esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Profile Generation and Modification
### Profile Generation
#### 2.1.2 The Meshing Equation  
When a cycloid gear is in motion, the trajectory of the point \(P\) observed in the cycloidal gear coordinate frame \(S_{c}\) is a cycloid profile. Since the rollers always mesh with the cycloidal gears, Equation (1) still holds, where \(n_{c}\) stands for the tooth number of the cycloidal disk and \(n_{p}\) stands for the roller number.  
\[\varphi_{c}\cdot n_{c}=\varphi_{p}\cdot n_{p} \tag{1}\]  
The conversion of coordinate and the meshing formula can be used to obtain the contour formula of the cycloidal disk using gearing theory:  
\[r_{c}^{(P)}=M_{cp}\cdot r_{p}^{(P)} \tag{2}\]  
\[\left(\frac{\partial r_{c}^{(P)}}{\partial\alpha}\times\overset{\rightarrow} {k}\right)\cdot\frac{\partial r_{c}^{(P)}}{\partial\varphi_{p}}=0 \tag{3}\]  
where \(r_{p}^{(P)}\) is the coordinate of \(P\) in \(S_{p}\) as shown in Equation (4), \(r_{c}^{(P)}\) represents the coordinate of \(P\) in \(S_{c}\), \(M_{cp}\) is the transformation matrix that converts from system \(S_{p}\) to system \(S_{c}\) as shown in Equation (5), and \(\overset{\rightarrow}{k}\) stands for the unit normal vector of the \(X-Y\) plane. After replacing \(r_{p}^{(P)}\) in the above equations and solving the mesh equations, Equations (6) and (7) are obtained.  
\[r_{P}^{(P)}=\begin{bmatrix}R_{P}+R_{rp}\cos\alpha\\ R_{rp}\sin\alpha\\ 0\\ 1\end{bmatrix} \tag{4}\]  
\[M_{cp}=M_{cf}\cdot M_{fp}=\begin{bmatrix}\cos\bigl{(}\varphi_{p}-\varphi_{c} \bigr{)}&-\sin\bigl{(}\varphi_{p}-\varphi_{c}\bigr{)}&0&-e\cos\varphi_{c}\\ \sin\bigl{(}\varphi_{p}-\varphi_{c}\bigr{)}&\cos\bigl{(}\varphi_{p}-\varphi_{c }\bigr{)}&0&e\sin\varphi_{c}\\ 0&0&1&0\\ 0&0&0&1\end{bmatrix} \tag{5}\]  
\[r_{c}^{(P)}=M_{cp}\cdot r_{p}^{(P)}=\begin{bmatrix}R_{p}\cos\frac{\varphi_{p} }{n_{c}}+R_{rp}\cos\Bigl{(}\alpha-\frac{\varphi_{p}}{n_{c}}\Bigr{)}-e\cos \frac{\varphi_{p}-n_{p}}{n_{c}}\\ -R_{p}\sin\frac{\varphi_{p}}{n_{c}}+R_{rp}\sin\Bigl{(}\alpha-\frac{\varphi_{p} }{n_{c}}\Bigr{)}+e\sin\frac{\varphi_{p}-n_{p}}{n_{c}}\\ 0\\ 1\end{bmatrix} \tag{6}\]  
\[\alpha=\tan^{-1}\biggl{(}\frac{\sin\alpha}{\cos\alpha}\biggr{)}=\tan^{-1} \biggl{(}\frac{-\sin\varphi_{p}}{\cos\varphi_{p}-R_{p}/n_{p}e}\biggr{)} \tag{7}\]  
\(R_{rp}\), \(n_{p}\), \(n_{c}\), and \(e\) are given values, then Equations (6) and (7) are utilized to generate the cycloidal flank profile.
## 5 Experimental Result
### Testbed
#### 5.1.3 Virtual Sensor  
The Virtual Sensor is an FPGA-based emulation system that allows feeding the SPI interface of the MCU with data taken from a trace. The trace was obtained from a real sensor deployment on the field. For characterisation and profiling purposes, the virtual sensor was connected to the MCU replacing the MEMS present in the sensor node board. The Virtual Sensor is completely implemented within the PXI system and is made of two modules sharing data through a DMA-controlled hardware FIFO:  
* PXIe-8880: The PXI system controller, which is a general-purpose CPU-based host running Windows 7 and LabVIEW. It loads the accelerations measured on the bridge from a CSV file and pushes them into a FIFO at the boundary of the FPGA system.
