esa-sceva/satcom-chunk-collection huggingface dataset:
# Heat Transfer Performance and Flow Characteristics of Helical Baffle-Corrugated Tube Heat Exchanger
## 3 Results and Discussion
### Optimization of Helical Baffle-Corrugated Tube Heat Exchanger
Furthermore, the contribution of **C**, **B**, and **A** based on the _CR_ values to the _EEC_ influence range are from 74.51% to 80.33%, around 15%, and between 2.95% and 9.73%, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Total Heat Transfer Coefficient (W/(K\(\cdot\)m\({}^{2}\)))**} & \multicolumn{4}{c}{**Pressure Drop (Pa)**} \\ \cline{3-13}  & & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & \(R\) & \(CR\) & \(k_{1}\) & \(k_{2}\) & \(k_{3}\) & \(R\) & \(CR\) \\ \hline \multirow{3}{*}{I} & **A** & 1772.66 & 2072.77 & 2135.59 & 362.93 & 0.457 & 1127.68 & 1121.31 & 1130.93 & 9.62 & 0.005 \\  & **B** & \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{_EEC_} \\ \cline{3-6}  & & \(\mathbf{k_{1}}\) & \(\mathbf{k_{2}}\) & \(\mathbf{k_{3}}\) & \(\mathbf{R}\) & \(\mathbf{CR}\) \\ \hline \multirow{3}{*}{I} & **A** & 4.096 & 4.260 & 5.559 & 1.463 & 15.10\% \\  & **B** & 5.019 & 4.820 & 4.076 & 0.943 & 9.73\% \\  & **C** & 1.427 & 3.778 & 8.710 & 7.284 & 75.17\% \\ \hline \multirow{3}{*}{II} & **A** & 3.774 & 4.600 & 5.043 & 1.269 & 16.72\% \\  & **B** & 4.441 & 4.376 & 4.599 & 0.224 & 2.95\% \\  & **C** & 1.471 & 4.378 & 7.567 & 6.096 & 80.33\% \\ \hline \multirow{3}{*}{III} & **A** & 3.192 & 3.843 & 4.137 & 0.945 & 16.02\% \\  & **B** & 3.510 & 3.595 & 4.068 & 0.558 & 9.47\% \\  & **C** & 1.469 & 3.840 & 5.863 & 4.393 & 74.51\% \\ \hline \multirow{3}{*}{I} & **A** & 1.631 & 2.055 & 2.230 & 0.599 & 15.53\% \\  & **B** & 2.078 & 1.948 & 1.889 & 0.188 & 4.88\% \\  & **C** & 0.523 & 1.800 & 3.593 & 3.070 & 79.59\% \\ \hline \multirow{3}{*}{V} & **A** & 0.810 & 1.026 & 1.113 & 0.303 & 15.22\% \\  & **B** & 1.046 & 0.974 & 0.929 & 0.117 & 5.89\% \\  & **C** & 0.248 & 0.882 & 1.820 & 1.572 & 78.89\% \\ \hline \end{tabular}
\end{table} 
Table 7: Range analysis of _EEC_. Figure 9: Level of influence of each factor: (**a**) total heat transfer coefficient; (**b**) pressure drop. Figure 10 presents the level of influence of each factor for the _EEC_. In the case of a low pipe flow rate (Conditions I, IV, and V), the optimum level of the helical baffle-corrugated tube heat exchanger is **A3**, **B1**, and **C3** in terms of heat exchanger efficiency evaluation coefficients; in the case of large pipe flow rates (Condition II and III), the optimum level of the helical baffle-corrugated tube heat exchanger is **A3**, **B3**, and **C3**. Heat exchanger research aims to improve equipment performance, but achieving an optimal balance between the heat transfer coefficient and pressure drop is challenging in production. The _EEC_ index can help address this issue by evaluating comprehensive performance. In this study, the _EEC_ index is used to optimize structural parameters, offering practical ideas for production.
