esa-sceva/satcom-chunk-collection huggingface dataset:
# Condition Monitoring of Wind Turbine Blades Using Active and Passive Thermography
## 5 Results and Discussion
### Image Processing and Quantitative Evaluation
#### 5.2.1 Matched Filters (MF)  
Applying MF to the step heating thermograms improved the quality of the results, as shown in Figure 7. All four versions of MF increased the visibility and contrast of the defects. Three of these filters, including the SAM, the ACE, and the \(F\)-statistic, showed very promising results where all defects could be detected. However, the \(t\)-statistic highly improved the quality of the raw thermal data and compared to other MF methods provided less details regarding subsurface defects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** \\ \hline
**A** & 16.3 & 18.5 & 18.5 & 13.7 & 3.4 \\
**B** & 10.2 & 15.8 & 17 & 11.9 & 2.9 \\
**C** & 2.5 & 4.2 & 13.1 & 4.9 & 2.6 \\
**D** & NA & 2.1 & 2.3 & 2.1 & NA \\ \hline \end{tabular}
\end{table}
Table 3: SNR related to raw data captured by flash thermography.  
Figure 7: Four matched filters including (**a**) Spectral Angle Map (SAM), (**b**) Adaptive Coherence Estimator (ACE), (**c**) \(t\)-statistic and (**d**) \(F\)-statistic when the specimen was being step heated.  
By using the above results, it can be seen that the diameter-to-depth ratio of the minimum detectable defect for each of SAM, ACE and \(F\)-statistic was 1.33, while this value for \(t\)-statistic was 2. To quantitatively evaluate the MF results, the SNRs for defective areas were determined. Figure 8a illustrates the signals of the \(F\)-statistic results obtained from the step heating data. The background noise around A2 is also shown in Figure 8b. These results were averaged to get the representative noise. It should be noted that all of the processed images have been normalized by dividing the intensity of each pixel over the difference between the maximum and minimum intensity available on the image.  
The SNRs are presented in Tables 4-7. The percentage improvement in the SNRs compared to the raw values is shown in parenthesis for each defect. These tables show that higher SNR values were obtained when MFs were applied to the step heating data. Therefore, the application of MFs to step heating data improved the resolution of the internal defects.  
Tables 4-7 also reveal that larger defects closer to the surface generated higher SNR values. There were some exceptions to this general conclusion, however, primarily due to the non-uniformity of the heating.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **1** & **2** & **3** & **4** & **5** \\ \hline A & 36.8 (99\%) & 35.3 (94\%) & 31.8 (78\%) & 26.9 (87\%) & 15.1 (396\%) \\ B & 33.5 (84\%) & 33.7 (87\%) & 31.2 (76\%) & 27.9 (116\%) & 17 (185\%) \\ C & 30.7 (72\%) & 29.3 (67\%) & 29.1 (70\%) & 26.3 (139\%) & 18.8 \\ D & 16.1 (31\%) & 18.7 (55\%) & 18.6 (116\%) & 13.9 & 5.5 \\ \hline \end{tabular}
\end{table}
Table 4: SNR (dB) for SAM on step heating thermal data.  
Figure 8: (a) Normalized signal values along the defect rows and (b) background noise around A2.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 7 Network, System, and Channel Model  
### Network Model  
In this scenario, a cellular network with \(\mathcal{L}\) gNBs is analysed. Each network consists of one associated gNB and at least one interfering gNB, and the UAVs' association with their  
Figure 10: Deep Q-learning for interference mitigation flow diagram.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline t & Time \\ \(s_{t}\) & States \\ \(\epsilon\) & Exploration rate \\ d & Exploration rate decay \\ \(a_{t}\) & Actions \\ r & Data Sample \\ L & No. of associated gNBs \\ B & No. of interfering gNBs \\ i & Total no. of gNBs \\ h & No. of UAVs \\ \(Q_{\pi}\) & State–Action Value Function \\ \(\varphi_{i}\) & Neural network weights \\ \(i[t]\) & experience \\ \(\delta\) & SINR \\ \(\delta_{\text{eff}}\) & Effective SINR \\ \(\delta_{\text{target}}\) & Target SINR \\ \(\delta_{\text{min}}\) & Minimum SINR \\ \(\delta_{\text{thresh}}\) & Threshold SINR \\ SGD & Stochastic Gradient Descent \\ \(P_{TX}\) & Transmit Power \\ \(\mathcal{D}\) & Set of transmit powers \\ \(\mathcal{D}\) & Experience Replay buffer \\ \(y_{l}\) & Received signal \\ \(\mathbb{E}\) & Estimated function \\ H & Neural Network Width \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of notations.  
associated gNB is based on their distance. A UAV can only be served by one gNB at a time, so the cell radius can be expressed as \(r>R/2\).
