esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CNV Screening on OCTA Images  
Our goal is to discriminate from OCTA images the three predefined classes: No AMD (healthy CC), advanced wet AMD (CNV) and intermediate AMD (drusen or CC impairment) (see Figure 1). OCTA imaging allows physicians to visualize blood vessels in the individual layers of the retina and choroid without dye injection. Thus, CNV, drusen and impairment within the CC (pigmentary changes) can be clearly identified on OCTA images. The healthy choriocapillaris appears on OCTA images as a grainy texture with bright and dark spots corresponding to blood flow and flow deficits, respectively [7]. Drusen and CC impairment are characterized by black nonflow areas of different sizes related to flow deficits that can appear anywhere on the OCTA image surrounded by the grainy texture of the choriocapillaris. Regarding CNV, neovascular membranes harbor the aspect of a vascular branching, surrounded by the grainy texture of the choriocapillaris.  
Nevertheless, these images are corrupted by speckle noise due to the physical principles of OCT, in addition to the image acquisition process and artifacts [24]. Moreover, included CNV lesions could have different sizes and locations with irregular shapes of neovascular membranes, thus small ones may be confused with the grainy texture of the choriocapillaris. This makes CNV detection on OCTA images a very challenging task. Figure 1 illustrates the OCTA choriocapillaris images of the three classes used for the classification in this work.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
## 2 System Model
Without loss of generality, we rearrange the all channel coefficients and the sorted channel coefficients are given as follows. \[h_{1}\leqslant h_{2}\leqslant\cdots\leqslant h_{i}\leqslant\cdots\leqslant h _{N} \tag{4}\]
# Fluid Net Models: From Behavioral Properties to Structural Objects
## 3 Bifurcations: Operation Mode Properties
### Bifurcations in a Manufacturing TCPN Model
In this case, \(\lambda_{2}\in(0,10]\). The starting roots (when \(\lambda_{2}=0\)) are represented by symbol '\(X\)', while the endings (when \(\lambda_{2}=10\)) are represented by symbol '\(O\)'. If we increase \(\lambda_{2}\) indefinitely, that is \(\lambda_{2}\to\infty\), one of the eigenvalues will increase (\(s\to-\infty\)) indefinitely, and the other two will tend to \(s=-1\) (this is obtained dividing \(s^{3}+(\lambda_{2}+3)s^{2}+(2\lambda_{2}+2)s+\lambda_{2}-1=0\) by \(\lambda_{2}\) and obtaining the limit when \(\lambda_{2}\to\infty\)). Notice that:  
* If \(\lambda_{2}<1\), the equilibrium marking is hyperbolic (eigenvalues are not on the imaginary axis), and there exists one eigenvalue of \(\mathbf{A_{C}}\) located in the right part of the complex plane, that is, the equilibrium marking of the reduced system is unstable within that region. The marking of the T-coverture increases until it reaches another region, activating another configuration. * If \(\lambda_{2}=1\), then the previous eigenvalue on the right part of the complex plane is relocated to the imaginary axis of the complex plane (a new zero eigenvalue); thus, the reduced system is non-hyperbolic and, in this case, critically stable. * If \(\lambda_{2}>1\), then the eigenvalue goes to the left part of complex plane, and thus, the equilibrium marking becomes hyperbolic and stable, and the marking of the reduced system evolves until it reaches a deadlock. Figure 6: Location of three of the eigenvalues of matrix \(\mathbf{A_{C}}\) on the complex plane when \(\lambda_{2}\) is varied from zero to ten. When \(\lambda_{2}\) is close to zero, the eigenvalues are located on the ‘\(X\)’ symbol. By increasing \(\lambda_{2}\), the eigenvalues move through the blue line until they reach the ‘\(O\)’ symbol, where the value of \(\lambda_{2}\) is ten. Consequently, a _bifurcation_ occurs for \(\lambda_{2}=1\). According to [12], this marking bifurcation explains the discontinuity of the equilibrium throughput with respect to the firing rate \(t_{2}\), which can be seen in Figure 2b.
