esa-sceva/satcom-chunk-collection huggingface dataset:
# A No Reference Image Quality Assessment Metric Based on Visual Perception +
## 3 Extraction Model of Visual Region of Interest  
With the increasingly development of information technology, images have become the main media of information transmission. How to analyze and deal with a large number of image information effectively and accurately is an important subject. Then, researchers have found that the most important information in the image is often focusing on a few key areas. They call these key areas salient regions or regions of interest. We will greatly improve the efficiency and accuracy of image processing and analyzing with extracting these salient regions accurately. So far, there are many regions of interest extraction algorithms. The detection technology of the ROI, which has been based on interaction and detection technology, has gradually developed into based on visual feature technology. The salient map model proposed by [PERSON] is the most typical ROI detection, based on visual features [33]. This method only considers the bottom-up signal, simple and easy to implement. It is the most influential visual attention model.
## 5 Analysis of Results and Discussion
### Effect of Mesh Density on Model Accuracy  
To investigate the effectiveness of utilizing different mesh densities on model accuracy, the load-time history and applied energy-penetration curves resulting from the numerical analysis were compared with the corresponding results obtained from the experimental investigation as portrayed in Figure 14a,b. Five different meshing densities were examined ranging from very fine to coarse. Table 4 displays the number of elements utilized in each case and the ratio between the results obtained from the finite element analysis (FEA) and the corresponding experimental results. It can be observed that as the number of elements increased, the shape of the predicted curves became smoother and progressively converged toward the experimental behavior. Cases 1 and 2 were far from the experimental curve. Conversely, cases 3 and 5 almost gave similar results to the experimental curves. Case 4 achieved the closest prediction of the experimental results. Based on the results in Table 4, the near optimum number of elements utilized in simulating the ECC specimens was 15,180 elements. Since the spherical steel ball, holder steel ring, and drop weight were considered rigid, their element sizes had no significant effect on the solution.  
Figure 14: Effect of different mesh densities on numerical and experimental results: (**a**) load-time history and (**b**) applied energy-penetration depth.  
Figure 13: (**a**) Initial conditions and (**b**) boundary conditions.
## 2 Background
### The IEEE 802.11p Standard
As mentioned previously, the bandwidth of the OFDM signal is given by 10 MHz and the radio frequency matches to 5.9 GHz. The data subcarriers are shown in green, while the pilots are shown in red and are uniformly distributed between the data subcarriers. Additionally, the null and Direct Current (DC) subcarriers are represented by black and blue colors, respectively. For the sake of simplicity, in our work, null and DC subcarriers are discarded. In Figure 4, the IEEE 802.11p packet preamble structure is illustrated. It can be seen that the preamble has 10 short training symbols of 1.6 us located at the beginning of the packet. These symbols are used to perform fine synchronization. In this work, the use of these symbols is discarded, since we assume a perfect synchronization to focus on the estimation/equalization issue. Furthermore, two long training symbols of 6.4 us can be distinguished, which are used to perform the channel estimation. Since these symbols are found for each subcarrier of the OFDM signal, they are better suited to calculating the channel estimation than the pilot signals [7]. Note that a guard interval to avoid out-of-band interference also exists, whose duration is 3.2 us.
## 5 Discussion and Conclusions
### Conclusions  
Thermal conductivity is important in several applications to different temperatures, i.e., aerospace industry; nuclear industry; nuclear control rods; radioactive waste containment; the phase change material; or items such as bearings, piston parts, pumps, compressor plate valves, cable insulation and medical implants used in different applications. Therefore, it is essential to measure this thermal property with the most accurate as possible.  
The percentage error obtained by ANSYS was 13.5% for the robust model, averaging the 4 volumes (2 copper bars, 1 aluminium bar, and the fibreglass insulator).  
The union of elements that interact between the interfaces of the materials is essential and considerably affects the results. In spite of this, it is possible to know with a 13.5% error the temperature distribution inside the system of cut bars [35].  
