esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of a Spiral Tube Damping Busbar to Suppress VFTO in 1000 kV GIS
## 3 Simulation Results
### Discussion  
As shown in Figure 7a, when the disconnector is opened, the VFTO for the whole process at the power supply side appears as a superposition of high-frequency transients and a sine wave. After SF\({}_{6}\) gas discharges, the disconnector gap restores the insulation state; then, the current disappears, the high-frequency transient components gradually decay, and, finally, the voltage wave continues as a sinusoidal wave with the supply voltage.  
In addition, the VFTO for the full process at the load side has an approximate step shape, which is a superposition of high-frequency transients and a stepped wave. Each step of the ladder wave corresponds to SF\({}_{6}\) gas breakdown, the step magnitude is the breakdown voltage, and the narrow pulse at the ladder edge is the highest frequency of the VFTO, as shown in Figure 7c.  
Moreover, the VFTC for the whole process is the superposition of high-frequency transients with zero current amplitude. When the critical breakdown voltage (BV) of the SF\({}_{6}\) gas is equal to the transient recovery voltage of the gap, the SF\({}_{6}\) gas collapses and the gap transfers from the insulation state into a conductive state; then, the circuit produces a high-frequency transient current, as shown in Figure 7e. Additionally, when the critical breakdown voltage of SF\({}_{6}\) gas exceeds the transient recovery voltage of the gap, the current disappears during discharge of the SF\({}_{6}\) gas.  
The simulation results show that the maximum amplitude of the VFTO decreased from 1.554 to 1.13 p.u., which means that the damping busbar functioned as a VFTO suppressor. The damping effect of the new design is evident from the simulation results, and the mitigation effect was about 27%. Table 3 illustrates the transient characteristics of the VFTO waveform under opening operation before and after installing the damping busbar. A singular switching operation (opening) of the disconnector was simulated in EMTP-ATP (Alternative Transients Program) software.  
Figure 6: The equivalent circuit of a 1000 kV GIS after installing the damping busbar.  
Figure 7: _Cont._
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 3 Methodology
### Multiple Representation Structure--BuildingTree  
The multiple representation data structure BuildingTree in this study is a partial hierarchy; it is an improved and extended version of the independent level data structure in [PERSON] et al. [21]. The previous CityTree model is mainly generated for building groups or blocks in LOD1 or LOD2 by the aggregation operation while the current version of the BuildingTree is designed for building objects, where the data are stored in LOD3 and LOD4 models (as shown in Figure 2):  
* LOD3 Exterior Shell (LOD3 ES) is a detailed building shell models with boundary representation. This is an LOD3 model which has the basic content of an LOD3 model but only consists of the exterior shell. This model is stored as a boundary representation. Compared with LOD3, LOD3 ES can provide high visualization similarity while greatly reducing the data volume. Meanwhile, the LOD3 ES can be processed faster for CSG detection than LOD3 since the number of polygons are reduced.  
* CLOD--a total hierarchical multiple representation model stored in CSG representation. The model stores features in LOD1 to LOD3.  
The CLOD sub-tree is built by the following four levels (Figure 3).  
Leve1 1: box models of buildings (LOD1); Level 2: building roof structure (LOD2); Level 3: building parts (extensions of the ground plan, windows, doors, roof details etc.) that are larger than a threshold (large CSGs); Level 4: building parts that are smaller than a threshold (small CSGs). This implies that a building in CLOD is built by all four levels. A higher level can only be shown if all the lower levels are shown. However, there is no ordering of the elements within each level. The elements that will be transferred and visualized on a level is dependent on the user's position. The distance between the user's position and the model is calculated to determine the visualization levels of the model. In this study, only landmark buildings have all four levels. For ordinary buildings, levels 3 and 4 can be merged.  
Figure 3: The levels in the CLOD total hierarchical tree.  
Figure 2: BuildingTree structure.  
