esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Edge AI for Early Health Prediction
### Prediction of Future Health Outcomes
#### 5.3.4 Federated Learning (FL)  
By implementing federated learning, healthcare providers can effectively train models on patient data that come from many different sources. This approach allows for a more comprehensive and diverse set of data from which the model can learn, leading to greater accuracy in predicting and detecting diseases in their early stages. In preliminary health diagnosis, the implementation of federated learning is an effective mechanism to develop predictive models that can predict the likelihood of diseases such as cancer, diabetes, and heart disease [72; 73; 74]. By thoroughly analyzing data collected from different patients, the model can efficiently identify patterns and risk factors that would be difficult to detect in a single patient. In addition, federated learning can help address privacy issues in healthcare. The risk of data breaches is significantly reduced because patients' data are stored on their devices rather than sent to a central server, which increases patient privacy [72; 75].
## 3 Near-Field Scanning Transmitarray Feed by a Cluster  
### Antenna Optics and Feed Cluster  
In a first approach to evaluate the viability of this concept, a cluster of five feeds is analyzed to illuminate the transmitarray through a 1D distribution along the \(\hat{x}\)-axis, with an angular distribution \(\theta=5^{\circ}\) and an arc of radius 100 mm. Table 1 outlines the location of the phase center of each feed in the \(\hat{x}\)-axis according to (1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feed** & \(\theta=\pm 0^{\circ}\) & \(\theta=\pm 5^{\circ}\) & \(\theta=\pm 10^{\circ}\) & \(\theta=\pm 15^{\circ}\) & \(\theta=\pm 20^{\circ}\) \\ \hline \(\hat{x}\) & 0 & \(\pm 8.71\) & \(\pm 17.36\) & \(\pm 25.88\) & \(\pm 34.22\) \\ \(\hat{y}\) & 0 & 0 & 0 & 0 & 0 \\ \(\hat{z}\) & \(-100\) & \(-99.61\) & \(-98.48\) & \(-96.59\) & \(-93.97\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Phase center location for different \(\theta\) according to (1).  
Figure 1: Spatial distribution of the feeds along the \(\hat{x}\) and \(\hat{y}\) axes.  
This cluster feeds a transmitarray made up of 576 elements on a regular gird of \(24\times 24\) elements with a periodicity of \(5\times 5\) mm\({}^{2}\) in both the \(\hat{x}\) and \(\hat{y}\) direction. The equivalent aperture of the transmitarray is \(169.70\) mm\({}^{2}\) (\(11.2\lambda\times 11.2\lambda\)) at the central frequency, 28 GHz. According to this configuration and using a standard gain horn of 15 dBi, the incident field is analyzed at different positions to evaluate their differences. In particular, the central position and the extreme feed are compared, which is the worst case. The comparison is carried out with a full-wave simulation using CST Microwave Studio [20]. The incident field at 28 GHz is obtained at these two positions, and the results are shown in Figure 2. Both incident fields behave similarly, and the difference is lower than 1 dB in an area of radius 50 mm (see Figure 3). These differences increase on the transmitarray edge due to the projection of the incident field on the transmitarray surface. The incident phase changes since it only depends on the phase center location. Hence, if a transmitarray is designed properly to focus the near-field on a given spot, with a single feed, it can be assumed that locating different feeds according to (1), the different incoming fields may be focused on different spots regarding the incident progressive phase variations.  
Figure 3: Differences of the amplitude taper (dB) on the transmitarray surface for the feeds \(\theta=0^{\circ}\) and \(\theta=20^{\circ}\). The solid black line limits the area with less than a 1 dB of difference.  
Figure 2: Evaluation of the incident field (amplitude (dB) and phase (deg)) for the feed at \(\theta=0^{\circ}\) and \(\theta=20^{\circ}\).
