esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Performance Analysis
### IP at Eavesdropper  
Theorem 3: _The IP event at eavesdropper \(E\) in transmission block \(t\) refers to IoT relays and devices decoded the messages successfully; however, the eavesdropper \(E\) cannot decode the messages successfully. IP at eavesdropper \(E\) is expressed as follows:_  
\[IP_{E\to D_{n}}(t)=1-\Pr\bigg{\{}\min_{i=\{N_{\
u},\dots,n\}}\left\{R_{R_{r}- x_{i}}^{T_{1}^{(t)}}\right\}\geq\mathcal{R},\min_{i=\{N_{\
u},\dots,n\}}\left\{R_{E -x_{i}}^{T_{2}^{(t)}}\right\}\geq\mathcal{R}\bigg{\}}. \tag{49}\]_In other words, expression (49) may be rewritten as follows:_  
\[IP_{E\to D_{n}}(t) =1-\Pr\biggl{\{}\min\biggl{\{}\min_{i=\{N,\ldots,n\}}\biggl{\{}R_{R_{ r}-x_{i}}^{T_{1}^{(l)}}\biggr{\}},\min_{i=\{N,\ldots,n\}}\biggl{\{}R_{E-x_{i}}^{T_{2}^{(l)}} \biggr{\}}\biggr{\}}\geq\mathcal{R}\biggr{\}} \tag{50}\] \[\triangleq 1-\Pr\biggl{\{}\min_{i=\{N,\ldots,n\}}\biggl{\{}R_{E-x_{i}}^{T_{2 }^{(l)}}\biggr{\}}\geq\mathcal{R}\biggr{\}}. \tag{51}\]  
_By substituting expressions (16), (17), (36)-(39) into expression (50), we always obtain expression (51) since \(\min_{i=\{N,\ldots,n\}}\biggl{\{}R_{R_{r}-x_{i}}^{T_{1}^{(l)}}\biggr{\}}>\min_ {i=\{N,\ldots,n\}}\biggl{\{}R_{E-x_{i}}^{T_{2}^{(l)}}\biggr{\}}\)._  
_By applying PDF over Rayleigh distribution, we obtain IP at eavesdropper \(E\) in closed-form as follows:_  
\[IP_{E\to D_{n}}(t) =\max_{i=\{N,\ldots,n\}}\Biggl{\{}1-\frac{\biggl{(}\alpha_{i}- \gamma\sum\limits_{j=1}^{i-1}\alpha_{j}\biggr{)}\sigma_{S,R_{r}}\sigma_{R_{r}, E}}{\biggl{(}\alpha_{i}-\gamma\sum\limits_{j=1}^{i-1}\alpha_{j}\biggr{)} \sigma_{S,R_{r}}\sigma_{R_{r},E}+\delta\gamma\sigma_{S,R_{r}}\sigma_{R_{r},E}}\] \[\times\exp\left(-\frac{\gamma}{\lambda_{R_{r}}\biggl{(}\alpha_{i} -\gamma\sum\limits_{j=1}^{i-1}\alpha_{j}\biggr{)}\rho\sigma_{S,R_{r}}\sigma_{R _{r},E}}\right)\Biggr{\}}, \tag{52}\]  
_where \(R_{r}\) is the selected relay and \(R_{r}\) is the rest relay, \(\delta=1\) if the rest IoT relay sent jamming signal or \(\delta=0\) if the rest IoT relay did not send jamming signal to the eavesdropper \(E\), and SINR threshold in expression (52) is with \(\gamma=2^{2\mathcal{R}}-1\) or \(\gamma=2^{4\mathcal{R}}-1\) for well known or unknown transmission script at eavesdropper \(E\), respectively._  
See Appendix C for proof.
