esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Comparison of the Detection Performance on the Keimpung University (KMU) Dataset  
We compare the performance of AED-CNN with a set of five well-known methods, including histogram-of-oriented-gradients (HOG) + support vector machine (SVM) [44], Hotspot + SVM [23], OCS-LBP + CaRF [21], YOLOv2 [20], and YOLOv2 + ABMS [30].  
As shown in Figure 5, our AED-CNN clearly shows the best performance when compared to other methods and achieves the highest AP of 97.5%, which significantly exceeds the two recent optimal results, YOLOv2 + ABMS [30] by 17%, and OCS-LBP + CaRF [21] by 5.1%, respectively. HOG + SVM [44] shows the worst results because the HOG feature is not suitable to characterize pedestrians in IR images. Although the hotspot regions of pedestrians can be detected in IR images, Hotspot + SVM [23] is still ineffective when the temperature of pedestrians is similar to the background, for example during a summer night. With the same limitations as Hotspot + SVM, OCS-LBP + RF [21] and YOLOv2 [20] showed worse performance than ours. ABMS is proposed by YOLOv2 + ABMS [30] to pre-process the IR images for enhancing pedestrians during hot weather. However, the YOLOv2 network cannot detect small-sized pedestrians in low-resolution IR images. These comparative results prove that our AED-CNN shows superior robustness compared to other methods under various circumstances.  
Figure 5: Performance comparison of precision versus recall using the Keimpung University (KMU) dataset.  
Figure 6 presents some detection results on the KMU test set using our method. It is clear that our method successfully detects pedestrians with various scale instances during a hot summer night. From this observation, we can conclude that our method is effective at detecting pedestrians in IR images and is robust under different weather conditions.
## 4 Training Efficiency Evaluation and Statistical Analysis
### Evaluation Results  
Figure 16a is a box plot that represents the mean scores of the evaluation results. In group A, the minimum and maximum values were 25 and 80, respectively, and the mean score was 47.2. In group B, the minimum and maximum values were 40 and 90, respectively, and the mean score was 63.5. As a result, both the minimum and maximum values of group B were more than 10 points higher compared to those of group A. Moreover, the mean score increased by 16.2, showing a 34% improvement in training efficiency compared to group A. Figure 16b is an acquired score graph for each content feature. Regarding the reduction in human error, the total acquired score of group B was 17.8, which was 37% higher than that of group A (12.9). It seems that the training efficiency was improved by visualizing the knowledge of veteran operators as a comment/tag in AR content. In terms of improving training efficiency, group B had an efficiency score of 29.2, an increase of 44% compared to group A. This is because the trainee can check the product hierarchy using the transparency control technique of the product subcomponents. In the virtual experience based on the manual, group B received a score of 16.4, which increased by 15% compared to 14.2 in group A. This is because the trainees can experience the functional check procedures given in the maintenance manual in AR.
## 3 Construction of Coherent Integration Time Optimization Model
### The Relationship between Model Parameters and Coherent Integration Time  
As discussed in Section 3.1, the altimetric precision is determined mainly by two parameters, \(S_{h}\) and \(N_{\text{eff}}\). In this section, the variables of these two parameters are derived to be expressed in terms of coherent integration time. The re-derived parameters are brought into Equation (7) to construct the coherent integration time optimization model.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 4 Discussion  
Both the median filter and parameter estimation offer the advantage of simplicity in operation, often allowing for direct use without frequent parameter adjustments in practical applications. However, both methods have their respective limitations. Specifically, the median filter may cause signal distortion and is not applicable to formations parallel to the wellbore axis, while parameter estimation only suppresses the amplitude of direct waves to a certain extent. In contrast, the F-K filter, high-resolution Radon transform and shear wave transform excel in extracting reflected waves from reflectors close to the wellbore. However, they rely on expert experience to adjust method parameters during use, resulting in uncertain required time, and are similarly inapplicable to formations parallel to the wellbore axis. In field logging data, the types of reflectors are often unknown, and each method has its specific limitations. Therefore, when extracting reflected waves, experts often need to judge the possible types of reflectors in each wellbore section based  
Figure 13: Reflection waves extracted from field logging data by Wave-U-Net. The area containing the reflected waves is highlighted with a solid red box. The values displayed in the variable density map are clipped to a range of \(+/-70\).  
on their experience and select appropriate signal processing methods for extraction. This process is not only time consuming but also inefficient. MC-ConvTasNet overcomes the dependence on expert experience and achieves automatic extraction of reflected waves across the entire wellbore section. Although the performance of MC-ConvTasNet may decline when reflectors are very close to the wellbore, its applicable range still covers the typical distances involved in single-well imaging, ranging from a few meters to several tens of meters.  