# Cognitive Diagnosis Method via Q-Matrix-Embedded Neural Networks
## 3 Experiments and Results
### Simulation Studies
#### 3.2.2 Results
When evaluating the effect of the sample size on the performance of the models, we observed that the classical models were sensitive to the  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Data** & \(N\) & **DINA** & **GDINA** & **NPC** & **GNPC** & **MLP** & **ANN** & \begin{tabular}{c} **Dual** \\ **Q-Net** \\ \end{tabular}  \\ \hline \multirow{4}{*}{HSD1} & 100 & 0.990 & 0.986 & 0.987 & 0.987 & 0.992 & 0.994 & 0.995 \\  & 200 & 0.993 & 0.990 & 0.989 & 0.989 & 0.9  
sample size, and the overall trend was that their performance improved as the sample size increased. GDINA and GNPC followed this trend closely, but DINA and NPC seemed to follow this trend only for the HSD1 and LSD1 datasets. This was because the HSD2 and LSD2 datasets were generated based on the GDINA model, which caused DINA and NPC to deviate from this trend on these two simulated datasets. On the other hand, the neural-network-based methods did not consistently adhere to this trend. This was because, in our work, the neural network models were trained using pre-training to initialize the model parameters, and then fine-tuning the model parameters based on sub-datasets with different sample sizes. Therefore, the robustness of the neural-network-based methods was more influenced by the sample imbalance of the test set than the sample size. Hence, the neural-network-based methods in this paper did not completely follow this rule. Previously, we analyzed in detail how each model performed for AAR on each simulated dataset. Next, we transfer our focus to their performance for PAR. Overall, our method Dual Q-Net outperformed the other models in PAR score on every simulated dataset, which is consistent with its performance for AAR. The difference was that the neural-network-based methods had a much larger advantage over the classical methods in PAR than for AAR under the same conditions. In particular, as the number of attributes examined increased and the data quality decreased (i.e., the guessing and slip parameters increased), the advantage of the neural-network-based methods becomes more prominent. For the classical models, on datasets with fewer attributes (e.g., the HSD1 and LSD1 datasets), the DINA model had a slight advantage over the other three, which was not significantly different. However, for datasets with more attributes (e.g., the HSD2 and LSD2 datasets) GDINA and GNPC were significantly ahead of DINA and NPC. For the neural-network-based methods, the overall performance was more satisfactory. Their PAR scores on all datasets were overall greater than 0.7. However, their performance was somewhat affected by the quality of the data, while it was not sensitive to the sample size.
# Feature Extraction in 5G Wireless Systems: A Quantum Cat Swarm and Wavelet-Based Approach
## 1 Introduction  
In the context of cognitive radio networks, spectrum detection plays an important role in determining the detection and use of available spectrum bands [1]. With recent advances in 5G networks, strong data retention with low-latency communication has become crucial in spectrum sensing applications. Generally, 5G signals can be deployed with various modulation techniques, different bandwidths, and different levels of interference when using traditional spectrum detection methods [2].  
In recent years, deep learning models have been used to detect spectrum band levels and identify signal strengths in 5G networks [3]. Convolutional and recurrent deep neural network models have been introduced in various spectrum-sensing applications. The performance of deep learning models in 5G networks was discussed in [4]. These models have shown the best performance in experiments and simulations based on 5G networks [5].  
In some real-time applications, the signals can be dynamic and change according to the noise levels; to address this problem, autoencoder models have been used. Moreover, in the work of [PERSON] et al., the segregation of the original and noisy signals was performed separately with the help of these models [6].  
Spectrum sensing is a key aspect of cognitive radio networks, facilitating the efficient use of the electromagnetic spectrum through the identification and exploitation of vacant frequency bands. In the context of 5G and beyond, where the demand for higher data rates and lower latency is constantly growing, spectrum sensing plays a crucial role in enabling dynamic access to available frequencies [7]. Deep learning techniques have emerged as valuable tools in spectrum sensing due to their ability to enable the automatic identification of complex features in raw signal data. These methods can be adapted tolearn from various signal characteristics, making them suitable for applications involving dynamic and heterogeneous 5G networks [8, 9]. In terms of spatial and temporal connections of the spectrum signals, it is possible to improve the performance of spectrum sensing technologies by integrating convolutional neural networks (CNNs) and recurrent neural networks (RNNs), as demonstrated by [PERSON] et al [6].