## 2 Vector-Tracking Loops
Code discriminator \[Y_{R,k}=\left(IE_{k}^{2}+QE_{k}^{2}\right)-\left(IL_{k}^{2}+QL_{k}^{2}\right)\] (6) where \(Y_{R,k}\) is the code discriminator output, which has been converted to a pseudo-range residual \(\widetilde{Y}_{R,k}\) (unit: m): \[\widetilde{Y}_{R,k}=Y_{R,k}\beta/2A_{F,k}^{2}=\rho_{e,k}+v_{R,k}\beta/2A_{F,k}^ {2}\] (7) where \(\rho_{e,k}\) is ideal pseudorange residual, \(\beta\) is the chip length (unit: m), \(A_{F,k}\) is the signal amplitude, and \(v_{R,k}\) is the discriminator output noise. Let \(\widetilde{v}_{R,k}=v_{R,k}\beta/2A_{F,k}^{2}\). The variance is:  
\[\sigma^{2}\Big{(}\widetilde{v}_{R,k}\Big{)}=\frac{\beta^{2}}{2(Tc/n_{o})^{2}} +\frac{\beta^{2}}{Tc/n_{o}}\bigg{(}\frac{\rho_{e}^{2}}{\beta^{2}}+\frac{1}{4} \bigg{)} \tag{8}\]  
where \(T\) is the predetection integration time and \(c/n_{o}\) is the carrier-to-noise ratio in Hz. 2. Carrier frequency discriminator \[ \begin{array}{l}Y_{RR,k}=IP_{k,2}QP_{k,1}-IP_{k,1}QP_{k,2}\\ \approx-A_{F,k}^{2}R^{2}(\varepsilon_{k})\Big{(}\pi f_{err,k}T\Big{)}/4+v_{ RR,k}\end{array} \] (9) where \(\varepsilon_{k}\) denotes the code phase error, \(R(\varepsilon_{k})\) is the C/A code correlation function, \(f_{err,k}\) is the frequency error, and \(v_{RR,k}\) is the noise term. The discriminator output is converted to the pseudo-range rate residual \(\widetilde{Y}_{RR,k}\) (unit: m/s):  
\[\widetilde{Y}_{RR,k}=\dot{\rho}_{err,k}+\widetilde{v}_{RR,k} \tag{10}\]  
In Equation (10), \(\widetilde{v}_{RR,k}\) denotes the pseudorange rate residual measurement noise. Its detailed formula and variance are as follows:  
\[\widetilde{v}_{RR,k}=-\frac{4}{A_{F,k}^{2}R^{2}(\varepsilon_{k})\pi T}\cdot \frac{c}{f_{L1}}\cdot v_{RR,k} \tag{11}\]  
\[\sigma^{2}\Big{(}\widetilde{v}_{RR,k}\Big{)}=\frac{2+2R^{2}(\varepsilon_{k})( Tc/n_{o})}{R^{4}(\varepsilon_{k})(Tc/n_{o})^{2}}\bigg{(}\frac{c}{\pi Tf_{L1}} \bigg{)}^{2} \tag{12}\]  
where \(f_{L1}\) is the Global Positioning System (GPS) L1 frequency and \(c\) is the speed of light.
# Improved Ensemble-Learning Algorithm for Predictive Maintenance in the Manufacturing Process
## 2 Current Trend of IIoT-Based PdM  
According to the latest forecast by IDC [5], the proportion of IIoT-related applications in the IoT market will be 18.3% in 2023. IIoT integrates a sensing element, hardware, software, and firmware to improve industrial management and manufacturing processes [6]. Such technologies include a cyber network, data connection, data storage and application, and cloud platform. IIoT could overgrow; for example, IndustryARC(r) reported that the IIoT market could reach USD 123.89 billion by 2021 [7]. In 2023, the IIoT market size could reach USD 310 billion [8]. Worldwide spending on software and hardware in IIoT could reach USD 982 billion in 2024 [9] and USD 949 billion by 2025 [10]. Thus, using IIoT technologies to develop PdM is the current trend. An IIoT vendor has advanced technologies with advantages of scalability, effective data management, and a real-time monitor module. Therefore, IIoT vendors, such as PTC Inc. (Boston, MA, USA), GE Digital, SAP Business Objects (Paris, France), Microsoft (San Francisco, CA, USA), and Oracle (Austin, TX, USA), provide cloud-based maintenance applications in the PdM market.  
