esa-sceva/satcom-chunk-collection huggingface dataset:
# Does Below-Above Canopy Air Mass Decoupling Impact Temperate Floodplain Forest CO2 Exchange?
## 2 Materials and Methods
### Data Handling  
#### 2.3.1 Turbulent Flux Calculation and Post-Processing  
Our whole analysis covers a multi-year period of EC measurements above the canopy (years 2015-2020), and a 1-year focal period (year 2019) with concurrent EC measurements above and below the canopy.  
Standardly, vertical turbulent fluxes were calculated with EddyPro(r) (version 6.2.0, LI-COR Biosciences, Rockville, MD, USA) using the eddy covariance method (described in detail by [13; 42]) both above and below the canopy with an averaging time of 30 min. The correct application of the EC method requires several corrections on the calculated covariances between vertical wind component \(w\) and the quantity of interest, e.g., CO\({}_{2}\). The planar-fit method [43] was used to ensure that the long-term mean of individual vertical wind measurements \(w\) is negligible or nullified. Furthermore, a rotation into the main wind direction is conducted with this method. Potential flux losses in the high frequency range, e.g., due to path length averaging or the spatial separation of sonic measurement path and gas analyzer were accounted for with the correction proposed by [44]. Additionally, the measured buoyancy flux was converted into the real sensible heat flux according to [45]. The EC software also includes a quality flagging scheme testing the data for stationarity and development of turbulence and finally, providing an overall quality flag combining the results of both tests [46].
## 2 Material and Methods
### Statistical Analysis  
All measurements were checked for normality using the Shapiro-Wilk test, and continuous variables were described as the mean and the standard deviation, while categorical variables were reported as proportions.  
Test-retest reliability was investigated as agreement between T0 and T1-pre data (in terms of MD, MA, HAs percentage, and RoM) for the dominant and non-dominant limbs on each plane of motion during the four gait phases. Intraclass correlation coefficient (ICC, 2,k) was used to investigate relative reliability and interpreted as poor (lower than 0.50), moderate (between 0.50 and 0.75), good (between 0.75 and 0.90), and excellent (greater than 0.90) [29]. Absolute reliability was investigated using the standard error of measurement (SEM), calculated as SEM = SD \(\surd\)1-ICC, with SD representing the standard deviation of the mean difference between T0 and T1-pre trials.  
When considering treatment effects, two-tailed \(t\)-tests for independent samples or chi square tests were used to assess between-groups differences in terms of participants' characteristics and outcome measures at baseline. Subsequently, mixed-model ANOVA (a 4 \(\times\) 2 design for MD, MA, HAs percentage, and RoM and a 2 \(\times\) 2 design for MVCs) with time as within-subject variable and group as between-subjects variable was used to investigate between-groups differences over time in terms of outcome measures. In the case of significant interactions or main effects, post hoc analysis with Bonferroni correction was performed to assess between-groups differences at each timepoint and within-groupdifferences over time. The statistical level of significance was set at \(\alpha\) = 0.05, and the analysis was carried out using the software SPSS 28.0 for Windows (IBM Corp, Armonk, NY, USA).
# Cosmeceutical Potentials of _Equisetum debile_ Roxb. ex Vaucher Extracts
## 2 Materials and Methods
### Determination of Tyrosine Inhibition (Skin-Whitening Activity)  
Melanin is a brown pigment that plays a major role to induce a darkened complexion of skin appearance. Lowering melanin production via an inhibition of tyrosinase activity has been recognized to brighten a dark skin shade, leading to a lighter or whitening appearance. Therefore, a skin-whitening property of _E. debile_ extracts was investigated via the inhibitory capacity against the activity of tyrosinase using a dopachrome 96-well plate method with some modifications [45]. Briefly, tyrosinase accelerated an enzymatic reaction to convert L-DOPA substrate to brown pigment products (pheomelanin and eumelanin) that were quantified using a UV-Vis spectrophotometer with an absorbance at 492 nm. Before testing, the _E. debile_ extracts were re-suspended in 50 mM phosphate buffer pH 6.8. The reaction was a mixture of 50 uL L-DOPA (15 mM in 50 mM phosphate buffer pH 6.8) and 50 uL plant extract, which was followed by an addition of 50 uL tyrosinase (100 units/mL). The experiment was carried out in a 96-well microplate, and the absorbance was measured before and after an activation of tyrosinase activity. The incubation condition was maintained at 37 \({}^{\circ}\)C (to mimic human skin temperature) for 60 min to initiate an enzymatic reaction. The following equation was used to compute the percent tyrosinase inhibition [32]:  
\[\%\;\text{inhibition}=[(\text{A}-\text{B})-(\text{C}-\text{D})]/(\text{A}- \text{B})\times 100,\]  
where A is an absorbance of plant samples before an enzymatic incubation with B as a blank. C is an absorbance of plant samples after the incubation with D as a blank.  
