esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation
### QoE Metrics Measurements  
The full reference (FR) model is an objective quality assessment utilised to estimate the QoE of video streaming. It performs a direct comparison between the video under processing called distorted and the actual video, named reference, in order to evaluate the video streaming quality. The two assessed videos are studied according to their properties frame-by-frame to inspect different characteristics, including colour processing and contrast features [46]. The Video Multimethod Assessment Fusion (VMAF), presented by Netflix [47] and the Structural Similarity Index Metric (SSIM) [48] are employed as FR measurement metrics for QoE assessment. Both metrics correlate well with human perception and allow an efficient computation. The metrics are computed off-line and as the average VMAF and SSIM over all the video frames. Table 2 represents a mapping of objective QoE (VMAF) and (SSIM) to the nominal Mean Opinion Score (MOS). MOS is a 5-point scale utilised to evaluate the end-users' satisfaction in a subjective manner [21].
## 4 Analysis and Findings of Research Questions  
In this section, each research question is assessed to determine whether it was answered in detail to fulfill the research objectives precisely. The analysis for each research question is presented below.  
Research Question 1: What Activity Is Carried out by the User on a Smartphone That Is Highly Vulnerable to Security Attack?  
This research question aimed to assess user activities and behaviors the attacker could exploit to compromise smartphones through sensors. The user's activity refers to how they use their device daily (for example, installing applications, clicking a link from an attached file that grants permission to an installed application, and other activities). Such user activities or behaviors, whether intentional or unintentional, may lead to a smartphone being targeted by a cyber attack and the vulnerability of the mobile device being exploited or compromised. The mobile device has certain vulnerabilities, such as small capacity, low-cost sensors, and constantly being switched on.  
Several security attacks occur from these vulnerabilities, including AndroRAT, Desert Falcon, and FineSpy. The asset may become an easy target for cyber attacks due to the user's behavior and limited awareness concerning the smartphone's sensors. Furthermore, unawareness can also be due to physical activity, such as leaving the mobile device unattended, leading to theft. Additionally, smartphones are particularly vulnerable to cyber attacks for two reasons: (a) user activity and (b) the development cycle for mobile applications with poor quality.  
As Figure 9 and Table 9 illustrate, user activity leads to the targeting of smartphones. Table 9 shows five modalities: sensors, services, physical, software, and applications. Each of them is connected to the related mobile sensors, which indicate the user's activities.  
Figure 8: Paper Selection Process.
###### Abstract  
In recent years, significant progress has been made in seizure prediction using machine learning methods. However, fully supervised learning methods often rely on a large amount of labeled data, which can be costly and time-consuming. Unsupervised learning overcomes these drawbacks but can suffer from issues such as unstable training and reduced prediction accuracy. In this paper, we propose a semi-supervised seizure prediction model called WGAN-GP-Bi-LSTM. Specifically, we utilize the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) as the feature learning model, using the Earth Mover's distance and gradient penalty to guide the unsupervised training process and train a high-order feature extractor. Meanwhile, we built a prediction model based on the Bidirectional Long Short-Term Memory Network (Bi-LSTM), which enhances seizure prediction performance by incorporating the high-order time-frequency features of the brain signals. An independent, publicly available dataset, CHB-MIT, was applied to train and validate the model's performance. The results showed that the model achieved an average AUC of 90.08%, an average sensitivity of 82.84%, and an average specificity of 85.97%. A comparison with previous research demonstrates that our proposed method outperforms traditional adversarial network models and optimizes unsupervised feature extraction for seizure prediction.  
EEG signals; seizure prediction; generative adversarial networks; long short-term memory network +
Footnote †: journal:  
_Applied_  
_sciences_
## 6 Simulation and Analysis of Protocol Based on BFGO-C in HWSN
### Comparison of the Data Transmission Volume  
The surviving nodes in the network transmit a data packet to the CH nodes every round, and then the CH nodes transmit it to the base station, and the base station counts the number of data packets collected. The data transmission volume represents the total number of packets sent, it reflects the throughput of the network. The data transmission volume of the four protocols is shown in Figure 16, and Figure 17 also shows the data transmission volume when the first node dies, half of the nodes die and the last node dies.  
