esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 4 Performance Evaluation
### Model Performance
#### 4.2.1 Hazard-Type Prediction Performance
By visually inspecting the images and further reflecting on the strategy of using building-focused bounding boxes when annotating the data, the authors speculate that for a tsunami image, by the mandatory attention of the visual cues on building objects using bounding boxes, this treatment may tend to miss other important visual cues, particularly water. In other words, by confining the visual cues within the bounding boxes for only the buildings, more subjective uncertainties are introduced to discriminate tsunami scenes against the other two. As observed previously, the best model for hazard-type prediction is \(M_{HT}(FRC,RN)\). To better assess its capacity and robustness, the PRC and ROC curves are illustrated in Figure 8. In the PRC plot, the iso-contours of the \(F_{1}\) values with various classification thresholds are illustrated, which lead to the \(F_{1}\) contours of 0.2, 0.4, 0.6, and 0.8. The baseline prediction line is marked in the ROC lines, indicating that any ROC above this diagonal line implies a useful classification model. From PRC and ROC plots, which are overall monotonic and symmetrically concave, it is evident that the model consistently has a strong capacity and robustness at most select thresholds. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Actual**Prediction**} & \multicolumn{3}{c}{\(M_{HT}(FRC,ZF)\)} & \multicolumn{3}{c}{\(M_{HT}(FRC,RN)\)} & \multicolumn{3}{c}{\(M_{HT}(SSD,RN)\)} \\ \cline{2-13}  & **Tornado** & **Tsunami** & **Earthquake** & **Total** & **Tornado** & **Tsunami** & **Earthquake** & **Total** & **Tornado** & **Tsunami** & **Earthquake** & **Total** \\ \hline Tomandi & 72 & 2 & 1 & 75 & 73 & 0 & 2 & 75 & 33 & 39 & 75 \\ Tsumami & 4 & 48 & 16 & 68 & 0 & 59 & 9 & 68 & 16 & 19 & 33 & 68 \\ Earthquake & 9 & 16 & 86 & 111 & 2 & 12 & 97 & 111 & 17 & 22 & 72 & 111 \\ \hline Total & 85 & 66 & 103 & 254 & 75 & 71 & 108 & 254 & 66 & 44 & 144 & 254 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix of hazard-type prediction. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(M_{HT}(FRC,ZF)\)} & \multicolumn{3}{c}{\(M_{HT}(FRC,RN)\)} & \multicolumn{3}{c}{\(M_{HT}(SSD,RN)\)} \\ \cline{2-13}  & \(AUC\) & \(F_{1}\) Score & \(AA\) & \(AUC\) & \(F_{1}\) Score & \(AA\) & \(AUC\) & \(F_{1}\) Score & \(AA\) \\ \hline Tornado & 0.98 & 0.90 & 0.96 & 0.99 & 0.97 & 0.97 & 0.64 & 0.47 & 0.44 \\ Tsunami & 0.83 & 0.72 & 0.705 & 0.92 & 0.85 & 0.87 & 0.57 & 0.34 & 0.28 \\ Earthquake & 0.86 & 0.80 & 0.774 & 0.91 & 0.88 & 0.88 & 0.55 & 0.57 & 0.65 \\ \hline OA & & 0.81 & & 0.90 & & & 0.49 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of hazard-type prediction.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 3 Methodology and Experimental Procedure
### Step 2: Radio Control Car  
The RC car, shown in Figure 1, is a testbed for research and development within the areas of autonomous driving and accurate positioning. This is able to achieve high position accuracy and low latency using low-cost hardware by fusing data from multiple sensors (accelerometer, gyroscope, odometry, and Real-Time Kinematic Satellite Navigation (RTK-SN)) to take advantages of their individual strengths [19]. This testbed is scalable to a wide range of model vehicles. The custom hardware and all involved software are open source for easy development and extension.  
The RC car has been the testing platform used for the real tests of the outdoor positioning system proposed in this work. This platform has been extended to embed the HW and SW related to our UWB data fusion solution. Furthermore, the RTK-SN system of this platform has been used as ground truth to compare the accuracy obtained.