Although our current research focuses on synthetic data, studies [42, 43] have shown that integrating synthetic and real data within datasets can significantly enhance model performance. Therefore, we plan to incorporate partially labeled actual field data into our future work to further improve the robustness of our models. It is recommended that readers first generate synthetic datasets using analytical algorithms to train the neural network. The trained model can then be effectively applied to extract clear reflection waves from field logging data. Subsequently, the reflection waves extracted from each well can be used as known labels to fine-tune the model with field logging data. Through fine-tuning, the model's generalization capability will be significantly enhanced, thereby further improving its performance in reflection wave extraction.  
This paper only focuses on the separation of flexural and reflected waves for dipole S-wave imaging. Further study is being conducted to apply this algorithm to the wave separation of monopole P-wave imaging.
## 3 Applications
### Geoacoustic Parameter Inversion
#### 3.4.2 Inversion of Geoacoustic Parameters by Genetic Algorithm
Figure 12: The 1D-PPD (one-dimensional posterior probability distribution) of each geoacoustic parameter (the red arrow shows the inversion result corresponding to the optimal value of the cost function).
## 3 Results and Discussion
### Impact of In Vitro Digestion on Fatty Acids Profile  
As can be seen in Figure 1, the fatty acid profile of fortified yogurts was significantly affected by gastrointestinal tract passage. In the COY samples (Figure 1A), small percentages (under 10%) of medium-chain fatty acids reached the intestine. In comparison, CBY samples showed higher recovery percentages, although no significant differences (\(p>0.05\)) were observed. In relation to the long-chain fatty acids (\(>\)C14), higher recovery percentages were verified in both coconut oil yogurts. Significant differences (\(p<0.05\)) were observed for myristic (C14) and oleic (C18:1 c9) acids in coconut yogurts. In these cases, CBY yogurts showed high recovery percentages (8 times higher than COY for myristic acid and 1.5 times for oleic acid). Indeed, the formulation with bigel continued to offer better gastrointestinal tract resistance in this study, and replacement of carboxymethylcellulose by alginate did not have a negative effect. More than 90% of the main coconut oil bioactive fatty acids were able to reach the intestine.  
Figure 1: Recovery percentages of fatty acids in fortified yogurts ((**A**) coconut oil yogurts, (**B**) avocado oil yogurts). Different letters mean significant differences between yogurts as determined by a one-way ANOVA test (\(p<0.05\)). CT—control yogurt, COY—coconut oil yogurt; CBY—coconut bigel yogurt; AOY—avocado oil yogurt; ABY—avocado bigel yogurt.  
Regarding AOY and ABY yogurts (Figure 1B), significant differences (\(p<0.05\)) were observed for myristic (C14), palmitic (C16), linoleic (C18:2), and \(\gamma\)-linolenic acid (C18:3 c6c9c12). In these cases, high recovery percentages were observed for ABY yogurts, except for myristic acid where AOY showed 1.5 times less degradation during the digestion. Although there were no significant differences (\(p>0.05\)), oleic acid the main bioactive fatty acid in avocado oil showed greater resistance to the gastrointestinal tract in ABY samples. The amount of this fatty acid was reduced by ca 53% in AOY and ca 37% in ABY yogurts. In summary, ABY showed less degradation during the digestion than the AOY yogurts. The recovery percentages of oleic acid obtained in this work were 2 times higher for AOY and 1.3 times higher for ABY when compared to the previous work [17].
