esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Inference
Secondly, the Mann-Whitney U test (Equation (2)) evaluated differences in age distributions across prediction outcome groups within age cohorts. Finally, the Chi-squared test (Equation (3)) was applied to evaluate the relationship between sex and the correctness of the predictions of the model. All analyses were performed using a two-tailed significance level of 0.05, a conventional threshold in hypothesis testing to control the probability of Type I error, since one in twenty (0.05) chances represents an unusual sampling event [35]. Statistically, this implies a 5% probability of falsely rejecting the null hypothesis; consequently, \(p\)-values above this threshold indicate insufficient evidence to reject it. In addition to significance testing, Confidence Intervals (CIs) were considered to quantify the precision of estimated parameters by providing a range over which the true value is expected to lie with a given probability. Unlike \(p\)-values, CIs convey both the magnitude and direction of effects [36]. In this study, a 95% confidence level was applied, corresponding to the 0.05 significance threshold defined. The 95% CI for a sample mean was calculated as \[\text{Cl}_{95\%}=\overline{x}\pm z\cdot\frac{\sigma}{\sqrt{n}},\quad z=1.96, \tag{4}\]  
where \(\overline{x}\) denotes the sample mean, \(\sigma\) the standard deviation, \(n\) the sample size, and \(z\) the \(z\)-score corresponding to the desired confidence level (1.96 for 95%) [36]. Additionally, to assess the sensitivity of each statistical test, a power analysis was conducted. Statistical power refers to the probability of correctly rejecting a false null hypothesis and, therefore, reflects the sensitivity of a test to detect true effects. It is mathematically defined as \(1-\beta\), where \(\beta\) is the probability of committing a Type II error (failing to reject the null hypothesis when it is false). To determine the power for each test, specific alternative hypotheses were defined, tailored to the observed data and reflecting plausible deviations from the null hypothesis. For the Shapiro-Wilk test, the power analysis assumed an exponential distribution as an alternative hypothesis, as it is a plausible deviation from normality for age data in medical contexts and is commonly used in normality test evaluations [37]. For the Mann-Whitney U test, the power analysis assumed a shift in medians as the alternative hypothesis, given its relevance as a plausible deviation in age distributions within predictive modelling contexts [38]. For the Chi-squared test, the power analysis assumed a small effect size ([PERSON]'s w = 0.1), as it reflects a subtle yet meaningful departure from independence in categorical data analyses [25]. Power was assessed as the proportion of tests rejecting \(H_{0}\) when using 10,000 simulations, corresponding to Monte Carlo simulation. By incorporating these statistical methods, potential biases related to demographic factors were thoroughly examined, contributing to a more rigorous evaluation of CNN diagnosis precision in the detection of GBM.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 4 Simulation Studies for a 6-DOF Manipulator
### Simulations of Error and Stiffness in PTP Trajectory  
Given the generalized force \(F=\left[\begin{array}{ccccc}5&10&20&0&0&0\end{array}\right]\) (N), the total end stiffness \(K\) and total generalized deviation \(\delta=\left[\begin{array}{ccccc}\Delta x&\Delta y&\Delta z&\Delta\phi_{x}& \Delta\phi_{y}&\Delta\phi_{z}\end{array}\right]\) were obtained in the point-to-point trajectory. \(\Delta x\), \(\Delta y\), and \(\Delta z\) are commonly employed to measure the positional deviation, which is the deviation in the end position in the X direction, Y direction, and Z direction, respectively.  
The motion trajectory of the manipulator was set from point (207, 444, 631) to point (\(-137\), 69, 241) (including 40 points). The stiffness and position error in the translation direction of X, Y, and Z in the process of motion were obtained by using the derived stiffness formulas, as shown in Figures 6 and 7.  
With the movement of the manipulator, the stiffness in the X, Y, and Z translational directions will also change. The stiffness in the Y direction was basically unchanged at approximately 350 rad\(\cdot\)N/m, and its error basically fluctuated at approximately 0.2 mm. During the movement of the manipulator arm, the stiffness in the Z direction decreased from 640 to 270 rad\(\cdot\)N/m, and the corresponding position error increased from 0.15 mm to 0.37 mm. When the stiffness in the X direction increased from 100 to 280 rad\(\cdot\)N/m, the corresponding error decreased.
