esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of Magnetic-Silica Magnetic Fluids by Laser Scattering
## 4 Conclusions  
The optical, structural, phase composition, and magnetic properties of the magnetite-silica magnetic fluid synthesized from a ferric chloride and ferrous sulfate aqueous solution were investigated.  
The granulometric composition of the magnetic fluid was studied by scanning and transmission electron microscopy. The magnetic fluid particles were found to be superparamagnetic with sizes of about 10 nm. SEM analysis and measurements of magnetic characteristics without a preliminary dilution or drying were performed for the first time. The phase composition was evaluated by X-ray diffraction and reflection high-energy electron diffraction, and magnetic properties were studied by vibrating sample magnetometry.  
To study the shape parameters of the particles, the authors developed an improved method of dynamic light scattering--DDLS. The DDLS method we proposed was used to investigate coefficients of rotational and translational diffusion of single magnetic nanoparticles and their aggregates for the first time.  
Longitudinal and transverse radii of the particle semi-axes were calculated on the assumption that the particle shape was ellipsoidal. The aspect ratio for the aggregates calculated by the DDLS method was 0.5-0.99, while individual nanoparticles were nearly spherical (the average aspect ratio was 0.9).  
The results of the study made it possible to describe the shape and sizes of magnetic nanoparticles, as well as their aggregates. The sedimentation stability of the diluted magnetic fluid was also investigated. It was found that when the liquid was diluted 200 times, it partially lost its stability, and the nanoparticles aggregated and formed clusters about 140 nm in size. Further research will be focused on revealing the exact concentration boundaries of the sedimentation stability of the magnetic fluid. The proposed DDLS method can be applied in studies of structural and morphological properties of nanoparticles not only in diluted magnetic fluids but also in a number of metallic as well as biological fluid solutions.  
E.N.V., Conceptualization, Supervision, Writing--Review & Editing, Funding Acquisition; E.K.N., Methodology, Writing--Original Draft, Investigation; K.G.G., Resources, Writing--Review & Editing, Validation, Investigation; J.M., Resources; M.C.M., Resources, Writing--Review & Editing. All authors have read and agreed to the published version of the manuscript.  
The work was supported by State assignment for basic research (Project No. FSEG-2020-0024).  
Not applicable.  
The data that support the finding of this research are not publicly available due to confidentiality constraints.  
The authors declare no conflict of interest.
## 1 Introduction
This leads to the problem of being able to tell the user in which frame the mismatch occurs but not being able to give them direction for improvement. In this study, we introduced a method that applies a coordinate correction network to enhance the accuracy of joint coordinates obtained through pose estimation. Our approach focuses on proposing an embedding method to accurately represent a golfer's swing, utilizing features such as geometric analysis, physical characteristics, and swing style. We aim to offer a comprehensive golf analysis system that suggests improved postures to learners using template-based image and video generation. The detailed benefits and unique aspects of our approach, including specialized information representation and the provision of interpretable insights, will be expanded upon in the Method section. The key contributions of our research are as follows:  
* We introduce a coordinate correction network to improve the performance of the joint coordinates extracted through human pose estimation technology, thereby enhancing the accuracy of posture analysis. * We propose a golf swing embedding technique that allows for more accurate representation of golf swing movements, enabling specialized golf swing analysis. * Unlike previous learning methods, we can provide specific advice for user movement correction through interpretable embedding analysis.
## 4 Topology Optimization of Valve Motion Components Based on Thermal-Fluid-Mechanical Coupling
### Topology Optimization Design
#### 4.3.4 Optimization Results Model Reconstruction  
The topology optimization calculation results are mesh data with different cell densities which are not smooth, discontinuous, and unclear. According to the topology optimization results shown in Figure 16 with \(u=40\%\), the calculated results were surface fitted and the model reconstruction was carried out using SolidWorks software to obtain an engineering feasible structural optimization model of the disc, as shown in Figure 17.  
The structural topology optimization undergoes a big change at the valve disc and the connection between the valve disc and the rocker. The valve disc is optimized from the original model's flat plate structure to a ball crown structure. The connection method is changed from the original fixed valve disc and rocker through the nut to the optimized valve integrated structure. The mass of the valve motion components before optimization is 2.2847 kg, and after optimization, the mass is 0.965 kg, with a weight reduction of 57.76%.