Kojic acid, a well-known tyrosinase inhibitor, was used as a standard. Kojic acid solution was prepared in various concentrations to obtain a calibration curve. The enzymatic reaction of kojic acid was performed similarly to the plant samples described above. The  
\begin{table}
\begin{tabular}{c c} \hline
**Instrument Parameters** & **Operational Conditions** \\ \hline ICP RF power (w) & 1300 \\ Plasma gas (L/min) & 15 \\ Auxiliary gas (L/min) & 0.2 \\ Nebulizer gas (L/min) & 0.8 \\ Viewing type & Axial \\ Read delay (seconds) & 15 \\ Replicates & 3 \\ Sample flow rate (mL/min) & 1.5 \\ Flush time (seconds) & 25 \\ Wavelength (mm) & Si 212.412 \\ \hline \end{tabular}
\end{table}
Table 1: Operational parameters of inductively coupled plasma–optical emission (ICP-OES) spectrometer for silicon (Si) determination from _E. debile_ extracts.  
inhibitory capacity of _E. debile_ against tyrosinase activity was expressed as KAE values (mg kojic acid equivalent per gram plant sample) using an equation derived from a kojic acid calibration curve and IC\({}_{50}\) value which is the inhibitor concentration leading to 50% activity loss.
# Enhancing Density Prediction of Agricultural Land Soil through Void Area Curve Analysis
## 2 Materials and Methods
### Image Processing  
The binary image obtained as a result of image thresholding is used to compute \(n^{\prime}\). The calculation process of \(n^{\prime}\) is as follows. For image processing, we used the OpenCV (Open Computer Vision) library in Python (www.python.org). The OpenCV library includes many functions for image processing, and it also includes the three thresholding methods used in this study [44; 45]. The original digital image is an RGB image comprising three channels of red, green, and blue. The original digital image was converted into an 8-bit grayscale image. The converted grayscale image was used for thresholding. Image thresholding is performed based on the determined threshold value. In simple thresholding, the threshold value is determined with an arbitrary value, and in the Otsu and Triangle method, it is determined according to each algorithm. Pixels with an image intensity of 255 are a soil solid, and pixels with an image intensity of 0 are a void. Then, \(N_{void}\) is determined by counting the pixels with image intensity = 0. Additionally, \(N_{void}\) can be computed as the sum of the frequencies in the histogram of a grayscale image that are less than a threshold value. The digital image used in this study has a total of 17,915,904 pixels, 5184 horizontal and 3456 vertical. Therefore, \(N_{total}\) is fixed at 17,915,904. Finally, \(n^{\prime}\) can be calculated according to Equation (7).
## 6 Application Scenario Based on Measured Temperature Aata
### Impact of Input Uncertainty on Bayesian RUL Prediction  
RUL prediction in Figure 12 is based on a fixed starting temperature swing of \(\Delta T_{0}=110.92\)\({}^{\circ}\)C measured for the LED system that served as the specimen system in the test data simulation in Section 4. Using the predictive distribution of \(\Delta T_{0}\) obtained from Section 5.1 (based on E1) instead of \(\Delta T_{0}\), we aim at studying the impact of production and application variability in \(\Delta T_{0}\) on RUL prediction. Figure 13b shows the resulting predicted increase in \(\Delta T_{j}\) over the PC runtime based on test data of \(n_{3}=500\) LED systems with different variability within the thermal resistances, i.e., \(\sigma_{R_{\mathrm{th}}}=1\) K/W and \(\sigma_{R_{\mathrm{th}}}=0.3\) K/W. Additionally, Figure 13a compares the respective pdfs of failure.  
For \(\sigma_{R_{\mathrm{th}}}=1\) K/W, the number of cycles until the predicted temperature swing reaches the failure threshold is estimated at \(\mathrm{N_{f}}=20,638\) (median 20,413). The corresponding lower and upper limits of the 95% credible interval are \([15,636;26,813]\) cycles. For less  
Figure 12: Bayesian RUL prediction of LED systems as proposed in [43]: Measured data (red circles) with error bars representing the measurement uncertainty obtained from 20 repeated measurements on a single LED device under constant conditions, i.e., \(\pm 2\,\tilde{\sigma}\) with \(\tilde{\sigma}\approx 0.5\)\({}^{\circ}\)C; estimated values (green circles); and predictions of \(\Delta T_{j}\) (green dashed line). Probability density function (pdf) of failure based on a failure threshold used to indicate end-of-life.  