## 3 DNN-Based Demodulation Evaluation
### Three Level Supervised Model Analysis  
From the results presented in Table 5, it is clear that the VGG16 model performed best with accuracy well above 95% for both cropped and full-sized frames and for both Softmax and Sigmoid activations in the last layer of the VGG16 model. As for the second-best model, it is the three layer convolution model with an accuracy well above 90% and below 95%. As a whole, full-size frames performed better than cropped frames, especially with Softmax as the activation function in the last layer. In comparison to 2 Level full-sized frames, the results for the 3 Level full-color frame model evaluations were much less accurate than the 2 level data. This was actually something that was expected because as quantization of the system increases, the risk of mistakes in interpreting the received signal also increases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Softmax Full** & **Softmax** & **Sigmoid Full** & **Sigmoid** \\  & **Image** & **Cropped Image** & **Image** & **Cropped Image** \\ \hline VGG16 & 98.89\% & 98.69\% & 97.99\% & 96.60\% \\ Resnet50 & 95.60\% & 68.20\% & 81.00\% & 57.00\% \\ Inception V3 & 76.80\% & 51.23\% & 63.30\% & 39.34\% \\
3 Layer & 97.56\% & 93.24\% & 94.20\% & 99.00\% \\ Convolution & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: 2 Level Full Color Model Evaluations.  
However, 3 level data seem promising given a more refined model with, perhaps, a large training session and larger training data size.
## 5 Conclusions and Future Work  
In this study, we have explored the requirements, models, input-output formats, and evaluation metrics relevant to generative AI systems. By addressing the research questions, we have provided valuable insights and a comprehensive understanding of these aspects. We identified three distinct categories of requirements--hardware, software, and user experience--for implementing generative AI systems. This knowledge is crucial for researchers, developers, and practitioners in effectively planning and designing such systems. Additionally, we presented a taxonomy of generative AI models based on their architecture, including popular models such as VAEs, GANs, diffusion models, transformers, language models, normalizing flow models, and hybrid models. This taxonomy serves as a guide for selecting appropriate models based on specific application requirements, fostering advancements in the field. Furthermore, we classified the input and output formats used in generative AI systems, providing a comprehensive and organized overview. This classification, along with the associated tasks and models, offers researchers and practitioners a valuable resource for developing customized generative AI systems for various applications. Moreover, we proposed a classification system based on output types and discussed commonly used evaluation metrics. This contribution enables the establishment of robust evaluation frameworks for generative AI models, enhancing their credibility and facilitating comparative analyses.  
Moving forward, there are several avenues for future research in the field of generative AI. Firstly, it would be valuable to investigate further the interplay between the identified requirements and the performance of generative AI systems. Understanding how different hardware, software, and user experience factors impact system outcomes can lead to more optimized and efficient implementations. Additionally, as the field continues to evolve, it is important to continually update and expand the taxonomy of generative AI models. New model architectures and variations emerge regularly, and incorporating them into the taxonomy would provide a comprehensive and up-to-date overview. Furthermore, exploring additional input and output formats for generative AI tasks can contribute to a more comprehensive understanding of system capabilities and limitations. Investigating novel formats and their suitability for specific tasks can open up new possibilities for generative AI applications. Lastly, expanding the evaluation metrics and developing standardized benchmarks can advance the field by providing more rigorous and consistent means of assessing the quality and performance of generative AI models. This would facilitate fair comparisons between different approaches and promote further advancements in the field.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** See Appendixes A and B.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Cough Data Processing
#### 2.2.2 Audio Feature Extraction  
Two multidimensional audio features, Mel-Frequency Cepstral Coefficients (MFCCs), and Constant Q Cepstral Coefficients (CQCCs), both representing perceptual cues of the audio spectrum, were extracted from the cough sounds. The choice of MFCCs was motivated by its ubiquity in the audio processing arena. MFCCs are the most commonly adopted acoustic features in automatic speech applications including speaker and language identification [24; 25]. CQCCs are features initially developed to detect spoofing in automatic speaker verification systems. They have been reported to outperform other features when detecting original audio from spoofed audio in automatic speaker verification systems [26; 27]. Since the children from the normal-voluntary cohort were instructed to cough voluntarily, and thus mimic a sick person, we therefore hypothesized that CQCCs might be able successfully detect such mimicking.
