esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation on NOx Emission of a Hydrogen-Fuelled Dual-Cylinder Free-Piston Engine
## 3 Simulation Results  
### NOx Emission Comparison  
Thanks to the fuel specialty of hydrogen (which contains H only), the only type of emission that requires concern is NOx. Figure 12 illuminates NOx emission from the two-stroke and four-stroke hydrogen free-piston engine WAVE model under the equivalence ratio of 0.4365 and compression ratio of 3.7, and the engine frequency from 5 Hz to 11 Hz.  
Figure 11: Cumulative heat release comparison between MATLAB and WAVE.  
Figure 10: Heat release rate comparison between MATLAB and WAVE.  
NOx is a type of high temperature emission, and the formation of NOx emission increases with the increase of the combustion temperature when the in-cylinder temperature is higher than 1800 K.  
Therefore, when the engine speed (frequency) increases, the value of NOx emission increases simultaneously. The NOx emission of the four-stroke FPE model is nearly doubled compared to the two-stroke FPE model, and this result is slightly lower than what was obtained by [PERSON].  
A comparison between the NOx emissions of this two-stroke hydrogen FPE model, the baseline two-stroke gasoline FPE model and the baseline two-stroke gasoline conventional engine model is presented in Figure 13. The equivalent ratios of the engines shown in Figure 13 are pre-set as 1.0, as the stoichiometric air/fuel ratio of gasoline is 14.7 and that of hydrogen is 34.3, so the air/fuel ratio that is pre-set in Ricardo WAVE: \(AFR_{petrol}=14.7\), \(AFR_{hydrogen}=34.3\). It is obvious in Figure 13 that conventional spark-ignition gasoline engines produce the largest amount of NOx, and the NOx emissions produced from gasoline free-piston engines and hydrogen free-piston engines are quite similar. Comparing with a conventional spark-ignition gasoline engine and free-piston engine, the NOx reduction potential is remarkable for the FPE; the reduction ranges are all higher than 51% from the engine frequency of 5 Hz to 11 Hz, and the largest reduction of 57.37% occurs at 6 Hz. It is worth mentioning that in the free-piston engine, gasoline produces less NOx emissions compared with hydrogen, but the difference is relatively small. The difference of NOx emissions of gasoline and hydrogen FPE increases with the increasing engine frequency, and the figure reaches 10.97% at 11 Hz.  
The reason that NOx emissions from hydrogen free-piston engines are higher than gasoline free-piston engine can be seen in Figure 14. As shown in Figure 14, the highest in-cylinder temperature in the gasoline FPE and hydrogen FPE is under the air/fuel ratio of \(AFR_{petrol}=14.7\),\(AFR_{hydrogen}=34.3\). For each engine frequency, the highest in-cylinder temperature of hydrogen FPE is slightly higher than that of gasoline FPE, and the temperatures are all higher than 2000 K, which has been mentioned above; NOx emission is strongly affected by temperature.  
Figure 12: NOx emission comparison between 2- and 4-stroke hydrogen FPE.
# Systematic Approach for Alignment of Light Field Mirage
## 6 Brightness Matching Technique between Upper and Lower LFDs  
A half mirror was used to integrate 3D images produced by the upper and lower LFDs. A metallic half mirror was used for the light field Mirage, as described in Section 3. Because light emitted from the LCDs is linearly polarized and is made incident into the half mirror at an angle of 45\({}^{\circ}\), the reflectance and transmittance of the metallic half mirror for light emitted from the upper and lower LFDs are not equal. Therefore, the 3D image ray intensities from the upper and lower LFDs are different. Figure 8a shows the intensity difference in the stitched images observed through the top hole. The left half image was generated by one of the upper LFDs, and the right half image was generated by one of the lower LFDs. The average luminance was 67.5 cd/m\({}^{2}\) and 164.3 cd/m\({}^{2}\) for the images generated by the upper LFDs and lower LFDs, respectively. The photographs shown in Figure 7 were captured by reducing the intensity of the upper LFDs to make the brightness of both images equivalent. In this case, the stitched images became dark. We developed the brightness matching technique to increase the brightness of the 360-degree images produced by the light field Mirage.  
