esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Ablation Study by Relationship Factors  
To further understand the contribution of each relationship factor (distance, attribute, and spatial relationships) within the proposed Ambient Detection model, we conducted an ablation study. Specifically, we evaluated seven different configurations, systematically activating or deactivating each factor to measure their individual and combined impacts on object detection performance. These seven configurations included the complete model (\(D_{norm}+A_{norm}+S_{R}\)), three two-factor combinations (\(D_{norm}+A_{norm}\), \(D_{norm}+S_{R}\), \(A_{norm}+S_{R}\)), and three single-factor models (\(D_{norm},A_{norm},S_{R}\)).  
Table 3 summarizes the detection performance of each configuration based on 232 test images, measured using the conservative criterion outlined in Section 3.1. The complete model, utilizing all three relationship factors (\(D_{norm}+A_{norm}+S_{R}\)), achieved the highest success rate of 93.10%. When one relationship factor was removed, performance consistently declined, but to varying degrees. The removal of the spatial relationship (\(D_{norm}+A_{norm}\)) and attribute relationship (\(D_{norm}+S_{R}\)) resulted in modest decreases, producing success rates of 88.79% and 87.50%, respectively. However, excluding the distance relationship (\(A_{norm}+S_{R}\)) led to a significant drop to 80.60%, highlighting the critical importance of proximity cues in concrete-pouring detection tasks.  
Further, single-factor analyses revealed that the distance relationship alone maintained a relatively high success rate of 81.90%, reinforcing its primary role. In contrast, single-factor models relying solely on attribute (75.43%) or spatial (76.72%) relationships performed notably worse, indicating these relationships mainly provide complementary information rather than independently strong cues.  
Overall, the ablation results demonstrate that the distance relationship factor (\(D_{norm}\)) plays the dominant role in object detection reliability, while attribute (\(A_{norm}\)) and spatial (\(S_{R}\)) relationships significantly enhance overall detection accuracy when combined effectively.
## 1 Introduction
There exists a diverse range of bio-mimetic or metaheuristic algorithms for optimization in wireless sensor networks including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), _etc._ In fact, optimization algorithms are far more diverse than the types of optimization, but the right choice of an optimization algorithm can be crucially important in finding the right solutions for a given optimization scenario. Optimization, especially bio-mimetic strategy-based optimization in WSNs, is a very active research area. Papers published in this area are highly diverse in their approaches and implementations. To the authors' knowledge, there is no article which provides survey of the area. However, some work has been done addressing the various issues individually (e.g., energy efficiency, QoS or security) and they tend to overlook the whole scenario of collective optimization approach which encompasses these two or three WSN issues. In [6], an extensive survey was done on WSNs taking into account the topic of overall computational intelligence, but with some focus on bio-mimetic strategies. The more recent survey [7] narrowed down its focus to an ant colony optimization (ACO)-based approach to solve several issues in WSNs. Moreover, in [8] the authors discussed a protocol based on ACO, and two fundamental parameters, QoS and reputation are used. Both works exclude other popular techniques like PSO and GA. In [9], some issues of WSNs have been addressed using only PSO. A number of papers have reported works on energy efficient clustering [10, 11, 12, 13] and prolonging network lifetime [14] in WSNs using PSO. Considering these points, we feel that now is an appropriate time to put recent works into perspective and take a holistic view of the field. This article takes a step in that direction by presenting a survey of the literature in the area of bio-mimetic optimization strategies in WSNs focusing on current,'state-of-the-art' research. This paper aims to present a comprehensive overview of optimization techniques especially used in energy minimization, ensuring security, and managing QoS in WSN applications. Finally, this work points out open research challenges and recommends future research directions. Section 2 presents a brief overview on optimization and Section 3 presents the rationale for optimization in WSN in details. Section 4 provides an overview of existing approaches of bio-mimetic optimizations including hybrid approaches in WSNs. Open research challenges and suggestions for future research directions are presented in Section 5. Finally Section 6 concludes the work and points to areas of potential future work.
# A VHF Circularly Polarized Turnstile Antenna with Balun for Meteor Radar
## 4 Experimental Results and Improved Design  
A low working frequency (less than 50 MHz) band indicates a large antenna size, and measuring the radiation pattern in an anechoic chamber can be expensive and infeasible.  
The fabricated antenna and array are shown in Figure 7a,b. To verify the radiation characteristic of the antenna, the fabricated antenna is erected in the open field. VSWR was measured using the Rohde & Schwarz ZNLE3 Vector Network Analyzer, and the simulated and measured results are presented in Figure 8. The measurements are in good agreement with the simulations. The measured VSWR in the impedance bandwidth ranges from 38.5 MHz to 39.5 MHz is less than 1.2. The slight frequency shifts are attributed to fabrication and welding imperfections.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(Z_{75\text{p}}\) & Impedance of 75 \(\Omega\) transmission line \\ \(Z_{75\text{p}}\) & Impedance of two parallel 75 \(\Omega\) transmission line \\ \(Z_{trans}\) & Impedance of impedance transformer \\ \(Z_{0}\) & Nominal impedance of the system \\ \(Z_{L}\) & Impedance of load (antenna) \\ \hline \end{tabular}
\end{table}
Table 2: Symbol definitions.