The trend of the data transmission volume shows that the protocol based on BFGO-C has the fastest and highest transmission volume. With all the nodes dead, the number of packets stopped growing, and the SEP protocol, HCR protocol, ERP protocol, and protocol based on BFGO-C had 14,725, 35,699, 628,07, and 122,068 data transmissions, respectively, with a protocol based on BFGO-C eventually transmitting the most data. The protocol  
Figure 16: Variation of the volume of transmission with the number of rounds.  
Figure 17: The volume of transmission for key rounds.  
Figure 15: Remaining energy for key rounds.  
based on BFGO-C was consistently ahead of the other protocols regarding the number of packets transmitted in the early and middle stages of the network.  
The protocol based on BFGO-C has achieved good performance in HWSN network clustering. Compared with the SEP, HCR and ERC protocols, it extends network life to a certain extent, reduces network energy consumption, and effectively improves network performance.
## 3 Results and Discussion
### PCR-DGGE Analyses  
The DGGE profiles of the milk and cheese samples are shown in Figure 3.  
Overall, the analyses showed differences in terms of the number of bands or relative abundance and intensity between the two types of samples and within the same group. In particular, the DGGE profiles of the CTR and ECO milk samples (lines 1 and 3, respectively) consisted of a few bands located at different heights within the gel, showing substantial differences between the two samples. In particular, one to two marked bands were observed in each sample, while both profiles were characterized by a greater number of weak bands, occasionally found in the corresponding cheese samples. Similarly, the DGGE profiles of the cheese (lines 2 and 4) shared some bands, of varying intensity, one of which was also with the ECO milk sample, where it was more prominent. Furthermore, the ECO cheese showed several weaker bands, not revealed in the CTR sample, presumably attributable to other species involved in the cheesemaking. These results confirm the importance to coupled different techniques (culture-dependent and independent) to study in greater depth microbial populations and their dynamics during cheese production.  
Figure 3: PCR-DGGE profiles of CTR milk (line 1), CTR cheese (line 2), ECO milk (line 3), and ECO cheese (line 4). CTR: milk and cheese made by dairy cows fed concentrate without olive cake supplements. ECO: milk and cheese made by dairy cows fed concentrate with 7% enriched olive cake as 7% of a DM.
# Autonomous Mobile Station for Artificial Intelligence Monitoring of Mining Equipment and Risks
## 4 Results
### Stage 5: Identification and Tracking
In synthesis, the developed model achieved exceptional performance in the identification and classification of mining equipment, validating the hypotheses put forward and guaranteeing its applicability in real environments. This robust performance, measured through accuracy and loss optimization metrics, confirms the viability of the system for practical implementation in mining operations.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 6 Application in Engineering Design
### Pressure Vessel Design Problem
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Optimizer** & **Min** & **Mean** & **Max** & **Std** & **Time** & **Rank** \\ \hline JADESCA & 5885.333 & 6308.676 & 7319.001 & 596.4261 & 2.423681 & 1 \\ MFO & 5885.333 & 6022.59 & 6607.425 & 234.8754 & 6.142237 & 2 \\ POA & 5885.333 & 5915.393 & 6039.32 & 60.19246 & 4.271487 & 3 \\ MPA & 5885.336 & 5885.359 & 5885.423 & 0.025197 & 4.873054 & 4 \\ FDA & 5885.487 & 6279.071 & 7314.904 & 460.3439 & 5.573389 & 5 \\ AHA & 5886.149 & 5907.304 & 5975.677 & 36.16151 & 3.353356 & 6 \\ GWO & 5902.452 & 6025.497 & 6943.246 & 322.0955 & 2.344287 & 7 \\ GJO & 5910.013 & 6439.871 & 7309.613 & 578.9419 & 2.713069 & 8 \\ COA & 5927.161 & 6225.893 & 7009.73 & 312.9504 & 4.008542 & 9 \\ HLOA & 6010.778 & 6528.31 & 7138.38 & 372.3133 & 4.164848 & 10 \\ MVO & 6096.298 & 6686.698 & 7282.354 & 389.2835 & 3.787784 & 11 \\ MGO & 6111.014 & 6810.098 & 7319.001 & 462.936 & 13.56634 & 12 \\ SCA & 6193.138 & 7509.86 & 8750.035 & 761.746 & 3.153723 & 13 \\ AOA & 6708.505 & 13887.01 & 29845.51 & 6427.656 & 2.613052 & 14 \\ \hline \end{tabular}
