esa-sceva/satcom-chunk-collection huggingface dataset:
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### End-to-End Latency QoS Parameter  
A close look at Table 1 in terms of end-to-end latency shows that a critical value which distinguishes scenarios 1 and 4 from scenario 3, is 100 ms. On the other hand, the scenario that tolerates higher latency values is scenario 5. Moreover, the scenarios that tolerate small latency values are 3 and 6. The calculation of end-to-end latency is performed using the equations that have been derived in [20]. These equations are as follows:  
\[t_{d1}=\left[\left(2\times TTI\right)+\left(k_{f}/R_{b}\times N\right)\right] \times V_{D} \tag{1}\]  
\[t_{d2}=\left[\left(2\times TTI\right)+\left(k_{f}/R_{b}\times N\times 1.8 \right)\right]\times V_{D} \tag{2}\]  
\[t_{d3}=\left[\left(2\times TTI\right)+\left(k_{f}/R_{b}\times N\times 2.8 \right)\right]\times V_{D} \tag{3}\]  
At Table 4 it is important to note that for the calculation of end-to-end latency for the first 8 scenarios of Table 2 we consider the worst case where all vehicles are involved. This is described from \(V_{D}\) parameter. The notation \(t_{d1}\) represents latency using SOVA, \(t_{d2}\) represents latency using max-log-MAP and notation \(t_{d3}\) represents latency using log-MAP. Furthermore, the number of turbo decoding iterations is \(N=3\), the short frame size is \(k_{f}\), the data rate is \(R_{b}\) and the TTI value is 1 ms (according to LTE turbo coding specifications) [20].  
Figure 12: Throughput vs. SNR for scenarios 7 and 8 of Table 2, considering different decoding algorithms, 3 iterations, frame length of 512 bits.  
At Table 4 the calculated end-to-end latency values are shown using equations 1, 2 and 3. We consider the three decoding algorithms and the first 8 scenarios of Table 2. The first observation is that irrespectively of the different parameters for the scenarios of Table 2 (short frame length, data rate, decoding algorithm, vehicle speed), scenario 2 can be implemented in all cases. This observation is similar in [20], where mainly a short frame length of 128 bits is considered.  
Additionally, our analysis shows that for scenarios 1, 3, 6 and 8 of Table 2, the implemented scenarios of Table 1 depend on the turbo decoding algorithm. At Table 5 our aim is to investigate the effect of vehicle density on the choice of Table 1 scenarios considering the calculated latency values. Here, we have constant values for frame length (512 bits), data rate (12 Mbps) and mobile speed (60 km/h). We notice that increasing vehicle density has no effect on the implemented Table 1 scenario considering end-to-end latency (scenarios 1, 2 and 4 can be implemented) irrespectively from the decoding algorithm.
# Variations in the Equatorial Ionospheric F Region Current during the 2022 Tonga Volcanic Eruption
## 1 Introduction
These results were consistent with previous observations, in which the F-region dynamo current was found to decrease with decreasing solar activity [14]. The FRC was diverted along the magnetic field line into the E region in both hemispheres, thus forming meridional current systems at noon and dusk. The dusktime meridional current was caused by the equatorial F-layer dynamo based on Magnetic Field Satellite (Magsat) observations [9]. These previous studies have provided interesting information that has aided our understanding of the FRC. It is essential to understand whether volcanic eruptions can affect the ionospheric current system, however, there has been little observational evidence to directly reveal the impact of these geological disasters on FRC for a long time. In this work, by analyzing FRC observations derived from the dual Swarm A and C satellites, we display the evidence characterizing the FRC in relation to the intense volcanic eruption event that occurred in Tonga on 15 January 2022. The focus of the present work is mainly on the polarity reversal of the FRC in response to the occurrence of neutral wind changes in the F region (above 400 km), which can be affected by volcanic eruptions. To this end, we use combined observations derived from Swarm and Ionospheric Connection Explorer (ICON) satellites. Section 2 briefly introduces the data-processing methods used in this work. Section 3 contains the observational results obtained from the satellites. Section 4 discusses the results in the context of previous studies. Finally, Section 5 presents a summary of this work.