The values near the borders are very far from the experiment; however, the values near the sample bar are too close to those obtained experimentally.  
The temperature deviation obtained through simulation and experimental work of the cold source is affected by the contact between it and the copper bar. On the other hand, the same is valid for hot source contact. In the simulation, losses due to bad contact or heat leakage to the environment are not considered. Other methods proposed in the literature to characterise materials have allowed us to verify experimental results [36; 37].  
The bars cut in CENAM must be designed to prevent radial heat leakage because, according to the simulation results, there is heat leakage in this direction. With the results obtained, a guard can be proposed that balances the gradients generated in the system. Because three distinct sections are noted for the latter case, it would be 213.33 \({}^{\circ}\)C, 277.77 \({}^{\circ}\)C, and 342.22 \({}^{\circ}\)C. As a proposal for improving the design of the CENAM cut bar system.  
According to the analysis of the results and the simulations obtained, the following design criteria are proposed. Improve the thermal contact between the hot source and the cold source with the reference bars, which can be achieved by a system that compresses the three bars. Implement a guard with a control system in the hot source and in the cold source, which, although it does not eliminate the radial temperature gradient, reduces it to a minimum. From simulation realised, it is possible to obtain the location of the temperature sensor for the control guard system.
## 4 Simulation Results
However, while ISI decreases, the delayed signals are not canceled, leading to errors caused  
Figure 7: ADR versus number of interfering users \((N-1)\) for different TN\(-\)RN distances. by the delay. In conclusion, as \(N_{h}\) increases, ISI decreases, but the reduction in the number of signal overlap cases leads to an increased error rate. Furthermore, as mentioned above, \(M=2\) shows a lower SER at all distances compared to \(M=4\), implying that lower-order modulations are more robust to channel impairments. Altogether, the choice of \(N_{h}\) and \(M\) depends on the trade-off between the desired data rate and the robustness against the communication channel.
## 3 Results and Discussion
### Material Characterization
#### 3.1.4 Zeta Potential (ZP)  
The zeta potential of each Hap sample was measured to assess the stability of the Hap dispersion in water. The results, shown in Table 2, indicate that the synthesized Hap samples exhibit low stability in water as they precipitate rapidly. The measured zeta potential values were \(-\)18.6 mV for Hap-H1, \(-\)19.1 mV for Hap-H2, \(-\)21.9 mV for Hap-H3, \(-\)22.6 mV for Hap-H3 750, and \(-\)22.8 mV for Hap-FCF. This finding aligns with [PERSON] et al. [38], who also reported the poor aqueous stability of hydroxyapatite. Generally, smaller Hap particles tend to exhibit higher stability in aqueous media; however, a suspension is typically considered stable only when zeta potential values exceed \(\pm\)30 mV [39].
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 3 Network Structure
