esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency for 5G and Beyond 5G: Potential, Limitations, and Future Directions
## 4 Possible Areas of Improvement  
In forthcoming investigations, we aim to mitigate the identified limitations of existing 5G networks and beyond 5G new networks' energy efficiency solutions, as outlined in Table 3, Advantages and limitations of energy efficiency studies of 5G networks, and Table 4, Studies' comparisons of beyond 5G new networks. Specifically, we recognize the following challenges:  
* Solutions based on reinforcement learning [12; 13] necessitate a learning phase, potentially delaying implementation;
* Approaches like [11; 14; 16; 22] may entail high computational complexity, posing scalability concerns;
* Heuristic algorithms such as [14; 15; 17; 18; 21] may not guarantee optimal solutions;
* Some solutions necessitate complex infrastructure, such as renewable power sources [9; 15] or specific backhauling technologies [17];
* Approaches need hybrid solutions to achieve maximum improvement of EE [24; 25];
* Approaches need to take into consideration real network constraints and complexities like the imperfection of CSI, different ES scenarios [24; 25], or computational load [26; 27].  
Our future research will explore strategies to overcome these constraints or use the advantages and develop more efficient, scalable, and readily deployable energy-saving mechanisms for cellular networks.  
The inherent complexity of networks, coupled with high user mobility, data throughput demands, and base station density, pose challenges in implementing algorithms that efficiently manage these factors. Existing approaches may lead to computationally intensive and potentially suboptimal solutions.  
This study has helped us establish the next step in our future research and has hopefully helped other researchers obtain a clear overview of the discussed topic. Thus, our goal will be to investigate online algorithms to determine optimal base station sleep mode strategies. We will evaluate the impact of these algorithms on energy efficiency, network signaling overhead, and user Quality of Service (QoS). Furthermore, we plan to explore the hybridization of online algorithms with other techniques such as clustering the BSs and HOs of users to lower consumption networks and renewable energy utilization, if available.  
To ensure practical relevance, we aim to measure the energy efficiency of these strategies and the algorithm's time to convergence, and to compute power needs using real-world network data collected with a service assurance solution monitoring a real 2G, 4G, and 5G core network. This data-driven approach will allow us to validate our findings and provide insights into the efficacy of proposed solutions in real-world scenarios.  
To make the theoretical solution more clear, please refer to the block diagram depicted in Figure 4.  
Figure 4: Proposed theoretical solution.
## 6 Simulation and Analysis
### Verifying the Correctness of CHCs Injection
#### 6.2.2 Phase-to-Phase (A-B) Faults with 0.1 \(\Omega\) Fault Resistance Occur at f\({}_{2}\)  
When a phase-to-phase (A-B) fault with 0.1 \(\Omega\) fault resistance occurs at \(f_{2}\), IBDG1 and IBDG2 detect the fault and initiate the injection of CHCs according to the injection strategy. The currents at the outlet of IBDG1 before and after the fault are shown in Figure 11. The distortion rate of A-phase CHC at the IBDG1 outlet is shown in Figure 12. The amplitude \(Mag_{17}\) of A-phase CHC and the characteristic harmonic signals \(I_{\text{hd}}\) and \(I_{\text{hq}}\) are shown in Figure 13.  
Figure 11: Currents at the outlet of IBDG1 before and after the fault.  
Figure 8: Currents at the outlet of IBDG1 before and after injection.  
Figure 10: The amplitude of A-phase CHC and the waveforms of characteristic harmonic signals.  
Figure 9: Distortion rate of A-phase CHC at the outlet of IBDG1.  
From Figure 11, IBDG1 performs LVRT at 0.3 s, and the currents at the outlet increase to 1.5 times the original one. There are CHCs in the output currents of IBDG1 after 0.3 s. Due to the inherent delay of FFT operation, the amplitude of the CHC reaches stability after one cycle, and the stable value is approximately equal to the amplitude of characteristic harmonic signals, as shown in Figure 13. From Figure 12, the distortion rate of CHC is stabilized at 4%. The above simulation results are consistent with the theoretical analysis in Section 3 and can verify the correctness of the CHCs injection system under the fault.
## 5 Conclusions  
The goal of the design methodology presented in this paper was to enable or simplify observer design for systems that are otherwise difficult to handle by allowing the designer to focus on a smaller, nonlinear subsystem. That is to say, we mainly focused on observing, for example, how the change of an internal parameter at the local level affects the global output at the global level.  
