esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Performance Evaluation Results and Discussion
### Effects of a on the FLRAF
When the delivery rate is at a lower level, the network does not reduce energy consumption, but a small number of received data packets will inevitably result in a higher average energy consumption. Therefore, when \(\alpha<0.3\), due to the limited number of received data packets, the average energy consumption is at a relatively high level, but as the forwarding area increases, the success rate of data transmission gradually increases, while the average energy consumption decreases as it is gradually averaged. On the other hand, after \(\alpha>0.6\), the forwarding area gradually expands, involving a greater number of nodes in the forwarding process, thus increasing the overall energy consumption. The slight decrease in the number of received data packets further contributes to a slight increase in average energy consumption. Figure 15 shows the effect of \(\alpha\) on the network lifetime. Thanks to the fuzzy reasoning system we introduced, we effectively avoided the possibility of selecting low-energy nodes as forwarding nodes in the early stage. As \(\alpha\) increases, the range of the forwarding area also increases, and more nodes will be included. The more nodes there are, the longer the time interval for a single node to serve as a forwarding node, and with a fixed initial energy of the node, the number of network simulation rounds it participates in will increase, thereby  
Figure 14: Effects of \(\alpha\) on average energy consumption. Figure 13: Effects of \(\alpha\) on average end-to-end delay. extending the network lifetime. The network lifetime will theoretically continue to extend as the number of nodes grows. Therefore, after comprehensively analyzing the impact of changes in forwarding regions on the performance of various network parameters, we found that when \(\alpha=0.6\), the data delivery rate remains high, while both average energy consumption and end-to-end delay perform well. Therefore, we ultimately determined \(\alpha=0.6\) as the optimal forwarding area configuration, and conducted performance parameter simulations comparing our proposed FLRAF protocol with other existing protocols in the following sections.
# Flexible Phased Antenna Arrays: A Review
## 1 Introduction
### Historical Background  
PAs faced challenges of cost, size, and component limitations in the implementation of Electronically Scanned Antenna Arrays (ESAAs) before 1980s [2, 3, 4]. These systems implemented Passive Electronically Scanned Arrays (PESAs) instead of Active Electronically Scanned Arrays (AESAs). The implementation of AESAs was made possible after the inclusion of Gallium Arsenide (GaAs)-based circuits at a monolithic scale during 1980s. Monolithic phase shifters utilizing GaAs technology have been actively incorporated in applications such as electronic warfare and Satellite Communications (SATCOM) ever since [5, 6]. The advancement to incorporate multiple functionalities on a single chip was utilized for phase and gain control across different frequency bands [7, 8, 9], consequently reducing the size of the arrays. But the lower yield and higher costs of the solution posed a hurdle to realize complex architectural choices for array design. The limitations were addressed through the implementation of silicon-based Radio-Frequency Integrated Circuits (RFICs), which offer enhanced integration, reduced size, and improved performance in PA, leading to an increase in the number of their applications. Silicon-based RFIC allowed the realization of fully integrated multi-element PA systems for the first time. The idea of a fully integrated PA system is to have a Transmitter (TX) and Receiver (RX) capable of beamforming, gain, and phase control, with integrated on-chip power amplifiers, and Low-Noise Amplifiers (LNAs) [10, 11, 12, 13].
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion
### The Inference Time  
Inference time is the model's mean duration to analyze a single input (sensor data) and generate recognition outcomes. It is essential for assessing the appropriateness of real-time applications.  
Figure 6: Accuracy comparison.  
\[\text{Inference Time}=\frac{\text{Total Processing Time for All sensor data (T)}}{\text{Total number of sensor data(V)}}(\text{milliseconds per sensor data}) \tag{13}\]  
In Equation (13), inference time, the average processing time for sensor data, is a key parameter for model computational efficiency. Divide the total processing time for all sensor data (T) by the total number of sensor data (V) in milliseconds per sensor data. Less inference time means faster processing and scalability in real-time applications.  
Figure 7 of the inference time series compares the execution timings of four models, HGE-ACN, DDSTGCNN, MSeqGraph, and BAE, under five scenarios. With a time of 45 ms, HGE-ACN is the quickest, whereas BAE takes 85 ms. With a minimum inference time of 48 ms, the variable signing speed remains the fastest, while other models exhibit a significant rise in time. All models experience an increase in signature variability, except HGE-ACN at 50 ms and BAE at 95 ms. As a result of environmental noise, inference times increase; for example, HGE-ACN takes 52 ms and BAE 98 ms. When tested under extreme conditions, HGE-ACN shows its best performance at 55 ms.  
