dev) (N/mm2)** \\ \end{tabular} & \(\boldsymbol{\sigma}\) **(Mean.Dev) (N/mm2)** & **\%c (Mean.Dev)** \\ \hline PS & 1832.7 (44.1) & 31.2 (2.0) & 1.9 (0.1) \\ PS3 NaMt & 1740.2 (75.1) & 25.9 (2.5) & 0.8 (0.2) \\ PS3 OO@NaMt & 1620.5 (69.4) & 23.9 (2.3) & 1.1 (0.1) \\ PS5 OO@NaMt & 1590.5 (71.0) & 22.7 (3.3) & 1.0 (0.2) \\ PScoMa & 1815.7 (36.7) & 25.6 (4.4) & 1.5 (0.1) \\ PScoMA3 OO@NaMt & 2270.5 (66.1) & 34.5 (4.1) & 1.3 (0.2) \\ PScoMA5 OO@NaMt & 2380.8 (43.4) & 35.9 (4.0) & 1.3 (0.2) \\ PS3 OrgMt & 1960.4 (61.2) & 32.8 (2.9) & 1.3 (0.2) \\ PS3 OO@OrgMt & 2056.3 (58.8) & 33.3 (3.6) & 1.4 (0.2) \\ PS 5 OO@OrgMt & 2120.5 (53.1) & 33.6 (3.9) & 1.5 (0.2) \\ \hline \end{tabular}
\end{table}
Table 2: Young’s Modulus (E), tensile strength (\(\sigma_{\text{uts}}\)), and elongation at break (\(\epsilon_{\text{b}}\)) values of all tested films.  
Figure 3: FTIR plots of all PS/ NaMt (**a**), PScoMANaMt (**b**), and PSOrgMt (**c**) films.  
As for the tensile strength (\(\sigma\)), the addition of coMa in PS causes a decrease in this property. In all the other cases the behaviour of this mechanical property is identical to the behaviour of the (E).  
As for the elongation at break values, it seems that the addition of NaMt decreases values more than the decrease causes the addition of OrgMt which, however, is higher than the decrease causes the coMa. The addition of OO seems to operate in an opposite way, increasing the elongation at break values.  
Figure 4 depicts a direct assessment of the stress-strain behaviour of the \"after growth\" films. It is obvious from this figure that the highest increase in stiffness and strength values are obtained for PScoMA5 OO@NaMt and PSCOO@OrgM films. According to the XRD results an exfoliated structure was indicated for PScoOM@NaMt samples, a partial intercalated structure for PScoMa\({}_{\text{x}}\)OO@NaMt samples, and an intercalated structure for PScoO@OrgMt samples. These results are following the tensile properties variation.
# A Wideband Metal-Only Patch Antenna for CubeSat
## 1 Introduction
A symmetric U-slotted patch on a 45.8 mm \(\times\) 45.8 mm ground plane was studied in [14]. The U-slotted patch antenna was printed on FR4 substrate and was placed 8 mm above the ground plane. As a result, both air and FR4 dielectric were used as substrate material, achieving an impedance bandwidth of 30% at 2.4 GHz. A circular microstrip patch loaded with shorting pins operating at X-band can be found in [17]. The antenna was printed on an RT duroid 5880 and the authors claimed that by adding two balanced shorting pins, the impedance bandwidth was doubled, reaching 8% at 10.8 GHz without affecting the antenna's radiation performance. Finally, the folded patch feeding technique was used in [19], achieving an impedance bandwidth of 73.8% at 2.5 GHz. The E-shaped patch was placed at 20 mm from the ground plane with a shorting wall used to miniaturize the structure while keeping a broad bandwidth. In this paper, we propose a wideband F-shaped patch antenna for CubeSats communications with an operating frequency of 2.45 GHz. The main idea is to feed the resonance arms of the upper F-shaped patch by a folded patch. This generates two resonant frequencies, and hence, achieves a wide bandwidth. Moreover, three shorting pins between the edges of the upper patch (F-shaped patch) and the ground plane are used to increase the effective electrical length of the patch and hence reduces its physical size. They are also used to lower the resonant frequency and widen bandwidth. The proposed antenna is a metal-only antenna with air substrate. This is important as it presents higher efficiency and improved radiation performance due to the absence of high lossy dielectric substrates [20]. The proposed antenna provides a linear polarization and can be used for establishing up- and downlinks with the ground station. Other proposed linear polarized antennas for satellite communications can be found in [21; 22; 23; 24]. Lastly, the performance of the proposed antenna is compared with some existing proposed patch antennas for CubeSat in terms of size, bandwidth and gain.