## 3 MT Method  
### MT Method Theory  
The MT method is a passive geophysical exploration that uses the electromagnetic concept to measure the variation in the natural electric field \(\mathbf{E}\) and the magnetic field \(\mathbf{H}\) at the surface to provide information about resistivity at the Earth's subsurface. The theory of the MT method was first introduced by [PERSON] [16] and detailed by [PERSON] [15] and [PERSON] [38]. The penetration depth of the electromagnetic wave in the Earth's subsurface depends on the period of the electromagnetic sounding and conductivity of the Earth's structure, which has been known as skin depth \(\delta\) and is given in this Equation (1) [15]  
\[\delta(T)=503\sqrt{T\rho_{a}} \tag{1}\]  
with \(T\) as the period and \(\rho_{a}\) as the apparent resistivity obtained from the impedance tensor of MT data. The ratio of electric field and magnetic field is known as an impedance tensor \(\mathbf{Z}\), known as the transfer function of the MT, which is given by Equations (2) and (3) [38]  
\[\mathbf{E}=\mathbf{Z}\mathbf{H} \tag{2}\]  
\[\mathbf{Z}=\begin{bmatrix}Z_{\mathbf{xx}}&Z_{\mathbf{xy}}\\ Z_{\mathbf{yx}}&Z_{\mathbf{yy}}\end{bmatrix} \tag{3}\]  
The basic information about vertical variations in the conductivity is given by the off-diagonal components \(Z_{\mathbf{xy}}\) and \(Z_{\mathbf{yx}}\). While the diagonal components \(Z_{\mathbf{xx}}\) and \(Z_{\mathbf{yy}}\) of the MT impedance tensor indicate the asymmetry of the medium, and they become zero if the medium is symmetric about a vertical plane passing through the observation site [39]. MT data can be expressed as apparent resistivity \(\rho_{a}\) (Equation (4)) and phase \(\phi\) (Equation (5)) [15; 16; 38].  
\[\rho_{a}=\frac{1}{\mu\omega}|Z|^{2}=\frac{1}{\mu\omega}|Z_{yx}|^{2} \tag{4}\]  
Figure 2: Rock stratigraphy of the study area and surroundings [13; 31].  
\[\phi=\tan^{-1}\left(\frac{\text{Im}Z}{\text{Re}Z}\right) \tag{5}\]  
where \(\rho_{a}\) is the apparent resistivity (\(\Omega\)m), \(\mu\) is the permeability, \(\omega\) is the frequency, Z is the impedance, and \(\phi\) is the phase.
## 5 Conclusions and Future Work  
The truck unloading system is a complicated integration of components with different levels of reliability. This study assessed and evaluated the reliability of the truck unloading system, explored and identified the components that have the lowest reliability within the system. The mean time between failures, failure modes of each component, and reliability of the system are analyzed. In addition, this study established a preventive maintenance strategy for improving the reliability of the truck unloading systems. Since the current level of the reliability of the truck unloading system is not satisfactory, it can be greatly enhanced through focusing on a few critical components.  
Figure 5: Unloading system reliability before and after preventive maintenance.  
According to reliability analysis, the best schedule for preventive maintenance to maintain system reliability above 90% is 45 days. Moreover, the positive displacement pump and the positive displacement meter are the most critical items. Therefore, it is recommended to avail spare parts for these two components and conduct preventive maintenance on regular basis; preferably every one and a half months as found from the reliability analysis. In addition, it is highly recommended that the operators should report any unusual observations at the earliest to avoid any further damage or poor function. Furthermore, the results indicated that implementing preventive maintenance improves the system reliability by almost 80% in the first year, and this ratio increases radically as the period increases.  
Furthermore, the replacement of the positive displacement meter with a more advanced Coriolis meter should be considered. Comparatively, it has a lower failure rate due to the lower number of internal components and the simpler mechanism of its operation. Second, filtration components such as a strainer should be periodically inspected and cleaned to avoid plugging. Third, although valves have medium failure rates, it is important to keep spare parts for the frequently failing parts such as seals. Finally, further studies are highly recommended for design improvement of the truck unloading system to enhance the system reliability and having a longer uninterrupted safe operation.  