## 2 Materials and Methods
### Model Evaluation and Validation  
To determine the accuracy and appropriate selection of the prediction method, we used two statistical measures, (1) mean absolute error (MAE) and (2) root mean square error (RMSE). A mean absolute error (MAE) measures the average magnitude of the errors in a set of predictions without considering their direction and the average of absolute differences between prediction and actual observation over the test samples with equal weight on all individual differences (Equation (7)) [19].  
\[MAE=\frac{1}{n}\sum_{j=1}^{n}\left|y_{j}-\hat{y}_{j}\right| \tag{7}\]where \(n\) denotes the number of samples. \(|\hat{y_{i}}-y_{i}|\) are the absolute residuals between the actual values and the predicted values, where \(y_{i}\) is the observed value for the observation and \(\hat{y_{i}}\) is the predicted value.  
This research also applied a root mean square error (RMSE) to measure the standard deviation of residuals. RMSE is the standard deviation of the prediction errors (residuals) and can measure residual dispersal. A basic formula of RMSE is shown below:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(\hat{y_{i}}-y_{i})^{2}}{n}} \tag{8}\]
# Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
###### Abstract  
Laser space networks are an important development direction for inter-satellite communication. Detecting the angle of arrival (AOA) of multiple satellites in a wide field of view (FOV) is the key to realize inter-satellite laser communication networking. The traditional AOA detection method based on the lens system has a limited FOV. In this paper, we demonstrate a system that uses a spatially distributed sensor array to detect the AOA in a wide FOV. The basic concept is to detect AOA using the signal strength of each sensor at different spatial angles. An AOA detection model was developed, and the relationship of key structural parameters of the spatially distributed sensor array on the FOV and angular resolution was analyzed. Furthermore, a spatially distributed sensor array prototype consisting of 5 InGaAs PIN photodiodes distributed on a 3D-printed structure with an inclination angle of 30\({}^{\circ}\) was developed. In order to improve the angle calculation accuracy, a multi-sensor data fusion algorithm is proposed. The experimental results show that the prototype's maximum FOV is 110\({}^{\circ}\). The root mean square error (RMSE) for azimuth is 0.6\({}^{\circ}\) within a 60\({}^{\circ}\) FOV, whereas the RMSE for elevation is 0.67\({}^{\circ}\). The RMSE increases to 1.1\({}^{\circ}\) for azimuth and 1.7\({}^{\circ}\) for elevation when the FOV expands to 110\({}^{\circ}\). The designed spatially distributed sensor array has the advantages of a wide FOV and low size, weight, and power (SWaP), presenting great potential for multi-satellite laser communication applications.  
Keywords:angle of arrival; spatially distributed sensor array; wide field of view; low-SWaP +
Footnote †: journal: Sensors
# TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data
## 4 Proposed Model  
### Problem Definition  
Anomaly detection of time series data is the process of identifying abnormal events or behaviors from normal time series. Time series data are divided into the training data \(x_{\text{train}}^{n\times m}\) and test data \(x_{\text{test}}^{k\times m}\), where \(n\) and \(k\) are the maximum length of the timestamp, and \(m\) is the feature dimension of each time series data. Our goal is to build an unsupervised model that can efficiently capture the non-linear pattern and multivariate distribution of multivariate time series, accurately discriminate the abnormal data in the test data \(x_{\text{test}}^{k\times m}\), and generate a probability vector \(y\subseteq R^{k}\), where \(y_{i}\in\{0,1\}\) indicates whether the \(i\)-th timestamp is abnormal or not. Our model is based on a generative adversarial network, which can conduct anomaly detection with its discriminator and generator, and Transformer [11], which uses a multi-head attention mechanism to capture feature correlation and time dependence in time series.
## 3 Proposed Method
### Feature Extraction Using Sinusoidal Transform Coding
For feature extraction, the LLDs were first calculated using a 30-ms analysis window, then their trajectories over the duration of 8 s were characterized by regression and statistical measures to obtain the spectral features as shown in Table 1. The features were extracted every 4 s (50% overlap). The overview of the proposed method is depicted in Figure 2, where \(X_{b}(f)\) represents Fourier transform coefficients corresponding to a sub-band among the six sub-bands. Figure 2: The proposed feature extraction method overview.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 3 Issues Faced by RPL during Mobility
### Issues with the Rank of Detached Mobile Nodes  
A mobile node that is disconnected from the graph can only change its current parent when it receives DIO that has a better rank than itself. A node can send DIS messages to receive new DIO messages, however, it stays disconnected if the rank in those DIO messages is worse than its own rank. This increases the time taken by the nodes to re-attach with the graph [41]. RPL does not specify how the inaccessible parent can be removed from the parent set. We note that RPL suggests some solutions for unreachability detection e.g., neighbor discovery, bidirectional forwarding detection, however, they are too complicated to use in practical environments.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Car-Following Behavior  
PRT is a critical factor influencing vehicle dynamics and traffic stability. Human drivers do not respond instantaneously to changes in their environment. The PRT encompasses the interval between a driver perceiving a stimulus (such as a vehicle slowing down) and initiating a corresponding response (such as braking). This delay can significantly impact vehicle-following behavior, especially in scenarios involving sudden decelerations or high-density traffic conditions.  