## 1 Introduction
Prior studies have demonstrated the feasibility of predicting weld bead shape and strength using ANN models based on welding current and voltage signals [27; 28]. However, these studies are well-suited for predicting the mechanical properties of weld joints using non-destructive testing data in single-pass welding, but they face challenges when applied to root-pass welding in multi-pass welding. Additionally, in components such as butt joints of small-diameter pipes, it is often difficult to verify the penetration condition, and many components exist where the penetration cannot be confirmed. Thus, there is a pressing need for methodologies to predict the penetration condition in such a structure. In this study, we aimed to propose a method for predicting the back-bead in actual component welding, particularly for parts where visual testing of the penetration is challenging. We developed a model capable of predicting penetration conditions with greater accuracy using data obtained from the welding current and voltage during welding. A V-groove joint specimen with a material thickness of 25 mm was prepared, and GMA welding was performed by varying the wire feed rate (WFR) in direct current (DC) pulse waveform within a gap range of 1.0 to 2.0 mm. The measured current and voltage were transformed into short-time Fourier transform (STFT) of dynamic resistances. During arc welding, the welding power source controls the current of the DC pulse, and the voltage reflects the arc length and wire extension length. The dynamic resistance, derived from current and voltage, was selected as an input variable because it is considered capable of reflecting the welding power source's control characteristics and the arc's physical phenomena during welding, such as spatter and arc blow. The transformed dynamic resistance STFT information was used as input variables, and a CNN model was developed to predict the penetration conditions. The penetration conditions were classified as partial joint penetration (PJP), complete joint penetration (CJP), and burn-through. The size of the dynamic resistance STFT was adjusted, and preprocessing steps, such as thresholding, were applied to generate the input variables for the CNN model. The prediction model adopted a CNN architecture consisting of three convolutional layers and two pooling layers, achieving a 97.8% accuracy when predicting the penetration conditions.
# Wind Tunnel Measurements of Surface Shear Stress on an Isolated Dune Downwind a Bridge
###### Abstract  
As part of a comprehensive environmental assessment of the Dun-Gel railway project located in Dunhuang city, Gansu Province, China, a wind tunnel experiment was proposed to predict surface shear stress changes on a sand dune when a bridge was built upstream it. The results show that the length of the wall shear stress shelter region of a bridge is about 10 times of the bridge height (H). In the cases that the interval of the bridge and sand dune (S) is less than 5 H, normalized wall shear stress on the windward crest is decreased from 1.75 (isolated dune) to 1.0 (S = 5.0 H, measured downwind bridge pier) and 1.5 (S = 5.0 H, measured in the middle line of two adjacent bridge piers). In addition, the mean surface shear stress in the downstream zone of the sand dune model is reduced by the bridge pier and is increased by the bridge desk. As for the fluctuation of surface shear stress (\(\zeta\)) on the windward crest, \(\zeta\) decreases from 1.3 (in the isolated dune case) to 1.2 (in the case S = 5.0 H, measured just downwind the pier) and increases from 1.3 (in the isolated dune case) to 1.6 (in the cases S = 5.0 H, in the middle of two adjacent piers). Taking the mean and fluctuation of surface shear stress into consideration together, we introduce a parameter \(\psi\) ranging from 0 to 1. A low value indicates deposition and a high value indicates erosion. On the windward slope, the value of \(\psi\) increases with height (from 0 at toe to 0.98 at crest). However, in the cases of S = 1.5 H, \(\psi\) is decreased by the bridge in the lower part of the sand dune at y = 0 and is increased at y = L/2 compared with the isolated dune case. In other cases, the change of \(\psi\) on the windward slope is not as prominent as in the case of S = 1.5 H. Downstream the sand dune, erosion starts in a point that exists between x = 10 H and 15 H in all cases.  
wind tunnel; surface shear stress; isolated dune; bridge +
Footnote †: journal: Applied Physics Letters
## 1 Introduction  
### Motivation  
In recent years middleware systems have been used in a wide variety of applications Essential applications of middleware systems have been found in real-time systems. One of these systems is the multi unmanned aerial vehicle system which has more advantages than a single UAV system due to its ability to perform complex tasks more effectively than a single UAV. Several development approaches have been cultivated based on the appropriate application to manage a group of agent systems. Multi-agent systems can work cooperatively in many of these applications to integrate specific activities or actions. Some researchers have utilized inflexible or unreliable middleware to share data between multiple vehicles, such as the Automotive Open Systems Architecture (AUTOSAR) and the Open Services Gateway Initiative (OSGi) that do not accept Quality of Service (QoS). In most of these researches the middleware has not been used with the formation or containment control of multiple vehicles. Other authors have utilized classical containment control for multi-agent systems without considering the issue of uncertainty in the parameters or they have considered a limit on the number of the agents, for example, by using classical state feedback, an output feedback controller, and a classical feedback linearization controller. In a similar way formation maintenance has been addressed by using a potential field approach and feedback linearization controller (see [1, 2]). Some advanced controllers have been applied to a single agent like the \(\mathcal{L}_{1}\) adaptive control for underwater vehicle-manipulator systems (UVMs) (see [3]), while, in [4, 5, 6] an integral SMC and adaptive SMC have been proposed and implemented to the quadrotor trajectory tracking control. This work can also be linked to the data-driven algorithms in [7]. Therefore, using the Data Distribution Service (DDS) middleware system has many advantages like increased flexibility, reliability, and portability.
