esa-sceva/satcom-chunk-collection huggingface dataset:
# EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
## 5 Results and Discussion
### Results on CelebAMask-HQ  
In this subsection, we compare our algorithm with several recently published methods, including DFANet [23], DANet [33], DABNet [24], CE2P [35], and UNet [5] on the CelebAMask-HQ test set. For fair comparison, we re-implement the above frameworks under the same hardware configuration without using extra training data or multi-scale testing. As depicted in Table 1 and Figure 5, our solution outperforms others by a large margin. Surprisingly, our approach surpassed CE2P (the winner of LIP Challenge 2018) ([[http://sysu-hcp.net/lip/index.php](http://sysu-hcp.net/lip/index.php)]([http://sysu-hcp.net/lip/index.php](http://sysu-hcp.net/lip/index.php))) by 0.32% mIoU (78.19% vs. 77.87%) while halving the running time. Restricted by the cumbersome ResNet101, CE2P is not suitable for real-time segmentation. On the contrary, benefiting from the lightweight ResNet18, our network achieved comparable results with CE2P while ensuring low latency. By modifying the backbone network to ResNet34, the accuracy of EHANet is much better than CE2P, which reflects that CE2P has a large number of redundant parameters. Moreover, it can be observed that compared with the fastest method DABNet, the accuracy of our method is more than 10% higher than the former. To further evaluate the effect, detailed per-category comparisons are also reported in Table 1, where our method achieves the highest IoU on 10 out of 19 categories. Overall, the improvements over the state-of-the-art methods confirm the effectiveness of our EHANet for face parsing. Lightweight models often lack context, resulting in poor segmentation for small objects. I.e., neither DFANet nor UNet detected a \"necklace.\" In contrast, our network can well alleviate this problem through the channel-wise attention mechanism in order to better encode the semantic categories. The qualitative results on CelebAMask-HQ test set are presented in Figure 6. From the dotted rectangle, we can observe that our results are smoother and more natural. Figure 5: Runtime and mIoU (mean intersection of union) on CelebAMask-HQ. “R” denotes ResNet backbone (i.e., R18 is equivalent to ResNet18). Our method achieved remarkable results in both accuracy and efficiency.
# Fractal Cardioid Slot Antenna for Super Wideband Applications
## 2 Proposed Antenna and Parametric Sweep
### Effects of the Parameter \(g\)  
The influence of dimension \(g\) at the point where the CPW line supplies the cardioid, on the reflection coefficient, is shown in Figure 5. It can be observed that the parameter \(g\) has a significant influence on the reflection coefficient level. The widest bandwidth characteristic is obtained when \(g=0.4\) mm, and further increases in the value of \(g\) considerably deteriorates the reflection coefficient in the entire operating band.  
Figure 4: The simulated reflection coefficients of the first order fractal antenna for different iteration factors \(\mathrm{IF_{1}}=a_{3}/a_{2}\) obtained by changing the value of parameter \(a_{3}\) when \(a_{1}=6.6\) and \(a_{2}=4.55\): (**a**) \(a_{3}=3.64\), (**b**) \(a_{3}=4.82\), (**c**) \(a_{3}=6.06\) and (**d**) \(a_{3}=6.55\).
# Propagation Mechanism of Pressure Waves during Pulse Hydraulic Fracturing in Horizontal Wells
## 4 Results and Discussion
### Influence of Fluid-Injection Scheme
#### 4.4.2 Influence of Injection Flow Rate Waveform  
In variable-flow hydraulic fracturing operations, there are differences in the manner of injecting fracturing fluid. This sub-section will discuss three representative injection waveforms, namely rectangular wave injection, sinusoidal wave injection, and sawtooth wave injection. Figure 14 depicts the entrance flow rate waveforms of the three different injection schemes, while Figure 15 shows the simulation results of the three distinct injection waveforms.  
Figure 11: Impact of the vertical section length on downhole pressure.  