Furthermore, integrating IIoT technologies into PdM-related technologies can effectively increase automated equipment maintenance processes and gain enterprise profits. The combination of IIOT-based PdM technologies is called \"IIoT-based PdM\". Figure 3 shows the global PdM-related market sales forecasted by consulting firms. For example, Statista(r) predicted that PdM-related sales would reach USD 9.1 billion in 2021 [11]. According to a report by Consultancy.org(r), the global market sales of PdM-related technologies were USD 6 billion in 2020 [12]. PdM-related technology sales could reach USD 11 billion in 2022, increasing by 83% in two years (2020-2022). According to a report by Consultancy.org(r), IoT analytics company reports have a similar prediction trend, i.e., the increase rate from 2020-2024 is up to 83% [12; 13]. A report published by Marketsand-Markets(tm) Comany researches that PdM-related applications will increase and that the PdM market size could reach USD 10.7 billion in 2024 [14]. Investment is increasing in PdM-related applications, IoT-related applications, artificial intelligence techniques, and ML technologies [14]. Most IoT vendors integrate data analytics technologies and data communication to develop IIoT-based PdM applications. For example, Microsoft Azure, IBM Watson, and Amazon Web Service (Seattle, WA, USA) provide data storage, IoT gateway, ML module, and other services for PdM manufacturing. These major factors may contribute to the PdM-related market growth during the forecast period. IIoT-based PdM vendors focus on cloud- and edge-based predictive analytics. The present study predicts the semiconductor and blister packing machine cases using the ML algorithm in data analytics.
## 3 The Acceptability and Acceptance Study of SHERPAM
### Measures
#### 3.3.3 Quantitative Measure  
_Unified Theory of Acceptance and Use of Technology._ A 42-item questionnaire measured on 7-point Likert scales was developed based on the material used by [PERSON] et al. [9] (see Appendix A). Participants completed a scale adapted for the SHERPAM platform. To assess _Performance Expectancy_\(11\) items were used (e.g., \"In your opinion, the use of the SHERPAM platform... to know the intensity of my physical activity\"). To assess _Effort Expectancy_\(11\) items were used (e.g., \"Based on the presentation of the SHERPAM system, would you say that its use... would be easy for me\"). _Social Influence_ was assessed by 7 items (e.g., \"In your opinion, the use of the SHERPAM platform... will have a positive impact on those around me\"); _Facilitating Conditions_ were assessed by 8 items (e.g., \"In your opinion, with the SHERPAM platform... I will be able to be medically monitored\"). And finally, _Behavioral Intention_ was assessed by 5 items (e.g., \"Based on the presentation of the SHERPAM system, would you say that... I intend to continue using this system after the clinical trial period.\").
