esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper investigates a NOMA-based uplink ISTN, in which each user can access the satellite or terrestrial BSs according to their individual QoS requirements. In order  
Figure 5: User association results.  
Figure 6: Achievable sum rate versus the backhaul link capacity.  
to maximize the achievable sum rate, an optimization problem is established to jointly optimize the transmit power, user association, and receive beamforming. A two-stage algorithm is developed to solve the non-convex problem. At the first stage, user associations are determined based on individual preference lists and the backhaul capacities of the APs. At the second stage, the power allocation and the receiving beamforming vectors are alternately optimized in an iterative process. Within each iteration, the closed-form solution for the transmit power is derived. Simulation results validate the effectiveness of the proposed algorithm and the benefits brought by NOMA. UEs prefer to access terrestrial BSs when their backhaul link capacity is sufficient; otherwise, they can rely on the satellite for QoS guarantees. Furthermore, the achievable sum rate achieves its peak when the number of UEs aligns with the number of receive antennas at the APs.  
Conceptualization, P.X. and Z.F.; methodology, Z.C. and J.J.; validation, Y.Z. and X.H.; writing--review and editing, J.Z.; funding acquisition, P.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the science and technology project of the state grid corporation of China: Research and application on critical technology of integrated communication, sensing, and computation in power wireless network under hierarchical cooperative control mode of the distribution network (No. 5700-202356629A-3-2-ZN).  
Not applicable.  
Data Availability StatementData are contained within the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] were employed by the company State Grid Economic and Technological Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Resonators: Materials and Geometries
### Material Types
Due to its high electrical conductivity, particularly in the basal plane, graphite is an excellent material for conductive components in resonator designs. Its low density makes it lightweight and easy to process into thin sheets, powders, or composites, allowing integration into various resonator geometries. Graphite also effectively absorbs and dissipates electromagnetic radiation, enhancing its utility in electromagnetic shielding applications. However, graphite has notable limitations. Its electrical conductivity is highly anisotropic, with much higher values in the basal plane compared to the perpendicular direction. Additionally, its brittleness limits its mechanical longevity and reliability, particularly under stress or vibration. Unlike materials with high dielectric constants, graphite does not reduce the wavelength of electromagnetic waves, limiting its ability to miniaturize resonators. Furthermore, graphite is prone to oxidation at high temperatures in the presence of oxygen, which can degrade its performance. Graphite is a valuable material for resonators requiring high conductivity, thermal stability, and electromagnetic shielding. It is particularly suitable for high-power, robust resonators with critical shielding properties, although its anisotropic conductivity and mechanical limitations restrict its broader usage [31]. Graphite is most useful for high-power applications and electromagnetic shielding, where conductivity is a valuable feature. Table 1 presents a detailed comparison of the main characteristics of DW, glycerin, and graphite, highlighting their key properties and suitability for various applications.
# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 4 Instance Validation
Figure 12: Grey relational coefficient distribution map of hybrid rocket engine internal ballistic experiment-simulation data: (**a**) Traditional grey relational analysis, which shows the distribution of gray correlation coefficients under the traditional approach; (**b**) Interpolation method, highlighting changes in the number of sample points for gray correlation coefficient calculations; (**c**) Deviation acceptability, highlighting changes in gray correlation coefficients; (**d**) Robust grey relational analysis, highlighting an overall increase in the correlation coefficient. Color blocks represent sample points and their corresponding grey correlation coefficients. Figure 12c shows the distribution of grey correlation coefficients under the use of bias acceptability only, which corresponds to a grey correlation of 81.3763% and an accuracy of 95.4465%; comparing Figure 12a with Figure 12c, it can be found that the essence of bias acceptability lies in the correction of grey correlation coefficients of the data points in the tolerance band of error, which acts directly on the data points in the tolerance band and does not change the original data distribution as well as the fitting accuracy, as follows: \(\text{TIC}_{\text{a}}=0.03170=\text{TIC}_{\text{c}}=0.03170\). Figure 12d shows the distribution of grey correlation coefficients after the combined use of through-valve interpolation as well as bias acceptability, at which point the grey correlation is 83.0590% and the accuracy is 96.0231%. By correcting the grey correlation coefficients of individual data points in the microscopic level and selectively increasing the sequence data points in the macroscopic level to indirectly enhance the distance weights, a robust grey correlation analysis method is constructed; when dealing with perturbed time series datasets and noisy data, the new method shows higher stability and reliability, and it can better take into account the uncertainties of perturbation in a small-scale range or the complexity of the noise in the practical application scenarios. The robustness of the grey correlation analysis model is improved.
