esa-sceva/satcom-chunk-collection huggingface dataset:
# Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine
## 5 Conclusions  
In lots of online learning applications, the sequentially arrived data usually have varying confidence coefficients. OS-ELM trains the neural network chunk-by-chunk, but at the same time, it cannot deal with varying confidence coefficients of each data chunk very well. Based on the OS-ELM algorithm, we propose a novel algorithm, AWOS-ELM, which assesses the confidence coefficient and determines the weight of each data chunk before using the chunk to train the network. The proposed AWOS-ELM algorithm can improve the learning process by picking out the abnormal samples which may mislead the subsequent learning process, thereby reducing the impact of these abnormal samples. Thus, the AWOS-ELM is able to learn sequentially similar to how the OS-ELM works, but at the same time deals with the varying confidence coefficients of each data chunk properly. Compared with OS-ELM, simulations on benchmark databases demonstrate that AWOS-LEM performs better in generalization performance, stability, and prediction accuracy. In addition, experimental results on a flexible robot arm identification problem demonstrate that AWOS-ELM has the ability to get higher prediction accuracy than OS-ELM, while the two algorithms have similar training speed and stability.  
This research was funded by the National Natural Science Foundation of China (under Grant 51276087).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the main idea, [PERSON] wrote the program and carried out the experiments, [PERSON] and [PERSON] analyzed the data and interpreted the results, [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 4: Comparison of the prediction ability of the OS-ELM algorithm and the AWOS-ELM algorithm. (**a**) Sigmoid function; (**b**) RBF function.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Hidden Node** & **Algorithms** & **RMSE** & \(\sigma\) & \begin{tabular}{c} **Hidden Nodes** \\ **Number** \\ \end{tabular} & \(\tau_{r}\) & **Chunk Size** & \begin{tabular}{c} **Training** \\ **Time/s** \\ \end{tabular} \\ \hline \begin{tabular}{c} Sigmoid \\ function \\ \end{tabular} & OS-ELM & 0.0207 & 0.0004 & 45 & - & 5 & 0.1401 \\ \begin{tabular}{c} AWOS-ELM \\ \end{tabular} & 0.0081 & 0.0004 & 45 & 0.04 & 5 & 0.1426 \\ \hline \
\begin{tabular}{c} Radial basis \\ function \\ \end{tabular} & OS-ELM & 0.0198 & 0.0006 & 45 & - & 5 & 0.3738 \\
\begin{tabular}{c} AWOS-ELM \\ \end{tabular} & 0.0084 & 0.0007 & 45 & 0.04 & 5 & 0.4115 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison between AWOS-ELM and OS-ELM on the robot arm example.
## 3 Results
### Best Performing TIs
Overall, MOM (\(n=58\)), which significantly boosted both AB and GB performances, was observed to add the most explanatory power to ensemble models: MOM (\(n=58\)) was the _best TI_ for both AB and GB models, and _second-best TI_ with RF. Per Table 1, across ensemble models MOM (\(n=58\)) on average reduced the RMSE and MAE by 3.58% and 3.17%, respectively, and increased the PCC by 2.72%. Furthermore, Table 2 highlights that MOM (\(n=58\)) increased the accuracy of a majority of AB and GB tail, and AB IQR forecasts: yielding a negative \(\tilde{\text{W}}+\tilde{\text{L}}\) and %Ws greater than 50%. MOM (\(n=58\)), however, did not increase the accuracy of a majority of GB IQR forecasts: yielding a positive \(\tilde{\text{W}}+\tilde{\text{L}}\) (2.83), as well as a %W below 50% (49.91%). Nonetheless, given the results in Tables 1 and 2, we consider MOM (\(n=58\)) to be our best ensemble model TI. Finally, analysing the performances of deep models, Table 1 highlights that, when added to deep models containing CONV layers, the ROC overwhelmingly outperformed a majority of TIs. While other indicators, including the %B and the MOM, struggled to add explanatory power to the four CONV models, on average the ROC significantly reduced CONV models' RMSE and MAE by 2.22% and 3.07% respectively, and increased their PCC by 1.09%. Beyond CONV models, Table 1 also highlights that the EMA performed best with the fully connected deep model. EMA (\(s=22\)) significantly increased 2 NN's predictive power: reducing its RMSE and MAE by 4.09% and 1.56% respectively, and increasing its PCC by 1.85%. Analysing deep models' TI