## 6 Results and Comparison
### Performance Comparison
#### 6.3.3 Virtex 6 Comparison  
This subsection presents a comprehensive comparison between the proposed cryptographic accelerator design and existing architectures on Virtex 6 [15,26]. The work in [26] requires 1.28 times higher hardware resources as compared to the proposed non-pipelined architecture while operating at a significantly higher frequency while achieving a better throughput/area ratio. On the other hand, the proposed 2-stage pipelined architecture consumes 1.42 times more resources but operates at 2.71 times higher clock frequency, resulting in a higher throughput/area ratio.  
In comparison to the architecture presented in [15], the proposed non-pipelined design employs 1.65 times fewer resources, operates at 1.04 times less clock frequency, and achieves a 1.16 times higher throughput/area ratio. Similarly, the proposed 2-stage pipelined architecture uses 33.5% fewer resources, operates at 1.55 times higher clock frequency, and achieves a 1.21 times better throughput/area ratio.  
The proposed architecture and the works in [29] are not directly comparable because they use different implementation fields. The implementation field in this article is \(GF(2^{233})\), while the implementation field for [29] is \(GF(p)\). However, a comparison of the two architectures is still possible by considering the clock frequency, hardware resources, and throughput/area ratio. The proposed non-pipelined architecture achieves a significant speedup and resource reduction over the work in [29]. For d = 59, the Virtex-6 implementation of the proposed non-pipelined architecture achieves a clock frequency that is 1.91 times higher and utilizes 4.125 times fewer resources. In addition, the proposed non-pipelined architecture achieves a higher throughput/area ratio than the work in [29]. The proposed pipelined architecture further improves the speedup and resource reduction. For d = 59, the Virtex-6 implementation of the proposed pipelined architecture achieves a clock frequency that is 3.12 times higher and utilizes 73.18% fewer resources. In addition, the proposed pipelined architecture achieves a higher throughput/area ratio.  
Overall, the comparison results demonstrate that the proposed non-pipelined design offers an excellent trade-off between hardware resources, clock frequency, and throughput/area ratio. However, the two-stage pipelined architecture may be a better choice for applications that prioritize higher throughput/area and can tolerate higher hardware resource usage.
## 2 Basic Principles
### Preprocessing
#### 2.4.1 Clutter  
Radio signals reflected by the ground lead to a deterioration of data quality, in general, as the ground contains information unrelated to the object or task. The difficulty of the determination and removal depends strongly on the situational conditions.  
In static environments, clutter can be removed by simply subtracting the data containing the relevant object from the data that were previously collected where the object was missing [44]. Nevertheless, quasi-static or dynamic environments, such as those that occur in mobile applications, storage areas, etc., are characterized by changing conditions that can affect the data.  
Numerous works have emerged in recent years that have been based on different approaches, e.g., sophisticated filters using eigenimages derived from _Singular-Value Decomposition_ (SVD) for filtering, combinations of _Principal Component Analysis_ (which is explained in Section 2.4.4), and filtering in the wavenumber domain using predictive deconvolution, Radon transform, or f-k filtering [72; 73].
# Reinforcement Learning in a New Keynesian Model +
## 2 The Standard Behavioural NK Model
### The Workhorse NK Model  
We first set out the most basic three-equation linearised workhorse NK model with RE  
\[y_{t} = \mathbb{E}_{t}y_{t+1}-(r_{n,t}-\mathbb{E}_{t}\pi_{t+1})+u_{1,t} \tag{1}\] \[\pi_{t} = \beta\mathbb{E}_{t}\pi_{t+1}+xy_{t}+u_{2,t}\] (2) \[r_{n,t} = \rho_{t}r_{n,t-1}+(1-\rho_{t})(\theta_{\pi}\pi_{t}+\theta_{y}y_{t })+u_{3,t} \tag{3}\]  
where \(y_{t}\), \(\pi_{t}\) and \(r_{n,t}\) are the output gap, the inflation rate and the nominal interest rate, respectively. All variables are expressed in log-deviation form about a zero net-inflation steady state. The shock processes \(u_{i,t}\,,\,i=1,2,3\) should be interpreted as exogenous shocks to demand (or preferences), the supply side, and monetary policy, respectively, and they are usually AR(1) processes. Expectations (\(\mathbb{E}_{t}\)) up to now are formed, assuming RE and perfect information of the state vector (which includes the shock processes). Equation (1) is the linearised Euler equation for consumption which is equated with output in equilibrium (there is no government expenditure). The value (2) is the NK Phillips curve, and (3) is the nominal interest rate rule in \"implementable form\" in that it responds to output relative to the steady state rather than the output gap (note that (1) assumes logarithmic utility and that the supply side shock is a composite of technology and marginal cost processes in the model developed in this paper. The AR(1) feature of shock processes is criticised by Reference [4], as it implies that persistence is exogenously generated. This paper addresses this critique in developing strong endogenous persistence mechanisms through learning).  
