esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 2 Related Work
Their model enables choosing the number of TIM groups that provides close to optimal energy consumption of PS stations and throughput of active stations. This paper presents the interplay between TIM segmentation and RAW grouping in demanding scenarios such as high-throughput or high-density considering reliable bidirectional traffic. It shows limitations and interconnections between the two features and suggests how to combine them in order to achieve the desired trade-off between scalability and throughput on one hand, and latency and energy consumption on the other hand, for bidirectional traffic patterns. As such, we aim to fill the existing gap in research on general network performance (considering throughput, reliability, latency and energy) with bidirectional traffic patterns by utilizing the two most popular MAC features of IEEE 802.11 ah, RAW and TIM, working in parallel. Furthermore, we demonstrate how IEEE 802.11 ah can be used in IoT to support legacy Internet traffic and discuss the issues of using TCP over IEEE 802.11 ah. This performance evaluation enables nearly optimal TIM and RAW configuration in compliance with network demands. We hope this work can contribute to improved interoperability possibilities between TIM and RAW and perhaps inspires the design of novel standard-compliant adaptive optimization algorithm(s) that would be applicable to use cases with heterogeneous traffic patterns.
# Adaptive 3D Imaging for Moving Targets Based on a SIMO InISAR Imaging System in 0.2 THz Band
## 3 Adaptive Tracking of the Moving Targets Using Multiple Beams
### SIMO Signal Model
As a result, the synthesized sum channel signal is expressed as:  
\[s_{0}(t_{m},\tau)=\sigma e^{-j2\pi(f_{e}+\gamma\tau)\frac{2R_{0}p(t_{m})}{c}} \tag{11}\]  
And the HRRP of the synthesized sum channel has the following form:  
\[Y_{0}(t_{m},r)=\text{sinc}[\frac{2B}{c}(r-R_{0P}(t_{m}))]e^{-j\frac{4B}{\lambda }R_{0P}(t_{m})} \tag{12}\]  
The above expressions indicate that the phase component in the HRRP of the synthesized sum signal is determined by the round wave path from the transmitter antenna to the scatter center, which is equivalent to placing a virtual receiving antenna in the origin. Since the synthesis of the sum channel signal is a coherent accumulation, the SNR will be higher than the single channel, hence the target detection can be performed based on the HRRP of the virtual sum channel.
# The Effects of Functional Ankle Taping on Postural Stability in Elite Judo Players
## 2 Materials and Methods
### Experimental Protocol
#### 2.2.3 Outcome Measures  
For biomechanical postural evaluation, motion trials were performed in a motion analysis laboratory surrounded by an 8-camera optical motion capture system (Vicon, Culver City, CA, USA). Further, we have attached reflective markers on the subject' lower body based on a plug-in-gait body model. Ground reaction forces (GRF) and moments under the feet for kinetic analyses were collected at 360 Hz using one force plate (AMTI, Watertown, MA, USA) mounted on the laboratory floor level. The COP data were obtained from the GRF and moments data.  
For the linear analysis of postural stability, a 95% confidence ellipse sway area (COPSWAY) was calculated around the COP motion along both the anteroposterior and mediolateral axes (Figure 2). Traditionally, a smaller value of COPSWAY indicates greater static balance or postural stability. The details regarding the procedure to calculate the COP  
Figure 1: Experimental protocol of the ankle taping intervention and postural standing trials on a force plate mounted on the laboratory floor.  
SWAY is described in the previous literature [18]. Further, the COP velocity (COPVEL) was computed for the bidimensional COP trajectory (both the anteroposterior and mediolateral directions) [19]. The COPVEL has been suggested to be one of the most sensitive objective measures for detecting changes in postural abilities, a lower COPVEL value indicates good balance whereas a higher value indicates poor balance [20].  
We extended the basic evaluation of postural stability by investigating the nature of the control signal using Approximate Entropy (ApEn) which is one of nonlinear measures [21]. By using ApEn which aims to measure structural patterns of COP data in a time series, dynamic stability during static posture was assessed. Based on typical values previously suggested in COP time-series data [22], the vector length and the tolerance range were defined as m = 2 and r = 0.2 of the standard deviation of the time series GRF data. A total of 10,800 data points (30 s 360 Hz) were utilized for ApEn calculations in anteroposterior (ApEnAP) and mediolateral (ApEnML) directions, and the surrogate data test was performed for the reliability verification of the calculated ApEn values [23]. In the extant literature, greater ApEn values were reflective of 'greater complexity' and a healthy biological system, whereas less values were denoted as 'less complexity' and an unhealthy system [21].