In Table 2, the computational complexity of the HGE-ACN is analyzed based on graph convolution and adaptive convolution operations. Graph convolution has a complexity of O\(\left(\text{NF}^{2}\text{L}\right)\), where N is the number of keypoints, F is the feature dimension, and L is the number of graph convolution layers. Adaptive convolution operates over spatial dimensions with complexity O\(\left(\text{HWCk}^{2}\right)\), where H and W are frame dimensions, C is the number of channels, and k is the kernel size. The combined complexity ensures scalable and efficient inference.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Component** & **Operation** & **Computational Complexity** \\ \hline Graph Convolution & Node feature aggregation and transformation & \(O\left(NF^{2}L\right)\) \\ Adaptive Convolution & Feature extraction over spatial dimensions & \(O\left(HWCk^{2}\right)\) \\ Total Complexity & Combined graph and convolutional computations & \(O\left(NF^{2}L+HWCk^{2}L_{C}\right)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of HGE-ACN model.  
Figure 7: Inference time.
# Sensitivity Analysis of an Implanted Antenna within Surrounding Biological Environment
###### Abstract  
The paper describes the sensitivity analysis of a wireless power transfer link involving an implanted antenna within the surrounding biological environment. The approach combines a 3D electromagnetic modeling and a surrogate model (based polynomial chaos expansion). The analysis takes into account geometrical parameters of the implanted antenna and physical properties of the biological tissue. It allows researchers to identify at low cost the main parameters affecting the efficiency of the transmission link.  
wireless power transfer; medical implant; stochastic model; sensitivity analysis Article
## Abbreviations  
AI Artificial Intelligence  
ML Machine Learning  
DL Deep Learning  
IoT Internet of Things  
5G Fifth Generation Mobile Network  
6G Sixth Generation Mobile Network  
UDNs Ultra-Dense Networks  
LBO Load Balancing Optimization  
HetNets Heterogeneous Networks  
QoS Quality of Service  
PPHO Ping-Pong Handover  
UAVs Unmanned Aerial Vehicles  
WWWW World Wide Wireless Web  
WISDOM Wireless System for Dynamic Operating Mega Communication  
MBB Mobile Broadband  
eMBB Enhanced Mobile Broadband  
URLLC Ultra-Reliable Low-Latency Communications  
mMTC Massive Machine-Type Communications  
SDN Software-Defined Networking  
RRHs Remote Radio Heads  
BSs Base Stations  
RF Radio Frequency  
SON Super Self-Organizing Network  
mmWave Millimeter-Wave
## 5 Developing the Models for Predicting UCS
### Delm  
Similar to other neural network models, the performance of the DELM model is also controlled by the numbers of hidden layers and the corresponding number of neurons. For this purpose, 3, 4, and 5 multi-hidden layers with different numbers of neurons (5, 10, and 15) were considered to predict UCS. To verify the performance of all DELM models, the RMSE and R\({}^{2}\) were also recorded in the training and testing phases, as shown in Table 3. As can be seen in this table, in the DELM model with numbers of 10-10-10 neurons in three hidden layers, the lowest value of RMSE and the highest value of R\({}^{2}\) in the training and testing phase were obtained and recorded, respectively. Therefore, the DELM model considering the numbers of 10-10-10 neurons in three hidden layers was selected to predict UCS in this study, as illustrated in Figure 7.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Model Multi-Hidden Layers**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{2-5}  & **Training** & **Testing** & **Training** & **Testing** \\ \hline
5-5-5 & 31.1924 & 30.0702 & 0.6211 & 0.6467 \\
**10-10-10** & **28.4753** & **27.6212** & **0.6843** & **0.7019** \\
15-15-15 & 28.5459 & 27.6791 & 0.6827 & 0.7006 \\
5-5-5-5 & 34.0340 & 32.3176 & 0.5490 & 0.5919 \\
10-10-10-10 & 35.5967 & 33.8719 & 0.5066 & 0.5517 \\
15-15-15-15 & 32.3059 & 30.5482 & 0.5936 & 0.6354 \\
5-5-5-5 & 32.1756 & 30.6333 & 0.5969 & 0.6333 \\
10-10-10-10 & 34.0784 & 33.2670 & 0.5478 & 0.5676 \\
15-15-15-15 & 40.7977 & 39.3185 & 0.3519 & 0.3959 \\ \hline \hline \end{tabular} Note: Line in bold represents the better solution.  
\end{table}
Table 3: Performance evaluation of DELM models with various numbers of hidden layers.  
Figure 6: Training process of the KELM-GWO model.
