esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Control of the Spatial Coherence in a Holographic Display System
After the SLM is in the optical path, a Fourier lens is inserted to reconstruct the Fourier hologram with a focal length of 100 mm. The mono-color CMOS sensor has a resolution of 4096 \(\times\) 3000 pixels, a diagonal of 1.1 inch, a pixel pitch of 3.45 \(\upmu\)m. The experimental setup is shown in Figure 2b. In addition, because the brightness of the captured image increases with the expansion of the spatial filter size, the exposure time of the image sensor is adjusted according to the spatial filter size to prevent the saturation of the captured images, which obstructs the proposed analysis. The Lena image is used as the object of the Fourier hologram and is positioned above the DC point. Figure 3 shows the captured images of the reconstructed Fourier hologram according to the various sizes of the spatial filter with 32.4, 75.6, 140.4, 205.2, 270.0, 507.6, 702.0, and 1090.8 \(\upmu\)m. The sharpness of the reconstructed Fourier hologram decreases and blurs as the spatial filter size increases. These results demonstrate that the reconstructed image becomes blurred owing to the decrease of the spatial coherence. It is worth noting that low temporal coherence causes radial blur, which appears clearly under a high spatial coherence. Figure 1: Relationship between the size of the light source and the spatial coherence.
## 2 Methodology
### Interlaminar Stresses
#### 2.3.2 Limit Interlaminar Stresses  
The normal and shear stresses' limits in an interlaminate depend on the amount of fiber and the type of resin used. Table 1 shows the limit values of the interlaminar stresses for a laminate with an orthophthalic polyester resin. In the case where one of these parameters is not available, it can be estimated using the following dimensionless relationship (Equation (21b)).  
\[\frac{Z_{t_{1}}}{S_{t_{1}}}\cdot\left(\frac{E_{m1}}{E_{i1}}\right)^{1/2}=\frac {Z_{t_{2}}}{S_{t_{2}}}\cdot\left(\frac{E_{m2}}{E_{2}}\right)^{1/2} \tag{21a}\] \[Z_{t_{2}}=Z_{t_{1}}\cdot\frac{S_{t2}}{S_{t1}}\cdot\left(\frac{E_{m1}}{E_{m2}} \right)^{1/2}\cdot\left(\frac{E_{t2}}{E_{i1}}\right)^{1/2} \tag{21b}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fiber**} & \multirow{2}{*}{\(\mathbf{f_{c}}\) (1)} & \multirow{2}{*}{\(\mathbf{S_{t}}\) (2)} & \multirow{2}{*}{\(\mathbf{Z_{t}}\) (MPa)} \\ \cline{3-3} \cline{5-5}  & & & (MPa) \\ \hline Uni-directional (UD) & 60\% & 15.00 & 14.60 (1) \\ Woven Roving (WR) & 50\% & 15.25 & 11.70 (3) \\ Chopped strand mat (CSM) & 30\% & 17.95 & 9.50 (3) \\ \hline \hline \end{tabular} (1) Lloyd’s Register [39], (3) \(Zt\) estimated using Equation (21b). Note: see laminate properties (\(E_{mj}\) y \(E_{ij}\)), in Appendix A.  
\end{table}
Table 1: Estimation of the failure stresses in the interlaminate.  
For cases in which the FRP panel is of sandwich-type, with a balsa core, additional consideration must be given to the tensile, compression, and shear stresses of the core, see Lloyd's Register [39]; Table 2.2.2 Minimum characteristics and mechanical properties of end-grain balsa.
## 2 Modeling Approach
### Model Fitting Methodology  
The family of models described in the previous section varies in the number of parameters from two to seven depending on the number of features we use at the linear function \(f(\cdot)\). Since we need to quantify the model uncertainty (Section 2.1), we estimate the whole distribution of its parameters. This can be conducted by using the Bayes formula:  
\[p(\theta|y_{i})=\frac{\mathrm{p}(y_{i}|\theta)*p(\theta)}{p(y_{i})} \tag{10}\]  
where \(\mathrm{p}(y_{i}|\theta)\) is the likelihood function of Equations (8) and (9), \(p(\theta)\) are the prior probabilities of the parameters, and \(p(y_{i})\) is the marginal likelihood [32; 33; 34; 35]. The likelihood function links the observations/measurements to the parameters, and it is multiplied by the prior distributions (i.e., beliefs before taken any measurements), posterior distribution of the unknown parameter is obtained (distributions incorporate the model uncertainty). Although this is conceptually simple, in practice it is challenging to compute due to the normalization factor \(p(y_{i})\). If we re-write the marginal likelihood as \(p(y_{i})=\int_{\Theta}\mathrm{p}(y_{i}|\theta)*p(\theta)d(\theta)\), it is clear that it is needed to be integrated over the domain of \(\Theta\), i.e., all possible values of \(\theta\), which is demanding.  
