esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CCD Assault Detection Using Euclidean Distance-Based Anomaly Detection Schemes
### Euclidean Distance-Based Anomaly Detection Scheme 2  
Using the Euclidean distance-based method, we can detect a CCD assault with improved accuracy when the labels (normal versus compromised) are given in the SE-MF dataset. In this subsection, we explain the second proposed scheme, EDADS-2, shown in Figure 4. In the beginning, the GA is applied to select the optimal features subset from the SE-MF historical dataset. The resulting optimal features data subset consisting of compromised and normal data is divided into the training dataset, \(X_{R}=\Big{\{}X_{R1}^{(n)},X_{R2}^{(n)},...,X_{Ra}^{(n)}\Big{\}}\), where \(X_{Rp}^{(n)}\in X,\forall p\in\{1,2,3,...,a\}\) ; and the testing dataset, \(X_{T}=\Big{\{}X_{T1}^{(n)},X_{T2}^{(n)},...,X_{Tb}^{(n)}\Big{\}}\), where \(X_{Tq}^{(n)}\in X,\forall q\in\{1,2,3,...,b\}\). The labels are used to select the normal set, \(X_{RN}=\Big{\{}X_{RN1}^{(n)},X_{RN2}^{(n)},...,X_{RNf}^{(n)}\Big{\}}\), where \(X_{RNs}\in X_{R},\forall s\in\{1,2,3,...,f\}\), and \(f\) is the number of training samples designated as normal. In Step 1, the centroid of the training data subset designated as normal, with the help of labels, is calculated to form vector \(C_{d}=\{\mu_{1},\mu_{2},...,\mu_{n}\}\), where \(\mu_{r}=\sum\limits_{i=1}^{\int\limits_{x_{RNi}^{(i)}}}\), \(\forall r\in\{1,2,...,n\}\). It is pertinent to mention here that for EDADS-2, the centroid is calculated taking into account the normal training samples only. On the other hand, for the proposed EDADS-1, the centroid is calculated including both normal and compromised samples. In Step 2, the Euclidean distance between each sample and the centroid is calculated to form distance vector \(D_{R}=\Big{\{}D_{RN1},D_{RN2},...,D_{RNf}\Big{\}}\) where \(D_{RNs}=\sqrt{\sum\limits_{i=1}^{n}\Big{\{}\big{(}\mu_{i}-X^{(i)}{}_{RNs}\big{)} ^{2}\Big{\}}},\forall s\in\{1,2,...,f\}\). In Step 3, the elements of distance vector \(D_{R}\), are sorted into descending vector \(S_{DRN1}=\Big{\{}S_{DRN1},S_{DRN2},...,S_{DRNf}\Big{\}},S_{DRN}\subseteq D_{RN}\) to choose the first 10% of its elements, which are at the farthest distance from the centroid, to make the vector \(\mu_{DRN}=\Big{\{}S_{DRN1},S_{DRN2},...,S_{DRN(0.1\times f)}\Big{\}}\). Employing vector \(\mu_{DRN}\), average distance \(D_{R,avg}=\sum\limits_{i=1}^{01\times f}\frac{S_{DRNi}}{01\times f}\) of each sample from the centroid is calculated. Finally, when a new test sample arrives, it is identified as being normal (if \(D_{Tq}\leq D_{R,avg}\)) or an outlier (if \(D_{Tq}>D_{R,avg}\)). EDADS-2 also has low computational complexity and may identify a new test sample as an outlier or normal in a very small amount time because it only has to calculate the distance of one sample to compare it with the average distance.  
Figure 3: Flowchart of the Euclidean distance-based anomaly detection Scheme 1, EDADS-1.