Figure 7: Effects of image stitching technique: (**a**) without image stitching, (**b**,**c**) with image stitching and without image interpolation, and (**d**) with image stitching and image.  
First, quarter-wave plates were used to make the light emitted from the upper and lower LFDs circularly polarized. The average luminance of images generated by the upper LFDs was increased to 106.0 cd/m\({}^{2}\), and that of the images generated by the lower LFDs was reduced to 121.5 cd/m\({}^{2}\). The stitched images are shown in Figure 8b. The brightness difference decreased, and the brightness of the stitched image increased.  
Next, half-wave plates were attached to the upper and lower LFDs. For the upper LFDs, the half-wave plates were placed to make their polarization axes parallel to the polarized direction of linearly polarized light emitted from the LFDs so that the polarization of light emitted from the upper LFDs did not change. For the lower LFDs, the half-wave plates were placed to position their polarization axes at an angle of 45\({}^{\circ}\) against the polarized direction of the linearly polarized light emitted from the LFDs, so that the linearly polarized light rotated by an angle of 90\({}^{\circ}\). The average luminance of images generated by the upper LFDs was increased to 143.8 cd/m\({}^{2}\), and that of the images generated by the lower LFDs was reduced to 150.8 cd/m\({}^{2}\). The stitched images are shown in Figure 8c. The brightness difference was reduced to 2.4%. The brightness of the stitched images increased by 2.2 times compared with that obtained without using the brightness matching technique. Therefore, we adopted the half-wave plates to the light field Mirage.
## Materials and Methods
### RTKLib Software
The Saastamoinen model computes the tropospheric delay \(T_{r}\) using the following expression [48]:  
\[T_{r}=\frac{0.002277}{\cos(z)}\Big{[}p+\Big{(}\frac{1255}{T}+0.05\Big{)}e-tan^{2 }(z)\Big{]} \tag{1}\]  
where:  
\(p\), is the total pressure (hPa);  
\(T\), is the absolute temperature (K) of the air;  
\(e\), is the partial pressure (hPa) of water vapor;  
\(z\), is the zenith angle (rad). In the Saastamoinen model [48], a standard atmosphere is considered as a reference, the geodetic height is approximated to the ellipsoidal height, and a humidity percentage is fixed to 70%. The model considers the troposphere as divided into two layers. The first layer, from the earth surface to 10 km on it, has a constant descent rate of temperature of 6.5 C/km. The second layer, from 10 km to 70 km on the earth surface, has constant temperature value. Therefore, for atmospheric refraction integral, the function of refractive index can be computed based on the zenith distance trigonometric functions and term wise integration. In this way, the ZTD is expressed as:  
\[ZTD=0.002277\frac{(P_{0}+\Big{(}0.05+\frac{1255}{P_{0}+273.15}\Big{)}e_{0}}{f (\varphi,\ h)} \tag{2}\]  
\[e_{0}=rh\text{-}6.11\cdot 10^{\frac{2.5N_{0}}{70}+2.33} \tag{3}\]  
\[f(\varphi,\ h)=1-0.00266 cos2\varphi-0.00028h \tag{4}\]  
where \(P_{0}\), \(T_{0}\), and \(e_{0}\) are, respectively, the surface pressure, the surface temperature, and the water vapor pressure at the surface level, \(rh\) is the relative humidity, \(f(\varphi,\ h)\) is the correction of gravity acceleration caused by the rotation of the earth, and \(\varphi\) and \(h\) are the latitude and the height of point. The Estimate ZTD model computes the tropospheric delay starting from the expression of the Saastamoinen model with the zenith angle and relative humidity equal to zero and employing the NMF (Niell Mapping Function), based on receiver geographical coordinates and measurement time [49]. The mapping function in terms of the elevation (\(El\)) and the azimuth (\(Az\)) angles between the satellite and the receiver is expressed as:  
\[M(El)=M_{w}(E)\big{[}1+\cot(El)\cdot\big{(}G_{N}\cos(El)\cdot\big{(}G_{N}\cos (\text{Az})+G_{E}\sin(Az)\big{)}\big{]} \tag{5}\]  
\[T_{r,z}=M_{h}(El)\cdot Z_{H}+M(El)\big{(}Z_{T}-Z_{H}\big{)} \tag{6}\]  
where \(Z_{T}\) (in m) is the tropospheric zenith total delay that is estimated from the Extended Kalman Filter together with the north (\(G_{N}\)) and the east (\(G_{E}\)) components of the tropospheric gradient. \(Z_{H}\) (in m) is the tropospheric zenith hydro-static delay computed using a tropospheric model, such as Saastamoinen, Hopfield, or modified Hopfield models with the zenith angle and relative humidity equal to zero. \(M_{h}\) (\(El\)) and \(M_{w}\) (\(El\)) are, respectively, the hydro-static and wet mapping-functions.
