esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Cyclostationarity
### LCL-PTV Optimal FRESH Filtering
The derivation can be simplified if the MMSE is performed in the frequency domain. The frequency domain optimal FRESH filter equation can be expressed as [19; 28]  
\[\hat{Y}(f)=\sum_{p}H^{\alpha_{p}}(f)\cdot R_{p}\left(f-\alpha_{p}\right)+\sum_ {q}H^{\alpha_{q}}(f)\cdot R_{q}\left(-f+\alpha_{q}\right) \tag{21}\]  
where \(H^{\alpha_{p}}(f)=DFT\{h_{p}(n)\}\), \(H^{\alpha_{q}}(f)=DFT\{h_{q}(n)\}\), \(R_{p}(f)=DFT\{r_{p}(n)\}\), \(R_{p}(f)=DFT\{r_{q}^{c}(n)\}\) and \(\hat{Y}(f)=DFT\{\hat{y}(n)\}\). The MSE between \(Y(f)\) and \(\hat{Y}(f)\) can be expressed by  
\[\mathbf{E_{mse}}(f)=\mathbb{E}\{\mathbf{E}(f)\cdot\mathbf{E}^{*}(f)\},\ \mathbf{where}\ \mathbb{E}\ \mathbf{ denotes expectation operator} \tag{22}\]  
The MSE can be obtained by taking its derivative with respect to the frequency domain filter weights \(\mathbf{H}^{\alpha_{p}}(f)\) and \(\mathbf{H}^{\alpha_{q}}(f)\), and setting equal to zero. According to the analysis in References [19; 28], the MMSE solution to the optimum FRESH filter can be expressed as an optimum Wiener filter solution:  
\[\mathbf{H_{opt}}=\mathbf{S}_{\mathbf{R}\mathbf{R}}^{-1}\ \mathbf{S}_{\mathbf{R} \mathbf{Y}} \tag{23}\]  
where \(\mathbf{R}=\left[DFT\left(\left[r_{1}(n),\ r_{2}(n)\ldots r_{p}(n),r_{1}^{c}(n), r_{2}^{c}(n),\ldots r_{Q}^{c}(n)\right]\right)\right]^{T}\), \(\mathbf{S}_{\mathbf{R}\mathbf{R}}=\mathbb{E}\{\mathbf{R}\cdot\mathbf{R}^{H}\}\) and \(\mathbf{S}_{\mathbf{R}\mathbf{Y}}=\mathbb{E}\{\mathbf{R
## 4 Case Study and Discussion
### Position and Orientation Deviation Computation of an Assembly with Three Mating Planes
The positions and the orientations approaching progressively over the whole process are shown in Table 3, and the variations of six contact points are shown in Figure 10.  
Similarly, the six contact points and PODs corresponding to the other five assembly states were determined, as shown in Figure 11 and Table 4, respectively. Figure 10: Variations of six contact points. The three contact points on the main mating plane are red, the two contacts point on the first auxiliary mating plane are green, and the contact point on the second auxiliary mating plane is yellow. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Different Positions and Orientations** & **Position and Orientation** & **Number of approaches** \\  & **Values** & **Number of approaches** \\ \hline Initial assembly position & \(\Omega^{0}=(0,0,0,0,0)\) & 0 \\ \hline Position and orientation after three contact points are determined & \(\Omega^{3}=(0,0,0.013,0.007,165.964,162.935)\) & Once, adjustment corresponding to the \(Z\)-, \(\chi\)-, and \(\chi\)- axes, respectively \\ \hline Position and orientation after four contact points are determined & \(\Omega^{4}=(0,0.011,0.014,0.614,90.572,87.271)\) & Twice, approach of moving \\ \hline Position and orientation after five contact points are determined & \(\Omega^{5}=(0,0.008,0.014,0.481,90.737,88.741)\) & Once, approach of rotating \\ \hline Position and orientation after six contact points are determined & \(\Omega^{5}=(5.37\times 10^{-7},0.009,0.011,0.521,90.456,90.458)\) & Once, approach of moving \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Positions and orientations approaching progressively over the whole process.