The general modern computational approach to calculate the posterior probabilities of model parameters avoids the labor-intensive approximation of \(p(y_{i})\), and to draw samples directly from \(p(\theta|y_{i})\). This computational efficient technique, called Markov Chain Monte Carlo (MCMC), constitutes a cornerstone of probabilistic machine learning and Bayesian statistics [32; 33]. Recent computational frameworks of probabilistic machine learning and Bayesian statistics [36; 37], where high accuracy and speed are critical factors, incorporate gradient-based information from \(\
abla\mathrm{log}\ p(\theta|y_{i})\). Those algorithms are called Hamiltonian Monte Carlo (HMC) and can significantly speed-up the calculations [38].  
By using MCMC and HMC, we can efficiently estimate the distribution of all model parameters and quantify their and model's uncertainty. Moreover, this Bayesian treatment of the candidate models enables us to select the best performing on for future bottarga samples (see next section). Due to the very small number of the samples, we cannot perform the usual cross-validation as we would have done in a traditional machine learning model [30; 39].
# Attention-Guided Wireless Channel Modeling and Generating
## 5 Conclusions  
In this paper, we propose a novel wireless channel modeling and generation scheme SE-GAN. SE-GAN is based on WGAN-GP to optimize the network structure by combining attention modules, aiming to improve the efficiency and quality of wireless channel data modeling and generation. Through a large number of experiments, we comprehensively evaluated the performance of SE-GAN in terms of the similarity, diversity, and time required to generate data. As a novel wireless channel modeling and generating scheme, SE-GAN has shown remarkable advantages in these aspects, indicating that SE-GAN has a wide range of application prospects in the field of wireless channel modeling and generating.  
Future research will further investigate the application of SE-GAN in various wireless communication environments, particularly focusing on its performance in complex channel models and multi-path propagation scenarios. The variations in these environments can significantly affect the accuracy and stability of the model, necessitating an in-depth exploration of how adjustments in network architecture and training strategies can enhance the model's adaptability and robustness.  
Additionally, we plan to investigate the development of fuzzy logic rule bases derived from real-world data to model channel behavior more accurately. Given the dynamic and uncertain nature of wireless communication environments, we aim to explore how these rule bases, constructed from high-quality real data, can be integrated into the system to enable real-time adjustments of channel characteristics.  
Conceptualization, Y.H. and N.X.; formal analysis, Y.H. and L.C.; funding acquisition, L.C.; methodology, Y.H. and L.C.; resources, Y.H. and H.Y.; software, Y.H. and N.X.; writing--original draft, Y.H.; writing--review and editing, Y.H. and L.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Time** & **ChannelGAN** & **SE-GAN** \\ \hline Data loading & 1.375484 s & 0.746142 s \\ Data generating & 0.572778 s & 0.502677 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data loading and generating times for different channel modeling and generating schemes.  
**Funding:** This work is supported by the National Natural Science Foundation of China under Grant No. 62401411 and the New Engineering Project of Hubei Province No. XGK03070, and is also supported by the Wuhan Municipal Natural Science Foundation under Grant No. 2024040801020323.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Acknowledgments:** The authors would like to express their sincere thanks to the editor and the anonymous reviewers for their valuable suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# High Reliability Evaluation and Lifetime Prediction of 50 GHz Athermal AWG Module
###### Abstract  
A 96-channel (50 GHz-spacing) at thermal AWG has been developed. It has a wide operating range due to reduced temperature dependence than conventional AWG. The temperature dependence of the center wavelength of the developed module satisfied the \(\pm\)0.05 nm range in all channels in the temperature range of \(-\)40 \({}^{\circ}\)C to 85 \({}^{\circ}\)C, and the insertion loss variation was also less than \(\pm\)0.5 dB. As a result of validating its reliability through tests based on Telcordia-GR-1209 and GR-1221, the temperature dependence of the center wavelength satisfied the \(\pm\)0.022 nm range, and the insertion loss variation was also less than \(\pm\)0.2 dB. Accelerated life testing showed an expected service life of over 36.7 years, ensuring long-term safety of communication quality in harsh indoor and outdoor environments.  
accelerated test; athermal; DWDM; lifetime prediction; reliability +
Footnote †: journal: Applied  
10 October 2021 18 November  
systems. However, for active temperature stabilization, the use of cooling devices such as heaters or Peltier Coolers requires power consumption and electronic control circuits, and issues such as price, device size, and reliability limit the widespread use of DWDM systems [17; 18].  
