esa-sceva/satcom-chunk-collection huggingface dataset:
# Sound Localization Framework for Construction Site Monitoring
## 4 Construction Sound Localization Framework (CSLF)
### Key Components of the Sound-Based Localization Approach  
#### 4.2.1 Sound-Based Localization Approach  
Sounds are waveform signals that propagate through the vibration of a medium, and there are various approaches for signal-based localization. Considering these approaches, we compared the pros and cons of existing localization methods and selected a sound-based localization technique suitable for construction sites.  
The time of arrival (TOA) localization technique uses a triangulation method to measure the absolute transmission time from the sound source to the sound receiver to determine the distance measured by three or more sound receivers [29]. Considering that it is necessary to synchronize the monitored target with the receiver, the target must be clear, and separate tags must be attached to the target. However, the goal of this study is to localize and identify multiple sound sources that naturally occur at a construction site. Therefore, the TOA technique, which requires the monitored target to be clear, is not suitable.  
The angle of arrival (AOA) localization technique calculates the location of the sound signal using the AOA calculated by each receiver [30]. While the AOA method can perform sound localization using only two sound receivers, the location of the sound is presented only as an AOA for each receiver, and calculations are performed using the triangulation method. Therefore, small differences in the estimated angle can significantly affect the accuracy. If the distance between the sound source and the sound receiver is too large or small, localization errors can become significantly large. Considering that construction sites require the monitoring of large spaces and sounds to occur in unexpected places, the AOA technique is not suitable for construction sites.  
The time delay of arrival (TDOA) localization technique, a beamforming monitoring method, performs sound localization based on the differences in the arrival times between sounds received by multiple receivers [31]. This technique is suitable for interior and exterior environments as it does not measure the absolute transmission time of the sound, but only measures the difference in the receiving times at each sound receiver. Therefore, there is no need for synchronization with the target, and the technique is easy to implement. Considering that unspecified noises occur from unexpected places at construction sites, the TDOA localization method is considered to be the most effective for this study.  
Moreover, the TDOA localization method [20; 31; 32; 33] can calculate the relative distance from the sound-source location to the receiver if the recorded sound data are captured by microphones (Sounds 1, 2, and 3) based on the coordinates of the installed receivers (M1, M2, and M3). The differences in the calculated arrival times between the microphones are used to iteratively calculate the approximate location of the recorded sound.
## 3 Decentralized Neural Block Control (DNBC-UKF)
By incorporating the UKF within the training process, the neural network can effectively capture and model the complex nonlinear dependencies present in the data, leading to an improved performance in tasks such as time series forecasting, speech recognition, and natural language processing [22; 23]. The initialization of a UKF involves determining the initial state estimate, the covariance matrix and the process noise covariance matrix, which can make the selection of these initialization parameters a complex task, especially when using heuristic techniques [18]. The challenge arises because these parameters significantly impact the filter's performance and are often problem-specific, requiring domain expertise and careful tuning. The manual selection of these parameters can be time-consuming, and may not guarantee optimal performance. In such scenarios, employing metaheuristic optimization methods proves to be a promising approach for selecting these initialization parameters. Metaheuristic optimization methods offer several advantages when applied to the selection of UKF initialization parameters. Firstly, these methods provide a systematic and automated approach to parameter tuning, relieving the burden of manual parameter selection. They can efficiently explore the vast parameter space, searching for the optimal combination that minimizes the error or maximizes a performance metric. By leveraging the search mechanisms inherent in metaheuristic algorithms, such as exploration and exploitation, the initialization parameters can be fine-tuned to enhance the convergence and accuracy of the UKF. Secondly, metaheuristic optimization methods can handle nonlinearity, multimodality, and non-convexity in the optimization landscape, which are common challenges in parameter selection for UKF initialization. These algorithms possess the flexibility to adapt and explore diverse regions of the parameter space, avoiding local optima and finding near-optimal or globally optimal solutions.
