esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
###### Abstract  
The proliferation of flying ad hoc networks (FANETs) enables multiple applications in various scenarios. In order to construct and maintain an effective hierarchical structure in FANETs where mobile nodes proceed at high mobility, we propose a novel FANET clustering algorithm by using the Kalman-filter-predicted location and velocity information. First, we use the Silhouette coefficient to determine the number of clusters and the \(k\)-means++ method is utilized to group nodes into clusters. Regarding the external disturbances in highly mobile scenarios, a Kalman filter is used to predict locations and velocities for all nodes. When clustering, the relative speeds together with relative distances are considered, and the previous selected cluster heads (CHs) are utilized to initialize current centroids. Furthermore, we propose two metrics, including the cluster stability and the ratio of changed edges, to evaluate the network performance. Relevant simulation results reveal that our proposal can yield a cumulative distribution function (CDF) of cluster stability values close to the sensor-measurement-based data. Moreover, it can reduce communication overheads significantly.  
FANET communications; network clustering; Kalman filter prediction; \(k\)-means++ algorithm; cluster stability metric +
Footnote †: journal: _Electronic Journal of Physics_  
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Footnote †: journal: _Electronic Journal of Physics_  
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Footnote 0: 0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Conjugate Gradient Hard Thresholding Pursuit Algorithm for Sparse Signal Recovery
## 4 Convergence Analysis of CGHTP
### Real-World Image Reconstruction
The optimal step size \(\alpha^{*}\) for reconstructing Lenna, Pens, Pepper and Baboon images by HTP algorithm are \(\alpha^{*}=5.5\), \(\alpha^{*}=5\), \(\alpha^{*}=6\) and \(\alpha^{*}=4\), respectively. The abbreviations PSNR and Rerr in the table represent the Peak Signal to Noise Ratio and relative reconstruction error of the reconstructed image, respectively.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 4 Comparison of the Optimized Clustering Approaches
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **CI/ML** & \begin{tabular}{c} **Data Delivery** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **Data** \\ **Aggregation** \\ \end{tabular} &
\begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & **Scalability** & **Nature** & **Network** & **Radio Model** & **Multihop** & **Multipath** \\ \hline FCH [51] & FL & - & no & high & low & centralized & homogeneous & first order & no & no \\ \hline CHEF [33] & FL & - & yes & high & medium & distributed & homogeneous & first order & no & - \\ \hline LEACH-FL [34] & FL & - & - & high & low & centralized & homogeneous & first order & no & yes \\ \hline ICT2 TSK [34] & FL & - & - & low & medium & centralized & homogeneous & first order & no & yes \\ \hline SEP-FL [55] & FL & - & - & average & low & centralized & heterogeneous & first order & - & - \\ \hline EAUC [60] & FL & - & yes & low & low & distributed & homogeneous & first order & yes & yes \\ \hline DFL [37] & FL & low & - & average & medium & distributed & homogeneous & first order & yes & yes \\ \hline SF [39] & FL/SI & average & no & low & medium & centralized & homogeneous & first order & yes & yes \\ \hline FUC [40] & FL & - & - & low & low & distributed & homogeneous & first order & - & yes \\ \hline EDCF [41] & FL & average & - & average & low & distributed & heterogeneous & first order & yes & yes \\ \hline
[43] & GA & - & no & average & medium & centralized & heterogeneous & first order & yes & - \\ \hline
[14] & GA & - & yes & average & medium & distributed & homogeneous & first order & - & - \\ \hline LEACH-GA [46] & GA & - & yes & high & low & distributed & homogeneous & first order & no & yes \\ \hline GABEC [46] & GA & - & yes & low & low & distributed & homogeneous & first order & - & - \\ \hline
[48] & NN & average & yes & average & low & distributed & homogeneous & - & - & - \\ \hline
[49] & NN & - & - & average & low & distributed & homogeneous & first order & yes & yes \\ \hline CLQUE [50] & RL & average & yes & low & medium & distributed & homogeneous & - & yes & yes \\ \hline
[51] & RL & - & - & average & low & distributed & homogeneous & - & - & - \\ \hline PSO-C [57] & PSO & average & yes & average & low & centralized & homogeneous & first order & no & no \\ \hline
[56] & PSO & average & yes & average & high & centralized & heterogeneous & first order & yes & yes \\ \hline PSO-HC [57] & PSO & - & - & average & high & centralized & homogeneous & CC2420 & yes & - \\ \hline MPSCA [58] & PSO & average & yes & high & distributed & heterogeneous & - & yes & yes \\ \hline TPSO-CR [59] & PSO & high & yes & average & high & centralized & homogeneous/ & CC2420 & yes & yes \\ \hline PSO-ECHS [40] & PSO & high & no & low & high & centralized & homogeneous & first order & - & - \\ \hline T-ANT [60] & ACO & - & yes & average & low & distributed & homogeneous & first order & yes & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the optimized clustering algorithms.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 10 Future Research Directions  