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 4 Design of PCEB-MS Protocol  
The PCEM-MS protocol involves two parts: the distributed power control algorithm (DPCA), based on the multisink DPCA, and the unequal clustering and energy-balanced algorithm located inside the sink (UCEBA).  
The DPCA based on the multisink is mainly used to adjust the power of the adjacent sink and ensure the overall multisink coverage. The UCEBA achieves energy balance and prolongs network lifetime by considering the long-strip structure of the coalmine tunnel.
## 4 Seismic Assessments with Capacity Spectrum Method  
### Capacity Spectrum Method  
The capacity spectrum method (CSM) was chosen due to its extensive adaptability and simplicity to various structural behaviors [39]. It is mentioned that this method does not require a priori-defined multilinear simplified behavior; therewith, experimental or numerical results can be applied directly. The CSM operates by converting a structurally intricate system into an equivalent single degree of freedom (SDOF) model and subsequently employing a static nonlinear analysis. The method's validity is considerably enhanced when the predominant natural vibrational mode aligns with each primary directional axis of the building [40]. Given that the concentrated mass \(M_{k}\) (representing the mass of the upper structure of the bridge) at the cap beams is significantly larger than the mass of the two-column bent itself, all three two-column bent specimens can be simplified as an SDOF system. The procedures of the CSM include the following:  
Step 1: the real structure is simplified to the equivalent idealized SDOF system; see Figure 13(a).  
Step 2: the cyclic force-displacement relation (hysteretic curve) and the skeleton curve are obtained from experiments; see Figure 13(b).  
Step 3: the hysteretic results are analyzed to obtain the values of equivalent viscous damping (EVD) factors \(\xi_{\rm eq}\) subject to each imposed displacement level (Figure 13(c)).  
Step 4: convert the skeleton curve to a capacity curve (Figure 13(d)), in a pseudo-acceleration versus displacement spectrum ordinate, with  
\[SA_{d}=\frac{F}{M_{k}}\ \ \ \ \ SD_{d}=\delta \tag{1}\]  
where \(F\) is the lateral force (unit: kN), \(\delta\) is the lateral displacement (unit: mm), and \(M_{k}\) is the structural mass, which is \(1.14\times 10^{5}\) kg (resulting in an axial compression ratio of 6% for the two columns).  
Figure 13: Capacity spectrum method for evaluating structural performance.  
Step 5: with the value of the EVD factor \(\xi_{eq,i}\) associated with a guessed displacement, \(\delta_{i}\), a seismic spectral demand curve is obtained; see Figure 14d.  
Step 6: plot capacity and demand curves together; see Figure 14d. The demand curve is plotted according to the initial guess value of the equivalent viscous damping (EVD) factor \(\xi_{eq,i}\). The performance point is the intersection between the capacity and demand curves, which defines the estimated load \(F_{est}\) and displacement \(\delta_{est}\).  
Step 7: if the estimated displacement value \(\delta_{est}\) deviates from the initial guess displacement \(\delta_{i}\), then update \(\delta_{i}\) through interpolation or extrapolation. The EVD factor \(\xi_{eq,i}\) is subsequently updated based on its corresponding relationship with \(\delta_{i}\) (Figure 14c).  
Step 8: the analysis is looped from step 5 until the convergence \(\delta_{est}=\delta_{i}\) (error less than 1%).
## 3 Results  
In different load grade conditions, electrical parameter testing experiments were conducted on sandstone, granite, and similar rock material specimens, and the measured results of microelement total resistance, total inductance, total capacitance, voltage, current, and frequency are presented in Table 1.  
