esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation
### Cache Performance Model
For cloud-edge caching systems, the cache hit efficiency is another way of expressing cache efficiency, which is more representative of the efficiency of caching in real scenarios because it is closely related to the transfer latency in various cases [15]. As the edge has a faster response time than the cloud, caching at the edge for a requested content is considered a cache hit. Taking into account the issue of transmission delay, in the \(t\)-th round of federation, the cache hit efficiency for all contents within a single BS can be represented as  
\[H_{t,n}=\sum\limits_{u\in\mathcal{U}}\sum\limits_{f\in\mathcal{F}}\frac{d_{U- B}}{d_{U-B}+x_{f,B}d_{B-B}+x_{f,C}d_{B-C}} \tag{6}\]  
where \(x_{f,B}\) and \(x_{f,C}\) are decision variables representing whether the requested content \(f\) comes from the neighboring BS (\(x_{f,B}=1\) if yes, 0 otherwise), or the cloud (\(x_{f,C}=1\) if yes,0 otherwise). Therefore, the formula for the cache hit efficiency of the system in the \(t\)-th epoch of federation is as follows:  
\[H_{t,system} =\sum_{n\in\mathcal{N}}H_{t,n} \tag{7}\] \[subject\ to:\] \[f\in\mathcal{F}\] \[n\in\mathcal{N}\] \[u\in\mathcal{U}\] \[x_{B}\ and\ x_{c}\in\{0,1\}\] \[\sum_{f\in\mathcal{F}}P_{u,f}=1\]  
Based on the paper [30], it is known that maximizing the cache hit efficiency, i.e., minimizing the average delay of the system, is an NP-complete problem. We prove this in the next section. Moreover, achieving satisfactory results through heuristic algorithms such as GREEDYAD is difficult [31]. The difficulty in obtaining effective strategies with heuristic algorithms for edge caching can be attributed to their inability to adequately address dynamic environments, heterogeneous resources, scalability, and collaboration requirements. Therefore, we introduce an approach to optimize the caching efficiency through DRL methods.
## 2 Materials and Methods
### Methodology  
#### 2.5.1 Spectral Analysis  
Spectral analysis is popularly known as spectrum analysis, which plots the time-dependent spectrum of frequencies [33]. In seismology, the spectral analysis is generally conducted for seismic signal processing, through an algorithm that displays the power of different frequency elements for a time-domain signal. Therefore, this is also called frequency domain analysis in signal processing.  
Signals are displayed in the form of a periodogram. A periodogram shows the spectral density with respect to frequency. Therefore, the periodogram for a sequence [x\({}_{1}\),..., x\({}_{n}\)] can be mathematically presented as:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Assessment** & **Raw Data** & **Input Parameters** & **Methods** & **Resolution** \\  & & & & **and Scale** \\ \hline Susceptibility to & Digital elevation & Amplification factor & & \\ seismic & model (DEM) & elevation & Weighted overlay & \\ assimilation (SSA) & and geological & Slope & using predefined & \\  & information & curvature & & weights \\ \hline  & & Elevation & & \\  & DEM, geological & slope & & \\  & data, earthquake & Curvature & & 1:30,000 \\ Earthquake & inventory, & magnitude density & & \\  & geo-structural & Depth density & Recurrent neural & \\  & information, & Epicenter density & network (RNN) & \\  & attenuation & Proximity to epicenters & & \\  & relationship & Fault density & & \\  & & Proximity to fault & & \\  & & Geology & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Raw data and input parameters used for assessments.  
\[\hat{\mathcal{D}}_{\mathrm{P}}(\omega)=\frac{1}{n}\biggl{|}\sum_{t=1}^{n}x_{t} \mathrm{e}^{-i\omega t}\biggr{|}^{2} \tag{1}\]  
The expression presented in Equation (1) can form a power spectrum of any seismic signal having values of [x\({}_{1}\),..., x\({}_{n}\)], where t is time and n = 1,2,3... etc. The window \((\omega)\) can be selected based on requirements. However, we implemented the Blackman window to improve continuous function. The power spectral density \(\hat{\mathcal{D}}_{\mathrm{p}}(\omega)/\mathrm{F}\) of a periodogram can be estimated using a nonuniform fast Fourier transform, where the frequency is 2\(\pi\) during no supply of the sampling frequency. However, the sampling frequency (fs) is applied when the frequency range is (0, 1). The main aim of the periodogram spectral estimator is to expose \"hidden periodicities\" in a time series analysis that defines the periodogram [34; 35].
