esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 5 Physical Experiment
### Measurement Accuracy Evaluation
Because some data of other methods are extracted manually, the calculation  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**NO. **} & \multicolumn{3}{c}{**Measurement Result(\({}^{\circ}\))**} & \multicolumn{3}{c}{**True Value(\({}^{\circ}\))**} & \multicolumn{3}{c}{**Error(\({}^{\circ}\))**} \\ \cline{2-10}  & **Yaw** & **Pitch** & **Roll** & **Yaw** & **Pitch** & **Roll** & **Yaw** & **Pitch** & **Roll** \\ \hline
1 & \(-\)0.9 & 17.7 & 0.2 & 0.0 & 15.0 & 0.0 & \(-\)0.9 & 2.7 & 0.2 \\
2 & 0.7 & \(-\)9.7 & 0.1 & 0.0 & \(-\)9.0 & 0.0 & 0.7 & \(-\)0  
speeds of the algorithms cannot be compared. The eight points extracted are shown in Figure 15.  
The measured RMSEs of aircraft attitude angle and depth distance are shown in Table 3. The RMSE of the three attitude angles are 1.1\({}^{\circ}\), 0.8\({}^{\circ}\) and 2.1\({}^{\circ}\). The data show no large gap between the accuracy of the proposed method in this article and the comparison method, but the proposed method can be applied to the aircraft attitude when some key points, such as the wing and tail part, are blocked. In the case of the comparison method, the extracted 2D point data do not correspond to the 3D points, and large deviations in the measurement results are noted. These deviations present a safety hazard during the flight of the aircraft. [PERSON]'s method has the same accuracy as the proposed method in distance measurement, but it has a larger angle error in attitude measurement. In [PERSON]'s method, when calculating three-dimensional space points, a large error will be introduced because of the long distance of the object. The attitude is calculated through the corresponding three-dimensional points. So the result of attitude calculation is inaccurate. Figure 16 shows some of the measured attitude angle data. The yaw and roll measured by the comparison algorithm produces serious errors in images 22 and 23. This error is caused by the UAV partially occluding the right wing in the image, which results in a corresponding point error. The pitch also shows obvious errors in images of 22-24, 34-40, and 46 because the wing plane is linearized in the image on account of the occlusion of the wing and the small pitch angle, thereby resulting in inaccurate lifting points. By comparison, the measured values of the proposed method are stable within a certain error range. This finding confirms the robustness and reliability of the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **RMSE of Yaw (\({}^{\circ}\))** & **RMSE of Pitch (\({}^{\circ}\))** & **RMSE of Roll (\({}^{\circ}\))** & **RMSE of Distance (\(m\))** \\ \hline Proposed & 1.1 & 2.1 & 0.8 & 0.61 \\ EPNP & 2.0 & 0.8 & 1.8 & 1.33 \\ PNP & 2.4 & 2.7 & 0.3 & 0.31 \\ Li’s Method & 4.6 & 3.2 & 4.7 & 0.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RMS of attitude angle and depth distance in the comparison method. Figure 15: Aircraft attitude measurement keypoints.
## 2 Materials and Methods
### Data Source
#### 2.2.2 Land Use Type  
The land use data were obtained from the China Multi-Period Land Use Remote Sensing Monitoring Database, released by [PERSON]'s team at the Institute of Geographic Sciences and Resources, Chinese Academy of Sciences at [[http://www.resdc.cn](http://www.resdc.cn)]([http://www.resdc.cn](http://www.resdc.cn)) (accessed on 12 August 2024). The data are generated on the basis of Landsat satellite image interpretation and are suitable for land use/cover change analysis on the regional and national scales, with a spatial resolution of 30 m \(\times\) 30 m. In this study, four land use types were extracted from this product for the year 2020: dry croplands, forests, grasslands, and wetlands. These types were used as the baseline data for land use classification. Historical data comparison and verification revealed that the primary land use types in the study area had remained stable over time, with no large-scale transformations occurring. To account for minor local changes, spatial buffer zones were established to ensure that the potential impact of data timeliness on the study results remained within an acceptable range.
# Hybrid MU-MIMO Precoding Based on K-Means User Clustering
## 7 Conclusions  
In this paper, a user grouping algorithm based on a modified K-means spatial clustering was proposed. The technique tried to address the complexity of scheduling spatially-compatible users, as required by different precoding schemes.  
It was shown that the user's channels inter-correlation depended on the transmit correlation matrix, for a base station with a uniform linear array of antennas and single antenna users, and that the user's channels inter-correlation depended ultimately on the angles between the users.  