For CAV, PRT constitutes the interval between the detection of a change in the driving environment and the initiation of an appropriate response by the vehicle's control system. This temporal delay encompasses several critical components, including the time required for sensor data acquisition and processing, communication latency inherent in V2V interactions, decision-making processes within the vehicle's control algorithms, and the actuation latency associated with executing control commands such as braking or acceleration. Understanding and accurately modeling PRT is essential for predicting and enhancing the responsiveness and safety of CAVs in dynamic traffic conditions
## 3 Extraction of Echo Envelope Fluctuation Features  
### Simulation Study  
#### 3.1.1 Simulation Study of Benchmark  
Based on the analysis above, the envelope fluctuation intensity was studied through simulation, where the target studied is the benchmark model [22] scaled by a ratio of 1:20, the length is 3 m, and the scaled model is made of steel. The incident pulse is a linear frequency modulation (LFM) signal, the frequencies are 10-20 kHz and 20-40 kHz, and the pulse lengths are 1 ms and 3 ms.  
The method of echo simulation is called the frequency indirect echo simulation method. The acoustic scattering of the target can be considered a linear system or target scattering channel, the incident signal is the system input, and the echo is the system output that is transferred by the target system and underwater sound channel. In this study, the transfer function of the target was calculated using special software that is based on the planar element method (PEM)--a numerical model that converts an integral calculation to arithmetic calculation [23; 24]. The software can rapidly calculate sonar echo characteristics from sonar targets of various shapes [25].  
Figures 1-3 are the simulated echo envelope fluctuation features. Figures 1a and 2a are the echo envelope fluctuation intensities with different pulse lengths, and Figures 1b and 2b are the corresponding STDs of echo envelope fluctuation with different pulse lengths.  
The simulation results show that the fluctuation intensity and its standard deviation change dramatically with the azimuth, with the minimum intensity on the beam, and the maximum on the keel-line. The cause is that the time difference of the echo highlight in the keel-line is greater than that in the beam direction; correspondingly, the phase difference is larger than that causing the strong fluctuation of the echo envelope. However, in the beam direction, the phases of different echo highlights are nearly the same, and the envelope is flatter than that in other azimuths.  
Figure 3 compares the fluctuation intensity features of the echo envelope for different frequencies, 10-20 kHz and 20-40 kHz, with 1 ms pulse lengths. Figure 3a is the comparison of fluctuation intensity, and Figure 3b is the comparison of STDs of echo envelope fluctuation intensity, correspondingly. The simulated results show that the echo envelope fluctuation intensity greatly depends on the carrier frequency: when the carrier frequency increases, the echo envelope fluctuation intensity increases correspondingly, which is consistent with the theoretical analysis.  
Figure 1: **(a)** Intensity of echo envelope fluctuation after normalization, the frequency is 20–40 kHz and the pulse length is 1 ms; **(b)** STD of echo envelope fluctuation after normalization.  
Figure 2: **(a)** Intensity of echo envelope fluctuation after normalization, the frequency is 20–40 kHz and the pulse length is 3 ms; **(b)** STD of echo envelope fluctuation after normalization.