## 4 Dictionary Pair Learning Algorithm
### Sparse coding phase
The values of frame bounds \(A\) and \(B\) are shown in Table 1. For example, when the noise level \(\sigma=40\), \(A\) and \(B\) are set to be 0.8 and 1.8, respectively. Table 2 shows the comparison results in terms of PSNR. There are three related image denoising methods involved, including the classical dictionary learning algorithm KSVD [9], the data-driven tight frame based denoising method [1] and the incoherent dictionary learning based method [21]. The patch size of KSVD [9] and the method in [21] are \(8\times 8\) with stripe 1 and the dictionaries are of size \(64\times 256\) at their optimal state according to the previous work. We point out that [1] works on filters of size \(16\times 16\) instead of image patches and initialized by 64 3-level Harr wavelet filters in size \(16\times 16\). All the three compared methods can achieve their best performance with 50 iterations. Table 2 shows that the incoherent dictionary learning method [21] outperforms the KSVD [9] in average as the mutual incoherent of dictionary can provide stable recovery. That [1] outperforms [21] implies that the tight frame is a more stable system. Then our stable sparse model based method outperforms [1] in average suggests that applying non-tight frame to approximate RIP can provide even better and more stable reconstruction results. Figure 3 shows two exemplified visual results on images 'Man' and 'Couple' at noise levels \(\sigma=50\) and \(\sigma=40\), respectively. The proposed method shows much clearer and better visual results than the other competing methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \(\sigma\) & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \hline A & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 \\ \hline B & 1.8 & 1.8 & 1.8 & 2.4 & 3.0 & 3.3 & 3.3 & 3.6 & 3.6 \\ \hline \end{tabular}
\end{table} 
Table 1: The valuesof \(A\) and \(B\).
# Improvement of the Zienkiewicz-Zhu Error Recovery Technique Using a Patch Configuration
## 2 Node Patch Zienkiewicz-Zhu (ZZ) Stress Error Recovery Technique  
The ZZ super-convergent patch recovery technique [4] for finite element solution errors recovery assumes that the node values of field variable derivatives (\(\sigma\)) belong to a polynomial expansion of the same complete order as that of the basis function and is valid over a patch of neighboring nodes of the vertex (Figures 1 and 2). The following polynomial expansion may be used for each component of stress:  
\[\sigma^{*}(x)=P(x)\cdot a, \tag{1}\]  
where \(P(x)\) is the basis function of the assumed polynomial, \(x=(x_{i},y_{i})\), are the coordinates of the sampling points, and \(a\) is the unknown parameters vector.  
Figure 1: Patch configuration in triangular mesh.  
Figure 2: Patch configuration in quadrilateral mesh.  
A least-squares fit of \(\sigma^{h}\) values over the nodes patch, may be made by minimizing the following functional:  
\[\pi_{f}(a)=\frac{1}{2}\sum_{i=1}^{np}\left[\sigma^{h}(x_{i},\,y_{i})-P(x_{i},\,y _{i}\,)\cdot a\right]^{2}, \tag{2}\]  
On simplification, it results into the following equation:  
\[A\ a=b, \tag{3}\]  
The matrices \(A\) and \(b\) are given as:  
\[A=\sum_{i=1}^{np}\left[P^{T}(x_{i},\,y_{i})\ P(x_{i},\,y_{i}\,)\right], \tag{4}\]  
\[b=\sum_{i=1}^{np}\left[P^{T}(x_{i},\,y_{i})\ \sigma^{h}(x_{i},\,y_{i}\,)\right], \tag{5}\]  
where \(np\) is the number of nodes in a patch.