performances across the target groups, Table 2 highlights that the ROC successfully improved the accuracy of a majority of CONV model IQR and tail forecasts. Unlike with linear and ensemble models, the best performing CONV model TI, the ROC, yielded both tail and IQR: \(\tilde{\text{W}}+\tilde{\text{L}}\)s below zero and %Ws greater than 50%. This was not the case with EMA (\(s=22\)), however. EMA (\(s=22\)) increased 2 NN tail forecast accuracies, but not IQR accuracies. Overall, we consider ROC (\(n=49\)) and EMA (\(s=22\)) to be our best performing deep model TIs. In addition to highlighting which TIs provide the most explanatory power to deep models, Table 1 also affirms an inverse relationship between model depth and accuracy improvement. We observe reductions in _best TI_ model improvement rates, %RMSE/%MAE/%PCC, with each additional CONV layer: from 2.73/4.21/1.85 (CNN), to 2.39/2.91/1.19 (2 CNN), to finally 1.38/1.75/0.82 (ResNet). A gradual reduction in performance improvement occurs because stacking multiple CONV layers allows models to identify larger and more complex features, reducing the need for feature engineering.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 2 The S-MIM System
### Selected NOMA Solution
For an IoT satellite-based system, it is important to maximize the first packet transmission successful reception at the gateway, in order to minimize the number of retransmissions for energy efficiency and latency reasons. For this reason, a reasonable target packet error rate (PER) is \(10^{-3}\) or less. Assuming this typical target PER, we observe that in the presence of a packet power unbalance, the E-SSA throughput is several orders of magnitude higher than SSA. The steep PLR vs. MAC load E-SSA characteristic also allows simplifying the congestion control as it is sufficient to keep the MAC average load below a certain critical value, which can be easily monitored at the gateway measuring the interference plus thermal noise over thermal noise ratio. Another interesting feature of E-SSA is that it can be operated with a single preamble despite the non-zero probability of preamble collision. This is because the asynchronous random access nature (i.e., random delay of arriving packets) combined with the carrier frequency uncertainty due to the terminal oscillator instabilities, as well as the possible differential Doppler, makes the preamble destructive collision probability low enough not to require multiple preambles. This makes the E-SSA demodulator implementation easier, limiting the need for a single preamble searcher. As a matter of fact, the preamble detection function is the most demanding demodulator functionality in terms of processing requirements. Figure 1: Simulated vs. analytical SSA and E-SSA throughput (**a**) and PLR (**b**) performance with and without lognormal power unbalance from [11], 3 GPP forward error correction (FEC) code rate 1/3 with block size of 100 bits, BPSK modulation, spreading factor of 256 and \(\rm E_{s}/N_{0}=6\) dB (© Copyright 2016 John Wiley and Sons).
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
## 5 Analysis of Experimental Data: High-Speed Machining (HSM)
### Surface Finish  
The ANOVA results of Ra observed in HSM experiments are presented in Table 14. All the cutting parameters have been observed to be statistically significant. In terms of percentage contribution, feed has the highest effect (91.86%), followed by depth of cut and cutting speed. Figure 10 presents the main effect plot of Ra with respect to cutting parameters. An overall improvement in surface roughness can be observed with the increase in cutting speed, and the lowest surface roughness has been achieved at the highest cutting speed (2500 m/min). This improvement in surface finish can be attributed to the fact that the tendency of built-up-edge (BUE) formation diminishes with the increase in cutting speed, which consequently improves surface finish [47]. In the case of the effect of feed, an expected deterioration of surface finish with an increase in feed has been observed. This relationship of feed and Ra was observed to prevail in all types of machining (CSM, TSM, and HSM). Ra has shown an erratic behavior with respect to the depth of cut. However, the minimum value of Ra was generally observed at the lowest level of depth of cut.