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 4 Combination of Multisource Data for Road Extraction  
### Combination of High-Resolution Images with Other Data for Road Extraction  
High-resolution images reveal very fine details of the earth's surface and geometry; however, high-resolution imaging also increases the geometric noise and only provides spectral and spatial information on the surface. LiDAR data, as a special data source, can provide 3D information about objects. [PERSON] et al. [233] proposed automatic road extraction methods through an integrated approach involving ALS altimetry and high-resolution imaging. The method was used to extract road information without background objects, and showed an increased road extraction accuracy of 90% when applied to Amsterdam data. [PERSON] et al. [234] proposed a grid-structured urban road network extraction method using LiDAR data and high-resolution imagery. A significant improvement in the road extraction accuracy was obtained using this method than using high-resolution imagery or LiDAR data. [PERSON] et al. [235] proposed a method to improve the accuracy of road centreline extraction using high-resolution images and LiDAR data. The method adopted the minimum area bounding rectangle-based interference-filling approach, multistep approach  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Category** & **Characteristic** & **References** & **Correctness** \\ \hline Hierarchical fusion and optimisation & ALS & Extract road centreline & [217], 2015 & 0.914 \\ Point-based classification & MS-ALS & Land cover classification & [226], 2017 & 0.920 \\  & & road detection and road & & 0.860 \\ Object-based image analysis and random forest & MS-ALS & surface classification & [227], 2017 & 0.805 \\  & & Three types of asphalt and a & & 0.947 \\ Support vector machine & MS-ALS & concrete class & [228], 2018 & (Overall accuracy) \\ Hybrid capsule network & MS-ALS & Land cover classification & [231], 2020 & 0.979 \\  & & & & (Overall accuracy) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of road extraction using LiDAR data.  
and Harris corner detection. The experimental results based on the datasets of Vaihingen, New York, and Guangzhou showed that the proposed method was efficient in identifying complex scenes.
## 7 Discussion
### Future Directions and Research Opportunities
#### 7.2.1 Standardization  
Standardization is a critical enabler for the widespread adoption of DTs throughout the lifecycle of transportation infrastructure. Currently, the lack of standardized frameworks for data exchange, modeling methods, and communication protocols poses significant challenges. Different DT systems use varying data formats and standards, making integration and interoperability difficult [119]. Developing universal standards is essential to enable seamless data exchange across DT platforms and transportation management systems [27].  
During the planning and design phase, standardized data models and protocols would facilitate the integration of diverse datasets, such as geographic, environmental, and traffic data, enabling more accurate simulations and optimized designs. In the construction phase, standardized communication protocols would ensure real-time data sharing between DT systems and construction management tools, improving quality control and progress monitoring. As infrastructure moves into the operation and maintenance phase, standardized sensor data formats would allow consistent data integration across different systems, enhancing real-time monitoring and predictive maintenance capabilities. Finally, during the decommissioning phase, standardized frameworks would support the efficient transfer of data and models to new systems, ensuring continuity and minimizing disruptions during infrastructure upgrades or replacements.  
Future research should focus on creating standardized data models and protocols tailored to the needs of transportation infrastructure. For example, standardized sensor data formats would allow for consistent data integration across different DT systems, regardless of the data source or system developer. Similarly, standardized communication protocols would enable DT systems to share results effortlessly with external applications, such as urban planning databases, Intelligent Transportation Systems (ITS), and public transit networks.  
While some progress has been made in developing data exchange standards--particularly in niche areas like building information modeling (BIM) and geographic information systems (GIS)--much work remains to establish a widely accepted framework for DTs in transportation [102]. Achieving this goal will require collaboration among stakeholders to address the diversity of technologies involved, including IoT devices, machine learning models, and 3D visualization tools.
# Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps
## 2 Related Work
### U-Net  
Semantic segmentation is the task of classifying objects in pixels in an image [18]. In deep learning, an object means a class to be classified [19]. Segmentation needs to obtain a label from the input image that each pixel belongs to. For this, one-hot encoding is used to create as many output channels as there are classes. After that, one output is calculated by means of argmax. In this way, semantic segmentation considers only whether pixels are binarily included for each class. It is mainly studied in the medical and transportation fields and has been recently applied to autonomous vehicle technology. Representative studies include the fully convolutional networks proposed by [PERSON] et al. [20] in 2015, DeepLab proposed by [PERSON] et at. [21] in 2015, and U-Net proposed by [PERSON] et al. [22].  