variability within the thermal resistances, \(\sigma_{R_{\rm th}}=0.3\) K/W, the predicted time to failure is 20,416 cycles (median 20,378), with a narrower credible region of \([18,956;21,971]\) power cycles. Obviously, inherent uncertainty in the initial temperature swing has a considerable impact on the resulting uncertainty in RUL prediction, and its variability decreases with decreasing variability in \(\sigma_{R_{\rm th}}\). Thus, sources of potential uncertainty of the system in focus have to be carefully considered for RUL prediction to be reliable. Beyond the quantification of uncertainty, from Figure 13, the need also arises to deal with the variability \(\sigma_{R_{\rm th}}\) to reduce the uncertainty on the RUL.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
## 2 Reference Assumption PLL Features
### The Definition of SSV RRAM  
The so-called 'dynamics', in a generic sense, are concerned with velocity, acceleration, and jerk of the relative movement between the GNSS satellite and the user vehicle. Considering the motion complexity of SSV users, a space vehicle equipped with a third-order PLL is treated as the baseline of current technology. A third-order PLL is recommended for SSV RRAM resulting from its unique attributes. First, a third-order PLL can track the variations of velocity and acceleration without any bias, which is prior to first-order and second-order PLLs. Second, the jerk is so small for non-maneuvering space vehicles that the steady state dynamic stress error caused by jerk is also small. With regard to the parameter design of the SSV RRAM, two core parameters, the LNBW and CIT, should be emphatically analyzed.  
The interaction effect of high-sensitivity and high-dynamics is reflected by these two parameters. Reducing the tracking loop bandwidth is the most effective way to achieve high-sensitivity, but a narrower LNBW is not in the best interest of high-dynamics. Secondly, increasing CIT is another way to achieve high-sensitivity, but the Doppler frequency changes dramatically in high-dynamic conditions, which results in an unacceptable integration loss caused by frequency misalignment. Apparently, neither reducing the LNBW nor increasing CIT is able to satisfy high-sensitivity and high-dynamic performance at the same time. The contradiction analyzed above brings about an issue about how to select proper LNBW and CIT for our SSV RRAM.
# A New Engine Fault Diagnosis Method Based on Multi-Sensor Data Fusion
## 3 The Proposed Method for Fault Diagnosis
### BPA Determination
#### 3.3.1 Support Degree Calculation of Singleton Propositions  
In this part, the support degree of the test model to proposition \(\{F_{i}\}\) denoted as \(Sup(\{F_{i}\})\) (\(i=1\), \(2\),..., \(M\)) is obtained.  
Taking into account the property of the model curve, firstly let us calculate the integral interval:  
\[InMin=\min(\bar{X}_{i}+3\sigma_{i},\bar{X}_{i}-3\sigma_{i},\bar{X}_{T}+3\sigma _{T},\bar{X}_{T}-3\sigma_{T}), \tag{19}\]  
\[InMax=\max(\bar{X}_{i}+3\sigma_{i},\bar{X}_{i}-3\sigma_{i},\bar{X}_{T}+3\sigma _{T},\bar{X}_{T}-3\sigma_{T}). \tag{20}\]  
Then, the intersection area between the test model curve \(\mu_{T}(x)\) and the fault model curve \(\mu_{F_{i}}(x)\) is calculated:  
\[Area(\{F_{i}\})=\int_{InMin}^{InMax}\min(\mu_{F_{i}}(x),\mu_{T}(x))dx. \tag{21}\]  
For example, \(Area(\{F_{1}\})\) is as shown in Figure 2a if \(M=3\).  
Finally, calculate the support degree \(Sup(\{F_{i}\})\) of the test model to proposition \(\{F_{i}\}\):  
\[Sup(\{F_{i}\})=\frac{Area(\{F_{i}\})}{Area_{T}}, \tag{22}\]  
where the \(Area_{T}\) is the area under the test model curve \(\mu_{T}(x)\), \(Area_{T}=\sqrt{2\pi}\sigma_{T}\).