### Hierarchical
#### 3.2.3 Fuzzy Logic Based  
During the operation of these protocols, the core functions of the network as data routing, cluster head election, the hotspot problem or network balancing, are executed with the assistance of fuzzy logic, which enables the network to combine and evaluate diverse parameters while operating efficiently. In what follows in this section, eight typical examples of fuzzy logic based energy efficient routing protocols are described. Fuzzy Logic-Based Energy Efficient Packet Loss Preventive Routing ProtocolFEEPRP (FEEPRP) [107] is an energy aware packet loss preventive routing protocol, which aims to save energy and, at the same time, control congestion, avoid malicious nodes, and prevent data losses. FEEPRP uses a fuzzy control mechanism that monitors the past records of residual energy, packets dropped at each node, and hop count to decide which route to select for sending messages. Each time, a different route is selected according to the output given by the fuzzy logic mechanism. The residual energy of each route is considered as one of the inputs in the fuzzy control to assure energy conservation as well. Energy conservation is enhanced more by letting nodes sleep when idle. The throughput achieved is high. However, processing overhead is notable. In addition, mobility of nodes is not supported. Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach (DUCF) [108] is a clustering protocol, which balances the energy consumed by nodes and reduces the energy consumed by cluster heads. One common problem DUCF faces, called hotspot, is that the cluster heads closer to the base station have to send their data and relay data from distant cluster heads, due to its multi-hop routing, resulting in increased consumption. To solve this, it utilizes a fuzzy approach achieving better network balance. In addition, to achieve energy balance, more consuming cluster heads, should meet the energy requirements within an area with a low number of nodes, closer to neighbor nodes and the base station. Therefore, during cluster head election, DUCF takes into account three parameters, which namely are: (a) the residual energy, (b) node degree, and (c) distance to the base station. By using DUCF, not only is network lifetime prolonged, but also the hotspot problem is solved. However, Coverage redundancy is not supported. Energy-Aware Unequal Clustering with Fuzzy approachEAUCF [109] is a clustering energy balance protocol which utilizes Fuzzy logic in order to solve the hotspot problem, a common issue of cluster heads in close proximity to the base station. Before EAUCF elects a cluster head, it considers two Fuzzy
## Appendix A Analytical Symbol-Error-Rate Formulation  
### Analytical Symbol-Error-Rate of FU in \(S_{1}\)  
In accordance with the concept of SM, both BS and D\({}_{1}\) transmit FU data bits through the activation of a single antenna at a particular time instant. Therefore, we can consider the estimation of any transmitted \(M\)-PSK/\(M\)-QAM symbol at D\({}_{1}\) and FU as a special case of \(1\times A_{D_{1}}\) and \(1\times A_{\text{FU}}\) MRC detection [12], respectively. Now by using [57] (Equation (41)), the error estimation of any transmitted \(M\)-PSK data symbol (SER) at D\({}_{1}\) and FU in S\({}_{1}\) can be obtained respectively as  
\[P_{\text{SER,FU}}^{S_{1},P_{1}} =\frac{1}{\pi}\int_{0}^{\frac{(M_{1}-1)\pi}{M_{1}}}\prod_{i=1}^{A _{D_{1}}}\biggl{(}1+\frac{\rho_{\text{SFU,1}}}{\sin^{2}\phi}\biggr{)}^{-1}d\phi \tag{42}\] \[P_{\text{SER,FU}}^{S_{1},P_{2}} =\frac{1}{\pi}\int_{0}^{\frac{(M_{2}-1)\pi}{M_{2}}}\prod_{i=1}^{A _{\text{FU}}}\biggl{(}1+\frac{\rho_{\text{SFU,2}}}{\sin^{2}\phi}\biggr{)}^{-1}d\phi \tag{43}\]  
where \(P_{\text{SER,FU}}^{S_{1},P_{1}}\) and \(P_{\text{SER,FU}}^{S_{1},P_{2}}\) denote SER at D\({}_{1}\) and FU during \(P_{1}\) and \(P_{2}\), respectively. Here, \(g_{\text{FU,1}}=\sin^{2}(\frac{\phi}{M_{1}})\), \(g_{\text{FU,2}}=\sin^{2}(\frac{\phi}{M_{2}})\), and \(M_{2}=2^{N_{1}}\). The average SNR, \(\rho\) at the receiving side is denoted by \(\rho\triangleq\frac{P_{\text{BS}}}{\sigma^{2}}\triangleq\frac{P_{D_{1}}}{ \sigma^{2}}\), where \(\sigma^{2}\) is the noise variance.  