This paper focuses on different techniques found in OpenFlow-based SDN. We observe that most existing techniques that appear in the literature complement the methods and subordinate them. Following are the possible future research directions for the research community. SDN controllers handle communication traffic, and as network traffic grows, the mapping of flow rules between controller and switches becomes overburdened, while a few controllers become unburdened. Due to poor throughput and long reaction times, such an imbalance impairs the performance of the SDN network. It is difficult to manage the load across several controllers dynamically. In SDN security, several SDN-based defensive primitives are addressed. To cope with contemporary cyber dangers, both novel primitives and modular protection systems that employ various primitives are required. Researchers should focus on improving the security of established network protocols (such as address resolution protocol (ARP), dynamic host configuration protocol (DHCP), domain name system (DNS), etc. ), as well as novel protocols such as neighbour discovery protocol (NDP) in Internet Protocol Version 6 (IPV6). With respect to controller scalability, separation of control and data planes, quantity of events/requests handled by a controller, and controller-switch communication delay are all bottlenecks in SDN. The relationship between different approaches used to optimize controllers and scalability difficulties are also examined. The majority of current storage systems have been evaluated on small-size networks, such as those with 7-28 nodes and 7-43 connections. As a result, these figures are incomparable to a large number of devices in a wide-scale network environment, such as Telco, Internet of things (IoT), and so on. In resource management (i.e., network components such as switches and controllers), ML approaches are extremely successful. The majority of ML algorithms, on the other hand, are focused on flow categorization and monitoring. Much less study has been done on estimating traffic flow for real-time applications and best-effort traffic and deciding which traffic flow to install ahead of time. These are multiple research areas to explore. In addition, the energy-efficient SDN networks, wireless networks, network virtualization techniques, cloud computing platforms, and SDN migration mechanisms are recommended for more detailed exploration.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Domain Adaptation
Data augmentation and sample generation using generative models: Generative models such as GANs can be explored to generate samples with target-domain characteristics during the training process, thereby enhancing the model's generalization ability in the target domain. Furthermore, generative models can be used for data augmentation to expand the training data for source and target domains and therefore increase the model's robustness. 4. Incorporation of multitask learning and domain knowledge: Models' generalization ability can be improved by learning multiple related tasks in a single model. Simultaneously, domain knowledge can be integrated to provide additional information about the source and target domains, which can guide the model in domain adaptation. 5. Enhancement of model interpretability: The interpretability of domain adaptation models can be increased to make the domain transfer process transparent. This task can be achieved through the introduction of interpretability metrics and visualization methods. It can also help researchers understand the model's behavior and influential factors during the transfer process. 6. Online domain adaptation and incremental learning: Online domain adaptation methods and incremental learning algorithms can be designed to enable a model to adjust in real time as it continuously receives new data and adapt to the changes in the target domain. This task can improve the model's adaptability and practicality in dynamic environments. 7. Incorporation of unsupervised or weakly supervised learning methods: Considering the scarcity of annotated data, domain adaptation techniques can be optimized by using unsupervised or weakly supervised learning methods. Doing so can effectively reduce annotation costs while enhancing the generalization ability of models in the target domain. 8. Multimodal data fusion: Multimodal data (such as images, point clouds, and depth information) can be utilized for domain adaptation to fully leverage information from different data sources. Fusing multiple data types can enhance the performance and robustness of domain adaptation models. 9. Knowledge-graph-based domain adaptation: Knowledge graphs can be employed to provide domain adaptation models with rich background knowledge and semantic information. Combining knowledge graphs with domain adaptation techniques can improve the model's ability to understand and transfer complex scenarios.
