esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Changes in Color  
Table 1 shows the changes in the color factors of blood plasma suspensions after HHP treatment. The appearance of food, mainly determined by the color of the surface, is the first quality parameter that influences the consumer to accept or reject the food [32]. Based on the results obtained, it can be concluded that the application of pressure treatment induced significant changes in all color factors of blood plasma. Regarding the lightness (L*) factor, it can be observed that the pressure treatment darkened the samples compared to the control sample. However, a slight lightening was observed with increasing pressure values. [PERSON] et al. [33] examined HHP-treated bovine blood and found a significant decrease in L* at 300, 400 and 500 MPa, similar to our results.  
In the case of a*, a reddish color was observed after treatment at 300 MPa, which decreases with increasing pressure values. At a pressure of 600 MPa, a* becomes negative and shifts towards the green tone. In the case of b*, it was observed that the initial yellowish color decreases and the color of the samples shifts towards blue with increasing pressure values. [PERSON] et al. [34] treated large white swine and wild boar meat with HHP. They found a significant decrease in the b* color factor after 300 MPa treatment in both animals. This result is consistent with our results. The change is not significant between treatments above 450 MPa. The control sample is significantly different from all other pressure-treated samples for all three color factors. The changes in the color of the samples are presumably due to partial denaturation and aggregation of serum albumin, the main component of plasma proteins. The denaturation of this protein is associated with the unraveling of its native structure, leading to the exposure of hydrophobic regions and aromatic amino acids [35]. Larger agglomerates were formed at 550 MPa and 600 MPa. These structural changes can result in increased light absorption or scattering, which is reflected in the increasing color values of samples. Changes can affect the functional properties of blood plasma [36], including its solubility and binding capacity. According to \(\Delta\)E*, it can be concluded that the values decrease with increasing pressure, but in all cases, the visually perceptible difference is in the \"very clearly visible\" range (6.0 < \(\Delta\)E*) [37].
# A Single Image Enhancement Technique Using Dark Channel Prior
## 3 Improved Image Defogging Method Based on Dark Channel Prior
### Treatment of Dark Channel Prior Failure Areas  
The sky region does not conform to the principle of dark channel prior, so the transmission of the sky region is mis-estimated, causing halo distortion in sky regions. As a result, various defogging algorithms based on sky region segmentation are emerging. For example, [PERSON] [15] uses gradient threshold and region growth algorithm to obtain the unicom region, and then identifies the sky region based on the pixel brightness threshold of the unicom region. However, the phenomenon of missed detection in the sky region and the time-consuming calculation of the unicom region occurred. Aiming at solving these problems, in this paper, we propose an improved method as follows. First, an algorithm based on a combination of edge detection and maximum inter-class variance is used to separate the sky and non-sky areas. Then, the enhancement algorithm via sequential decomposition is used for the sky area, and the optimization method of Section 3.1 is used for the non-sky area. Finally, the enhanced sky and non-sky areas after defogging are fused to obtain a clear defogged image.