# Accurate Phase Calibration of Multistatic Imaging System for Medical and Industrial Applications
## 5 Imaging Result
This artifact appears at the location of the Rx antenna and is considered to have been generated during the subtraction of the reconstructed image. Next, the phase distribution of the reconstructed image is compared. The phase distribution without phase calibration is asymmetric for the \(y\)-axis. In contrast, when phase calibration is performed, the distribution is symmetrical about the \(y\)-axis. Symmetrical phase distributions were obtained by performing phase calibration on all channels. Figure 9 shows a 3D representation of the reconstructed images of Figure 8 at different heights. The graph shows that the amplitude distribution and the frequency used for reconstruction is 6.5 GHz. The x- and the y-axes show the same axes as in Figure 8. The z-axis shows the height at which the reconstructed image was calculated, from the bottom of the antenna array to a height of 75 mm, divided at 12.5 mm intervals. The color bar values indicate the intensity of scattering, and a transparency is set to the color of the color map for visualization. The black circles in the reconstructed image at each height indicate the outer diameter of the antenna array, the inner diameter, and the position of the metal column. The location and shape of the metal columns were confirmed and are presented in Figure 9. The metal columns are visible in the reconstructed images at four different heights from 25 mm to 62.5 mm. In the reconstructed images at 0 mm, 12.5 mm, and 75 mm, the metal columns are obscured. Several previous studies have shown that measurements at the Tx and Rx antennas located in different planes (cross-planes) are required to improve the image accuracy of microwave imaging systems [31; 32; 33; 34]. It has been reported that measurements that do not include cross-planes reduce the vertical resolution of the reconstructed image. The fact that some of the reconstructed images in this study are blurred may indicate the same tendency as in the aforementioned previous studies.
## 5 Results
### Throughput Analysis without Nonlinear Effects
#### 5.2.3 Comparison  
The results achieved for the Rayleigh and ray-tracer radio channels were compared in terms of the 10 th, 50 th (median), and 90 th percentiles, as shown in Figure 7. The main observation is that the average UE rates achieved under the Rayleigh radio channel are much higher than those achieved under the ray-tracer channel. This is because the Rayleigh channel is not spatially correlated, which plays a crucial role in achieving the full performance of any M-MIMO system. From this perspective, the results obtained for this channel model may be overly optimistic, i.e., the UE rates are overestimated compared to real-world scenarios. However, even after rate normalization, the i.i.d. Rayleigh channel cannot accurately evaluate the relationship between the network-centric and UCCF M-MIMO network architectures. For example, while the highest median rate in the i.i.d. Rayleigh channel is achieved by the network-centric approach or UCCF M-MIMO architecture with two RUs, the ray-tracer-based simulations reveal that it is the best to use the UCCF M-MIMO network architecture with more than three RUs, depending on the MNO's goal. As a result, accurate radio channel models should be used for the evaluation of any M-MIMO system, especially when demonstrating the opportunities related to the UCCF M-MIMO network. By comparing the plots, it can also be seen that utilization of the UCCF M-MIMO network architecture in the considered network provides greater benefits for the realistic ray-tracer radio channel. This is because it allows for coordinated suppression of interference between RUs (achieved through ZF precoding applied within each RU) and increased array gain through the utilization of M-MIMO transmission from multiple RUs.
## 2 Related Works
Under this protocol, a number of concentric zones are formed around the sink. In turn, each first-zone sensory node aims to establish an SPT with other neighbour source nodes. For this, a greedy tree establishment algorithm is used to incrementally interconnect nodes from outer to inner zones. Hence, a tree trunk is formed by the inter-zone ties, whereas branches are established using the intra-zone paths. An SPT is completed when the last zone is interconnected. Zone-based mobile agent aggregation (ZMA) [4] reactively forms optimal paths for MA data aggregation according to a bottom-up approach. Similarly to TBID, ZMA forms zones with which to route MAs. However, this utilises a data-centric (DC) zone partitioning technique according to the consumer's DI. ZMA utilises a weight function to select a number of sensory nodes (zone mobile agent coordinators) at each zone to manage MA movements. MAs utilise a bottom-up migration approach to move from target regions to the sink for sensory data collection and aggregation. They record the traversed itineraries to avoid loop and collision. There are a number of route planning protocols that are dedicatedly designed to forward hardware MAs--mainly UAVs [22; 23]. Three-dimensional itinerary planning for UAVs was proposed by [24]. This utilises glow-worm swarm optimisation to route drones over a field of randomly located 3D objects. The target is to find an optimal path to fly over the field with no collisions. For this, a bio-inspired technique, glow-worm swarm, is utilised, which allows the UAVs to move through a path to achieve the journey targets, search the environment and avoid collisions with the existing obstacles on the path. MAs (e.g., drones) may use machine learning techniques for itinerary planning, path optimisation and communication link prediction [25]. One study [26] utilised a deep reinforcement learning technique for autonomous UAV path planning, and [27] extended the work to utilise reinforcement learning for accident avoidance. In [28] a reinforcement learning technique was combined with fuzzy logic to enhance the link connectivity and reduce the path length. MINDS offers WSN data aggregation applications a number of contributions, especially minimised network traffic, reduced data collection latency and maximised data aggregation robustness. They are achieved as MINDS establishes a data-centric, minimum hop-count tree infrastructure with reduced network transmission, for MAs to move and capture the maximum number of sensory recordings. The conventional MA data aggregation protocols still suffer from high network transmission rates in route planning. This results in increased network resource consumption, especially power and bandwidth. Moreover, they need to have a data-centric and hop-count-aware route planning through which MAs are forwarded via minimised hop-count paths to capture the maximum number of sensory data samples. This results in enhanced data aggregation robustness and reduced data collection latency at the same time.