# Challenges in Physical Layer Security for Visible Light Communication Systems
## 2 Differences in RF and VLC Security Environments
However, the legitimate user must consider different constraints on hardware requirements, user convenience, communication efficiency, etc., of various applications, which limit the gain of the legitimate user. On the other hand, eavesdroppers can ignore these issues, since its primary goal is eavesdropping. Moreover, the LED transmitters should be spatially distributed over the ceiling to illuminate the entire room evenly. This system design requirement enables the eavesdropper to selectively receive a light signal being emitted from a particular LED by modifying its receiver's field-of-view (FoV) and adjusting its orientation, avoiding beamforming and jamming interference, which will be discussed in more detail in Section 3.2. Thus, in PLS-enabled VLC systems, an eavesdropper with an additional improvement and/or modification on its receiver architecture may be able to achieve a higher SNR than the legitimate user. Overall, the differences in the security environments between RF and VLC systems, summarized in Table 1, impose an additional challenge to secure the VLC transmission. Most of the previous PLS studies for VLC systems proposed various transmission techniques and verified their excellent secure communication performance. However, if the eavesdroppers attempt more intelligent approaches to enhance reception, state-of-the-art PLS techniques can be easily avoided and secure communication performance could be degraded significantly.
# Research and Design of the RF Cavity for an 11 MeV Superconducting Cyclotron
## 4 Design and Simulation of the RF Cavity
### Structural Analysis of the RF Cavities
In addition, since the RF cavity utilizes a classical 180\({}^{\circ}\) Dee, tip effects need to be avoided at the edges, and therefore the corners of the Dee are suitably chamfered. From Figure 5, it can be observed that the size of the Dee's edge fillet radius has a limited effect on the resonant frequency of the RF cavity. In addition, the electric field strength at the edge of the Dee can be significantly reduced by increasing the radius of the edge fillet. Therefore, it is sufficient to ensure that the size of the Dee's edge fillet radius meets the requirement of the maximum electric field strength during design. Figure 3: The RF cavity with different shapes of Dee and Stem structures: (**a**) rectangular Stem and (**b**) cylindrical Stem.
# Vision-Based Path Guidance to Achieve Dies-Free Roller Hemming Process
## 2 Experimental Apparatus and Methodology  
### Experimental Apparatus  
A universal fixed platform was designed to replace a specific die, which is used to support and fix parts in the hemming process. As shown in Figure 1, the platform consists of an aluminum extrusion frame, rotating motor, and multiple pneumatic clamps and vacuum chucks, which together allow for changes in the fix position and angles to fit different types of parts.  
Figure 1: A universal fixed platform for a hemming process: (**a**) front view; (**b**) enlarged view of upper part.  
As there is no special die to serve as the underneath supporting surface when the sheet metal part is hemmed by rollers, the traditional hemming tool such as the single roller as shown in Figure 2a has to be redesigned and transformed into the active double-roller as shown in Figure 2b. In the active double-roller design, the lower roller or wheel serves as the supporting structure whereas the upper roller can be actively controlled pneumatically to open and close against the lower roller to exert force onto the sheet metal. The shape of the upper roller can be changed to meet the needs of different forming angles.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Control Models
#### 5.3.3 Reactive vs. Proactive Control
Proactive control has difficulty adapting to new or unknown threats, as pre-computed policies may not affect such threats. A proactive approach for mitigating reconnaissance attacks using the technique of IP and port shuffling is presented in [356]. Network congestion control using a joint reactive and proactive approach that couples the activity of both users and the network controller using an optimization technique has been studied in [363]. In proactive flow management, all required flow entries are installed in the switches by the controller, which causes additional requirements such as switch memory hierarchy optimizations [364]. A proactive approach to protecting blockchain nodes from domain name service amplification attacks by using a stateful mapping scheme, an entropy calculation scheme, and a DDoS mitigation module has been effective [365]. A proactive attack and failure resilience mechanism that uses a sandboxing technique to isolate the controller from the host and employs live remote checkpointing and migrating between different hosts to evade failures and attacks has been studied in [366]. A framework that can proactively adapt to the attack surface of networks and dynamically optimize defense strategies using moving target defense techniques has been realized in a cost effective manner [367]. A proactive admission control and resource management strategy for virtualized networks that maximizes the high-priority networks subjected to substrate limitations and memory requirements of virtual network requests is presented in [368]. A proactive routing rule placement technique for KDN that updates the flow rules proactively, however, considering whether the routing path actually changed or not, has been studied in [369]. By enabling the controller to add backup paths proactively along with the working paths and enabling switches to perform recovery actions locally, a failure recovery mechanism for KDN has been studied in [370].
