esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Characteristics, Principles, and a Case Study of SPH
### A Case Study of SPH
In this context,  
\(W\) represents the kernel function used for the smooth calculation of distances between particles;  
\(H\) denotes the smoothing length, which determines the influence range of the kernel function;  
Grad \(W\) refers to the gradient of the kernel function, utilized for force calculations;  
Equation_of_state pertains to the state equation, which is employed to convert density into pressure;  
Figure 6: The simulation steps of the SPH method. Viscosity_term is a term that accounts for viscosity and is used to calculate the viscous force. ```
//Initializeparticles
1:for each particle i in fluid
2: initialize_position(particle i)
3: initialize_velocity(particle i)
4: initialize_mass(particle i)
5: initialize_other_properties(particle i)
6:endfor
7:Main simulation loop
8:whiletime < end_time
9://Compute density for each particle
10:for each particle i in fluid
11: rho_i = 0
12:for each neighbor j of particle i
13: rho_i += mass(j) * W(position(i) - position(j), H)
14:endfor
15:density(i) = rho_i
16:endfor
17://Compute pressure for each particle
18:for each particle i in fluid
19: pressure(i) = equation_of_state(density(i))
20:endfor
21://Compute forces for each particle i in fluid
22: force_i = 0
23:for each neighbor j of particle i
24:force_i += -mass(i) * (pressure(i) + pressure(j))/(2 * density(j)) * gradW(position(i) - position(j), H)
25: + viscosity_term
26:endfor
27:force(i) = force_i
28:endfor
29: // Integrate motion equations
30:for each particle i in fluid
31: velocity(i) += time_step * force(i)/density(i)
32: position(i) += time_step * velocity(i)
33:endfor
34: // Handle boundary conditions
35:for each particle i in fluid
36:if position(i) is outside boundary
37: handle_boundary(position(i), velocity(i))
38:endif
39:endfor
40: // Advance time
41: time += time_step
42:endwhile
```  
**Algorithm 1**Smoothed particle hydrodynamics simulation algorithm
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
## 2 Audio Fingerprint Extraction Based on LLE
### Pre-Processing  
As the audio signal has short-time stability, pre-processing steps, such as pre-emphasis, are required before the fingerprint extraction. First, we down-sample the input audio signal to 8 KHz because the sensitive frequency area of human is concentrated on 4 KHz and lower which can also reduce the amount of data involved in calculation. Audio fingerprinting needs high frequency resolution and wide time coverage so we use frame of length 100 ms overlapped with an interval of 10 ms.This framing strategy can reduce the error caused by the inconsistency of the boundary between test and sample and have a good retrieval efficiency even in the worst case (5 ms off). In order to reduce the poor impact of muted segments in the matching process we sift the original signal roughly using voice activity detection (VAD) with frame energy.
# Extracellular Matrix Tunes the Regenerative Potential of Fetal Stem Cells
## 3 Results
### Assessment of Surface Marker Expression in Fetal Stem Cells after Expansion on dECMs  
To characterize surface marker expression and evaluate the cell proliferation capacity, flow cytometry was used to measure the surface phenotypes CD73, CD90, CD105, CD146, and SSEA4, as well as relative EdU incorporation in fetal stem cells following expansion on dECMs (Figure 2). We found that the percentage of CD73, CD90, CD146, and SSEA4 in fSDSCs remained stable in a range between 97% and 100% after growth on dECMs. In the fNPC groups, CD73 and CD90 remained stable at similar levels to the fSDSC groups. However, the percentage of CD146 was 95% in the Plastic group, which dropped to 65%in the NECM group and 82% in the SECM group. Interestingly, the percentage of SSEA4 was 50% in the PL group and increased to 65% in the NECM group and 78% in the SECM group. The above data indicate that fSDSCs exhibit a higher expression of MSC surface markers and a less marked response to dECM expansion compared to fNPCs. Intriguingly, the median fluorescent intensity (MFI) in both fetal stem cells showed the same trend in response to dECM expansion. Following expansion on dECMs, the MFI of CD73 and SSEA4 increased, while that of CD90 and CD146 decreased. Compared to the different responses of fNPCs and fSDSCs in the percentage of CD105 expression, dECM expansion increased relative EdU incorporation in both fetal cells, particularly for the SECM group.  
