esa-sceva/satcom-chunk-collection huggingface dataset:
# GenConViT: Deepfake Video Detection Using Generative Convolutional Vision Transformer
## 4 Evaluation
### Experimental Setup
#### 4.1.3 Video Preprocessing  
We preprocessed the frames of the videos in the datasets so that we could work with images that only contained information about faces and were correctly labeled. The preprocessing component in DL plays a critical role in preparing raw datasets for training, validation, and testing. The proposed model focuses on the face region, which is crucial in deepfake generation and synthesis mechanisms. We therefore preprocessed the videos using a series of image processing operations. These operations included the following steps:  
Figure 2: Generated images (\(I_{B}\)): (**a**) input samples, (**b**) reconstruction from network \(B\).  
Extracting the face region from each video using the OpenCV, face_recognition [82], and BlazeFace [83] face recognition deep learning libraries;
2. Resizing the input (facial) image to a 224 \(\times\) 224 RGB format, where the dimensions of the input image are \(H\times W\times C\), with \(H=224\) representing the height, \(W=224\) representing the width, and \(C=3\) representing the RGB channels;
3. Verifying the quality of extracted face-region images manually (only for the DFDC dataset).  
After the face regions were extracted, we manually reviewed DFDC image frames to fix two problems. As noted in [74], (1) DFDC deepfake videos may contain pristine frames and (2) face regions may not always be accurately detected by the face recognition frameworks used to extract them, leading to some frames in the training dataset containing no faces [60]. To address this issue, we manually reviewed the images and excluded images that did not contain a face or were deemed to be a real image within the fake class. This approach allowed us to curate a fake class dataset comprising only relevant and potentially manipulated face images. In the future, we could opt for using automated methods rather than achieving this manually. For example, to filter real from fake frames within a deepfake video, an initial model could be trained on a small, manually filtered subset of images and could then classify the remaining subset into real and deepfake images. This classification could significantly speed up the manual verification process.  
As a result of applying the preprocessing steps to the datasets, we collected a total of 1,004,810 images. To train, validate, and test our model, the images were divided into a ratio of approximately 80:15:5, resulting in 826,756 images for training, 130,948 images for validation, and 47,106 images for testing. Note that this preprocessed test set was only used for internal evaluation; the main experiments conducted in this paper were performed on videos (in contrast to individual frames). Namely, for our evaluation, we held back 3972 videos from the DFDC, DeepfakeTIMIT, Celeb-DF (v2), and FF++ datasets for testing. To create a single prediction per video, we extracted 15 frames from each video and averaged the resulting predictions.
# Predicting Missing Values in Survey Data Using Prompt Engineering for Addressing Item Non-Response
## 4 Experiments
### Ablation Study
#### 4.5.2 The Impact of n-Shot Respondents  
The number of n-shot respondents, which refers to the number of relevant examples provided to the model, plays a critical role in determining the overall performance of our method. To assess its impact, we conducted experiments by fixing top-k at 25--based on  
Figure 3: The impact of top-k responses and n-shot respondents on the model’s performance, as measured by F1-Score. (**a**) The average F1-Score trend across different top-k values (ranging from 5 to 100) shows that performance generally increases until top-k reaches 25, after which the scores plateau with minimal improvements. (**b**) The average F1-Score trend across different n-shot values (ranging from 1 to 4) indicates that the highest performance is achieved at 2-shot, with scores slightly declining as n-shot increases beyond this point.  
our previous findings that this value consistently yields strong performance across various questions--and varying n-shot from 1 to 4.  
We limited our exploration to a maximum of 4-shot respondents for several reasons. First, increasing the n-shot significantly adds to the computational burden, as each additional example increases the context length that the model must process. This, in turn, can lead to longer inference times and greater resource consumption, which may not be practical in large-scale survey analysis. Additionally, empirical observations from our experiments showed that beyond 4-shot, the marginal gains in F1-Score tend to diminish, with little to no improvement in prediction accuracy. Thus, a cap of 4 was chosen to balance performance with computational efficiency.  