* PXIe-7858R: The PXI FPGA module, which loads the measurements the DMA move from the controller to its FIFO. It acts as a digital MEMS that samples structural accelerations at a constant rate and stores them in an on-board buffer which can be accessed in-order through an SPI interface.
# 3D Ear Normalization and Recognition Based on Local Surface Variation
## 3 Technical Approach
### Normalization of the Ear Data
#### 3.2.1 The Local Surface Variation  
In 3D object recognition, one of the key problems is how to represent surfaces effectively. In the following, a surface representation called the local surface variation (LSV) is employed, and used for ear recognition. Compared with other surface representations, the LSV is more sensitive to the concave and convex areas of the surface. An ear has stable and rich structural features, so the LSV is more suitable as a 3D ear representation.  
In order to describe the variation of the local surface, the neighborhood of 3D mesh vertices are defined using the method of [36]. A set of rings can be defined around a mask vertex \(v_{0}\). The ring \(R_{1}\) includes all the directly connected neighbors of vertex \(v_{0}\), and the ring \(R_{2}\) is made up of all the directly connected neighbors of the vertices in the first ring, and so on. The ring \(R_{i}\) can be defined as follows: for one vertex \(v\) of \(R_{i}\), there is the shortest path from \(v_{0}\) to \(v\) with \(i\) edges. Then the \(N\)-ring neighborhood of vertex \(v_{0}\) is defined by the point set \(Re=\{R_{i}\colon i\leq N\}\).  
Eigenvalues of the covariance matrix of a local 3D surface neighborhood can be utilized to describe local surface properties [33]. The covariance matrix \(\mathbf{C}\) for a sample vertex \(p\) is given by  
\[\mathbf{C}=\left[\begin{array}{c}p_{i_{1}}-\overline{p}\\ \vdots\\ p_{i_{k}}-\overline{p}\end{array}\right]^{T}\cdot\left[\begin{array}{c}p_{i_{ 1}}-\overline{p}\\ \vdots\\ p_{i_{k}}-\overline{p}\end{array}\right],\ \ \ i_{j}\in N_{p} \tag{1}\]  
where \(\overline{p}\) is the centroid of the neighbors \(p_{i_{j}}\). Eigenvectors \(\mathbf{v}_{l}\) can be obtained from  
\[C\cdot\mathbf{v}_{l}=\lambda_{l}\cdot\mathbf{v}_{l}\,,\ \ \ l\in\ \{0,1,2\} \tag{2}\]  
\(\mathbf{C}\) is a positive semi-definite and symmetric matrix, the eigenvalues \(\lambda_{l}\) are real numbers. As such, corresponding to the principal components of the point set defended by \(N_{p}\), eigenvectors \(\mathbf{v}_{l}\) compose a set of orthogonal basis vectors. The variation of \(p_{i},i\in N_{p}\) along the direction of the corresponding eigenvectors is measured by \(\lambda_{l}\). Then the sum of square distances between \(p_{i}\) and its centroid as the total variation is given by  
\[\sum_{i\in N_{p}}\left|p_{i}-\overline{p}\right|^{2}=\lambda_{0}+\lambda_{1}+ \lambda_{2} \tag{3}\]The Surface Variation \(\sigma_{n}(p)\) of the 3D local surface which measures the variation quantitatively is defined by [PERSON] [33].  
\[\sigma_{n}(p)=\frac{\lambda_{0}}{\lambda_{0}+\lambda_{1}+\lambda_{2}} \tag{4}\]  
If \(\sigma_{n}(p)=0\), that means the neighborhood vertices of \(p\) are all in a plane; when the neighborhood is comprised of the vertices with the isotropically distribution, \(\sigma_{n}(p)\) obtains maximum value \(1/3\).