Figure 12: The amplitude of downhole pressure fluctuations in purely vertical and purely horizontal states.  
Figure 14: Flow waveform at the entrance.  
Figure 13: Influence of injection flow rate amplitude.  
Figure 15: Influence of injection flow rate waveform.  
It is noteworthy that when fracturing fluid is injected in the form of rectangular waves, the abrupt changes in injection lead to corresponding pressure transient responses at the downhole. The rectangular waves, compared to sinusoidal and sawtooth waves, are capable of generating greater pressure fluctuations at the downhole, resulting in more effective fracturing.
# Characteristics of GMAW Narrow Gap Welding on the Armor Steel of Combat Vehicles
## 2 Experimental Procedures and Results  
### Welding Joint Design  
The material used for this study is a MIL-STD-12560-grade armor steel, which has excellent impact toughness. The chemical and mechanical properties of the armor steel are shown in Tables 1 and 2. AWS A5.10, MIL-100s grade filler metal was used. As is typical of NGW, a lack of fusion frequently occurs on the lateral side on the inside of the narrow gap, which stems from the low thermal inflow and small molten pool. This is a critical defect that negatively influences the bond strength. Moreover, due to the long, narrow gap, welding defects also occur when the welding torch cannot reach the first layer welding or when it is insufficiently protected by the protective gas. These all aggravate the quality and welding strength of the weld. To obtain a reliable welding part when applying the GMAW (gas metal arc welding) process while considering these factors, the shape of the welding joint shown in Figure 1 was designed and applied. GMAW is a welding process in which an electric arc forms between a consumable wire electrode (filler metal) and the workpiece metals (base metals). Through the macro test, the soundness of the welding part was confirmed, as shown in Figure 2. To ensure the convenience of the industrial application, only the GMAW process was applied without developing an additional device or process. The macro test is performed on the cross-section, longitudinal section, or any direction as an independent test to evaluate the subsurface conditions or as a subsequent step of another test to reveal the effects on the subsurface. The order of the welding deposition of the NGW and X-groove are shown in Figures 1 and 3, and the welding conditions are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**C** & **Si** & **Mn** & **Ni** & **Cr** & **Mo** & **P** & **S** \\ \hline
0.16 & 0.24 & 0.22 & 2.94 & 1.46 & 0.37 & 0.006 & 0.003 \\
0.06 & 0.33 & 1.35 & 1.75 & 0.10 & 0.34 & 0.004 & 0.008 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of base metal and filler metal (wt %).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Yield Strength** & **Tensile Strength** & **Elongation** \\ \hline
970 & 1040 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of base metal (wt %).
# The Effect of Particle Type and Size on CoCr Surface Properties by Fine-Particle Shot Peaning
## 3 Results
### Microstructure Analysis  
The microstructure of the surface material pre- and post surface modification were evaluated by electron probe microanalysis, including secondary electron (Figure 3), which can evaluate the surface morphology compared between pre- and post-peening process and backscatter electron (Figure 4) techniques, which show the change of surface morphology. (The light areas are the dimples made by the peening process.) The results showed that the fine-particle shot peening process can change the surface microstructure by creating surface dimples with both ceramic and silica particles.  
Figure 3: Surface morphology evaluated by electron probe microanalysis (secondary electron) and comparing different types of particles.  
Figure 2: Surface morphology evaluated by optical microscope and comparing different sizes and types of particles.  
It was also demonstrated that ceramic particles can create deeper and clearer surface dimples than silica particles. This difference may be affected by the hardness of the particle. In general, the higher the hardness value of a particle, the greater the change on the surface microstructure.