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array  
### Five-Element Microphone Array Localization Algorithm in the X0Y Plane  
For the X0Y plane, M1, M2, M3, M4 and M0 form the first single-plane five-element microphone array, setting the rectangular coordinate parameters of the sound source as \(S_{0}(x_{0},y_{0},r_{0})\), and the spherical coordinates as \(S_{0}(r_{0},\theta_{0},\varphi_{0})\). According to the principle of sound source geometric localization, using the distance formula between S and M0, M1, M2, M3, and M4, \(S_{0}(x_{0},y_{0},r_{0})\) is derived:  
\[\left\{\begin{array}{l}x_{0}\approx\frac{(\tau_{30}-\tau_{10})(4u^{2}-c^{2} n_{0})}{4 am_{0}}\\ y_{0}\approx\frac{(\tau_{40}-\tau_{20})(4u^{2}-c^{2}n_{0})}{4 am_{0}}\\ r_{0}=\frac{4u^{2}-c^{2}n_{0}}{2m_{0}c}\end{array}\right. \tag{1}\]  
where \(m_{0}=\tau_{10}+\tau_{20}+\tau_{30}+\tau_{40}\), \(n_{0}={\tau_{10}}^{2}+{\tau_{20}}^{2}+{\tau_{30}}^{2}+{\tau_{40}}^{2}\), and the values of \(x_{0}\), \(y_{0}\), and \(r_{0}\) are related only to the time-delay values \(\tau_{10}\), \(\tau_{20}\), \(\tau_{30}\), and \(\tau_{40}\), the sound velocity c, and the array element spacing a.  
\begin{table}
\begin{tabular}{c c} \hline
**Basis of Judgment** & **Quadrant** \\ \hline \(\tau_{30}>\tau_{10};\tau_{40}>\tau_{20};\tau_{60}>\tau_{50}\) & first quadrant \\ \(\tau_{30}<\tau_{10};\tau_{40}>\tau_{20};\tau_{60}>\tau_{50}\) & second quadrant \\ \(\tau_{30}<\tau_{10};\tau_{40}<\tau_{20};\tau_{60}>\tau_{50}\) & third quadrant \\ \(\tau_{30}>\tau_{10};\tau_{40}<\tau_{20}  
According to Figure 1, it can be concluded that:  
\[\left\{\begin{array}{l}x=\textit{rcos}\theta cos\varphi\\ y=\textit{rcos}\theta sin\varphi\\ z=\textit{rsin}\theta\end{array}\right. \tag{2}\]  
Combining Equations (1) and (2), we obtain \(S_{0}(r_{0},\theta_{0},\varphi_{0})\):  
\[\left\{\begin{array}{l}r_{0}=\frac{4a^{2}-c^{2}n_{0}}{2m_{0}c}\\ \theta_{0}=\textit{arccos}\frac{c\sqrt{\left(\tau_{30}-\tau_{10}\right)^{2}+ \left(\tau_{40}-\tau_{20}\right)^{2}}}{2a}\\ \varphi_{0}=\textit{arctan}(\frac{\tau_{40}-\tau_{20}}{\tau_{30}-\tau_{10}}) \end{array}\right. \tag{3}\]  
According to Equation (3), when the array element spacing a and sound velocity c are fixed, the value of the distance \(r_{0}\) from the sound source S to the coordinate origin, the elevation angle \(\theta_{0}\), and the horizontal angle \(\varphi_{0}\) are related only to \(\tau_{10}\), \(\tau_{20}\), \(\tau_{30}\), and \(\tau_{40}\).