# Design, Plant Test and CFD Calculation of a Turbocharger for a Low-Speed Engine
An analysis of more than 15 different works carried out by the authors allowed to draw the following conclusions on the choice of a turbulence model for various CFD programs: for ANSYS CFX, the Shear Stress Transport (SST) turbulence model gives the best results, for Numeca FINE/Turbo the SA turbulence model is more preferable, for Star-CCM +, the \(k-\varepsilon\) one. The calculation is performed in a stationary setting without taking into account the heat input. To achieve grid independence, the authors considered four computational grids with the number of cells from 27 to 84.4 million. The comparison was made for two modes: design rpm and about half of design rpm. The calculated and test characteristics are in good agreement both in magnitude and in the area of operation. In this case, good agreement is also achieved at reduced rpms, for which the authors of [1] obtained a significant discrepancy between the calculated and experimental data, explained by not taking into account the heat supply in CFD calculations. Such a discrepancy in the results of CFD calculations with practically the same formulation (in both cases, the process was considered adiabatic during calculations) shows that it is difficult to speak about the reliability and repeatability of CFD calculations without their preliminary comprehensive identification. The authors of [6] investigated a new geometry generator that was developed for the design of curvature-smooth blades (vanes). The blade cascade can be initially designed by a conventional way and then smoothed. The flow was simulated in a stage 3D impeller + vaneless diffuser (VLD) of a turbocharger. The computational grid was built for one channel; the total number of cells was 2.3 million. The SST turbulence model and the ANSYS CFX program were used. Calculated and test data comparison demonstrated significant overestimation of the calculated pressure ratio and a shift of the calculated characteristics to the right, which is typical for a number of other comparisons of CFD calculations with experiments [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. We can also observe unusually large overestimation of calculated efficiency. Obviously, CFD calculations of engine turbochargers (like other centrifugal compressors) cannot yet replace engineering design and calculation methods. The aim of the research is to design a centrifugal compressor for an 8 ChN26/26 gas engine, measure its gas-dynamic characteristics at four different rpm, check the compliance of the CFD-calculated gas-dynamic characteristics of a centrifugal compressor of an internal combustion engine turbocharger with the characteristics obtained on the test rig.
# LEACH-MTC: A Network Energy Optimization Algorithm Constraint as Moving Target Prediction
## 3 Method: LEACH-MTC Based on Moving Target State Prediction
### Construction of Elliptical Area Based on State Prediction
\[c(k)=\frac{1}{2}|\text{F}_{1}\text{F}_{2}|=\lambda|v(k)| \tag{19}\]  
where \(\lambda\) is the weight coefficient. According to the relation between the axis and the focal length of the ellipse, the relation is \(a(k)^{2}=b(k)^{2}+c(k)^{2}\). Combining the elliptical coverage area \(S_{ab}(k)\) in the Equation (14), the ellipse long semi-axis \(a(k)\) and the short semi-axis \(b(k)\) are obtained as follows, respectively:  
\[a(k)=\sqrt{2}S_{ab}(k)\bigg{/}\Bigg{(}\pi\sqrt{\frac{\sqrt{\big{(}\lambda|v(k )|\big{)}^{4}\pi^{2}+4S_{ab}(k)}}{\pi}-\big{(}\lambda|v(k)|\big{)}^{2}}\Bigg{)} \tag{20}\]  
Figure 2.0  
Thus, based on the moving target's current position \(F_{1}\) and the velocity \(v(k)\), the position of the front focus \(F_{2}\big{(}J_{x}(k),J_{y}(k)\big{)}\) and the center \(\big{(}O_{x}(k),O_{y}(k)\big{)}\) of the ellipse can be obtained:  
\[\left[ \begin{array}{c}O_{x}(k)\\ O_{y}(k)\end{array} \right]=\left[ \begin{array}{c}T_{x}(k)\\ T_{y}(k)\end{array} \right]+\lambda|v(k)|\left[ \begin{array}{cc}1&0\\ 0&1\end{array} \right]\left[ \begin{array}{c}cos\tilde{\theta}(k)\\ sin\tilde{\theta}(k)\end{array} \right] \tag{22}\]  
\[\left[ \begin{array}{c}J_{x}(k)\\ J_{y}(k)\end{array} \right]=\left[ \begin{array}{c}O_{x}(k)\\ O_{y}(k)\end{array} \right]+\lambda|v(k)|\left[ \begin{array}{cc}1&0\\ 0&1\end{array} \right]\left[ \begin{array}{c}cos\tilde{\theta}(k)\\ sin\tilde{\theta}(k)\end{array} \right] \tag{23}\]  
where \(\tilde{\theta}(k)\) is the predicted of \(\theta(k)\), which is the angle between the target and the horizontal direction at time \(k\). In summary, the shape and size of the elliptical coverage area constructed can be determined by the ellipse area \(S_{ab}(k)\), the long semi-axis \(a(k)\), the short semi-axis \(b(k)\), the focal point \(F_{2}\), and the ellipse center \(O(k)\). According to the coverage area of the ellipse, nodes in the ellipse should be awakened and nodes out of ellipse should be kept sleeping.