## 3 Results and Discussion
### Applications on Real Samples  
Shaanbei millet samples purchased from the northern Shaanxi local market were processed in accordance with the national standard GB 7650-87. The results obtained from spiked solutions with different concentrations of Val, Cys, and Trp are listed in Table 1. The analysis revealed a Trp concentration of 1.68 \(\times\) 10\({}^{-3}\) M in millet, whereas Cys and Val were not detected. The detection of these three amino acids in millet resulted in recoveries within a reasonable range. The recoveries for Cys ranged from 96.0% to 102.3%, with an RSD value in the range of 7.82-9.95%. The Trp recoveries were within the range of 95.2-110.0%, with RSD values in the range of 5.17-9.04%. The Val recoveries ranged from 98.4% to 105.9%, with an RSD ranging from 2.86% to 6.01%. These results indicate good homogeneity and accuracy of the substrate, confirming the applicability of the prepared ZIF-8@AuNPs substrate for real sample detection.  
An L-cysteine-containing healthcare product was chosen, and a standard addition recovery experiment was conducted (Table 2). The L-cysteine content in healthcare products was determined to be 1.13 \(\times\) 10\({}^{-2}\) M, with a recovery rate falling within the reasonable range of 97.0-105.3%. Compared to high-performance liquid chromatography, SERS technology exhibits superior efficiency and sensitivity [36,37]. This study presented a viable approach for the detection of complex samples.
## 3 Proposed Handover Scheme
### Handover Decision
#### 3.4.2 Handover Initiation  
Based on the confidence probability, we determine the optimized handover initiation time. This is a tradeoff problem. The higher the handover rate, the more network resources would be consumed to reroute the communication. However, when the handover rate is low, handover may not be performed promptly resulting in outage. Thus the determination of handover time should meet the unnecessary handover probability requirement and the outage probability requirement concurrently.  
A handover from AP1 to AP2 occurs at time index \(i\) when the following two criteria are satisfied,  
\[Criterion1:1-Pr_{con}(i)\leq\alpha \tag{10}\]  
\[Criterion2:Pr_{con}(i)\leq\beta \tag{11}\]  
where \(\alpha\) is the required unnecessary handover rate and \(\beta\) is the tolerable outage probability. Criterion 1 is to ensure the unnecessary handover rate requirement, while Criterion 2 is to ensure the outage probability requirement.  
As shown in Figure 5, \(\hat{s}_{i,\min}\) is the point where Criterion 1 is met, \(\hat{s}_{i,\max}\) is the point where the Criterion 2 is met. Thus any \(i_{opt}\in[i_{\min},i_{\max}]\) satisfies the two criteria concurrently. If \(i_{opt}\) is chosen closer to \(i_{\min}\), the outage probability could be further reduced, which is shown as outage gain. Conversely, if \(i_{opt}\) is closer to \(i_{\max}\), the handover rate could be further reduced, which is shown as handover rate gain. As outage is crucial for BSNs, we choose the point \(i_{opt}=i_{\min}\) as the optimal handover initiation time, when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized.
# Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems
## 3 Proposed Scheme
### Integrity Preservation Trace of Reretouched Video Files
In the module \"Stored Integrity Analysis Search\", the mechanism searches for the comparison result of the retouched video and the integrity analysis result of its corresponding original video. This module calculates the hash value of the retouched video file and utilizes it as the index of the searching operation. The core parts of source code are stored in their original forms, and the rest of the non-essential source parts are stored as a shortened form of their entire hash value, similar to the mechanism explained in Figure 3. The core parts are the signature comparisonprocedure and the search procedure of stored results, and they are constructed with a program language designed for the blockchain system, called smart contract.
## 2 Wavelet Denoising  
Wavelet transform has been widely used in representing signals in the time-frequency domain. The wavelet transform decomposes a time-domain signal into its wavelet coefficients through a mother wavelet function. These coefficients are obtained by performing shifting and dilation of the mother wavelet as shown in Equation (1):  
\[\Psi_{a,b}(t)=\Psi\left(\frac{t-b}{a}\right) \tag{1}\]  
where \(a\) is the scaling parameter and \(b\) is the shifting parameter [13; 14].  
When the noisy version is available, the problem is to restore the information contained in the signal. In traditional wavelet denoising, the wavelet coefficients are modified by taking advantage of their local properties and then inverting the transformation to obtain a clean version of the signal. A basic flow chart of wavelet-based denoising is shown in Figure 1. An eyeblink corrupted EEG signal of 15 sec duration is shown in the top panel of Figure 2. The corrupted EEG signal is then decomposed into wavelet coefficients up to level 5 using Daubechies wavelet with the vanishing moment as 4, and the corresponding detail and approximation coefficients are shown in the Figure 3. After thresholding both the detail and approximation coefficients, the corrected coefficients are used in inverse operation, and an artifact-free EEG signal is shown in the middle panel of Figure 2 in black.  