Across most questions, we observed that setting n-shot to 2 typically resulted in the best performance. As shown in Figure 2(b), the average F1-Scores generally peaked at 2-shot before stabilizing or slightly declining with higher values. This finding suggests that, in many cases, providing two well-chosen examples strikes an optimal balance, offering the model enough context to effectively generalize without overwhelming it with excessive information. When combined with a robust column selection process, this 2-shot setting appears to enhance the model's ability to capture the nuances of the survey questions, leading to more accurate and reliable predictions.
# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 6 Conclusions  
In this paper, we have presented a traffic perception visibility estimation algorithm that combines laser transmission and image processing. Nighttime road visibility was estimated by current atmospheric transmissivity and images characteristics, which include road image transmittance, target image transmittance, target brightness, and contrast. Multivariate nonlinear regression analyses were performed on various visibility indicators; two regression models with ambient brightness and without ambient brightness were established. Different classifications and multiple data improved the accuracy of the estimation. This algorithm was suitable for road visibility estimation under different weather conditions, without camera calibration. Finally, the adjustment suggestions for nighttime road visibility were also given. Experiments show that our proposed method can accurately estimate nighttime road visibility. When applied to highways, the estimated visibility can be used to adjust the speed limit of expressways, thereby reducing the occurrence of traffic accidents.  
Conceptualization, H.Q. (Huibin Qin); methodology, H.Q. (Huibin Qin) and H.Q. ([PERSON]); software, H.Q. ([PERSON]); validation, H.Q. ([PERSON]); formal analysis, H.Q. (Huibin Qin) and H.Q. ([PERSON]); investigation, H.Q. (Huibin Qin) and H.Q. (Hongshuai Qin); resources, H.Q. (Huibin Qin); data curation, H.Q. ([PERSON]); writing--original draft preparation, H.Q. (Hongshuai Qin); writing--review and editing, H.Q. (Hongshuai Qin); supervision, H.Q. (Huibin Qin); project administration, H.Q. (Huibin Qin). All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **V1** & **Error** & **V2** & **Error** & **V** & **Error** & **Visibility Meter** \\ \hline Figure 8a & 9951 & 0.49\% & 9294 & 7.06\% & 9776 & 2.24\% & 10,000 \\ Figure 8b & 7906 & 1.26\% & 6188 & 22.72\% & 8046 & 0.49\% & 8007 \\ Figure 8c & 3691 & 28.76\% & 6803 & 31.3\% & 4966 & 4.15\% & 5181 \\ Figure 8d & 1250 & 44.44\% & 2315 & 2.9\% & 2230 & 0.89\% & 2250 \\ Figure 8e & 924 & 7.32\% & 1585 & 84.08\% & 970 & 12.64\% & 861 \\ Figure 8f & 368 & 107.91\% & 1845 & 942.12\% & 124 & 29.78\% & 177 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimated results of the images in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **V1** & **V2** & **V** \\ \hline MAE & 482 & 446 & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average absolute error of different visibility estimation methods (MAE).  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Experimental Evaluation and Results
### Executor Scheduling Algorithm Performance Analysis
The computational burden is strategically distributed across operational phases, with initialization processes such as credibility index establishment performed during system design and testing phases, while runtime parameter adjustments are only triggered by specific events such as vulnerability discoveries or maintenance cycles. Furthermore, leveraging the cloud-edge collaborative paradigm, computationally intensive operations can be offloaded to cloud platforms with batch parameter updates distributed to edge nodes, thereby minimizing real-time computational overhead while maintaining security benefits.
# An Efficient Method to Learn Overcomplete Multi-Scale Dictionaries of ECG Signals
## 3 Multi-Scale Dictionary Derivation  
In this section, we describe the novel approach for off-line construction of a single overcomplete and multi-scale dictionary using QRS complexes extracted from multiple ECGs recorded from healthy patients. The database used to construct the dictionary is described first in Section 3.1, and the method is described next: the pre-processing stage in Section 3.2 and the dictionary creation stage in Section 3.3. Finally, the obtained dictionary was stored and applied to attain a sparse reconstruction of the desired ECGs (which may be in the database or not) using the LASSO approach, as described in Section 4.