This study performed reliability and accelerated life tests on a 96-channel (50 GHz-spacing) athermal AWG module fabricated by installing a temperature compensation board on the back of a silicon substrate. We tried to verify the reliability for stabilizing the communication quality in the network environment and predict the life by checking the life of more than 10 years [19] required for optical communication parts.
## 4 Result and Discussion
### Discussion  
As shown in Figure 15c, the maximum absolute value of the detection indicator depends on the starting position of the measurement, even for the same defect. Only the maximum value obtained by each starting position is summarized by traveling speed in Figure 17. When the traveling speed was 0.25 m/min, the distribution was \(-\)0.085\(\sim\)\(-\)0.067, and when the traveling speed was 1.0 m/min, the distribution was \(-\)0.073\(\sim\)\(-\)0.014. The distribution approached zero as the traveling speed increased. Assuming that a defect is judged to be detected when the indicator is less than \(-\)0.05, the detection probability at each traveling speed is as shown in Figure 17, and it was confirmed analytically that the slower the traveling speed, the easier the defect is detected.  
On the other hand, there are different ways to increase the detection probability beside the lower traveling speed such as increasing the laser repetition rate. When considering the traveling speed of 0.5 m/min in the middle of Figure 17 as a standard, halving the traveling speed to 0.25 m/min is equivalent to doubling the laser repetition rate to 200 Hz (refer to the equivalent repetition rate in the right-most column of Figure 17). If the traveling speed is 0.5 m/min with a threshold value of \(-\)0.05, it is predicted that a repetition rate of 200 Hz is sufficient for stable detection. Therefore, the required repetition rate of the generating laser can be determined from the required detection accuracy and threshold setting.  
When traveling measurements were conducted on the same defect, the value of the detection indicator varied depending on the starting position of the measurement. Therefore, it was found that defect sizing is difficult based on the proposed indicator values. A different detection method needs to be developed for sizing and localization. However, the application of UT is originally difficult for thin sheets such as those used in this study, and it will be challenging to propose a new method.
## 4 Experimentation  
### Datasets  
There are numerous standard datasets [5; 8; 14; 24; 54; 55] available for the anomalous event detection task. Despite that, we chose to perform our experimentation using the proposed framework, DMRMs, on two very recent and challenging datasets for the anomalous event detection task: UCF-Crime [14] and ShanghaiTech [5] datasets. These datasets have videos that are long in duration, large in size, and consist of real-world scenarios of anomalous events. Moreover, to train our network efficiently and accurately, we needed a large number of anomalous and normal videos.  
The UCF-Crime dataset consists of a total of 1900 long, untrimmed real-world videos, including 950 normal videos and 950 anomalous event videos. It has 13 classes of real-world anomalous events such as robberies, car accidents, fighting, and more. We also trained our activity recognition network, 3D ResNet-34 [21], using this dataset.  
The ShanghaiTech dataset consists of a total of 437 videos (with 13 different scenes and 316, 154 frames)-that is, 330 training videos (normal videos) and 107 test videos (anomalous videos like cycling and skating on the pedestrian-only path). This dataset contains videos with different camera angles and lighting conditions.  
**Training and Testing Datasets:** In DMRMs, the network was trained individually on both datasets. First, the UCF-Crime dataset was divided into a training dataset and a testing dataset. Whereas the training group contains 800 normal and 810 anomalous event videos, the testing group contains 150 normal and 140 anomalous event videos. It is worth mentioning that the dataset was divided in such a way that both the training and testing datasets each contained all 13 classes of anomalous events. Second, our network was also trained on the ShanghaiTech dataset, which was already divided into training and testing datasets. However, we needed both normal and anomalous videos to train our network; therefore, we used a portion of the given testing dataset (randomly chosen 40 testing videos) as an anomalous video group for the training phase. Moreover, we used only 30% of the given training dataset (normal event videos). Moreover, both the training and testing datasets contain all 13 scenes. Using less part of the training dataset was an advantageous part of our proposed framework, DMRMs.