Before relaxing the RE assumption, two points about this formulation need to be made. First, there are not a lagged term in \(y_{t}\) in the demand curve (1) nor a lagged term in \(\pi_{t}\) in the Phillips curve (2) (as, for example, in Reference [23]). These can enter through the introduction of external habits in the consumers' utility function and price indexing, respectively, but we choose to focus on learning as a persistence mechanism; thus, both these features are omitted. Second, the linearisation even without these persistence terms is only correct for a zero-inflation steady state.
# Model for the Prediction of Rain Attenuation Affecting Free Space Optical Links
## 3 Comparison with Analytical Methods
### Rain Attenuation Models for Terrestrial Radio Links  
#### 3.1.1 ITU-R P.530-16 Model  
The prediction model included in recommendation ITU-R P530-16 [10] relies on the following steps:  
1. Compute the effective path length of the link, \(d_{\mathit{eff}}\), by multiplying the actual path length \(L\) (km) by the path reduction factor \(r\), which can be expressed as: \[r=\frac{1}{0.44L^{0.633}R_{0.01}^{0.073\alpha}f^{0.123}-10579[1-\exp(-0.024L)]},\] (3) where \(f\) is the frequency in GHz; \(L\) is the path length; \(\alpha\) is extracted from Table 1; and \(R_{0.01}\) is the rain rate exceeded for \(0.01\%\) of the time in a year, to be extracted from the local \(P(R)\). According to [10], if \(r>2.5\), its value is bounded to \(2.5\).  
Figure 5: \(P(A)\)s obtained from the whole set of rain attenuation maps for Hyderabad, Pakistan (\(L=5\) km, different values of \(\mu\)).  
The path attenuation exceeded for 0.01% of the yearly time is calculated as:  
\[A_{0.01}=\gamma_{R}d_{eff}=\gamma_{R}Lr. \tag{4}\]
3. The prediction of the full \(P(A)\), for 0.001% \(\leq P\leq\) 1%, is given by: \[A(P)=A_{0.01}C_{1}P^{-(C_{2}+C_{3}\log_{10}P)},\] (5) with \(C_{1}\), \(C_{2}\), and \(C_{3}\) being empirical coefficients depending on frequency \(f\)[10].
## 4 Data Generation in Machine Fault Detection
### Gan  
GAN is a technique designed by [PERSON] to generate new data from a fixed training data set. In this technique, the discriminative and the generative neural networks compete in a zero-sum game to improve themselves. Using a limited training set, the GAN techniques learn by themselves to generate data using the specific structure. The most well-known GAN applications are those in computer vision, in which a photograph set is trained to generate new output with realistic characteristics for human observers. Although previous studies were mostly focused on unsupervised learning, GANs were then used in semi-supervised learning and reinforcement learning. The core idea is to \"indirectly\" train a generator using the discriminator. Based on this idea, the generator is trained to fool the discriminator but not to minimize the total loss of the function, which leads to the ability to generate new data in a different manner.  
GAN was first proposed in Reference [33] to generate images similar to the original image. In a practical situation, GAN can be considered as the competition between a discriminative network \(D\) and a generative network \(G\). With normal contribution or random noise, which ranges from 0 to 1 as the input, the generative network \(G\) generates the \"fake\" data \(G(z)\), in which distribution \(p_{g}\) is close to that of the data distribution \(p_{data}\). The role of the discriminative network \(D\) is to distinguish the true data sample \(x\sim p_{data}(x)\) and the generated sample \(G(z)\sim p_{g}(G(z))\). In the original GAN, this adversarial training process was formulated as follows:  
\[min_{G}max_{D}\ V(D,G)=E_{x\sim p_{data}(x)}\ [\log D(x)]+E_{z\sim p_{z}(z)}\ [\log(1-D((G(z)))]. \tag{7}\]  
The adversarial procedure is illustrated in Figure 9. Most existing GANs perform a similar adversarial procedure in different adversarial objective functions. In this paper, the GAN algorithm is used to generate the broken machine data signal; therefore, only broken training data is fed into the generator. The generator generates the broken data using random noise, which ranges from 0 to 1 with normal distribution to guarantee the difference in the output data.  