## 3 Broadband PA Implementation
### The Synthesized Transformer Output Matching Network
Therefore, to minimize the reflected energy, the resonance frequencies are adjusted closer together so that S\({}_{11}\) stays below \(-27\) dB throughout the \(BW_{\text{d}}\). To complete the OMN topology, a DC-blocking capacitor \(C_{2}\) with an initial value of 2 pF was added to the adjusted STN, as depicted in Figure 4b, with the inductor \(M\) serving as the drain bias line. In the end, optimization of the OMN's parameters was carried out by applying the aforementioned rapid dual-objective optimization method combined with the PSO algorithm, leading to a notable reduction in the final \(E_{\text{tol}}\) to 0.3, compared to the \(E_{\text{ini}}\) of 0.73. Figure 6 shows the trajectory of \(Z_{\text{omn}}\)(\(f_{\text{j}}\)), where only at 22 GHz does the transformed impedance fall outside the S-OID, while at mid and high frequencies,  
Figure 4: STN: (**a**) magnetic coupling resonance model; (**b**) equivalent T-network. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(R_{\text{opt}}\) & \(C_{\text{out}}\) & \(L_{2}\)-\(M\) & \(M\) & \(L_{1}\)-\(M\) & \(C_{1}\) & \(R_{\text{L}}\) \\ \hline Initial & 26 \(\Omega\) & 0.27 pF & 0.9 pH & 190.47 pH & 177.56 pH & 0.14 pF & 50 \(\Omega\) \\ Adjusted & 26 \(\Omega\) & 0.27 pF & 6.19 pH & 176.35 pH & 174.69 pH & 0.14 pF & 50 \(\Omega\) \\ \hline \end{tabular}
\end{table} 
Table 3: The parameters of the STN. \(Z_{\text{omm}}(f_{\text{i}})\) closely matches the respective \(Z_{\text{opt,s}}(f_{\text{i}})\). Although the deviation between \(Z_{\text{omm}}(f_{\text{i}})\) and \(Z_{\text{opt,s}}(f_{\text{i}})\) is somewhat greater at lower frequencies, \(Z_{\text{omm}}(f_{\text{i}})\) remains within the P-OID. Such a deliberate matching strategy ensures compliance with large-signal performance requirements over the \(BW_{\text{d}}\) and a flat frequency response.
## 3 Results
### The Effects Home Confinement on PA, Sedentary Behavior, and Satisfaction with Life (SL)
There were no interaction effects (Table 5) of region (range of \(\mathrm{n_{p}}^{2}\): 0.000-0.09), playing position (range of \(\mathrm{n_{p}}^{2}\): 0.00-0.02; Figure 1) or playing level (range of \(\mathrm{n_{p}}^{2}\): 0.01-0.08; Figure 2). Figure 1: Sitting hours per weekday depending on playing positions. Figure 2: Sitting hours per weekday depending on level of handball league. Based on the change in PA from before to during confinement measured by \(\Delta\)d and \(\eta_{\text{p}}^{2}\), moderate PA (minutes/week) demonstrated the largest group difference (\(\Delta\)d = 1.11, \(\eta_{\text{p}}^{2}\) = 0.86). The smallest change (\(\Delta\)d, \(\eta_{\text{p}}^{2}\)) over the time was observed for walking (days/walk for at least 10 min: \(\Delta\)d = 0.73, \(\eta_{\text{p}}^{2}\) = 0.678). The effect sizes for both samples (Europe vs. MENA) were similar (d\({}_{\text{MENA}}\) = 2.34, d\({}_{\text{Europe}}\) = 1.61; Table 3). A detailed variance analysis based on the 23 involved countries (number of subjects range: 1-86) was only possible and senseful for countries/samples with more than 30 subjects. Five countries fulfilled the criterion: Kuwait (n = 86), Saudi Arabia (n = 55), Iraq (n = 39), Romania (n = 50), Germany (n = 37), France (n = 31). Regarding demographic and anthropometric parameters only the age (\(p\) < 0.001, \(\eta_{\text{p}}^{2}\) = 0.179) was different between countries. The mean age ranged from 22.0 years (Romania) to 31.5 years (Iraq). The comparison of physical activity parameters between countries before and during confinement revealed interaction effects (time \(\times\) country) for four parameters (Table 6). The largest interaction effect (\(\eta_{\text{p}}^{2}\) = 0.285) was displayed for vigorous PA (days/week) (Figure 3). The effect sizes d ranged from 0.78 (Romania) to 4.09 (Germany). \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Activities**} & \multicolumn{2}{c}{**Interaction Effects (Time \(\times\) Country)**} \\  & **Parameters** & \(\boldsymbol{p\) (\(\eta_{\text{p}}^{2}\))} \\ \hline \multirow{2}{*}{**Vigorous physical activities**} & **Days/week (d)** & **<0.001 (0.29)** \\  & **Minutes/week (min)** & 0.102 (0.03) \\  & **MET-minutes/week** & **<0.001 (0.19)** \\ \hline \multirow{2}{*}{**Moderate physical activities**} & **Days/week (d)** & \textless{0.001 (0.07)} \\  & **Minutes/week (min)** & **<0.001 (0.11)** \\  & **MET-minutes/week** & **<0.001 (0.08)** \\ \hline \multirow{2}{*}{**Walking**} & **Days/walk for at least 10 min (d)** & \textless{0.001 (0.07)} \\  & **Minutes per walking day (min)** & \textless{0.001 (0.07)} \\  & **MET-minutes/week** & **<0.001 (0.13)** \\ \hline \multirow{2}{*}{**Sitting**} & **Hours per weekday day (h)** & \textless{0.001 (0.07)} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of physical activity parameters between countries before and during confinement. Meaningful interaction effects (criteria: \(p\) < 0.05 and \(\eta_{\text{p}}^{2}\) > 0.10) highlighted in bold.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Fairness Measurements  