## 2 Dark Current Noise Correction Method Based on Dark Pixels
### Definition and Properties of Dark Pixels  
Dark pixels with functionally intact readout circuits were constructed as follows: a material filter was laminated to normal imaging pixels' surfaces so that the pixels could not receive radiation from outside the window, the target, or the dewar. Dark pixels' and imaging pixels' response characteristics do not differ; external factors lead to dark pixels not registering a target response. The LWIR QWIP's cooling temperature is lower than that of liquid nitrogen; as a result, the background radiation of the filter on the dark pixels' surface is very low.  
Although some naturally formed blind pixels cannot receive external radiation, dead pixels cannot effectively replace dark pixels because (1) there are uncertainties in the formation of dead pixels; (2) there is no guarantee that their function will be effective in the long term; (3) the dark current characteristics of dead pixels and normal pixels are not necessarily the same; and (4) the response characteristics of different dead pixels vary [24,25].
## 5 Analysis of Results
### Dynamic Performance Analysis of Oblique Navigation of Submarine
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Value** & \multicolumn{4}{c}{**1500 rpm**} & \multicolumn{4}{c}{**2000 rpm**} \\ \hline \(v\) & 0.27915 & 0.54763 & 0.82666 & 1.09514 & 0.21374 & 0.43238 & 0.6431 & 0.85541 \\ \(Y\) & \(-1.94843\) & \(-4.17379\) & \(-6.14388\) & \(-8.54343\) & \(-1.34179\) & \(-2.94849\) & \(-4.12274\) & \(-5.35104\) \\ \(v\) & 0.45881 & 1.99588 & 3.58731 & 5.13674 & 0.92834 & 2.94728 & 5.03789 & 7.05684 \\ \(N\) & 0.48056 & 1.3427 & 2.02753 & 2.57477 & 1.06143 & 2.23848 & 3.22291 & 4.91051 \\ \hline \multicolumn{8}{c}{**2500 rpm**} & \multicolumn{4}{c}{**3000 rpm**} \\ \hline \(v\) & 0.18051 & 0.35932 & 0.55053 & 0.73119 & 0.13805 & 0.28205 & 0.4155 & 0.55502 \\ \(Y\) & \(-0.85828\) & \(-1.77425\) & \(-2.41393\) & \(-3.55213\) & \(-0.54895\) & \(-1.2577\) & \(-1.70517\) & \(-2.23974\) \\ \(v\) & 1.29901 & 3.70099 & 6.13262 & 8.56425 & 1.96623 & 5.09473 & 8.18122 & 11.23806 \\ \(N\) & 1.71263 & 3.52252 & 5.33241 & 7.35631 & 2.50446 & 5.24681 & 7.86688 & 10.64286 \\ \hline \end{tabular}
\end{table} 
Table 3: Relation data of \(Y\) and \(N\) with \(v\). Through Equation (15), one can clearly analyze the relationships between various physical quantities during the submarine motion process. For example, an increase in lateral velocity leads to larger numerical values of lateral force and lateral moment. Additionally, an increase in the rotational speed of the spiral mixed-flow pump results in an augmentation of the total thrust produced by the pump-jet vector thrust, consequently generating significant lateral force, thereby inducing deviation in the pump-jet vector thrust submarine's hull. This establishes vital conclusions regarding the interplay of various physical quantities during the motion of the submarine. \[Y_{\text{\tiny{p}}}=\frac{Y^{\prime}{}_{\text{\tiny{p}}}}{0.5\rho L^{2}V} \tag{11}\]  
\[Y_{\text{\tiny{p}}|\text{\tiny{p}}|}=\frac{Y^{\prime}{}_{\text{\tiny{p}}|\text {\tiny{p}}|}}{0.5\rho L^{2}} \tag{12}\]  
\[N_{\text{\tiny{p}}}=\frac{N^{\prime}{}_{\text{\tiny{p}}}}{0.5\rho L^{3}V} \tag{13}\]  