Again by using [57] (Equation (41)), the error estimation of any transmitted \(M\)-QAM data symbol at D\({}_{1}\) and FU in S\({}_{1}\) can be obtained respectively as  
\[P_{\text{SER,FU}}^{S_{1},P_{1}} =\frac{4}{\pi}\biggl{(}1-\frac{1}{\sqrt{M_{1}}}\biggr{)}\int_{0}^{ \frac{\pi}{2}}\prod_{i=1}^{A_{D_{1}}}\biggl{(}1+\frac{\rho_{\text{QAM,1}}}{ \sin^{2}\phi}\biggr{)}^{-1}d\phi \tag{44}\] \[-\frac{4}{\pi}\biggl{(}1-\frac{1}{\sqrt{M_{1}}}\biggr{)}^{2}\int_ {0}^{\frac{\pi}{4}}\prod_{i=1}^{A_{D_{1}}}\biggl{(}1+\frac{\rho_{\text{QAM,1} }}{\sin^{2}\phi}\biggr{)}^{-1}d\phi\] \[P_{\text{SER,FU}}^{S_{1},P_{2}} =\frac{4}{\pi}\biggl{(}1-\frac{1}{\sqrt{M_{2}}}\biggr{)}\int_{0}^{ \frac{\pi}{2}}\prod_{i=1}^{A_{\text{FU}}}\biggl{(}1+\frac{\rho_{\text{SQAM,2} }}{\sin^{2}\phi}\biggr{)}^{-1}d\phi\] (45) \[-\frac{4}{\pi}\biggl{(}1-\frac{1}{\sqrt{M_{2}}}\biggr{)}^{2}\int_ {0}^{\frac{\pi}{4}}\prod_{i=1}^{A_{\text{FU}}}\biggl{(}1+\frac{\rho_{\text{ SQAM,1}}}{\sin^{2}\phi}\biggr{)}^{-1}d\phi\]  
where \(g_{QAM,1}=\frac{3}{2(M_{1}-1)}\) and \(g_{QAM,2}=\frac{3}{2(M_{2}-1)}\). Here, \(P_{\text{SER,FU}}^{S_{1},P_{1}}\) and \(P_{\text{SER,FU}}^{S_{1},P_{2}}\) denote SER at D\({}_{1}\) and FU during \(P_{1}\) and \(P_{2}\), respectively.
# An Approach for a Spatial Data Attribute Similarity Measure Based on Granular Computing Closeness
## 4 Case Study  
### Research Area and Data Sources  
Guangxi is a sea passage in southwestern China, with the advantage being located along the border, along the coast and along the Yangtze River. Guangxi has a complete array of land types, with high and low landforms including mountains, plateaus, middle mountains, hills, terraces and plains.  
Figure 2: Spatial distribution of three land use types and spatial distribution of indexes.  
Having entered a new era of industrialization and rapid economic development in Guangxi, land use has changed dramatically. This paper will use the 2010 data on land use in Guangxi (Figure 3b), the Guangxi GDP kilomet network data in 2010 (Figure 4a), the data of the Guangxi population density kilomet network in 2010 (Figure 4b), the data of Guangxi DMSP/OLS in 2010 (Figure 4c), Guangxi Digital Elevation Model (DEM) data (Figure 4d) and Guangxi road data (Figure 4e), using the method proposed in this paper to verify the spatial similarity of different land types in Guangxi. The road data source of this study is the open street map ([[https://www.openstreetmap.org](https://www.openstreetmap.org)]([https://www.openstreetmap.org](https://www.openstreetmap.org))), and other data are from the data collection of the Resource and Environmental Science Data Center of the Chinese Academy of Sciences ([[http://www.resdc.cn/Default.aspx](http://www.resdc.cn/Default.aspx)]([http://www.resdc.cn/Default.aspx](http://www.resdc.cn/Default.aspx))). There were seven types of first-level land use and 22 types of second-level land use in Guangxi in 2010, where cultivated land, forest land and grassland areas dominated, and the proportion of unused land and sea areas was small, as is seen in Table 2.