# LEO-Based Satellite Constellation for Moving Target Detection
## 3 Results
### Clutter Suppression Results  
The theoretical upper limit and lower limit of DFR is discussed in Section 2.2, in which the former requires that the echoes related to the target and the clutter can be separated in DFR domain. Figure 7a,b illustrates the distribution results of the target and that of the clutter, respectively. Here, only the clutter corresponding to the antenna main lobe is considered when setting the DFR interval, and the main lobe beamwidths along the pitch direction and the azimuth direction are set to 6 degrees and 9 degrees in turn. The target velocity is 20 knots, and its direction deviates from the \(x\) axis by 45 degrees in the clockwise direction.  
Figure 5: The multi-source fusion result with LEO-based satellite constellation.  
Figure 6: The unambiguous Doppler interval.  
While the latter requires that the multi-source echoes can be integrated to the same cell, Figure 8 illustrates the relationship between the target velocity and the optional interval of \(\Delta_{fdr}\), in which the optional interval of \(\Delta_{fdr}\) is located between the dotted line and the solid line, and the constraint interval of \(\Delta_{fdr}\) gradually relaxes with the increase of the target velocity.
## 4 Methodology
### Current Trajectory Embedding
#### 4.1.2 Spatial Trajectory Embedding  
For a trajectory traveling between adjacent points \((C_{A},C_{B})\), based on its spatial reconstruction result, its spatial trajectory can be represented as an m-dimensional vector \(S=[s_{1},s_{2},\ldots,s_{m}]\), where \(s_{i}\) represents the i-th spatial point in the trajectory.  
As for the current trajectory, we use a time-state vector \(S_{t}\) to capture its total travel time feature, expanding the total travel time \((at_{A}-at_{B})\) into an m-dimensional vector (with the same dimension as the spatial vector S), \(S_{t}=[S_{1}^{1},S_{t}^{2},\ldots,S_{t}^{m}]\). The calculation method for \(S_{t}\) is shown below:  
\[S_{t}=(at_{B}-at_{A})\times S \tag{5}\]  
Next, a congestion-state vector is used to represent the traffic condition when the vehicle is driving on the road, and a method for the calculation of the congestion state of the vehicle is provided. When the vehicle passes through the road at time t, the probability \(P^{i}(t)\) of it being on the road can be calculated using a formula. Based on this, we can represent the congestion-state vector \(S_{S}\) as an m-dimensional vector, which can be obtained using the following formula:  
\[S_{s}=P^{i}_{move}(at_{B}-at_{A})\times S \tag{6}\]  
In addition, to represent the influence of the departure time on the spatiotemporal relationship of the vehicle, a periodic vector is used to represent the vehicle's driving cycle. The time of day is divided into C time slots, and \(C_{S}\) is used to represent the departure time of the journey \(at_{A}\) in the time slot of a day. Then, the periodic vector of the journey can be represented as \(S_{C}\) and can be calculated using the following formula:  
\[S_{c}=\frac{C_{s}}{C}\times S \tag{7}\]  
According to the trajectory embedding method for the current trajectory, the embedded current trajectory C can be represented as  
\[C=Concat(S,S_{t},S_{s},S_{c}) \tag{8}\]
## 3 Results and Discussion
### Relationships between the Net Primary Productivity and Ozone Concentration
The NPP estimation model, which reflects the effects of ozone, is shown in Table 3.  