###### Abstract  
Polyacrylonitrile (PAN)-derived carbon nanofiber mats were fabricated using electrospinningning and further carbonization. Scanning electron microscopy (SEM), X-ray diffraction (XRD) and electrochemical characterization were used to investigate the effects of precursor concentration, thermal stabilization and carbonization temperature, addition of multi-walled carbon nanotubes (MWCNTs), activation of nitric acid and sulfuric acid on the morphologies, conductivity, flexibility and electrochemical properties of the fabricated carbon nanofiber mats. The results reveal that the carbon nanofiber mats with uniform fiber diameter of 200 nm and sheet resistance of 154 \(\Omega\)/sq could be achieved with a PAN mass fraction of 12 wt% and a thermal stabilization and carbonization temperature of 270 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, respectively. Due to the good conductivity and high strength of the MWCNTs, the sheet resistance of the carbon nanofiber mats decreases to around 60 \(\Omega\)/sq by adding MWCNTs to precursor, and the mats exhibit excellent bend and fold flexibility. The electrochemical performance of the co-spun carbon nanofiber mats could be further improved by the activation treatment of acids, and the maximum specific capacitance of the carbon mat reaches 113.5 F/g at a current density of 0.1 mA/cm\({}^{2}\) in the case of 1:3 HNO\({}_{3}\):H\({}_{2}\)SO\({}_{4}\). The investigation provides a reference for improving the performance of spun carbon nanofiber mats, which can be used as the electrodes or current collectors to further load other active materials in the applications of energy storage devices.  
electrospinning; carbon nanofiber mat; performance improvement +
Footnote †: journal: Applied Sci. 2019, 9, 3683; doi:10.3390/APP9183683
## 3 Processing Method of Vibration Piezoelectric Signal
### Statistical Validation  
A Chi-square test was employed in this study to evaluate the statistical significance of differences in burn rate between the control group and the experimental group. The experimental setup utilized a newly designed drying tray (as illustrated in Figure 4), incorporating a PVDF piezoelectric film sensor composed of a 28 \(\upmu\)m piezoelectric film with filamentary silver paste electrodes. The test sample consisted of a uniform batch of corn seeds with a total mass of 10,000 g for both groups.  
1. Make assumptions  
**Hypothesis 0 (H0):**: _There is no statistically significant difference in burn rates between the control group and the experimental group._  
**Hypothesis 1 (H1):**: _There is a statistically significant difference in burn rates between the control group and the experimental group._  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Vibration Frequency (Hz)**} & \multirow{2}{*}{**Scald Rate (\%)**} & **Moisture Change Monitoring Accuracy (\%)** & **Signal Outage Probability (OP)** \\ \hline
4.5 & 2.1 & 95.3 & 0.08 \\
5.0 & 1.8 & 96.2 & 0.06 \\
5.2 & 1.5 & 97.0 & 0.05 \\
5.5 & 1.6 & 96.5 & 0.06 \\
6.0 & 1.9 & 95.8 & 0.07 \\ \hline \end{tabular}
\end{table}
Table 4: Select different vibration frequencies to carry out the experimental results.  
2. Collect data Multiple experimental trials were conducted. As shown in Table 5, one representative dataset is presented as an example for statistical analysis.  
3. Calculate Chi-square statistics The calculation formula of Chi-square statistics is \[\chi^{2}=\frac{20,000\times(230\times 9960-40\times 9770)^{2}}{(230+40)\times(2 30+9770)\times(9770+9960)\times(40+9960)}\] \[=135.533\]  
4. Definite degree of freedom (df) The calculation formula for degrees of freedom is df = (number of variable rows \(-1\)) \(\times\) (number of variable columns \(-1\)) = (\(2-1\)) \(\times\) (\(2-1\)) = 1
5. Find critical value The critical value was determined using the Chi-square distribution table based on the degrees of freedom and the significance level (typically set at 0.05). For a degree of freedom (df) of 1 and \(\alpha\) = 0.05, the corresponding critical value is 3.841.  
6. Compare Chi-square statistics and critical values. If \(\chi^{2}\) is greater than the critical value, reject the null hypothesis. If it is less than or equal to the critical value, the null hypothesis cannot be rejected. This paper calculates that \(\chi^{2}\) = 135.533 is greater than the critical value of 3.841, so the null hypothesis is rejected.  
7. Based on the results of the Chi-square test, it can be concluded that there is a statistically significant difference in the burn rate between the control group and the experimental group. This indicates that the newly developed vibrating piezoelectric drying tray significantly reduces the occurrence of grain burning during the drying process.