## 4 Materials and Methods
### Experimental Setup and Imaging
#### 4.1.2 Wheat Grown on the Field  
Wheat (_Triticum aestivum_), _cv_ Chilham, was cultivated in the field at Rothamsted Research, Harpenden, UK, from October 2020 to August 2021. Rows were planted 15 cm apart, with 350 seeds/m\({}^{2}\) sowing density, on a 9 m \(\times\) 1.8 m plot. The soil phosphorus (P) treatments were established by successive cropping and differential P application over several years at 12 Olsen phosphorus rates (approximately 3, 6, 9, 12, 15, 18, 21, 25, 30, 40, 50, and 60 ppm). The phosphorus deficient treatments induced color variations, especially on the leaves, veins, and stems, resulting in a purple color [31]. Digital images were taken weekly, starting from the jointing to booting stage, producing over 200 images. To capture these color variations, a digital camera -Sony Alpha a6300 (Sony Electronics, Sony UK, Weybridge, (Surrey), UK) was used. The camera was mounted on a 1.5 m monopod [32] and held perpendicular to the ground, approximately 2 m above the canopy. A cable release trigger (UR-232-Tasman) was attached to the monopod to control the camera trigger. The setup was centered in the middle of the plots. The camera was set to automatic exposure mode to compensate for the changes in natural light.
# Development and Testing of an Active Noise Control System for Urban Road Traffic Noise
## 4 Single- and Dual-Frequency Noise Reduction Tests  
### Methodology  
The developed active noise control (ANC) prototype system consists of a reference speaker, a controller, a secondary source speaker, and an error sensor. The experimental design is illustrated in Figure 8.  
To evaluate the performance of the ANC system, we conducted comparative indoor tests following these steps:  
(1) Program Burn-in: The computer is connected to the controller through a simulator for program burn-in. Once the program is burned in, the controller can operate independently from the computer. During this time, the lab environment is kept quiet to prevent irrelevant noise from affecting the test results.  
(2) Environmental Noise Measurement: A UMIK-1 measuring microphone connected to the first computer is used, with the REW V5.20.3 software (a professional acoustics measuring and analysis tool) recording the noise data throughout the experiment. Firstly, we measure and record the environmental noise in the laboratory. Before the measurement, all other devices are switched off to avoid interference.  
(3) Sound Pressure Level Measurement: With the ANC system turned off, the UMIK-1 microphone is combined with the error sensor to measure the sound pressure level of the area to be silenced. The primary source speaker is connected to the second computer, using the REW V5.20.3 software to generate simulated noise sources of different frequencies. For convenience of comparison, the volume of the primary source speaker is adjusted so that the sound pressure level measured at the microphone is always maintained at 70 dBA.  
(4) Observation of Noise Reduction Effect and Parameter Adjustment: Once the ANC system is activated, we can observe real-time noise reduction effects using the second computer. To achieve the best noise reduction effect, we adjust the iteration step size via the VIO module (a user interface for controlling system parameters), modify the program  
Figure 8: Schematic diagram of the developed active noise control system test.  
to adjust the filter order, and change the distance between the primary source, reference speaker, secondary source, and error sensor to obtain the optimal parameter combination. The presented measurements are from multiple tests, and we selected the results with the best noise reduction effect.  
(5) Comparison of Test Results: Finally, we compare the sound pressure level data of the area to be silenced before and after turning on the ANC system to assess the effectiveness of the ANC system.
## 3 Experimental Characteristics of Multicolored Pulses
### Output Power/Energy of Multicolored Pulses  
Table 1 shows the average power of AS1-AS3 obtained with five different bulk media. The external cross-angle of the two pump beams was 1.8\({}^{\circ}\), while the input powers of Beam 1 and Beam 2 were set at 6.5 and 25 mW respectively. CaF\({}_{2}\) had the highest AS1 output power, and the lowest AS3 output power of all five media. Conversely, in BBO crystal, the powers of the sidebands decreased the most rapidly with increasing order number. This phenomenon can be explained by the different phase-matching conditions and dispersion properties of the five materials.  