\[N_{\text{\tiny{p}}|\text{\tiny{p}}|}=\frac{N^{\prime}{}_{\text{\tiny{p}}|\text {\tiny{p}}|}}{0.5\rho L^{3}} \tag{14}\]  
\[\left\{ \begin{array}{l}Y_{\text{\tiny{p}}}=-0.042{+}5.48\times 10^{-5}n-2.41 \times 10^{-8}n^{2}+3.45\times 10^{-12}n^{3}\\ N_{\text{\tiny{p}}}=0.0069-8.81\times 10^{-6}n+3.78\times 10^{-9}n^{2}-5.27 \times 10^{-13}n^{3}\\ Y_{\text{\tiny{p}}|\text{\tiny{p}}|}=0.04-5.2\times 10^{-5}n+2.15\times 10^{-8}n^{2}- 2.89\times 10^{-12}n^{3}\\ N_{\text{\tiny{p}}|\text{\tiny{p}}|}=-0.009+1.2\times 10^{-5}n-5.01\times 10^{-9}n^{2}+ 6.78\times 10^{-13}n^{3}\end{array} \right. \tag{15}\]
## 3 Results and Discussion  
### Validation of ZDIH Data Extracted from ERA-5  
For the purpose of validation, the ZDIH data (ERA-5 Satellite Data) used for this study were compared with the available Durban radiosonde data obtained from the South African Weather Service (SAWS). The radiosonde, which was sounded daily at the Durban International Airport, measures atmospheric parameters such as temperature, pressure,relative humidity, dew point, wind speed, etc., as it ascends from the ground's surface to the upper atmosphere. The available data presented in Figures 2a and 3a are the daily vertical profile of the atmospheric temperature for the month of March and July 2015, respectively.  
Figure 3: (**a**) Daily vertical profile of atmospheric temperature and (**b**) monthly mean temperature for July 2015 at Durban.  
Figure 2: (**a**) Daily vertical profile of atmospheric temperature and (**b**) monthly mean temperature for March 2015 at Durban.  
The corresponding monthly average temperature profiles, from which the mean ZDIH values are derived, are presented in Figures 2b and 3b. It was observed that the mean ZDIH for March and July is about 4623 m and 3820 m, respectively. This aligned with the corresponding Autumn (March-May) and Winter (June-August) ZDIH mean values of 4453 m and 3710 m obtained from ERA-5 data, with a percentage difference of 3.2% and 2.9%. Previous research studies have consistently shown that ERA-5 data have insignificant differences with locally measured data [33; 34; 35; 36]. Moreover, the satellite provides global data for several years, which makes it sufficient for data learning and training. This informed the decision to adopt satellite data for this work. It should be noted that the ZDIHs in Figures 2b and 3b do not represent the rain height but are only employed to validate the ERA-5 ZDIH data used for this study, as indicated in Equation (1).
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 4 V2X Communication Architecture
### V2X Communications Protocols
#### 4.1.1 IEEE 802.11p--DSRC
As a result, the network becomes vulnerable, allowing road users to be misled by misleading safety warnings such as accident messages, traffic conditions and traffic management. IEEE 802.11p ensures constant functionality in terms of reliability and latency in low-traffic-volume and congestion conditions. However, the performance suffers dramatically with high traffic, speed, and traffic volumes [86]. As a result, upgrading to IEEE 802.11 ax-based repeaters for DSRC V2X systems could boost load management capability, alleviating previously cited congestion issues.