# Review of Target Geo-Location Algorithms for Aerial Remote Sensing Cameras without Control Points
## 4 Discussion
However, as mentioned in Section 2.6, the filtering algorithm inherently causes the low efficiency of the camera, which performs well for tracking the target. The challenge of how to eliminate or reduce the adverse effects of random errors in the positioning process for a single image remains to be addressed. Therefore, selecting an appropriate auxiliary coordinate system or adding a corrected rotation matrix and other solutions are necessary for reducing the influence of random errors of the positioning algorithm. Figure 16: Schematic diagram of positioning accuracy change. (3) _Focus on error terms ignored by traditional algorithms_. With the gradual reduction in the measurement errors of various angle and ranging sensors, the influence of error sources that are neglected by traditional algorithms will be more obvious, such as atmospheric environment, image distortion, and the height of the target itself. In the research work of papers [96; 97], it is shown that in the case of high angle measurement accuracy, the target positioning accuracy can still be improved by more than 30% only by correcting distortion and target height. In addition, we found through simulation analysis that when the atmospheric environment causes the imaging LOS to deviate by 0.01\({}^{\circ}\), the target positioning error will increase by more than 25.2 m (imaging distance is 10 km, imaging angle is 60\({}^{\circ}\)). Hence, these factors need to be taken into account in the algorithm in subsequent research, and the accuracy of the positioning algorithm can be improved by adding auxiliary coordinate systems, correcting the position of the projected point in the coordinate system, and adding a scale factor to correct the LOS equation. (4) _Combining the traditional algorithms together_. The algorithms mentioned above can be fused to compensate for respective deficiency. For instance, scholars have greatly improved the precision of positioning algorithms by combining DEM models and filtering algorithms, fusing intersection algorithms and filtering models [55; 56; 63]. Some positioning algorithms that combine multiple models can improve the accuracy to within 5 m. However, it should be pointed out that most of these algorithms require multiple sets of image data at the same time and fail to achieve high accuracy positioning of a single image. This problem remains to be solved.
## 4 Discussion  
Normal distributions are widely represented in natural phenomena, social phenomena, science and technology, and in activities associated with production. Many random phenomena encountered in practice completely or approximately obey normal distributions. While emphasizing the status of the normal distribution, it is important to note that many random phenomena cannot be depicted by a normal distribution. The use of the cloud model to describe such randomness extends the normal distribution to a generalized normal using a new independent parameter, superentropy, to measure deviations from the normal distribution, with the advantage of being more generalizable than the normal-terrestrial conditional distribution [48]. Use of technical methods in the construction of indicator systems and research methods have a greater impact on evaluation results of LCCC. The normal cloud model allows for the conversion from qualitative to quantitative assessment, especially when the amount of data is small [49]. For example, in this study there were only 14 study units and a lot of uncertainty in the data. Hence, using the normal cloud model had the advantage of simplifying the study population when applied to small sample data processing.  