## 3 Results and Discussion
### Results of Parametric Study
#### 3.2.3 Results in Terms of Number of Neurons  
In this section, the influence of the number of neurons in the hidden layer on the performance of the ANN model is explored. To this end, other parameters such as training function was fixed as Levenberg-Marquardt, activation function was fixed as tansig and cost function was fixed as mean square error. Figure 8a-d show the box plot distribution of R\({}^{2}\), Slope, RMSE and MAE, respectively, as a function of number of neurons, which was ranked from one to 30 with a resolution of one. The presentation is the same as in Figure 6: median, mean, 25-75% and 9-91% percentiles of the probability distribution over 1000 random sampling runs are shown. Results showed that a small number of neurons (i.e., fewer than five) gave a poor performance for the ANN model, with respect to all error measurement criteria (in both median and standard deviation). However, all the median values formed a concave curve, which indicated greatest performance around 15 artificial neurons.  
Figure 7: Evaluation of activation functions: box plot over 1000 random sampling runs with respect to (**a**) R\({}^{2}\), (**b**) Slope, (**c**) RMSE and (**d**) MAE.  
This observation confirmed that there is an optimal number of neurons for the given problem (i.e., beyond a certain point, increasing the number of neurons does not always improve performance). Therefore, the optimal number of neurons was finally chosen as 15.  
Figure 8: Evaluation of number of neurons: box plot over 1000 random sampling runs with respect to (**a**) R\({}^{2}\), (**b**) Slope, (**c**) RMSE and (**d**) MAE.  
The influence of cost functions on the performance of the ANN model was also evaluated. However, all considered cost functions produced similar results on the ANN's performance. Mean square error was finally selected.
# An Insight to the Outage Performance of Multi-Hop Mixed RF/FSO/IWOC System
### Contributions
\begin{table}
\begin{tabular}{l l l} \hline \hline
5. & \(\tilde{\tau}_{RF}\) & Average SNR of RF link \\ \hline
6. & \(\gamma\) & Instantaneous SNR of the RF/FSO/UWOC link \\ \hline
7. & \(\Gamma(.,.)\) & Upper incomplete Gamma function \\ \hline
8. & \(L_{RF}\) & RF channel path loss \\ \hline
9. & \(\alpha\) & Path loss exponent \\ \hline
10. & A & Constant related to the transmission environment and frequency of the RF information signal \\ \hline
11. & \(d_{RF}\) & Distance between source and relay 1 \\ \hline
12. & m & Nakagami-m fading parameter \\ \hline
13. & K & Rician factor \\ \hline
14. & \(\tilde{\tau}_{FSO}\) & Average SNR of FSO link \\ \hline
15. & \(\rho\) & Optical-to-electrical efficiency \\ \hline
16. & \(P_{t_{1}}\) & Source transmitted optical power \\ \hline
17. & \(\sigma_{n_{1}}\) & Standard deviation of Gaussian noise \\ \hline
18. & \(\sigma_{\theta}^{2}\) & Variance of Tx–Rx misalignment orientations \\ \hline
19. & \(\zeta\) & Pointing error in FSO link \\ \hline
20. & \(\sigma_{s}\) & Standard deviation of the pointing error displacement \\ \hline
21. & \(w_{e}\) & Equivalent beam width at the receiver \\ \hline
22. & \(\alpha_{f},\beta_{f}\) & Atmospheric turbulence parameters \\ \hline
23. & \(\Gamma(.)\) & Standard Gamma function \\ \hline
24. & \(erf(.)\) & Error function \\ \hline
25. & \(h_{l}\) & Atmospheric path loss \\ \hline
26. & \(F_{T_{FSO}}(\gamma)\) & CDF of SNR of FSO link \\ \hline
27. & \(f_{T_{FSO}}(\gamma)\) & PDF of SNR of FSO link \\ \hline
28. & \(F_{T_{UW}}(\gamma)\) & CDF of SNR of UWOC link \\ \hline