U-Net is a model based on an end-to-end fully convolutional network (FCN) proposed for the purpose of biomedical image segmentation. U-Net uses data augmentation to learn enough with only a small amount of data. Data augmentation improves model performance by increasing the amount of data and is important because it simply improves most of the model performance [23; 24]. U-Net is shaped like a U and mainly consists of a contracting path and an expansive path. The contracting path is the stage that captures the context of the input image. Context refers to the relationship between neighboring image pixels and can be thought of as understanding the overall image context by looking at a part of the image. In general, it consists of a convolutional layer and a pooling layer. In each step, the number of channels in the feature map is doubled, but the size is cut in half. The expanding path is a step for accurate localization, and up-sampling is performed several times to increase the resolution that has gone through the contracting path. It plays a role in more accurate localization by combining with the context of the feature map captured in the contracting path. U-Net removes the fully connected layer and uses only convolution to enable segmentation even when images of arbitrary size come in by the overlap tile strategy. Overall, the contracting path and expanding path are symmetrical and constitute a U-shaped network.
## 3 Results
### Comparative Analysis between Groups, ACL and NI, Considering DL or NDL  
The comparative analysis in peak actEMG for DL and NDL showed no significant differences between groups. However, strong to excellent effect sizes for NDL were observed in VM (d = 1.075) and in GMax (d = 1.124). For NDL, VM peak actEMG was higher in NI players, while in GMax peak actEMG was higher in ACL players (Table 2).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**DOMINANT LIMB (DL)**} & \multicolumn{4}{c}{**NO DOMINANT LIMB (NDL)**} \\ \cline{2-10}  & **ACL** & **NI** & **NI** & **NI** & **NI** & **NI** & **NI** & **NI** & **NI** & **NI** & **NI** \\ \hline  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline BF\_peak & 85.81 \(\pm\) 85.81 & 90.06 \(\pm\) 5.05 & 0.157 & 0.73 & (\(-\)10.33 to 1.83) & 91.22 \(\pm\) 5.59 & 93.61 \(\pm\) 5.45 & 0.464 & 0.43 & (\(-\)9.29 to 4.52) \\ ST\_peak & 83.79 \(\pm\) 83.79 & 89.38 \(\pm\) 7.65 & 0.320 & 0.49 & (\(-\)17.29 to 6.11) & 91.34 \(\pm\) 6.98 & 86.44 \(\pm\) 5.96 & 0.204 & 0.75 & (\(-\)3.07 to 12.86) \\ UT\_peak & 87.04 \(\pm\) 87.04 & 89.18 \(\pm\) 8.69 & 0.647 & 0.23 & (\(-\)11.86 to 7.59) & 86.14 \(\pm\) 8.59 & 89.39 \(\pm\) 5.16 & 0.407 & 0.46 & (\(-\)11.55 to 5.05) \\ VM\_peak & 82.68 \(\pm\) 82.68 & 87.44 \(\pm\) 10.10 & 0.347 & 0.47 & (\(-\)15.19 to 5.68) & 78.84 \(\pm\) 7.34 & 86.78 \(\pm\) 7.43 & 0.087 & 1.07 & (\(-\)17.23 to 1.35) \\ GMax\_peak & 87.26 \(\pm\) 87.26 & 90.08 \(\pm\) 6.39 & 0.509 & 0.34 & (\(-\)11.76 to 6.11) & 94.37 \(\pm\) 4.67 & 88.09 \(\pm\) 6.38 & 0.085 & 1.12 & (\(-\)1.01 to 13.58) \\ GM\_peak & 89.68 \(\pm\) 89.68 & 88.04 \(\pm\) 6.16 & 0.611 & 0.26 & (\(-\)5.11 to 8.39) & 91.59 \(\pm\) 4.86 & 91.08 \(\pm\) 6.16 & 0.879 & 0.09 & (\(-\)6.67 to 7.69) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of peak actEMG outcomes during maximal voluntary isometric contraction between groups, ACL and NI, considering DL or NDL.  