Evaluating carrying capacity is complex, hence multi-factor, multi-index, and multi-level evaluation theories are widely used [28]. As a result, it is challenging to evaluate carrying capacity comprehensively using a simple index system. The qualitative evaluation descriptions in this study were transformed into quantitative values through a normal cloud model, generating accurate and unified quantitative evaluation results. The normal cloud model method used three numerical features to describe the results, and by combining the meanings of the different numerical features, the accuracy of the evaluation results were judged and the influence of ambiguity on the results was greatly reduced [23; 24; 25; 45]. Through comparative analysis with the Liaoning Department of Natural Resources Land Carrying Capacity Evaluation Project [50], the expert consultation method was simple and subjective, and this study improved the reliability and generalizability of the research results in the case of a complex LCCC system with high uncertainty, while considering the balance between the index system and assessment methodologies. However, the indicators selected in this study are still subjective even though they are selected on the basis of existing studies. In addition, the study data are cross-sectional, and the comparative analysis of the study results over time is lacking, which has certain limitations.  
Figure 7: Distribution of coordination stage types of LCCC in Liaoning Province.
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 2 The Proposed Antenna Design
### Ultra Compact 2 \(\times\) 1 UWB Linear VNSA Array (Array 2)  
At the UWB frequency band (3.1-10.6 GHz), the compact UWB 3:1 NTL WPD [27] is selected here only to show its applicability as an antenna array's feeding network. As a contribution and to test the 3:1 NTL WPD performance, a 3:1 TTL WPD is designed, fabricated, and tested using the same concept as in [30]. The 33.31% size reduction of 3:1 NTLIs as compared to TTLIs is shown in Figure 7a. The prototype of 3:1 TTL WPD is shown in Figure 7b. Based on the detailed parametric values on the effect of the isolation resistors on the divider performance, the R\({}_{1}\), R\({}_{2}\), and R\({}_{3}\) values for 3:1 TTL WPD are 180 \(\Omega\), 910 \(\Omega\), and 240 \(\Omega\), respectively. However, they are equal to 270 \(\Omega\), 390 \(\Omega\), and 240 \(\Omega\), respectively, for 3:1 NTL WPD. The results of the two dividers are demonstrated in Table 3 and Figures 8 and 9, which show the effectiveness of using NTL theory [27; 29] to compact the 3:1 NTL WPD without degrading its performance. In terms of matching, isolation, and group delay, both dividers operate well over the UWB frequency spectrum. It can be observed that the measured insertion losses for both dividers, S\({}_{12}\) and S\({}_{13}\), are close to the theoretical values, \(-1.24\) dB and \(-6\) dB, respectively. As a novelty in this work, the advantage of NTL [27; 29] and VNSPA [28] theories is exploited to design the compact version of Array 1 by integrating two elements of the predesigned compact UWB VNSA [28] with 3:1 unequal split NTL WPD (in the dashed box of Figure 7) [27], resulting in the compact VNSA array (Array 2), as illustrated in Figure 10. Since the size of VNSA [28] is 51.94% smaller than VTSA [1], 52% compactness is achieved in the resulting array with a 6.76% BW enhancement and this illustrates the efficiency of applying the NTL and VNSPA theories to miniaturize the size of UWB WPD and VTSA, respectively, while maintaining good performance. The next section explains the parametric studies performed on Array 2 for performance enhancement. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Calculated** & **Optimized (VTSA [1])** & **Optimized (Array 1)** \\ \hline r & - & 0.17 & 0.174 \\ \hline \(Wmax\) (mm) & 24.45 & 21.03 & 27.46 \\ \hline \(L_{T}\) (mm) & 27 & 25 & 26.3 \\ \hline \(L_{qw}\) (mm) & 6.57 & 5.7 & 5.5 \\ \hline \(W_{min}\) (mm) & - & 0.3 & 0.34 \\ \hline \(radsl\) (mm) & 3.285 & 1.89 & 2.35 \\ \hline \(Dis\) (mm) & - & 37.2 & 45 \\ \hline \(sp\) & 52.36 & - & 83.3 \\ \hline \(Wa\) (mm) & 1.819 & 1.2 & 0.96 \\ \hline \(W_{p1}\) (mm) & 1.819 & - & 1.85 \\ \hline \(W_{p2}=W_{p3}\) (mm) & 1.819 & - & 1.85 \\ \hline \(Lar\) (mm) & - & - & 56 \\ \hline \(War\) (mm) & - & - & 135 \\ \hline \end{tabular}
\end{table} 
Table 2: Calculated and optimized parameters of Array 1 with its single element (VTSA).