29. & \(f_{T_{UW}}(\gamma)\) & PDF of SNR of UWOC link \\ \hline
30. & r & Constant for modulation and detection technique \\ \hline
31. & \(\mu_{r}\) & Average SNR of the UWOC link \\ \hline
32. & \(w,\lambda,a,b,c\) & Parameters associated with the EGG distribution of the UWOC link \\ \hline
32. & \(\gamma_{DF}\) & SNR of the received signal at destination node \\ \hline
33. & \(\gamma_{RF},\gamma_{FSO},\gamma_{UW}\) & Instantaneous SNR of the RF, FSO and UWOC links resp. \\ \hline
34. & \(F_{T_{DF}}(\gamma)\) & CDF of the end-to-end source to destination link \\ \hline
35. & \(\gamma_{th}\) & Threshold SNR \\ \hline
36. & \(P_{out}^{DF}(\gamma_{th})\) & Outage probability of the system \\ \hline
37. & \(P_{out}\) & Outage probability of the system at high-SNR region \\ \hline
38. & \(G_{C}^{xy}\) & Coding gain \\ \hline
39. & \(G_{D}^{xy}\) & Diversity gain \\ \hline
40. & \(d_{FSO}\) & FSO link distance \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
# Parallel Simulations of the Sharp Wave-Ripples of the Hippocamp on Multicore CPUs and GPUs
## 3 Experimental Results
### Performance Evaluation  
The processing time of all the serial and parallel codes have been measured on the system described in Section 3.1. In addition to the experiment concerning the same network described in [5] i.e., considering 800 pyramidal cells and 160 interneurons, two other networks have been considered to evaluate the scalability of the simulation. In particular, a smaller network with half the neurons (i.e., 400 excitatory and 80 inhibitory cells) and a bigger network with 1200 pyramidal cells and 240 interneurons have been simulated. In the following, the smallest network will be denoted as Network1, the original as Network2, and finally the biggest network will be referenced as Network3. All the simulations were repeated 10 times and the mean processing times, together with the standard deviations, have been computed. The serial processing of the three networks on the Intel i9 13900K took 192.25 s, 740.54 s, and 983.76 s, respectively. The same networks took 552.84 s, 2141.91 s, and 2504.97 s on the Intel Xeon Silver 4314. The memory footprint of the three networks is 8.7 GB, 15.2 GB and 21.2 GB, respectively. These memory footprints impose a limitation on the GPUs that can carry out the simulations.  
For the OpenMP codes, the experiments considered a variable number of threads, from 16 to 64, to find the optimal number of processing elements for each processor. Figure 6 shows the mean processing times of the Intel i9 13900K and of the Intel Xeon Silver 4314 for the three Networks for the serial and parallel codes, and with a variable number of threads. In all these cases, the standard deviation was less than 2%.  
Figure 5: Voltage traces of two cells included in the network.  
Figure 6: Processing times of the multicore CPUs with varying number of threads. The y-axis is in logarithmic scale.  
The same methodology, apart from varying the number of threads, was repeated for the two GPUs described in Section 3.2. Network3 has been simulated only on the NVIDIA RTX 4090 board due to memory constraints. Figure 7 reports the mean processing time for these devices for the three networks. Again, the standard deviation is low since it is always less than 2%.  
Finally, the speed-up between the serial processing on the serial processors and the best OpenMP for each multicore CPU and the GPUs have been computed to characterize the impact of the parallelization of the hippocampus CA1 network simulation. Table 4 reports these best processing times, together with the speed-up indicated between brackets.