# Erosion Rate Measurements for DART Spacecraft Ion Propulsion System +
## 4 Discussion
Carbon deposition may exert the most influence. Figure 17: Photo of grid periphery showing the erosion-deposition pattern. Net erosion emanates radially from the peripheral set of holes, with directionality influenced by the hole pattern. Other factors to consider in relation to radial velocity bias of sputtered molybdenum are alignment offset between screen and accel apertures, electric-field driven radial velocity bias of discharge chamber ions as they encounter the screen grid plasma sheath, and ion density gradients behind screen apertures. The effect of each of these factors has been simulated, and experimental data showed that they produce an offset of the pit and groove erosion pattern on the accel downstream face [33]. The offset is in the direction of the beamlet steering. An offset toward the small-radius side of the aperture is apparent in Figure 17, which is inconsistent with expected radial velocity bias (whether it is due to electric field or plasma density gradient) for discharge ions near the perimeter. Grid alignment offset combined with carbon deposition that correlates with the radial coordinate may therefore be the primary cause of biased molybdenum radial velocity. If correct, we would expect to measure something different on the opposite side of the grid. For the present study measurements were performed on one side only.
## 2 Theoretical Background and Literature Review  
### The Core of 5G Networks: Advanced Ceramic Antenna Technologies  
#### Overview of 5G Communications and the Significance of Antenna Technology  
The advent of the fifth generation (5G) of mobile communications marks a significant departure from its predecessors, heralding a new era in telecommunications. This transformation is characterized by groundbreaking advancements over 3G and 4G technologies. 5G stands out with its ultra-high data rates, minimal latency, and unprecedented connectivity, laying the foundation for innovative services and applications across diverse sectors. It boasts data transmission speeds reaching up to an astonishing 20 Gbps, a leap that is about twenty times faster than 4G's 1 Gbps and more than a thousand-fold increase over 3G's 384 Kbps. This quantum leap in speed is pivotal in enhancing high-bandwidth services, including ultra-high-definition video streaming, real-time gaming, and immersive virtual and augmented reality experiences. Designed to achieve sub-millisecond response times, 5G technology brings exceptional precision and stability, essential for real-time services and applications. Its advanced connectivity capabilities are set to meet the burgeoning demands of smart cities, autonomous vehicles, and industrial automation. This technology's ability to support a vast array of devices concurrently is a game-changer, opening up possibilities for interconnected systems on a scale previously unimaginable [23,24].  
Central to the success of 5G is the evolution of antenna technologies, particularly in supporting higher bandwidths and data transfer rates, while also offering increased directional flexibility. Ceramic antennas, in this context, have emerged as a focal point due to their potential to reduce antenna size while maintaining, or even enhancing, performance efficiency. These advancements in antenna technology are crucial for realizing the high data speeds and reliability promised by 5G [25, 26, 27, 28, 29]. Concurrently, there is a growing interest in integrating blockchain technology in the manufacturing of 5G communication components. This emerging field of research aims to address key challenges such as transparency, security, and efficiency within the manufacturing process. The potential of blockchain to transform supply chain management through smart contracts is being explored actively. However, this innovative approach is not without its challenges. Issues like processing speed, security concerns, cost implications, and the need for standardization are at the forefront of ongoing research endeavors. Addressing these challenges is crucial for the successful integration of blockchain technology in the manufacturing of 5G components, particularly as the world increasingly embraces the potentials of this new era of communication technology [30, 31, 32].
## 2 Related Work
As additional research, a framework for developing security-related applications was proposed in [36]. To secure an SDN network, most of these approaches generate new attack scenarios and focus on defending against each attack. Rosemary [23] defends against system crashes and data compromise attacks. TopoGuard [24] identifies misused APIs and defends against new attacks such as host location hijacking attacks. Finally, Avant-Guard [25] defends against control plane saturation attacks. However, these types of defenses are restricted to new types of attacks. Policy based approaches (i.e., access control) have been researched to secure the north bound APIs of an SDN controller. SE-Floodlight [37] implements security features that utilize trust model and policy mediation logic. The operation checkpoint permission system [38] ensures that controller operations are performed only by trusted applications. The authors in [39] implemented read, notification, write, and system access permissions for OpenFlow applications in order to isolate the controller and apps in thread containers. Although these policy-based approaches introduce the idea of providing access control for applications, they do not secure the core modules of the SDN controller or do not effectively resist attack traffic on an SDN data plane. Overall, the mechanisms for traffic engineering, measurement and monitoring, and security and dependability fail to interconnect in order to prevent internal faults and anomalies, defend against outside attacks, and maintain the availability and efficiency of a network even when anomalies are present. Indeed, none of the current approaches provides a reasonable solution to resolve these problems. The primary objective of this study is to provide a comprehensive reactive monitoring and defense system. Table 1 briefly compares existing works to our proposed system.