Figure 1: Fetal MSC morphology and stemness gene expression following expansion on dECMs. (**A**) Before and after the decellularization of ECMs deposited by fNPCs and fSDSCs. Scale bar: 100 μm. (**B**) Cell morphology of fetal MSCs after expansion on dECMs deposited by fNPCs (NE) or fSDSCs (SE), with tissue culture plastic (PL) as a control. Scale bar: 100 μm. (**C**) Assessment of stemness gene expression, including _SOX2_, _NANOG_, _POUSF1_, _NOV_, _NES_, _KLF4_, _MYC_, and _BMI1_ in fNPCs and fSDSCs, following expansion on NE, SE, or PL using TaqMan(r) real-time qPCR. _GAPDH_ served as the internal control. Data are shown as a bar chart. * indicates a statistically significant difference from the corresponding PL group (\(p<0.05\)). * indicates a statistically significant difference from the corresponding NE group. 5 indicates a statistically significant difference from the corresponding PL group of fNPCs (\(p<0.05\)).
# Vehicle Pose Estimation Method Based on Maximum Correntropy Square Root Unscented Kalman Filter
## 6 Conclusions  
The contribution of this paper is to address the problems of the SLAM algorithm in mobile robot applications, with a focus on researching robot-pose estimation algorithms  
Figure 14: Comparison of the estimated paths in the car park: (**a**) EKF-SLAM; (**b**) UKF-SLAM; (**c**) SRUKF-SLAM; (**d**) MCUKF-SLAM; (**e**) MCSRUKF-SLAM.  
under non-Gaussian noise. Furthermore, in response to the problem of low estimation accuracy of the traditional Kalman filtering algorithm in non-Gaussian noise, we proposed the MCSRUKF-SLAM algorithm to improve the accuracy of the robot pose estimation. The proposed MCSRUKF-SLAM algorithm incorporates the square root form of sigma points into the Unscented Kalman Filter, and uses relatively simple QR decomposition and Cholesky decomposition to perform square root operations on the error covariance matrix. Moreover, the proposed MCSRUKF-SLAM algorithm which combines with the Maximum Correntropy criterion, effectively improves the problems of poor pose estimation accuracy in non-Gaussian noise.  
Conceptualization, S.L. and Y.G.; methodology, S.L.; software, S.L.; validation, Y.G.; writing--review and editing, S.L.; supervision, Y.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are available on request.  
The authors declare no conflicts of interest.
## 9 Lessons Learned
### The Process Perspective
#### 9.2.2 The Information Exchange Process Needs Formalization  
In our case study, there were no formal procedures in place for data exchange among the different disciplines. Data were provided and exchanged in various formats, leading to inefficiencies and interoperability issues. Furthermore, there was a lack of clarity regarding the timing and scope of data exchange in this project, mainly due to ambiguity surrounding deliverables from different disciplines. As a result, the VDC service provider had to invest additional effort in developing the project model, resulting in increased costs. Moreover, since the VDC service provider was exclusively contracted by the general contractor, inefficiencies arose when other team members sought access to the model for information extraction or the review of specific design conditions. Hence, having a structured information exchange process in place that explicitly outlines responsibilities for delivering specific items in a designated format and timeframe would be very beneficial to projects. Additionally, it is necessary to clarify accessibility to the federated model for all team members. The necessity of a formalized information exchange process has been highlighted in numerous research works, including [69], where the authors investigated the formalization of information exchange by using model view definitions. In a related work [70], the same research team investigated the mechanisms and challenges of such formalization.
## 6 Challenges and Future Research Directions
### Market Structure for Virtual Inertia Systems  
Currently no market for virtual inertia nor for inertia from conventional SGs exists. SGs and some loads in the power system inherently provide inertial response and are treated as a free resource. As the power system becomes inverter-dominated, the inertial requirements will become a valuable tradeable commodity, and generating units will demand financial compensation. A market-based approach can be a cost effective solution to ensure sufficiency of inertial services in the future power market [113]. The inertial response can be provided by wind turbines or even PV systems with inherent storage technologies [114]. Schemes to operate PV systems below their MPP with reserve for inertial response is also a possible option with the suitable market for such resources. A scheme to trade inertia is presented in [11]. Furthermore, the paper argues inertia should not be traded in terms of power or energy, but rather in terms of an inertia metric. A unit commitment framework for fast frequency services in the power system with transient stability constraints representing the dynamic performance requirements was proposed in [115]. It was shown that additional inertia prevented expensive units being committed post-frequency event and reduced the overall system production cost in a power system. Other papers propose a penalty factor for generators that do not provide inertial response, but so far there is no clear structure on how the inertia market should operate and is an open research area.  