Fairness is a measure to calculate how fairly the base station is allocating resources to the requests made by multiple users. In the case of heavy traffic, a user request may have to wait longer to get resources and may even starve. Hence, a fairness analysis is important for any scheduling scheme. To ensure the fairness of our proposed SRA algorithm, we have used [PERSON]'s fairness measure. [PERSON] et al. [84] proposed a methodology to calculate the fairness index among the users. If the measured goodput of the users is \(T=\{T_{1},...,T_{n}\}\) and the required fair goodput is \(O=\{O_{1},...,O_{n}\}\), then the normalized goodput is \(X_{n}=\frac{T_{n}}{O_{n}}\). The overall goodput fairness is measured as follows:  
\[FairnessIndex=\frac{(\sum X_{n}^{2})}{n\sum X_{n}^{2}}\]  
The delay fairness can be derived similarly from the above equation. Figures 5 and 6 show the results of fairness analysis in terms of average goodput and delay. The fairness is bounded between zero and one (i.e., 100%). If Jain Fairness Index equals to 1, perfect fairness is achieved. On the other hand, zero fairness means starvation. There are more general fairness indicators [88; 89]. In this paper, however, our main objective is to compare the performance of our approach to that of the advanced greedy baseline [13]. For fair comparisons, we use the same fairness metric and simulation settings as [13]. Using the more general fairness indicators and using other waveforms are reserved for future work.  
In this paper, we consider two dominant 5G waveforms, OFDM and FBMC, as discussed before. A major implementation comparison between them is the number of complex symbol multiplications. In OFDM, the number of multiplications per symbol that the split-radix algorithm has is:  
\[C_{FFT/IFFT}~{}=~{}M((~{}log(M)-3)~{}+~{}4)\]  
where \(M\) is the number of complex symbols. \(FFT\) and \(IFFT\) stand for the \(FFT\) and \(IFFT\) on the receiver and transmitter side, respectively.  
In FBMC, the number of multiplications per symbol is:  
\[C_{SFB/AFB}~{}=~{}2M((~{}log(M)-3)~{}+~{}4K)\]  
where the \(SFB\) and \(AFB\) are the Synthesis and Analysis Filter Banks at the transmitter and receiver side, respectively.  
OFDM suffers from poor spectral selectivity, which leads to marginal degradation in fairness as the number of users increases, as plotted in Figures 5 and 6. FBMC has higher selectivity, but requires more symbol multiplications.
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 7 Conclusions and Future Work  
This paper describes the integration of IoT devices, UAVs, and satellites in the 5G environment. A brief description of the different IoT technologies was presented, along with the state-of-the-art, the different use cases of UAVs and IoT, and the role of satellites in the IoT. Different architectures were presented by differentiating the action of IoT devices, UAVs, and satellites. The approach where UAVs were 5G-UE, satellites were RNs, and 5G-gNB was located in the terrestrial portion was discussed as the first step for the mentioned integration. Possible challenges were also presented. Future works will concern the implementation of the proposed approach. Mathematical analysis and numerical simulations will be the next step of this research activity, which will involve the practical implementation of (at least) one of the proposed architectures through an emulated/simulated testbed.
## 2 Formation Mechanisms of Effective Reservoir in Ultra-Deep
### Structural Fracturing Enhances the Permeability of Clastic Reservoirs  
Oil and gas drilling practices have demonstrated that structural fractures are a key factor in improving the flow capacity of deep elastic reservoirs. Nearly all deep and ultra-deep elastic reservoirs in China have been influenced by tectonic activity, making accurate predictions of \"sweet spots\" with well-developed fractures crucial for achieving high and stable production [66; 70; 71; 72]. The development of fractures can significantly enhance the matrix permeability of ultra-low permeability or tight reservoirs by providing flow channels for fluid migration.  
In recent years, exploration efforts targeting tight sandstone reservoirs using the \"fault-fracture body\" approach have achieved remarkable success [73; 74; 75]. Fractures can even serve as primary storage spaces for hydrocarbons [76; 77]. In the Tongjiang-Malubei area of the Sichuan Basin, extensive exploration has shown that fractures can form during early tectonic uplift. Additionally, fractures can develop through processes such as compression, tectonic compaction, and stratigraphic inversion. Various types of fractures can improve the matrix reservoir to differing extents; the reservoir properties of fractured intervals are significantly better than those of non-fractured intervals (Figure 9a).  