## 3 Results and Analysis
### Particle Pore Characteristics  
As shown in Figure 7, the AP particles were divided into several hollow spheres with the centre of mass of the particles as the centre of the sphere, spaced 10 \(\upmu\)m in the  
Figure 6: The pore distribution characteristics for porous AP nano-CT slices.  
radial direction. The porosity of the nonhollow spheres was calculated separately, and the uniformity of the pores and the internal distribution of the particles were analysed. For the mass loss of 9.5%, the calculated results are shown in Figure 8a, where there are no pores within 40 \(\upmu\)m of the centre, and the number of pores gradually increases as distance increases. This shows that the initial decomposition of AP particles occurs near the surface of the particles and gradually extends to the interior. When the mass loss reaches 20.2%, as shown in Figure 8b, there are no pores near the centre of mass, with a number of pores beyond 20 \(\upmu\)m, and porosity increases as distance increases. When the mass loss was 29.9%, as shown in Figure 8c, the maximum degree of low-temperature decomposition of AP was reached, and the pore distribution varied little with distance, resulting in a uniform pore distribution.  
Figure 8: _Cont._  
Figure 7: A schematic diagram of the pore space distribution.  
The equivalent diameter of the pores was calculated using the maximum sphere method, and the variation process of the different pore size ratios with mass loss was analysed. Figure 9 shows the distribution of the pore equivalent diameter of AP particles with mass loss of 9.5%, 20.2% and 29.9%. At 9.5%, there were more small pores, and 31.7% of all pores were below 2. When the mass loss reached 20.2%, those below 2. When the mass loss reached 29.9%, the number of smaller pores was greatly reduced, and the pore distribution looks approximately Gaussian. The peak pore size was about 4.  
By analysing the characteristics of pore distribution and pore equivalent diameter in the AP particles, it can be seen that in the initial stage of decomposition the pores were mainly concentrated near the particle surface, with most of them being small pores, indicating that the decomposition of AP particles first occurred near the particle surface during low-temperature heating. AP decomposition generates gas, and the gas escapes the surface, leading to the formation of pores. As decomposition progresses, the pores increase in size and expand into the interior of the particles. Typically, when the pores reach approximately 5 in diameter they stop increasing; however, the process continues inside the particles until finally, at a mass loss of approximately 30%, the low-temperature decomposition of AP stops. At this point, the interior of the particles is full of irregular pores, with almost all pores connected and relatively evenly distributed.  
Figure 8: Pore space distribution of AP particles with different mass losses.  
Figure 9: _Cont._
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 4 Performance Evaluation
Empirically, the range from 20 m to 30 m is considered to be appropriate according to this comparison, allowing practical movement speed \({}^{2}\)m/s-5 m/s' for the applications discussed above.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 1 Introduction
Spatial locality also is likely to reduce the page fault frequency, further improving the performance. Additionally, huge pages are commonly employed in cryptographic systems and secure data transmission to improve the performance and predictability when handling secret or sensitive data, making them a natural fit for covert communication channels that rely on timing stability. The combination of prefetching and huge pages amplifies these individual benefits, leading to enhanced covert channel efficiency. Huge pages facilitate more effective prefetchers, enabling data fetching across larger contiguous memory regions with fewer interruptions driven by page faults. This integration ensures faster address translations, higher cache hit rates, and a reduced latency, resulting in a significantly improved throughput and accuracy in covert channels. Our methodology constructs a cross-core covert channel by exploiting prefetch-induced cache coherence transitions, specifically using the PREFETCHW instruction to trigger measurable state changes in L1 caches. It further introduces multi-line encoding, wherein messages are encoded by accessing varying numbers of cache lines in a huge-page-backed memory region. This enables more robust and high-bandwidth transmission. Our proposed approach capitalizes on the strengths of prefetching and huge pages to enhance the covert channel performance, demonstrating notable improvements in the throughput and accuracy while addressing associated challenges in a controlled computing environment.
## 4 Experiments and Result Analyses
### Experimental Results
#### 4.4.2 Performance Improvement by Each Module
After using the body and edge decoupling module and decoder, F1 score and IoU improved significantly, proving that the body and edge decoupling module can accurately extract edge features and improve the detection accuracy in the boundary area, and the decoder can effectively merge the body, edge, and multi-scale features to restore an accurate segmentation map. Notably, the appearance of defects in the weld defect dataset is simpler than that in other datasets, and the detection effect of the body and edge decoupling module cannot greatly improve the detection effect on the weld dataset. As shown in Figure 16, after introducing each module, the PR curve became nearer the upper right corner, and the AP continued to increase, indicating that the various proposed modules have an improved effect on defect detection. Figure 16: Improvement of PR curve and AP by each module.
# Potential Imaging Capability of Optical Coherence Tomography as Dental Optical Probe: A Mini-Review
## 3 Application of OCT for Subgingival Area Visualization
### Peri-Implant Condition  
The subgingival area is not only present around the natural tooth, but also in the peri-implant area. Radiographic examination and probing around the implant are considered as standard diagnostic tools for evaluating peri-implant conditions [25,26]. However, several shortcomings, such as metal artifacts [27] and underestimation of peri-implant bone loss [28], have been associated with radiographic examinations. Probing around implants is relatively technique-sensitive and can be affected by the presence of the abutment and prosthesis [29]. It can also cause more discomfort and pain compared to probing around the teeth [30,31]. Considering these limitations, the evaluation of peri-implant conditions through the use of OCT may provide essential additional information.  