The proposed grouping algorithm identified clusters of users where the average angle between users was minimized. Given that in real life, the locations of users follow a distribution with a clustered structure, the proposed approach could be a very efficient way of identifying users with separable channels. In this case, we showed that it had a linear complexity with the number of users.  
The performance of various precoding schemes like linear RZF, BD, non-linear THP, and the hybrid, BD-THP, was evaluated, and it was shown that linear precoding schemes, like RZF, were more affected by users with correlated channels than the non-linear ones. For that reason, hybrid precoding is a good tradeoff between complexity and performance.  
Finally, the performance of the hybrid BD-THP precoding scheme was evaluated on a realistic user distribution using the proposed clustering algorithm.  
Although we showed that the AoA errors had a limited impact on the clustering results, more work is required to evaluate the overall precoding algorithm performance correctly under non-ideal channel estimation and AoA errors.  
Conceptualization, R.-F.T.; methodology, A.-A.E.; formal analysis, C.P.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Precise Point Positioning (PPP) Technique
The term \(\overline{N}^{s}_{r,IF}\) is usually decomposed to achieve PPP ambiguity resolution, i.e., to recover the integer property of ambiguity:  
\[\overline{N}^{s}_{r,IF}=\Bigg{(}\frac{cf_{2}}{f_{1}^{2}-f_{2}^{2}}N^{s}_{r, WL}+\frac{c}{f_{1}+f_{2}}\overline{N}^{s}_{r,NL}\Bigg{)}/\lambda_{IF} \tag{10}\]  
where \(N^{s}_{r,WL}\) is the integer \(WL\) ambiguity and \(\overline{N}^{s}_{r,NL}\) is the float \(NL\) ambiguity. Typically, the WL ambiguity is resolved by utilizing the Hatch-Melbourne-Wubbena (HMW) combination observable [48; 49; 50]. A tight constraint can be reconstructed and imposed on the ionosphere-free float ambiguities once the \(WL\) and \(NL\) integer ambiguities have been resolved and validated. \[r_{N_{IF}}=\overline{N}^{s1}_{r}-\overline{N}^{s2}_{r}-N^{s1,s2}_{r}-d^{s1,s2} \tag{11}\]  
In this form, PPP ambiguity resolution can be attained at the end-user level. Following the resolution of undifferenced ambiguities utilizing code and phase bias products, PPP may acquire a global positioning precision of millimeters, which aligns with traditional network analysis methods. The PPP-AR technique is more sensitive than the conventional PPP technique.
## 2 Background
### IoT Definition, Benefits, and Type of Applications  
There are several definitions of the IoT available in the literature. Based on [24], the IoT is a platform that involves the process of gathering raw data from smart devices and allowing communication between devices and humans. In the context of business, the IoT is defined as technology that involves devices that not only communicate among themselves but also solve issues and challenges [25]. The IoT is also called the \"Internet of the Future\" [26].  
The prime benefits of the Internet of Things are as follows.  
1. Cost reduction: The IoT reduces costs by setting priorities, assigning specialized work to specialized people, and improving the production process by reducing internal processes so that they are efficient and effective.
2. Business opportunities: With the Fourth Industrial Revolution, the Internet of Things has become an indispensable entity in the modern world, and companies rely mainly on artificial intelligence and smart networks. IoT solutions have a substantial role in the development of companies and large organizations.
3. Improved safety and security: The IoT provides safety and security solutions as it provides password services to protect against intrusion and illegal access and provides multifactor authentication. Applications of the IoT are countless and are in all walks of life. In Table 2, major applications of the IoT are discussed regarding Industrial Revolution 4.0 and beyond.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Domain** & **Contribution** \\ \hline
[27] & Medical field & The authors propose important entities that can possibly influence users’ acceptance of IoT-based disease management services. \\ \hline
[28] & Smart city & The authors explain the data security and privacy techniques that can be used in future smart cities. Salient features of smart cities are discussed along with the technology supporting that paradigm shift. The paper also highlights the research challenges in this journey. \\ \hline
[29] & Home automation & In this paper, the authors used a grouping of sensors, microcontrollers, and various communication protocols to detect user conduct at different access points and execute a sensing algorithm. \\ \hline
[30] & Smart industry & In this work, the authors have proposed the use of an Arduino ATmega 2560 board that gathers the temperature and humidity parameters from sensors and records them in an online database. This application is useful in realizing smart industries. \\ \hline
[31] & Commercial applications & In this work, a novel method is given with the aim of systematizing the generation of a quality of service (QoS)-aware service, offering real-time checking that is beneficial in various commercial applications. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Applications of the IoT in various domains.