# Intelligent Neural Network Schemes for Multi-Class Classification
## Appendix A  
The iterative self-constructing clustering (SCC) algorithm [32] is an extension of the one proposed in [50]. Let \(J\) be the number of existing clusters, initialized to be 0. SCC proceeds iteratively. In the first iteration, for pattern \(i\), \((\mathbf{x}_{i},\mathbf{y}_{i})\), \(1\leq i\leq N\), the membership degree of pattern \(i\) to existing cluster \(G_{j}\), \(1\leq j\leq J\), is calculated as  
\[\mu_{G_{j}}(\mathbf{x}_{i})=\prod_{\ell=1}^{n}\textit{exp}\left[-\left(\frac {x_{\ell,i}-c_{\ell,j}}{v_{\ell,j}}\right)^{2}\right]. \tag{1}\]  
If \(\mu_{G_{j}}(\mathbf{x}_{i})<\rho\) where \(\rho\) is a pre-defined constant, it is assumed that pattern \(\mathbf{x}_{i}\) is not similar to any existing group. Then, \(J\) is increased by 1, i.e., \(J\gets J+1\), and a new group \(G_{J}\) is generated, having  
\[\mathbf{c}_{j}\ \leftarrow\mathbf{x}_{i},\ \mathbf{v}_{j}\leftarrow(v_{0}, \ldots,v_{0}),\ S_{J}\gets 1 \tag{2}\]  
where \(v_{0}\) is a pre-defined constant. On the contrary, if \(\mu_{G_{j}}(\mathbf{x}_{i})\geq\rho\) and pattern \(i\) belongs to the same category of \(G_{j}\), let cluster \(G_{a}\) be the one with the highest membership degree. Then, pattern \(i\) is added to cluster \(G_{a}\) by  
\[c_{a}\ \leftarrow\ \frac{S_{a}\times\mathbf{c}_{a}+\mathbf{x}_{i}}{S_{a}+1} \tag{3}\]  
\[v_{\ell,a}\ \leftarrow\ \sqrt{\frac{\left(S_{a}+1\right)\left(v_{\ell,a}-v_{0} \right)^{2}+S_{a}\times c_{\ell,a}^{2}+x_{\ell,i}^{2}}{S_{a}}-\frac{S_{a}+1}{S _{a}}\times c_{\ell,a}^{2}}+v_{0},\ 1\leq\ell\leq n, \tag{4}\]  
\[S_{a}\ \gets S_{a}+1. \tag{5}\]  
When all patterns are processed, SSC stops with \(J\) clusters.  
In the next iteration, for pattern \(i\), \((\mathbf{x}_{i},y_{i})\), \(1\leq i\leq N\), it is removed from the cluster \(G_{b}\), \(1\leq b\leq J\), to which it was assigned. One of the following three cases occurs:* If there is no pattern left in \(G_{b}\), \(G_{b}\) is deleted, and the number of existing clusters is decreased by 1, i.e., \(J\gets J-1\).
* If there is only one pattern left in \(G_{b}\), the characteristics of \(G_{b}\) are reset by Equation (29), taking the left pattern as the single member.