## Appendix A Further Modelisation Details
### Simulation Parameters  
The whole list of our simulation parameters is presented in Table 11. Every simulation has used such parameters unless other values are explicitly specified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(\gamma_{CPU}\) & CPU Unit Resource Costs (URC) & \((0.19,0.6,0.05)\) \\  & (for each cloud provider) & \((0.48,1.2,0.1)\) \\ \(\gamma_{MBW}\) & Bandwidth URC & \((0.9,2.5,0.25)\) \\ \(c_{max}\) & Maximum resource provision parameter & \(20\) \\  & (assumed equal for all the resource types) & \(5\) \\ \(c_{min}\) & Minimum resource provision parameter & \(5\) \\  & (assumed equal for all the resource types) & \(0.2\) \\ \(\phi_{p}\) & Payload workload exponent & \(0.2\) \\ \(\phi_{b}\) & Bit-rate workload exponent & \(0.1\) \\ \(\rho_{cpu}^{*}\) & Optimal CPU Processing Time & \(5\times 10^{-3}\) \\  & (baseline of over-usage degradation) & \(5\times 10^{-3}\) \\ \(\rho_{mem}^{*}\) & Optimal memory PT & \(1\times 10^{-3}\) \\ \(\rho_{bw}^{*}\) & Optimal bandwidth PT & \(5\times 10^{-2}\) \\ \(\psi_{cpu}\) & CPU exponential degradation base & \(100\) \\ \(\psi_{mem}\) & Memory deg. b. & \(100\) \\ \(\psi_{bw}\) & Bandwidth deg. b. & \(100\) \\ \(l_{ch}\) & cache VNF Instantiation Time & \(10\),\(000\) \\  & Penalization in ms (ITP) & \(8000\) \\ \(I_{st}\) & streamer VNF ITP & \(7000\) \\ \(I_{co}\) & compressor VNF ITP & \(7000\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: List of simulation parameters.
## 4 Conclusions  
Binary (anatase TiO\({}_{2}\) and wurtzite ZnO) and ternary (ZnTiO\({}_{3}\) and Zn\({}_{2}\)TiO\({}_{4}\)) oxide NFs with engineered architecture and surface were prepared via electrospinning and subsequent calcination from precursor solutions with different Ti:Zn molar ratios. The comparative evaluation of the as-calcined NFs as photocatalysts for the removal of MB from an aqueous solution with a dye concentration of 15 \(\upmu\)M under UV irradiation indicates that the higher band gap and lower crystallinity of the ternary oxides reflect in a lower photocatalytic activity towards the dye degradation. Capture centers generated by properly tuning the preparation conditions were responsible for the very high activity of the binary oxide nanofibers. Their optimal loads were determined to maximize the efficiency of the photocatalytic process, which is a fundamental recipe for its industrial use. By optimizing the load of wurtzite ZnO NFs (0.66 mg mL\({}^{-1}\)), a higher rate constant (7.94 \(\times\) 10\({}^{-2}\) min\({}^{-1}\)) than previously reported was achieved. More importantly, when  
Figure 5: (a) Representative AFM image of NF-composing nanoparticles in sample TZ10. (b) Diameter and (c) height distributions. anatase TiO\({}_{2}\) NFs are used, optimizing the efficiency of the photodegradation process requires a lower catalyst load (0.33 mg mL\({}^{-1}\)) and, besides, the reaction rate constant (1.12 \(\times\) 10\({}^{-1}\) min\({}^{-1}\)) exceeds most of the values reported for state-of-art electrospun photocatalysts and for the commonly used P25-TiO\({}_{2}\) benchmark, as well. This behavior is due to the NF architecture and the presence of oxygen vacancies acting as capture-centers to reduce the recombination rate of the photogenerated \(e^{-}\)-\(h^{+}\) pairs. This finding proves the wider validity of the conclusions previously drawn on the key role of defects in the design of ZnO photocatalyst. By using only 0.33 mg mL\({}^{-1}\) oxygen-deficient \(a\)-TiO\({}_{2}\) NFs, the solution fully discolors after 40 min. Remarkably, after reuse without any regeneration procedure, their activity in the second cycle decreases only by 5.2%. In the third one, it further undergoes a diminishing of 18.7%. This result opens the path for industries to lower their operational cost of the large-scale process cleaning waters from MB dye. This laboratory-scale study highlights the crucial importance of determining the optimal concentration of the photocatalyst, proving that very small relative amounts of it allow the dye to be effectively degraded. Furthermore, thanks to the scalability of the electrospinning technique, it provides a new perspective towards the sustainable large-scale manufacture of photocatalysts for the treatment of industry effluents.