## 4 Experiment and Analysis
### Results and Analysis
#### 4.2.2 Evaluation of Consistency with the Original Meaning of Entropy
Hence, their consistency with the original definition of entropy was not verified, and their reliability and measurement ability were not assessed. Euclidean-distance-based and slope terrain entropy methods were used to calculate and evaluate the entropy values of the dataset. In the slope terrain entropy method, slopes were calculated and divided into five categories using the natural breaks method before terrain entropy calculation. The results of Euclidean-distance-based and slope terrain entropy methods are shown in Figure 8.  
As shown in Figure 8, both Euclidean distance and slope terrain entropy values exhibit an increasing trend as the number of mixed iterations increases. Thus, it is inferred that both of them align with the original concept of entropy. To verify this hypothesis, the regression equations and the coefficients of determination were further calculated for each indicator. The regression equation for the Euclidean distance terrain entropy is \(y=0.0107 lnx+0.4633\), and the coefficient of determination is \(94.73\%\). The regression equation for slope terrain entropy is \(y=0.2142 lnx+0.3445\), and the coefficient of determination is \(79.5\%\). The regression results support our hypothesis. The first coefficients of the regression equations for both methods are positive, and the coefficients of determination are both greater than \(50\%\). It indicates that these two methods are both consistent with the original meaning of entropy. The assessment results of the validity, reliability, and measurement ability of the two methods are shown in Table 2.  
The lower the values of \(R\) and \(A\), the better the reliability and measurement ability of the entropy. Therefore, it can be concluded that the Euclidean distance terrain entropy is superior to the slope terrain entropy in measurement ability. However, the \(R\) value of Euclidean distance terrain entropy is higher than that of slope terrain entropy, indicating that the former performs worse than the latter in reliability. The reason may be the higher sensitivity of Euclidean distance terrain entropy.
# A Frequency-Reconfigurable Dual-Band RF Crossover Based on Coupled Lines and Open Stubs
## 2 Theoretical Analysis
\]  
The S-parameters can be calculated using  
\[\mathrm{S_{11}}=\frac{\mathrm{\Gamma_{ee}}+\mathrm{\Gamma_{e0}}+\mathrm{ \Gamma_{oe}}+\mathrm{\Gamma_{oo}}}{4} \tag{14}\]  
\[\mathrm{S_{21}}=\frac{\mathrm{\Gamma_{ee}}-\mathrm{\Gamma_{e0}}+\mathrm{ \Gamma_{oe}}-\mathrm{\Gamma_{oo}}}{4} \tag{15}\]  
\[\mathrm{S_{31}}=\frac{\mathrm{\Gamma_{ee}}-\mathrm{\Gamma_{e0}}-\mathrm{ \Gamma_{oe}}+\mathrm{\Gamma_{oo}}}{4} \tag{16}\]  
\[\mathrm{S_{41}}=\frac{\mathrm{\Gamma_{ee}}+\mathrm{\Gamma_{e0}}-\mathrm{ \Gamma_{oe}}-\mathrm{\Gamma_{oo}}}{4} \tag{17}\]  
where the input reflection coefficients can be calculated using  
\[\mathrm{\Gamma_{ee,e0,oe,oo}}=\frac{1-\mathrm{Z_{o}}\mathrm{Y_{ee,e0,oe,oo}}}{ 1+\mathrm{Z_{o}}\mathrm{Y_{ee,e0,oe,oo}}} \tag{18}\]Then, by substituting (14)-(18) in (11)-(13), the following equations are found:  
\[\Gamma_{\rm ee}=\Gamma_{\rm oo}=-\Gamma_{\rm ee}=-\Gamma_{\rm oe} \tag{19}\]  
\[\rm Y_{ee}=Y_{\rm oo} \tag{20}\]  
\[\rm Y_{eo}=Y_{\rm oe} \tag{21}\]  
\[\rm Z_{o}^{2}\,Y_{ee}\,Y_{eo}=1 \tag{22}\]  
Also, the tuning range [39] can be calculated using  
\[\rm Tuning\ Range\ (\%)=\frac{f_{High}-f_{Low}}{\sqrt{f_{High}\ f_{Low}}} \times 100\% \tag{23}\]  