The NDVI and the net primary productivity of forests estimates derived from the results are shown in Figure 3. Figure 3 shows the annual mean value of NDVI, the net primary productivity of forests used as input data, and the 10-year mean value in the future. The values of NDVI were about 0.58-0.61 and were within the range of the NDVI value of forest [74]. The values of NDVI in this study are similar to the values that are found in other studies [75]. The net primary productivity of forests for the last decade was about 630-696 tC/km\({}^{2}\)/year, and the average NPP for the last 10 years was about 663 tC/km\({}^{2}\)/year (SD: 21 tC/km\({}^{2}\)/year). The average value of the total NPP of Korea is about 40 million tC/year. The year 2003 had the lowest NPP, at about 38 million tC/year, and the highest NPP value was about 42 million tC/year in 2009. The independent variables in 2003 showed that precipitation, NDVI, and temperature range were higher than in other years, but solar radiation was notably low. The low productivity in 2003 was due to this comparatively lower solar radiation. In addition, the NPP of forests was the highest in 2009 because of that year's comparatively higher amount of solar radiation; precipitation and NDVI values were similar to those of other years, but the highest level of radiation was observed in 2009. Based on all of the available information, we concluded that the distribution of these input variables affected the results. Furthermore, both NPP and NDVI are expected to decrease in the 2050s, when compared to their current levels, and they are expected to increase in the 2090s compared to their 2050s levels. However, NDVI increases compared to its present level in the 2090s, but NPP is expected to decrease compared to its current level during the same period. This implies that NDVI estimation is related to variables such as temperature and precipitation, but it can be assumed that the change in ozone concentration influenced NPP estimation. However, the empirical model has a disadvantage in that it can be only applied to the region for model developing [76], and there is a limitation that the actual technology is reflected [41; 77]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Coef.** & **Std. Error** & \(Z\) & \(p\)**-Value** \\ \hline (Constant) & \(-3548.925\) & 569.91 & \(-6.23\) & 0.000 \\ NDVI & 1.175 & 0.06 & 18.97 & 0.000 \\ Solar Radiation (MJ/m\({}^{2}\)) & 1.036 & 0.07 & 14.76 & 0.000 \\ Elevation (m) & \(-4.752\) & 1.46 & \(-3.25\) & 0.001 \\ Ozone concentration in 2 nd and 3 rd quarters (ppm) & \(-6810.416\) & 3393.69 & \(-2.01\) & 0.045 \\ \hline \hline \end{tabular}  
* Dependent Variable: NPP (Number of obs: 630, Number of groups: 10), ** rho: 0.08218871, *** R-sq: 0.5743.  
\end{table} 
Table 3: The results of the model for estimating and predicting the net primary productivity of forests in Korea.
# Screening Mechanism and Properties of a Cantilevered Vibrating Sieve for Particles Processing
## 5 Analysis of Simulation Results
### Influence of Geometric Parameters on Screening Efficiency  
Figure 7 demonstrates the screening efficiency with different length of cantilevered sieve rod. In this case, the sieve inclination angle \(\beta\) was set to be \(0^{\circ}\), and the simulated vibration parameters are: \(\gamma=40^{\circ}\), \(\omega=16\) Hz, D = 2 mm. Note that the vibration parameters are tentatively determined by the parameters available in Reference [23].With the increase of rod length, screening efficiency has an obvious increasing trend. When rod length is small, many particles are thrown out of the screening area after bouncing on the sieve, which leads to the reduction of undersized product and screening efficiency. With the increase of rod length, the screening time of particles on the sieve becomes longer, leading to a gradually increased screening efficiency. When rod length increases to about 380 mm, screening efficiency reaches its maximum of 68.60%. A further increase of the length causes screening efficiency to decline to some extent while staying high.  
Figure 6: Schematic of the screening experiment process.  
For the case when the length of cantilevered sieve rod was 380 mm, in which case the vibration parameters are: \(\gamma=40^{\circ}\), \(\omega=16\) Hz, D = 2 mm, respectively, the simulation also investigated the screening efficiency under sieve inclination angles of 0\({}^{\circ}\)-16\({}^{\circ}\), and the results are shown in Figure 8. Clearly, with the increase of sieve inclination angle, screening efficiency has a declining trend. The reason is perhaps that the increase of sieve inclination angle increases the motion velocity of materials on the sieve. As a result, some particles at the top of the material layer leave the working area before screening. According to Figure 8, when the sieve inclination angle is 14\({}^{\circ}\), screening efficiency drops below 50%. On the other hand, when the angle is small, the material moves slowly and receives adequate screening, and thus high screening efficiency can be maintained.
# Secure Cooperative Routing in Wireless Sensor Networks
## 4 Discussion
### Performance Evaluation Parameters
#### 4.1.1 Number of Alive Nodes  
The size of the network is 200 nodes. Figure 5 shows the quantity of alive nodes with the section of rounds that has appeared. Alive node's recurrence is straightforwardly relative to the estimation of energy. As the energy diminishes, the quantity of alive nodes decreases. Hence, the system's lifetime diminishes.  