Generally, there are two types of wavelet transform: one is the discrete wavelet transform (DWT) and another is a continuous wavelet transform (CWT) [15]. DWT is regarded as a non-redundant and extremely dynamic wavelet transform for obtaining wavelet representation of signal [16,17]. In DWT, the signal is passed through a half-band high-pass and a half-band low-pass filter, resulting in detail coefficients and approximate coefficients, respectively. The process continues until the expected frequency is obtained. The time-variance of DWT is a severe flaw, which is especially critical in statistical signal processing applications such as EEG [18]. The stationary wavelet transform (SWT) solves the DWT's translation invariance problem; however, it has redundant information and is sluggish [19]. The filter at each stage is the design difference between DWT and SWT [20]. At each level of decomposition, the approximate and detail sequences are the same length as the original sequence. Wavelet coefficients extracted through SWT are shown in Figure 4, and the corrected EEG signal is illustrated in the bottom panel of Figure 2 in blue.  
Figure 1: Basic flow chart of wavelet-based signal denoising.  
Figure 2: Eyeblink corrupted EEG signal in red, corrected EEG signal based on DWT in black and SWT in blue.  
Figure 4: SWT coefficients up to level 5 of the corrupted EEG shown in the top panel of Figure 2 using Daubechies wavelet with vanishing moment as 4.  
Figure 3: Wavelet coefficients up to level 5 of the corrupted EEG shown in the top panel of Figure 2 using Daubechies wavelet with vanishing moment as 4.
## 4 Discussion
### Strategic Actions and Policy Implications
#### 4.5.2 In the Context of Life Cycle Analysis  
Given the LCA findings, it is evident that the utilisation of cleaner sources of electricity and heat can significantly reduce GHG emissions (up to 21.55%). It underscores the strategic need for policies to promote the development and deployment of cleaner energy sources, like wind and solar, in the aviation fuel sector [67]. It is demonstrated that utilising offshore wind electricity could produce SAF with a GWP that is substantially lower than the current UK mandate [51]. Governments can raise the bar by setting more stringent emission standards, thus pushing industries to adopt cleaner production methods. The 54% reduction in carbon footprint from conventional methods suggests the effectiveness of advanced production scenarios [71]. Policymakers should consider providing incentives for research and adoption of these advanced scenarios. A study by [PERSON], [PERSON] [54] indicated the possibility of achieving significant SAF output using forest residue. This necessitates sustainable forest management policies to ensure long-term availability of feedstock and avoid deforestation. [PERSON] [58] emphasises the superiority of green hydrogen over blue hydrogen from a life cycle perspective. Policies should, therefore, not only focus on the immediate benefits but consider the entire life cycle impacts of technologies. Given the potential synergy of local production combined with imported hydrogen or fuels highlighted in [58], international collaborations and trade agreements can be fostered to optimise energy production and consumption.
## 2 Experimental Procedures
### Functionality of JP
#### 2.4.5 Phenolic Profile  
Sample Preparation  
The protocol described by [PERSON] et al. [22] was followed with minor changes. Briefly, 1 g of each JP and JCON was extracted using 20 mL of ethanol (80%, \(v/v\)), followed by ultrasonication in a water bath for 30 min. Subsequently, the extract was filtrated through a 0.45 \(\upmu\)m Whatman filter paper, and the filtered solution was additionally filtered through a 0.22 \(\upmu\)m syringe membrane into dark HPLC glass vials. Additionally, standard phenolic stock solutions (0.1, 31.25, 62.5, 125, 250, and 500 \(\upmu\)g/mL) were prepared via dilution in methanol (HPLC grade).  
HPLC-UV Detection  
The phenolic fraction was determined using a Shimadzu LC 20A system (Shimadzu Incorporated, Tokyo, Japan) according to a method previously reported by [PERSON] et al. [22] with slight modifications. The analytes of interest were eluted on an Agilent ZORBAX-SB C-18 column (4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m particle size, Agilent Technology, Santa Clara, CA, USA).  