## 2 System Model
### Indirect Link SNR  
From \(y_{\rm rd}\) of (7), the instantaneous SNR of the indirect link is expressed as  
\[\gamma_{\rm id}=\frac{P_{\rm s}G^{2}(1-\rho)|h_{\rm sr}|^{2}|h_{\rm rd}|^{2}}{ G^{2}|h_{\rm rd}|^{2}\sigma_{r}^{2}+\sigma^{2}}. \tag{8}\]  
At high SNR regimes (i.e., when \(P_{\rm s}\gg\sigma^{2}\)) [28], the above expression can be approximated as  
\[\gamma_{\rm id}\simeq\frac{\gamma_{\rm sr}\beta_{\rm rd}}{\beta_{\rm rd}+1} \tag{9}\]  
where \(\gamma_{\rm sr}\), \(\gamma_{\rm rd}\), and \(\beta_{\rm rd}\) represent the instantaneous SNRs of the SR and RD links and the normalized SNR of the RD link, respectively, defined as  
\[\gamma_{\rm sr} \triangleq\frac{(1-\rho)P_{\rm s}|h_{\rm sr}|^{2}}{\sigma_{r}^{2}} \tag{10}\] \[\gamma_{\rm rd} \triangleq\frac{\eta\rho P_{\rm s}|h_{\rm rd}|^{2}}{\sigma^{2}}\] \[\beta_{\rm rd} \triangleq\frac{\gamma_{\rm rd}}{\tilde{\gamma}_{\rm sr}/\Omega_ {\rm sr}}.\]  
Furthermore, the average SNRs of each link are given by  
\[\tilde{\gamma}_{\rm sr} =\frac{(1-\rho)P_{\rm s}\Omega_{\rm sr}}{\sigma_{r}^{2}} \tag{11}\] \[\tilde{\gamma}_{\rm rd} =\frac{\eta\rho P_{\rm s}\Omega_{\rm rd}}{\sigma^{2}}\] \[\tilde{\beta}_{\rm rd} =\frac{\tilde{\gamma}_{\rm rd}}{\tilde{\gamma}_{\rm sr}/\Omega_ {\rm sr}}.\]
## 3 Results
### Experiment II--augDXA Dataset  
The results of Experiment II are presented in Table 3. After data augmentation, improvement could be observed (a decrease in the average MAE up to 1.2 months) for each fold with the exception of the first (the model MobileNetV2 DW).The calculated average MAE ranged from 13.77 months to 15.82 months (the mean MEA was equal to 15.02 months). Figure 5 shows the learning curves for the model MobileNetV2 DW using the augDXA dataset.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Fold** & **MobV2 DW** & **MobV2 GA** & **VGG16 DW** & **InceptionV3 DW** & **ResNet50V2 DW** & **Inception ResNetV2 DW** \\ \hline
1 & 13.39 \(\pm\) 0.11 & 15.70 \(\pm\) 0.09 & 15.64 \(\pm\) 0.10 & 15.63 \(\pm\) 0.07 & 14.67 \(\pm\) 0.16 & 15.27 \(\pm\) 0.10 \\
2 & 16.29 \(\pm\) 0.09 & 17.73 \(\pm\) 0.06 & 16.25 \(\pm\) 0.05 & 16.72 \(\pm\) 0.13 & 15.72 \(\pm\) 0.15 & 16.99 \(\pm\) 0.14 \\