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
## 1 Introduction
The remainder of this paper is structured as follows. Section 2 presents the Loop-Back QKD protocol in a linear topology, covering its Single-Pulse, Hybrid Loop-Back-BB84, and Multi-Pulse variants, along with a comparative analysis of single-pulse and double-pulse configurations. Section 3 extends the protocol to multiuser scenarios, describing its implementation in a ring topology. Section 4 explores scalability considerations and the optical hardware requirements for practical deployment. Finally, Section 5 summarizes our findings and outlines directions for future research.
## Materials and Methods
### Electromagnetic Interference (EMI) Shielding Properties  
The examination of electromagnetic interference (EMI) shielding efficiency was conducted utilizing a P9372A Keysight Streamline Vector Network Analyzer (Keysight, Santa Rosa, CA, USA) paired with two sets of standard microwave waveguides with a 15 dB gain (specifically, WR 187 and WR 147, procured from Advanced Technical Materials Inc. (ATM), Patchogue, NY, USA). These setups facilitated coverage across a comprehensive C frequency bandwidth from 3.2 to 7.0 GHz, a spectrum relevant for long-range telecommunication applications such as satellite communications, Wi-Fi connectivity, cordless phones, and weather radar operations. For the testing process, samples were precisely aligned at the center within each pair of waveguides, allowing for the detailed measurements of their scattering parameters (S-parameters; S11, S12, S22, S21).
## 4 Experimental Verification
### Test Results and Analyses  
#### 4.2.1 Creep Curve of One-Layered CGS  
Figure 10 shows tested creep curves (\(\varepsilon\)-\(t\) curve) of the one-layered CGS as well as their predicted ones with different values of the shape factor \(\rho\) (\(\rho\) = 1-1.8, Equation (2)) for comparison. It is seen that all of the tested creep curves consist of three stages: instantaneous deformation, attenuation deformation, and stable creep deformation. Obviously, the higher the content (volume fraction) of pebble (L1 > L2 > L3 in Table 4), the smaller the creep deformation of the one-layered CGS (Figure 10a < Figure 10b < Figure 10c).  
Figure 10: Tested and predicted creep curves of different one-layered CGS. (**a**) L1, (**b**) L2, (**c**) L3.  
Figure 9: Deformations of CGS specimens after creep test. (**a**) L1, (**b**) L1 + L2, (**c**) L2 + L3, (**d**) L1 + L3, (**e**) L1 + L2 + L3.  
It is also found that all of the predicted creep curves of the one-layered CGS decreased with the increase of \(\rho\), since the larger the value of \(\rho\) (i.e., the more irregular the shape of pebble), the larger the contacted surface area of pebble with sand-clay matrix and hence the smaller the creep deformation. Furthermore, the predicted curves of \(\rho\) = 1 (ideal sphere pebble) are all larger than the tested curves. That is because the actual irregular polyhedron of pebble with a large contacted surface area would lead to small creep deformation.  
Comparison of the tested and predicted creep curves of different sand-clay matrix (L1, L2, L3), indicates they are all in good agreement at \(\rho\) = 1.35, 1.3, 1.25, respectively. This means the one-layered CGS with a higher content of pebble (L1 > L2 > L3) has a greater possibility of irregular pebble and thus a larger value of \(\rho\).