###### Abstract  
Wind turbine blade structural dynamics are crucial in the turbine structural design phase. Blade deflections and loads can affect the weight of the rotor as well as the power performance of a wind turbine if the deflections are extremely high. Predictions of the turbine's blade deflections and loads can lead to informative decisions on optimizing the design of the blade. In this work, a multivariate machine learning (ML) approach is used to predict the blade's dynamics based on the wind flow conditions and control actions of the turbine. Three different datasets were generated using the OpenFAST software tool for three different wind turbulence classes. Various ML algorithms were trained to predict the blade deflections at the tip and blade loads at the root in the edgewise and flapwise directions. The ML models were tested for generalization of the model to different flow conditions. A model is trained for one dataset with one of the turbulence classes and then used to predict the outputs of the other two datasets. The random forest ML algorithm gave the best accuracy for predicting the outputs for the dataset it was trained for, as well as the other two datasets. The accuracy of predictions was found to be higher in the edgewise direction for both load and deflection outputs. In the flapwise direction, the model could predict the outputs of the data it was trained for with an accuracy of around 99% and for the other two datasets with an accuracy of over 75%. While in the edgewise direction, the model trained on only one dataset gave a prediction accuracy above 95% for all three datasets.  
wind turbines; structural dynamics; blade design; machine learning; artificial intelligence; random forest
## 4 Results
### Computational Efficiency  
Beyond the model accuracy of the five models for the training and testing random split, it is of particular relevance to compare the computational efficiency (time required to the model during training and validation processes) of the models, as it is an important criterion for operational algorithms. All models were implemented in a Python environment on an Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10 GHz processor and installed memory (RAM) of 32.0 GB. The computational efficiency is recorded from the 20 repetitions in Section 4.1. The averaged results of 20 repetitions are illustrated in Figure 6. Large differences among the five ML models are clearly found. SVR performs incredibly fast (less than 1 s). However, GBRT is frustrating, owing to the large amounts of its hyperparameter tuning processes. In general, SVR is able to deliver near real time operational products with a highly efficient processing speed, whereas the GBRT model is not recommended for this kind of application, because the GBRT model is computationally more demanding. RF, GPR and ANN showed moderate performances in terms of computational efficiency.  
Figure 5: The \(R^{2}\), RMSE and MAE distribution of 20 repetitions between the predicted LAI values and corresponding measured LAI. (**a**) \(R^{2}\); (**b**) RMSE; (**c**) MAE.
## 6 Numerical Results  
In this paper, we use as an example a square area of \(50\times 50\) km\({}^{2}\) in the suburban environment, where \(S\) UAV nodes are uniformly distributed. The simulation parameters are shown in Table 2. All UAV nodes operate at a carrier frequency of 2.4 GHz with an available bandwidth of 20 MHz. The data rates of UAV nodes for control packets and data packets are \(R=20\) Mbps. In reference [38], the value of the attenuation factors \(\eta_{los}\) is 1. Considering the difference in noise floor between suburban and urban environments, the variance of noise \(\sigma^{2}\) is set to \(5\times 10^{-19}\) W/Hz. The communication range can be calculated, and is 2225.4 m. Based on the above parameters, we simulate the static grid and dynamic topology cases using MATLAB R2018a.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency \(f\) & 2.4 GHz \\ Bandwidth \(B\) & 20 MHz \\ Channel parameter \(\eta_{los}\) & 1 \\ Variance of noise \(\sigma^{2}\) & \(5\times 10^{-19}\) W/Hz \\ UAV transmit power \(p_{t}\) & 0.5 W \\ Data rate \(R\) & 20 Mbps \\ Communication range \(d_{C}\) & 2225.4 m \\ Slot & 100 \(\upmu\)s \\ Repetition interval & 500 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameter settings.
# WON-OCDMA System Based on MW-ZCC Codes for Applications in Optical Wireless Sensor Networks
## 5 Theoretical Analysis
### Obtained Results  
The values for the exploited parameters in the investigation were selected to allow for comparison with previous research and are summarised in Table 2.  