The generator \(G\) and discriminator \(D\) have the ANN structure and are implemented as shown in Tables 5 and 6, respectively. The generator \(G\) has a complex structure to generate a broken high-quality signal, whereas the discriminator \(D\) has a simpler structure for classification. Note that the output of the generator \(G\) has the same shape as the input,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ANN** & **K-Mean Clustering** & **SVM** \\ \hline SA of FFT (100\%) & 100\% & 62.94\% & 91.17\% \\ \hline SA of FFT (10\%) & 95.32\% & 45.29\% & 64.71\% \\ \hline SA of FFT (Jittering) & 94.35\% & 45.29\% & 63.23\% \\ \hline SA of FFT (Scaling) & 95.13\% & 45.29\% & 61.53\% \\ \hline SA of FFT (Slicing) & 95.32\% & 45.29\% & 62.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of ANN, K-mean clustering, and SVM with statistical analysis.
## Results and Discussion  
### Influence of PAW Activation Time on Inactivation Effect of Microorganisms  
The numbers of surviving microorganisms on thin sheets of bean curd before and after treatments are shown in Figure 1. The initial bacteria count was 6.05 log\({}_{10}\) CFU/g, while that of total yeasts and molds was 5.70 log\({}_{10}\) CFU/g. As expected, there was no significant reduction of the total microorganism population on the thin sheets of bean curd after being subjected to soaking in SDW for 20 min. This means the agitating procedure does not have an impact on the inactivation of microorganisms on the thin sheets of bean curd.  
As shown in Figure 1A, after being treated with PAW15 for 20 min, 5.76 log\({}_{10}\) CFU/g of bacteria was found on the thin sheets of bean curd, which was a significant reduction compared with the control group. For PAW30, the effect of bacteria inactivation was more obvious than that of PAW15. However, lengthening the PAW activation time to 60 s did not give rise to more significant inactivation, while, for a longer PAW activation time (PAW90), the bacteria number decreased to 5.21 log\({}_{10}\) CFU/g and the inactivation effect of bacteria was the most significant. For total yeasts and molds, PAW15 showed poor inactivation effect and had no significant difference with SDW treatment. In contrast, the PAW60 and  
Figure 1: The numbers of surviving bacteria (**A**) and yeasts and molds (**B**) on thin sheets of bean curd before and after plasma-activated water (PAW) treatments with different activation times. Different letters in the bars indicate statistically significant differences (\(p<0.05\)).  
PAW90 groups presented the best inactivation effects (Figure 1B). All of these results indicated that the plasma activation time for PAW generation had a positive effect on the inactivation of bacteria as well as total yeasts and molds. This may be attributed to the higher ORP and electrical conductivity values in the PAW activated for a longer time. The tendency was consistent with reports by [PERSON] et al. (2018) and [PERSON] et al. (2019).
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
## 3 SVM-Based Antenna Allocation
### Task 1: Designing a Training Sample Set
#### 3.1.3 Subtask 1-3. Class Design and Labeling  
From the interpretation of the antenna allocation process and multiclass classification, it is clear that designing the labeling is equivalent to designing the antenna allocation scheme. As shown in Section 2.2, the mapping from antenna allocation \(\mathbf{s}_{l}\) to the index \(l\) is a one-to-one mapping. Thus, we can use the index set \(\mathcal{L}\) for the labeling in the machine learning system. Let \(\mathbf{c}=[c_{1},\cdots,c_{D}]^{T}\) denote the class label vector for the training data matrix \(\mathbf{T}\), where \(c_{i}\in\mathcal{L}\) and \(i\in\{1,\cdots,D\}\). Thus, we use this metric in the following content. The labeling procedure is summarized as follows:  
* Evaluate the target KPI, \(\eta\), for the \(d\)th channel gain sample with a particular antenna allocation \(\mathbf{s}_{l}\) corresponding to label \(l\in\mathcal{L}\).