It should be noted that the BuildingTree only stores limited semantic information (e.g., private or public). The reason that not more semantic information is stored is that the BuildingTree is only used for visualization, and the rich semantic data that are available in e.g., CityGML models are not necessary. However, a BuildingTree contains the reference to the original LOD3 or LOD4 model which means that semantic information could be added to the model. The preservation of the semantic data could benefit further interactive visualization and analysis.  
The P-Tree structure presented in [23] is mainly focused on the multiple buildings or building groups while our method deals with single buildings. In [24], the CLOQ (Continuous Level of Quality) is composed by floor, wall and roof structure, while in this paper, the CLOD is composed of CSGs which represent a part of building including floor, wall and roof structures.
# Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi
## 3 Proposed Methodology
### Face Detection  
After capturing the facial images in real time using Pi-cam, the first and foremost task is to separate the faces. To remove the unwanted and redundant information from the facial images like the background, a variety of methods are available. The most well-known method was the Viola Jones algorithm that was invented in the early 20s. We are using another method known as Histogram of Oriented Gradients, or HOG, to detect the facial images. Raspberry-Pi captures the emotive facial images in real-time via Pi-Cam from real time video frames. This image is real-time captured input image \(I^{r}\) which is converted to grayscale to extract HOG features to extract the facial part in the input image \(I^{r}\). Finally the facial image is cropped, \(I^{r}_{cr}\), and is fed to a feature extraction unit to recognize faces in real time. Figure 2a shows the basic steps of face detection using HOG. As shown in Figure 2, the gradients are calculated for the entire grayscale image, and this is done by calculating the gradients for \(16\times 16\) pixels at a time.  
Figure 1: Architecture for face recognition and facial emotion recognition in real time using Raspberry-Pi (Dataset from [25]).  
This calculation is repeated for the entire grayscale image, and we will end up with an image of gradients. The next step is to calculate the strongest gradients in 16 \(\times\) 16 windows of pixels and replace the gradients in that window with the strongest gradient. These will result in a basic image that consists of basic structure of face. To locate the face in the real-time captured input image or on real time video, we only located that part of the image which looks remarkably like a known HOG pattern and crop that part of the input image \(I^{r}\), as a result we get the cropped facial image \(I^{r}_{cr}\).
## 4 Discussion  
A close observation of Figure 12 confirms that one of the main reasons for the measured high _Sa_ values for down-facing surfaces was unmelted or partially melted powder. The presence of  
Figure 11: Effect of each parameter (normalized values) on the surface roughness model for a \(35^{\circ}\) down-facing surface.  
Figure 10: Effect of each parameter (normalized values) on the surface roughness model for down-facing surfaces.  
partially melted powder on the down-facing surface was almost unavoidable. This was caused during the printing process when the meltpool/dross that was formed came into contact with loose unsupported powder, thereby causing the partial melting of this lose powder and thereby the attachment/embedding of the loose powder within the surface of the desired printed part.  
This aspect, coupled with the dorsal formation, which is formed by the full melting of loose powder in the overheated zones adjacent to the meltpool, were the major causes of high surface roughnesses in down-facing surfaces; this can be clearly seen in Figure 12.  
Another observation made is that the 35\({}^{\circ}\) down-facing surfaces exhibited higher surface roughnesses than the 45\({}^{\circ}\) down-facing surfaces. It can be concluded that this occurred because, even though both surfaces were printed on top of loose powder, the 45\({}^{\circ}\) down-facing surface had a less steep slope when compared to the build platform, which resulted in a smaller overheating zone as some of the heat was conducted away through the solid bulk of the part. However, for the 35\({}^{\circ}\) down-facing surface a larger amount of energy was transferred into the powder, causing a larger meltpool and larger cross formation.  
Additionally, while visually examining the samples it was found that some of the samples that showed relatively low surface roughness values in fact exhibited a significantly large dimensional deviation from the CAD of the samples. The down-facing surface was thicker than intended for many of the samples. This clearly indicated a large dress formation on the surface. This could be attributed to the larger energy input on some samples, resulting in the formation of larger meltpools. As a result, this could cause large dress with less measured roughness, as the larger meltpools had better wettability and would cause more interconnected melt pools which cause a uniform dress, and cannot be detected just by measuring roughness. Therefore, factors such as line energy, which could relate to the size of the meltpool formed, could lead to a better understanding of optimum overlap parameters in order to optimize the process for minimizing cross formation and surface roughness.  