Based on Table 1, the electrical signal pattern of the tested specimen showed periodic oscillation. The resistance (\(R_{\sigma}\)) and capacitance (\(C_{\sigma}\)) of the specimen were measured using an LCR impedance tester. When \(P=0\), the LCR impedance tester measured the inductance (\(L_{\sigma}\)) of the specimen as negative, indicating there was no inductance in the specimen. However, when \(P\
eq 0\), the oscilloscope obtained a signal waveform showing attenuation oscillation. At this point, the dynamic total impedance (\(Z_{\sigma}\)) of the specimen was obviously smaller than the resistance (\(R_{\sigma}\)). The total impedance (\(Z_{\sigma}\)) was determined by using open-circuit voltage and short-circuit current. For instance, in the case of the 8\(\#\) granite sample, when \(P=2\) kN, \(Z_{\sigma}\) was measured to be 264.9 \(\Omega\) (\(Z_{\sigma}=U_{\sigma}/I_{\sigma}\)), whereas \(R_{\sigma}=131,\!000.0\)\(\Omega\). The inductance values in Table 1 were obtained using the formula \(2\pi f=(L_{\sigma}C_{\sigma})^{-1/2}\). Further  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Load** & **Resistance** & **Inductance** & **Capacitance** & **Voltage** & **Current** & **Frequency** \\
**P (kN)** & \(R_{\sigma}\) (M\(\cdot\)\(\Omega\)) & \(L_{\sigma}\) (mH)** & \(C_{\sigma}\) (pF) & \(U_{\sigma}\) (mV) & \(I_{\sigma}\) (mA) & \(f\) (kHz) \\ \hline \multicolumn{8}{c}{8\# Granite sample} \\ \hline  
\hline \end{tabular}
\end{table}
Table 1: Measurement results of rock specimens’ physical parameters under compressive load.  
discussion will be needed to address the issue of the dynamic AC impedance \(Z_{\sigma}\) being much smaller than the resistance \(R_{\sigma}\).
## 3 Results
### Landslide Results Under Different Return Periods  
Using the trained RF model, predictions were made for a 1 km \(\times\) 1 km grid across the entire study area. Landslide susceptibility probabilities were categorized into six levels using the natural breaks method: extremely low risk (0-0.18), low risk (0.18-0.33), moderate risk (0.33-0.50), elevated risk (0.50-0.67), high risk (0.67-0.84), and extremely high risk (0.84-1). These results are illustrated in Figure 9.  
Figure 8: The ROC curves, AUC results, and accuracy evaluation indices for the RF, MLP, XGB, KNN, SVM, and GBM models.  
Figure 9: Landslide susceptibility assessment under different return periods: (**a**) 5 years, (**b**) 10 years, (**c**) 20 years, and (**d**) 50 years.  
From the results, we identify three distinct high-susceptibility zones in Zhejiang Province: the southern coastal mountainous region, the western mountainous area, and the hilly region of Shaoxing-Taizhou in central Zhejiang. In contrast, the Hangjiahu Plain, the Ningshao Plain, and the Jinqu Basin are mostly categorized as low-risk zones. The rugged and undulating terrain of the southern mountainous region aligns with higher susceptibility levels, demonstrating a consistent relationship between landslide susceptibility and fundamental geographic patterns.  
Next, we further examined the landslide susceptibility map generated by the XGB model for the 20-year return period. To evaluate the accuracy of the susceptibility map rigorously, we overlaid confirmed landslide points for the 20-year return period, with the aim of verifying the alignment between the mapped landslide areas and actual landslide events. The verification results, as shown in Figure 10, highlight four notable landslide clusters: Region 1 near Huzhou-Anji; Region 2 in Quzhou, western Zhejiang; Region 3 in the eastern part of the province (Shaoxing-Shengzhou); and Region 4 in the southern mountainous area around Wenzhou. The analysis reveals that most landslide points fall within high-risk or extremely high-risk zones, with high-risk levels for Regions 1, 2, 3, and 4 of 60.7%, 68.95%, 94.25%, and 98.60%, respectively. The close correspondence between actual landslide locations and the predicted susceptibility map underscores the XGB model's effectiveness in forecasting landslide susceptibility and provides valuable insights for geological disaster risk assessments and management.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### Simulation and Evaluation of DEC-KM
#### 5.2.4 Comparison of Clusters and Link Distances between CHs for DEC and DEC-KM
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**CH** & **Node Shortest Distance (m)** & \multicolumn{2}{c}{**Node Longest Distance (m)**} \\ \hline  & **DEC** & **DEC-KM** & **DEC** & **DEC-KM** \\ \hline CH5 & 2.524 & 2.524 & 38.619 & 18.410 \\ CH6 & 6.731 & 6.731 & 29.357 & 8.120 \\ CH7 & 12.232 & 12.232 & 31.256 & 28.614 \\ CH9 & 5.180 & 5.180 & 29.499 & 15.924 \\ CH10 & 7.954 & 7.954 & 17.691 & 14.775 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the nodes’ shortest and longest link distances to CH for DEC and DEC-KM. The results indicate that the proposed algorithm outperforms the old ones. A summary of improvements of DEC-KM to DEC protocol is gathered in Table 6.