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 5 Conclusions  
In this paper, we propose a GNSS spoofing interference source localization method based on a moving array antenna. The method has strong usability and is suitable for navigation nulling antenna hardware platforms, enabling it to have both anti-interference and deceptive interference detection and localization capabilities. This paper elaborates on the antenna model and navigation signal acquisition and tracking methods used in the implementation of the proposed method. Based on this, GNSS spoofing interference source localization is proposed in this paper. The proposed method first uses a spoofing interference extraction method based on a double-differenced carrier phase to effectively separate the deceptive and real signals. After the spoofing interference is extracted, the original carrier phase single-difference data of the spoofing signal from multiple observation points is fused through a moving antenna array to directly localize the spoofing interference. The procedure for calculating the CRB of the proposed method is also provided.  
Simulations are used to test the effectiveness of this approach. Simulation results show that this approach effectively solves the spoofing interference extraction and location problems. Simulation results also show that the proposed method is nicely adapted to carrier phase measurement errors and antenna positioning errors and that the proposed method is close to CRB. Based on the test results, the recommended monitoring path and the number of observation points for the proposed method are given. Subsequent work will test the method in combination with a physical platform to test the detection and location effects of spoofing interference in real-world scenarios. At the same time, the effectiveness of the method in the multi-path scenario will be the focus of our physical platform tests. The effectiveness of multi-path weakening means, and the effects of the parameter settings (such as the number requirement of observation points) will be explained in combination with the physical platform test effect of this method.  
Conceptualization, Z.Y. and G.L.; Methodology, R.L. and Q.C.; Resources, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under Project 2018 YFB0505100.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.  
Figure 14: Comparison of localization effects at different frequency points.
# A Class of Perfectly Secret Autonomous Low-Bit-Rate Voice Communication Systems
## 7 Experimental Evaluation
### Comparison with State-of-the-Art QKD Methods  
Both Quantum Key Distribution (QKD) systems and our LSP-based artificial speech synthesizer provide a foundation for perfectly secret communication by enabling the real-time generation of symmetric keys. However, they differ significantly in implementation, autonomy, and practicality when applied to low-bit-rate secure speech communication. In Tables 5-9, we compare the two approaches across key aspects. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Audio Channel** & **BER** & **BER** \\  & **Without FEC** & **with FEC** \\ \hline GSM 3G & 1.20\(\cdot\)10\({}^{-3}\) & 1.02\(\cdot\)10\({}^{-3}\) \\ GSM VoLTE & 1.50\(\cdot\)10\({}^{-3}\) & 1.20\(\cdot\)10\({}^{-3}\) \\ WhatsApp & 5.50\(\cdot\)10\({}^{-3}\) & 4.70\(\cdot\)10\({}^{-3}\) \\ Google meet & 7.90\(\cdot\)10\({}^{-3}\) & 3.40\(\cdot\)10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BER for different audio channels with and without ECC. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **LSP-Based Synthesizer** & **QKD Systems** \\ \hline Sequential Key & Public-authenticated channel used for error & Quantum transmission \\ Distillation (SKD) & correction and privacy amplification & requires classical reconciliation \\ \hline Man-in-the-Middle & Authentication prevents key & QKD offers provable security \\ Resistance & injection attacks & against eavesdropping \\ \hline Key Refresh Rate & \
