esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Blockchain-Based Federated Learning
## 1 Introduction
[26] explored the integration of blockchain and FL, taking into account communication costs and resource allocation in mobile edge computing scenarios. [PERSON] et al. [27] delved into this topic within the context of the Internet of Things. They provided detailed discussions on both blockchains and FL separately, and presented structures and perspectives on their integration. [PERSON] et al. [28] examined the architecture of BFL, covering aspects such as types, design, model enhancement, and incentive mechanisms. It is evident that these surveys predominantly focus on the BFL framework and its prospective applications in the field of AI, yet lack an in-depth analysis of the pivotal issues addressed by BFL, as well as a comprehensive discussion on its applicability in more expansive scenarios. Therefore, this work originates from the framework of BFL, providing an incisive discourse on key challenges in FL that are ameliorated through the integration of blockchains. It further elaborates on the prospective applications of BFL in multiple domains, including the Internet of Things (IoT), Industrial Internet of Things (IIoT), Internet of Vehicles (IoV), and healthcare services. The paper undertakes a holistic and rigorous analysis and comparative evaluation across three critical dimensions--fundamental architecture, core technologies, and future applications--to ultimately summarize the innovative directions and applicative frontiers where blockchains and FL converge. The main contributions of this work are as follows:  
* We offer an overview encompassing the definition, architectural design, and challenges of both blockchains and FL. We also delve into the motivations driving the application of blockchains in the context of FL. * We categorize BFL frameworks into three distinct classes based on how blockchains participated in the FL process within individual nodes. * We elaborate on how to use blockchain technology to mitigate the challenges of FL from the perspectives of decentralization, incentive mechanisms, attack resistance, privacy protection, and efficiency enhancement. * We compile a comprehensive list of current viable applications for BFL and engage in discussions regarding the promising future directions and unresolved issues in the field of BFL. The rest of this article is organized as follows. In Section 2, we introduce the basics of FL and blockchains, and we present the frameworks and functions of BFL in Section 3. In Section 4, we investigate the applications of BFL in different domains. Discussions of the current challenges and future research directions of BFL are presented in Section 5, and we conclude the paper in Section 6.
## 2 Materials and Methods
### Laboratory Processing and Soil Salinity Measurement  
The samples were processed and analyzed in the laboratory. First, the collected soil samples were air-dried, impurities were removed, and soil particles smaller than 2 mm were selected. Then, a soil solution was prepared using 20 g of soil sample and 100 mL of deionized water at a soil-to-water ratio of 5:1, from which the EC 1:5 value was extracted and measured using a conductivity meter. Subsequently, the soil samples were dried in an oven at 105\({}^{\circ}\) until a constant weight was reached, and the dry weight of the samples was measured to calculate salinity content and conductivity using our laboratory's formula for soil salinity content and conductivity:  
\[\mathbf{ssc}=\mathbf{2.18}\times\mathbf{EC}+\mathbf{0.727}\]  
converting the EC value to soil salinity content (SSC) in g/kg. Descriptive statistics were conducted on the soil salinity data from the sampling points. Table 1 describes the average, standard deviation, range, minimum, maximum, and coefficient of variation (CV) for the samples. The results showed significant variation in soil salinity across the sampling area, ranging from 0.02 to 36.08 g/kg, with a CV of 176%, indicating that it nearly covered all salinity levels in the study area.
## 2 Materials and Methods
### Design  
The design is non-experimental, retrospective, and cross-correlational. The study sought to identify relationships between the variables during the 2020/2021 season.  
The variables under study were divided into dependent and independent. The dependents are as follows:  
* Weekly load (Session RPE): [PERSON]'s adaptive scale was used with values from 1 to 10 [51]. Multiplying the RPE value by the duration of the session gives the session RPE load in arbitrary units [52]. Adding the load of each training session of the week, the weekly training load is obtained, not including that of the game [12; 37; 53].
* Game load: It is calculated by multiplying the game RPE by the playing time of each player [4; 19].