* Assign the \(d\)th element of \(\mathbf{c}\), \(c_{d}\), with \(l^{*}\), which stands for the best choice among all the antenna allocation schemes.
* Repeat the previous two steps for \(D\) times to go through all \(D\) training set.  
Remark on the reduction of the number of classes: We can further improve the labeling by exploiting the knowledge of a wireless communication system. It is known that multiple antennas with less spatial correlation results in better communication performance, which is also confirmed by our numerical experiments. Therefore, we reduce the number of classes to \(L^{\prime}<L\) by deleting some less selected classes, which correspond to the schemes with highly correlated antennas. This elimination can reduce prediction complexity, with a tradeoff in classification performance. Note that even with classes that are uniformly selected, a designer can still reduce \(L\) to \(L^{\prime}\) to reduce the complexity of the learning system if the resultant performance degradation is marginal. On the other hand, the number of clusters can also be automatically determined for unsupervised clustering systems by using Davies-Bouldin or Dunn indices (refer to [55] and references therein).
## 3 Additional Experiments
Having confirmed the appropriateness of the choice of elementary metrics, the last step of the verification of the proposed approach concerned the calculation of the combined metrics based on the F-Measure and pseudo-F-Measure for the extended dataset of 4260 binary images originating from the SmartDoc-QA database. The obtained values of the averaged PLCC obtained for all three considered metrics are presented in Table 4 separately for the Tesseract and EasyOCR engines, as well as for the Nokia and Samsung subsets. \begin{table}
\begin{tabular}{l c c c c} \hline
**OCR Engine** & \multicolumn{2}{c}{**Tesseract**} & \multicolumn{2}{c}{**EasyOCR**} \\ \hline
**Quality Metric** & **Nokia** & **Samsung** & **Nokia** & **Samsung** \\ \hline Precision & 0.2275 & 0.2966 & 0.2697 & 0.3151 \\ Recall/Sensitivity & 0.3539 & 0.3286 & 0.4111 & 0.4385 \\ F-Measure & **0.4811** & 0.4569 & **0.5480** & 0.5490 \\ Pseudo-Precision & 0.3571 & 0.1585 & 0.3814 & 0.1935 \\ Pseudo-Recall & 0.2293 & 0.2914 & 0.2719 & 0.3922 \\ Pseudo-F-Measure & 0.4523 & **0.4865** & 0.5170 & **0.5756** \\ Specificity & 0.3402 & 0.4343 & 0.3634 & 0.4932 \\ Balanced Classification Rate (BCR) & 0.3829 & 0.2832 & 0.4476 & 0.3857 \\ S-F-Measure & 0.4272 & 0.3829 & 0.4835 & 0.4832 \\ Accuracy & 0.4789 & 0.3729 & 0.5073 & 0.4021 \\ Geometric Accuracy & 0.4169 & 0.3807 & 0.4737 & 0.4761 \\ Peak Signal-to-Noise Ratio (PSNR) & 0.2236 & 0.3603 & 0.2580 & 0.3559 \\ Border Distance (chessboard) [10] & 0.2129 & 0.3253 & 0.2386 & 0.3191 \\ Border Distance (city-block) [10] & 0.2130 & 0.3273 & 0.2395 & 0.3197 \\ Border Distance (Euclidean) [10] & 0.2134 & 0.3254 & 0.2391 & 0.3190 \\ Distance Reciprocal Distortion (DRD) [7] & 0.4536 & 0.3534 & 0.4878 & 0.3782 \\ Misclassification Penalty Metric (MPM) [8] & 0.0655 & 0.1453 & 0.0748 & 0.1264 \\ \hline \end{tabular}
\end{table} 
Table 3: Averaged Pearson’s linear correlation coefficients (PLCC) of individual IQA metrics with the Levenshtein Distance for the considered images from the SmartDoc-QA subsets. The highest values in each column are marked with bold font. Comparing the best results achieved for the elementary metrics marked with bold fonts in Table 3 to the results reported in Table 4, the increase in the Pearson's correlations with the Levenshtein Distances may be observed, similar to the WEZUT OCR Dataset. This observation confirms the validity of the proposed approach, also using some other binarization methods for a more demanding database. Figure 7: A sample image from the SmartDoc-QA database and its selected binarized versions.