The potential use of OCT to evaluate peri-implant conditions has been demonstrated in animal subjects (ex vivo study). [PERSON] et al. reported that OCT images can clearly depict the implant body when the mucosal thickness is <1 mm. OCT images were also able to detect cement remnants at the submucosal area of the implant with a fixed crown when the sulcus depth was <2 mm and the mucosal thickness was <3 mm [32]. It has also been demonstrated that OCT images can be used to quantitatively measure peri-implant bone defects by comparing them with measurements using digital calipers as a reference (intradacs correlation coefficient = 0.99) [33]. Therefore, OCT may be considered as a potential novel imaging technique in implant dentistry, owing to its high resolution in peri-implant conditions. However, it should be noted that in vivo studies on human subjects have not been reported, and further clinical studies are required to provide evidence that OCT can be used to evaluate peri-implant conditions.
# Early Warning Signals for Bearing Failure Using Detrended Fluctuation Analysis
## 1 Introduction
In this paper, using detrended fluctuation analysis (DFA), we propose a new health indicator (scaling exponent) to anticipate bearing degradation. DFA has been used extensively in diagnostic techniques to identify and classify faults. As vibration signals are known to be non-stationary, DFA is one of the reliable methods to study the long-range correlations in these signals. [PERSON] et al. have used DFA and variational model decomposition (VMD) to diagnose and classify the acquired vibration signals at different fault severities [16]. [PERSON] et al. have applied DFA to gear vibrational signals for fault diagnostic purposes and concluded that DFA is capable of identifying gear faults to a reasonable extent [17]. [PERSON] et al. have applied DFA to roller bearing fault data to diagnose the faults and showed that DFA delivers excellent extraction of features of non-stationary and nonlinear data [18]. The main contributions of this paper can be summarized in three main points. Firstly, the DFA method combined with Kendall's tau introduce a novel health indictor of bearing degradation. The proposed indicator is corroborated using vibration signals collected from 17 bearings under different operating conditions. Secondly, the novel combined approach shows evidence of critical slowing down in the bearing vibration signals. The existence of the critical slowing down could provide a universal EWS for the failure of bearings and other components in rotary machinery. Finally, most of the classical health indicators used in the prognostics have shown a fluctuating trend toward bearing failure; however, the proposed method reveals a persistent trend in the scaling exponent of the vibration signals at an early stage of bearing degradation. The rest of the paper is organized as follows. Section 2 provides a description of the testing platform and the vibration data sets. In Section 3, the DFA method and the Kendall's tau coefficient are explained. Section 4 summarizes the results and the proposed approach. Finally, Section 5 presents the conclusions of the paper.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 4 Network Indicator System Construction
### Link Load Rate  
During task transmission in a network, traffic flows through the network links. The ratio of the actual traffic transmitted by a link to the theoretical capacity of the link is defined as the average load rate of the link. This metric provides insight into the utilization of network links; a higher value under no packet loss signifies increased network link utilization. Consequently, this paper introduces the link load rate factor to assess the performance of the IoV mesh network.  
\[S\_link=\frac{\sum\frac{\overline{\Psi}_{i,j}}{R_{i,j}}}{E} \tag{9}\]  
In the equation above, \(\overline{\Psi}_{i,j}\) represents the actual traffic rate of the link from \(i\) to \(j\), \(R_{i,j}\) represents the theoretical rate of the link from \(i\) to \(j\), and \(E\) represents the total number of network links. When the network is disturbed, resulting in packet loss for some tasks, the load on some links decreases, thereby resulting in performance degradation of the network.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Further Study
#### 4.5.3 Effects of \(\ell_{2,1}\) Norm  
As shown in Section 3.3, the \(\ell_{p,q}\) norm, i.e., \(\ell_{2,1}\), is used to improve the robustness for outliers. Therefore, in this section, to verify its effectiveness, we denote a variant of DSAH, named DSAH-Nm, which replaced the term \(||\mathbf{R}^{\top}\mathbf{B}-\mathbf{L}||_{2,1}\) in Equation (8) with \(||\mathbf{R}^{\top}\mathbf{B}-\mathbf{L}||_{F}^{2}\). The mAP results on two datasets with the code length varying from 8 bits to 128 bits are reported in Table 4 and 5. From the table, we can observe that the \(\ell_{2,1}\) norm is effective to improve the performance of DSAH, the reason may be that the label information often inevitably contains some noises or subjective factors.