## 5 Performance Evaluation
### Simulation Results  
#### 5.2.1 Changes in Network Density vs. Energy Consumption  
In the first simulation, we varied the number of nodes from 100 to 1000 and fixed the size of the sensor field. As the sensor field size remained unchanged, increasing the number of nodes was analogous to increasing the network density. We deployed nodes using a non-uniform distribution system. Hence, node density was not the same in different areas of the network. Here, we used a duty cycle of 0.5, meaning the nodes would maintain their radios active for half of the timeframe and sleep for the remaining half. The energy levels shown in the graphs were obtained based on the node energy consumption for the specific runtime of the simulation. Owing to a large number of nodes, we ran each simulation for approximately 10 to 30 rounds at a time and obtained their average. The average energy consumption per node was determined by varying the number of nodes using four OR protocols for comparison with low and high-traffic events. As shown in Figure 5a and Figure 5b, low traffic represents one packet/10 s, and high traffic represents five packets/10 s. Based on these figures, the ORW and ORR protocols showed a considerable increase in energy consumption as the non-uniform node density increased. This is because an increase in the density resulted in an increased number of redundant transmissions.  
The proposed protocol, which used the FCM clustering mechanism, outperformed existing protocols. As the network density increased, the energy consumption remained stable and did not exhibit any noticeable changes. This is because as the non-uniform density increased, the protocol used the FCM clustering method to drive the packet toward a lower-density area such that fewer nodes could listen to the packet and retransmit it. Accordingly, the average energy consumption decreased, and the load could be balanced at different densities. In low-traffic and high-traffic environments, FCM-OR could reduce the average energy consumption per node by approximately 12% and 26% compared with the ORW and ORR protocols, respectively.  
Moreover, according to our logic, to avoid collisions, the node will back-off and sleep whenever it senses that the channel is busy before transmitting any new packet. Hence, in the case of high traffic, most nodes detect a busy channel and sleep for random back-off before waking up and performing carrier sensing again. This back-off time increases exponentially with the repeated detection of a busy channel. Therefore, during high traffic, the energy consumption is comparatively lower than that of other protocols.  
The effects of the duty cycle under the same simulation setup, where the average energy consumption per node was calculated for a single-packet transmission to observe the fundamental performance of each protocol are shown in Figure 5(a),b. The performance of the protocols when the duty cycle was 0.6 and 0.8 are shown in Figure 5(a) and Figure 5(b), respectively. As shown in the figures, the proposed protocol outperformed the existing protocols, even at a higher duty cycle.
## 3 Results and Discussion  
### Morphology of CuSNP  
Due to the low stability of CuSNPs in aqueous media, corn starch was used as a stabilizing agent to prevent the aggregation of these particles and increase their dispersibility in aqueous solutions. The morphology of the CuSNP was observed using FETEM, as shown in Figure 1, and the particle size was analyzed using ImageJ. CuSNP nanoparticles hadan irregular shape and size ranging from 2 to 10 nm with an average of 6.2 \(\pm\) 1.7 nm, consistent with the previously described data of guar gum stabilized CuSNP [34].
## 5 Performance Analysis
### Secrecy Rate  
#### 5.2.1 Single-Carrier DM Scheme  
Using Equation (17), the signal-to-interference-plus-noise ratio (SINR) of the LU for the \(l\)th transmit antenna array can be obtained by  
\[\gamma_{l}^{sc,LU}=\frac{P_{s}\beta_{1}^{2}(1-\xi)^{2}}{\sigma^{2}}=r^{sc,LU}. \tag{37}\]  
Since the normalized narrow-band AWGN channel is assumed, by means of Equation (37), the achievable rate of the link from the \(l\)th transmit antenna array to the LU can be expressed as  
\[R_{l}^{sc,LU}=\log_{2}(1+\gamma_{l}^{sc,LU}). \tag{38}\]  
In light of Equation (19), the SINR of the Eve for the \(l\)th transmit antenna array can be calculated by Equation (39).  