An interconnected observer is designed to estimate both the state and unmeasured interconnection at the local and global levels. As a result, both local and global dynamics can be observed, as well as the influence of local dynamics on global dynamics. In particular, the interconnection is not supposed to be accessible to measurement. In order to achieve this purpose, firstly, an existing observer is extended to estimate the states of the actuator subsystem. Particularly, the information of the actuator subsystem output is substituted by their estimates, achieved by the observer of the process subsystem. Secondly, a kind of extended, high-gain observer is produced to estimate the states of the process subsystem, which is subjected to a precise unknown input. The unknown input is considered a new state of the process subsystem, and it is expressed as a function of the inputs, derivatives of the inputs, and the states of the actuator subsystem. Thus, an interconnected observer is proposed by using the estimates of the states and the unmeasured interconnection, and the convergence is investigated.  
Finally, satisfactory simulation results are obtained to confirm the effectiveness and robustness of the proposed method.  
In this paper, it is clear that the physical motivation for the decomposition of a control system into the actuator and the process parts is physically motivated. In this respect, for control analysis purposes, the condition for decomposition of an independent control system may be the target of future investigations, like the inverted pendulum on a cart. Another open question worth addressing is the demonstration of stability and sensitivity of the estimation error, like the use of ISS to investigate the stability of the estimation error in [32].  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] and [PERSON] performed the observer design, carried out simulations, and wrote the original draft. [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, Grant No. 62003106 and No. 51867006, Talent Project of GZU (2018) 02. Key Lab construction project of Guizhou Province (2016) 5103.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Evaluation Metrics  
In this study, we employed the accuracy, precision, recall and \(F1\)-score [44] to assess the performance of all deep neural networks. All the performance metrics were computed as follows:  
\[Accuracy=\frac{TP+TN}{TS} \tag{1}\]  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Tumor Type** & **Number of Images** & **Format** & **Type** \\ \hline Meningioma & 937 & & \\ Pituitary & 898 & JPG & Grayscale \\ Glioma & 926 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Brain tumor classification (MRI) dataset details.  
Figure 3: Samples of the brain tumor classification (MRI) dataset, upper row: Glioma tumor, middle row: meningioma tumor, and lower row: pituitary tumor.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
\[Sensitivity\left(Recall\right)=\frac{TP}{TP+FN} \tag{3}\]  
\[F1\text{-}Score=2\cdot\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
_TN_ stands for true negative, _TP_ for true positive, _FP_ for false positive and _FN_ for false negative, and _TS_ denotes the whole number of samples.
## 4 Trust Evaluation Mechanism
### Direct Trust Value of Nodes
On the one hand, if a node in the network hears a data packet in which the layer of the receiver is greater than that of the sender, it implies that the sender may be an MN. In this case, \(N_{a,d}=N_{a,d}+1\). Otherwise, \(N_{n,d}=N_{n,d}+1\). On the other hand, after forwarding a data packet, the node switches to the listening mode to determine whether the selected next-hop node is an MN. If no data packet is heard from the next-hop node during RTT (Round-Trip Time), it implies that the selected next-hop node may be an MN. In this case, \(N_{a,d}=N_{a,d}+1\). Otherwise, \(N_{n,d}=N_{n,d}+1\). RERTF: In UANs, the energy of nodes is limited. Unbalanced energy consumption has a great impact on network performance, such as the network lifetime. The reasonable and balanced use of node energy can effectively improve network performance. However, the energy consumption of the nodes is not taken into account in some of the existing trust mechanisms. Consequently, the residual energy of nodes is considered a trust factor (i.e., \(RERTF\)) to balance the energy consumption of UANs. Assuming that the initial energy of a node is \(E_{init}\), and the residual energy of node k is \(E_{resi}\), then the \(RERTF_{\text{n, k}}\) of node k calculated by node n can be expressed as  
\[RETT_{\text{n,k}}=\frac{E_{resi}}{E_{init}} \tag{4}\]  