# Evading Logits-Based Detections to Audio Adversarial Examples by Logits-Traction Attack
## 3 Proposed Method
### Detection Based on Logits Dispersion by Other Three Features  
This section further explores the effectiveness of our attack method. Given that the logit distribution of adversarial examples differs from that of normal speech, this paper designs detection mechanisms that focus on different features. The statistical features of logits in several other dimensions are calculated, such as intra-frame mean difference, decision frame variance, and the number of delineation statistics. It is checked whether Logits-Traction adversarial examples affect the detection accuracy based on these features.  
As described in Section 3.2, the number of \"decision frames\" in dense logits is small, and each frame has too many possible categories. Adding noise to the logits by [PERSON] et al. cannot distinguish between normal speech and adversarial examples. Another drawback of this method is that the random noise mechanism may produce inconsistent findings for numerous detections of the same speech. To this end, this paper designs new detection methods, and the detection system deployment [36] is shown in Figure 4:  
This detection system consists of three fixed logits feature calculation algorithms, a hyperparameter \(p\) and a threshold \(k\). \(p\) and \(k\) are only related to the protected model. When a text transcribed by ASR is recognized as an executable command, the detection system performs feature calculation on its logits. At first, the algorithm screens out logit values that satisfy the \(p\) condition, then applies the feature calculation algorithm to acquire the evaluation value and compares this value with the threshold k. Those exceeding the range of \(k\) are considered adversarial examples.
## 5 Simulation and Experimental Results of the Proposed System  
The proposed method is simulated using MATLAB. Here, in the DC-DC converters, IGBTs are utilized to reduce the switching power losses. The MPPT algorithm regulates the control signal produced by the PI controller, PWM pulses for the LUO converter and the input to the three-phase inverter. The proposed method is developed, modeled and simulated under starting and steady-state conditions.
## 2 Existing ASs and the Related Market Design  
ASs encompass activities associated with ensuring the security of distribution systems. These services include tasks such as maintaining frequency response stability, offering backup, supplying reactive power, facilitating system restoration, and initiating black start procedures. In traditional electric PSs, where power primarily flows from transmission systems to distribution systems, ASs are typically delivered by power plants connected to the transmission system [32]. The primary actor in defining and managing AS is often identified as the TSO. Within this framework, the European Network of TSOs characterizes an AS as the implementation of diverse functions aimed at maintaining the TSO's system security [33].
## 2 Applications of U-WSN
### Functionalities of UAV as a Communication Node
#### 2.3.2 Data Collection
A heuristic algorithm based on a column generation approach was proposed and the results perform well. The main idea of these research is to extend the data collection time through extending the network life time. They pay little attention on the impact of the network topology on data collection. In U-WSN, the dynamic topology has a critical influence on the connectivity of the wireless network. It will directly affect whether a route is created between two nodes and the endurance of the connection. However, most of existing works address the static network topology. The authors concentrated on the dynamic topology in [17; 18; 19], both the mobility of the UAV and sensors are considered. The authors fail to present all the relationships between the mobility and the data collection because of space limitations. In Section 4.2, we address the impact of the dynamic topology on the connection. In Table 5, we provide a summarisation and comparison of using UAV for data collection in the existing applications.