## 2 Materials and Methods  
The analysis of crack propagation history demands knowledge of appropriate material parameters to define crack propagation behaviour. Figure 1 is the crack propagation specimen geometry with initial crack for S355 and S960 structural steel in the experimental studies. The fatigue crack growth rate was performed according to ASTM E647 [23] with an electromagnetic resonance testing machine using crack gauge and specialized optical measurement system comprising an industrial camera with telecentric lens and LEDs. The crack initiation and propagation with the steel specimen in the fatigue rig under constant amplitude loading (CAL) at each stress ratio. The material data generated with the high-strength steel S355 and S960 plate with a thickness of 5 mm were used to determine the crack growth parameters, C and the slope m with the Paris' law diagram where the crack is stable.  
The traveling camera with a telecentric lenses trails the crack evolution in the test rig, suitable for a thin test piece. However, for a thicker specimen the surface observation is unreliable where the crack front tends to bend [24]. The test rig is paused at an interval to take the best image quality. Crack gauge is applied directly on the specimen surface and the crack length is continuously measured during the test with constant current, and the resistance-based voltage drop is measured with the measurement of the change in resistance, resulting in correlation of the whole crack surface or area favouring crack propagation measurement in steel material. The fatigue test configuration is illustrated in Figure 2 and both measurements were taken at opposite sides of the specimen. The crack measurement is amplified with a precision voltmeter for crack gauge of the RUMUL electromotive resonance machine. The measurement system is linked to the test machine to record crack length and number of load cycles simultaneously.  
Figure 1: Specim geometry based on SENT.  
Fracture mechanics often plays a role in predicting components that are subject to time-dependent crack growth mechanisms. The SIF (K) is given by the following relation and non-dimensional function (\(f\)) is the effect of SENT geometry in Equation (2).  
\[\text{K}=\frac{P}{BW}\;f\;(a/W), \tag{1}\]  
\[f\Big{(}\frac{a}{W}\Big{)}=1.12-0.231\Big{(}\frac{a}{W}\Big{)}+10.55\Big{(} \frac{a}{W}\Big{)}^{2}-21.72\Big{(}\frac{a}{W}\Big{)}^{3}+30.39\Big{(}\frac{a} {W}\Big{)}^{4} \tag{2}\]  
Based on the Paris law model for crack growth rate, Equation (3) describes the relationship of the rate of growth of a fatigue crack according to the load characteristic of the SIF around the crack front, which is a function of the range of stress intensity to accomplish the load cycle. Therefore, a linear approximation of the upper limit of the curve in a logarithmic scale for horizontal and vertical axes is plotted. The material constant (C) is the curve intercept with the \(\text{da}/\text{dN}\) axis, whereas m is the slope of the curve determined.  