## 3 Results
### Characteristics of Fresh and Packed Nettle Leaves
#### 3.4.5 Phenolic Compounds  
The total polyphenolic content of nettle leaves packed in different packaging materials and stored for 14 days is presented in Figure 7. In fresh nettle leaves, the TPC content was 398.88 mg GAE/100 g fm, the TNFC content was 218.68 mg GAE/100 g fm, and the TFC content was 180.2 mg CTH/100 g fm, which are significantly higher values compared to the content of polyphenolic compounds determined in the packaged leaves during the 6- and 14-day storage periods. All tested factors, days of storage (D), packaging material (M), and their interaction (D \(\times\) M) significantly affected the content of all analyzed polyphenolic compounds (\(p\)\(\leq\) 0.0001). The highest TPC content was found in nettle leaves packaged in LDPE after 14 days of storage, and the lowest TPC value was found in nettle leaves packaged in PA/PE, also after 14 days of storage. The same trend was observed for TNFC and TFC content, with the highest values in nettle leaves packaged in LDPE and the lowest in leaves packaged in PA/PE, both after 14 days of storage. While observing factors individually (Figure 7b), and regarding days of storage, all total polyphenolic compounds remained better preserved during the first 6 days of storage compared to the second storage period (14 days).  
The composition of 14 selected individual phenolic compounds in nettle leaves packed in different packaging materials is presented in Table 4. The interaction of varied factors (D \(\times\) M) significantly affected all analyzed individual polyphenolic compounds except quercetin-3-glucose, and individual factors (D and M) also influenced all phenolic compounds, with the exception of ellagic acid, which was not influenced by storage duration (D). Caffeoylmalic acid was found to be the most dominant individual phenolic compound in all samples. The high content of chlorogenic acid was also observed. The content of caffeoylmalic acid in nettle leaves packed in different materials varied from 55.99 mg/100 g (PA/PE 14 days) to 258.70 mg/100 g (LDPE 6 days), while for chlorogenic, it varied from 33.92 mg/100 g (PA/PE 14 days) to 162.12 mg/100 g (LDPE 6 days). As can be seen, both compounds, caffeoylmalic acid and chlorogenic acid content, were the highest in samples packed in LDPE after 6 days, and the lowest in PA/PE after 14 days. When considering packaging material (M) as the individual variable tested, it can be seen that caffeoylmalic and chlorogenic acid content were the highest in nettle leaves packed in LDPE, while the lowest in PA/PE corresponds to the TPC content determined in nettle leaves. Significantly lower contents of gallic, protocatechuric, vanillic acids, kaempfer, naringin, quercetin, quercetin-3-glucose, and rutin trihydrate were measured both in unpacked and packed samples, while some other compounds (\(p\)-hydroxisbenyoic, comamic, and ellagic acid) were found only in trace amounts.
## 2 Design and Performance of Ultra-Intense \"J-KAREN\" Laser System
### Improvements of Pointing Stability  
We have installed and tested three automatic beam alignment systems at the front end of the laser system. The systems have been used for the pump lasers for the Ti:sapphire oscillator in the first CPA stage, for the OPCPA in the second CPA stage, and for the seed laser for the OPCPA after the first CPA stage. There are two main reasons for installation of the alignment system. The first is to bring the beams back to their set positions after switch-on of the laser system and, hence, to speed up the set-up procedure. The second is to compensate for thermal and mechanical drifts to keep the beam positions for minimizing the temporal variation in energy and pulse duration during laser operations. Beam positions of the seed laser for the OPCPA, for example, are monitored by two position-sensitive detectors (PSD) using leakage pulse energy through the back of two high-reflection mirrors (BeamLock, TEM Messtechnik GmbH, Hanover, Germany). The PSD is an analog device that operates with a 10-100 \(\upmu\)s response time, so it is fast enough to monitor short-term fluctuations attributed to causes such as air turbulence and vibrations of the optical elements and benches. The software controlling the system calculates the centroid positions. If a deviation from the predetermined set-point values is detected, the beam is steered back by means of motorized actuators fitted to mirror mounts located upstream from the PSDs. Motorized mirrors are preferred to piezo driven mirrors because they hold their position when powered off. To avoid large fluctuations during stabilization due to the steep displacement, we introduce analog circuit to carefully adjust a proportional-integral-derivative controller. We also modify the circuit to hold the peak value of the pulsed signal from the PSD sensor because the sensor does not generate a signal during the shot-to-shot time period to detect pulses beams. Figure 11 shows the beam positions normalized by the beam size after switch-on during a typical day. As can be seen from this figure, beams are initially displaced by some amount, corresponding to up to one beam diameter. After about 1 s, displacements are corrected. The typical value of the pointing stability after the off-axis parabolic mirror (OAP) just before the target is 4-5 \(\upmu\)rad. Actually, the stability of the whole system is perfect throughout the day. The result clearly demonstrates that the automatic alignment system has greatly improved the stability and consistency of the system's performance. We plan to extend this stabilization system throughout the J-KAREN laser chain.  