The comparative analysis in average actEMG for NDL obtained statistical signification between groups in BF (\(p\) = 0.023) and GMax (\(p\) = 0.038). Moreover, for NDL, these outcomes showed strong to excellent effect sizes in BF (d = 1.384) and GMax (d = 1.351), as well. For NDL, the BF average actEMG was higher in NI players, while the GMax average actEMG was higher in ACL players (Table 3).  
No significative differences were observed for DL or NDL in the comparative analysis of peak torque of force between ACL and NI groups. However, for DL, strong to excellent effect sizes were obtained for peak torque of force in Gm (d = 0.942) (Table 4).
## 3 Results and Discussion
### Optimal Green Solvent Design for Antioxidant Extraction from Lemon Balm
The higher viscosity and density of the DES containing low amounts of water led to a compact intermolecular structure, which hindered contact between the bioactive compounds and the DES. Moreover, for the same levels of the first two independent variables (ChCl:U and WC), the UAE of total antioxidants from LB increased significantly with the increase in extraction time (t). For comparison, various molar ratios of ChCl and Eth (1:2, 1:3, 1:4, 1:5, and 1:6, mol/mol), water contents (0, 5, 10, 20, 30, and 40%), and conventional extraction times (30, 40, 50, 60, 70, and 80 min) were investigated to find the most effective extraction yield of rutin from _S. japonica_[23]. The highest extraction yield of rutin from this herb (about 23%) was achieved when ChCl:Eth was 1:3 containing 10% of water after 60 min. Additionally, an increase in water amount in ChCl:CitA (1:1) increased polyphenol yields from _Chamaenerion angustifolium_ (L) Scop. [22]. The optimal conditions of polyphenol extraction were found: temperature = 58 \({}^{\circ}\)C, time of UAE = 35 min, and WC = 70 wt%. At these conditions, TPC = 301 mg/mL, TFC = 74 mg/mL, TAC = 54 mg/mL, and DPPH = 70%.
# Power Flow Calculation Methods for Power Systems with Novel Structure UPFC
## 3 Three Power Flow Calculation Methods
### Three Power Flow Calculation Methods
\begin{array}{l}\Delta{P_{l}}^{\prime}={P_{ls}}-{P_{lm}}^{ \prime}-{u_{l}}{\sum\limits_{i\in l}{u_{i}}({G_{li}}\cos{\theta_{li}}+{B_{li}} \sin{\theta_{li}})}\\ \Delta{Q_{l}}^{\prime}={Q_{ls}}-{Q_{lm}}^{\prime}-{u_{l}}{\sum\limits_{i\in l }{u_{i}}({G_{li}}\sin{\theta_{li}}-{B_{li}}\cos{\theta_{li}})}\\ \Delta{P_{m}}^{\prime}={P_{ms}}-{P_{ml}}^{\prime}-{u_{m}}{\sum\limits_{i\in m }{u_{i}}({G_{mi}}\cos{\theta_{mi}}+{B_{mi}}\sin{\theta_{mi}})}\\ \Delta{Q_{m}}^{\prime}={Q_{ms}}-{Q_{ml}}^{\prime}-{u_{m}}{\sum\limits_{i\in m }{u_{i}}({G_{mi}}\sin{\theta_{mi}}-{B_{mi}}\cos{\theta_{mi}})}\\ \Delta{P_{n}}^{\prime}={P_{ns}}-{P_{ne}}^{\prime}-{u_{n}}{\sum\limits_{i\in n }{u_{i}}({G_{ni}}\cos{\theta_{ni}}+{B_{ni}}\sin{\theta_{ni}})}\\ \Delta{Q_{n}}^{\prime}={Q_{ns}}-{Q_{ne}}^{\prime}-{u_{n}}{\sum\limits_{i\in n }{u_{i}}({G_{ni}}\sin{\theta_{ni}}-{B_{ni}}\cos{\theta_{ni}})}\\ \end{array} \right. \tag{24}\]  
Then, the system power deviations can be represented by the left side of Equation (22). It should be noted that, in principle, at the \(i\)th iteration, \(\Delta{\mathbf{\theta_{2}}}\) and \(\Delta{\mathbf{U_{2}}}/{\mathbf{U_{2}}}\) in Equation (23) should be the results of this iteration. To calculate the new equivalent power injections of the UPFC in explicit form, \(\Delta{\mathbf{\theta_{2}}}\) and \(\Delta{\mathbf{U_{2}}}/{\mathbf{U_{2}}}\) are estimated as follows [26]:  
\[\left[ \begin{array}{c}\Delta{\mathbf{\theta_{2}}}^{i}\\ \Delta{\mathbf{U_{2}}}^{i}/{\mathbf{U_{2}}}^{i}\end{array} \right]\approx\lambda\left[ \begin{array}{c}\Delta{\mathbf{\theta_{2}}}^{i-1}\\ \Delta{\mathbf{U_{2}}}^{i-1}/{\mathbf{U_{2}}}^{i-1}\end{array} \right] \tag{25}\]  