The mobile phases comprised 0.1% acetic acid in HPLC grade water (mobile phase A) and 100% acetonitrile, HPLC grade (mobile phase B). The linear gradient protocol was set as follows: 0-10 min, 5-10% mobile phase B; 10-15 min, 10-20% mobile phase B; 15-25 min, 20-38% mobile phase B; 25-30 min, 38-40% mobile phase B; 30-31 min, 40-100% mobile phase B; 31-35 min, 100% mobile phase B; 35-36 min, 100-5% mobile phase B; 36-50 min, 5% mobile phase B. The flow rate was kept constant at 0.8 mL/min, and the chromatograms were recorded at 260, 360, and 520 nm for phenolic acids, flavonoids, and anthocyanins, respectively. The column temperature was 30 \({}^{\circ}\)C, and the injection volume was 10 \(\upmu\)L.  
Qualitatively, phenolic compounds were determined by comparing their retention times and absorption spectra with those of the pure standards and quantitatively by peak areas using the pure standards calibration curves. The phenolic compound was expressed as milligrams per 100 g fresh weight of JP (mg/100 g FW).
## 4 Results and Discussion
### Overall Effective Factor  
In this research, each of the uniformity indicators discussed is affected by the planting density of the crop (\(P_{d}\)) and the air change rate (_ACH_). In order to comprehensively analyze the environmental parameters including air velocity, temperature, relative humidity, and various uniformity indices, a comprehensive assessment of the validity of different planting density (\(P_{d}\)) and air change rate (_ACH_) has been conducted using the validity factor \(\theta\) in Equation (16). First, a discussion case of \(P_{d}\) = 0.9 is presented in this research. Figure 16 illustrates the profiles of \(\theta\) for five different crop planting density (\(P_{d}\)) with varying _ACH_ values. The figure demonstrates that, as _ACH_ increases, the majority of \(\theta\) values for all five \(P_{d}\) conditions tend to increase, indicating a positive correlation between _ACH_ and \(\theta\). Additionally, it is observed that lower crop planting density generally exhibit higher \(\theta\) values. However, it is worth noting that, for the case of \(P_{d}\) = 0.1 and _ACH_ > 60 h\({}^{-1}\), the \(\theta\) values are lower compared to the case of \(P_{d}\) = 0.3. Even when _ACH_ is set to 100 h\({}^{-1}\), the \(\theta\) values for \(P_{d}\) = 0.1 are lower compared to \(P_{d}\) = 0.5. Since _OU_ plays a dominant role in determining the \(\theta\) values, the trend of \(\theta\) is similar to that of _OU_, as evident from Figures 6 and 16. From the findings discussed above, it can be observed that, while increasing _ACH_ and decreasing \(P_{d}\) generally leads to higher overall \(\theta\) values in the room, there are cases (e.g., _ACH_ = 100 h\({}^{-1}\) and \(P_{d}\) = 0.1) where high _ACH_ and low \(P_{d}\) result in a decrease in \(\theta\). It is important to note that increasing _ACH_ in the room will also lead to higher energy consumption. Furthermore, it is observed that, in the case of \(P_{d}\) < 0.3, reducing the crop planting density does not significantly improve the \(\theta\) value. This is because the difference between the _RSDT_ and _RSDRH_ values under each \(P_{d}\) condition is not substantial. It is worth noting that reducing the planting density too much may result in lower crop yield. To enhance the overall \(\theta\), it is recommended to increase the indoor _ACH_ and lower the crop planting density (\(P_{d}\)) to an appropriate extent. However, it is crucial to select an optimal combination of _ACH_ and \(P_{d}\) that strikes a balance between energy consumption and crop yield.  
Figure 16: The effective factor(\(\theta\))as a function of _ACH_ for all different \(P_{d}\) cases.  
Figure 15: The _RSDRH_ as a function of _ACH_ for all different \(P_{d}\) cases.
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
## 4 Results  
### Near-Field Enhancement  
Near-field enhancement produced by NPs when electromagnetically irradiated is essential for applications like SERS (surface enhanced Raman spectroscopy) or SEF (surface enhanced fluorescence). In these spectroscopy techniques, most of the NP surface is covered with the molecules under study, so the electromagnetic field acting on them can be approximated by an average of the electric field produced over the whole NP surface [6; 21; 51]. Figure 2 shows the near-field enhancement averaged over the NP surface \(\langle\,\mid\,E\,\mid\,/\,\mid\,E_{0}\,\mid\,\rangle\). Spheres with \(R\) varying from 30 to 90 nm have been considered. With dashed and solid white lines are highlighted the energy at which \(\varepsilon_{2}=1\) and the starting energy of the UV regime respectively (3 eV). For reference, \(\langle\,\mid\,E\,\mid\,/\,\mid\,E_{0}\,\mid\,\rangle\) for two common metals for UV  
Figure 1: Real (solid line) and imaginary (dashed line) parts of the dielectric permittivity (\(\varepsilon=\varepsilon_{1}+\mathrm{i}\varepsilon_{2}\)) of (**a**) AIP [45], (**b**) AlAs [46], (**c**) AlN [47], (**d**) diamond, (**e**) CeO\({}_{2}\)[48], (**f**) TiO\({}_{2}\)[49], (**g**) Al, (**h**) Rh [50] and (**i**) dielectrics taken as reference like Si (blue), GaAs (yellow) and Ge (red) [50]. The region in which \(\varepsilon_{2}<1\) and \(\varepsilon_{1}>4\) within the UV is shadowed in gray. In the case of the reference materials (Al, Rh, Si, GaAs and Ge) the shadowed region indicates the UV spectral range.  