* Pre-game recovery: The Total Quality Recovery (TQR) scale was used, with values from 1 to 10 [54]. The players had to mark their sensations of recovery.
* (Missed field shots + Missed free throws + Turnovers + Blocks received + Fouls committed). To normalize this metric, the value obtained for each player is divided by the playing time [9; 56]. The independent variables are as follows:
* Perceived individual performance: A scale from 1 to 10 is used [51].
* Playing position: Defined by the first coach that divided the players into 3 groups: (a) guards, (b) forwards and (c) centers [9; 36].
* Previous experience: 3 groups according to the years practicing basketball: (a) a lot of experience (more than 17 years), (b) medium experience (between 14 and 17 years) and (c) little experience (less than 14 years) [9].
* Game location: (a) local or (b) away [19].
* Score-line: (a) equal game (final difference was a maximum of 8 points) or (b) unequal (final difference was more than 8 points) [19].
* Playing time: 3 groups according to the average number of minutes per game: (a) high (more than 22 minutes), (b) medium (between 15 and 22 minutes), or (c) low (less than 15 minutes) [9; 36; 57].
* Season phase: Period of the year in which the game was played: (a) preseason (4 weeks before the start of the competition), (b) first round (between matchdays 1 and 13), or (c) second round (between matchdays 14 and 26) [9; 37].
* Recovery cycle: Days between games were counted: (a) short cycle (less than 7 days) or (b) long cycle (7 or more days) [9; 37].
* Previous game outcome: Result of the previous game: (a) victory or (b) defeat [9; 19; 37].
* Next game outcome: Result of the next game: (a) victory or (b) defeat [9; 19; 37].
* Quality of previous opposition: (a) high-level team (15 wins or more), (b) medium-level team (between 9 and 15 wins), or (c) low-level team (less than 9 wins). The level of the last opponent that had been played was considered [9; 36; 37].
* Quality of next opposition: (a) high-level team (15 wins or more), (b) medium-level team (between 9 and 15 wins), or (c) low-level team (less than 9 wins). The level of the next opponent to be played was taken into account [9; 36; 37].
## 4 Numerical Modeling with Anisotropic Slate
### Numerical of the Brazilian Tests
#### 4.2.2 Numerical Model Verification  
To verify the correctness of the numerical model, Brazilian tests were performed by FCEM under bedding angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), and divider type. The experimental and numerical load-displacement curves are shown in Figure 12, where their shapes are fairly consistent and the corresponding ultimate failure patterns are also fairly well identified for the corresponding bedding cases. Additionally, for the typical bedding angles of 0\({}^{\circ}\), 90\({}^{\circ}\), and divider, the tensile strengths of specimens were 1.43 MPa (vs. the lab test mean value of 1.45 MPa), 0.69 MPa (vs the lab test mean value of 0.69 MPa), 1.61 MPa (vs the lab test mean value of 1.64 MPa), respectively. In such results, the peak load of slate is reduced with the increase of a bedding angle of 0\({}^{\circ}\)-90\({}^{\circ}\), as shown in Figure 12a-e, while increasing sharply with a bedding angle of 90\({}^{\circ}\) to the divider type, as in Figure 12e,f, showing a V-type trend similar to the results of laboratory Brazilian test in this work, This indicates that the bedding direction significantly affects the tensile strength due to the relatively anisotropic mechanical properties of the weak structural planes among the layered slate.  
For the macro failure patterns of all cases in the present work, as loading is continuously applied, there are many micro-cracks that emerged near the two loading ends, which contribute to small-scale crush. For the bedding angles of 0\({}^{\circ}\) and 90\({}^{\circ}\), the fracture paths run through the two loading ends, and the shear failure occurs between the horizontal bedding planes when the bedding angle is 0\({}^{\circ}\), as shown in Figure 12a. When the bedding angle is 90\({}^{\circ}\), straight tensile failure is formed between the vertical bedding planes near the loading end, as shown in Figure 12e. Figure 12b-d, it is of note that for bedding angles of 30-60\({}^{\circ}\), the numerical results often partially present shear fracture along the inclined bedding planes. The simulated failure patterns are fairly consistent with the Brazilian test results of slate in the literature [5; 6; 18; 19; 20]. For the divider model, the vertical principal crack appears along the vertical loading line and accompanies a small arc, which is consistent with the failure patterns of typical experimental specimens, as shown in Figure 12f.  
