esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model  
Consider an uplink synchronous M-MIMO system with the OFDM and UFMC waveform. The OFDM and UFMC systems have been adopted in 4G and B5G wireless communication systems to accommodate high data rate services and combat multi-path interference [30; 31; 32]. Therefore, in this section we describe the architecture of OFDM systems [33; 34; 35], UFMC systems [36; 37; 38; 39], the M-MIMO channel model [40; 41; 42], the least-square (LS) channel estimation method, and traditional MMSE linear detectors [43; 41]. MMSE is used as a benchmark for comparing BER performance. It is worth mentioning that in order to present more realistic massive antenna environments, we use the imperfect spatial correlation channels considered by the \"comm.MIMOChannel\" command in the MATLAB software tool as the simulation scenario within this study.  
We first list the indices and constants used in this section, as Table 1.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Parameters** & **Characterization** \\ \hline \(n\) & time index \\ \hline \(k,m\) & frequency index \\ \hline \(N\) & number of subcarriers \\ \hline \(B\) & number of sub-bands at the UFMC system \\ \hline \(L\) & length of the Dolph-Chebyshev filter \\ \hline \(N_{T}\) & number of transmitter antennas \\ \hline \(N_{R}\) & number of receiving antennas \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and characterization.  
To improve readability and maintain clear symbol definition, uppercase symbols represent frequency domain signals and lowercase symbols represent time domain signals, while bold typeface is used to indicates vectors or matrices.
## 5 Simulation Results and Analysis  
### Temperature Field and Heat Flux Analysis  
A large temperature difference between the inlet and outlet can be observed in the gas pressure regulator during the operation process, adversely affecting the normal operation of the regulator and the regulator structure during long-term operations. When the valve opening is small, the working conditions for the gas pressure regulator are more severe, and the overall structure will be subject to greater stress and strain. The analysis of the temperature field and heat flux for the gas pressure regulator provides an important reference for the subsequent study of the stress and deformation and the determination of the optimization parameters.  
Based on the THM coupling theory, gas pressure regulator models with nine openings (10% to 90%) were simulated. The temperature field and heat flux results for the model with a 50% opening are shown as an example in Figure 3. When the opening is 50%, the regulator has a large temperature drop on the wall at the valve spool, and the temperature in some areas drops below 0 \({}^{\circ}\)C. At the same time, the heat flux increased significantly at  
Figure 2: FEA model for the gas pressure regulator. (**a**) Structural model, (**b**) Fluid model.  
the outlet spool position, and a large amount of heat exchange can be observed at the outlet spool position, which is derived mainly from the heat lost when the gas flows through the part.  
To visualize the trend of temperature field and heat flux at the outlet spool position for the gas pressure regulator, the operating conditions, including nine openings (from 10% to 90%), are summarized in Figure 4. The figure shows that under the small valve opening, the temperature at the outlet spool is very low. Some areas even reach \(-50\)\({}^{\circ}\)C or less. At the same time, the heat flux is also high, indicating that when the valve opening is small, a large heat exchange can be observed at the spool position, and the working condition is more severe. As the valve opening increases, the temperature at the outlet spool rises, the heat flux decreases, and the operating condition improves. Therefore, the THM coupling effect should be considered in the analysis of the regulator structure.  
Figure 3: Simulation results for the gas pressure regulator at the 50% opening. (**a**) Temperature field, (**b**) Heat flux.