## 5 Validation Tests
### Accuracy Analysis for Different Observation Environment  
The horizontal, vertical, and 3D root mean square error (RMSE) were calculated relative to the positions provided by CSRS-PPP. The mean RMSE values of the positioning errors were computed using the six weighted models. Figures 10-12 show the results for the OS, CS, and UC cases, respectively. In the figures, \"w/o weight\" refers to the case where no weight was applied, and \"HK model\" refers to the model developed in this study. As shown in Figures 10-12, the HK model significantly improved the accuracy compared with the other existing models. In the case of IHU4, as shown in Figure 10, the horizontal and vertical errors without a weight model were 4.6 m and 5.3  
to the \"w/o weight\" case. The accuracy improved by more than 10 m, indicating that the multipath effect could be significantly reduced. Because the weight was derived based on the SNR, which reflects the characteristics of multipath signals, the model is more effective when it is used for urban canyons rather than for the open sky. Among existing models, the exponential model exhibits the highest accuracy. Moreover, the SNR-based models were more effective than the elevation-angle-based models as the observation environment worsened.
## 3 Proposed Scheme
### Application Usage of ZWNet  
Once ZWNet has been trained, the generation of a zero-watermark involves the following steps:  
(1) Input the host image with a size of \(224\times 224\) and jpg format into ZWNet to compute the dense features. Note that other image sizes are also compatible, as the preprocessing step will resample the input image to \(224\times 224\).  
(2) Provide the key and copyright information to ZWNet to generate the final zero-watermark.  
(3) Register the zero-watermark and record the key and copyright information.  
If an image with an uncertain copyright is encountered, the copyright can be identified through the following steps:  
(1) Input the suspected copyright image into ZWNet to generate the dense feature.  
(2) Select the corresponding recorded key and copyright to generate the verification zero-watermark.  
(3) Calculate the normalized correlation coefficient (NC) to assess the similarity between the original zero-watermark and the verification zero-watermark. NC is calculated as follows:  
\[\text{NC}=\frac{\sum_{i=1}^{N}W_{i}W_{i}}{\sqrt{\sum_{i=1}^{N}W_{i}^{2}}\sqrt{ \sum_{i=1}^{N}W_{i}^{2}}}\]  
Here, \(W\), \(W\), and \(N\) represent the original zero-watermark sequence, the verification zero-watermark sequence, and the sequence length.  
(4) If the NC value exceeds a predefined threshold, it indicates that the copyright of the image matches the registered copyright information. Otherwise, the image does not belong to the recorded copyright.
# Enhanced Speech Emotion Recognition Using Conditional-DCGAN-Based Data Augmentation
## 3 Proposed Methodology
### Proposed Model
#### 3.2.1 Conditional-DCGAN Architecture  
Conditional-DCGAN is a model that combines GAN and DCGAN, designed to effectively learn the complex patterns of emotional speech data and generate high-resolution Mel-spectrograms that reflect emotional features. This model uses Mel-spectrograms extracted from voice data as training data and was implemented using the PyTorch 2.2.0 framework. The loss function applied is Binary Cross Entropy, and to enhance training efficiency, the Adam\(W\) optimizer--a variant of the traditional Adam optimizer with a weight decay strategy--was used along with the Cosine Annealing Warm Restarts scheduler. In particular, the Cosine Annealing Warm Restarts scheduler helps generate a wide variety of samples by dynamically adjusting the learning rate. This can mitigate the mode collapse that often occurs in GANs by periodically fluctuating the learning rate, forcing the generator to learn different patterns rather than getting stuck on a particular pattern.  
The generator is responsible for creating Mel-spectrograms by receiving a latent vector generated from random noise and labels as inputs. The latent vector, combined with the embedded labels, gradually increases the resolution through five deconvolutions [36], eventually generating a Mel-spectrogram of size (1, 64, 64). After each deconvolution, batch normalization [37] is performed to prevent gradient vanishing and improve training stability. The ReLU function [38] is used as the activation function, and the output layer uses the Tanh function to adjust the pixel values of the output Mel-spectrogram within the range of [\(-1\), \(1\)].  