Conceptualization, A.M. and M.A.-S.; methodology, A.M. and M.A.-S.; software, A.G.; validation, A.G. and K.A.-O.; formal analysis, A.M., M.A.-S. and A.G.; investigation, A.M.; resources, A.G.; data curation, M.A.-S.; writing--original draft preparation, M.A.-S.; writing--review and editing, A.G. and A.M.; supervision, K.A.-O.; project administration, K.A.-O. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors would like to thank King Fahd University of Petroleum and Minerals for the support that contributed to carrying out this research. They also thank the anonymous reviewers for their insightful comments. The authors declare no conflict of interest.
# Practical Model for Metamaterials in Wireless Power Transfer Systems
## 1 Introduction
A numerical model is proposed in order to obtain the quality factor of the MTM slab. A practical circuit model of WPT systems with MTMs is established. The power efficiency is calculated by the resonance frequency and the quality factor of the MTM slab. An optimization algorithm is used to optimize transmission power efficiency. The proposed MTM-enhanced WPT system is validated via physical experimental studies. Unlike the calculation methods in high-frequency electromagnetics, the proposed model is more suitable for MTM-based low-frequency near field systems. The circuit model provides a more accurate and suitable way to predict the performance of WPT systems with MTM slabs.
# Structural Modifications in Epitaxial Graphene on SiC Following 10 keV Nitrogen Ion Implantation
## 2 Materials and Methods  
Substrates of 4H-SiC (0001) was used for the growth of EG. The graphene was grown inside an inductively heated furnace by thermal decomposition of SiC substate at 2000 \({}^{\circ}\)C having an argon pressure at 1 atm [25]. Nitrogen ion (N\({}^{+}\)) implantation was carried out at Inter University Accelerator Center (IUAC), New Delhi, India employing 30 kV tabletop accelerator. N\({}^{+}\) ions with 10 keV energy were used for ion implantation at varying fluences between \(1\times 10^{12}\) ions/cm\({}^{2}\) to \(1\times 10^{14}\) ions/cm\({}^{2}\). Stopping and range of ions in matter (SRIM) software was exploited for calculating various parameters like projected range of ions and different types of energy losses [26].  
The atomic force microscopy (AFM) by Veeco instruments with 3100 dimensions was done in the tapping mode on both as grown and ion-implanted samples. Raman-AFM WITEC model alpha 300 instrument with excitation laser of 532 nm was used for measuring Raman spectra. An axis Ultra DLD instrument having monochromatic Al (K\(\alpha\)) radiation (hv = 1486.6 eV) was used for doing the XPS measurements. The base pressure in analysis chamber was \(<\)1.5 \(\times\) 10\({}^{-7}\) Pa when the XPS spectra were recorded. The C 1s, Si 2p and O 1s core level spectra obtained from XPS further were deconvoluted using Casa XPS software package [27].
## 2 Materials and Methods
### Dynamic Time Warping Comparison  
In order to clearly compare the measurements between different shim-angle combinations, a dynamic time warping algorithm was applied to the data for Specim 1. The purpose of performing this dynamic time warping was to allow for clearer comparison of data during each trial and to assess the uncertainty that operational differences may have contributed to the data. The main purpose was to compare the measurements obtained in different trials (e.g., for different shim sizes) and standardize them by time, since variation was expected in how long it would take a surgeon to cycle the knee through flexion and extension. The algorithm implemented included the following steps:  
1. Data for time periods without movement (during which the knee was actively being flexed or extended) were detected and filtered out.  
Figure 6: Technique is shown for performing an adjustment cut to the manual left TKR set at 3 mm with a 2.5\({}^{\circ}\) inferiorly angled orientation guide. (**a**) Patellar clamp and custom adjustment cutting guide set. (**b**) Adjustment cut performed with patellar clamp and custom guide for a 3 mm 2.5\({}^{\circ}\) inferiorly angled cut in place. (**c**) Resected patella is shown after adjustment cut is completed.  