# Ellipsoidal Path Planning for Unmanned Aerial Vehicles
## 4 Simulation and Results
#### Comparison with State of the Art Algorithms
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Algorithm** & \multicolumn{2}{c}{**Map 1**} & \multicolumn{1}{c}{**Map 2**} & \multicolumn{1}{c}{**Map 3**} & \multicolumn{1}{c}{**Map 4**} \\ \hline \multirow{2}{*}{TLBO} & mean & 25.2912 & 61.5385 & 32.9653 & 34.9789 \\ \cline{2-6}  & STD & 2.2956 & 34.9912 & 2.5192 & 9.3455 \\ \hline \multirow{2}{*}{DE} & mean & 29.7900 & 51.7444 & 35.8763 & 39.0414 \\ \cline{2-6}  & STD & 4.9021 & 22.3003 & 3.5688 & 8.2239 \\ \hline \multirow{2}{*}{PSO} & mean & 27.8994 & 46.1655 & 32.5432 & 33.5899 \\ \cline{2-6}  & STD & 4.5297 & 29.2296 & 1.8954 & 7.6463 \\ \hline \multirow{2}{*}{FF} & mean & 25.6914 & 46.5815 & 31.9210 & 32.5309 \\ \cline{2-6}  & STD & 3.1752 & 19.2983 & 2.1017 & 5.0559 \\ \hline \multirow{2}{*}{RRT*} & mean & 37.7490 & 37.0365 & 39.6117 & 40.9821 \\ \cline{2-6}  & STD & 8.5628 & 5.1285 & 3.4001 & 4.3958 \\ \hline \end{tabular}
\end{table} 
Table 3: Experimental results, mean and standard deviation of the distance of the found path for each algorithm. Figure 13: RRT* results of the maps 1 to 4. We show the constructed tree in black and the best found path in red. In Table 4, we include the number of steps in each evolutionary algorithm and the number of nodes in the RRT* algorithm. We proved the novel algorithm in a real environment. We used the map from [19], and we created the ellipsoidal map. In Figure 15, we offer the resulting path planning. Notice that this path planning has a good performance even in non-structural environments. In Table 5, we show how the Cholesky factorization allows lower complexity in the RRT* algorithm. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **Map 1** & **Map 2** & **Map 3** & **Map 4** \\ \hline TLBO & 40.70 & 47.60 & 52.80 & 50.80 \\ DE & 52.84 & 47.10 & 61.30 & 59.30 \\ PSO & 46.33 & 45.30 & 49.40 & 47.93 \\ FF & 42.57 & 56.23 & 51.83 & 51.43 \\ RRT* & 1412.34 & 1115.10 & 1342.47 & 1516.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results, mean steps to goal point. Figure 14: Comparison results for four experimental maps and for the five proposed methods.
# A Real Option Pricing Decision of Construction Project under Group Bidding Environment
## 3 The Model Application and Analysis
### Simulation Results Analysis in the Independent Bidding Environment  
#### 3.2.1 The Optimal Bidding Price Analysis  
The changing trend of the winning probability and the option value before and after weighted by the winning probability is obtained, as shown in Figure 2.  
When other conditions remain fixed, the option value increases continuously with the higher bidding prices. In other words, without considering the impact of bidding prices on the winning probability, the enterprise will obtain greater benefits with the higher bidding price. However, in the group bidding environments, the option value must be weighted by the winning probability.  
The winning probability increases first and then decreases due to the deviation degree of the price and the group characteristics. Therefore, the option value weighted by the winning probability has the maximum objective function and the optimal bidding price. The optimal numerical solution of the model is 5.50 \(\times\) 10\({}^{7}\) CNY, which is 0.15% higher than the analytical solution, mainly affected by the iteration step size. It is considered that the numerical solution is consistent with the analytical solution, and the numerical solution model can be used for correlation analysis.  
The random competitive prices environment was selected 1000 times to obtain the distribution of the optimal prices group, as shown in Figure 3. It can be seen that the \(P^{*}\) is mainly concentrated in the range of CNY [5.41 \(\times\) 10\({}^{7}\), 5.54 \(\times\) 10\({}^{7}\)], with a concentration of 95%. The pricing interval is about CNY 1.3 million, accounting for 2.6% of the costs, which has reduced the pricing range effectively. The average value of the pricing range is taken as the final bidding price, that is, (\(P^{*}\), \(V\)) = (54,734,934, 1,251,837).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Meaning** \\ \hline \(C_{t}\) & CNY 50 million & estimated investment costs \\ \(\sigma\) & 20\% & estimated costs volatility level \\ \(r\) & 1\% & risk-free rate of return \\ \(T-t\) & 0.5 years & The option period \\ \(n\) & 1.1 & calibration parameter \\ \(b\) & 10 & calibration parameter \\ \(u\) & 0.3 & calibration parameter \\ \(N\) & 50 & Number of competitors \\ \(P_{i}\) & - & the bidding price of the \(i\)-th competitor \\ \hline \hline \end{tabular}
\end{table}
Table 1: The Value and meaning of parameters.  