\end{table} 
Table 8: Statistical results of the pressure vessel design problem. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Optimizer** & **Best Score** & **X1** & **X2** & **X3** & **X4** \\ \hline JADESCA & 5885.333 & 12.4507 & 6.154387 & 40.31962 & 200 \\ MFO & 5885.333 & 12.4507 & 6.154387 & 40.31962 & 200 \\ POA & 5885.333 & 12.4507 & 6.154387 & 40.31962 & 200 \\ MPA & 5885.336 & 12.45071 & 6.154399 & 40.31967 & 199.9993 \\ FDA & 5885.487 & 12.45214 & 6.155101 & 40.3243 & 199.9348 \\ AHA & 5886.149 & 12.45719 & 6.15776 & 40.34002 & 199.7166 \\ GWO & 5902.452 & 12.4873 & 6.237621 & 40.43423 & 198.4494 \\ GJO & 5910.013 & 12.49145 & 6.208815 & 40.40736 & 199.1607 \\ COA & 5927.161 & 12.50861 & 6.351544 & 40.47129 & 197.9056 \\ HLOA & 6010.778 & 13.19716 & 6.72448 & 42.73693 & 168.8822 \\ MVO & 6096.298 & 14.03449 & 6.909339 & 45.23691 & 141.3225 \\ MGO & 6111.014 & 14.28094 & 7.059075 & 46.24656 & 131.222 \\ SCA & 6193.138 & 12.97374 & 6.605777 & 42.00596 & 186.8397 \\ AOA & 6708.505 & 12.85614 & 9.456333 & 40.53201 & 200 \\ \hline \end{tabular}
\end{table} 
Table 9: Results of the pressure vessel design problem.
# Influence of Different Fibers on Performance of Bitumen Binders and Thin-Overlay Bitumen Mixtures
###### Abstract  
Thin-layer covers easily crack under traffic load, shortening their service life. Incorporating fiber materials into the mix can enhance crack resistance thanks to their abundance, affordability, and flexibility. However, different types of fibers have different performances in bitumen and mixtures due to different material properties. To explore this problem, basalt fiber, polypropylene fiber, and glass fiber were selected in this paper. The surface characteristics, internal structure, and adsorption capacity of oily substances were observed via scanning electron microscopy and oil absorption rate testing. The effects of fibers on the high-temperature and low-temperature properties of styrene-butadiene-styrene block copolymer-modified bitumen were investigated using the dynamic shear rheometer and the force ductility method. Ultimately, through indirect tensile testing and semi-circular bending tests, and the introduction of the toughness index and fracture toughness, a comprehensive evaluation was conducted on how varying fiber types and content affect the crack resistance and toughness of bitumen mixtures. The results show that the density and dispersion of the bundle fibers are the key to the oil absorption capacity under similar internal and external structural conditions. The oil absorption rate of polypropylene fiber is the best, reaching 5.423. Fiber incorporation can significantly improve the high-temperature rheological properties of bitumen. At 4% dosage, \(G^{*}\)/sin\(\delta\) increased by about 107.04% on average at 76 \({}^{\circ}\)C. At low temperatures, the increase in fiber content leads to a decrease in bitumen elasticity, and the influence of glass fiber is more obvious. The area of toughness did not reach 2000 N-mm at 4% dosage. After adding fibers, the toughness index and fracture toughness of the mixture increased by more than 2% and 35%, respectively. The maximum increases in fracture energy and crack initiation energy of the mixture are 14.29% and 47.29%, respectively. It shows that the fiber enhances the toughness, crack resistance, and crack propagation resistance of the mixture. The research results can provide some reference for the application of fiber-reinforced bitumen mixtures.  