2. Troughs in the values obtained for the combined summed load sensors were detected using a peak detection algorithm with specified prominence.
3. The signal was segmented into single movement pieces, and labeled based on the different test conditions.
4. Dynamic time warping was implemented to match each segmented signal in time. In doing so, this reduced the need for the surgeon performing the trial to move the knee at exactly the same speed each time by dynamically stretching or compressing the signal, to achieve optimal Euclidean distance matching.
5. Time matched signals from each test were combined to provide an average and standard deviation for each point in the knee motion, including the maximal load obtained for each trial.
# A Survey on Change Detection and Time Series Analysis with Applications
## 2 Decomposition Methods into Frequency Domain
### Recent Methods of Mitigating Spectral Leakages in Spectrum
#### 2.3.3 AntiLeakage Least-Squares Spectral Analysis (ALLSSA)  
The ALLSSA is an iterative method based on LSSA that usually uses a preselected set of frequencies to accurately estimate the statistically significant spectral peaks ([PERSON], 2016). After simultaneously suppressing several significant spectral peaks with the highest percentage variance from the spectrum, ALLSSA revisits the previously estimated frequencies to estimate them more accurately in the next step, and so it can significantly reduce the computational cost while it estimates the signal frequencies and amplitudes more accurately than LSSA. Once the frequencies and amplitudes are estimated, they can be used to reconstruct the time series to any desired equally spaced series while attenuating noise down to a certain confidence level. A flowchart of ALLSSA is shown in Figure A4.  
Unlike IMAP, ALLSSA simultaneously considers the correlations among sinusoids of different frequencies and constituents of known forms, such as trends, as well as the covariance matrix associated with the time series if provided. The ALLSSA, in many practical applications, usually performs fast; however, its computational cost may increase for large size time series with many components.
## 3 Modeling and Methods  
Modeling was carried out in order to evaluate the developed model and analyze the effects of operating parameters to the produced braking torque. The braking torque in the ECB system was obtained from the interaction between the main magnetic field and the magnetic field generated by the eddy currents. The amount of braking torque depends on the magnitude of the main magnetic field that produces the eddy currents. The eddy currents further produce a second magnetic field that affects the braking force. The main factors that influence the performance are the magnetic field strength in the air gap and the amount of eddy currents in the inductor.  
The amount of braking torque can be calculated by using several approaches, including the eddy currents loss equation. The amount of braking torque is proportional to the magnitude of eddy currents losses that occurs in the inductor material. The determination of the amount of braking torque can also be applied by using the Lorentz force equation and the Maxwell stress equation.
# Practical Performance Assessment of Water Vapor Monitoring Using BDS PPP-B2b Service
## 3 Methodology
### PWV Conversion Based on Estimated ZTD  
To achieve the water vapor monitoring based on GNSS technique, the conversion of PWV from the estimated ZTD will be conducted after GNSS data processing. First, the ZHD is calculated through the Saastamoinen model and the in-situ pressure observations from the monitoring system, as follows:  
\[Z_{h}=\frac{2.279\pm 0.0024}{1-0.00266 cos(2\varphi)-0.00028h}\times P \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Title 1** & **Post-Processing Mode** & **NRT Processing Mode** \\ \hline Data length for processing & 24 h & 6 h window \\ Processing interval & 30 s & 30 s \\ Satellite ephemeris & WUM & WUM (for verification) \\  & & CAS or B2b (for evaluation) \\ \hline Estimator & Least-squares (LSQ) + backward smoothing \\ Tropospheric priori model & Saastamoinen + GPT3 \\ Mapping function & VMF3 & \\ Stochastic model for ZTD & Estimated as piece-wise constant every 3600 s with random walk process \\  & between pieces (20 mm/sqrt(h)) \\ Horizontal tropospheric & Estimated as constant \\ gradients & Tightly constrained to double-differenced solutions \\ Receiver clock offsets & Estimated as white noise \\ Ambiguity & Estimated as arc-constant \\ \hline \end{tabular}
\end{table}
Table 1: GNSS data processing strategies for the post-processing model and NRT processing mode.  