Figure 2: Optimal decision determination of bidding price. (**a**) Fluctuation trend of winning probability; (**b**) Fluctuation trend of option value and weighted option value.
# Respiration Symptoms Monitoring in Body Area Networks
## 2 Related Work  
This section presents the research work performed for detecting the respiratory patterns. With regard to breathing pattern assessment, polysomnography (PSG) used pressure transducers in the nostrils to record the airflow pressure [6]. The subject has to sleep while wearing multiple sensors including an Electroencephalography (EEG) monitor, an Electromyography (EMG) monitor, an Electrooculography (EOG) monitor, nasal probes, etc. [7]. The system introduced in [8] exploits the pressure sensor array equipped with a mat to detect the subject's breathing pattern, and body motion. The camera-based system introduced in [9] monitors the patient and extract the respiration rate. However, the sleeping mat equipped with sensors is expensive, uncomfortable at times and the vision-based system is light dependent, computationally expensive, and raises privacy issues.  
As an alternative, radio frequency (RF) signals can be used to track the subject's chest movements due to the advantage of less complexity and independence of light intensity. The core idea is to exploit the change in wireless signal propagation between transceiver pair due to minute chest movement. Leveraging RF technologies, [PERSON] et al. [10] used the Frequency Modulated Carrier Waves (FMCW) radar to detect subject's breathing pattern. The main limitation of FMCW radar is that the transmitted signals interfere with the nearby RF systems since it exploits large range of frequencies.  
[PERSON] et al. [11] used the commercially available small wireless devices to track breathing pattern and exploited the received signal strength indicator (RSSI). Further improvements were brought up by [12] where the RSSI measurements were obtained using single transceiver pair to track the subject's respiration. The RSSI data, however, do not report the subtle chest movement due to breathing, as a result, the desirable results can be greatly affected by environmental noise. In order to cope with this issue, the RSSI measurements are essentially termed as sinusoidal signals, and the amplitude information is generally estimated on the basis receive signal measurements under particular periodic assumption [13]. Also, the performance of RSSI dramatically degrades in complex situations due to temporal dynamics and multipath fading [14]. However, the RSSI-based systems give us a blueprint to use wireless channel features that are more sensitive than RSSI, in extracting the normal breathing pattern and abnormal breathing pattern of a patient suffering from hyperthyerosis, sleep apnea and SIDS. Thus, in order to extract the subject's normal and abnormal respiration, we use S-Band sensing technique which is highly sensitive to body motion by exploiting the wireless channel information.
## 5 Performance Evaluation of the Charging Circuit
### Temperature Dependence of the ADC Circuit  
In the temperature-dependence experiment of the ADC circuit, we also placed the ADC circuit into the high-low-temperature test chamber with the same temperature range. The input voltage of the ADC circuit was generated by a nominal 5 V voltage reference chip and a potentiometer with an input range from 1 to 3 V in intervals of 0.5 V. The results in Figure 14a demonstrate that the minimum value of the reference voltage was 3.302 V at \(-\)50 \({}^{\circ}\)C, maximum value was 3.308 V at \(-\)30 \({}^{\circ}\)C, average value of the output was 3.306 V, and standard deviation was 0.0015 V. We calculated the coefficient of variation of the reference voltage as 0.048%. The results in Figure 14b show that the five measured ADC outputs can be considered stable in a temperature range of \(-\)50-30 \({}^{\circ}\)C.  
Figure 14: Temperature dependence of the (**a**) reference voltage, and (**b**) output of the ADC circuit (the measured and ideal voltage values of 1, 1.5, 2, 2.5, and 3 V).  
Figure 13: Temperature dependence of the output of detection circuit and the average error.
## 2 Data and Methods
### Study Area and Data
#### 2.1.1 Remote Sensing Data  
NPP-VIIRS luminous remote sensing data are from the National Oceanic and Atmospheric Administration (NOAA) and compiled by Suomi NPP [26]. The data were detected by a visible infrared imaging radiation instrument.  