## 3 Results
### Analysis of LEP, NDVI, RI, and CN Values of All Parks  
Based on the comprehensive analysis of LEP, NDVI, RI, and CN values presented in Table 7, a prevailing pattern emerges. Parks with a Softscape value exceeding 65% tend to exhibit a reasonable NDVI value and a positive CN value, signifying the potential to offset construction-related carbon emissions through increased planting and vegetation. The NDVI demonstrates an upward trajectory as Softscape percentages rise, with NDVI and RI largely unaffected by water body proportions. Decreasing Hardscape proportions correspond to increased NDVI values. In synthesis, the optimal LEP area ratio (Softscape:Waterbody:Hardscape) for water-containing parks is 6.5:2:1.5, while for waterless parks, it is 8.3:1.7. This ratio, founded on observed trends from referenced data, can serve as both the baseline for carbon-neutral parks' LEP area ratio and a guideline to ensure satisfactory NDVI and CN values by maintaining Softscape above 65% and Hardscape below 15%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **Park Name** & **Cut and Fill** & **Construction** & **Worker** & **Planting** & **Total Carbon** & **Carbon** & **Sequestration** & **Carbon** & **Neutrality** \\ \hline
1 & Xindu Park & 36,314 & 34,941 & 365 & 2152 & 73,773 & 89,100 & 20.78\% \\ \hline
2 & Lixin Park & 237,588 & 75,946 & - & 3198 & 316,733 & 389,700 & 23.04\% \\ \hline
3 & Maple Garden & 186,181 & 108,188 & 3263 & 1194 & 298,826 & 98,100 & \(-\)67.17\% \\ \hline
4 & Yongquan Park & 1,220,008 & 1,919,717 & 356 & 6199 & 3,146,280 & 514,800 & \(-\)83.64\% \\ \hline
5 &
\begin{tabular}{c} Litchi Old Tree \\ Park \\ \end{tabular} & 36,549 & 34,500 & 265 & 1154 & 72,468 & 88,200 & 21.71\% \\ \hline
6 & Dayuan Park & 57,567 & 43,301 & 1705 & 875 & 103,448 & 100,800 & \(-\)2.56\% \\ \hline \hline \end{tabular} Note: Unit (kg CO\({}_{2}\)e).  
\end{table}
Table 6: Statistical table of the total carbon footprint of each park landscape project.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Park Name** & \multicolumn{4}{c}{**Landscape Element Proportion (\%)**} & **NDVI** & **RI** & **CN (\%)** \\ \cline{3-8}
1 & Xindu Park & 78.96 & 5.85 & 15.19 & 0.62 & 4.06 & 20.78 \\ \hline
2 & Lixin Park & 67.57 & 6.54 & 25.89 & 0.54 & 3.69 & 23.04 \\ \hline
3 & Maple Garden & 32.23 & 11.43 & 56.34 & 0.34 & 3.64 & \(-\)67.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistical table of LEP, NDVI, RI, and CN.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
## 2 Height Systems
### Heights Defined Without Assumptions About Mass Distribution
#### 2.1.2 Dynamic Heights  
Another type of height defined without assumptions about mass distribution is dynamic heights, which are obtained by dividing the geopotential number by the normal value of gravity on the ellipsoid surface at ellipsoidal height \(h=0\) and geodetic latitude \(\varphi=45^{\circ}\) :  
\[H^{DYN}=\ \frac{C}{\gamma_{0}^{45}}. \tag{5}\]  
This value is constant for a given ellipsoid and is calculated using the expression of Somigliana [5]:  
\[\gamma_{0}=\ \frac{a\tau_{a}cos^{2}\varphi+b\tau_{b}sin^{2}\varphi}{\sqrt{a^{2} cos^{2}\varphi+b^{2}sin^{2}\varphi}}, \tag{6}\]  
where \(a\) and \(b\) are the semimajor and semiminor axes of the used level-ellipsoid, \(\gamma_{a}\) the normal gravity at the equator, \(\gamma_{b}\) the normal gravity at the pole, and \(\varphi\) the geodetic latitude. The value of normal gravity \(\gamma_{0}\) is expressed in ms\({}^{-2}\), provided that \(\gamma_{a}\) and \(\gamma_{b}\) are also expressed in the same unit. The consequence of dividing the geopotential number by a constant is that dynamic heights have the dimension of height but not the character of height, as they represent purely a physical quantity, specifically the potential relative to the geoid, expressed in units of meters [4].  