## 3 Finite Element Analyses
### Slot Opening Width Analysis  
#### 3.2.1 Rectangular Slot  
The slot-opening \(w_{so}\) is the first analysed parameter. An array of slot-opening values was selected, and its extreme values corresponded to a narrow slot opening (see Figure 8b) and to the widest one (see Figure 8a), i.e., to a geometry without tooth shoe.  
Varying the slot opening width does not significantly reduce, or increment, the additional losses for a rectangular slot, see Figure 9. In fact, considering the highest value of the total power losses (68.65 W) corresponding to the narrowest slot-opening, the percentage reduction corresponding to the widest slot-opening is 4.52%. On the other hand, \(\Delta Z\%\) passes from 40.39% to 83.56%, ranging from the narrowest to the widest slot-opening; see Figure 10.  
FEA results on slot-opening variation are antithetical, because the considered KPIs lead to different design choices.  
Figure 8: Rectangular slot with the widest (**a**) and the narrowest slot opening (**b**).  
Figure 9: Rectangular slot—Total Losses comparison.
## 2 Signal Model
After downconverting the received signal to the baseband, the formulation of the received signal is\[z(t) =\sum_{l=0}^{L-1}\sum_{p=0}^{p-1}\sum_{n=0}^{N_{t}-1}\sum_{s=0}^{S-1 }\psi_{l}u_{s,n}e^{2\pi s\Delta f(t-nT_{s}-pT_{p}-T_{cp}-\frac{2R_{l}(t)}{c})} \tag{6}\] \[e^{2\pi f_{d_{1}}t}rect[\frac{t-nT_{s}-T_{cp}-pT_{p}-(2R_{l}(t)/c)} {T_{s}}]\] \[+n(t).\]  
The sample interval is \(\tau=1/f_{s}\), and the time-discrete signal of the \(k\)th time sample could be reformulated as  
\[z(k) =\sum_{l=0}^{L-1}\sum_{p=0}^{P-1}\sum_{n=0}^{N_{t}-1}\sum_{s=0}^{ S-1}\{\psi_{l}a_{m,n}e^{\mu 2\pi s\Delta f(k-nT_{s}-pT_{p}-T_{g}-\frac{2R(t)}{c})} \tag{7}\] \[e^{2\pi f_{d_{1}}t}rect[\frac{k-nT_{s}-T_{cp}-pT_{p}-(2R_{l}(k)/c )}{T_{s}}]\}\] \[+n(k).\]  
Suppose that the sampling frequency is \(f_{s}=1/S\Delta f=T/S\), the sample interval is \(\tau=1/f_{s}\), and the time-discrete \(S\) time samples can be obtained in one symbol. Removing the CP, the \(\mathrm{s}\)th OFDM symbol, \(p\mathrm{th}\) pulse, and \(k\mathrm{th}\) time sample can be represented as  
\[z_{n,p}(k) =\sum_{l=0}^{L-1}\sum_{s=0}^{S-1}e^{-2\pi f_{c}\left[2R_{l}-2r_{l }(k\Delta t+T_{cp}+nT_{s}+pT_{p})\right]/c} \tag{8}\] \[\cdot\psi_{l}a_{s,n,p}e^{-\mu 2\pi s\Delta f\left[2R_{l}-2r_{l }(k/s\Delta f+T_{cp}+nT_{s}+pT_{p})\right]/c}\] \[+n_{n,p}(k).\]
## 4 Results
with an alloy with the addition of TiO\({}_{2}\), and the highest for the diode soldered with the reference alloy. It is worth noticing that diodes S02 and S03 soldered with the reference alloy were damaged quickly--before 100 h. Similarly, diode S05 was destroyed during the test. For diode S09, the R\({}_{\rm th}\) value was almost doubled. For diodes S04, S06, S07, and S08, the R\({}_{\rm th}\) changed no more than by a few percentages. Figure 10 shows the impact of aging on the luminous efficacy (\(\eta_{\rm LL}\)) emitted by individual diodes. Luminous efficacy is a measure of how well a light source produces visible light. It is the ratio of luminous flux to electrical power [42]. The duration the aging process (up to 1000 h) did not significantly affect the luminous efficacy value for most LEDs. Only for diodes S05 and S09 is a significant drop in the \(\eta_{\rm LL}\) value visible, an even 80%. This drop results from damage to the diode lens during heating. It is also worth noting that the most minor changes in the value of \(\eta_{\rm LL}\) during aging are observed for diode S06. Figure 11 shows the measured optical power values for diodes under test during the aging process. It is visible that the most significant value of optical power is obtained before the aging process (at t = 0). The optical power slowly increases for diodes that were not damaged during the aging process. The observed changes in the value of this parameter do not exceed 2%. Figure 12 shows the measured luminous flux values for diodes under test during the aging process. It is visible that the most significant value of luminous flux is obtained before the aging process (at t = 0). The luminous flux slowly increases for diodes that were not damaged during the aging process. The observed changes in the value of this parameter do not exceed a few percentages. For tested devices S05 and S09, a significant decrease in the value of \(\Phi_{\rm V}\) for time t > 0 was observed. This decrease is a result of the thermal degradation of the lens of the mentioned LEDs. Figure 13 shows the transient thermal impedance curves of chosen diodes under test. After 1000 h of the aging test, the differences between the Z\({}_{\rm th}\)(t) values do not exceed 0.2 K/W. In turn, for the diode marked with symbol S09, a two-fold change in the Z\({}_{\rm th}\)(t) value is visible. After 1000 h of operation, there is a significant improvement in the efficiency of dissipating the heat generated in this diode. This is probably due to the improved quality of the soldered joint during the aging test. Figure 13: Waveforms of the transient thermal impedance of LEDs during the aging process.