In the Shuntuugoule Low Uplift area of the Tarim Basin, fractures effectively connect individual sand bodies in deep sandstone reservoirs, increasing reservoir permeability by 1-3 orders of magnitude (Figure 9b). Furthermore, in most fracture-developed reservoirs, oil traces are visible in the core samples (Figure 9b), indicating that fractures serve as excellent pathways for oil and gas migration. Therefore, overlapping zones of fault-associated fractures, fold-associated fractures, and large-scale high-energy sand bodies represent the \"sweet spots\" for development.  
Fractures can enhance sweet spot grades and even convert non-reservoirs into reservoirs. In the Jurassic-Early Cretaceous phase of the Kuqa Depression, braided river delta sand bodies formed, but their poor matrix porosity has hindered large-scale development. Previous wells drilled by Sinopec and PetroChina failed to achieve industrial oil and gas flow standards. However, the Dibei-5 well and others successfully explored and tested industrial flows using the fault-fracture network without relying on in situ source rocks [33].
## 2 DPS Modeling of LEO-SIN
### Performance Analysis
#### 3.3.3 MSE of DPS Analysis  
MSE of DPS is used to quantitatively analyze the second-order deviation of different simulation models, which can be expressed as,  
\[MSE_{i}=\frac{1}{N}\sum_{n=1}^{N}\left|R_{z_{i}z_{i}}(n)-\widetilde{R}_{z_{i}z_ {i}}(n)\right|^{2} \tag{31}\]  
Figure 10: ACF error of MEDS-SP and RMEDS-SP.  
Figure 9: ACF of different models.  
where \(N\) is the number of sample points within the relative time, \(\widetilde{R}_{z_{i}z_{i}}(\tau)\) denotes the ACF error of the simulation model, \(R_{z_{i}z_{i}}(\tau)\) is the ACF error of the reference model. MSE of ACF of different models is calculated, and the simulation accuracy of the model is reflected by comparison. Since the random results of simulations, we get the average of \(MSE_{i}\),  
\[MSE=\frac{1}{N_{\text{s}}}\sum_{1}^{N_{\text{s}}}MSE_{i} \tag{32}\]  
where, \(N_{\text{s}}\) denotes the number of simulations times, and \(MSE_{i}\) is each MSE obtained from each simulation. According to Equation (32), we can obtain \(MSE\) results of MEDS-SP and RMEDS-SP, as Table 2 shows.  
As Table 2 shows, the number of sinusoids is set as \(N_{1}=20,N_{2}=21,K=4\), the MSE of DPS by RMEDS-SP improves the accuracy by about 31.24% compared with the MEDS-SP model. The corresponding DPS of a different model is operated by the Fourier transform of the ACF, which is shown in Figure 11.  
As Figure 11 shows, the DPS of RMEDS-SP is more similar to the referenced model than the DPS of MEDS-SP. The MSE of DPS of RMEDS-SP and MEDS-SP are 12.85 and 18.95, which have about a 32.19% performance improvement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **MSE** \\ \hline \multirow{2}{*}{MEDS-SP} & \(N_{1}=20\), & \\  & \(N_{2}=21\), & \\  & \(K=4\) & \\ \hline \multirow{2}{*}{RMEDS-SP} & \(N_{1}=20\), & \\  & \(N_{2}=21\), & \\  & \(K=4\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: MSE of simulation errors of different models.  
Figure 11: DPS of MEDS-SP and RMEDS-SP.
## 4 BPSK FDA System Design
### Channel Capacity  
To ensure that appropriate channel capacity exists at our desired fast-time chip frequency, we evaluate the Shannon-Hartley theorem  
\[C=B\log_{2}\left(1+\frac{S}{N_{o}}\right) \tag{13}\]  
with bandwidth \(B=10\) MHz and a signal-to-noise ratio of 7 dB (determined analytically in Section 4.3), and find that \(C=25.8\) Mb/s \(\geq f_{chip}=1\) Mb/s. Note here that \(S\) and \(N_{o}\) represent the signal level and noise level of the system, respectively.  
Figure 4: BER analysis to determine transmitted modulation size. The minimum amplitude of different packet lengths (**left**) are color-coded to match their respective points on the BER curve (**right**). The unit amplitude point (**left**) is assumed to have \(E_{b}/N_{0}=12\) dB.