Figure 3: The general schematic diagram for the NRT processing mode.  
where \(Z_{h}\) represents the ZHD, \(\varphi\) and \(h\) are the latitude and height of GNSS antenna, and \(P\) is the pressure measurement in hPa unit. Afterwards, the ZWD can be obtained after removing ZHD from the estimated ZTD. Finally, PWV can be converted with ZWD (\(Z_{w}\)) and the conversion factor as follows:  
\[W_{g}=\Pi\times Z_{w}=\frac{10^{6}}{\rho_{w}R_{V}(\frac{k_{3}}{l_{m}}+k_{2}^{ 2})}\times Z_{w} \tag{2}\]  
where \(W_{g}\) represents PWV converted from ZWD (\(Z_{w}\)) and the conversion factor (\(I\)I). \(\rho_{w}\) and \(R_{V}\) are the density of liquid water and universal gas constant for wet air. \(k_{2}^{\prime}\) and \(k_{3}\) represent coefficients of atmospheric refractivity. \(T_{m}\) is the weighted mean temperature, which can be calculated through the air temperature measurement and linear coefficient from [PERSON] et al. (2014) [41].
# Biosynthesis of Phytocannabinoids and Structural Insights: A Review
## 4 Biosynthesis and Pathway of Phytocannabinoids
\begin{table}
\begin{tabular}{c c c c} \hline
**Biosynthesis** & **Enzyme** & **Resulting Product** & **References** \\ \hline Precursor supply & Olivetol synthase & & [41] \\ Type III PKS & Olivetolic acid cyclase & & OH & [42] \\  & & HO & OH & \\ Tetaketidase cyclase & Cannabigerolic acid synthase & & \\ C-Phenyltransferase & Cannabichromeic acid synthase & & \\  & & HO & [44] \\  & & HO & [45] \\  & & HO & [46] \\  & & HO & [47] \\  & & HO & [48] \\  & & HO & [49] \\  & & HO & [47] \\  & & HO & [48] \\  & & HO & [49] \\  & & HO & [48] \\  & & HO &A native prenyltransferase from _C. sativa_ L. that completes the step was identified as CsPT1 (or CsPT4), or geranylpyrophosphate: olivetolate geranyltransferase (GOT), in 1998. Geranylpyrophosphate (GPP) is assumed to be supplied through the 2-C-methyl-D-erythritol-4-phosphate (MEPs) pathways owing to cannabinoid synthases [47]. The pathway can be separated into three distinct functional activities, and the polyketide pathways can produce OLAs and isoprenoids and can produce cannabinoid acid (CBGAs), as the first cannabinoids in collaboration with the prenyltransferase [48] (Figure 2). Recent work has focused on capitate stalked glandular (CSG) trichomes, which consist of two parts--a nearly spherical resin head (gland head) atop a multicellular stalk. The resin head incorporates a rosette of secretory disk cells at its base, covered by a thin, distensible sheath or cuticle. Cannabinoids and terpenoids accumulate in a secretory cavity between the disk cells and the cuticle [49]. Disk cells also secrete biosynthetic enzymes, such as THCA synthase, into the secretory cavity [46]. The pathway, with key chemical structures, is illustrated in Figure 2. See the chapter by [PERSON] et al. [49] for an elaboration. Cannabinoid content differs in terms of quantity and quality. Quantity and quality have different modes of inheritance [15]. Cannabinoid quantity (dry weight percentage) is polygenic and influenced by environmental factors. This is achieved through differential cyclization of the C10 moiety from GPP via the actions of independent synthases [49]. The discussion on synthetic biology approaches will be organized according to these individual enzymatic steps in the pathways, along with structural and mechanistic insights, to facilitate the subsequent framing of strategies for producing novel cannabinoids [50]. Figure 2: Cannabinoid biosynthetic pathway, leading to the two major phytocannabinoids, THC and CBD.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 2 Methods
### Adversarial Attacks  
The idea of using adversarial samples to cause the misclassification of a DL model was first explored by [PERSON] et al. [22]. There are two kinds of adversarial samples that can be crafted. Given a dataset \((\mathbf{x},y)\) where \(\mathbf{x}=(x_{1},x_{2},...,x_{n})\) is a normal sample and \(y=f(\mathbf{x})\) is the corresponding label of the sample, an untargeted adversarial sample \(\mathbf{x^{\prime}}\) is crafted to make \(f(\{x\