\(\rm f_{High}\): the highest resonant frequency that can be obtained in a band from tuning. \(\rm f_{Low}\): the lowest resonant frequency that can be obtained in a band from tuning. In addition, there are constraints regarding any CL to enable or facilitate its fabrication process. The even impedance must be greater than the odd impedance. Otherwise, the gap between the two TLs will be big, leading to uncoupling. Another condition that must be met is that the even impedance to odd impedance ratio should not be very high. If so, the gap between the two TLs will be very small, making the fabrication process a hard or impossible task to accomplish. Also, it may lead to high coupling, which can ruin the design. While keeping in mind these considerations, the proposed crossover can be designed to operate in different frequency bands based on the selection of the varactor diode and the parameters of the CLs and open stubs. Also, the change in capacitance affects the quality factor, which accordingly will modify the BW. Therefore, the circuit parameters were selected to work on frequencies that realize the previously mentioned applications, and these parameters are shown in \[\mathrm{S_{21}}=\frac{\mathrm{\Gamma_{ee}}-\mathrm{\Gamma_{e0}}+\mathrm{ \Gamma_{oe}}-\mathrm{\Gamma_{oo}}}{4} \tag{15}\]0  
Afterwards, MATLAB [40] was used to ensure the correction of the previously stated equations. Additionally, ADS was utilized to verify the validation of the design and to further aid in the optimization process. Figure 3 shows an illustration of the theoretical S-parameters of the proposed design and the change in frequencies that result from changing the capacitance of the varactor diodes. It can be seen that the increase in capacitance leads to a decrease in operating frequencies.
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## 3 Materials and Methods
### PAOC Technique for Subcarrier Multipath Mitigation
Subtracting the subcarrier multipath error estimate \(\Delta\hat{\phi}_{MEF}\) from the S-P discriminator output \(\Delta\phi_{p}\), an unbiased subcarrier estimate with small noise errors can be obtained as:  
\[\Delta\phi_{PAOC}=\Delta\phi_{p}-\Delta\hat{\phi}_{MEF}=\Delta\phi_{d}+ \epsilon_{p}-\epsilon_{res} \tag{21}\]  
Equation (21) shows that the PAOC subcarrier estimate \(\Delta\phi_{PAOC}\) contains no multipath error. Moreover, the thermal noise error of \(\Delta\phi_{PAOC}\) is smaller than that of \(\Delta\phi_{oc}\) but lager than that of \(\Delta\phi_{p}\). The processing described in Equations (18)-(21) can be modeled as a PAOC complementary filter, which is shown in Figure 4. When the smoothing constant is 1, \(\Delta\hat{\phi}_{MEF}[k]\) is equal to \(\Delta\hat{\phi}_{ME}[k]\), and the PAOC subcarrier estimate \(\Delta\phi_{PAOC}\) is equal to the S-OC discriminator output \(\Delta\phi_{oc}\). That is to say, the OC technique is a special case of the PAOC technique with a smoothing constant of 1. Figure 4: Schematic diagram of the PAOC technique for subcarrier multipath mitigation.
## 3 Results
### Aberrometric Measurements  
The values of the Zernike coefficients and M, J0, and J45 were collected and compared among each other for the four measurements: baseline and after performing tests under different types of lighting (white, red, and blue). Each order of aberration from order 2 to order 6 was analyzed separately. Order 2 includes LOAs (defocus, C0 (2, 0); and astigmatism, C0 (2, 2) and C0 (2, -2)), whereas HOAs consist of orders from 3 to 6.  
For the M values (Figure 8), statistically significant differences (\(p\)\(\leq\) 0.05) were observed when the lighting conditions were compared with the baseline measurement, with the baseline being more myopic in all cases. Among the lighting conditions, significant differences were found only between white and red light (\(p\) = 0.012). With respect to the J0 and J45 values (Figure 8), no significant differences were found except when blue light was compared with red light for J45 (\(p\) = 0.030).  
Within LOAs, significant differences were found in order 2 for the coefficient C0 (2, 0) among all lighting conditions except when comparing blue and red light (Table 2). In HOAs, significant differences were obtained in the central terms of even orders and in  
Figure 7: The comparison of the correct answers (**A**) and reaction times (**B**) for each stimulus appearance position in Test 3 between each type of lighting. Each \(p\) value is represented on the bars, where * and bold letters indicate statistically significant differences (\(p\)\(\leq\) 0.05).  
coefficients C0 (3, \(-\)1) and C0 (6, 2). No statistically significant differences were found in the rest of the coefficients.  