Figure 7 illustrates the performance of WON-OCDMA system with 8 MW-ZCC code words as a function of the received optical power (ROP) per chip. As can be observed, users with higher code-weights provide greater performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Value** \\ \hline \(\theta\) & Beam divergence & 0.5 mrad \\ \(\lambda\) & Operating wavelength & 1550 nm \\ \(D\) & Receiver aperture diameter & 8 cm \\ \(B_{c}\) & Electrical bandwidth & 2.5 GHz \\ \(\sigma_{R}^{2}\) & Rytov variance & 1 \\ \(\Omega\) & Weather attenuation & 3 dB/km \\ \(P_{c}\) & Crosstalk power & \(-\)30 dBm \\ \(P_{t}\) & Total transmitted power & 20 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the parameters used for system investigation.  
The calculated number of simultaneous users (data services and vibration point sensors) using MW-ZCC codes as data carriers is shown in Figure 8. It was assumed that services are evenly represented in the systems. Assuming the minimum allowable BER of \(10^{-3}\), \(10^{-9}\) and \(10^{-12}\) to support triple-play services, WON-OCDMA employing novel MW-ZCC codes can support up to 32 simultaneous subscribers. In the OOK modulation, a BER of \(10^{-3}\) is equivalent to an SNR of 15 dB.  
The performance degradation due to increased transmission distance in the atmosphere under moderate turbulence with 32 simultaneous users' (including transmission  
Figure 8: Probability of error versus total number of users as a function of code weight W of 2, 4, 8.  
Figure 7: Probability of error versus ROP per code for concurrently transmitting users using code weight W of 2, 4 and 8.  
from 10 sensing points) was investigated using simulation set-up in OptiSystem software. The result is illustrated in Figure 9, indicating a successful transmission up to 2 km.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 4 Results
### Presenting Detailed Use Cases
#### 4.2.6 Timesheets  
Supervisors can access timesheets via the web application on the page shown in Figure 13. By selecting a month and year, the supervisor initiates a GET request to the REST API, which retrieves all recorded hours worked by both the supervisor and their subordinates. The data is displayed in a table for easy review of working hours. Additionally, supervisors have the option to download this data as a CSV file for further analysis and manipulation.  
Figure 12: Sequence diagram of adding a task and updating it.  
Figure 13: Timesheet page for supervisors.  
In order for an employee to record their shift, they need to connect to the smartwatch, which would automatically send a POST request to the REST API to add a new entry in the EmployeesTimesheets, which would record the date and time of the starting shift of the employee. When the employee wants to end their shift, they would complete that from the smartwatch, which will send a PUT request to the API to record the time of the end shift. In Figure 14, the flow of actions based on the presented information is presented, showing how the web application gets data for timesheets and how that data is updated when changes are made from the employee regarding an end of shift.
## 5 Experimental Verification of Different Combinations of Fault Conditions
Due to the existence of three planetary gears, it can be seen in  
Figure 13: Vibration signal of sun gear missing teeth and planetary gear pitting experiment: (**a**) Time domain waveform; (**b**) Fourier spectrum. the figure that peaks appear at the positions \(f_{m}\pm nf_{s}/3\pm nf_{c}\) of meshing frequency with a sideband interval of \(nf_{s}/3\) times the characteristic frequency of the sun gear fault, where \(n\) is an integer. For example, at 2, 6, and 7 are \(f_{m}-f_{s}/3\), \(f_{m}+f_{s}/3\), and \(f_{m}+f_{s}/3+f_{c}\), respectively. Peaks are found at the positions of frequency \(f_{m}\pm nf_{p}\pm nf_{c}\) associated with the planetary gear fault characteristic frequency \(f_{p}\), such that 1, 5, 8, and 9 are \(f_{m}-2f_{p}-f_{c}\), \(f_{m}+f_{p}\), \(f_{m}+2f_{p}\), and \(f_{m}+2f_{p}+f_{c}\), respectively. The experimental signal is basically consistent with the simulated signal. Therefore, the comprehensive diagnosis is that both the sun gear and planetary gear are faulty. From the experiment, it can be seen that when both the sun gear and the planetary gear are faulty, the spectrum peaks are more intensive compared with normal operating conditions. Although the types of fault combinations are different, they are all faulty for the planetary gearbox system, so the spectrum peaks are roughly the same in terms of distribution. In addition, there are manufacturing errors and assembly errors in the test bench, which lead to the appearance of an interference spectrum in the experimental signal,  
Figure 14: Vibration signal of the sun gear missing teeth and planetary gear missing teeth experiment: (**a**) Time domain waveform; (**b**) Fourier spectrum. but it can still be found that the frequency distribution of the model signal is basically consistent with the test through the comparison between the simulation and the test, and the frequency spectrum is mostly distributed around the engagement frequency, thus verifying the accuracy of the digital twin model. The accuracy of the digital twin model is verified, and it provides a theoretical basis for the health monitoring and fault diagnosis of the planetary gearbox system.