## 1 Introduction  
The widespread adoption of smartphones, tablets, and other types of digital devices has led to an enormous surge in traffic on the internet in the past decade. Consequently, there is a need to effectively use the wireless spectrum due to a lack of more wireless resources and resources on the radio spectrum. Cognitive radio is a promising solution for addressing the issue of bandwidth scarcity. Unauthorized customers may utilize previously reserved frequencies for authorized individuals thanks to cognitive wireless technology. Cognitive radio technologies are classified into two types: frequency-sharing and heterogeneity. Numerous conceivable variations of the transfer of data in cognitive radio networks (CRNs) necessitate taking into account various queueing systems as their descriptors; for an example, refer to [1]. Here, we apply the resulting assumptions regarding the CRN's operational circumstances. It is assumed that the bandwidth of each network cell is split into identical sub-bands or channels, each available for usage by high-priority users (HPUs) and low-priority users (LPUs). There might be more than one channel. The single-server example widely discussed in the literature barely applies to CRNs. There is no waiting buffer in the unlikely event that all available bandwidth is used [2]. Spectrum measurement is perfect because LPUs can precisely measure or predict how much of a channel is occupied by HPUs or other LPUs. The so-called overlay mode is used by the cell. Under these conditions, if an HPU arrives while every channel is occupied, one LPU's service must be stopped. It involves a spectrum handoff wherein an LPU can quickly settle an additional idle channel if it becomes available after an HPU reclaims its track. If the inactive channel is unavailable, the LPU may attempt to join the service later, at random.
# Practical Submodule Capacitor Sizing for Modular Multilevel Converter Considering Grid Faults
###### Abstract  
Submodule (SM) capacitors are key elements in the modular multilevel converter (MMC), the design of which influences the entire system performance. In practical cases, SM capacitor sizing must consider the abnormal system operation (e.g., grid faults). In order to find a clear design boundary for SM capacitors, a practical capacitor sizing method is presented for the first time in this paper, considering the grid-fault-ride-through operation of the MMC, impact of MMC control system, and aging mechanism of capacitors. The SM capacitor rated voltage, capacitance, ESR, thermal resistance, and lifetime can be decided to ensure reliable operations of the MMC during grid faults. The effectiveness of the proposed method has been verified through experimental tests on a down-scale MMC system.  
modular multilevel converter (MMC); submodule capacitor design; grid faults; transient state; reliability +
Footnote †: journal: Applied Sciences
# Steering Pull Model and Its Sensitivity Analysis
## 2 Tire Pulling Force
### Wheel Alignment
#### 2.3.2. Camber  
Camber angle is the inclination angle of a tire with respect to the vertical line. This camber angle produces distortion of the contact surface of the tire and it causes a lateral force called 'camber force' or 'camber thrust'. A camber force can be obtained by Equation (8) [12].  
\[F_{y,c}=C_{\gamma}\gamma=C_{C}(F_{zl}+F_{zr})\gamma \tag{8}\]  
where \(F_{y,c}\) = lateral force due to camber angle, \(C_{\gamma}\) = camber stiffness, \(C_{C}\) = camber stiffness coefficient, \(\gamma\) = camber angle.  
Typically, a camber angle is set under 2 degrees and thus, the camber force caused by the camber angle is smaller than the lateral force by a slip angle. However, it can have a significant effect on the steering pull since the lateral force by a small slip angle is also very small. For a small tire angle, the camber and lateral forces can be treated as the independent as shown in Equation (9) [12].  
\[F_{y,t}=F_{y,\alpha}+F_{y,c}=F_{y,\alpha}+C_{C}(F_{zl}+F_{zr})\gamma \tag{9}\]  
where \(F_{y,t}\) = total lateral force, \(F_{y,\alpha}\) = lateral force due to slip angle.
## 4 Results
### Model Generalization and Cross-Domain Validation  
To evaluate the model's generalizability beyond our primary test site, we conducted extensive validation across multiple facilities with diverse architectural designs, occupancy patterns, and cultural contexts. The analysis extended from the original Singtel Serangoon TEPL building to encompass a high-density commercial complex in Kuala Lumpur (4200 occupants), an educational facility in Melbourne (2800 occupants), and a healthcare facility in Sydney.  
Initial testing revealed significant variations in usage patterns stemming from distinct operational characteristics. The Kuala Lumpur site exhibited concentrated usage peaks during prayer times, while the Melbourne campus displayed distributed patterns aligned with academic schedules. Despite these variations, the base model maintained strong performance, achieving RMSE values of 0.0165 and 0.0158, respectively, comparable to the original 0.0140 RMSE at Singtel.