1. Construct the evaluation set \(V=\{v_{1},v_{2},v_{3}\}\) to indicate the degree of trust for each trust factor (i.e., \(u_{1}\), \(u_{2}\), \(u_{3}\)), where \(v_{1}\), \(v_{2}\), and \(v_{3}\) represent untrusted, trusted, and very trusted, respectively. 2. Get the membership degree matrix \(R\). Membership degree of trust factors \(u_{1}\), \(u_{2}\) and \(u_{3}\) is obtained via the triangle membership function, which is shown in Figure 3. Membership degree matrix \(R\) is defined as \[R=\left\{ \begin{matrix}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{matrix} \right\}\] (5) In \(R\), each element \(r_{ij}\) is the membership degree of \(u_{i}\) to the evaluation set element \(v_{j}\): 1. Obtain the evaluation result. Given a fuzzy subset \(V=(w_{1},w_{2},w_{3})\), \(\sum\limits_{i=1}^{3}w_{i}=1\), where \(w_{i}\) represents the weight of the \(i\)th trust factor, and the weight is adjustable. Then, by utilizing the weighted average model \(M(\cdot,+)\)[25], the evaluation result \(B\) can be obtained: \[B=W\circ R=(w_{1},w_{2},w_{3})\circ\left\{ \begin{matrix}r_{11}&r_{12}&r_{13} \\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{matrix} \right\}\] (6) \[=(b_{1},b_{2},b_{3})\] By normalizing \(B\), we obtain the evaluation result \(B^{\prime}\) \[B^{\prime}=(\frac{b_{1}}{\delta},\frac{b_{2}}{\delta},\frac{b_{3}}{\delta})\] (7) where \(\delta=\sum\limits_{i=1}^{3}b_{i}\), \(B^{\prime}\) is considered as a weight. Given \[V=(0.15,0.5,0.95)^{T}\] (8) Then, the DTV of node k calculated by node n can be obtained as Equation (9) \[DTV_{\text{n,k}}=B^{\prime}V\] (9)
## 3 Results and Discussion
### Biochar Characterization
This behavior indicates that as the milling time increases, the disorder and defective structure of the biochar decreases. The FWHM of the biochar narrows with increased milling time. However, the FWHM of the biochar milled for 240 min is wider than that of the other samples, indicating that this biochar has a low crystalline quality. Figure 3: Deconvoluted and fitted Raman spectra of the biochar samples obtained at different (**a**) pyrolysis temperatures and (**b**) ball-milling times. The black circle and blue line are the original raw data and fitted data, respectively. The D and G bands are depicted in red and green, respectively. Figure 4 shows the FTIR spectra of the biochar samples at different pyrolysis temperatures and milling times. Evidently, the FTIR spectra of all the biochar samples show almost similar trends. The band at 1070 cm\({}^{-1}\) is attributed to the stretching vibration of C-O, and the peak at 1651 cm\({}^{-1}\) corresponds to the stretching vibration of C=C. The O-H stretching mode is associated with the broad band at 3416 cm\({}^{-1}\)[35]. The biochar samples produced at different pyrolysis temperatures and milling times show significantly different morphological structures, resulting in changes in the electrical potentials on their surfaces. The surface electrical potential of the biochar samples was investigated using a zeta potential analyzer. Zeta potential is a crucial factor for determining the effect of particles in a suspension on particle agglomeration, sedimentation, and interactions [36]. Figure 5a shows the zeta potentials of the biochar surfaces at different pyrolysis temperatures. The zeta potential gradually decreases with increased pyrolysis temperature. This result indicates that the negative charge of the biochar surface gradually increases, resulting in a strong electronegativity. Figure 5b shows the zeta potential of the biochar surface after different milling times, indicating that the zeta potential gradually decreases with increased milling time. This result may be related to particle agglomeration, because milling time minimizes particle agglomeration. This result is in good agreement with the SEM images shown in Figure 1b.
## 3 Materials and Methods
### Modeling
#### 3.2.1 Random Forest Modeling
For models incorporating country fixed effects, the categorical country variable (geo) was first converted into binary indicators (one-hot dummies) and then included in the feature matrix subject to the same standardization procedure. This unified preprocessing pipeline was embedded within the cross-validation framework applied to the training set, effectively preventing data leakage. Model hyperparameters were tuned using five-fold cross-validation conducted strictly within the training data. For Random Forests, mtry (the number of predictors sampled at each split) was optimized; for XGBoost, tuning covered nrounds, max_depth, and eta;for SVM with a linear kernel, only the regularization parameter C was tuned. Elastic Net models were optimized over alpha and lambda using the glmnet framework. At no point was the holdout test set used in parameter tuning. This separation between training and evaluation ensures robust generalization and mitigates overfitting, in line with best practices in applied machine learning [18; 46]. All models were implemented in R 4.3.1 using caret and caret Ensemble and trained on a MacBook Air (Apple M3 chip) with RStudio 2024.12.1.