## Hierarchical Bayesian Data Analysis in Radiometric SAR  
System Calibration: A Case Study on Transponder Calibration  
with RADARSAT-2 Data  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen,  
D-82234 Wessling, Germany; E-Mails: [PERSON] (K.S.); [PERSON]M.J.); [PERSON]S] ([PERSON]); [PERSON]SS] (J.R.); [PERSON]] ([PERSON]); [PERSON]J.W.A.); [PERSON]] (M.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-8153-28-2246; Fax: +49-8153-28-1449.  
_Received: 19 October 2013; in revised form: 20 November 2013 / Accepted: 28 November / Published: 4 December 2013_  
**Abstract:** A synthetic aperture radar (SAR) system requires external absolute calibration so that radiometric measurements can be exploited in numerous scientific and commercial applications. Besides estimating a calibration factor, metrological standards also demand the derivation of a respective calibration uncertainty. This uncertainty is currently not systematically determined. Here for the first time it is proposed to use hierarchical modeling and Bayesian statistics as a consistent method for handling and analyzing the hierarchical data typically acquired during external calibration campaigns. Through the use of Markov chain Monte Carlo simulations, a joint posterior probability can be conveniently derived from measurement data despite the necessary grouping of data samples. The applicability of the method is demonstrated through a case study: The radar reflectivity of DLR's new C-band Kalibri transponder is derived through a series of RADARSAT-2 acquisitions and a comparison with reference point targets (corner reflectors). The systematic derivation of calibration uncertainties is seen as an important step toward traceable radiometric calibration of synthetic aperture radars.  
**Keywords:** synthetic aperture radar; radiometric calibration; external calibration; Bayesian data analysis; transponder
## 3 Results and Discussion
### Influence of Porosity on River Ice Impact Forces at Bridge Abutments  
The initial velocity of the river ice is established at 1 m/s, the thickness of the ice row is taken as 0.5 m, and the river ice porosities of 5%, 10%, 15%, 20%, 25%, and 30% are chosen to simulate the effect of the river ice porosity on the maximum impact force when the impact damage occurs. The simulated ice load time course is illustrated in Figure 9.  
Figure 8: Comparison of simulated and formula-calculated maximum impact force for different ice row thicknesses.  
Figure 9: _Cont._  
Table 2, showing the role of ice load, clearly shows that as porosity increases, ice force at the abutment exhibits a consistent decrease. This phenomenon indicates that river ice porosity has a significant effect on the river ice impact force on the bridge abutment. When the porosity of the ice row increases, its internal structure becomes looser, and the strength of the river ice decreases, leading to a reduction in the ice row's force on the bridge abutment in the process of breaking the ice.  
\begin{table}
\begin{tabular}{c c} \hline
**Porosity** & **Ice Load Extreme/KN** \\ \hline
0.05 & 2739.8 \\
0.1 & 1625.19 \\
0.15 & 1062.8 \\
0.2 & 669.1 \\
0.25 & 375 \\
0.3 & 271 \\ \hline \end{tabular}
\end{table}
Table 2: Extreme values of ice load.  
Figure 9: Time course of ice loading with different porosities: **(a)** 5%; **(b)** 10%; **(c)** 15%; **(d)** 20%; **(e)** 25%; **(f)** 30%.  
In addition, as the porosity increases, the peak of the contact force is gradually lagged, and the time point at which the interaction between the bridge abutment and the ice raft begins to occur is delayed.  
Finally, with the further increase in porosity, when the ice load reaches the extreme value and lags back, the subsequent time course begins to show the changing characteristics of random loading and unloading, which may be due to the complexity and instability of the internal structure of the ice row. In the ice row with higher porosity, the distribution and size of ice pores are more uneven, and the unbroken river ice extrudes forward to the abutment after the impact force reaches the peak, and encounters different sizes and shapes of pores during the penetration process, presenting the ice force time course with strong randomness and a lower peak value.  
According to the ice row damage phenomenon under different porosities in Figure 10, it can be found that the damage pattern of the ice row changes with the increase in porosity. The damage is gradually concentrated near the face in contact with the ice-breaking column, and at high porosity, the abutment mainly has a destructive effect on the ice rows in the contact region, while it has less effect on the ice rows away from the contact region.  