# Modeling and Analyzing Preemption-Based Service Prioritization in 5G Networks Slicing Framework
## 3 Mathematical Model
Use the expression  
\[N_{s}=\sum_{\mathbf{n}\in\Omega}P(\mathbf{n})\,\mathbf{n}\cdot\mathbf{e}_{s}, \quad s\in\mathcal{S}, \tag{17}\]  
to calculate the mean number of servicing requests at the \(s^{\text{th}}\) NSI, the expression  
\[N=\sum_{\mathbf{n}\in\Omega}P(\mathbf{n})\,\mathbf{n}\cdot\mathbf{j} \tag{18}\]  
to calculate the mean number of servicing requests at the system, and the expression  
\[\mathsf{P}\{\mathcal{A}\}=\sum_{\mathbf{n}\in\mathcal{A}}P(\mathbf{n}),\quad \mathcal{A}\subseteq\Omega, \tag{19}\]  
to calculate the probability of an event \(\mathcal{A}\) determined using the main sets (10)-(13). Let \(P_{s}^{\text{adm}}\) and \(B_{s}^{\text{block}}\) represent the probability of events \(\Omega_{s}^{\text{add}}\cup\Omega_{s}^{\text{vpad}}\) and \(\Omega_{s}^{\text{block}}\), respectively, i.e., the admission and blocking probabilities for arriving requests at the \(s^{\text{th}}\) NSI. Moreover, use the expression  
\[\varrho_{s}=\frac{1}{\mathcal{Q}_{s}}\sum_{\mathbf{n}\in\Omega}P(\mathbf{n})\, \,(\mathbf{n}\odot\mathbf{b})\cdot\mathbf{e}_{s},\quad s\in\mathcal{S}, \tag{20}\]  
to calculate the average utilization of the guaranteed network capacity of the \(s^{\text{th}}\) NSI, and the expression  
\[\sigma=\frac{1}{\mathcal{C}}\sum_{s=1}^{S}\sum_{\mathbf{n}\in\Omega}P(\mathbf{ n})\,\,(\mathbf{n}\odot\mathbf{b})\cdot\mathbf{e}_{s} \tag{21}\]  
to calculate the average utilization of the total network capacity of the 5G BS. Given the above expressions, evaluate and analyze the efficiency of the proposed Pre-emption-based Prioritization (PP) scheme versus the classical Resource Reservation (RR) scheme in the following section.
## 2 Peculiarities of ATR Measurements
### Polarisation Analysis with Atr
The realisation of the polarisation rotation on the sample stage is established in visible light polariscopy, where it is based on an electrically controlled optical phase retardance in a liquid crystal cell [32]. The high sensitivity ATR detection is realised with multi-reflection prisms, where the number of reflections at the sample-prism interface can occur over ten times before the light is detected [33]. Amplitude and phase changes for such a situation become cumulative, and the reflection (for the E-field) can be expressed as follows [34] (for ATR geometry):  
\[\tilde{r}(\omega)=\frac{n_{1}-[n(\omega)+ik(\omega)]}{n_{1}+[n(\omega)+ik( \omega)]}\equiv r(\omega)e^{i\theta(\omega)}, \tag{5}\]  
where \(n_{1}\) is the refractive index of the ATR prism, \(\tilde{n}(\omega)=n(\omega)+ik(\omega)\) is the complex index of the sample, \(r(\omega)=\sqrt{R(\omega)}\) is the field reflection coefficient calculated from the experimentally measured reflectance \(R\) spectrum, and \(\theta\) is the phase change introduced by the ATR prism. The refractive index \(\tilde{n}=n+ik\) of the sample is then the following [34]:  
\[n=\frac{1-r^{2}}{1+r^{2}+2r\cos\theta}\times n_{1};\;\;k=\frac{-2r\sin\theta} {1+r^{2}+2r\cos\theta}\times n_{1}, \tag{6}\]  
and can be determined at each wavelength where the reflectivity \(r(\omega)\) was measured, while the phase \(\theta(\omega)\) is derived via Kramers-Kronig relation from \(r(\omega)\). It is usually possible to calculate the reflectivity and phase changes from a single reflection for the entire number \(N\) of reflections (see Equation (5)) as \(\tilde{r}(\omega)=[r_{one}(\omega)]^{N}\times e^{iN\theta_{one}(\omega)}\), where \(r_{one}\) and \(\theta_{one}\) are the corresponding changes occurring at the single reflection [35].
## 4 Tire-Friction Coefficient Estimation and Vehicle Safety  
The fidelity of tire-road interaction modeling is a critical factor in predicting vehicle behavior under various driving conditions. We present a simulation study using CarSim software to explore the vehicle dynamics during lane-change maneuvers on a low-friction surface, focusing on the impact of the tire-friction coefficient on vehicle safety.  