The discriminator receives Mel-spectrogram and label data as inputs and determines the authenticity of the input data and the consistency of the labels. The label data is embedded and matched in size with the Mel-spectrogram for combined use. The discriminator consists of five strided convolutions [39], utilizing batch normalization and the LeakyReLU  
Figure 2: (**a**) Traditional Conditional-DCGAN; (**b**) Newly Proposed Conditional-DCGAN.  
activation function [40]. The final output uses the sigmoid function to produce a probability value within the range of [0, 1].  
The computational cost of conditional-DCGAN grows approximately \(O\big{(}N^{2}\big{)}\) with the resolution of the image (e.g., 64 \(\times\) 64) \(N\). The detailed architecture of the model can be found in Table 3.  
The Mel-spectrogram generated by this process was used in experiments as an input to the speech emotion recognition model in combination with the original speech data to show its impact on the performance improvement of the speech emotion recognition model. The model was trained on an NVIDIA GeForce RTX 3090 GPU for about 36 min, and an example of the data generated by the generator can be seen in Figure 3.
## 4 Results
### Generated Sinkhole Susceptibility Map  
This study presents a sinkhole susceptibility map generated using SHAP values. The study area consists of a total of 40,213,167 pixels, with SHAP values calculated for each criterion at every pixel. The SHAP value of each criterion in the overall model  
Figure 9: SHAP dependence plots. The plots are arranged based on their importance ranks: (**a**) PTF; (**b**) MAP; (**c**) DBC; (**d**) DPH; (**e**) DDO; (**f**) LTC; (**g**) WDS; (**h**) DCH; (**i**) DDC; (**j**) DMG; (**k**); DSU; (**l**) DPT; (**m**) PST; (**n**) ELV; (**o**) PRD; (**p**) SLP; (**q**) GWD; (**r**) NDVI; (**s**) FDR; (**t**) SDP; (**u**) DTI; (**v**) PRC; (**w**) ASP.  
was multiplied by the corresponding SHAP value of that criterion at each pixel, and a susceptibility index was computed for the entire area.  
The susceptibility indices, representing the likelihood of sinkhole formation, were then classified into distinct categories to facilitate interpretation and visualization of the results. Several classification methods, such as equal interval, quantile, and geometric interval, are commonly used in susceptibility mapping. However, the Jenks Natural Breaks Classification Method was selected for this study due to its ability to identify natural groupings within the data and effectively highlight significant variations in susceptibility levels [96]. Unlike equal interval or quantile methods, which may produce arbitrary or misleading class boundaries, the Jenks method maximizes the differences between classes while minimizing the variance within each class, ensuring a natural distribution of susceptibility values. The calculated susceptibility indices were classified into five categories using the Jenks Natural Breaks Classification Method. These categories are defined as very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The sinkhole susceptibility map generated for the study area is visualized in Figure 10.  
Figure 10: Sinkhole susceptibility map.  
Susceptibility map given in Figure 10 reveals the high-resolution spatial distribution of sinkhole formation potential within the study area. The obtained classes provide detailed information for identifying sensitivity areas, and SHAP-based approach enables the calculation of sensitivity indices not only with pixel-based data but also by associating them with variable effects across the model. An illustration of the spatial distribution of the susceptibility classes (see Figure 11) reveals that a significant portion of the study area falls within the moderate and high susceptibility categories. According to the susceptibility map, 15.75% of the study area exhibits very low susceptibility, 18.14% displays low susceptibility, 20.53% is moderately susceptible, 25.99% is highly susceptible, and 19.59% is very highly susceptible.