bundled fiber; bitumen mixture; composite modification; crack resistance; toughness; fiber reinforcement; oil absorption properties; the fiber dispersibility
# Target-Aware Feature Bottleneck for Real-Time Visual Tracking
## 5 Conclusions  
In this paper, we proposed a novel visual tracking framework which involves the target-aware feature bottleneck module, where our proposed module can elicit target-specific information to perform group-wise feature modulation in an efficient manner. The lightweight feature maps produced by our proposed module can be fed to the lightweight prediction heads for faster real-time visual tracking, where the evaluation of the performance of the proposed tracker is performed on recent visual tracking datasets LaSOT and GOT-10k, which are large-scale benchmarks. The proposed tracking algorithm shows high performance measures on these two benchmark datasets where it can operate at a real-time speed. The ablation experiments also show that our method can improve the performance under various challenging circumstances of visual tracking, and our proposed target-aware, group-wise feature modulation scheme is a crucial factor for the performance improvements.  
**Funding:** This work was supported by the Republic of Korea Government (Ministry of Science and ICT (MSIT)) through the research fund of the National Research Foundation of Korea (NRF) under Grant NRF-2021R1C1C2095450.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets used in the study are available from the corresponding authors upon reasonable request.  
**Conflicts of Interest:** The author declares no conflict of interest  
Figure 7: Visualization of target-adaptive feature modulation on LaSOT test set. For the group-wise multiplier value plots of \(\alpha_{z}^{q}\) and \(\alpha_{x}^{q}\), \(x\)-axis indicates the channel dimension and \(y\)-axis indicates the multiplier values.
###### Abstract  
Previous research has explored associations between accelerometry and Global Navigation Satellite System (GNSS) derived loads. However, to our knowledge, no study has investigated the relationship between these measures and a known distance. Thus, the current study aimed to assess and compare the ability of four accelerometry based metrics and GNSS to predict known distance completed using different movement constraints. **Method:** A correlational design study was used to evaluate the association between the dependent and independent variables. A total of 30 physically active college students participated. Participants were asked to walk two different known distances (DIST) around a 2 m diameter circle (small circle) and a different distance around an 8 m diameter circle (large circle). Each distance completed around the small circle by one participant was completed around the large circle by a different participant. The same 30 distances were completed around each circle and ranged from 12.57 to 376.99 m. **Instrumentation:** Acceleration data was collected via a tri-axial accelerometer sampling at 100 Hz. Accelerometry derived measures included the sum of the absolute values of acceleration (SUM), the square root of the sum of squared accelerations (MAG), Player Load (PL), and Impulse Load (IL). Distance (GNSSD) was measured from positional data collected using a triple GNSS unit sampling at 10 Hz. **Results:** Separate simple linear regression models were created to assess the ability of each independent variable to predict DIST. The results indicate that all regression models performed well (R = 0.960-0.999, R\({}^{2}\) = 0.922-0.999; RMSE = 0.047-0.242, \(p<0.001\)), while GNSSD (small circle, R = 0.999, R\({}^{2}\) = 0.997, RMSE = 0.047 \(p<0.001\); large circle, R = 0.999, R\({}^{2}\) = 0.999, RMSE = 0.027, \(p<0.001\)) and the accelerometry derived metric MAG (small circle, R = 0.992, R\({}^{2}\) = 0.983, RMSE = 0.112, \(p<0.001\); large circle, R = 0.997, R\({}^{2}\) = 0.995, RMSE = 0.064, \(p<0.001\)) performed best among all models. **Conclusions:** This research illustrates that both GNSS and accelerometry may be used to indicate total distance completed while walking.  