It should be noted that since defocus, C0 (2, 0), was the predominant aberration in all cases, whenever significant differences were found in this coefficient, it was expected that they would also be found when comparing M values.
# CFD Based Non-Dimensional Characterization of Energy Dissipation Due to Verticle Slosh
## 5 Scaling of Violent Sloshing Systems
### Froude Scaling Rules  
For experimental scale, it is possible to achieve perfect geometric scaling \(\lambda=h/h_{fs}\), where \(h_{fs}\) is the tank height at full scale, and consequently that this scaling factor applies to all the geometric ratios. As noted above, slosh fluid physics is then commonly scaled using the Froude number:  
\[Fr=\omega_{fs}\sqrt{\frac{h_{fs}}{g}}=\omega\sqrt{\frac{h}{g}} \tag{22}\]  
hence,  
\[\frac{\omega}{\omega_{fs}}=\sqrt{\frac{h_{fs}}{h}}=\lambda^{-\frac{1}{2}} \tag{23}\]  
Consequently, the Froude scaling for time \(t\) is  
\[\frac{t}{t_{fs}}=\lambda^{\frac{1}{2}} \tag{24}\]  
Ensuring geometrical and Froude similarity constrains two of the three fundamental units (length and time). The rule for mass is generally achieved by simply using a test fluid of the same density as the full scale (in fact when possible the same fluid is used). This assumption leads to  
\[\frac{m}{m_{fs}}=\frac{\rho h^{3}}{\rho_{fs}h_{fs}^{3}}=\lambda^{3} \tag{25}\]  
All other scaling rules then follow from those derived above for length, time and mass such that:
# The Correlation Factors and Mechanisms of Diffusion for P and S in the Cu Single Crystal
###### Abstract  
The full description of the mechanisms for the diffusion of substitutional impurities requires an account of the correlation of the atomic jumps. This study investigated the diffusion of phosphorus (P) and sulfur (S) in the fcc copper (Cu) single crystal using density functional theory (DFT). Vacancy formation energies and impurity-vacancy interactions were calculated, revealing attractive interactions of P and S with the vacancies. The attractive interactions between S and a vacancy were roughly twice as strong as those between P and a vacancy. The 5-frequency--or 5-jump--model was employed to describe the correlation effects during diffusion. The potential energy profiles and activation energies were determined for the different jump paths necessary for the model and to account for all the correlation effects in substitutional impurity diffusion in the single crystal. The results indicated that S diffuses significantly faster than P in Cu, primarily due to lower activation energies for certain jump paths and a more favorable vacancy-impurity interaction. This occurs because when bonding with the crystal, S tends to prefer atomic sites with larger volumes and more asymmetric geometric arrangements when compared to P. This favors the interactions between S and the vacancies, and reduces friction with the matrix during the diffusion of S. The effective diffusion coefficients were calculated and compared with experimental data. The findings provide insights into the diffusion mechanisms of P and S in Cu and how these can be affected by the presence of extended defects such as grain boundaries.  
m +
Footnote †: journal: applied sciences
# A Fingerprint-Based Verification Framework Using Harris and SURF Feature Detection Algorithms
## 4 Experimental Results  
In this section, the experimental results obtained are shown and discussed in order to demonstrate the performance of the proposed fingerprint verification system. All testing and evaluation experiments are performed on two public domain databases, widely used in the related works, namely the Fingerprint Verification Competition's FVC2000 DB1 [28] and FVC2002 DB1 [29] databases where each of them consists of a total number of 80 fingerprint images with a resolution of \(300\times 300\) pixels for FVC2000 DB1 and \(388\times 374\) pixels for FVC2002 DB1. Both datasets are publicly downloadable for free [30], in which the fingerprint images were acquired from ten persons; eight acquisitions for each person of the same finger [31]. For realistic fingerprint verification, a user initially needs his or her fingerprint to be fed into the fingerprint verification system which is saved in some format with the user's identity. The system begins with the fingerprint enhancement process, and then extracts the local feature points (both Harris and SURF keypoints) from fingerprint images. These feature points are then compared with the feature points of the fingerprints in a database and the outlier's features are eliminated by applying the RANSAC algorithm.  
Final fingerprint matching is done by calculating the weighted average of the matching (or similarity) score between the fingerprint to be identified and each fingerprint in the database in order to decide whether they are a matching pair (i.e., belonging to the same finger), based on whether the similarity score is above or below a predefined matching threshold (0.3,0.4). Figure 9 presents an example of matching similarity of two different fingerprints and another example of matching results between two impressions of the same finger.