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is closely linked to the photosynthesis of plants and has the potential to estimate gross primary production (GPP) at different temporal and spatial scales. However, remotely sensed SIF at a ground or space level is usually instantaneous, which cannot represent the daily total SIF. The temporal mismatch between instantaneous SIF (\(\mathrm{SIF}_{\mathrm{inst}}\)) and daily GPP (\(\mathrm{GPP}_{\mathrm{daily}}\)) impacts their correlation across space and time. Previous studies have upscaled \(\mathrm{SIF}_{\mathrm{inst}}\) to the daily scale based on the diurnal cycle in the cosine of the solar zenith angle (\(\mathrm{cos}(\mathrm{SZA})\)) to correct the effects of latitude and length of the day on the variations in the SIF-GPP correlation. However, the important effects of diurnal weather changes due to cloud and atmospheric scattering were not considered. In this study, we present a SIF upscaling method using photosynthetically active radiation (PAR) as a driving variable. First, a conversion factor (i.e., the ratio of the instantaneous PAR (\(\mathrm{PAR}_{\mathrm{inst}}\)) to daily PAR (\(\mathrm{PAR}_{\mathrm{daily}}\))) was used to upscale in-situ SIF measurements from the instantaneous to daily scale. Then, the performance of the SIF upscaling method was evaluated under changing weather conditions and different latitudes using continuous tower-based measurements at two sites. The results prove that our PAR-based method can reduce not only latitude-dependent but also the weather-dependent variations in the SIF-GPP model. Specifically, the PAR-based method gave a more accurate prediction of diurnal and daily SIF (\(\mathrm{SIF}_{\mathrm{daily}}\)) than the \(\mathrm{cos}(\mathrm{SZA})\)-based method, with decreased relative root mean square error (RRMSE) values from 42.2% to 25.6% at half-hour intervals and from 25.4% to 13.3% at daily intervals. Moreover, the PAR-based upscaled \(\mathrm{SIF}_{\mathrm{daily}}\) had a stronger correlation with the daily absorbed PAR (APAR) than both the \(\mathrm{SIF}_{\mathrm{inst}}\) and \(\mathrm{cos}(\mathrm{SZA})\)-based upscaled \(\mathrm{SIF}_{\mathrm{daily}}\), especially for cloudy days with a coefficient of determination (R\({}^{2}\)) that increased from approximately 0.5 to 0.8. Finally, the PAR-based \(\mathrm{SIF}_{\mathrm{daily}}\) was linked to \(\mathrm{GPP}_{\mathrm{daily}}\) and compared to the \(\mathrm{SIF}_{\mathrm{inst}}\) or \(\mathrm{cos}(\mathrm{SZA})\)-based \(\mathrm{SIF}_{\mathrm{daily}}\). The results indicate that the SIF-GPP correlation can obviously be improved, with an increased R\({}^{2}\) from approximately 0.65 to 0.75. Our study confirms the importance of upscaling SIF from the instantaneous to daily scale when linking SIF with GPP and emphasizes the need to take diurnal weather changes into account for SIF temporal upscaling.  
solar-induced chlorophyll fluorescence; temporal upscaling; instantaneous scale; daily scale; photosynthetically active radiation; gross primary production 2018101663; doi:10.3390/rs10101663
## Materials and Methods
### Fabrication of GO and rGO  
GO was synthesized from natural graphite by using a modified Hummers method [18]. Graphite powder was added to 98% H\({}_{2}\)SO\({}_{4}\), and KMnO\({}_{4}\) was then added slowly while stirring at a temperature below 10 \({}^{\circ}\)C. Stirring was continued for 2 h at 10 \({}^{\circ}\)C, and the mixture was then stirred for five days at room temperature until a brown paste was obtained. Subsequently, a 5% H\({}_{2}\)SO\({}_{4}\) aqueous solution was added, and the mixture with stirred for 2 h. A 30% H\({}_{2}\)O\({}_{2}\) aqueous solution was then added and the mixture was stirred for a further 2 h, and a bright yellow mixture was obtained. To remove residual metal ions, the mixture was centrifuged and washed several times with 3% by weight (wt%) H\({}_{2}\)SO\({}_{4}\) and a 0.5 wt% H\({}_{2}\)O\({}_{2}\) aqueous solution. The resulting mixture was centrifuged with pure water until the pH value of the supernatant was neutral (pH 7) through the removal of the remaining sulfuric acid. The rinsed mixture was dried at room temperature for 24 h in a vacuum chamber, and graphite oxide powder was obtained. Finally, GO was obtained through the ultrasonic treatment of the graphite oxide powder dispersed in pure water, and to obtain a GO sheet, the supernatant of the centrifuged GO solution was filtered by using a vacuum filter. Finally, rGO was synthesized from the GO solution (1 mg GO/1 mg DDI water ratio) via a reduction process that used N\({}_{2}\)H\({}_{4}\)-H\({}_{2}\)O while stirring for 2 h at 90 \({}^{\circ}\)C [19].