The VIIRS/DNB presents a significant improvement over the former sensor (the DMSP/OLS), with daily images provided for free and higher spatial resolutions (740 m, instead of about 3 km for the DMSP). Additionally, there are some more important reasons for using it for this study. Firstly, it provides radiometrically calibrated data, which are sensitive to lower light levels and do not saturate in urban areas and areas with reduced overglow [27; 28]. Secondly, the revisit time of NPP-VIIRS is short, so it is usually used in studying large regional scales. Thirdly, NPP-VIIRS DNB luminous remote sensing data can obtain daily data with continuity; these are suitable for the immediate assessment of disasters [4] because they can directly reflect the distribution and concentration of lights before and after disasters with high temporal resolution. Thus, they are utilized as the data source for this study.
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 3 Methodology
### System Requirements  
To ensure optimal performance and the correct implementation of the Microsoft SEAL library, specific system requirements must be met. In this work, we are working with the latest stable version of Microsoft SEAL to date, version 4.1.1. It is advisable to try to use the most recent version in order to take advantage of the possible improvements that this library may provide.  
The requirements are fundamental for projects that demand high resource usage or have particular needs, and they might vary to accommodate the specific characteristics of a project. The requirements include sufficient hard disk space for installing all necessary software and tools; compatibility with various operating systems such as Linux, macOS, or Windows (with a preference for Windows 10 for its compatibility with SEAL); at least 4 GB of RAM (although 8 GB is recommended for efficient cryptographic operations); and a 64-bit processor architecture for optimal performance. Additionally, an appropriate development environment, particularly Visual Studio 2022, is essential for C++ project development, ensuring high performance when using the Microsoft SEAL library. The inclusion of specific work packages during the Visual Studio installation, such as the \"Desktop development with C++\" package and \"CMake Tools,\" is also necessary for the project's successful execution.
## 3 Results and Discussion
### Structural Characterization of the Magnetic Nanocomposites
#### 3.1.4 N\({}_{2}\) Adsorption-Desorption Isotherms  
Figure 4 presents the N\({}_{2}\) adsorption-desorption isotherms for the synthesized magnetic nanocomposites.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Ms** & **Hc** & **Mr** & **Mr/Ms** \\  & **(emu/g)** & **(KOe)** & **(emu/g)** & **(emu/g)** \\ \hline PM1 & 13.5 & 0.199 & 2.087 & 0.15 \\ PM2 & 6.94 & 0.051 & 0.401 & 0.05 \\ PM3 & 4.87 & 0.045 & 0.177 & 0.03 \\ PM4 & 4.82 & 0.089 & 0.348 & 0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Magnetic properties of magnetic nanocomposites.  
Figure 3: Magnetization curves of magnetic nanocomposites.  
According to IUPAC classification [56], all magnetic nanocomposites present type IVa adsorption-desorption isotherms with an H4 hysteresis, indicating that micropores are present.  
The pore size distribution of the powders (Figure 5) indicates that all samples exhibit pores with diameters around 3.8 nm.  
The specific surface area of the AC used as the raw material had a positive effect on the specific surface of the synthesized powders (Table 3). The increase in the specific surface of the AC determined an increase in the specific surface and the total pore volume. At the  
Figure 4: N\({}_{2}\) adsorption–desorption isotherms of magnetic nanocomposites.  
Figure 5: Pore size distribution of magnetic nanocomposites.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Specific** & **Total Pore** & **Micropores** & **Micropores** & **External** & **Average Pore** \\  & **Surface Area** & **Volume** & **Volume** & **Area** & **Surface Area** & **Diameter** \\  & **(m\({}^{2}\)/g)** & **(cm\({}^{3}\)/g)** & **(cc/g)** & **(m\({}^{2}\)/g)** & **(m\({}^{2}\)/g)** & **(nm)** \\ \hline PM1 & 656 & 0.29 & 0.25 & 624.46 & 31.75 & 3.82 \\ PM2 & 708 & 0.44 & 0.22 & 522.68 & 184.89 & 3.76 \\ PM3 & 715 & 0.45 & 0.23 & 531.12 & 183.88 & 3.82 \\ PM4 & 761 & 0.57 & 0.22 & 527.93 & 233.35 & 3.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the magnetic nanocomposites.  
same time, it can be observed that the time span of the thermal treatment determined a change in the specific surface. The reduction in the treatment time from 90 min (sample PM3) to 60 min (sample PM4) determined a slight increase in the specific surface area, from 715 m\({}^{2}\)/g to 761 m\({}^{2}\)/g, which could be explained by the preserving of a larger pore volume.  