* Otherwise, \(G_{b}\) is updated as \[\mathbf{c}_{b}\ \leftarrow\ \frac{S_{b}\mathbf{\times}\mathbf{c}_{b}-\mathbf{x}_{i}}{S_{b}-1}\ (33)\] (A6) \[v_{\ell,b}\ \leftarrow\ \sqrt{\frac{\left(S_{b}-1\right)\left(v_{\ell,b}-v_{0} \right)^{2}+S_{b}\mathbf{\times}c_{\ell,b}^{2}-x_{\ell,i}^{2}}{S_{b}-2}-\frac{S_{b} -1}{S_{b}-2}\times c_{\ell,b}^{2}}+v_{0},\ 1\leq\ell\leq n,\] (A7) \[S_{b}\ \leftarrow\ S_{b}-1\.\] (A8)  
Then, the membership degree of pattern \(i\) to \(G_{j}\), \(1\leq j\leq J\), is calculated by Equation (28), and a new cluster is created by Equation (29) or pattern \(i\) is added to an existing cluster by Equations (30)-(32). When all the training patterns are processed, (1) if the cluster assignment of any pattern is changed, the next iteration proceeds; (2) otherwise, the algorithm stops with \(J\) clusters together with their centers and deviations.
## 5 Experimental Validation of the Repair Strategy
### Test Protocol  
Cyclic tests were carried out on the ASB and RR columns at the sustainable and innovative bridge engineering research center (SIBERC) Laboratory of Fuzhou University (China) using the same test protocol. First, the vertical constant load P = 266 kN was applied on the column top by the steel frame in Figure 10a,b. This load is the vertical load due to the bridge deck weight on a scale of 1:36, using the adopted scale factors in [PERSON] et al. [36]. Then, the horizontal displacement progression outlined in Figure 7b was applied to the column top by the hydraulic actuator shown in Figure 10a. The cyclic displacement progression used was the one recorded on the column top in [PERSON] et al. [36] during  
Figure 10: Cyclic test apparatus: (**a**) Photo of the Fuzhou University laboratory, (**b**) design scheme, and (**c**) applied instrument to measure at the column base: Vertical (V) and diagonal (X) deformations; (**d**) horizontal (H) and concrete jacket strains (W).  
a pseudo dynamic test performed on a bridge with the same configuration as the one studied here (Figure 7a). The tested column was the one repaired following the previous proposed strategy in [PERSON] et al. [36].  
The pseudo dynamic test permitted us to investigate the behavior of the full-scale RC bridge that was numerically simulated, except for a pier that was tested on a scale of 1:6 at the proof testing and research in structures and materials laboratory (PRISMA) at the Department of Architecture of the University of Roma Tre. For that reason, the selected displacement history was representative of the one recorded on the pier when the bridge was subjected to an earthquake. This test protocol permitted us to study the response of the columns when asymmetric displacements in the two load directions were applied, as in case of an earthquake. Usually, the cyclic test on columns is performed by imposing symmetric displacements in the two load directions with increasing amplitude, which is not representative of an earthquake excitation.  
The first part (Tol) and the second part (Tol \(\times\) 2) of the displacement history (Figure 7b) were recorded on the column when the Tolmezzo accelerogram from the earthquake in Italy in 1976, and the Tolmezzo accelerogram scaled to double were applied to the bridge in sequence. The Tolmezzo earthquake was selected because it produces a peak response on the acceleration spectrum for the bridge with a period of 0.7 s. For that reason, the Tol part is representative of a strong earthquake for the studied bridge, whereas the Tol \(\times\) 2 part is representative of a very strong aftershock. The Tol and Tol \(\times\) 2 parts have five cycles characterized by great displacement values, which permit the study of low cycle fatigue response of the column.  
Preliminary elastic cyclic tests on the columns permitted us to evaluate the column yield displacement that was equal to ~5 mm for each column. The selected displacement history permitted us to investigate the column response at large displacement ductility values (~6-7).