## 4 Conclusions  
The microstructure, crystal structure, and hydrogen sorption properties of Ti\({}_{1.56}\)V\({}_{0.18}\)(FeV)\({}_{0.18}\)Cr\({}_{1.08}\) + 4 wt % Zr\({}_{7}\)Ni\({}_{10}\) and Ti\({}_{1.26}\)V\({}_{0.315}\)(FeV)\({}_{0.315}\)Cr\({}_{1.11}\) + 4 wt % Zr\({}_{7}\)Ni\({}_{10}\) were investigated. Both of the alloys consisted of two phases. The main phase had a BCC structure and a composition that was close to the nominal one, but with minor inclusions of iron, zirconium, and nickel. For the Ti\({}_{1.56}\)V\({}_{0.18}\)(FeV)\({}_{0.18}\)Cr\({}_{1.08}\) + 4 wt % Zr\({}_{7}\)Ni\({}_{10}\), the secondary phase is depleted in vanadium. In the case of Ti\({}_{1.26}\)V\({}_{0.315}\)(FeV)\({}_{0.315}\)Cr\({}_{1.11}\) + 4 wt % Zr\({}_{7}\)Ni\({}_{10}\), the secondary phase is depleted in vanadium and chromium, while titanium is more abundant than in the main phase. For both alloys, the secondary phase has much higher proportion of iron, zirconium, and nickel than in the main phase.  
For both compositions, we evidenced that a replacement of 50 at% of vanadium by ferrovanadium did not significantly change the lattice parameter but it increased the incubation time of the first hydrogenation. However, the replacement of vanadium by ferrovanadium did not have a significant impact on the hydrogen capacities, heat of formation, and entropy. The main drawback of these FeV substituted alloys is an important loss of capacity after only one cycle. This behaviour should be studied more thoroughly.  
Conceptualization, J.-L.B. and J.H.; Formal Analysis, T.B.; Investigation, T.B and C.G.; Supervision J.-L.B. and J.H.  
This research was funded in part by NSERC grant.  
The authors declare no conflict of interest.
## 3 System Model  
In this section, we describe the dynamic path and charging system in detail. The notation table and nomenclature table in this paper are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Description** \\ \hline V & Road node set in graph \(G\). \\ V\({}_{cs}\) & Set of charging stations. \\ \(cs_{i}\) & Charging station at road node \(i\). \\ \(\Theta\) & Road section set in graph \(G\). \\ \(\Theta_{e}\) & ES set in graph \(G\). \\ \(t_{i}^{k}\) & Time for AEV \(k\) to reach node \(i\). \\ \(t_{i_{k}}^{k}\) & Time for AEV \(k\) to leave node \(i\). \\ \(t_{i_{k}}^{\infty,c}\) & AEV \(k\) charging time at \(cs_{i}\). \\ \(t_{cs_{i}}^{x,w}\) & AEV \(k\) queuing time at \(cs_{i}\). \\ \(t_{ij}^{k,r}\) & AEV \(k\) travel time on segment \((i,j)\). \\ \(E_{ij}^{k}\) & AEV \(k\) energy consumption on segment \((i,j)\). \\ \(C_{i}^{k}\) & AEV \(k\) SoC at road node \(i\). \\ \(\Omega\) & The SoC that AEV needs to charge. \\ \(\eta_{ij}\) & Traffic flow density on segment \((i,j)\). \\ \(L_{ij}\) & Length of road segment \((i,j)\). \\ \(Q_{ij}^{max}\) & Maximum number of AEVs on segment \((i,j)\). \\ \(Q_{ij}(t)\) & The number of AEVs on segment \((i,j)\) at \(t\). \\ \(LS_{kh}\) & The variation in the position between two vehicles \(k\) and \(h\). \\ \(D_{kh}\) & The distance difference between the vehicle \(k\) and \(h\) to the R. \\ \(S_{kh}\) & The total number of data packets sent from vehicle \(k\) to vehicle \(h\). \\ \(F_{kh}\) & The total number of data packets forwarded by vehicle \(k\) to vehicle \(h\). \\ \(M_{kh}\) & The total number of packets lost by vehicle \(k\) to vehicle \(h\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notations.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Description** \\ \hline AEV & autonomous electric vehicle \\ VANETs & vehicular ad hoc networks \\ RSU & roadside unit \\ V2V & vehicle-to-vehicle \\ V2R & vehicle to roadside unit \\ V2G & vehicle to gird (charging infrastructure) \\ CS & charging station \\ ES & energy segment \\ TFD & traffic flow density \\ SoC & state of charge \\ DRT-A* & dynamic real-time A* \\ DRT-CS & dynamic real-time charging selection \\ \hline \hline \end{tabular}
\end{table}
Table 3: Nomenclature.