One method that deserves further exploration is deploying inertia as \"service\" for power quality. For instance, as a microgrid operator, one can offer inertial services based on certain criteria such as maximum allowable ROCOFs and/or frequency deviation. The Quality of Service (QoS) metrics which have been proposed for cloud computing services (e.g., [116]) can be garnered for power systems to measure the power quality in terms on inertial response availability. The quality may be assessed in terms of response time after a frequency disturbance and/or inertia made available. This will foster a framework for microgrid operators to incorporate inertial response services in the system based on the requirements of its end-users.
## 4 Experimental Results and Evaluation  
The proposed ABC algorithm with the Pareto-based approach was implemented in MATLAB version R2020. Calculations were performed using an AMD Ryzen 7 3700U processor with Radeon Vega Mobile GFX 2.30 GHz, a 64-bit Windows 10 operating system, and 8 GB of RAM. Three-step sequential experiments were conducted. Experiment A was designed to identify the optimal parameters for the ABC algorithm with the Pareto-based approach, and the \(\overset{\sim}{C}\) metric method was used to compare its performance. Experiment B aimed to evaluate the performance of the ABC algorithm with the Pareto-based approach by a LINGO optimization solver on a small problem dataset sourced from the OR-Library database [36]. To compare the results with previous studies and provide a clear comparison of solutions, the well-known Loh and Nee (LN) test cases [37] for medium to large problems were employed. We compared the results of the proposed algorithm with heuristics and meta-heuristics. The algorithms compared included spatial representation [37], layering algorithm [38], development heuristics [39], genetic algorithm [40], and tabu search [41]. As a result, the relative improvement (RI) value was utilized to facilitate the comparison process. The \(RI\) value was estimated using two equations: the first Equation (37) evaluated the number of boxes left (BL), and the second Equation (38) evaluated the volume utilization  
\begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Key Parameter** & **Value** \\ \hline
1 & Colony size \((C_{s})\) & \(1,\ldots,100\) \\
2 & Number of employed bees \((E_{o})\) & \((C_{s})/2\) \\
3 & Number of onlooker bees \((O_{r})\) & \((C_{s})/2\) \\
4 & Mutation rate \((M_{r})\) & \(0,\ldots,1\) \\
5 & Swap rate \((S_{r})\) & \(0,\ldots,1\) \\
6 & Crossover rate \((C_{r})\) & \(0,\ldots,1\) \\
7 & Number of iterations \((I_{o})\) & \(1,\ldots,50,000\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Key parameters of the ABC algorithm with Pareto-based approach [35].  
(VU). It should be noted that a higher RI value indicates that the ABC algorithm with the Pareto-based approach outperforms the comparative research solution.  
\[RI=((Sol_{orig}-Sol_{ABC})/Sol_{orig})\times 100 \tag{37}\]  
\[RI=-((Sol_{orig}-Sol_{ABC})/Sol_{orig})\times 100 \tag{38}\]  
where \(RI\) represents the relative improvement (%) between \(Sol_{orig}\) and \(Sol_{ABC}\), and \(Sol_{orig}\) represents the solution obtained from the comparative research. \(Sol_{ABC}\) represents the solution obtained from the ABC algorithm with the Pareto-based approach.  
As a final step, Experiment C aims to evaluate the performance of the proposed algorithm on the largest and most complex problems by testing it on a set of generated instances. In addition, the complexity indicator (CI) will be utilized to measure the complexity of the problem. This will facilitate the identification of the proposed algorithm's capabilities to solve complex and real-world problems efficiently and effectively.