\begin{tabular}{c} Matches low-bit-rate vocoder \\ encryption rates (1.2–2.4 kbps) \\ \end{tabular} &
\begin{tabular}{c} Limited by quantum transmission rate, \\ often lower than needed for \\ real-time speech \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Key agreement and distribution over public channel. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **LSP-Based Synthesizer** & **QKD Systems** \\ \hline \begin{tabular}{c} Perfect Secrecy \\ Guarantee \\ \end{tabular} & \begin{tabular}{c} Achieved through one-time pad encryption \\ with real-time key agreement \\ \end{tabular} & \begin{tabular}{c} Provably secure key exchange, but \\ requires additional \\ encryption (e.g., AES) \\ \end{tabular} \\ \hline \begin{tabular}{c} Information- \\ Theoretic Security \\ \end{tabular} & \begin{tabular}{c} Security based on shared \\ entropy extraction \\ \end{tabular} & \begin{tabular}{c} Quantum no-cloning theorem ensures \\ eavesdropper detection \\ \end{tabular} \\ \hline \begin{tabular}{c} Resistance to Active \\ Attacks \\ \end{tabular} & \
\begin{tabular}{c} Immune to classical \\ cryptanalysis and quantum computing threats \\ \end{tabular} &
\begin{tabular}{c} Secure against quantum \\ computing but vulnerable to side-channel \\ attacks on \\ implementation \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Infrastructure and practical deployment.
# RotJoint-Based Action Analyzer: A Robust Pose Comparison Pipeline
## 3 Benchmark
### PoseAMBench Metric  
We define the Torsion-Ambiguity-Aware Similarity Metric (TAASM) to measure action similarity in the Torsion-Ambiguity Pose set, extending the limb torsion metric (LTM) for body segments (torso, arms, and legs). The TAASM accounts for torsion differences even when body shape and joint angles are identical. By evaluating torsion separately for each segment, the metric provides a more interpretable similarity score, focusing on torsion-induced variations.  
**Torsion metric calculation**: The body is divided into five parts: torso (including the head), left arm, right arm, left leg, and right leg. For each body part \(g\), the torsional difference is given by  
\[LTM_{g}=\frac{1}{n}\sum_{i=1}^{n}\left|\theta_{g,i}^{\text{actual}}-\theta_{g, i}^{\text{reference}}\right| \tag{1}\]  
where \(\theta_{g,i}^{\text{actual}}\) and \(\theta_{g,i}^{\text{reference}}\) are the actual and reference torsion angles for body part \(g\) at frame \(i\).  
**Normalized similarity calculation**: The torsional difference for each body part is normalized as  
\[S_{g}=1-\frac{LTM_{g}}{\pi} \tag{2}\]  
where \(\pi\) represents the maximum torsion angle (180\({}^{\circ}\) or \(\pi\) radians), and \(S_{g}\) ranges from 0 (maximum difference) to 1 (perfect similarity).  
**Overall similarity**: The overall action similarity is the average of the individual body part similarities:  
\[TAASM=\frac{1}{5}\Big{(}S_{\text{torso}}+S_{\text{left\_arm}}+S_{\text{right\_ arm}}+S_{\text{left\_leg}}+S_{\text{right\_leg}}\Big{)} \tag{3}\]  
Figure 3: Comparison of statistical measures across different sets in PoseAMBench. \(\sigma\) means standard deviation.  
where \(S_{\text{torso}}\), \(S_{\text{left\_arm}}\), \(S_{\text{right\_arm}}\), \(S_{\text{left\_leg}}\), and \(S_{\text{right\_leg}}\) are the similarity scores for the torso, left arm, right arm, left leg, and right leg, respectively.  