Dimensional accuracy tests are also required in order to investigate this phenomenon of formation of low roughness and large dress. It clearly becomes evident that, in the SLM process, the process parameters display significant degrees of interdependency that affects the final quality of printed parts.
## 7 Results and Discussion
### Experiment 2: Comparison of the GD Algorithm with Previous Reported Algorithms
Figure 14 displays the total energy consumption when increasing the simulation time from 1 h to 12 h. Since ToA increases with the SF value, the SF assignments can then be used to justify the results. For EXPloRa, where there is a greater number of large SF nodes, the energy consumption is clearly higher than other algorithms. On the other hand, the default ADR scheme offers the lowest energy consumption as almost 90% of the nodes are SF7. The proposed GD algorithm offers relatively the same energy consumption as the  
Figure 12: The DER values vs. the payload size (bytes) within different algorithms. Figure 13: Time-on-Air from 1 h to 12 h of simulation time within different algorithms. SD but slightly higher than that of the QCVM due to neither SF10, SF11, nor SF12 being assigned for QCVM.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### PM Detection Methods
#### 2.3.2 PM2.5 and PM10 Detection Methods  
PM emissions are the most important air pollutant in shipping, and it causes health damage costs [40], so it is important to monitor airborne PM today. The properties of PM10 and PM2.5 are the same, and the difference lies in the different aerodynamic diameters, so the detection method applicable to PM2.5 is also applicable to PM10. The manual analysis method for PM10 and PM2.5 detection and the method for TSP detection are both gravimetric methods, while the commonly used automatic analysis methods include the micro-oscillation balance method, the light scattering method, and the beta-ray method [41]. These are the main methods of detecting air pollutants in the port, which can be divided into manual monitoring and automatic monitoring according to the method of detecting the concentration of air pollutants. The main difference between these two types of monitoring lies mainly in the experimental conditions required for their monitoring and the continuity and time interval of their monitoring. Because the detection methods for various pollutants in the laboratory are relatively mature and because the detection data accuracy is high, the development direction of manual monitoring is mainly the research and improvement of samplers. Due to technical reasons, the current detection methods used by traditional detection instruments are all automatic analysis methods. To ensure the accuracy of the data, these instruments usually include many auxiliary tools, so the prices of such traditional instruments are relatively expensive, as shown in Table 5.  
For current ports, automatic monitoring is a trend. Since the air pollution situation in the port area is highly correlated with human activities (e.g., ships entering and leaving the port), the concentration of air pollutants in the port may change significantly in a short period of time. Therefore, for air pollutant monitoring in ports, the monitoring equipment should have the characteristics of continuity and high temporal resolution. Continuous automatic monitoring equipment will be more suitable for the port environment. The automatic monitoring is achieved by various types of pollutant detection equipment and sensors based on the principle of automatic analysis methods, and the development direction is to improve the accuracy of detection equipment, anti-interference, and monitoring types. The high cost of traditional testing instruments puts a high burden on their coverage in the port area. Relatively speaking, various sensors will be much cheaper, and the sensor
# Non-Magnetic Circulator Based on a Time-Varying Phase Modulator
## 2 Gyrator Design
### Circuit of Gyrator
The control signals of the on/off switch are different. The control signal ctr1 is ahead of that of ctr2. The intermediate buffering unit is the delay line, and the delay time is one-quarter of the period of the control signal. Then the period of the control signal can affect the actual output phase of the gyrator. The period of the input signal and control signal is _Tm_, _Tc_, respectively. Without considering the loss of the transmission, the scattering matrix of the gyrator considering the phase change of the buffering unit is:  