# Sharpness-Based Distance Detection
## 2 Target Identification and Location
### Target Detection Experiment  
The experimental results obtained by using YOLO for target detection are shown in Figure 3. The objects on the screen are identified and localised and the target objects are extracted. The result graph shows that the confidence level of the target object is very high. The so-called confidence contains two aspects: one is the probability that the bounding box contains an object, and the other is the accuracy of the bounding box. The former is recorded as Pr (object)--when there is a target Pr (object) = 1, when there is no target Pr (object) = 0--and the accuracy of the bounding box is determined by the IOU of the predicted box and the actual box (intersection over union, intersection and union ratio). The confidence level is displayed after the name of the predicted object for easy observation. This demonstrates that the deep learning approach can be used for target recognition, where the loss function for training is composed of CIoU, _obj_ and _cls_.  
\[L\big{(}t_{p},t_{gt}\big{)}=\sum_{k=0}^{K}\Bigg{[}\alpha_{k}^{balance}\alpha_{ box}\sum_{i=0}^{S^{2}}\sum_{j=0}^{B}1_{kij}^{obj}L_{CIoU}+\alpha_{obj}\sum_{i=0}^{S^{2}} \sum_{j=0}^{B}1_{kij}^{obj}L_{obj}+\alpha_{cls}\sum_{i=0}^{S^{2}}\sum_{j=0}^{ B}1_{kij}^{obj}L_{cls}\Bigg{]} \tag{1}\]  
where \(L\) is the loss function; \(K\), \(S^{2}\), and \(B\) are the output feature map, cell and the number of anchors on each cell respectively, and \(\alpha_{*}\) is the corresponding weight, the default value is \(\alpha_{box}\) = 0.05, \(\alpha_{obj}\) = 0.7, \(\alpha_{cls}\) = 0.3; \(1_{kij}^{obj}\) indicates whether the \(k\)th output feature map, the ith cell, and the jth anchor box is a positive sample or not--if it is a positive sample then it will be 1, and if it is not, it will be 0; \(t_{p}\), \(t_{gt}\) are the prediction vectors and ground-truth vectors; \(\alpha_{k}^{balance}\) is used to balance the weights of the output feature maps for each scale--the default value is [4.0, 1.0, 0.4], which corresponds to the output feature maps of 80 \(\times\) 80, 40 \(\times\) 40, 20 \(\times\) 20 in turn. \(L_{CIoU}\), \(L_{obj}\), and \(L_{cls}\), function on CIoU, obj, and cls respectively.  
Figure 3: Target detection and target object region extraction map.  
Figure 2: DeepSORT flowchart.
# Spoofing Detection Algorithm Based on Pseudorange Differences
## 2 Spoofing Detection Algorithm
### Algorithm Summary and Flowchart  
To sum up, the proposed algorithm has three steps: (1) pseudorange differences calculation; (2) iterative solution; (3) comparison with the traditional least squares method. The conclusion can be drawn as: the spoofing detection algorithm's result approximates the traditional least squares method's result when the signal is real. Otherwise, the results of these two algorithms are different. Based on this, the authenticity of the signal can be determined. To implement the algorithm, we need to get the pseudorange and the ephemeris from the GNSS receiver. The satellite position is determined based on the information of the ephemeris. Using the information of the pseudorange, the difference calculation and the iterative solution are carried out. Explicit algorithm flows are given in Figures 2 and 3. In Figure 2, the detailed spoofing detection process is presented. In Figure 3, we give the analysis explanation of the pseudorange double-difference model establishment and solution.  
When there are \(n\) satellites with the same pseudo-random noise (PRN) number at \(t_{k}\) and \(t_{k+1}\), the first difference calculation needs to be executed once, and the second difference calculation needs to be executed \(\left(n-1\right)\) times. Therefore, the algorithm complexity is \(O\left(n\right)\).
## 3 Message Passing for JP-CoMP Clustering
### Main Idea  
Determining an appropriate cell partner by maximizing the sum capacity (3) is a challenging problem. The sum capacity is a nonlinear problem without an efficient solution available to solve it. The combinatorial optimization is required in this case to provide an optimal clustering method. To solve the combinatorial problem requires a centralized process, which, in turn, incurs a high computational burden and high backhaul capacity. Therefore, we propose a distributed algorithm that works by message passing to reduce both the computational complexity and backhaul overload.  
Nonlinear optimization (3) cannot easily be realized in this case. Instead, the approximation of the objective function is required. Hence, we approximate the original nonlinear objective function into a linear form. Utilizing this transform, multi-layer message passing is introduced as the proposed algorithm for cell clustering in JP-CoMP. This proposed algorithm will provide the lower computational complexity and backhaul capacity level.  
The main idea of multi-layer message passing is to perform distributed optimization by exchanging messages at all base stations in the multi-layer assessment. The proposed algorithm assesses the cell cluster based on the specific objective function of each layer. Multi-layer message passing will divide the clustering process into two layers, in this case the cluster (first layer) and the super-cluster (second layer). The cluster is represented by an exemplar, while the super-cluster is represented by a super-exemplar. Although our approach is suboptimal, our simulation results confirm that the proposed algorithm outperforms previous schemes.