Figure 12: _Cont._
## 4 Results
### Study Characteristics  
Among the 16 included articles, 1 article aimed to find interchangeability between EPTS systems [29]. The rest (\(n=15\)) were developed to assess the validity or accuracy of LPS systems, and among them, five articles evaluated reliability [17; 23; 30; 31; 32] (Appendix B).  
Despite the fact that LPS was developed to track players' positioning in indoor environments, the majority of the studies included in this systematic review were carried out outdoors (\(n=9\)) during soccer training tasks [18; 22; 23; 24; 29; 31; 33; 34; 35]. Most of the studies carried out in indoor environments (\(n=6\)) were performed during basketball bouts [17; 30; 36], while 1 was performed on ice hockey [37], 1 with handball players [38] and 1 was not specified [39]. In addition, 1 article was carried out in both environments [32] (Appendix B).  
LPS has been used based on different technologies. Four studies used UWB technology with 6 antennae around the field and, in general, 18 Hz [17; 23; 24; 36]. Local Position Measurement (LPM) was used in 10 studies in which between 11 and 19 antennae were used as a reference and, overall, at 45 data points per second (\(n=6\)) [18; 22; 29; 30; 31; 33; 34; 35; 38; 39]. The rest used Wireless Ad hoc System for Positioning (WASP) technology [32] with 12 antennae and 10 Hz, and other radio-based systems at 10 Hz and an unspecified number of antennae [37] (Appendix B).  
All articles assessed the precision of LPS technology on the measurement of kinematic variables such as time-motion at different intensities during linear and nonlinear locomotion. The precision was assessed comparing the LPS with a criterion measurement device (considered as a gold standard in each article). Among them, tape measurement (\(n=4\)), timing gates (\(n=5\)), trundle wheel (\(n=1\)), VICON optic-system (\(n=4\)), Laser measurement (LAVEG) (\(n=1\)) and Geographic Information System (GIS) (\(n=1\)) were used as comparison methods. Moreover, one article analyzed the accuracy of UWB to measure collective tactical behavior variables (i.e., surface area), comparing its validity using GIS [24] (Appendix B).
## 3 Performance Evaluation
### Acquisition Performance
#### 3.2.3 Acquisition Probability  
We next analyze the acquisition probability. From (20), it is easy to observe that the \(SNR\) of all signals at the output of the correlator is the same if the delay offset \(\tau\) and Doppler offset \(f_{d}\) are zero and the received signal power and coherent integration time are equal, where coherent integration time is an integer multiple of the signal period. However, with the same total false alarm probability, the higher the number of Doppler frequency searches, the smaller the false alarm probability per search, and the lower the corresponding detection probability.  
The acquisition of navigation signals is essentially a detection problem, the Doppler estimation accuracy depends mainly on the frequency search step. The correlation loss introduced by the frequency offset is at its maximum when the true Doppler frequency falls exactly at 1/2 of the two search frequency points, and the acquisition probability is the worst. In the following, we analyze the acquisition performance of the proposed signals under different Doppler frequencies and significant Doppler shift scenarios. We set the signal bandwidth to 10.23 MHz, the sampling frequency to 20.46 MHz, and the total false alarm probability to \(10^{-3}\), \(N=10\), \(T_{coh}=1\) ms. The theoretical detection probabilities of different signals can be obtained as shown in Figure 8. Where 0 Hz and 40 kHz in parentheses are the minimum and maximum channel Doppler, respectively. In addition, 37.25 kHz and 37.5 kHz in parentheses are the middle frequencies between two adjacent frequency search points for continuous and pulsed signals, respectively.  