## 3 Experiments and Results
### Signal Enhancement and Reconstruction  
Signals from knocking, walking, and manual mining collected in the practical environment are nonlinear and nonstationary. CEEMDAN-KL overcomes the problem of signal aliasing in decomposition and reduces the number of iterations compared with ensemble empirical mode decomposition (EEMD). Furthermore, CEEMDAN-KL introduces white noise and KL divergence in the calculation process to optimize the signal reconstruction accuracy, which can meet the requirements of noise reduction and feature enhancement in a strong noise environment.  
After the standardization, CEEMDAN-KL was used to enhance the features of the three types of typical signals. The comparison results are shown in Figure 9. Among them, the abscissa represents time, where the unit is ms. The ordinate represents intensity, and the unit is mV.  
Figure 8: Schematic diagram of the \(\Phi\)-OTDR system. CW: Continuous Wave, OC: Optical Coupler, AOM: Acoustic–Optical Modulator, EDFA: Erbium-Doped Optical Fiber Amplifier, Cir: Circulator, PD: Photodetector, DAQ: Data Acquisition Card, PC: Personal Computer, and FUT: Fiber Under Test.  
Figure 9: _Cont._  
From the comparison of the three types of typical signals before and after CEEMDAN-KL processing, the characteristics of the mutant type of the signal are more clear. In Figure 9, a,c,e on the left are the original signals of standard knocking, walking, and manual mining, respectively. It can be found that the original signals had a low SNR, and the high-frequency waveform characteristics of the target signal were not obvious. After the CEEMDAN-KL processing, the enhanced signals were obtained and are shown in Figure 9b,d,f. In the case of a low SNR, the high-frequency parts of the time-domain waveform signals were effectively enhanced, which improved the discrimination between the target signal and noise. This is consistent with the observation in Section 2.2. Additionally, for the single-point step of the signal, as shown in Figure 9e,f, the influence of such abnormal points could be reduced. These results indicate that the CEEMDAN-KL effectively improved the distinguishing characteristics of the signals.
# Effect of Different Acoustic Parameters on NOx Emissions of Partially Premixed Flame
## 3 Results and Discussion
### The Relationship between EINOx and Acoustic Frequency  
The effects of acoustic frequency on NOx emissions are shown in Figure 5. There was a clear linear relationship between EINOx and frequency when frequency was less than \(f_{\rm c}\), as seen in Figure 5. The positive slope of the linear relationship between EINOx and acoustic frequency ranges from 0.001 to 0.003. However, the relationship between the frequency and EINOx is not clear when \(f>f_{\rm c}\). This is related to the flame response to acoustic excitation at different frequencies.
# Passive Location for 5G OFDM Radiation Sources Based on Virtual Synthetic Aperture
## 2 Theoretical Analyses of Typical Source Localization Methods
### Doppler Frequency  
The Doppler frequency localization method is realized by using the instantaneous change rate of Doppler frequency caused by the movement between satellite and source. The acceleration changes of the satellite and source can be obtained by the change rates of Doppler at various times to obtain the source location [10; 11; 12; 13].  