# Analysis of Cascading Failures and Recovery in Freeway Network Under the Impact of Incidents
## 4 Case Study
### Cascading Failure Simulation and Analysis
#### 4.2.2 Attack with Reduced Road Capacity  
In the previous section, we observed the impact of different initial attack quantities on the cascading failure outcomes. In traffic networks, large-scale rainfall, fog, and snow can lead to a decrease in the traffic capacity of road segments. The Highway Capacity Manual (HCM) provides reduction factors for road capacity under different weather conditions. To study the cascading failure phenomenon under different capacities, the research was conducted by adjusting the capacity reduction factor \(\gamma\) (ranging from 0.4 to 1).  
The parameter \(\gamma\) represents the capacity of edges within the network. A reduction in edge capacity inevitably increases the load on individual links, which in turn degrades the overall performance of the network. Comparing the results in Figure 7, it can be observed that when \(\gamma\) is set to 1, the scale of cascading failures is minimal, and the network can support its designed maximum traffic volume. When \(\gamma\) is between 0.7 and 0.9, the additional load on other roads during the failure process leads to more failure propagation. When \(\gamma\) is between 0.4 and 0.6, it indicates a high-risk failure, making the initial attack more likely to trigger large-scale failures in the freeway network.  
The results for the network connectivity indicator are presented in Figure 7a. In the HLA strategy, when k = 25, changes in \(\gamma\) have a more significant impact on cascading failures. The results for network efficiency and connectivity reliability are similar, as shown in Figure 7c,d.  
Figure 6: Results of cascading failure under different \(k\) values. (**a**) SCC; (**b**) NR; (**c**) EFF; (**d**) CR.
## 4 Methodology
### Temperature-Sensitive Variable Selection
#### 4.1.2 Correlation Analysis Method  
Correlation analysis mainly studies the correlation between multiple variables. It is often expressed by correlation coefficient \(R\). For the continuity between temperature variables and thermal errors, the Pearson correlation coefficient [54] is adopted to represent the correlation between them. Equation (3) is the formula of the Pearson correlation coefficient.  
\[\left\{\begin{array}{c}S_{xy}=\frac{\sum\limits_{i=1}^{N}{(X_{i}-\overline{ X})(Y_{i}-\overline{Y})}}{N-1}\\ S_{x}=\sqrt{\frac{\sum\limits_{i=1}^{N}{(X_{i}-\overline{X})}^{2}}{N-1}}\\ S_{y}=\sqrt{\frac{\sum\limits_{i=1}^{N}{(Y_{i}-\overline{Y})}^{2}}{N-1}}\\ R_{xy}=\frac{S_{xy}}{S_{x}S_{y}}=\frac{\sum\limits_{i=1}^{N}{(X_{i}-\overline{ X})(Y_{i}-\overline{Y})}}{\sqrt{\sum\limits_{i=1}^{N}{(X_{i}-\overline{X})}^{2}} \sqrt{\sum\limits_{i=1}^{N}{(Y_{i}-\overline{Y})}^{2}}}\end{array}\right. \tag{3}\]  
where, \(R_{xy}\) is the correlation coefficient of the object; \(S_{xy}\) is the covariance of the object; \(S_{x}\) is the standard deviation of sample \(X\) of the temperature variables; \(S_{y}\) is the standard deviation of sample \(Y\) of thermal errors; \(N\) is the sample size; \(X_{i}\) is the \(i\)th temperaturevariable; \(\overline{X}\) is the mean of all temperature variables; Y\({}_{i}\) is the \(i\)th thermal error; and \(\overline{Y}\) is the mean of all thermal errors.  
Pearson correlation coefficient \(R\) ranges from \([-1,\,1]\). If \(R\) is in the range \([0,\,1]\), it is a positive correlation, meaning that when one variable goes up or down, another variable goes up or down. If \(R\) is in the range \([-1,\,0]\), it is a negative correlation, meaning that when one variable goes up or down, another variable goes down or up.  