Figure 11: Vertical and horizontal deviations of beam positions normalized by the beam size after switch-on of the automatic beam alignment system.
## 3 Development of Prediction Model of Ground Subsidence
### Evaluation Metrics  
Evaluation metrics were established to evaluate the performance of the machine-learning models. These metrics are important for understanding the accuracy of model predictions and classifications. They are used to check the prediction accuracy of a model in each iteration and reduce the loss value, thereby minimizing the error between the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range** & **Value** \\ \hline _colsample\_bytree_ & [0.8, 1.0] & \(8.41\times 10^{-1}\) \\ _gamma_ & [0, 1] & \(3.61\times 10^{-5}\) \\ _learning\_rate_ & [0.01, 0.3] & \(9.16\times 10^{-2}\) \\ _max\_depth_ & [2, 128] & 102 \\ _min\_child\_weight_ & [0.1, 10] & 3.17 \\ _reg\_alpha_ & [10\({}^{-8}\), 10] & \(4.38\times 10^{-5}\) \\ _reg\_lambda_ & [10\({}^{-8}\), 10] & \(8.60\times 10^{-7}\) \\ _subsample_ & [0.8, 1.0] & \(9.82\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hyperparameter tuning results using Optuna for prediction model using XGBoost.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Range** & **Value** \\ \hline _bugging\_function_ & [0.8, 1.0] & \(8.75\times 10^{-1}\) \\ _feature\_fraction_ & [0.8, 1.0] & \(9.78\times 10^{-1}\) \\ _lambda\_1_ & [10\({}^{-8}\), 10] & \(4.67\times 10^{-8}\) \\ _lambda\_1\(2\) & [10\({}^{-8}\), 10] & \(6.69\) \\ _learning\_rate_ & [0.01, 0.3] & \(2.74\times 10^{-1}\) \\ _max\_depth_ & [2, 128] & 28 \\ _min\_child\_weight_ & [0.1, 10] & \(5.98\) \\ _num\_lawes_ & [2, 1024] & 961 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter tuning results using Optuna for prediction model using LightGBM.  
actual and predicted values. This helps in assessing the effectiveness of the model and contributes to preventing overfitting. The mean absolute error (_MAE_) [64], a commonly used evaluation metric in regression analysis, was adopted for evaluation. Metrics based on absolute or squared errors are called scale-dependent metrics [65]. _MAE_ is a metric used to measure the average magnitude of the absolute errors between the predicted value and the actual value [66]. The _MAE_ is often called the mean absolute deviation (MAD) [67; 68; 69].  