## 4 The Impacts of Microalgae/Cyanobacteria and Their Extracts on Crops' Production
### Soil Improvement
#### 4.1.1 Nitrogen Fixation  
Blue-green algae (or cyanobacteria) are able to fix atmospheric nitrogen (N\({}_{2}\)), converting it into organic nitrogen forms, which are more easily assimilated by higher plants. Therefore, the use of cyanobacteria in soils increase the availability of nitrogen, which is one of the essential nutrients for plants' growth [6, 8, 96, 97]. The beneficial effects of cyano-bacteria in agricultural soils (for the production of different crops) has already been demonstrated [90]. For example, using a cyanobacterial inoculum in a rice paddle, [PERSON] and [PERSON] [80] concluded that this inoculum improved the grain and straw yields and increased the availability of nitrogen in the soil. Similarly, using _Anabena variabilis_ strains in a rice field, [PERSON] and [PERSON] [81] demonstrated that the used strains promoted an overall improvement in the rice crops (e.g., increase in plant height and leaf length, and improvement of seeds, grain and straw productivities). When applying _Nostoc_ sp. vegetative cells to a rice field, [PERSON], et al. [82] obtained an increase in grain yields comparable to the one obtained with a chemically-based nitrogen fertiliser. Although the majority of the studies refer to the use of cyanobacteria in rice fields, recent studies have also referred the positive effect of cyanobacteria in other cultures. [PERSON], et al. [83] inoculated the soil intended for pea plant production with the cyanobacterial species _Nostoc entophyllum_ and _Oscillatoria angustissima_, observing a significant increase in the germination percentage, photosynthetic pigments and growth parameters of this plant. In addition, the authors observed that this increase was associated with a higher N\({}_{2}\) fixation activity by both cyanobacteria. In the study performed by [PERSON], et al. [84], the use of a biofilm composed by the cyanobacterium _Anabena torulosa_ in a wheat crop significantly increased the availability of nitrogen in the soil. [PERSON], et al. [85] evaluated the potential of several cyanobacterial-bacterial consortia as fertilisers for soybean and mungbean production, presenting positive results in terms of nitrogen availability in the soil and plant fresh weight. Considering the improvements observed in terms of nitrogen availability, it is possible to conclude that cyanobacteria could be used in replacement of chemical nitrogen fertilisers, which can reduce crops' production costs by 25-40%. The use of these microorganisms also promotes nitrogen recycling, avoiding the continuous production of chemically-based nitrogen fertilisers. Finally, cyanobacteria are known for their ability to form soil crusts that avoid nitrogen leaching/runoff and, therefore, the risks of water contamination with nitrogen [96].
# On Transform Domain Communication Systems under Spectrum Sensing Mismatch: A Deterministic Analysis
## 3 Spectrum Sensing Mismatch
### Deterministic Analysis of Performance in AWGN Channels
Based on this, the bit error probability for the TDCS using the CCSK method is given using the well-accepted BER expression for \(M\)-ary orthogonal signaling [16]:  
\[\left(P_{b}\right)=\frac{2^{\log_{2}\left(M\right)-1}}{2^{\log_{2}\left(M \right)}-1}\cdot\frac{1}{\sqrt{2}}\cdot\int\limits_{-\infty}^{+\infty}\left[1- \left(\frac{1}{\sqrt{2\pi}}Q(y)\right)^{M}\right]\cdot\exp\left[-\frac{1}{2} \left(y-\sqrt{\left(1-\alpha\right)\left(2\frac{\varepsilon_{S}}{N_{0}} \right)}\right)^{2}\right]dy \tag{20}\]  
where \(Q(y)=\left(1/\sqrt{2\pi}\right)\int_{y}^{\infty}\exp\left(-u^{2}/2\right)du\) denotes the error function. Obviously a larger value of \(\alpha\) indicates a worse spectrum match pattern, leading to an inferior BER performance. For the QPSK-based TDCS model, the transmitted signal remains the same as in (5), except that \(k\in\Omega^{Tx}\) and \(\lambda=\sqrt{N/N_{Tx}}\). In the receiver, after multiply with a receiver reference waveform, the frequency domain signal, i.e., \(\mathbf{R}_{2}=\mathbf{R}_{1}\cdot\left(\mathbf{D}\right)^{*}=\left\{R_{2}(0),R _{2}(1),...,R_{2}(N-1)\right\}\), is:  
\[R_{2}(k)=\overbrace{R_{QPSK}(k)}^{signal}+\overbrace{n_{2}(k)}^{noise} \tag{21}\]  
where \(R_{QPSK}(k)=\lambda e^{i\varphi}\) is valid only when \(k\in\Omega^{C}\), and \(n_{2}(k)\) is of the same expression as in [16]. After de-spreading, the estimated QPSK symbol is given by:  
\[\hat{S}_{Q}=\frac{1}{N}\sum_{k=0}^{N-1}R_{2}(k)=\frac{N_{C}}{N}\lambda e^{i \varphi}+n_{3} \tag{22}\]  
\[(SNR)^{(QPSK)}=\frac{\left(\frac{N_{C}}{N}\lambda e^{i\varphi}\right)^{2}}{ \frac{N_{Rx}}{N^{2}}N_{0}}=(1-\alpha)\underbrace{\left(\frac{\varepsilon_{S}}{ N_{0}}\right)}_{\text{d}traditional} \tag{23}\]  
Similarly, \(\alpha=\left(1-(N_{C})^{2}/N_{Tx}N_{Rx}\right)\) remains the same as in (22) despite a different modulation method is employed. Following the typical SNR-BER expression for QPSK [17], the related bit error performance is:  
\[(P_{b})^{(QPSK)}=2Q\Bigg{(}\sqrt{\frac{2\varepsilon_{b}}{N_{0}}}\Bigg{)}\left[ 1-\frac{1}{2}Q\Bigg{(}\sqrt{\frac{2\varepsilon_{b}}{N_{0}}}\Bigg{)}\right] \tag{24}\]  
where \(\varepsilon_{b}=(1/2)\varepsilon_{S}\) denotes average bit power for QPSK signaling.