## 6 Simulation Results and Discussion
### Optimization Results
#### 6.1.3 PV/Wind/Diesel/Battery System  
Of the hybrid systems considered in this study, the PV/wind/diesel/battery system, having LCOE of USD 0.304/kWh, NPC of USD 207,388, and emissions of 18,324 kg/yr, was composed of a better energy mix than the other systems discussed in Sections 6.1.2, 6.1.4 and 6.1.5. Even though the PV/wind/diesel/battery system had a better in energy mix than the other configurations considered in this study, the higher magnitudes of NPC, LCOE and emissions, as shown in Table 3, could lead us to search for another cost effective-alternative to electrify Kibran Gabriel island.
# Establishment and Application of Crowd-Sensing-Based System for Bridge Structural Crack Detection
## 3 Development of the Crowd Sensing Crack Collection System  
The transmission network in the crowd sensing crack collection system shown in Figure 1 can utilize thd existing broadband and mobile network facilities, as smartphones have become almost ubiquitous mobile devices nowadays. What needs to be built is the cloud server, the cloud-based mobile APP management platform, and the mobile APP installed on mobile phones. The cloud server can be rented with selected configurations according to the needs. The architecture of the mobile APP and the management platform in the cloud server are shown in Figure 2. The main functions of the cloud-based management platform contain participant account management, crack information management, and crack information inquiry. The main functions of the mobile APP contain crack image capturing, location recording, text description of cracks, and record inquiry.  
Figure 1: Components of the crowd sensing crack collection system.  
The crowd sensing approach adopted by this system is the active sensing by people in the loop. Widely distributed participants carrying smartphones will come close to bridge structures such as railings, road surfaces, beams, and bridge piers in their daily travels. Participants can use the mobile APP to capture crack images; meanwhile, the APP will record the locations of the participants at the time and upload them to the cloud server. The users can process the collected data through the management platform in the cloud server by logging in via the web.
# Facial Paralysis Detection on Images Using Key Point Analysis
## 2 Methodology
### Facial Measures Computation  
As stated before, in this methodology the evaluation of the input image was performed aiming to detect a level of asymmetry within the two sides of the face. The proposed measures extract information from the eyebrows, eyes, nose, and mouth. As depicted on Figure 3, 34 distances were calculated using the facial key points. Some of those distances (A to K) were inspired by the work of [17], who aimed to evaluate objective measures from face photographs with a target other than facial paralysis detection, but they seem to be a good reference to characterize the healthy human face. Here, distances A to Q were proposed to detect any level of asymmetry among face sides (or regions), while measures R to Z were proposed to identify the facial movement executed by the patient; then, the asymmetry level was computed.  
Figure 2: Facial model adapted from the work proposed by [PERSON] and [PERSON] [3]. (**a**) Standard 68 key points model; (**b**) our 51 key points model.  
The proposed facial measures aim to identify any level of asymmetry between the left and right sides of the face, although locating the paralyzed side (or region) of the face is out of the scope of this work. Most measures were compared and ratios were computed to obtain a value in the range of \([0,1]\), where 0 means fairly asymmetric and 1 is assumed to be closer to a healthy face. Before describing our proposed measures, it is worth noticing that the angle between two points was computed according to Equation (1), the slope between points was computed following Equation (3), the Euclidean distance was employed in this work according to Equation (4), and the perimeter of a close segment was computed using Equation (5):  
\[m(Pa,Pb)=\left|\frac{Pa_{y}-Pb_{y}}{Pa_{x}-Pb_{x}}\right| \tag{3}\]  
\[d(Pa,Pb)=\sqrt{(Pa_{x}-Pb_{x})^{2}+(Pa_{y}-Pb_{y})^{2}} \tag{4}\]  
Figure 3: Facial distances to compute spatial relations between facial landmarks. (**a**) Distances A to K; (**b**) distances L to Q; (**c**) distances R to Z.  
\[\overline{S}(P_{s},\ldots,P_{l})=\sum_{x=s}^{l-1}d(P_{x},P_{x}+1)+d(P_{s},P_{l}) \tag{5}\]  
where \(\overline{S}\) is a close segment, \(P_{s}\) is the start point, and \(P_{l}\) is the last one and represents the end point within the segment.  