Figure 10: _Cont._
## 1 Introduction
Indeed, previous studies and measurements have shown that antennas of this type can be very suitable for THz detection systems [23; 24]. To obtain impedance matching we introduce an improved structure, in which the junction detector is not placed in the center of the antenna [25]. Instead, the junction is placed at the end of two matching strips remote from the center. The basic concept and simulations, for several configurations, have been presented in previous works [26; 27; 28; 29; 30]. In this work, we present the implementation and characterization of THz detectors based on this structure. The investigation starts with a theoretical analysis, exploring the influence of the junction parameters on the input impedance of the antenna, as described in Section 2.1. Using extensive simulations based on this analysis, we were able to optimize the system design, including the antenna structure, the matching strip dimensions, and the junction configuration, as detailed in Section 2.2. The various parameters of Josephson junctions, including the junction geometry, impedance, and substrate effects, were taken into account in the simulations [31]. In order to examine the effects of impedance matching on the performance of the detection system, we implemented two bow-tie antennas with different matching strip width. The configuration of the two systems was based on the results of our simulations. The HTSC junctions were implemented by growing a thin film of YBa\({}_{2}\)Cu\({}_{3}\)O\({}_{7}\) on the substrate. Layers of YBCO thin films and Au were deposited over the (100)MgO bicrystal substrate, by Theva GmbH, Germany [32]. In the space between the two antenna poles, only the Au layer was etched, exposing a thin layer of YBCO. Thus, the YBCO Josephson junction was located exactly at the bicrystal misorientation point. The final structure of the planar antenna and the detector was obtained by etching of these layers, performed by STAR Cryoelectronics, USA [33]. In Section 3, we present experimental results, demonstrating the high performance of the fabricated antennas.
## 3 Methods
### Data Processing Flow
#### 3.4.1 Signal Filtering  
Motion artifacts, baseline, and power-line interference are primary noise sources in EMG signals [20; 21]. The frequency of power-line interference is typically 50 Hz, depending on the location. The range of baseline and motion disturbances was between 0 and 20 Hz.  
Figure 1: Electrode attachment positions.  
Figure 2: Diagram of data processing flow.  
Power-line interference also tends to induce harmonic noises that yield high spikes at multiples of 50 Hz, which are observable in the frequency domain. A typical process used to address this a signal-filtering technique. In the previous experiment, a Butterworth high-pass filter was able to generate a clean signal [8]. Nevertheless, conventional digital filters still leaves undesirable noise within non-muscle-contracting signals such as a relaxed event.  
One commonly used digital filtering method is wavelet denoising (WD). In contrast to Butterworth filters, which produce flat or smooth frequency responses, WD applies wavelet transform to decompose the signal into frequency components with matched resolutions [22]. Consequently, unlike Butterworth filters, WD offers superior time-frequency localization, ensuring the retention of precise signal features while minimizing noise. The process of wavelet denoising involves an initial decomposition of the signal through a wavelet transform (WT), followed by the application of appropriate thresholds to the detail coefficients. This step entails setting all coefficients below the associated thresholds to zero. Subsequently, the denoised signal is reconstructed based on the modified detail coefficients [23].  
Parameter selection for wavelet denoising is essential. Preserving the muscle-contracting amplitude of the EMG signal is mandatory when eliminating undesirable noises. Additionally, addressing the non-stationary behavior of EMG signals remains a challenge. Thus, selecting incorrect parameters may result in poor noise removal or the elimination of essential amplitudes. A previous study investigated the optimum wavelet function to identify and denoise EMG signals [24]. According to the study findings, the Daubechies1 (db1 or haar) wavelet with a hard transformation at 0 dB signal-to-noise ratio (SNR) achieved the best denoising performance. In this study, we combined the Butterworth filter and wavelet denoising techniques. We employed high-pass and bandstop Butterworth filters to eliminate the specific frequencies of the noises. Wavelet filters with a db1 function, hard transformation, and level 1 decomposition were employed to eliminate the remaining noises within the relaxed event signal. Figure 3 illustrates the result after the signal filtering process.