3 & 16.58 \(\pm\) 0.07 & 17.58 \(\pm\) 0.42 & 16.49 \(\pm\) 0.13 & 17.14 \(\pm\) 0.37 & 16.03 \(\pm\) 0.10 & 17.56 \(\pm\) 0.08 \\
4 & 14.85 \(\pm\) 0.01 & 16.31 \(\pm\) 0.07 & 16.74 \(\pm\) 0.05 & 17.09 \(\pm\) 0.03 & 15.95 \(\pm\) 0.10 & 17.69 \(\pm\) 0.12 \\
5 & 16.67 \(\pm\) 0.07 & 18.11 \(\pm\) 0.07 & 18.30 \(\pm\) 0.03 & 18.8 \(\pm\) 0.065 & 16.39 \(\pm\) 0.11 & 18.17 \(\pm\) 0.07 \\ \hline \(\overline{MAE}\) & **15.56**\(\pm\) 1.26 & 17.08 \(\pm\) 0.92 & 16.69 \(\pm\) 0.89 & 17.09 \(\pm\) 1.04 & 15.75 \(\pm\) **0.58** & 17.14 \(\pm\) 1.00 \\ \hline \hline \end{tabular} MAE [months]: mean and standard deviation  
\end{table}
Table 2: Average values and standard deviations of MAE obtained in Experiment I—different models, DXA dataset. The mean MAE and standard deviation in the last row were calculated from all folds.  
Figure 5: Training process for the MobileNetV2 DW model using the augDXA dataset. Both figures present mean and standard deviation calculated from all folds in a single test. The top right part of the figure shows the scaled part of the plot for the last 100 epochs.
# Effect of Coriolis Force on Vibration of Annulus Pipe
## 4 Results and Discussions
### Hinged Pipe  
Considering the pipe only and solving Equation (17), it is possible to evaluate the frequencies:  
\[\omega=-2.47714\sqrt{\frac{EI}{L^{4}m}} \omega=2.47714\sqrt{\frac{EI}{L^{4}m}} \omega=-27.5349\sqrt{\frac{EI}{L^{4}m}} \omega=27.5349\sqrt{\frac{EI}{L^{4}m}}\omega=\frac{10.9545\sqrt{EI}}{L^{2} \sqrt{m}} \omega=-\frac{50.1996\sqrt{EI}}{L^{2}\sqrt{m}} \omega=\frac{50.1996\sqrt{EI}}{L^{2}\sqrt{m}}\]  
and the theoretical value is:  
\[\omega_{t}=\frac{\pi^{2}}{4L^{2}}\sqrt{\frac{EI}{\rho}}\]  
Introducing \(m=m_{1}\) for the fluid mass, it is possible to calculate the frequencies, due to the water velocity. Figure 7 shows the result for the first frequency.  
Figure 8 shows all the four frequencies associated with the finite element solution of the hinged structure.  
Figure 6: Frequencies respect to the velocity.  
Figure 7: Frequencies with respect to the velocity.  