Figure 9: **(a)** Measured (red), retrieved (black) spectral intensity profile and retrieved spectral phase (blue) of AS1; **(b)** Retrieved temporal intensity profile (black), temporal phase (blue), and calculated transform-limited temporal intensity profile (red) of AS1; **(c)** Measured (red), retrieved (black) spectral intensities and retrieved spectral phase (blue) of AS2; **(d)** Retrieved temporal intensity profile (black), phase (blue), and calculated transform-limited temporal intensity profile (red) of AS2 [63].  
Figure 10a shows the power dependence of AS1 on the power of Beam 1, with the power of Beam 2 fixed at 19 mW and the cross-angle set at 1.8\({}^{\circ}\). The output power of AS1 was sensitive to the pump power with a low pump rate, and saturation occurred when the power of Beam 1 increased to about 11 mW. Similarly, when the power of Beam 1 was set to 11 mW and Beam 2 had a high pump power, saturation of the output power of AS1 appeared, as shown in Figure 10b. This saturation may have helped to obtain sidebands with high stability. The power stability of AS1 and Beam 1 were 0.95% and 0.62% in RMS, respectively, as shown in the inset of Figure 10a.  
By optimizing the spatial and temporal overlap of the two pump beams, the maximum pulse energy of S1 and AS1 reached was higher than 1 \(\upmu\)J [66]. An even higher output power was achieved by enlarging the pump beam size and increasing the pump power.
## 4 Results and Discussion
In [31], the MSE values ranged from 7.9 to 16.3, while [25] reported varying MSE values between 0.11 and 0.61 for the proposed model. The work of [32] reported an MSE of 1.5 \(\times\) 10\({}^{-3}\) for a fully connected ANN model, while the best MSE of 0.81 \(\times\) 10\({}^{-3}\) was achieved by a proposed hybrid connected model. Study [17] achieved an MSE of 2 \(\times\) 10\({}^{-3}\), and [33] obtained approximately 98% prediction accuracy for the proposed ANN model.
## Materials and Methods
### HCV RNA and HCVcAg Measurement  
Blood samples were collected in EDTA (ethylenediaminetetraacetic acid) containing tubes and plasma was separated. Plasma samples for HCV RNA and HCVcAg measurements were stored at \(-70\)\({}^{\circ}\)C until the time of testing. Serum HCV RNA quantitative levels were determined by  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{1}{c}{**Characteristics**} & **All; \(N=34\)** \\ \hline Age, years [median (IQR)] & 50 (41.5–58.5) \\ \hline Males [\(n\); \%] & 10; 59 \\ \hline BMI, kg/m\({}^{2}\) [median (IQR)] & 26.2 (23.2–28.6) \\ \hline HCV RNA, log10 IU/mL [median (IQR)] & 5.9 (5.6–6.3) \\ \hline HCVcAg, fmol/L [median (IQR)] & 2653 (1168–4716) \\ \hline HCV genotype 1a/1b/3a/4 [\%] & 6/88/0/6 \\ \hline Prior HCV treatment history & - \\ null response [\(n\); \%] & 8; 24 \\ partial response [\(n\); \%] & 4; 12 \\ relapse [\(n\); \%] & 9; 26 \\ naive [\(n\); \%] & 12; 35 \\ unknown [\(n\); \%] & 1; 3 \\ \hline PLT—\(\times\) 10\({}^{3}\) cell/mm\({}^{3}\) [median (IQR)] & 154 (91–201) \\ \hline Hemoglobin—g/dL [median (IQR)] & 15.1 (13.73–15.9) \\ \hline Albumin—mg/dL [median (IQR)] & 4.3 (3.9–4.7) \\ \hline ALT—IU/mL [median (IQR)] & 75.5 (40.3–140) \\ \hline Bilirubin—mg/dL [median (IQR)] & 0.7 (0.5–0.9) \\ \hline AFF—ng/dL [median (IQR)] & 6.2 (2.8–20.2) \\ \hline Liver stiffness—kPa [median (IQR)] & 10.2 (7.8–17.9) \\ \hline Liver cirrhosis [\(n\); \%] & 14; 42 \\ \hline MELD score [median (min–max)] & 7 (6–15) \\ \hline Child Pugh score [median (min–max)] & 5 (5-9) \\ \hline BMI—body mass index; HCV RNA; ribonucleic acid hepatitis C virus; HCVcAg—hepatitis C core antigen; PLT—platelets; ALT—alanine aminotransferase; AFP—alpha-fetoprotein; kPa-kilopascal; MELD—Model of End-Stage Liver Disease; IQR—interquartile range; log10—decimal logarithm. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics.  