We assign labels of 0 (dissimilar) and 1 (similar) to pose pairs. In the Shape-Variations and Viewpoint-Variations Sets, label 1 is used when actions are the same but other conditions differ, and label 0 otherwise. For the Torsional-Pose Set, labels are based on TAASM: if the value exceeds 0.6, the label is 1; otherwise, it is 0.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Communication Overhead Objective  
When Software systems consist of highly constrained hardware and communication resources, it is advisable to limit high frequent transmissions between software components. For this purpose, the communication overhead (CO) objective was proposed, which imposes minimal data communication for system parameters and components. As a network- and deployment-dependent measure, the overall communication overhead of the system is utilised to represent this viewpoint. This measure was initially developed by [PERSON] and [PERSON] [9] and defined as:  
\[o(d)=\sum_{i=1}^{n}\sum_{j=1}^{n}\mathbf{f}(c_{i},c_{j})\cdot\mathbf{y}( \mathbf{d}(c_{i}),\mathbf{d}(c_{j}))+\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{\mathbf{ f}(c_{i},c_{j})\cdot\mathbf{m}(c_{i},c_{j})}{\mathbf{b}(\mathbf{d}(c_{i}), \mathbf{d}(c_{j}))\cdot r(\mathbf{d}(c_{i}),\mathbf{where \(\mathsf{m}:\mathcal{C}\times\mathcal{C}\rightarrow\mathbb{N}\) is the component message size, with \(\mathsf{m}(c_{i},c_{j})=0\) if \(c_{i}=c_{j}\), or in the case that no communication between the two components exists, \(\mathsf{f}:\mathcal{C}\times\mathcal{C}\rightarrow\mathbb{R}\) is the communication frequency between \(c_{i}\) and \(c_{j}\), \(\mathsf{b}:\mathcal{H}\times\mathcal{H}\rightarrow\mathbb{N}\) is the network bandwidth, with \(\mathsf{b}(\mathsf{d}(c_{i}),\mathsf{d}(c_{j}))=0\) if \(\mathsf{d}(c_{i})=\mathsf{d}(c_{j})\) or there is no network connection between \(\mathsf{d}(c_{i})\) and \(\mathsf{d}(c_{j})\), and \(\mathsf{y}:\mathcal{D}\times\mathcal{D}\rightarrow\mathbb{N}\) is the network delay, with \(\mathsf{y}(\mathsf{d}(c_{i}),\mathsf{d}(c_{j}))=0\) if \(\mathsf{d}(c_{i})=\mathsf{d}(c_{j})\) or there is no network connection between \(\mathsf{d}(c_{i})\) and \(\mathsf{d}(c_{j})\).
###### Abstract  
As one of the precious cultural heritages, Chinese landscape painting has developed unique styles and techniques. Researching the intelligent generation of Chinese landscape paintings from photos can benefit the inheritance of traditional Chinese culture. To address detail loss, blurred outlines, and poor style transfer in present generated results, a model for generating Chinese landscape paintings from photos named Paint-CUT is proposed. In order to solve the problem of detail loss, the SA-ResBlock module is proposed by combining shuffle attention with the resblocks in the generator, which is used to enhance the generator's ability to extract the main scene information and texture features. In order to solve the problem of poor style transfer, perceptual loss is introduced to constrain the model in terms of content and style. The pre-trained VGG is used to extract the content and style features to calculate the perceptual loss and, then, the loss can guide the model to generate landscape paintings with similar content to landscape photos and a similar style to target landscape paintings. In order to solve the problem of blurred outlines in generated landscape paintings, edge loss is proposed to the model. The Canny edge detection is used to generate edge maps and, then, the edge loss between edge maps of landscape photos and generated landscape paintings is calculated. The generated landscape paintings have clear outlines and details by adding edge loss. Comparison experiments and ablation experiments are performed on the proposed model. Experiments show that the proposed model can generate Chinese landscape paintings with clear outlines, rich details, and realistic style. Generated paintings not only retain the details of landscape photos, such as texture and outlines of mountains, but also have similar styles to the target paintings, such as colors and brush strokes. So, the generation quality of Chinese landscape paintings has improved.  
Chinese landscape painting generation; generative adversarial networks; shuffle attention; perceptual loss; edge loss Article