In this work, it is assumed that a healthy face is symmetric with respect to the position of the face elements (e.g., the eyelids, the mouth corners, the eyebrows) independently of the face gesture. In the case where those elements are not symmetric to each other, it is assumed that a level of paralysis will be diagnosed. Therefore, 29 symmetry measures are proposed and described in Table 1; please refer to Figures 2 and 3 if further detail is required.
## 3 HPMSM System with Output LC Filter
### Passive Damping Method  
In passive damping, a resistor is connected in parallel to the capacitor ends of the filter. The control diagram of the network is shown in Figure 5, where \(R_{\text{C}}\) is the parallel resistance. The transfer function from the output voltage of the inverter to the current of HPMSM can be calculated as  
\[\phi(s)=\frac{I_{a}(s)}{U_{Inv}(s)}=\frac{1}{LL_{1}C\textsl{s}^{3}+(RL_{1}C+ \frac{LL_{1}}{R_{\text{C}}})\textsl{s}^{2}+(L+L_{1}+L_{1}\frac{R}{R_{\text{C}} })\textsl{s}+R}. \tag{24}\]  
Choose RC as 1 ohm, 10 ohm, and infinity, respectively, the Bode diagram of the system is shown in Figure 6. It can be seen that when the parallel resistance of the filter capacitor is infinite, there is no suppression effect on the reso  
Figure 4: Bode diagram of LC filter when inductance varies.  
Figure 5: Control diagram of LC type HPMSM with parallel connection of the filter capacitor and the resistor.  
decrease of the resistance, the resonance peak is restrained more effectively. However, the introduction of resistors will result in additional power losses, which can be obtained as  
\[P_{n}=3\frac{{{U_{c}}^{2}}}{{{R_{c}}}}. \tag{25}\]  
According to (25), the power absorbed by the resistance will be extremely high when RC is small, hence affecting the efficiency of HPMSM, which becomes the main drawback of the passive damping method. Therefore, it was replaced by the later active damping method.
## 2 Analysis of the Reverse Torque and Vibration in the Mud Pump
### Relationship between Mud Pump Vibration and Input Parameters  
#### 2.4.1 Analysis Modeling and Pre-Processing Setting  
Due to the unstable force exerted by the mud on the mud pump, the operation of the mud pump inevitably generates certain vibrations. To analyze the effect of vibration on the safety and stability of the mud pump and the return pipeline, a mud pump rotor model, as shown in Figure 10, was established to investigate the effect of mud-induced vibration on the mud pump rotor using a one-way fluid-structure coupling analysis.  
Figure 8: Meshing of the three-dimensional model of the mud pump flow channel.  
Figure 9: Relationship between reverse torque and rotational speed.  
In the one-way fluid-structure coupling analysis, the calculations of the fluid and solid were performed independently. Firstly, in the fluid calculation, the fluid parameters, such as velocity, pressure, and temperature, were computed to obtain the force exerted by the fluid on the solid. Then, in the solid calculation, the force was applied as the external load on the solid to compute parameters, such as stress, strain, and deformation. In the ANSYS software, the workflow for the one-way fluid-structure coupling analysis mainly involved using ANSYS Fluent for the fluid calculation and transferring the results to ANSYS Mechanical for the solid calculation. Specifically, in ANSYS, the input for the fluid calculation included the initial state of the fluid, boundary conditions, mesh, and so on. The output included fluid velocity, pressure, temperature, and other related parameters. For the solid calculation, the input included material properties, geometric shapes, boundary conditions, and so on, while the output included stress, strain, deformation, and other relevant parameters.  
The mud pump rotor was divided using a hexahedral meshing method, with a total of 247,947 cells. The material used for the mud pump rotor was stainless-steel 022 Cr22 Ni5 Mo3N, and its material properties are shown in Table 4.