a Roche COBAS AmpliPrep HCV test (Roche Molecular System, Pleasanton, CA, USA) with level of quantification 15 IU/mL, and level of detection 11 IU/mL. For quantification of serum HCV core antigen (HCVcAg) samples were tested with the fully automated Architect HCVcAg assay (Abbott Diagnostics, Chicago, IL, USA) according to the manufacturer's recommendations. Concentration of HCVcAg was expressed in femtomoles (fmol/L) per liter (1.0 fmol/L = 0.02 pg/mL). According to the manufacturer, the detection cut-off for a negative value was 3.0 fmol/L, the gray zone was 3-10 fmol/L and the upper detection limit was 180,000 fmol/L. HCV genotype was determined by direct sequencing of the PCR (polymerase chain reaction) product using genotype-specific primers.
# A Review of Parallel Heterogeneous Computing Algorithms in Power Systems
## 3 PHC in Power Systems
### Gpu
#### 3.1.7 Optimal Power Flow  
As seen for contingency analysis, GPU is used for OPF to reduce the simulation time of power flow analysis [1; 147; 148; 149; 150; 151; 152; 153; 154; 155]. OPF has been implemented using hybrid CPU-GPU algorithms where common instructions are parallelized using the SIMD architecture of GPUs. Metaheuristic methods and the Newton-Raphson algorithm are two of the strategies most often parallelized for OPF application. The metaheuristic methods are used to find the power system's optimal operating point, considering the state and control variables and the optimization constraints (node voltages and generation limits).  
All tasks performed in the GPU are constituted by common instructions that fit the SIMD technique. Data transfer tasks between the CPU and GPU generate a bottleneck since the communication channel's bandwidth between both processing units is low. One of the challenges of CPU-GPU algorithms is minimizing the number of data transfer tasks to avoid bottlenecks as much as possible for the communication channel.  
The authors in ref. [155; 150] implemented an OPF with a metaheuristic method and the Newton-Raphson algorithm using a CPU-GPU platform. Particle Swarm Optimization (PSO) is used to optimize the total generation cost, transmission losses, and pollutant emissions of large-scale power systems. The initialization of the particle position and velocity, calculation of the fitness of all particles, the particle's best position update, the swarm's best position update, and the movement of all particles for all PSO iterations are parallelized in a GPU.
# Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective
## 4 Experimental Assessment
### Temporal Efficacy  
The temporal efficacy deals with the delay computation for the varied number of data instances. For comparative analysis, three explicit ML models of random forest, decision tree, and logistic regressions are considered. However, it is important to mention that only the ML model is updated in the experimental simulation, while the remaining framework is unaltered. Temporal delay computes the running time of the ML model for detection of the vulnerable data instances for attack detection in the IoT-UAV scenario. The detailed analysis is depicted in Figure 13. It can be seen that in the varied number of data instances, the proposed model of hybrid LRRF is temporally more efficient as compared to other models. Specifically, the proposed model registers an average temporal delay of 34.56 s in comparison to 45.60 s for random forest, 67.34 s for decision tree, and 90.23 s for the logistic regression. It shows that the presented techniques are temporally more effective for the identification of attacks in comparison to other classification techniques. Moreover, computation time incorporates the total time from data generation to computation. However, for the comparative analysis, the trained model of LR and MF is considered for the comparative analysis. Since the trained model can classify vulnerable data in real time, the computation time is lower as compared to the conventional static model, which has to classify data statistically and therefore incorporates more time. Moreover, the computational time complexity for random forest is computed as O(n(tree)+m(try)+nlog(n)). Similarly, for decision tree and logistic regression, the computational complexity is computed as O(v(variables)+n (records) log(n)) and O(n(data points)d(size)). However, in the current scenario, the proposed model's asymptotic computational complexity is computed as O(n(iterations)logn), which is theoretically better than comparative techniques. It can be concluded from the fact that the average of multiple classification techniques is utilized. Therefore, the temporal efficacy tends to be less in the generation of optimal results.