_MAE_ is calculated as follows [70]:  
\[MAE=\frac{1}{N}{\sum_{i=1}^{N}}|y_{i}-\hat{y}_{i}|, \tag{1}\]  
where \(N\) is the number of validation sets, \(y_{i}\) is the actual value of the validation set, and \(\hat{y}_{i}\) is the predicted value of the model. This measurement allowed us to determine how far the model's predicted values were from the actual values without considering the direction of error.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model
### Communication Model
#### 2.2.2 The Channel Gain \(h_{k}^{u}\)  
The channel gain from UAV to GU \(k\) in \(A_{UAV}\) is denoted as \(h_{k}^{u}\). It can be expressed as  
\[h_{k}^{u}=\sqrt{G_{u}G_{k}\bigg{(}\frac{c}{4\pi f_{c}d_{k}^{u}}\bigg{)}^{2}}, \tag{3}\]  
where \(d_{k}^{u}\) is the distance between UAV and GU \(k\), and \(G_{u}\) is the UAV antenna gain. According to [39; 40], the directional antenna half-power beamwidth of UAV is denoted as \(2\phi_{u}\). Thus, the UAV antenna gain can be approximated by  
\[G_{u}(\theta_{u})=\begin{cases}G_{0}/\phi_{u}^{2},&-\phi_{u}\leq\theta_{u}\leq \phi_{u},\\ g_{0}\approx 0,&\text{otherwise}\;,\end{cases} \tag{4}\]  
where \(\theta_{u}\) is the sector angle, \(G_{0}\approx 29000/2\phi_{u}\) with \(\phi_{u}\) in degrees [41]. Note that in practice, \(g_{0}\) satisfies \(0<g_{0}\ll G_{0}/\phi_{u}^{2}\), and for simplicity in this paper, \(g_{0}=0\).
## 3 Methodology  
In order to investigate the effectiveness of the FEM for the calculation of the resonance frequency of a Helmholtz resonator, different cases were considered in the methodology. Initially, single-necked Helmholtz resonators were modeled (spherical body, cylindrical body) and compared with the results of the lumped element approximation formula (single-neck) and with the [PERSON] et al. [33] theoretical formulation. The reason for this approach is to confirm that our FEM modeling results are in line with the literature determining a (single-neck) Helmholtz resonator's resonance frequency (e.g., [47]). The above would be an indication that our single-neck models are structured correctly, behave as expected and are a good starting base for modeling of the multi-neck Helmholtz resonators, as in this case the literature is not as extensive. Next, various models of multi-neck Helmholtz resonators were tested to access the effectiveness and applicability of the FEM for calculating the resonance frequency. The cases that were tested were a resonator with a spherical body with identical necks, a resonator with a cylindrical body with identical necks, a resonator with a cylindrical body with different necks and finally a resonator with a cylindrical body with different necks and one flanged neck. Again, results were compared with the [PERSON] et al. [33] and the lumped element approximation formula.  
For the estimation of the resonance frequencies in the case of [PERSON] et al.'s [33] theoretical formulation, Equation (4) was utilized. Accordingly, for the lumped-element approximation approach (single neck, multi-neck), Equations (10) and (11) were used, respectively. For all formulae, the speed of sound was set to 343 m/s and the density of air to \(\rho=1.2\) kg/m\({}^{3}\). For all calculations, the end correction coefficient was set to 0.75 [44] (unflanged neck) and 0.85 [44] (flanged neck). For the calculations, the software Matlab R2021a (Mathworks, MA, USA) was used.  
For the implementation of the FEM formulation as presented in Section 2.2, the commercial Comsol Multiphysics v.6.0 software (Comsol inc., Burlington, NJ, USA) was used. For the creation of the 3D models of the multi-neck Helmholtz resonators, the software Inventor v.2024 (Autodesk, San Francisco, CA, USA) was utilized. The general rule of thumb \(\lambda/\text{h}=5\) was used to create the mesh, where \(\lambda\) and h, respectively, stand for the wavelength of the upper limit frequency and the maximum nodal distance. For acoustic modeling, five elements per wavelength were utilized, as is customary in the frequency domain [48]. A quadratic Lagrange triangular unstructured mesh was used to discretize the domain. For all FEM models, the speed of sound was set to 343 m/s and the density of air to \(\rho=1.2\) kg/m\({}^{3}\).
## 6 Conclusions  
An analytical model to compute the throughput and energy efficiency of a burst transmission mechanism using microsleep operation under network saturation was presented in this paper. This mechanism represents a simple implementation of the IEEE 802.11 ac TXOP PSM mechanism on the top of the fundamental IEEE 802.11 DCF mechanism using burst transmissions. When the TXOP PSM-based mechanism is employed, a device can send a burst of \(\alpha\) data frames to another device while a third device can microsleep during the data exchange. Such device enabling microsleep operation spends a given time and consumes power in the transitions to enter and exit a low-power sleep state. The proposed model takes into account the influence of these transitions, since the requirements of these transitions can compromise the feasibility of microsleep operation.  