**Keywords:** wearable technologies; accelerometers; GNSS; GPS; monitoring; training load; physical activity
## 5 Performance Study
### Underwater Acoustic Channel Model  
As the underwater environment is remarkably different from its terrestrial counterpart [4; 35], the existing wireless modules cannot be deployed without specific re-engineering. The internodal distance as well as the link orientation of the underwater network highly affects the underwater channel characteristics [35]. Thus the underwater channel is attributed as the combination of the worst aspect of terrestrial mobile for poor quality and satellite radio channel for high latency [8]. Therefore, it is crucial to accurately emulate underwater channel to warrant the outcome of a simulation process. A lot of research in this context [41; 42] have tried to include the impact of loss and delay of the underwater communication into their acoustic channel design.  
In order to accurately emulate the underwater communication environment for simulation, we have used an underwater acoustic channel model designed in [43]. In [43], the absorption at a given frequency is approximated based on the Thorp's approximation shown in [44]. To estimate the total attenuation and eventually obtain the SNR that is used by the NS2 simulator, the model in [43] also incorporates spreading loss and various underwater noises, such as turbulence, shipping, wind, and thermal noises.  
The authors of [43] also modeled another important underwater communication factor, the underwater acoustic propagation speed based on [45]. Therefore, in our simulation, the propagation speed \(s_{p}\) in meter per seconds is calculated according to the model shown in [43,45], which is as follows  
\[s_{p}=1449.05+45.7t-5.21t^{2}+0.23t^{3}+(1.333-0.126t+0.009t^{2})(S-35)+16.3z+0.1 8z^{2}\]  
where \(t\), \(z\), and \(S\) represents one tenth of the water temperature, the depth and the salinity of the water, respectively.  
For example, we obtain approximately 0.33 s as a propagation delay over a distance of 500 m at a depth of 2,000 m from the surface level.
## 3 Problem Description  
To perform vehicle flow detection for multiple lanes, a simple choice is the conventional method whose functional procedure is shown in Figure 4. In step 1, based on the average FFT magnitudes of environmental noises observed at the road site, an appropriate and fixed threshold is pre-selected for removing noises (e.g., motorcycles and bicycles) from the FFTs of received return signals from all lanes. Moreover, the threshold has to be far below the FFT main lobe of a vehicle signal within each lane. Because the peaks of the FFTs of clutters (e.g., buildings) are usually large and appear at fixed positions, it is ensured that the FFT main lobes of reflected vehicle signals and those of clutters are distinguishable. Such a threshold selection can reduce the chance of noise being mistaken as side lobes, which in turn may minimize the vehicle counting errors. In step 2, FFTs of vehicle signals from all lanes are edged by the threshold to yield resulting FFTs. The number of the resulting FFT lobes within each lane is taken as the instantaneous vehicle number within each lane.  
Accordingly, the conventional method utilizes no additional accessible information for refining edged FFTs to yield vehicle flow detection. In this way, the estimated traffic flow within a lane and the estimated directional traffic volume may be quite inaccurate. This mainly results from lane-crossing  
Figure 4: The functional procedure of the conventional approach.  
Figure 3: Frequency versus time characteristics for the FMCW.  
FFT side lobes from adjacent lanes. An example is shown in Figure 5 in which the edged FFT main lobe of a vehicle signal lies within Lane A while the edged FFT side lobe of the same vehicle signal lies within Lane B. The conventional scheme incorrectly assigns \"1\" as the instantaneous vehicle number within Lane B. Therefore, the foregoing unfavorable situation motivates us to develop smart methods for improving the accuracy of vehicle flow detection.
## 5 Numerical Results
### Analytical Model Validation
Typically, an SN detects a collision when a _CTS_ packet is not received after the transmission of the _RTS_ packet. Upon detecting a collision, an SN switches to the _sleep_ mode. Then, the time spent in the _sleep_ mode is larger for classes that experience traffic congestion than for classes that do not experience it. For the results presented in the following subsections, analytical results are represented by black lines with markers, while simulation results are represented by red markers. As it will be observed, the analytical and simulation markers completely overlap. This indicates a perfect match between the analytical and simulation results and proves that the results obtained by the approximate analytical model are very accurate.