The simulation replicates a lane change at 60 km per hour on a snowy road with a friction coefficient (\(\upmu\)) of 0.2. The steering wheel angle input is modeled as a sine wave, representing the response to the driver-initiated maneuver. This input is particularly used to study the effect of minimal steering on the side-slip angle of the vehicle in low-friction environments. The results in Figures 8 and 9 highlight a pronounced sensitivity of the vehicle side-slip angle to small steering inputs under low friction conditions. The simulated lateral dynamics in Figure 8 show that even with minimal steering angle inputs, the side-slip angle can significantly increase, leading to potential instability, as shown in Figure 9. These findings are critical as they demonstrate the nonlinear vehicle response to steering inputs under lowfriction surfaces.  
Figure 8: Simulation studies: lateral dynamics.  
These findings underscore the critical role of accurately estimating the tire-road friction coefficient for the development and refinement of advanced driver-assistance systems (ADAS) and active safety systems. Given the challenges in obtaining extensive experimental data under varied conditions, our study utilizes the highly realistic CarSim simulator to model vehicle responses. This simulation-based methodology supports our analysis of vehicle stability limits and the activation of countermeasures, offering a viable alternative to direct experimental comparison yet delivering credible insights into enhancing vehicle safety.
## 2 Setup and Methods
### Moving Averaging-Moving Differential
Figure 3: SNR dependence on the MA and MD window size for 500 Hz (**a**) and 8000 Hz (**b**) events; the commercial laser obtained DAS data, processed using MA-MD technique with MA and MD window sizes (**c**) 19 and 24 counts, respectively; (**d**) 7 and 1 counts, respectively. In [39], it is also stated that the window sizes are completely interchangeable, and it is possible to calculate only half of this matrix (Figure 3a,b) to determine the SNR distribution by the MA-MD window sizes in a specific frequency range. However, practice has shown that this is true, with some error. In those frequency regions for which the optimal MD window size is relatively large, the symmetry can, indeed, be observed, with a slight deviation (the line of symmetry passes through the points (11; 10) and (33; 32)). Apparently, the same shift does not allow one to fully see the symmetry for the 8 kHz band, since one of the areas of optimal window sizes goes beyond the allowable values (Figure 3b, top). Nevertheless, in each case, in order to obtain a good SNR increase, one can calculate the MD window size based on Equation (1) and use it to select the most appropriate MA window size. An obvious disadvantage of the algorithm is the partial data loss, which takes place at the end of the processed time series. The value of this loss depends on the sum of the MA and MD window sizes.
## 3 Results  
Descriptive statistics and qualitative interpretations of the Pearson's correlation between IMTP assessment, sprint mechanical variables, and performance are presented in Table 1. The intraclass correlation coefficient (ICC) between IMTPF and 30 m sprinting performance trials was very high (0.97 and 0.99, respectively).  
Pearson's product-moment correlation analysis revealed large linear associations between IMTPF and v\({}_{0}\) (r = 0.65, R\({}^{2}\) = 0.42, \(p\) = 0.009) (Figure 1). In addition, IMTPF demonstrated large negative linear relationships with sprint times to 15 m (r = \(-\)0.53, R\({}^{2}\) = 0.28, \(p\) = 0.043), 20 m (r = \(-\)0.55, R\({}^{2}\) = 0.30, \(p\) = 0.033), 25 m (r = \(-\)0.57, R\({}^{2}\) = 0.33, \(p\) = 0.025), and 30 m (r = \(-\)0.60, R\({}^{2}\) = 0.36, \(p\) = 0.036) (Figure 2). The F\({}_{0}\), P\({}_{\rm max}\), and sprint times to 5 m and 10 m were not significantly related to IMTPF (r = 0.20, R\({}^{2}\) = 0.04, \(p\) = 0.474; r = 0.44, R\({}^{2}\) = 0.19, \(p\) = 0.102; r = \(-\)0.38, R\({}^{2}\) = 0.14, \(p\) = 0.169; r = \(-\)0.50, R\({}^{2}\) = 0.25, \(p\) = 0.057, respectively). However, a trend of relationship between IMTPF and time to 10 m (r = \(-\)0.50, \(p\) = 0.057) was noted.