If only the correlation coefficient \(R\) is used as the criterion for selecting temperature-sensitive variables, the robustness of the thermal error model may be decreased. To better express the correlation between thermal errors and temperature variables, the absolute mean correlation coefficient \(\overline{R}_{i}\) is adopted, as shown in Equation (4).  
\[\overline{R}_{i}=\frac{1}{5}(|R_{ix1}|+|R_{ix2}|+\big{|}R_{iy1}\big{|}+\big{|} R_{iy1}\big{|}+|R_{iz}|) \tag{4}\]  
where, \(R_{ix1}\), \(R_{ix2}\), \(R_{iy1}\), \(R_{iy2}\), and \(R_{iz}\) indicate the correlation coefficient between the \(i\)th temperature variable and the thermal error of \(X_{1}\), \(X_{2}\), \(Y_{1}\), \(Y_{2}\), and \(Z\) direction, respectively.
## 3 Results and Discussion
### Improvement of the LBFC Due to Longitudinal, Linear Beam Oscillation
There is another facet of quality improvement due to beam oscillation for mild steel. Typically, mild steel with a plate thickness of 12 mm is cut with oxygen as an assisting gas [5; 7]. However, an oxygen layer results on the cut edge, which is usually removed for subsequent production steps [5; 8]. An inert assist gas as nitrogen avoids such oxygen layer and requires no post-treatment of the cut edge. A high-quality laser beam fusion cut of mild steel needs more laser power due to the missing energy of the oxygen reaction and the high heat conductivity coefficient. On that account, state of the art cutting machines apply more than 8 kW laser power to enable this cutting task. In contrast, beam oscillation makes it possible to cut thick mild steel with nitrogen and just 3 kW laser power as presented in Figure 4d.
## 4 Experimental Implementation and Results
### Classification Model Training and Testing Process
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Activity**} & **No. of Video Clips** & \multirow{2}{*}{**Training Clips (70\%)**} & **Testing Clips** & **Total Clips** \\  & **per Stream** & & **(30\%)** & **(RGB + PC)** \\ \hline
**Approach** & 98 (RGB *) + 98 (PC **) & 68 (RGB) + 68 (PC) & 30 (RGB) + 30 (PC) & 196 \\
**Digging** & 98 (RGB) + 98 (PC) & 68 (RGB) + 68 (PC) & 30 (RGB) + 30 (PC) & 196 \\
**Dumping** & 98 (RGB) + 98 (PC) & 68 (RGB) + 68 (PC) & 30 (RGB) + 30 (PC) & 196 \\
**Idle** & 98 (RGB) + 98 (PC) & 68 (RGB) + 68 (PC) & 30 (RGB) + 30 (PC) & 196 \\
**Leveling** & 98 (RGB) + 98 (PC) & 68 (RGB) + 68 (PC) & 30 (RGB) + 30 (PC) & 196 \\
**Total** & 980 (RGB + PC) & 680 (RGB + PC) & 300 (RGB + PC) & 980 \\ \hline \hline \end{tabular}  
* RGB = RGB Video data clip; ** PC = Point cloud data clip.  
\end{table} 
Table 3: Data distribution for different datasets. 99.62%. The dataset was split into training and testing data with a 70% and 30% ratio, respectively, to create distinct subsets for training and testing purposes.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
A band-stop FSS unit cell was constructed over an FR4 substrate of thickness T, as displayed in Figure 6. The specification of the unit cell is presented in Table 3. Figure 7 represents the reflection factor of the FSS unit cell. An inverted array of an FSS was combined with the MIMO element at the backside, as displayed in Figure 8.  
Figure 5: MIMO antenna: various S-parameters. The proposed MIMO antenna was converted to a MIMO with an FSS structure to increase gain and directivity further. The FSS unit cell was transformed into an array and placed over a 1.6 mm thick FR4 substrate. This design was inverted and placed at the bottom of the MIMO antenna with an air gap of 10.44 mm, as shown in Figure 8. The scattering factor of the MIMO elements represents no mutual coupling even after placing  
Figure 6: FSS unit cell. Figure 7: S-parameter of the FSS unit structure. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values (mm)** \\ \hline
**W** & 20 \\ \hline
**L** & 20 \\ \hline
**W1** & 17.5 \\ \hline
**L1** & 17.5 \\ \hline
**W2** & 15.5 \\ \hline
**L2** & 15.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dimensions of the FSS unit cell. the FSS structure, as displayed in Figure 9. The scattering factor of the MIMO antenna with an FSS was achieved for the required bands with a good reflection coefficient.