\[S_{ gyrator}= \begin{bmatrix}0&\exp(j(\frac{Tc}{4T_{m}}\pi+2\varphi_{A\text{ \ or }B}))\\ \exp(j(\frac{Tc}{4T_{m}}\pi+\varphi_{A}+\varphi_{B}))&0\end{bmatrix}  \tag{5}\] \begin{table}
\begin{tabular}{c c} \hline \(\pi\) & \(t_{1}\) \\ \hline
0.75 & 0.25 \\ \hline \end{tabular}
\end{table} 
Table 1: Solutions of Equation (4). \(\varphi_{A\ or\ B}\) in Equation (5) indicates \(\varphi_{A}\) or \(\varphi_{B}\), which is a time-varying quantity. In fact, \(\varphi_{B}\) and \(\varphi_{A}\) differ by \(180^{\circ}\) at the center frequency, so the value of item \(S_{12}\) in Equation (5) is constant. When the frequency of the input signal is changed, the phase difference between \(S_{12}\) and \(S_{21}\) can still be maintained at \(180^{\circ}\) within a considerable bandwidth. In addition, the value of \(Tm\) also affects the phase change of the gyrator. Figure 4: (**a**) Propagating transmission. (**b**) Opposite transmission. Figure 5: The actual circuit structure of the gyrator. Figure 6 shows the phase difference between the forward and the opposite transmissions. The designed TVPM is used to compose the circuit of the gyrator. The differential phase shifter structure can be maintained in a considerable bandwidth. The phase difference caused by forward and reverse transmissions within a certain bandwidth is always maintained at 180\({}^{\circ}\). When the center frequency is 1 GHz, the phase difference of 180\({}^{\circ}\) is maintained within the bandwidth of 400 MHz (\(-3\) dB). In fact, when \(T_{m}/Tc\) is odd, the phase change of the output of the two ports is \(\pm\)90\({}^{\circ}\) respectively. Figure 7 shows the phase change caused by the periods of the control signals. When the value of \(T_{m}/Tc\) is 5, the scattering matrix of the gyrator in a time-harmonic form satisfies the following relationships: \(S_{12}=j,\ S_{21}=-j\).
# Iteratively Refined Multi-Channel Speech Separation
## 5 Discussion  
Our experimental results demonstrated the significant effectiveness of the proposed method in both multi-channel speech separation and dereverberation tasks. The iterative results in Table 2 reveal that the performance of SDR and SI-SDR improved by 17.46% and 22.66% after the first iteration, indicating that the initial iteration effectively enhanced the model's performance. The spectrogram of separated speech at different stages further proves the effectiveness of our method. As the processing stages increased, the quality of the spectrograph output of the neural network gradually improved, demonstrating the advantages of neural networks in processing speech signals.  
When our method was compared with other popular multi-channel speech separation methods, such as FaSNet, NB-BLSTM, Beam-TasNet, beam-guided TasNet and Beam-TFDPRNN, the proposed iBeam-TFDPRNN demonstrated superior performance. As shown in Table 3, our method significantly outperformed these methods in terms of SDR, SI-SDR, PESQ, and SIR. Notably, our model included only 2.8 M parameters, which was smaller in parameter size than most other methods.  
In the joint separation and dereverberation tasks, our method achieved significant improvements over the reference methods. As shown in Table 4, our method achieved an SDR of 20.2 dB, achieving much better results than the oracle mask-based MVDR, Beam-TasNet, and beam-guided TasNet. In addition, this result was also very close to the performance of the oracle signal-based MVDR. These results highlight the potential of our method for real-world applications.  
Despite our method having achieved good experimental results, there are still some limitations. For example, the dataset used in the experiment did not contain noise, meaning it may not have fully represented all real-world scenarios. Furthermore, each additional processing stage increased the RTF of the model, which may have impacted the model's practicality for real-time applications. In future work, we will explore more diverse datasets and optimization of the computational efficiency of the model to address these limitations.