## 2 Related Work
### Physical-Layer Key Generation  
Physical-layer key generation (PLKG) has been widely adopted in various wireless communication scenarios. Many studies have leveraged Channel State Information (CSI) as a shared random source for key generation and conducted experiments. CSI can be further divided into Channel Impulse Response (CIR) and Channel Frequency Response (CFR). In OFDM systems, the CFR can be directly obtained. For instance, [20; 21] proposed methods for generating keys using the CFR. Compared to the CFR, the CIR is a finer-grained parameter that contains more detailed channel information. It can better capture multipath propagation characteristics and is more suitable for dynamic environments. Ref. [22] applied a PLKG scheme based on the CIR to the Internet of Vehicles. In addition to the CSI, other shared random sources can be used for key generation. For example, in [10], the authors proposed a PLKG method based on the Received Signal Strength (RSS). However, the RSS is only a scalar value and may not provide sufficient key entropy in certain cases. Despite this, the RSS offers the advantage of being easy to measure and low in complexity. In FDD systems, where uplink and downlink use different frequencies, the assumption of channel reciprocity no longer holds. In [23], the authors employed random precoding for key generation in FDD systems, using it as an alternative to channel randomness for key material.  
Due to factors such as asynchronous measurements, channel noise, and hardware differences between the transmitter and receiver, the random physical sources extracted by both parties are typically not perfectly symmetrical [7]. Traditional solutions involve techniques like differential compensation to simultaneously simulate measured channels [24; 25], while low-pass filtering is used to suppress noise [26]. More recent research has explored the use of artificial intelligence algorithms to optimize the extraction of shared random sources.  
For instance, [PERSON] et al. [27] applied Principal Component Analysis (PCA) to improve the correlation of channel measurements between two nodes in a time-division duplex (TDD) MIMO system. [PERSON] et al. [28] deployed a trained autoencoder in the communication nodes, enabling the transmitter and receiver to extract reciprocal features from weakly correlated channel estimates. These advancements have broadened the application of PLKG.  
Classic physical-layer key generation is typically divided into four steps: shared random source acquisition, quantization, information reconciliation, and privacy amplification. The primary objective of the first two steps is to extract a shared random source, with both parties quantizing the sampled values into initial bit sequences. Due to the reciprocity of the channel, the initial sequences generated by both parties should be approximately identical. The main purpose of the latter two steps is to correct any discrepancies and map the initial sequences to generate a perfectly consistent symmetric key. Our protocol follows these classic steps, which are described in detail in Section 3.
# Modeling the Formation of Urea-Water Sprays from an Air-Assisted Nozzle
## 1 Introduction
Detailed data about the atomization process is not presented due to the focus of this study being on the mixing element. A more detailed investigation of the spray atomization process of air-assisted nozzles was conducted by [PERSON] et al. [16]. In this study, an atomizer with the external mixing of urea and compressed air was investigated by means of phase Doppler anemometry (PDA) measurements and CFD simulations. The authors also used a blob injection approach to initialize the liquid urea-water solution. A good agreement could be achieved for the velocities and the characteristic spray parameters like the SMD and arithmetic diameter. In the present study, a spray model of an air-assisted atomizer with an internal mixing chamber was generated in a commercial CFD code. Detailed experimental data for the evaluation of the model comprising droplet velocities and droplet sizes at different axial and radial positions was already published by [PERSON] [17; 18] and was provided for the present investigations. The frequently used blob injection approach is not very well-suited for this kind of nozzle, since the liquid and the atomizing air form a complex two-phase flow upstream of the nozzle exit. Therefore, the modeling approach was to extend the computational domain in order to include the outflow channel of the nozzle. This way, defined boundary conditions could be provided for the gas flow. The compressed air was modeled by a gas jet initialized inside this outflow channel. Thereby, modifications of the turbulence model were conducted to adapt the shape of the emerging gas jet. The liquid phase was initialized by a spatially averaged droplet spectrum measured 50 mm downstream the nozzle. The WAVE breakup model, which is quite common for the modeling of breakup processes in the case of diesel injection, was applied to model the atomization of the droplets. Finally, a good agreement with the experimental data could be achieved over a wide range using the presented modeling approach. Particularly, the radial distributions of droplet sizes and velocities could be predicted correctly for different operating conditions, which is important for a correct description of the spray preparation. Further on, the turbulence level inside the jet is discussed due to its influence on the droplet dispersion, as reported by [PERSON] et al. [19]. The findings in the present study contribute to provide and to improve modeling approaches for air-assisted atomizers.