## 3 Results and Discussion
### Photocatalytic Performance Results
#### 3.5.3 Comparative Analysis  
The TiO\({}_{2}\)/zeolite photocatalyst was compared with previously reported photocatalysts for the photodegradation of organic dyes, and the results are noted in Table 7. It should be noted that while the presented photocatalyst is comparable and even improved with respect to those reported in the literature, direct comparisons are challenging due to differences in dye type and its initial concentration, light source, catalyst morphology, irradiation time, and source of zeolite. In this research, the source of zeolite was inimbrite, which is a good source of zeolites used in the synthesis of photocatalytic materials. Therefore, these considerations suggest that the TiO\({}_{2}\)/zeolite presented here is a novel and suitable material for the photodegradation of methyl orange.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **Degradation Efficiency (\%)** & **Concentration** \\ \hline TiO\({}_{2}\)/zeolite (First Test) & 92.80 & \\ TiO\({}_{2}\)/zeolite (Second Test) & 88.15 & 10 ppm \\ TiO\({}_{2}\)/zeolite (Third Test) & 87.64 & \\ \hline TiO\({}_{2}\)/zeolite (First Test) & 70.20 & \\ TiO\({}_{2}\)/zeolite (Second Test) & 67.69 & 20 ppm \\ TiO\({}_{2}\)/zeolite (Third Test) & 18.06 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Reutilization of TiO\({}_{2}\)/zeolite for the photodegradation of methyl orange.  
Figure 9: Photodegradation efficiency of MO using various amounts of TiO\({}_{2}\)/zeolite for 30 min.
###### Abstract  
In the steelmaking industry, the state of the blast furnace tuyere is an important basis for obtaining the internal information of the blast furnace. Traditional detections mainly rely on manual experience judgment, which is a time-consuming and tiring procedure for a human. In order to improve the efficiency of the detection, this paper is devoted to applying artificial intelligence methods to blast furnace anomaly detection. However, because of the low imaging degree of the abnormal state monitoring of the furnace mouth, the difference in the abnormal category is inconspicuous, and it is difficulty to extract the features with the existing intelligent models. To solve these problems, a novel and stable method is proposed in this paper to classify the image recognition of the abnormal state of the tuyere into one category; this is a new architecture that combines multiple technologies. For the fine-grained image classification task, an improved abnormal state recognition algorithm of the blast furnace tuyere based on the channel attention residual mechanism is proposed. In the model, the dataset is augmented by rotating it at random angles to balance the amount of data in each category; then, the residual module is used to integrate high- and low-order feature information and optimize the network; then, the multi-layer channel attention module is added based on the channel attention residual mechanism, and it obtains the optimal parameter combination of the model through k-fold cross-validation. Moreover, the number of channels was reduced by half after channel fusion, which could effectively reduce the model parameters and model complexity. It is shown in our experiments that the proposed method has an accuracy rate of 97.10% in identifying the abnormal state of the tuyere in our collection of blast furnace tuyere datasets. In order to test the performance of the proposed method, some existing models, such as SERNet, ResNeXt, and repVGG, are involved for comparison, and the proposed method has a better classification effect in comparison to them.  
blast furnace tuyere; residual network; attention mechanism; tuyere image; image recognition Article