## 2 Methods
### Heatmaps  
A heatmap generated by machine learning regression algorithms shows how a predicted value changes in a specific area. Different colors represent different levels of the value, with warmer colors indicating higher levels. The machine learning model is taught to predict this value based on other information, and the heatmap visualizes the model's predictions for different points. To create a heatmap using machine learning regression, the process (see Figure 3) starts with data collection, which involves gathering data that include input features (the set of variables as described in Section 2.1) and the target variable (electric field strength) for various points. The next step is model training, where a regression model (such as linear regression, random forest, support vector regression, or neural networks) is trained using the collected data to learn the relationship between the input features and the target variable. Once the model is trained, it is applied to make predictions across a grid or spatial area, generating predicted values for a dense set of locations or feature combinations, even in places where original data were not available. These predicted values are then visualized on a heatmap, where different colors represent varying ranges of predicted values, such as red for high values and blue for low values, creating a gradient effect. Regression-based machine learning heatmaps show predictive values rather than actual measurements, which makes them useful for forecasting in unsampled areas.
## 4 Mathematical Formulation
### Constraints
#### 4.1.2 Operation Zone and Overtaking Station
\begin{array}{l}J_{s+1,i}=J_{s,i+1},J_{s+1,i+1}=J_{si},\\ \text{if }T_{s,J_{si},J_{si+1}}=1,\text{ or }\Delta d_{s,J_{si}}^{(0)}<0,\tau_{s,J_{si}} \tau_{s,J_{si}+1}=0\text{ \,}\forall s\in\mathcal{S},i<I+2n\\ J_{s+1,i}=J_{si},J_{s+1,i+1}=J_{s,j+1},\text{ otherwise}\end{array} \right. \tag{13}\]  
If the operator allows \(n\) trains (\(n>1\)) to overtake or be overtaken, such as \(n=2\) in Figure 1, it is necessary repeatedly calculate the \(p\) planned intervals \(\Delta d_{s,J_{si}}^{(p-1)}\) and exchange train orders if necessary, where the \(n\)-th planned departure time \(d_{s,J_{si}}^{(n-1)}\) is the sum of the \((n-1)\)-th planned departure time and additional dwell time. Thus, in the STN, the waiting arc can be described as \(e_{si}^{(w)}=(v_{sJ_{si}},v_{sJ_{si+1}}),\forall i=J_{sj}\in\mathcal{I},s\in \mathcal{S}\). After \(n\) times of conflict resolutions, we must also ensure that consecutive trains satisfy the safety headway according to (14), because the interval between the overtaken train \(J_{si}\) and other follow-up trains, such as train \(J_{si+2}\), may decrease. The safety headway \(\widetilde{h}_{s,J_{si+1},J_{si+1}}\) of trains \(J_{s+1,i}\) and \(J_{s+1,i+1}\) at station \(s\) is taken from \(h_{dd},h_{dd}^{*},h_{dt},h_{tt}\), and \(h_{td}\) according to their stopping conditions and overtaking status. \[\tau_{s,J_{s+1,i}}\tau_{s,J_{s+1,i+1}}(d_{s,J_{s+1,i+1}}-d_{s,J_{s+1,i}})\geq \tau_{s,J_{s+1,i}}\tau_{s,J_{s+1,i+1}}\widetilde{h}_{s,J_{s+1,i},J_{s+1,i+1}}, \forall s\in\mathcal{S},i\leq I+2n \tag{14}\]  
Figure 9: Analysis diagram of the five reasonable overtaking situations. The operation experience of several lines in Japan shows that when train \(J_{si+1}\) stops at station \(s+1\) and overtakes train \(J_{si}\) at station \(s\), \(J_{si}\) will greatly increase dwell time, so it is not used. This study regards such situations as infeasible solutions.