## 4 Results and Discussion
### Transient and Steady Responses
Structures with higher levels of porosity will respond more quickly to a thermal variation and reach the equilibrium conditions in a faster way. Figure 5: Influence of semi-vertex angle on the transient temperature (**a**), radial displacement (**b**), and stress state (**c–f**) for a center point of truncated cone (D1-GPL X; \(e_{0}=0.4\); \(\gamma_{GPL}=0.01\); \(\omega=100\) rad/s). Figure 7 also analyzes the sensitivity of the response for different weight fractions of nanofillers. While the weight fraction of nanofillers increases, the steady state response significantly decreases due to an increased structural stiffness. For higher weight fractions of nanofillers, indeed, the structure will be steady faster. As also plotted in Figure 8, we repeat the same systematic analysis for a varying circular velocity, where it is clearly evident that the response for different weight fractions of nanofillers is more stable than the steady state response. Figure 6: Influence of porosity coefficient on the transient temperature (**a**), radial displacement (**b**), and stress state (**c**–**f**) for a center point of truncated cone (D1-GPL X; \(\phi=30^{\circ}\); \(\gamma_{GPL}=0.01\); \(\omega=100\) rad/s). that an increased circular velocity causes an increased steady state stress field (Figure 8a-d) and radial displacement (Figure 8e). Figure 7: Influence of \(\gamma_{GPL}\) on the transient temperature (**a**), radial displacement (**b**), and stress state (**c-f**) for a center point of truncated cone (D1-GPL X; \(e_{0}=0.4\); \(\phi=30^{\circ}\); \(\omega=100\) rad/s). The influence of various GPL patterns on temperature, radial displacement, and stresses along the \(r\) direction (at \(z=L/2\)) is depicted in Figure 9. Note that the effect of a GPLV and GPLUD pattern on the overall thermo-mechanical response is quite similar. As also plotted in Figure 10, for different porosity patterns, the D1, D3, and D2 patterns provide a higher level of temperature and radial displacement, respectively. The effect of D1  
Figure 8: Influence of the circular velocity on the transient stress state (**a**–**d**) and radial displacement (**e**) for a center point of truncated cone (D1-GPL X; \(e_{0}=0.4\); \(\phi=30^{\circ}\); \(\gamma_{GPL}=0.01\)). Figure 10: Temperature (**a**), radial displacement (**b**), and stress state (**c**–**f**) at \(z=L/2\) for various porosity distribution. (GPL V; \(e_{0}=0.4\); \(\dot{\phi}=30^{\circ}\); \(\gamma_{GPL}=0.01\); \(\omega=100\) rad/s). Figure 11: Temperature (**a**), radial displacement (**b**), and stress state (**c–f**) at \(z=L/2\) for various porosity coefficient (D1-GPL X; \(\phi=30^{\circ}\); \(\gamma_{GPL}=0.01\); \(\omega=100\) rad/s).
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Data Description
#### 3.1.1 Massachusetts Roads Dataset  
The Massachusetts Roads Dataset comprises high-resolution (1m per pixel) remote sensing imagery, with a total of 1171 images, each of 1500 \(\times\) 1500 pixels, accompanied by meticulously annotated ground truth labels. The dataset is partitioned into 1108 training images, 14 validation images, and 49 test images, each subset replete with precise label information. To align with the experimental framework of this study, the original 1500 \(\times\) 1500-pixel images underwent preprocessing to yield 512 \(\times\) 512-pixel image blocks,culminating in a corpus of 10,098 images. These images were further distributed into training, validation, and test sets at a ratio of 7:2:1.