eq y\) yet \(\mathbf{x}\) and \(\mathbf{x^{\prime}}\) are close according to certain metric. Another more powerful but harder attack uses targeted adversarial samples. Given a normal sample \(\mathbf{x}\) and a target label \(y^{\prime}\
eq f(\mathbf{x})\), the attacking algorithm searches for an adversarial sample \(\mathbf{x^{\prime}}\) such that \(f(\mathbf{x^{\prime}})=y^{\prime}\) where \(\mathbf{x}\) and \(\mathbf{x^{\prime}}\) are close. To measure the similarity between \(\mathbf{x}\) and \(\mathbf{x^{\prime}}\), \(L_{0},L_{2},L_{\infty}\) are the three most widely used distance metrics among all \(L_{p}\)-norms for generating adversarial samples [38]. The \(L_{p}\) distance is also written as \(||\mathbf{x}-\mathbf{x^{\prime}}||_{p}\), where \(||\cdot||_{p}\) of \(\mathbf{x}\) is defined as  
\[||\mathbf{x}||_{p}=(|x_{1}|^{p})+|x_{2}|^{p}+\cdots+|x_{n}|^{p})^{1/p} \tag{1}\]  
For the three distance metrics, \(L_{0}\) norm measures the number of points that differ between \(\mathbf{x}\) and \(\mathbf{x^{\prime}}\), \(L_{2}\) norm is the standard Euclidean distance, and \(L_{\infty}\) norm measures the maximum change to any of the points. Although all three distance metrics approximate to the human perceptual similarity, \(L_{\infty}\), also known as max-norm, is the most commonly used one due to its better consistency to human perception [39].  
There are three types of adversarial attacks: white-box, gray-box, and black-box attacks. In this work, we focus on untargeted white-box attacks under the \(L_{\infty}\) norm distance metric. White-box attacks require attackers have the highest-level knowledge of the model among the three types of attacks, which then have a greater impact on the performance of DL-based models than other two types of attacks. Since many DL-based plant disease identification schemes are built based on pre-trained DNN models, adversarial samples generated in the white-box manner against a fine-tuned model based on a pre-trained DNN model can be successfully transferred to the target model [24]. In the following, we describe four popular white-box attacks considered in this study: fast gradient sign method (FGSM) [40], basic iterate method (BIM) [41], projected gradient descent (PGD) [39], and Carlini and Wagner attack (CW) [38].
## 4 Discussion
### ROC Curves and Clinical Implications  
The ROC curves (Figure 4) complement the confusion matrices shown in Figure 2, which compare classification results with and without SMOTE optimization. While confusion matrices show performance at a specific threshold, ROC curves evaluate performance across all possible thresholds. The improved detection of abnormal cases after SMOTE optimization, as evidenced by the reduced \"false-normal\" rate from 7.34% to 5.39%, is reflected in the high AUC values. This demonstrates how the model was calibrated to prioritize sensitivity in detecting pathological conditions.  
For clinicians interpreting OCT images, these ROC curves demonstrate that the developed ML models provide highly reliable classification with minimal risk of missing pathological cases. The AUC of 0.98-0.99 suggests that if presented with one normal and one abnormal OCT image, the model would correctly rank the abnormal image with higher probability approximately 98-99% of the time. This clinical model optimization approach, detailed in Equation (12), further underscores how overall discriminative performance (i.e., AUC) is balanced with the specific clinical need to minimize false negatives in retinal disease detection.