## 1 Introduction
Although the existing literature offers valuable insights into disaster patterns, there are still some notable gaps that need to be addressed. For example, many studies tend to focus on individual types of disasters, such as floods or earthquakes, without providing a comprehensive analysis that considers multiple disaster types within a single framework. This research intends to address that gap by examining both natural and human-made disasters over the past century. Moreover, there is a noticeable lack of studies that effectively integrate geospatial and temporal analysis to predict future disaster patterns, particularly when considering the use of emerging technologies like AI and big data. This study aims to close that gap by providing an integrated approach that could improve disaster preparedness and response strategies. Although some studies examine disaster patterns, there is a lack of research evaluating how different regions implement disaster management strategies based on these patterns. This study will explore this by offering a comparative analysis. Recent advancements in AI and machine learning have revolutionized disaster management by enabling more precise predictions and real-time response strategies. However, integrating these technologies with traditional geospatial analysis is still in its early stages, and further research is necessary to explore their full potential in disaster risk management. While previous studies have contributed significantly to understanding disaster patterns, many are constrained by their reliance on historical data without accounting for the dynamic nature of climate change and human activities. This study aims to overcome these limitations by incorporating recent data and advanced analytical methods to provide a more accurate and comprehensive understanding of disaster risks. Also, insights from analyzing the geospatial and temporal patterns of disasters are crucial for informing policy decisions and enhancing disaster preparedness. However, more research is needed to translate these findings into practical strategies for policymakers. This study aims to contribute to this area by offering policy recommendations based on a comprehensive analysis of disaster patterns.
## 4 Results
### Feature Fusion Results  
In this section, the three most successful models, DenseNet169, MobileNet and ResNet152, were used as feature extractors and the features obtained from the three different models were combined by feature fusion. Then, these fused feature vectors were classified by SVM, RF and XGBoost algorithms. The classification performances of the ML algorithms are presented in Table 8.  
The experimental results show that the SVM model achieves the highest performance when the sigmoid kernel function is used. SVM (sigmoid) outperformed other ML models with 92.00% accuracy, 91.93% precision, 92.00% recall, 91.91% F1-score and 0.8930 kappa value. Notably, SVM's polynomial and RBF kernels also provided high accuracy but were inferior to the sigmoid function. The SVM (polynomial) model achieved 91.14% accuracy, while the SVM (rbf) model achieved 90.11% accuracy.  
Figure 7: Confusion matrices of three best-performing three CNN models (0: Healthy, 1: Stage 1, 2: Stage 2, 3: Stage 3).  
When other ML algorithms were analyzed, the RF model showed a relatively low performance with 87.97% accuracy, 87.98% precision, 87.97% recall and 87.91% F1-score. The XGBoost model performed slightly better than RF with an accuracy of 88.60%. In general, the SVM (sigmoid) model achieved the highest performance in terms of both accuracy and other evaluation metrics and was selected as the best ML model.  
The results of this stage show that combining features from different CNN models improves classification performance and provides better results compared to traditional single model approaches. Therefore, in the next stage, feature selection was performed using metaheuristic optimization algorithms and classification was performed with the best ML model.  
The confusion matrix in Figure 8 shows the classification performance after feature fusion with the SVM (sigmoid) model. Compared to the individual CNN models, the accuracy increased in all classes. In the 0 (Healthy) class, with 95 correct predictions; the success rate was higher than the previous models. In the 1 (Stage 1) class, 80 correct predictions were made, while misclassifications decreased. Especially in the 2 (Stage 2) class, the best result was achieved with 93 correct predictions and a significant improvement compared to the individual CNN models. In the 3 (Stage 3) class, the highest accuracy was achieved with 77 correct predictions. In general, the SVM (sigmoid) model after feature fusion produced better results than the performance of CNNs alone and made the discrimination between classes more successful.
## 4 Materials and Methods
### Environmental Observation  
Routine meteorological factors, including photosynthetically active radiation (Model LI-190 SB, Li-Cor Inc., Lincoln, NE, USA), air temperature (Model HMP45C, Vaisala, Helsinki, Finland), precipitation (Model 52203, Young Co., Traverse City, MI, USA), and soil water content (Model C5616, Campbell Scientific, Logan, UT, USA). The depth of measuring surface soil temperature was 5 cm. The daily, 8 days and monthly average SIF were also calculated. Due to the malfunction of sensor, soil water content during January to March was abnormal and was excluded in the analysis.