## 3 Dataset Description and Processing
### HD Maps Lanelet2
All other primitives are composed directly or indirectly of points. This fundamental nature of points makes them central to geographic information processing and cartography, providing a solid foundation for the establishment of geographic information systems and spatial analysis. Linestrings  
A linestring is an ordered array of two or more points with linear interpolation between them. It is used to represent the shape of elements in a map, such as road markings, curbs, building facades, fences, and more. Linestrings can also be virtual, for instance, when they form implicit boundaries of a lane. The reason for choosing linestrings as a representation is their ability to describe any one-dimensional shape and discretize it to a high degree if necessary. In comparison to spline curves, they are computationally efficient and can be used to represent sharp angles. Lanelet  
Lanelets define directional atomic segments within a map, including examples such as regular lanes, pedestrian crosswalks, and rail tracks. Atomic implies that within a lanelet, the currently applicable traffic rules remain constant, and the topological relationship with other lanes does not change. A lanelet is defined by a linestring serving as the left boundary and another linestring as the right boundary. Additionally, it can contain multiple regulatory elements expressing traffic rules applicable to the lane. Lanelets can also overlap or intersect. Area  
Zones are map segments where directed or undirected movement is not permitted. Examples include parking areas, squares, green spaces, or buildings. They are defined by one or more linestrings that collectively establish a closed outer boundary. Additionally, they may contain one or more linestrings that collectively define multiple inner boundaries, thus creating voids within the area. Figure 1: The single-frame images extracted from the video for each location display various road types [13]. (5) Regulatory elements  
Regulatory elements define traffic rules, such as speed limits, right-of-way rules, or traffic signals. Due to the various types of traffic rules, the exact structure of regulatory elements can vary significantly. Typically, they reference elements that define the rules (e.g., traffic signs) and, when necessary, reference elements that revoke the rules (e.g., signs indicating the end of a speed limit zone). They can also reference features like stop lines. The attributes of regulatory elements specify the type of rule they represent.
## 2 Theoretical Basis
of the LC into consideration, the dispersion relation of the planar waveguide can be obtained as follows [37]:  
\[\tanh\left(\frac{k_{1}W}{2}\right)=-\frac{k_{2}\varepsilon_{1}}{k_{1}\varepsilon_ {2}} \tag{3}\]  
\[k_{i}^{2}=k_{z}^{2}-k_{0}^{2}\varepsilon_{i},i=1,2 \tag{4}\]  
where \(k_{z}\) is the propagation constant of EM waves in the \(z\) direction; \(k_{1}\) and \(k_{2}\) are the wave number in the LC and metal in the \(x\) direction, respectively; \(k_{0}\) is the wave number in the free space; \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the relative permittivities of LC and metal, respectively. The relative permittivity of LC can be obtained using its refractive index \(n\) and absorption coefficient \(\alpha\) by the following equation:  
\[\varepsilon_{1}=\left(n-j\frac{\alpha c}{4\pi f}\right)^{2} \tag{5}\]  
Meanwhile, copper is used as the metal in our device throughout the paper. In order to express the properties of copper accurately, its relative permittivity is given by the Drude model:  
\[\varepsilon_{2}=\varepsilon_{\infty}-\frac{\omega_{p}^{2}}{\omega^{2}-j\gamma\omega} \tag{6}\]  
where \(\omega_{p}\) and \(\gamma\) are the plasma frequency and collision frequency of copper, respectively, and \(\varepsilon_{\infty}\) stands for the dielectric constant at infinite frequency. For copper in the terahertz range, \(\omega_{p}\), \(\gamma\) and \(\varepsilon_{\infty}\) are set to \(1.123\times 10^{16}\) rad/s, \(2.196\times 10^{12}\) Hz and \(1\), respectively [38]. By numerical computation, the dispersion curves of the LC-infiltrated planar waveguide are shown in Figure 3, where the geometry of the waveguide is \(L=240\)\(\upmu\)m and \(W=5\)\(\upmu\)m. The propagation constant is normalized in terms of \(k_{\text{unit}}=2\pi/L\). Figure 3 shows that the propagation constant can be changed with different bias electric fields of the LC. For a fixed frequency, the propagation constant is larger with a higher bias electric field. The limited length of the MIM waveguide will induce reflections that lead to the so-called FP resonance and thus multiple peaks in the transmission spectrum. The resonance in the transmission spectrum should follow the FP cavity resonant condition [39]:  
\[k_{z}L=m\pi\]  
Figure 3: Dispersion curves of the LC-infiltrated MIM waveguide for different bias electric fields of LC; \(k_{\text{z}}\) is normalized in terms of \(k_{\text{unit}}=2\pi/L\). where \(k_{z}\) is the propagation constant along the main channel of the MIM waveguide, \(L\) is the longitudinal length of the waveguide, and \(m\) = 1, 2, 3, \(\dots\), \(\infty\) is the order of the FP mode. By representing \(k_{z}\) by \(2\pi/\lambda\) in Equation (7), where \(\lambda\) can be substituted by \(c/\left(f\cdot\text{Re}\left\{n_{eff}\right\}\right)\), the FP resonant frequencies of mode \(m\) can be expressed as:  
\[\alpha_{E}(\omega) = \sum_{i=0}^{M_{\mathrm{g}}}h_{i,E}\omega^{i} \tag{2}\]0  
where \(\text{Re}\left\{n_{eff}\right\}\) is the real part of the effective refractive index seen by EM waves propagating along the MIM waveguide.