## 5 Results and Discussion
#### Experimental Investigation
The comparison analysis performed based on packet loss for the proposed and existing routing protocols is shown in Figure 13. The graph is sketched between various techniques and packet loss in percentage on both the X- and Y-axes. The packet loss for the proposed routing approach is 58.3% less than EEGRA, 45.83% less than I-LEACH, 33.3% less than EBAR, and 23.02% less than ER-SRP. Figure 14 illustrates the comparison study carried out using the number of received packets in the existing and proposed routing protocols. The number of received packets for the proposed method is 5.70% greater than EEGRA, 3.80% greater than I-LEACH, 3.04% greater than EBAR, and 1.90% greater than ER-SRP. Based on this comparison study, it is proved that the performance of the proposed method is better than that of the existing routing methods. The comparison study carried out based on encryption and decryption times for the proposed and existing routing approaches is displayed in Figures 15 and 16. The encryption time for the proposed approach is 18 ms, and for the existing techniques such as EEGRA, I-LEACH, EBAR, ER-SRP, the encryption time is determined to be 22 ms, 22 ms, 21 ms, and 19 ms, respectively. Similarly, the decryption time for the proposed methodology is 0.4 ms and for the technique such as EEGRA, I-LEACH, EBAR, ER-SRP the decryption time is determined to be0.49 ms, 0.48 ms, 0.47 ms, and 0.44 ms respectively. Finally, based on this comparison study, it is revealed that the performance of the proposed architectural framework is superior to that of the conventional approach. By utilizing the optimal routing design and encryption algorithm, less energy utilization and improved security can be achieved in a better way. The analysis also revealed that a grid-based routing design is more suitable for smart building applications. Figure 14: Comparison on the amount of packet received. Figure 15: Comparison on the encryption time.
## 3 Results
### Complexity Analysis
#### 3.1.3 Static MSL Signs Classification  
Fully Connected Neural Network (FC-NN):  
* Description: The FC-NN used for static sign classification consists of several fully connected layers.
* Complexity: For a neural network with \(L\) layers, where each layer \(i\) has \(n_{i}\) neurons, the complexity of forward propagation is approximately \(O\left(\sum_{i=1}^{L}n_{i}\cdot n_{i-1}\right)\). This accounts for the matrix multiplications performed at each layer. Training complexity is higher because of backpropagation, approximately \(O(T\cdot\sum_{i=1}^{L}n_{i}\cdot n_{i-1})\), where \(T\) is the number of training epochs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Accuracy** & **Recall** & **Precision** & **F1 Score** \\ \hline
91.09\% & 91.05\% & 91.25\% & 90.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Metric results for the 29 letters.  
Convolutional Neural Network (CNN):
* Description: The CNN processes cropped images of the hand to classify static signs.
* Complexity: For a CNN with \(L\) convolutional layers, each layer with a kernel size of \(k_{i}\times k_{i}\) and an input feature map of size \(m_{i}\times m_{i}\), the complexity for one forward pass is approximately \(O\left(\sum_{i=1}^{L}k_{i}^{2}\cdot m_{i}^{2}\cdot c_{i}\right)\), where \(c_{i}\) is the number of channels in layer \(i\). The complexity increases with more layers or larger input sizes.
## 3 Workflow Description
### Mesh Generation  
The most significant challenge encountered during the development of T-WorkFlow was the automatic generation of the computational mesh, which posed two critical constraints. Firstly, to meet the specific requirements of Tatuus S.p.a. concerning available computational resources and to minimize the time required for the overall CFD process, the mesh should not exceed \(8\times 10^{6}\) cells. Secondly, to enable meaningful comparisons between CFD results of different radiator models, it is critical to ensure that the automatically generated mesh has a consistent level of quality, regardless of the specific geometry. In this regard, the pre-processing operations described in Section 3.2 were essential to achieve the highest possible mesh quality while respecting these constraints.  
In this work, the grid generation process is based on the _blockMesh_ and _snappyHexMesh_ tools from the OpenFOAM suite, which require properly defined input files (dictionaries) that are automatically generated by T-WorkFlow, as is discussed below.