Due to these characteristics, it is expected that the as-synthesized powders will exhibit excellent adsorption efficiency, especially due to the large specific surface area. In addition, the powders present magnetic properties, which would be an advantage for their separation from aqueous solutions.
# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Evaluation Indicators  
The _PA_ represents the proportion of correctly classified pixels to all pixels and is calculated as in Equation (6).  
\[PA=\frac{\sum_{i=1}^{k}p_{ii}}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}} \tag{6}\]  
where \(k\) denotes that there are \(k\) classes of all pixel points, \(p_{ij}\) denotes the number of pixel points in class \(i\) that are predicted as class \(j\), and \(p_{ii}\) denotes the number of pixel points in class \(i\) that are correctly predicted as class \(i\). In the confusion matrix, \(p_{ij}\) can be found in the \(i\)-th column and \(j\)-th row, and \(p_{ii}\) in the same way. Therefore, after calculating the confusion matrix, we can easily calculate the pixel classification accuracy.  
_MPA_ is a boost to _PA_, which is calculated by figuring out the proportion of correctly classified pixel points of each class to the pixel points of the whole class, and then calculating the average value of pixel classification accuracy for all classes, which is calculated as in Equation (7).  
\[MPA=\frac{1}{k+1}\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}} \tag{7}\]  
As the name suggests, \(IoU\) represents the ratio of the intersection of the target region and the predicted region to the concurrent set, expressing the overlap between the target region and the predicted region, and the higher the intersection ratio indicates, the higher the accuracy of the segmentation is, as calculated in Equation (8).  
\[IoU=\frac{p_{ii}}{\sum_{j=1}^{k}p_{ij}+\sum_{j=1}^{k}p_{ji}-p_{ii}} \tag{8}\]  
Like \(IoU\), \(MIoU\) is also the ratio of the intersection of the target region and the predicted region to the concatenated set, which is calculated for each pixel category and then for the average value of the intersection ratio for all categories. The higher the value indicates, the higher the accuracy of the segmentation is, and the formula is as in Equation (9).  
\[MIoU=\frac{1}{k+1}\sum_{i=0}^{k}IoU \tag{9}\]  
\(FWIoU\) is a boost for \(MIoU\). This method uses the frequency of occurrence of each class as a weight to calculate the mean value of IoU, which can better represent the accuracy of semantic segmentation, which is calculated as in Equation (10).  
\[FWIoU=\frac{1}{\sum_{i=0}^{k}\sum_{j=0}^{k}p_{ij}}\sum_{i=0}^{ak}\frac{p_{ii} }{\sum_{j=1}^{k}p_{ij}+\sum_{j=1}^{k}p_{ji}-p_{ii}} \tag{10}\]
###### Abstract  
To accurately estimate the service delay behavior of IEEE 802.11 networks, this paper comprehensively considers four main factors that affect the performance of IEEE 802.11 networks and establishes a service delay model with statistical characteristics. We analyzed the operation mechanism of 802.11 DCF, using the backoff stage and the backoff counter to portray the dynamic change characteristics of the system regarding the data frame transmission states. Afterward, we calculated the one-step transition probability of these states, establishing a 2-D Markov model, including the ICS procedure and the backoff procedure. Based on this model, we constructed steady-state equations to derive a relationship between the transmission probability and collision probability for each node transmission queue. By analyzing the ICS delay and the backoff delay, we obtained the probability generating function (PGF) of the average idle time. The analytical expressions of other service delays, such as the successful transmission time and collided transmission time, were derived to obtain the PGF of the total service delay. In the numerical simulation, we compared the first two statistical moments of the PGF with the Nav model, and it was found that our delay evaluation results were significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios was larger than that of the proposed model due to the lack of the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency.  
1