## 4 Results and Discussion
### Flow Field and Stress Filed
#### 4.1.3 Verification of the Flow Field  
To verify the accuracy of the simulation results, the proposed model was used to simulate the experimental case. The jet inlet pressure was set at 15 MPa, and the ambient pressure was set to be 10 MPa, parameters also used for nozzle and perforation structures [13]. The length-to-nozzle diameter ratio was 2, while the contraction angle was 30.5\({}^{\circ}\). As shown in Figure 15, when compressed SC-CO\({}_{2}\) flowed through the nozzle section, the static pressure gradually decreased, while the fluid velocity increased significantly, indicating that the static pressure was converted to dynamic pressure. After SC-CO\({}_{2}\) flowed through the annular and entered the perforation tunnel, static pressure increased from the minimum value in the perforation tunnel of the casing and cement section. Meanwhile, the velocity decreased sharply. These results were in accordance with those reported by [PERSON] [13]. Comparing simulation results with experiments results, the tunnel pressure was 11.79 MPa by numerical simulation, and the measured tunnel pressure was 12.63 MPa. The small relative error indicated that the FSI model can accurately simulate SC-CO\({}_{2}\) in a perforation tunnel.  
Figure 14: Flow field and stress field before fracture initiation.
# Predicting the Onset of Freezing of Gait Using EEG Dynamics
## 2 Materials and Methods
### EEG Processing  
The EEGLAB toolbox [23] was used for processing EEG data, as shown in Figure 2. The raw EEG was band-pass filtered between 1 and 30-Hz to eliminate low- and high-frequency noises. The line noise was removed with the pop_cleanline function in EEGLAB. Further, channels with at least three seconds of flatlines were corrected with clean_flatlines functions, and all channels were cleaned with clean_channels. There were 3 \(\pm\) 0.5 channels removed on average, and these channels were interpolated. Afterwards, normal walking, transition to FOG, and transition to voluntary stopping trials were extracted to provide input to the deep learning models.  
In this study, a total of 178 trials of transition to FOG episodes, 55 transitions to voluntary stopping, and 233 trials of normal walking were extracted from the continuous EEG data of 17 subjects. These transitions to FOG were extracted from both standard TUG and TUG tasks for voluntary stopping based on the occurrence of FOG episodes, and each trial was 2 seconds in length. These data were reformatted to a matrix with the shape number of trials \(\times\) time points \(\times\) number of channels format (466 \(\times\) 1000 \(\times\) 30) before providing it to the DL models.  
We also performed two-class classifications with the transitions to FOG and the transition to voluntary stopping. This matrix was with the shape number of trials \(\times\) time points \(\times\) number of channels format (233 \(\times\) 1000 \(\times\) 30) before providing to the DL models.  
We performed a grid search to select the optimal hyperparameters for the three CNN models: EEGNet, Shallow ConvNet, and Deep ConvNet. The data were shuffled and randomly divided into three separate sets, the training (60%), validation (20%), and testing sets (20%). The performances of these models were obtained by 5-fold cross-validation. We also performed leave-one-subject-out cross-validation to evaluate the performance of these models.