###### Abstract
By learning from past experiences, the courier node as receiver can improve its communication strategies, enhancing network efficiency and reducing collisions. It allows the system to scale effectively in complex environments, ensuring better performance and resource management in dynamic networks with energy-efficient collision avoidance. Additionally, DQN transforms conventional Q-learning by utilizing neural networks to estimate the Q-value function, effectively addressing the constraints of Q-tables in extensive state-action spaces. By leveraging function approximation, DQN enhances scalability and adaptability, enabling efficient decision-making in dynamic and continuous state environments. This makes DQN particularly suitable for complex scenarios such as underwater acoustic communication networks, where state transitions are highly dynamic. The proposed framework introduces the following noted contributions in the MAC protocol for effective power control and collision avoidance in UWSN:  
* The protocol minimizes energy consumption by dynamically adjusting wake-up schedules and integrating adaptive power control mechanisms, thereby significantly extending network lifespan. Figure 1: Structural design of underwater wireless sensor networks. * Enhanced synchronization between transmitter and receiver nodes and courier nodes as transmitter and as receiver improves communication reliability, reduces delays, and mitigates packet collisions, ensuring efficient operation under diverse conditions. * A novel collision-avoidance mechanism combined with priority-based communication optimizes resource utilization, guarantees timely delivery of critical data, and significantly improves system throughput. * The adaptable and versatile design caters to different traffic priorities and network configurations, allowing the protocol to be customized for a diverse set of underwater applications, all while preserving excellent performance. The paper follows this structure: Section 2 presents the literature review, Section 3 outlines the proposed system model, Section 4 showcases the performance evaluation, and Section 6 provides the conclusion of the paper.
###### Abstract  
Recently, camera sensors have been widely used in a variety of applications, including advanced driver assistance systems (ADASs), surveillance systems, and unmanned aerial vehicles (UAVs). These sensors are often integrated with intelligent algorithms to automatically analyze information and perform specific functions. However, during rainy weather, droplets on the camera lens surface can obstruct the view, leading to degraded image quality and reduced algorithm performance. This paper quantitatively evaluates the effect of droplet contamination on image quality and object detection performance. Image quality degradation was analyzed using the modulation transfer function (MTF), with droplet volume and number as variables. Results show that the MTF50 decreased by up to 80% when the droplet volume reached 10 \(\upmu\)L. To assess the effect on object detection, performance changes were evaluated across different detection algorithms. The findings reveal that droplet contamination can reduce the detection performance of small objects by up to 90%, as measured by the mean average precision (mAP) metric. Furthermore, degradation was more severe on hydrophilic surfaces compared to hydrophobic ones. This study demonstrates that droplet characteristics such as volume, number, and shape significantly influence both image quality and object detection performance. It provides critical insights into selecting appropriate camera lens materials by comparing hydrophilic and hydrophobic surfaces while also highlighting the susceptibility of intelligent algorithms to environmental factors and underscoring the importance of effective cleaning techniques.  