Suppose that at time \(t_{1},t_{2},\cdots,t_{n}\), the location of the satellite is \(r_{o}=[x_{oi},y_{oi},z_{oi}]^{T}\), the velocity of the satellite is \(v_{oi}=[v_{xi},v_{yi},v_{zi}]^{T}\), the acceleration of the satellite is \(a_{oi}=[a_{xi},a_{yi},a_{zi}]^{T}\), and the location of the source is \(\mathbf{r}_{T}=[x,y,z]^{T}\). The Doppler generated by relative motion is \(f_{d}=f_{0}(1-\mathbf{r}^{T}\mathbf{v}/(\mathbf{r}_{C}))\), where \(f_{0}\) is the signal carrier frequency, \(\mathbf{r}\) is the relative location vector of the source and satellite, \(\mathbf{v}\) is the relative velocity vector of the source and satellite, \(c\) is the light speed. Differentiate the above equation with respect to time to obtain the Doppler rate of change \(\hat{f}_{d}=-\frac{\hat{f}_{0}}{c}[\frac{\mathbf{r}^{T}\mathbf{v}\mathbf{r}^{T}\mathbf{a}}{ \mathbf{r}}\frac{(\mathbf{r}^{T}\mathbf{v})^{2}}{\mathbf{r}^{T}}]\), where \(\mathbf{a}\) is the relative acceleration vector of the source and satellite. The Doppler change rate at a different time can be expressed as a matrix that \(\mathbf{\Omega}=\mathbf{G}f_{0}+\mathbf{E}\), where \(\mathbf{\Omega}=[\hat{f}_{d1},\hat{f}_{d2},\cdots,\hat{f}_{dN}]^{T}\) is the change rate of Doppler at \(N\) times. \(\mathbf{G}=[u_{1}(x,y,z),u_{2}(x,y,z),\cdots,u_{N}(x,y,z)]^{T}\) is \(N\) expressions about the source location, \(\mathbf{E}=[\delta_{f_{d1}},\delta_{f_{d2}},\cdots,\delta_{f_{dN}}]^{T}\) is the error of the Doppler change rate. The source location can be obtained by solving the nonlinear equations. The solutions of nonlinear equations include Chan, Newton iteration method, grid search method, particle swarm optimization algorithm, etc. [26].  
Doppler method is simple in principle and system structure, but it requires multiple times of measurement taking to obtain high location accuracy, thus a long time is required to realize localization. It is also difficult to obtain a high-accuracy measurement of the instantaneous Doppler change rate, which greatly affects the location accuracy [10].
## 1 Introduction
However, semi-active systems have received more attention because they offer impressive adaptability and a very low power requirement [57; 58; 59; 60; 61]. As most flexible structures, such as heavy and tall buildings and bridges, have minimal structural damping and are prone to dynamic excitation from wind or seismic vibrations, different control mechanisms are used to prevent structural failures. There are three types of control systems for building structures: passive, active, and semi-active or hybrid systems. Although passive control systems have been extensively studied [62], active and semi-active systems have also been investigated as a control for structures [63].
## 4 The Air Void Size Distribution and Dynamic Modulus Analysis of the Hardened Stage
### Analysis of Compressive Strength Loss and Air Void Content  
As shown in Table 2, the added air entrainment agent can lead to obvious compressive strength loss. Compared to the Type 1 sample, the compressive strength of Types 2 and 3 samples decrease by 7.39% and 38.40%, respectively. In addition, the air void content of Types 2 and 3 Samples increase by 3.4% and 6.6% separately, as compared to the Type 1 sample. Hence, for every 1% increase in air void content, the compressive strength decreases by 2.17% and 5.82% for Types 2 and 3 samples, respectively. It is reported that the compressive strength loss rate was in the range of [2%, 6%] for a 1% increase in air void content [26]. Though the strength loss rate of Types 2 and 3 samples are both in this range, the loss rate of the Type 3 sample is still much higher than the value of the Type 2 sample. Hence, the correlation between compressive strength and air void content cannot be simply treated as a linear relationship. It is considered that the air void distribution can affect the compressive strength, and not only the air void content.  
In particular, the distributions of small air voids along the ITZ area are demonstrated in Figure 10d-f for Type 1, 2, and 3 samples, respectively. It is clear that the ITZ area of the Type 1 sample is relatively dense and small air voids can seldom be detected. Similarly, only limited small air voids can be found in the ITZ area of the Type 2 sample, as shown in Figure 10e. For the Type 3 sample, numerous small air voids can be found along the ITZ area as depicted in Figure 10f. The existence of small air voids in the ITZ area can lead to a weaker bonding strength between the cement paste and aggregate, which can be the main reason for the severe strength loss in sample Type 3. This finding demonstrates that clustered air voids can play an important role for the compressive strength loss.