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Variables** & **Mean (SD)** & **95\% CI** & **Relationship with** & **ES** \\  & & & **IMTPF (r)** & **ES** \\ \hline IMTPF (N) & 2366 \(\pm\) 511 & 2082–2648 & & & \\ F\({}_{0}\) (N\(\cdot\)kg\({}^{-1}\)) & 7.50 \(\pm\) 0.95 & 6.98–8.02 & 0.20 (\(-\)0.35 to 0.65) & Small \\ v\({}_{0}\) (m\(\cdot\)s\({}^{-1}\)) & 10.13 \(\pm\) 0.66 & 9.76–10.50 & 0.65 **\({}^{**}\)** (0.21 to 0.87) & Large \\ P\({}_{\rm max}\) (W\(\cdot\)kg\({}^{-1}\)) & 19.02 \(\pm\) 2.90 & 17.41–20.62 & 0.44 (\(-\)0.09 to 0.78) & Moderate \\ Time to 5 m (s) & 1.30 \(\pm\) 0.08 & 1.25–1.34 & \(-\)0.38 (\(-\)0.74 to 0.17) & Moderate \\ Time to 10 m (s) & 2.00 \(\pm\) 0.10 & 1.94–2.06 & \(-\)0.50 (\(-\)0.81 to 0.01) & Large \\ Time to 15 m (s) & 2.62 \(\pm\) 0.13 & 2.55–2.69 & \(-\)0.53 * (\(-\)0.82 to & Large \\  &
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)
### Frequency-Domain Methods  
The frequency-domain technique uses the estimation of power spectral density (PSD) of the HRVs as the basis for the parameter's extraction. The parameters of the frequency domain are determined through the application of the Fast Fourier transform (FFT) (Figure 3) and autoregressive modelling (AR) (Figure 4). The PSDs of the very low frequency (VLF; 0.003-0.04 Hz), the low frequency (LF; 0.04-0.15 Hz), and the high frequency (HF; 0.15-0.4 Hz) are considered as the frequency domain HRV features. Absolute power components, like ms\({}^{2}\), and relative power components, such as percent, are typically used to measure the VLF, LF, and HF power components, respectively. However, one may also use the normalized units to measure the LF and HF components (n.u.). The power dispersal over numerous constituents of the PSD is unfixed and changes with the autonomic variation of the heart [6]. This is because the PSD is not a fixed function. It has not been determined what precise functional procedure is accountable for producing the VLF power constituent [6], but research is continuing in this direction. Atropine has been reported to eliminate the VLF component. As a result, the VLF power constituent is regarded as a pointer of parasympathetic commotion [29]. Nevertheless, the nature of the LF power constituent is complicated. Both sympathetic and parasympathetic nerve innervations influence the LF power [29]. This makes the LF power component a problematic variable to analyze. Accordingly, the LF/HF proportion is generally utilized to describe the sympathetic activity [29] or the sympathovagal balance [31] rather than the LF power component.  
Figure 3: A typical FFT spectrum of a 5 min HRV, plotted using LabVIEW.  
Figure 2: A sample histogram of a 5 min HRV, plotted using LabVIEW (National Instruments Corporation, Austin, TX, USA).
## 3 System Architecture
### Overall Architecture
#### 3.1.2 Computing Cluster  
The computing cluster is a collection of computing nodes which can be edge servers, data centers, cloud centers, etc. Every computing node can provide several different services and each service with a unique service identifier (SID). Computing clusters can collect relevant information about these services and report it to the corresponding computing resources management gateway. After further processing through the computing management gateway, the information is announced to the network nodes. Currently, the information that needs to be collected by the computing cluster includes the service ID, historical processing time of the service, service quality level, computing resource availability, and remaining capacity of the computing cluster.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 2 Related Works
### Image Retrieval Methods for Cross-View Geo-Localization
TransGeo [14] was the first fully Transformer-based geo-localization network architecture. The Transformer encoders were trained with the complete images at the first training stage. The Transformer encoder was trained with cropped images in the second training stage. The requirement for two-stage training in TransGeo makes the training complex and time-consuming.