In Figure 3, points \(A\) and \(B\) lie on the same level surface, which implies that they have the same geopotential numbers and, consequently, the same dynamic heights, even though due to the non-parallelism of the level surfaces, they are not equally distant from the geoid.  
It was previously stated that one of the methods for transitioning to a dynamic height system based on field measurements using geometric leveling is to convert the benchmark heights and height differences between them into the system of geopotential numbers. Then, dividing by the normal value of gravity on the ellipsoid surface at \(h=0\) and \(\varphi=45^{\circ}\), the dynamic height system is obtained. However, dynamic heights can also be obtained by converting the benchmarks into dynamic heights and correcting the height differences obtained from geometric leveling with a dynamic correction (DC), resulting in dynamic height differences. Then, through adjustment, the dynamic heights of all the points in the  
Figure 2: Geopotential number definition (according to [PERSON] and [PERSON] [4]).  
network are determined without transitioning to the system of geopotential numbers. The expression for calculating dynamic correction is as follows [4]:  
\[DC_{AB}=\sum_{A}^{B}\frac{g-\gamma_{0}^{4S}}{\gamma_{0}^{4S}}\delta n, \tag{7}\]  
where g denotes the mean value of gravity between the start and the end point of the leveling line and \(\delta n\) is the leveled height difference.  
The dynamic height difference is obtained using Equation (7):  
\[\Delta H_{AB}^{DYN}=\ \Delta n_{AB}+DC_{AB}. \tag{8}\]  
where \(\Delta n_{AB}\) denotes the sum of leveled height differences between \(A\) and \(B\).
## 3 Experimental Results and Analysis
### Explainable Analysis
#### 3.5.2 Local Interpretation  
The SHAP local interpreter was employed to perform local interpretation on four samples, including continuous samples from two types of flow channels. The SHAP waterfall plots are presented in Figure 11. It can be observed that there are substantial differences in feature importance between the straight channel and the hexagonal channel within a single sample. In the two samples of the straight channel, the features RE and Dh exhibit the greatest impact on the model output, whereas in the hexagonal channel, the  
Figure 10: Statistical diagram of SHAP. (The blue background on the left represents feature contribution. Each point represents a sample, and the color represents whether the corresponding feature on that sample contributes positively or negatively. In the figure on the right, the horizontal coordinate is the sample serial number, and the black column on the right represents the feature contribution degree.) (**a**) Straight flow channel; (**b**) hexagonal flow channel.  
features u and Tinv demonstrate a more significant influence. Additionally, even within the same type of flow channel, feature importance varies across different samples. For instance, in the straight channel, features RE and Dh have a greater impact on sample 75, while their influence is relatively smaller on sample 76, with an increased contribution from feature Tinv. Moreover, the direction of feature contributions to the model output also differs across the different flow channels and samples. For example, feature RE predominantly contributes negatively in the straight channel but positively in the hexagonal channel.  
Figure 11: SHAP local interpretation results of four samples (\(X\) represents the SHAP value, and \(E[f(x)]\) represents the expectation of all samples \(f(x)\)): (**a**) Straight flow channel, sample number 44; (**b**) straight flow channel, sample number 45; (**c**) hexagonal flow channel, sample number 44; (**d**) hexagonal flow channel, sample number 46.  
From the results of both global and local explanations, in practical applications, the appropriate features should be selected for optimization based on the specific type of flow channel and sample characteristics to accurately predict the heat transfer coefficient. For straight flow channels, Dh, RE, Qw, Tin, PR, and D should be given priority; for hexagonal flow channels, Tinw, Qw, PR, and pin should be emphasized.