c 2025 15 2690
# Side-Channel Power Analysis Based on SA-SVM
## 1 Introduction
Several researchers have used heuristic algorithms for feature extraction. [PERSON] et al. [21] proposed a framework of GA-CPA, which combined genetic algorithms (GAs) and CPA. This framework utilizes genetic algorithms to extract characteristic values, followed by a CPA attack. [PERSON] et al. [22] described an intelligent neural network algorithm, which leverages particle swarm optimization (PSO) to detect hardware trojans. The experimental results indicate that the detection accuracy of the PSO neural network method surpasses that of conventional BP neural network methods. Several researchers have used heuristic algorithms for parameter optimization, which differs from traditional exact-solving methods in prioritizing search in the approximate solution space. Several heuristic algorithms have been studied to optimize SVM parameters, such as genetic algorithms (GAs) [23], particle swarm optimization (PSO) [24], ant colony optimization (ACO) [25], and simulated annealing (SA) [26; 27]. These studies have shown improved classification accuracy compared with other methods such as grid search. The GA algorithm is close to the best solution, but it is difficult to encode the problem and then decode the solution. The PSO algorithm has fewer variables to tweak and a straightforward principle, but it has poor local search capability and insufficient search precision. The ACO method converges slowly and tends to fall into the local optimal. Moreover, ACO cannot handle continuous space optimization issues and is only suitable for discrete problems. Simulated annealing (SA) enables the discovery of maximum or minimum values by allowing for random selection of suboptimal solutions, making it easier to escape local optima. In this study, SA and SVM methods were combined to establish an SA-SVM model, which was established and applied to the side-channel power analysis. The experiment was performed on the DPA Contest v4.1 public dataset. First, the Pearson coefficient was used to select the eigenvalues of the dataset of power traces. Then, the HW model was used as a label for the SA-SVM model. The SA-SVM model uses a certain probability of accepting negative increments to jump out of local optima and find optimal parameters more easily. We evaluated the performance of the parameters in terms of accuracy and guessing entropy. The results of the experiments show that, when compared with the grid search method, through simulated annealing, global optimization can be improved by jumping out of the local optimum.
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 3 Proposed System Implementation
### Front-End Tracking Based on Semi-Direct Methods
#### 3.2.2 Keyframe Selection  
After initialization, the system determines whether the current frame is a keyframe based on scene changes and IMU pre-integration. The overall output accuracy of the system depends largely on the quality of the keyframes that were inserted. The selection criteria for keyframes are as follows: First, the average parallax of tracked points between the current frame and the latest keyframe is beyond a certain threshold. Second, the number of tracked points goes below a certain threshold. Third, the translation calculated by IMU pre-integration exceeds a preset threshold after the latest keyframe is inserted. If any of the above criteria are met, the current frame is considered a keyframe.
## 6 Conclusions and Future Studies  
This study employed a multioptimization technique that uses the Taguchi method with the RSM method and that depends upon the concept of the desirability function (DF). The optimal parameter combinations for SLA printing of a dental bridge model obtained from a simple CAD model were investigated. The essential conclusions drawn from the present work are summarized as follows:  
Figure 16: Comparison between the EXOCAD bridge model and the SLA-printed version in front and back views.  
Figure 15: Bridge scanned with Medit–T710 3D scanner.  
* The surface roughness shows a significant decrease with the decrease in exposure time and a slight decrease with the increases in bottom exposure time and bottom layers.
* The normal exposure time gives a lower average dimensional deviation at its middle level, but the bottom layers show a significant decrease in dimensional deviation. The bottom exposure time shows a slight increase in dimensional deviation.
* The mathematical models developed by utilizing the Taguchi method with the RSM method revealed that input-output relationships in the SLA process could be determined with reasonable accuracy and reliability to produce a dental bridge model based on a simple CAD model.
* Analyses of the main effects of the process variables (normal exposure time, bottom exposure time and bottom layers) showed that they were significant for the process outputs except in the case of the insignificant effect of bottom exposure time on surface roughness.
* The multioptimization method used in this research gives ideal parameter sets, i.e., the highest levels of normal exposure time, highest bottom layers and moderate levels of bottom exposure time, for achieving the best combination of low dimensional deviation and low surface roughness.  
Conceptualization, N.K.A.-K.; methodology, N.K.A.-K.; software, N.K.A.-K. and T.R.M.; validation and investigation, N.K.A.-K. and T.R.M.; formal analysis, N.K.A.-K. and T.R.M.; writing--original draft preparation, N.K.A.-K. and T.R.M.; writing--review and editing and supervision, N.K.A.-K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data used are shown in the text. The authors will make the data available free of charge upon request.  
The authors of this publication would like to thank the technical staff in the King dental lab, especially [PERSON] and [PERSON].  
The authors declare no conflict of interest.