\[\gamma_{l}^{sc,E}=\frac{P_{s}\beta_{1}^{2}|\mathbf{h}^{H}(\theta_{el}) \mathbf{h}(\theta_{dl})|^{2}}{P_{s}\beta_{1}^{2}\sum\limits_{k=1,k\
eq l}^{K}| \mathbf{h}^{H}(\theta_{el})\mathbf{h}(\theta_{dk})|^{2}+P_{s}\beta_{2}^{2} \sum\limits_{k=1}^{K}|\mathbf{h}^{H}(\theta_{el})\mathbf{T}_{k}\mathbf{z}_{k} |^{2}+\sigma^{2}}. \tag{39}\]  
According to Equation (39), the achievable rate of the link between the \(l\)th transmit antenna array and the Eve can be given by  
\[R_{l}^{sc,E}=\log_{2}(1+\gamma_{l}^{sc,E}). \tag{40}\]Therefore, the secrecy rate of the proposed single-carrier DM scheme can be defined as [24]  
\[R^{sc}=\sum\limits_{l=1}^{K}{[R^{sc,LU}_{l}-R^{sc,E}_{l}]}^{\dagger}. \tag{41}\]
# Acoustic TDOA Measurement and Accurate Indoor Positioning for Smartphone
###### Abstract
At the system level, our system has the following advantages: it is able to achieve decimeter positioning accuracy and it is easy deployment. Moreover, it is suitable for smartphone applications. At the methodological level, we have made the following improvements: (1) in TDOA estimation, we adopt a robust method to extract the first path, which is based on the idea of multi-threshold grouping. Compared with the cross-correlation method [41], our method significantly improves the accuracy of TDOA estimation. (2) In the static positioning experiment, we find that the maximum likelihood (ML) positioning method is more reliable for acoustic positioning systems. Thus, we combine the ML algorithm with the proposed TDOA measurements to obtain the location of the smartphone. The paper is organized as follows: The second section introduces the acoustic positioning system. In the third section, the design of the acoustic signal, the TDOA detection method of the signal, and the positioning simulation of the actual scene are introduced. According to the actual simulation results, the ML method is determined as the positioning algorithm in this paper. The fourth section summarizes the experiment and results. The fifth section summarizes this article.
## 4 Simulation Results and Discussion
### Lateral Displacements of the Surrounding Rocks
Additionally, the lateral deformations of the surrounding rocks when steel bracings were set to 8 m were relatively smaller than those when the steel bracings set at 9 m.  
At excavation depths of 24 m and 31 m  
The lateral displacements of the surrounding rocks when the excavation of the foundation pit reached 24 m and 31 m beneath ground are illustrated in Figure 8. When the excavation depths were 24 m and 31 m, the lateral displacements of the surrounding rocks under supporting schemes A, B, and C still presented a P-shape, while those under supporting scheme D presented a B-shape, indicating that setting two-layer prestressed anchorages to 8 m and 22.5 m underground was effective in restraining lateral displacements due to improvements in the integrity of the surrounding rocks. Figure 7: Lateral displacements of the surrounding rocks at an excavation depth of 16 m.  
Meanwhile, the lateral displacements of the surrounding rocks under the supporting conditions of scheme A were less than those under the supporting conditions of scheme B, and those under supporting conditions of scheme C were the largest, demonstrating that when the excavation depth was greater than 16 m, the transverse support arrangements of scheme C were less effective in terms of restraining the lateral displacements of the surrounding rocks compared to those of scheme D. Furthermore, when the excavation depths were 24 m and 31 m, the maximum lateral displacements of the surrounding rocks were 7 mm and 8.1 mm, respectively, which are significantly lower than those for excavating deep foundation pits in soft soils or rocks reported in [22], with maximum lateral deformation being more than 60 mm when adopting FE numerical calculation model; in [23], with maximum lateral displacements being more than 50 mm when adopting PLAXIS v.9 software; in [24], with maximum horizontal displacement being 34.5 mm at a depth of 5 m when adopting Midas GTS NX 2022 R1; and in [25], with the maximum horizontal displacement observed being about 35 mm when excavation was completed.