In this specific application, a comparison with the theoretical solution is possible since and the analytical solution is known:  
\[w(x,t)=\sum_{n}A_{2n-1}Sin((2n-1)\frac{\pi x}{L})Sin(\omega_{i}t)+\sum_{m}A_{2n} Sin(2m\frac{\pi x}{L})Cos(\omega_{i})t \tag{19}\]  
Equation (19) is substituted in Equation (1). The result can be rewritten by grouping the trigonometric terms and, in particular, it is possible to expand with a Fourier series the cosines terms:  
\[Cos\bigg{(}\frac{(-1+2n)\pi x}{L}\bigg{)}=\sum_{m}\frac{2n-1}{(2n-1)^{2}-(2m)^{ 2}}\]  
\[Cos\bigg{(}\frac{2m\pi x}{L}\bigg{)}=\sum_{m}\frac{2m}{(2m)^{2}-(2n-1)^{2}}\]  
Setting the similar trigonometric terms equal to zero and deleting all the modes except for the first two, the following condition can be written from the homogeneous system  
\[-\frac{32\rho v\omega}{3L\bigg{(}\frac{EJ\pi^{4}}{L^{4}}-\frac{\pi^{2}\rho^{2} \rho}{L^{2}}-m\omega^{2}\bigg{)}}=-\frac{3L}{8\rho v\omega}\bigg{(}\frac{1  
In particular, Figure 10 shoes the error for the first frequency calculated as  
\[Err=\left|\frac{\omega_{t}-\omega_{n}}{\omega_{t}}\right|\]  
where \(w_{t}\) is the theoretical result of the frequency and \(w_{n}\) is the numerical result.  
The error increases with the velocity as expected, suggesting the use of more elements for high velocities.
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
## 4 Experimental Research  
In adaptive optics systems, the FSM is usually regarded as a special DM that only produces tilt aberration compensation. To verify the importance of theoretical results, an experimental platform for a dual-deformable-mirror wavefront correction system was built indoors to correct wavefront distortion. The experimental setup is illustrated in Figure 6. The beam is emitted by a 650 nm semiconductor laser and extended and collimated through a 4f system composed of lens L\({}_{1}\) and lens L\({}_{2}\). The collimated beam is incident on the surface of the FSM, and then on the surface of the DM. The FSM and DM correct the specified aberrations as needed. The beam corrected by FSM and DM is incident onto a 4f system composed of lens L\({}_{3}\) and lens L\({}_{4}\) for beam reduction. The wavefront sensor detects at the focal point of lens L\({}_{4}\) and transmits the detected wavefront information to the wavefront controller (computer). The computer calculates the control voltage using an appropriate control algorithm and sends it to the FSM and DM for closed-loop correction of the distorted  
Figure 5: Coupling coefficient curve between the FSM and the DM.  
wavefront over multiple iterations. This forms a typical adaptive optics dual-deformable-mirrors closed-loop correction system.  
According to the experimental diagram constructed in Figure 6, the experimental parameters of the PI controller are: \(k_{i\_w}=0.999\), \(k_{p\_w}=0.1\), \(k_{i\_t}=0.999\), \(k_{p\_t}=0.1\), and the decoupling algorithm studied in this paper is used for indoor experiments. Figure 7 shows the coupling coefficient curve between the FSM and the DM.  
Figure 6: Experimental diagram of typical adaptive optics dual-deformable-mirrors wavefront correction system.  
Figure 7: _Cont._  
Figure 7a presents the coupling coefficient curve when the FSM only corrects the 1 st and 2 nd-order aberrations, and the DM only corrects the 3 rd-order aberrations. The approximate trend of the curve indicates that the coupling coefficient is approximately \(1.17\times 10^{-3}\). Figure 7b displays the coupling coefficient curve when the FSM only corrects the 1 st and 2 nd-order aberrations, and the DM only corrects the 10 th order aberrations, with a coupling coefficient of approximately \(1.814\times 10^{-2}\). Figure 7c shows the coupling coefficient curve when the FSM only corrects the 1 st- and 2 nd- order aberrations, and the DM only corrects the 25 th-order aberrations, with a coupling coefficient of approximately \(7.81\times 10^{-3}\). Through experiments, it was demonstrated that the decoupling algorithm is effective in suppressing the coupling effect between the FSM and the DM. It can perform graded correction on aberrations, selectively allocate any order of aberrations for correction, and achieve independent correction of any order of aberrations. The coupling coefficients reached levels of \(10^{-2}\) and below, effectively suppressing the coupling effect between the FSM and the DM.