A comprehensive performance evaluation of the TXOP PSM-based mechanism, called TXOP PSM for simplicity, was provided using the proposed model and computer-based simulations. The performance of TXOP PSM was compared to those of DCF and PSM using burst transmissions as a function of various system parameters. The results presented in this paper show a strong dependence of TXOP PSM performance on the system parameters. More specifically, TXOP PSM using \(\alpha=3\) improves the throughput of TXOP PSM with \(\alpha=1\) (no burst transmission) by up to 75% in scenarios with heavy traffic, small frames, high PHY rates, and many devices. When \(\alpha=3\) is employed, TXOP PSM also enables microsleep operation and increases the energy efficiency of DCF and PSM by up to 424% in scenarios with heavy traffic, large frames, low PHY rates, and many devices. In addition, the throughput and energy efficiency of TXOP PSM can increase up to 48% and 483%, respectively, as the value of \(\alpha\) increases from \(\alpha=1\) to \(\alpha=10\).  
In future work, the authors plan to evaluate the performance of TXOP PSM using frame aggregation and block acknowledgment in scenarios including different traffic classes and multiple shared channels. Moreover, the authors aim at testing the mechanism in real-life environments by using programmable wireless platforms.
# Grayscale Iterative Star Spot Extraction Algorithm Based on Image Entropy
## 1 Introduction
In contrast to the existing pixel-to-object extraction algorithms, this algorithm belongs to an object-to-pixel star spot extraction method in the presence of a stray light background. The main contributions can be summarized as follows:  
1. Based on the star spot imaging model, three types of background stray light distribution models in star images are analyzed in detail. A grayscale iterative image preprocessing algorithm is proposed, which fully utilizes prior information regarding the local contrast of star spot targets, effectively filtering out background noise to a certain extent. 2. After image preprocessing, using the algorithm proposed in this paper, we establish a sliding window for star spot extraction based on inner-outer templates to traverse the star image and extract star spots using the image entropy algorithm. This method treats N neighboring pixels of a star spot as a single entity, directly matching all pixels of the star spot with the target. This represents a significant breakthrough compared to the existing pixel-to-object extraction methods. 3. Numerical simulations and experimental results demonstrate that, compared to traditional detection algorithms, this method exhibits superior resistance to stray light, increases star spot extraction rates, reduces the number of false stars, and shows good robustness (see Sections 6 and 7). The content of this paper is arranged as follows: In Section 2, we provide a detailed introduction to the star spot imaging model. In Section 3, we analyze the mechanism for star image background stray light. In Section 4, we propose a grayscale iterative image preprocessing method. In Section 5, we establish a numerical method to extract star spots from the preprocessed star image. In Sections 6 and 7, we implement a series of numerical simulations and experiments. Finally, we conclude in Section 8.
## 4 Results and Analysis  
### Simulation Mobility Model  
The study is conducted in a C++ environment using the NS3 simulator [44], based on the Random Waypoint and ManhattanGrid mobility models for MANETs to evaluate the performance of routing protocols. The authors compare the performance of various scenarios of MCW_OLSR and OLSR. The simulation is executed for 100 s. Identical mobile nodes numbering 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 are arranged in a terrain of 1000 m by 1000 m. An examination of graphs is presented to discuss simulation results. Table 4 summarizes all parameters used during the simulation.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline Terrain Size & 1000 m \(\times\) 1000 m \\ \hline Max Number of Nodes & 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 \\ \hline Radio Range & 250 m \\ \hline MAC Layer & IEEE 802.11 peer-to-peer mode \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
## 5 Discussion and Future Directions
### Future Directions  
Future engineering advancements in SCI treatment are likely to focus on the miniaturisation of devices, the increased biocompatibility of implants, and more sophisticated algorithms for decoding brain signals and predicting motor intentions. Developing flexible and stretchable electronics will also be critical, allowing implants to more seamlessly interface with the spinal cord or brain, enhancing the longevity and comfort of the devices. Additionally, advances in wireless power and data transfer could enable fully implantable systems, eliminating the need for external power sources and further improving patient mobility and adaptability. Integrating wireless data transfer with AI and adaptive algorithms can increase device usability by using machine learning models that can predict motor intentions or optimise stimulation patterns. Future research should not only focus on the technical performance of the SCI device but also on designing devices that align with the evolving regulatory frameworks to ensure safe and effective clinical practice. As these technological improvements evolve, biomedical engineering will continue to play a crucial role in advancing SCI treatment, potentially leading to more widespread functional recovery for individuals with spinal cord injuries.