## 3 Results and Discussion
### Establishment of a Novel Multiplex PCR Method  
A novel multiplex PCR method was developed to detect seven LM canola events, including Topas 19/2, Rf3, Dp-073496-4, Ms8, GT73, Mon88032, and T45. To increase the primer efficiency and accuracy, they were all designed to be contained within the canola genome region and transgenic region. For visible analysis on the agarose gel, primer pairs of various sizes in the range of 95-550 bp were selected. The PCR product sizes for the seven LM canola events were expected to be 95 bp (Topas 19/2), 139 bp (Rf3), 219 bp (Dp-73496-4), 249 bp (Ms8), 317 bp (GT73), 407 bp (Mon88032), and 550 bp (T45) (Table 1).  
To develop a novel multiplex PCR method to detect the LM canola events, primer pairs of various sizes for each event were designed and tested to confirm the efficiency and accuracy of the PCR analysis. The specificity of the primer pairs was confirmed using one event genomic DNA from each CRM (seven events and one non-LM). The agarose gel image of the results showed a high efficiency for the primer pairs, as they detected only a single band for each event. All the bands for the PCR products were confirmed to be their expected sizes (data not shown). These results indicated that each of the seven designed primer pairs amplified only the desired event. Although the novel primer pairs were confirmed to be able to decipher one event from another using genomic DNA, many stacked LM products, including those with more than one gene, were imported into South Korea. To detect multiple genes simultaneously in the seven LM canola, various PCR parameters, such as primer concentration and Tm value for multiplex PCR analysis, were tested (Supplementary Figure S1). Conclusively, the touchdown PCR method was chosen to increase the efficiency and visibility of the PCR amplification (Figure 3A). The newly developed multiplex PCR methods were confirmed using a mixture of seven primer pairs. The resulting agarose gel image showed a high efficiency by representing only a single band for each event and multiple bands in the DNA mixture (Figure 3B). The developed multiplex PCR method was, thus, determined to be suitable for the quantitative analysis of the LM canola events.  
Figure 3: Establishment of a novel multiplex PCR for seven LM canola events. **(A)** Schematic diagram of the multiplex PCR conditions. **(B)** Agarose gel image of the products from the multiplex PCR method for the LM canola, in which each line indicates a non-LM PCR product (line 1), Topas 19/2 (line 2), Rf3 (line 3), Dp-073496-4 (line 4), Ms8 (line 5), GT73 (line 6), Mon88032 (line 7), T45 (line 8), and the multiplex PCR product (line 9).
# Comparative Analysis of Skeleton-Based Human Pose Estimation
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Domain** & **HPE Library** & **Year** & **Purpose of Application** \\ \hline Video & OpenPose [6] & 2018 & Kidnapping detection–using HPE to classify kidnapping cases and normal cases in an intelligent video surveillance system. \\ Surveillance & OpenPose [5] & 2019 & A child abuse prevention decision-support system–using OpenPose to classify adults and children in CCTV. \\ \hline \multirow{6}{*}{Medical Assistance} & OpenPose [15] & 2020 & A fall detection system–using OpenPose to extract features of human body. \\  & PoseNet [8] & 2020 & Automatic feedback on incorrect posture for physiotherapy exercises. \\  & PoseNet [10] & 2021 & A telehealth system providing in-home rehabilitation. \\  & \multirow{2}{*}{OpenPose [11]} & \multirow{2}{*}{2021} & Measure joint angles and conduct semi-automatic ergonomic postural assessments to evaluate the risk of musculoskeletal disorders. \\  & & & A healthcare system that measures patient’s strength, balance, and range of motion during physical therapy activities. \\  & & MediaPipe Pose [12] & 2022 & A fall detection system. \\  & & & A posture corrector system–to notify people who are spending most of their time sitting in front of the computer with bad posture to avoid long-term health issues. \\ \hline \multirow{6}{*}{Sport Motion Analysis} & OpenPose [28] & 2018 & A basketball free-throw shooting prediction system–using OpenPose to generate body keypoints. \\  & OpenPose [21] & 2020 & A real-time push-up counter–to classify the correct and incorrect push-ups. \\  & \multirow{2}{*}{OpenPose [20]} & \multirow{2}{*}{2021} & A system to evaluate baseball swinging poses and help baseball players \\  & & & correct their poses. \\  & & & A mobile application–to analyze, improve, and track cricket players’ \\  & \multirow{2}{*}{PoseNet [25]} & \multirow{2}{*}{2021} & A real-time workout analyzer–allows fitness enthusiast to perform their workout accurately at home and with proper guidance. \\  & & & A fitness tutor–to maintain the correctness of the posture during workout exercises. \\  & \multirow{2}{*}{PoseNet [26]} & \multirow{2}{*}{2021} & A fitness application–provides instant feedback to users to ensure the accuracy of their workout exercise poses. \\  & & & \\ \multirow{2}{*}{MediaPipe Pose [22]} & \multirow{2}{*}{2022} & To score the human body’s balance ability on the wobble board. \\  & & & A free weight exercise tracking software–allows users to learn and correct their exercise poses. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applications of PoseNet, MoveNet, OpenPose, and MediaPipe Pose in different domains. experimental results, in terms of image and video datasets. Section 5 analyzes the strengths and weaknesses of each HPE library. The last section presents the conclusion of the study.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 4 Health Monitoring Techniques for Wind Farms
### Issues of Concern and their Mitigation in Wind Turbines  
Since the wind turbine blades are directly exposed to the harsh environment, they are vulnerable to failures. In these environments, wind loads vary constantly, and undergo cyclic fatigue loads under their own weight. Erosion and corrosion experienced by the wind turbines are the results of extreme temperature and humidity changes. Blade failures could also lead to a major economic loss. For example, \(\xi\)1.25 million was exhausted due to the significant downtime and repair costs attributed to the failure of blades in an onshore wind turbine at Dunbar, Scotland in 2005 [193]. The major modes of failure of wind turbine blades include failure of adhesive joints and load-carrying laminates being delaminated. Several non-destructive testing techniques are used to detect these failures. When triggered by the external forces, the vibrational analysis evaluates the dynamic response of blades and hence investigates their health condition [186]. If multiple vibration transducers are used in sequence, that can also be used to detect the damage locations, which is attributed to the reason that fiberglass composite material is used to manufacture the blades of the wind turbine [194]. For the purpose of commercial testing of WT blades, strain gauges are popularly used, which are made up of piezoelectric ceramic materials [195]. These gauges are attached to the surface of the blades for measuring the localized strain arising due to bending and stretching loads [194]. Another technique to monitor structural health monitoring is acoustic emission. When a crack is originated and propagates in the blade, acoustic emission perceives the energy of the elastic waves, which helps in its identification [196]. Other SHM techniques include TFRF, which is developed recently and is trustworthy in both faults as well as damage detection. Along the span direction of the blade, TFRF-based blade SHM measures the dynamic response of the blades. To fulfill this measurement, a distributed strain sensor and high-resolution stereo imaging camera are the two prospective tools, but they require more research for being applicable in the future [194] (Figure 16).
# Method of Suppressing Rayleigh Waves Based on the Technology of Time-Domain Differential Detection
## 2 Differential Detection Method for Suppressing Rayleigh Waves at Common Shot Points
### Analysis of the Impact of Operating Boundaries on Time-Domain Differential Technology
#### 2.2.1 Shear Wave Reflections from Lateral Interfaces Parallel to the Survey Line
Hammer excitation is commonly used in seismic exploration engineering, featuring effective frequency ranges from several hertz to hundreds. This range differs from that of ideal wavelet numerical simulation signals. In practical applications, longitudinal, shear, and Rayleigh wave tails persist for multiple effective periods. However, differential processing generally experiences minimal interference when suppressing Rayleigh waves, thus maintaining its effectiveness. Consequently, reflected longitudinal waves from deeper interfaces can be selectively preserved after differential processing. The degree of preservation is limited by the differential time and is indirectly associated with the influential frequency bands and propagation speeds of both Rayleigh and reflected longitudinal waves and the distance between detectors.
## 4 Experimentation and Results
### Proposed CNN Architecture  
When considering the architecture that was studied, a model with 350 nodes in the fully connected 1 with patch sizes of (36, 48, and 48) achieved maximum sensitivity for the dataset. The proposed CNN architecture for efficient lung nodule classification is presented in Table 1.  