## 5 Research Methodology and Data Analysis
### The Methodological Justification and Model Selection Criteria
* Visualize trends using time series plots in Python. The study's quantitative element is based on using operational data from the airline's reliability records. Information on AOG occurrences, use of spare parts, and maintenance schedules were obtained over one year. This information includes the AOG events, nature and frequency of the spare parts used, average time lost per instance, and total cost per event. This approach incorporated records from inventory control systems and longitudinal computer records of spare part movements to assess the spare part distribution and locate vulnerabilities in the demand forecasting methods [37]. Quantitative methods, including regression analysis, correlation studies, and time series, were used to articulate sound empirical analysis concerning the collected data. These methods facilitated the identification of patterns and trends of AOG occurrences, which were subsequently used as the basis of predictive maintenance modeling. The objective was to minimize the amount of time equipment was down and, therefore, increase overall organizational efficiency [38]. AOG rate measurement at research base airlines was performed during the past twelve months by collecting quantitative data into key performance indicators that will be used for the assessment of spare parts distribution and maintenance rate. The primary metrics analysis included the following:  
* Number of AOG events: This was the number of AOG that occurred each month, and, thus, it gave an idea of the regularity of aircraft grounding due to technical malfunctions or lack of spares. * Average downtime per AOG incident: This metric was calculated by assessing the number of hours it took for each AOG event to be addressed from the time the aircraft was delayed until it was again ready for use. It helped to identify the effectiveness of the current maintenance and supply chain of spare parts operations. * Time to deliver spare parts: The measure captured the time from the beginning of a request for a part to the delivery of such part and its installation, helping decide on areas of the spare parts chain that needed improvement. * Spare parts stockout rate: To identify the need for further improvement in AOG forecasting and inventory, the number of times a year that specific parts required were not available in inventory due to an AOG was calculated. * AOG incident cost: To further illustrate the precise AOG cost, several costs, such as money for the replacement part, labor time, and losses of otherwise valuable production time, were determined for each AOG event. Through the implementation of these metrics, researchers were able to generate numerical evidence on which they designed means of correcting AOG-affiliated ineffectiveness.
## 2 Bilinear and Weibull/IPL General Background  
In fatigue analysis, it is fundamental to understand the models for evaluating the service life of materials. This section provides the fundamentals of the fatigue models, focusing on the S-N curve and the Basquin equation. We also discuss the bilinear model for fatigue characterization and present the Weibull distribution and the Weibull/IPL model concepts, which are essential in the P-S-N analysis.
# A Two-Stage Multi-Objective Optimization Algorithm for Solving Large-Scale Optimization Problems
## 3 Proposed Algorithm  
In this section, we proposed a two-stage large-scale optimization algorithm based on decision variable clustering to solve large-scale multi-objective optimization problems, named LSMOEA-VT. The main framework of LSMOEA-VT was shown in Algorithm 1.  
```
Input: N (population size), nSel (number of selected solutions for decision variable clustering), nPer (number of perturbations on each solution for decision variable clustering), nCor (number of selected solutions for decision variable interaction analysis) a set of uniformly distributed reference lines \(\mathbf{r}=\{r_{1},r_{2},\ldots,r_{n}\}\). Output: P (final population)
1. \(\mathrm{P}\leftarrow\mathrm{Initialize(N)}\);
2. [DV, CV] \(\leftarrow\) VariableClustering (P, nSel, nPer);
3. \(\mathrm{subCVs}\leftarrow\)InteractionAnalysis (P, CV, nCor);
4. **while** termination criterion not fulfilled do
5. \(\mathrm{P}^{\prime}\leftarrow\) ConvergenceOptimization(P, \(\mathrm{subCVs}\));
6. \(\mathrm{Q}\leftarrow\) Find some Pareto-optimal solutions of \(f(x)\)
7. \(\Omega\leftarrow\) Learn the subspace based on \(\mathrm{Q}\);
8. Update the given reference lines \(r=\{r_{1},r_{2},\ldots,r_{n}\}\);
9. \(\mathrm{P}\leftarrow\) Diversity maintaining with \(r\) in \(\Omega\);
10. Return P.
```  
**Algorithm 1** Main Framework of LSMOEA-VT  
In the first stage of the algorithm, we use clustering methods to divide all decision variables into two categories, one related to convergence and the other related to diversity. Therefore, we can apply specific treatments to each group. Due to the fact that the number of decision variables after clustering may still be relatively large, we can conduct interdependence analysis on these variables to test the interdependence relationships between them. In the second stage, we can search for a set of Pareto optimal solutions for a given large-scale problem, then learn POS from the found Pareto optimal solutions, and finally obtain a final solution with convergence and diversity by updating the given reference line. Several crucial components of LSMOEA-VT are further illustrated in the coming parts.