where \(\lambda\) is a scale factor, and the value range of \(\lambda\) is [0,1]. At the first iteration, \(\Delta{\mathbf{\theta_{2}}}\) and \(\Delta{\mathbf{U_{2}}}/{\mathbf{U_{2}}}\) are both equal to \(\mathbf{0}\). With this method (which is referred to as the \"improved method\"), after the power flow calculation converges, \(\Delta{\mathbf{\theta_{2}}}\) and \(\Delta{\mathbf{U_{2}}}\) both approach \(\mathbf{0}\), so that the new equivalent power injections of the UPFC approach the original power injections. Therefore, the power flow obtained by the improved method is the same as that of the original method. Besides, because the improved method uses the same Jacobian matrix as the simplified method, it can be easily implemented in existing power flow calculation programs. Since the impact of the Jacobian matrix modifications is considered in the new equivalent power injections, the improved method will have better convergence characteristics than the simplified method.
## 2 Interconnect Technologies in Power Electronics and SSL  
### Soldering  
Soldering is the most dominant interconnect technology in electronics production due to the simple processing for mass production. The standard solder paste application method is a stencil-printing process of viscous solder paste over a thin metal stencil onto the substrate. The solder paste consists of small metal spheres and a binder material including flux. Components are placed by an automatic pick and place machine into the printed solder reservoirs, and the assembled substrates are carried through a reflow oven with multiple temperature sections to fulfill an optimal solder profile for the paste. The soldering profile can be separated into the three processing zones, namely, preheat/soak (outgassing of binders and oxide reduction from the solder spheres by flux), reflow (solder is liquid), and cool down (solder solidifies). To increase the quality of solder interfaces, protection atmospheres, e.g., nitrogen gas, can additionally be used, or a vacuum can be applied [13; 14].  
A common issue in soldering is gas inclusion in the solder interconnect, referred to as voids. Voids reduce the cross-section for heat transfer and, therefore, have to be capped under a certain limit to be unproblematic [15]. By applying vacuum while the solder is liquid, most of the voids can be sucked out of the solder joint. More critical than voids are nonwetted areas due to residues on substrate or component pads, drastically reducing the contact area for electrical and thermal transfer. Reasons for residues can be contamination or inadequate handling of substrate and component or inappropriate process parameters [16]. A third issue involves cracks in the solder interconnect induced by a CTE mismatch (coefficient of thermal expansion) of component and substrate. Several cycles of heating and cooling over the operating lifecycle of the device slowly damage the solder interconnect leading to the growth of thin cracks. Heat and current transfer are no longer possible through these cracked areas and the thermal performance is reduced [17], until the interconnects finally fail electrically due to an open contact.
# Reliable Semantic Communication System Enabled by Knowledge Graph
## 3 System Model
### Semantic Extraction Method
Consequently, the details of the proposed semantic extraction method can be summarized in Algorithm 1. ```
0: the transmitted sentence \(S\)
1: Build entity set \(E\) by Equation (5)
2:for each \(en_{i}\in E\)do
3: Compute the embedding \(e_{i}\) by Equations (6) and (7)
4:endfor
5: Construct the relation set according to Equation (8)
6: Compute loss function \(\mathcal{L}_{\texttt{S2G}}(\theta)\) according to Equation (10)
7: Train \(\theta\rightarrow\theta^{*}\)
```  
**Output**: The knowledge graph \(G\) ```
**Algorithm 1** The proposed semantic extraction method