In this calculation, up to the 500 th round, none of the nodes completely lost its energy and had an energy level higher than the threshold energy \"0.01\" and consequently stayed alive after that as the round increment nodes started depleting energy even after the proposed MIDS algorithm productively finished its 3500 th rounds.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 3 Results
### Objectness Threshold Influence  
Objectness may be considered as model confidence regarding the occurrence of an object in the image. As is expected, the objectness threshold has a significant impact on the precision of the model detection. It is worth noting that if the objectness threshold is too low, the false-positive number grows significantly. The optimal in terms of precision and recall trade-off value is approximately 0.9, which is presented in Figure 10. For the value of 0.9, precision, recall, and mAP have comparable values. However, beyond this value, there is a rapid decline in the performance. More detailed examination of Figure 10 reveals how deceptive mAP metrics may be. For the objectness threshold value of 0.1, mAP is almost 0.75, but precision is around 0.3, which is low. Therefore, it is important to consider several metrics to show the complete picture of the model performance. This ambiguity was also one of the reasons we decided to introduce the extended confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Ground Truth**} \\ \hline  & & **Lymphoma** & **Mastocytoma** & **Histiocytoma** & **Not Present** \\ \hline \multirow{3}{*}{Predictions} & lymphoma & 285 & 0 & 0 & 309 \\  & mastocytoma & 0 & 1010 & 0 & 1152 \\  & histiocytoma & 0 & 0 & 669 & 669 \\ \hline \hline  & not detected & 44 & 116 & 71 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Extended confusion matrix for the best model for the ‘divided’ dataset.  
Figure 9: IoU threshold influence on model performance (YOLOv3 trained with the ‘divided’ dataset, results for validation subset).
## 4 Experimentation and Results
### Case Study of Using Quadtree in Data Analysis
Additionally, to quantify the difference between the eddies not detected by AMEDA in the processed data and the actually existing eddies, we introduce the concept of eddy recognition error (E). This error is derived by calculating the ratio of the number of undetected eddies to the total number of eddies in the region. The specific formula for calculating the eddy recognition error is as follows:  
\[E=1-\frac{N_{valid}}{N_{ameda}} \tag{14}\]  
In the eddy detection comparative experiments, we calculated the eddy recognition error (E) to be 0.15. This result indicates that only 15% of the eddies in the analyzed region were not captured by the detection algorithm. This low error rate reflects the effectiveness of the data after compression processing in the subsequent analysis tasks.
## 4 Homogeneous Broadening
### Homogeneous Line Shapes  
For direct bandgap transitions without the k-vector conservation constraint in the broadening, the lineshape is obtained by convolving the conduction and valence band Lorentzians [16]. Two Lorentzian functions convolve into a new Lorentzian line with \(\tau\) equaling the reduced relaxation time \(\tau_{r}\) (obtained also by Matthiessen's rule for the summing of two scattering frequencies)  
\[\tau_{r}=\tau_{e}\tau_{h}/(\tau_{e}+\tau_{h}), \tag{9}\]  
where \(\tau_{e}\) and \(\tau_{h}\) are the original electron and hole relaxation times in the conduction and valence bands, respectively.  
Taking the k-vector conservation into account also in the broadening, the homogeneous lineshape for interband transitions between two Lorentzian broadened band states is [9]:  
\[L_{h}(\Delta E)=\frac{\tau_{be}+\tau_{bh}}{\pi\hbar}\times\frac{1}{1+\tau_{be }^{2}\Delta E^{2}/\hbar^{2}}\times\frac{1}{1+\tau_{bh}^{2}\Delta E^{2}/\hbar^ {2}}, \tag{10}\]  
where  
\[\tau_{be}=\tau_{e}\cdot m_{r}/m_{e},\quad\tau_{bh}=\tau_{h}\cdot m_{r}/m_{e} \tag{11}\]  
are the effective relaxation times and \(m_{e}\) and \(m_{h}\) the effective electron and hole masses, respectively and \(m_{r}=m_{e}m_{h}/(m_{e}+m_{h})\). Tensile or compressive quantum well strains are often applied to remove the light-hole heavy-hole band degeneracy [17]. Strain also makes the effective mass ratios solitary and unique for Equation (10).  