A crucial stage in lowering false positives is the comprehensive training phase. Due to this, the training for this study was performed iteratively on a range of unique subsets of the training dataset. These subsets were built utilizing a healthy mix of false positive cases and genuine positive cases that were not identified during the algorithm evaluation. The false positive cases were manually separated from the other examples for the next training iteration once each training session was complete. Consequently, training in the recommended CNN architecture was required to reduce false positives. The training algorithm for false positive reduction is presented in Figure 8.
## 5 Experimental Results  
### Experimental Apparatus and Procedure  
To verify the accuracy of the theoretical analysis, the experimental setup shown in Figure 9 was created. There is a spring with a spring constant of 1.43 N/mm and an original length of 0.2 m above the holder. A pressure force is supplied to the holder by rotating the screw to change the length of the spring. This is equivalent to determining the value of \(h_{d}\). The entire system can move liberally within the stroke of the linear bearing. The wiper blade is clamped by the holder and driven by the actuator to perform a reciprocating motion on the wiping surface. Before each experiment, water was sprayed on the wiping surface.  
An enlarged view of the wiper blade is shown in Figure 10. There are three white dots marked on the neck, the center of rotation of the second link, and the tip of the wiper blade.  
The motion of the wiper blade was filmed by a high-speed video camera (FASTCAM-APX RS 250 K, Photron Inc., San Diego, CA, USA) with a frame rate of 3000 fps at a 1024 \(\times\) 512 pixel resolution on a screen measuring 3.165 \(\times\) 10\({}^{-2}\) m \(\times\) 1.583 \(\times\) 10\({}^{-2}\) m. Thus, each pixel had dimensions of 3.091 \(\times\) 10\({}^{-5}\) m. We conducted two experiments for attack angles of 0, 10, and 18 degrees. In the first experiment, the camera recorded the location at which the jump phenomenon occurs during the wiping process. In the second experiment, the filming position was located around the reversal point of the wiper blade.
## 2 Preliminaries and Assumptions  
### Terminology  
**Definition 1** (Sensing Range).: _The sensing range of a sensor \(s_{i}\) is the area where any occurring event inside that area is sensed by \(s_{i}\)._  
**Definition 2** (Communication Range).: _The communication range of a sensor \(s_{i}\) is the area where \(s_{i}\) communicates with any other sensor \(s_{j}\) that is within the area._  
**Definition 3** (Coverage).: _Coverage involves the placement and coordination of sensor nodes to ensure that the target area or points of interest are accurately monitored. The goal is to maximize the sensing capabilities of the field by considering factors such as sensing range, communication range, energy efficiency, and redundancy._  
**Definition 4** (\(k\)-Coverage).: _A point in a field of interest is said to be \(k\)-covered if every point in this field is covered by at least \(k\) sensors. \(k\) is called the degree of coverage._  
**Definition 5** (Nature-inspired Algorithms).: _Nature-inspired algorithms are optimization algorithms that mimic nature processes to find solutions to complex solutions like the traveling salesman problem (TSP). Some examples of nature-inspired algorithms include particle swarm optimization (PSO), genetic algorithm (GA), and lion optimization (LO)._
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 6 Extension to Two- and Three-Dimensional Lattice Models
### Analysis of Circuit Width for Different Designs  
In required qubits terms, all three designs need at least the following qubits to store input and output data for \(N_{M}=4\) and \(N_{E}=3\):  
\[|eu2|eu1|eu0\rangle : N_{E}\text{-qbit representation of the exponent of }u\] \[|mu2|mu1|mu0\rangle : (N_{M}-1)\text{-qbit representation of the mantissa of }u\] \[|ev2|ev1|ev0\rangle : N_{E}\text{-qbit representation of the exponent of }v\] \[|mv2|mv1|mv0\rangle : (N_{M}-1)\text{-qbit representation of the mantissa of }v\] \[|ek2|ek1|ek0\rangle : N_{E}\text{-qbit representation of the exponent of kinetic energy}\] \[|mk2|mk1|mk0\rangle : (N_{M}-1)\text{-qbit representation of the mantissa of kinetic energy}\]  
This means that, more generally, \(3N_{E}+3(N_{M}-1)\) qubits represent the absolute minimum circuit width. In the following, the number of additional qubits required for the different designs is detailed.  
Figure 8: Quantum circuit design _Ekin2D1_ for the computation of \(u^{2}+v^{2}\). The floating-point representation uses four mantissa qubits (\(N_{M}=4\)) and three exponent qubits (\(N_{E}=3\)).