###### Abstract  
As the Global Navigation Satellite System (GNSS) is widely used in all walks of life, the signal structure of satellite navigation is open, and the vulnerability to spoofing attacks is also becoming increasingly prominent, which will seriously affect the credibility of navigation, positioning, and timing (PNT) services. Satellite navigation signal authentication technology is an emerging technical means of improving civil signal anti-spoofing on the satellite navigation system side, and it is also an important development direction and research focus of the GNSS. China plans to carry out the design and development of the next-generation Beidou navigation satellite system (BDS), and one of its core goals is to provide more secure and credible PNT services. This paper first expounds on the principles and technical architecture of satellite navigation signal authentication, then clarifies the development history of satellite navigation signal authentication, and finally proposes the BDS authentication service system architecture. It will provide technical support for the construction and development of the follow-up Beidou authentication service.  
satellite navigation; Beidou navigation satellite system; credible navigation; signal authentication; anti-spoofing 20231514620233 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Metasurfaces
### Non-Linear Metasurfaces  
Non-linear metasurfaces have been deployed to protect sensitive electronics and reduce interference on a shared platform. Non-linearity was introduced using diodes and capacitors which were incorporated in the design process and controlled the response of the metasurface. This surface absorbed the high-powered radio frequency signals while causing a minimal distortion to the low powered ones [9]. In non-linear electrodynamics, metasurfaces and metamaterials can be used to enhance the magnitude of the non-linearity. They also allow targeting of the magnetic non-linear phenomenon and can combine it with the electric non-linearity [59]. One of the most important aspects of non-linearity is the second harmonic generation which is the process where two photons combine to form a single photon. This new photon has twice the energy and its frequency are also double in comparison to the original photon(s). This phenomenon happens when two photons interact with a non-linear material [60; 61]. Metasurfaces have been deployed to enhance this process. Using fishnet metasurfaces in the near infrared region, an enhancement in the resonance of the second harmonic generation spectrum was observed using spectroscopy [61]. It needs to be noted that if a medium is inversion symmetric, it cannot generate the second harmonic and thus breaking the symmetry is imperative in order to observe the second harmonic generation.
# A Voting-Based Star Identification Algorithm Using a Partitioned Star Catalog
## 3 Star Identification
### Impact of Number of Stars on Identification Speed  
Identification speed is also a key parameter for evaluating the performance of a star identification algorithm. Typically, identification speed is closely related to the number of stars in the star image. More stars correspond to more feature matching, resulting in longer identification time and slower identification speed. To assess the impact of number of stars on the identification speed, the proposed algorithm is applied to 1000 frames of actual star images containing different numbers of stars. The identification time for star images with the same number of stars is recorded and compared with other subgraph isomorphism algorithms. The experimental results are shown in Figure 11.  
The results in Figure 11 show that, under the same experimental conditions, the identification time of the proposed algorithm is significantly lower than that of the other algorithms. As the number of stars increases, the identification time of the proposed algorithm rises at the slowest rate. Furthermore, for the same number of stars, the identification time of the proposed algorithm is shorter than that of the other algorithms. The analysis of the experimental results is explained below. The Mortari algorithm performs star identification across the entire celestial sphere, with a wide retrieval range for navigation stars and a high number of feature matches, leading to a longer identification time. The You, Liu, and Wang algorithms firstly perform coarse identification across the celestial sphere and  
Figure 10: Identification rate with different numbers of false stars.  
then fine identification within a local area, reducing the retrieval range for navigation stars, which somewhat decreases the number of feature matches and shortens the identification time. However, the proposed algorithm only performs star identification within the basic unit of the navigation feature library, leading to a smaller retrieval range for navigation stars and fewer feature matches, which results in a shorter identification time.  
Moreover, since the basic unit of the navigation feature library contains all features of stars within the FOV, even as the number of stars increases, the retrieval range for navigation stars remains unchanged, making the identification time of the proposed algorithm less sensitive to the number of stars.