In Figure 3, the combined inhomogeneous and homogeneous broadenings are plotted with four different \(\tau_{be}\) and \(\tau_{bh}\) ratios in Equation (10). The inhomogeneous lineshape in the convolution was the Urbach broadened spectrum, where the JDOS follows the Heaviside step function at \(E_{0}\), and the exciton enhancement is included using Equation (5).  
Figure 3: Simulation for the homogeneous broadening in a qw-LED, using Equation (10) and inhomogeneous line according to Equation (8) with parameters: \(\rho_{Jdos}\propto H(E_{0}),E_{0}=1.4\) eV, \(E_{b}=E_{u}=8\) meV and T = 293 K.  
Equation (10) can also be written as the difference of two Lorentzian functions [9] revealing that for large relaxation time ratios, it converges to the simple Lorentzian lineshape with the larger relaxation time (\(\tau_{be}\) or \(\tau_{bh}\)) as the linewidth parameter. It additionally obeys the momentum conservation rule in the broadening, and is, therefore, applied as the default homogeneous lineshape. The slimmest lines are obtained with \(\tau_{be}\), being equal with \(\tau_{bh}\).
# UAV-Based Privacy-Preserved Trustworthy Seamless Service Agility for NextG Cellular Networks
## 6 Security and Performance Analysis
### Metrics and Baselines  
In this set of results, we evaluate our designed authentication solution's time performance compared to the standard srsRAN package connection procedure. Hence, we have two baselines:  
* **Normal Connection Time**: shows the time taken by the normal _rrc_ message connection procedure, including the USIM supporting XOR/Milenage authentication for the UE. This baseline presents time-based compatibility to a standard situation connection setup on the same testing environment.
* **Authenticated Guest Porting Process Time**: shows the time performance of our proposed UAV-assisted user-agility supported connection setup, including our all parties authentication in Figure 3 from message 1 to 15.
## 4 Field Tests
### Correlation between \(\theta\) and \(\rho\)  
To investigate the resistivity variation with depth, the profiles of resistivity estimated at each position for PTDR are plotted in Figure 11. For the three positions, the resistivity values slightly decreased as the depth increased, and the variation in resistivity along the depth was significant at P2. To compare the apparent resistivity with the water content estimated by using PTDR, the data estimated at each depth is plotted in Figure 12. The volumetric water content decreased with an increase in the apparent resistivity. Thus, the relationship between the electrical resistivity (\(\rho\)) and the volumetric water content (\(\theta\)) can be established by regression analysis as follows:  
\[\theta=\mathrm{a}\cdot\rho^{-\mathrm{b}} \tag{3}\]  
where \(\mathrm{a}\) and \(\mathrm{b}\) are constants determined by regression analysis, and the values of \(\mathrm{a}\) and \(\mathrm{b}\) are summarized in Table 2. Assuming that the resistivity value estimated at the 100 cm depth of P1 can be overestimated and disregarded from all of the data, the coefficient of determination of 0.61 increased to 0.84, while the standard deviation of regression decreased from 0.34 to 0.22. The results showed that the variation in volumetric water content estimated using PTDR closely correlated with  
Figure 10: Resistivity tomograms of a selected line up to: (**a**) 5 m depth; (**b**) 2 m depth.  
the electrical resistivity distribution. Compared to the results of previous studies reported by [PERSON] et al. [48], the electrical resistivity measured at this site was greater, as most of the soil particles at this site were larger than 2 mm. Note that the grain size distribution of the soils at this site is similar to that reported by [PERSON] et al. [36]. However, the relationship of the electrical resistivity and the volumetric water content should be carefully considered, given that the amount of data used for regression analysis was not sufficient, and the spacing among the electrodes was greater than the minimum distance between the two depths of the PTDR. Nevertheless, the results demonstrate that the electrical resistivity measured at this site was mainly affected by water content. After all, the water content profile at a position can be estimated by using the PTDR. Then, the water content distribution can be approximately estimated from using the PTDR and electrical resistivity survey. In short, the application of PTDR with electrical resistivity survey may become a promising technique for the evaluation of volumetric water content distribution in the active layer.  
Figure 11: Profiles of electrical resistivity estimated at the three different locations.  