\[\text{log}\Big{(}\frac{\text{da}}{\text{dN}}\Big{)}=\text{m}\,\text{log}( \Delta\text{K})+\;\text{log}\text{C}, \tag{3}\]
## 3 Results  
### In Vitro Study  
The results of the determination of the total antioxidant activity with the FRAP assay are presented in Figure 1. Antioxidant activity of mountain tea presented significantly statistical differences with all the analyzed samples, namely blueberries (p < 0.005), cranberries (\(p\) < 0.001), pomergnate (\(p\) < 0.001), grapefruit (\(p\) < 0.001), red peppers (\(p\) < 0.001) and almonds (\(p\) < 0.001). More specifically, mountain tea had the greatest antioxidant activity 335.19 \(\pm\) 5.07 mmol Fe\({}^{2+}\)/g of sample, followed by pomergnate with 106.29 \(\pm\) 2.82 mmol Fe\({}^{2+}\)/g, blueberries with 74.05 \(\pm\) 1.92 mmol Fe\({}^{2+}\)/g and almonds with 67.80 \(\pm\) 1.67 mmol Fe\({}^{2+}\)/g, respectively, without exhibiting statistically important differences. Red peppers with 36.55 \(\pm\) 4.91 mmol/L, cranberries 36.73 \(\pm\) 5.85 mmol Fe\({}^{2+}\)/g and grapefruits with 28.07 \(\pm\) 2.67 mmol Fe\({}^{2+}\)/g, had lower total antioxidant activity, but this was not statistically significant. The mean values and the statistically important differences of the total phenolic compounds quantified in the food extracts by Folin-Ciocalteau analysis are presented in Figure 2. The highest content of phenolic compounds was determined in the mountain tea extract with a mean value of 867.95 \(\pm\) 6.89 mg gallic acid/g of food sample. Next were almonds with 355.00 \(\pm\) 2.17 mg gallic acid/g, blueberries with 171.25 \(\pm\) 0.84 mg gallic acid/g, and pomergnate with 90.51 \(\pm\) 1.13 mg gallic acid/g. Grapefruit with 78.52 \(\pm\) 0.61 mg gallic acid/g, cranberries with 66.65 \(\pm\) 0.86 mg gallic acid/g and red peppers with 49.51 \(\pm\) 0.76 mg gallic acid/g were the samples with the lowest concentrations of total phenolic content. Statistically important differences were observed between the mountain tea and all the analyzed food samples (\(p\) < 0.001), except for almonds (\(p\) > 0.05). The total phenolic content of almonds also exhibited statistically important differences compared to all analyzed foods, except for mountain tea (\(p\) < 0.001). In addition, statistically important differences were observed between the total phenolic content of blueberries and red peppers (\(p\) < 0.05). Figure 1: Mean values of total antioxidant activity of different food extracts as determined by Ferric Reducing Antioxidant Power (FRAP) assay. Statistically significant differences are portrayed with superscripts (different letters). As summarized in Table 1, there are several studies in the literature referring to the total antioxidant and phenolic content of the selected foods determined with FRAP and Folin-Cioalteu assay.
## 5 The Experiment and Analysis
### Fault Data from QPZZ-T
#### 5.2.2 Comparison Analysis  
To verify the effectiveness of PGMCKD, the MCKD and MCKD based on Shannon entropy are selected to compare with PGMCKD. For MCKD, the theoretical value of \(T\) is 96, the value of \(L\) is 175. Shannon entropy is used to set the value range of \(T\) as [94; 98],  
Figure 8: Power spectrum of reconstructed signal.  
Figure 10: Power spectrum of reconstructed signal by MCKD.  
Figure 9: Power spectrum by KMVMD-PGMCKD.  
the step size is 1, the search range of \(L\) is [100, 500], the step size is 5, and the final result of \([L,T]\) is [98, 395]. The processed signal power spectrum is given in Figures 10 and 11.  
In Figure 11, after the MCKD is employed to deal with the reconstructed signal, the fault features still cannot be extracted. In Figure 5, fault frequency \(f_{0}\) (123 Hz) has the largest amplitude, and 1/2 harmonic of the rotating frequency (11.74 Hz) can also be found. However, compared with Figure 9, harmonic of the fault frequency occurs. Therefore, the experimental comparison results prove the effectiveness of PGMCKD.