## 3 Performance Analysis
### Outage Probability  
The outage probability is an important statistical measurement to assess the quality of service provided by the wireless communication network. Typically, the outage probability should be in the range \(10^{-5}-10^{-6}\), and a smaller outage probability value denotes better diversity performance. It can be defined as the probability that the end-to-end instantaneous SNR is lower than a specified SNR threshold value \(\gamma_{\mathrm{th}}\) which is required for satisfactory reception. By replacing \(x\) with \(\gamma_{th}\) in Equation (20), the outage probability can be calculated by [PERSON] (1994):  
\[P_{out}=\Pr\left(\gamma\leq\gamma_{th}\right)=F_{\gamma}(\gamma_{th})=\frac{ \exp(-tr(\lambda))|\mathbf{\Psi}(x\sigma^{2}/\delta^{2}\gamma_{th})}{| \mathbf{V}|\left(\Gamma(N_{Hr}-N_{rH}+1)\right)^{N_{rH}}} \tag{26}\]  
We can observe that \(P_{out}\) is a function of \(N_{H}\), \(N_{r}\), \(\gamma\), and \(\gamma_{th}\). Their significance on the diversity performance based on multiple HAP networks will be evaluated in Section 4.
# Enhancing Predictive Accuracy of Landslide Susceptibility via Machine Learning Optimization
## 3 Methodology  
The methodology developed in the present study involves three phases of analysis: data collection and preprocessing, model selection and parameter optimization, and performance evaluation. The first phase involved collecting all the necessary data, which included landslides, non-landslide areas, and landslide-related variables, along with preprocessing, which involved classification based on standard classification schemes and weighting of the classes using their degrees of influence. For the weighing process, this study utilized the certainty factor method. The first phase also included the preparation of the training and testing subsets. The second phase involved the implementation of the four models (MLP, NB, CDT, and RF), along with a parameter optimization process. Finally, a comparative analysis was carried out in the third phase, evaluating the model's accuracy using ROC curve analysis and landslide density calculations to validate the models' predictive  
Figure 1: Study area.  
reliability. The following section provides a brief description of the models and techniques used (Figure 2).
# Temporal EEG Imaging for Drowsy Driving Prediction
## 3 Approach
### Temporal EEG Image  
One of the challenges in drowsy driving prediction is that some drowsy trials may have similar patterns of the alert trials. The driving performance might not degrade immediately, even if the alertness level of the drivers begins falling, which means that drivers can respond well to lane-departure events before they fall asleep (but the drowsy pattern of the EEG has appeared). In that case, the generated EEG images between the drowsy trial and its previous alert trials can be very similar. As the drivers wake up by themselves, their vigilance level dramatically recovers, and the RT returns to the alert state; then, the EEG pattern becomes completely different from the drowsy trials. Based on these findings, the drowsy state should be estimated not only using the current trial but also by examining the previous trials. This study proposes a temporal EEG image that is generated by a linear combination of a sequence of EEG images, as shown in Equation (2):  
Figure 4: Schematic illustration used to transform the time domain electroencephalography (EEG) signals into a pixels in a color image.  
\[\begin{array}{l}{l}{l}^{\prime}_{t}=\sum\limits_{t=1}^{N}{c_{t}l_{t}}\\ \sum\limits_{t=1}^{N}{c_{t}=1}\\ \end{array} \tag{2}\]  
where \(I^{\prime}_{t}\) is the generated temporal EEG image, \(I_{t}\) is the array of the EEG image data, \(c\) is a scalar coefficient and \(c_{i}<c_{j}\) when \(i<j\). \(N\) in this study is set to five. Figure 7 illustrates the schematic diagram of the temporal EEG image. Instead of asingle frame EEG image \(I_{N}\), this approach estimates the drowsiness state using \(I^{\prime}_{N^{\prime}}\), which includes the information of the brain activity from multiple time points.  
Figure 5: Interpolation scheme of the EEG image. The transformed image data points described in Figure 4 are arranged corresponding to the location of the EEG electrodes. These image pixels are interpolated over a \(32\times 32\) color image via Clough–Tocher scheme.  
Figure 6: Examples of the generated EEG images. (**a**) The alert trials. (**b**) The drowsy trials.  
Figure 7: The schematic diagram of the temporal EEG image. \(I_{N}\) is the single frame EEG image generated from the current trial. Temporal EEG image \(I^{\prime}_{N}\) is acquired by linearly combining \(I_{N}\) and its previous trials.