## 3 Materials and Methods  
Transdisciplinary and multi-scale fracture network characterisation was performed utilising digital satellite imagery, drainage patterns, and geological scrutinization acquired during the fieldwork on the Niah-Subis Limestone. First, the structural geological investigations of the linear features were examined, marked, and recorded on the Digital Elevation Model (DEM). The linear features of the DEM represent geological lineaments, which range from fractures, faults, folds, sedimentary bedding, and possible lithological boundaries. The present-day Niah-Subis drainage pattern map helped the lineament delineation of the DEM to enhance the patterns for lineament interpretation. The potential karst depression at the regional scale was identified using DEM, and the relationship between the karst depression and the surface drainage system was then evaluated. Fieldwork was conducted in Traders' Cave, Great Cave, and Painted Cave with visible observation for the Subis Limestone outcrops. During the fieldwork, a close geological examination was performed to differentiate karst features and fractures in the outcrops. Measurements of the fracture orientations were recorded for comparison with the lineament analysis conducted on the DEM data. Lastly, the network topology analysis is combined with the two-dimensional (2D) discrete fracture (DFN) modelling of the regional lineaments and outcrop-scale fractures. The discrete fracture network (DFN) model comprises a series of geometric objects representing individual fractures [54]. When integrated, the network topology emphasises the relationships between individual structures and can reveal how fault interactions and connectivity occur [55; 56]. The summary of the workflow is displayed in Figure 2.
# Age Estimation from fMRI Data Using Recurrent Neural Network
## 3 Age Estimation
### Proposed Model  
The proposed GRU-based model takes an input \(X\), where the last GRU is followed by fully connected (FC) layers, including a batch normalization (BN) layer and ReLU activation. We describe the structure of the proposed model in Figure 3a. The model hyperparameters, including the number of layers and hidden dimensions, are optimized as described in Section 4.  
Figure 1: An example of normalization and registration of an fMRI image. (**a**) Original fMRI image of sub12855 in the Berlin_Margulies project; (**b**) normalized fMRI image; (**c**) registered fMRI image after normalization, where each color represents the clustered brain region.  
Figure 2: Data preprocessing procedures. We use FEAT to normalize the fMRI data (4D data), and then use FLIRT to register the data. Finally, we average voxel values for each 94 brain region.  
Other Baseline Models  
Another estimator we considered is a Transformer, which shows extraordinary performances in NLP such as translation. The Transformer generally includes two parts. The \"encoder\" takes an input sequence then maps to a vector where the \"decoder\" outputs a sequence based on the encoded vector. Since we want to estimate age (real number), we are only interested in the Transformer encoder. Similar to Transformer-based models in NLP, we first map the preprocessed data \(X\) to a vector using positional encoding. Then, we apply multiple Transformer encoder layers that consist of self-attention and feed-forward layers. The final fully connected layers estimate the age. The structure of Transformer-based model is described in Figure 2(a).  
We also considered the other RNN-based models, including vanilla RNN and LSTM, where vanilla RNN and LSTM also have similar structures to the GRU-based model.  
Finally, we constructed a multilayer perceptron (MLP) that consists of FC layers only. We treat the preprocessed fMRI data as data with two dimensions: time series and brain regions. We constructed a multi-layer model learning data with the same structure. Each layer contains an FC layer, a batch normalization (BN) layer, a ReLU activation, and a dropout [60].