Figure 12: Relation between water contents and electrical resistivity, with a comparison of the data range reported by [PERSON] et al. [48]. The red dashed curve indicates the trend, excluding the data point marked with a red circle.
## 2 Transmission and Multipath Effect of OFDM Signals
For this reason, a cyclic prefix must be attached to each symbol in OFDM, so as to overcome the multipath effect and guarantee the orthogonality of OFDM sub-carriers. The cyclic prefix is taken as the protective time interval between symbols [21]. The structure of the cyclic prefix and the corresponding OFDM symbols is indicated in Figure 1 as follows:  
The length of the cyclic prefix is denoted by \(\Delta\), so that the period length \(T^{\prime}\) of each symbol with a cyclic prefix change to \(T+\Delta\). According to the principles of OFDM modulation, the time domain signal of the \(m\)th symbol in OFDM is written into Equation (3):  
\[x_{m}(t)=\sum_{m=-\infty}^{+\infty}\sum_{k=0}^{V}X_{m}(k)e^{j2\pi k(t-mT^{ \prime})/T}g(t-mT^{\prime}) \tag{3}\]  
Figure 1: Symbol structure of OFDM symbols. where \(g(t)\) is the rectangular window function with the width \(T^{\prime}\); \(X_{m}(k)\) is the frequency domain value of the \(k\)th subcarrier in the \(m\)th symbol; \(V\) indicates the number of points in IFFT. It is assumed that the impulse response of the underwater acoustic channel is defined by Equation (4):  
\[h(\tau,t)=h_{0}\delta(t)+\sum\limits_{s=1}^{P_{1}+P_{2}}h_{s}\delta(t-\tau_{s}) \tag{4}\]  
where \(h_{i}\) and \(\tau_{i}\) (\(i=1,2\ldots P_{1}+P_{2}\)) represent the attenuation factor and time delay of the \(i\)th path, respectively; \(P_{1}\) indicates the number of paths satisfying \(\tau_{i}\leq\Delta\); \(P_{2}\) is the number of paths satisfying \(\tau_{i}>\Delta\). Following the principles of OFDM signal demodulation, if noise is ignored, the \(m\)th symbol received is fast Fourier-transformed with Equations (3) and (4) and based on the cyclic prefix \(x_{i}(t)=x_{i}(t-T)\). It is derived that the restored information \(Y_{m}(k)\) is as follows:  
\[ \begin{array}{c}Y_{m}(k)=\frac{1}{\tau}\int\
olimits_{T^{\prime}m+T}^{T^{ \prime}m+T}[h_{0}x_{m}(t)+\sum\limits_{s=1}^{P_{1}+P_{2}}h_{s}x_{m}(t-\tau_{s} )]e^{-j2\pi k(t-T^{\prime}m)/T}dt\\ =[h_{0}+\sum\limits_{s=1}^{P_{1}+P_{2}}h_{s}e^{-j2\pi kt_{s}/T}]\cdot X_{m}(k )\\ -\sum\limits_{s=1}^{P_{1}+P_{2}}\sum\limits_{d=0}^{+\infty}\frac{\tau_{s}- \Delta}{T}h_{s}X_{m-1}(d)\cdot Sa[\frac{\pi(k-d)(\tau_{s}-\Delta)}{T}]e^{-j\frac {\pi(k-d)(\tau_{s}-\Delta)}{T}}\\ +\sum\limits_{s=P_{1}+1}^{P_{1}+P_{2}}\sum\limits_{d=0}^{+\infty}\frac{\tau_{s }-\Delta}{T}h_{s}X_{m-1}(d)\cdot Sa[\frac{\pi(k-d)(\tau_{s}-\Delta)}{T}]e^{j \frac{2\pi d|2T^{\prime}-\tau_{s}|}{T}}\end{array}  \tag{5}\]  
where \(Sa(t)=\text{sin}t/t\) is the sampling function. Evidently, the multipath effect affects the OFDM signals in three aspects. As shown in Equation (5), the first aspect is the frequency selective fading caused by multipath channels. The second aspect is the interference between subcarriers since multipath effect wrecks the orthogonality of OFDM subcarriers. The third aspect is inter-symbol interference [22].