plasmonic applications are included: Rh and Al. In the case of Al, a realistic 5 nm thick oxide (Al\({}_{2}\)O\({}_{3}\)) shell has been considered [52; 53].  
Unlike Si, all the HRI dielectric materials analyzed in this research, exhibit its electric field enhancement at energies above 3 eV. AlP, AlAs, CeO\({}_{2}\) and TiO\({}_{2}\) also show similar performance and high values of \(\langle\)1 \(E\)1 / _I_E_0 1\(\rangle\) can be reached between 3 and 3.4 or 4 eV depending on the material. We would like to emphasize that TiO\({}_{2}\) shows the largest enhancement at energies above 3 eV. AlN and diamond also have very similar behavior since their absorption bands are above 6 eV: they have a HRI dielectric like behavior all along the studied spectral range. In this case, diamond presents the highest enhancement.  
The values of \(\langle\)1 \(E\)1 / _I_E_0 1\(\rangle\) for Al (with its native oxide shell included) and Rh NPs are also shown for comparison. Rhodium presents very low enhancement because its LSPRs are into deep UV (above 6 eV) [20; 30]. On the contrary, Al/Al\({}_{2}\)O\({}_{3}\) nanospheres present wider peaks with moderate intensity. Consequently, the value of \(\langle\)1 \(E\)1 / _I_E_0 1\(\rangle\) is almost constant for all sizes in the studied spectral range.
# Review: A Survey on Objective Evaluation of Image Sharpness
## 2 Evaluation Methods and Analysis
### Learning-Based Methods
#### 2.3.4 Other Methods Based on Learning  
[PERSON] et al. [84] proposed a new local learning method for blind image evaluation, which uses the perceptually similar neighbors of the searched test image as its training set and evaluates the image through a sparse Gaussian process. [PERSON] et al. [85] presented a content insensitive blind image ambiguity evaluation index by using Weibull statistics. This method models the gradient amplitude of blur image by adjusting the scale parameter, shape parameter and skewness in Weibull distribution and uses sparse extreme value learning machine to predict the final image evaluation. [PERSON] et al. [86] proposed a no-reference image sharpness evaluation method based on sorting learning and block extraction. Performance evaluation shows that the method is highly relevant to human perception and robust to image content. [PERSON] et al. [87] proposed a depth model combining the spatial and visual features of images for image quality assessment. The algorithm takes multiple image features as input and learns feature weights through end-to-end training to obtain evaluation results.  
Through the analysis of learning-based methods, it is found that most of the above methods combine the structural features, local or perceptual features of images. The parameters of the corresponding indicators are estimated by the regression analysis, and the evaluation results are obtained.  
The advantages and disadvantages of different learning-based methods are shown in Table 2.
## 4 Results and Discussion
### Possible Use  
This study enables rapid identification of areas highly prone to landslides along a route. By analyzing slope stability and integrating key hazard indicators--such as displacement, rainfall, and crack development--into a monitoring system, it efficiently locates typical high-risk slopes and supports road safety during operation. The approach emphasizes efficiency in early detection and risk mitigation, which can also further support prioritization of slope reinforcement and drainage upgrades along the route.  
It also aids in strategically positioning emergency supply depots (e.g., rescue materials) by concentrating resources in high-risk road sections characterized by steep terrain, significant elevation fluctuations, and intense snowmelt/rainfall during warm seasons, and prioritized due to their heightened vulnerability.  
Figure 13: Bi-modal violin plots of SHAP feature value distributions for four models—(**a**) NB, (**b**) SVM, (**c**) SVM-RF, (**d**) XGBoost—under two sampling methods: GLHM and random.