###### Abstract  
In reactive power compensators applied in drives with asynchronous motors, a control strategy focusing on the compensation of higher-order current harmonics is implemented. Control schemes of such compensators typically employ low-pass Butterworth filters with fixed cut-off frequencies to isolate the reactive power component. However, the impact of alternative filter types on compensator performance remains insufficiently explored. Furthermore, in the control systems under consideration, stator phase current signals of the asynchronous motor are used as reference inputs. This approach proves effective under the steady-state operating conditions of the drive. Under non-steady-state operating conditions--typical for traction drive systems--this approach becomes ineffective due to the increased complexity in obtaining accurate reference current signals. As a result, the performance of the filters also deteriorates. It is therefore proposed to investigate the impact of alternative filter types on the efficiency of compensator operation. To address this challenge, the following strategies are suggested: implement higher-order harmonic compensation in the system of stator phase supply voltages of the asynchronous motor; use the control signals from the Field-Oriented Control (FOC) algorithm as reference inputs; and adapt the cut-off frequencies of the filters dynamically to match the frequency of the supply voltage. The simulation results indicate that the use of an elliptic filter in compensator control systems yielded the highest effectiveness. Moreover, the results confirmed the efficiency of the proposed solutions under both steady-state and non-steady-state operating conditions of the traction drive. These approaches support the development of reactive power compensators integrated into traction drive systems for railway rolling stock.  
reactive power; higher harmonics compensation; high-pass filters Article
## 4 Cryptographic Analysis
### Encryption/Decryption Key Sensitivity  
Another important characteristic of correlation analysis is the key sensitivity test. A good audio encryption scheme should be sensitive with respect to the secret key, that is, a slight modification of the secret key. We encrypted an audio file with key set 1 and then tried to decrypt it with the very slightly modified key set 2: \(x_{1d}\) = 0.62337047692752, \(y_{1d}\) = 0.7415988807484, \(x_{2d}\) = -0.91983014150675, and \(y_{2d}\) = 0.45066640271424.  
Figure 5a,b present the original and encrypted file a-strange-dream; Figure 5c presents the decrypted file a-strange-dream with different key, Figure 5d is decrypted with the first key set. The proposed audio scheme demonstrates unsuccessful decryption even with a very similar key set. The right decryption needs the entire and correct knowledge of all of the secret key digit.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Audio File** & **File Size** & **File Length** & **Encryption Time** \\ \hline usb-headset-weird & 200 kb & 2.32 s & 0.865 s \\ cartoon-mumble-speak & 298.1 kb & 3.45 s & 1.284 s \\ grito-wav & 385.3 kb & 4.47 s & 1.676 s \\ a-strange-dream & 495 kb & 5.74 s & 2.188 s \\ radio & 691 kb & 8.02 s & 3.195 s \\ insult & 832 kb & 9.66 s & 3.691 s \\ sharpening-blade-long-blade & 1228.8 kb & 14.48 s & 5.651 s \\ airport-security-message & 1740.8 kb & 20.28 s & 8.193 s \\ shakuhachi-grave-5 & 2048 kb & 23.41 s & 8.831 s \\ Ref. [5] & - & 7 s & 0.012 s \\ Ref. [8] & 2.33 mb & 13.85 s & 5.767 s \\ AES [31] & 800 kb & - & 0.003 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Speed performance test.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Audio File** & **SNR** & **PSNR** \\ \hline usb-headset-weird & \(-\)42.3697 dB & 4.6583 dB \\ cartoon-mumble-speak & \(-\)44.6930 dB & 4.7036 dB \\ grito-wav & \(-\)43.0053 dB & 4.7551 dB \\ a-strange-dream & \(-\)44.8190 dB & 4.6431 dB \\ radio & \(-\)40.7581 dB & 4.6431 dB \\ insult & \(-\)43.2195 dB & 4.7109 dB \\ sharpening-blade-long-blade & \(-\)28.5052 dB & 4.7690 dB \\ airport-security-message & \(-\)44.7026 dB & 4.6590 dB \\ shakuhachi-grave-5 & \(-\)43.3945 dB & 4.6990 dB \\ Ref. [5] & \(33.7464\) dB & 59.7989 dB \\ Ref. [8] & \(-\)16.0483 dB & 1.4524 dB \\ Ref. [27] & \(-\)133.0000 dB & - \\ AES [27] & \(-\)1.4461 dB & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak signal-to-noise ratio.