Analyzing the results shown in Figure 8, we can conclude the following. Firstly all of the signals have the same and highest acquisition probability at 0 Hz and 40 kHz; this is because despite 40 kHz being a significant Doppler shift, it falls exactly at the frequency search points at which it does not introduce correlation loss. Secondly, the acquisition probabilities of the Pulse-OTFS and Pulse-BPSK signals at 37.5 kHz and OTFS and BPSK at 37.25 kHz are about 0.9 dB lower than that at 0 Hz and 40 kHz, respectively, which represents the worst detection probability. Finally, Pulse-OTFS and Pulse-BPSK have about 1 dB better detection performance than OTFS and BPSK at the same Doppler frequency offset and carrier power-to-noise density ratio \(C/N_{0}\). The proposed signal improves the acquisition probability compared to the continuous signals with the same total false alarm probability and \(C/N_{0}\).  
Figure 8: Detection probability of different signals.
## 4 Results
### Questionnaire Results
#### 4.1.2 Gesture-Based User Interface  
The participants responded to 10 statements regarding the gesture-based UI of Four Seasons. The mean and standard deviations of the responses are depicted in Figure 5. The total mean and standard deviation for these statements were 4.21 and 0.76, respectively, with Cronbach's alpha being 0.62. The overall clarity, usability, and learnability of the gesture-based interface were found to be high, as evidenced by statement 2 (\(\mu\): 4.43, \(\sigma\): 0.75), statement 3 (\(\mu\): 4.57, \(\sigma\): 0.60), statement 4 (\(\mu\): 4.62, \(\sigma\): 0.50), and statement 5 (\(\mu\): 4.76, \(\sigma\): 0.54). The results also show that the gesture-based interaction method helped to immerse the participants (statement 6, \(\mu\): 4.43, \(\sigma\): 0.60), and most participants did not find that the interaction method strained their body (statement 7, \(\mu\): 4.14, \(\sigma\): 0.91). Statement 1, which was on the resemblance of the interaction method to the interaction in the real world, received lower ratings (\(\mu\): 3.81, \(\sigma\): 0.87). This is aligned with the results of statement 9 (\(\mu\): 3.767, \(\sigma\): 0.73), which measured sensory stimulation experienced by the participants. Figure 4: Means and standard deviations of the statements related to presence. The scale ranges from strongly disagree (1) to strongly agree (5). 1. The interaction method was similar to interaction in the real world. 2. It was easy to figure out how to proceed with the content. 3. The guidance on progressing the content was easy to read and understand. 4. The operation method for each scene was consistent and easy to use. 5. The interaction method could be understood just by seeing and hearing it, without having to memorize it. 6. The control method using body movements helped immense in the content
7. There was no strain on the body due to the way the body was moved for controlling
8. Interaction with this content felt lively. 9. I felt sensory (visual, auditory, tactile) stimulation from this content
10. The content responded immediately to my actions
# Research on Image Denoising in Edge Detection Based on Wavelet Transform
## 3 Materials and Methods
### Wavelet Transform
With good regularity, the db\(N\) wavelet is characterized by nonlinear phase energy concentrated near the beginning of its support. For the db\(N\) wavelet, as the order (sequence \(N\)) increases, the greater the order of the vanishing moment, where the higher the vanishing moment the better the smoothness, the stronger the localization ability of the frequency domain, and the better the division of the frequency band, but it will make the time domain tight support weaken, while the computational volume increases greatly and the real-time performance becomes worse. sym is short for Symlet, and the Symlet wavelet is usually denoted as sym\(N\) (\(N\) = 2, 3..., 8). sym\(N\) wavelets also have good regularity. The wavelet is consistent with the db\(N\) wavelet in terms of continuity, branch length and filter length, but the sym\(N\) wavelet has a better symmetry, i.e., it can reduce phase distortion when analyzing and reconstructing the signal to some extent. coif is short for coiflet and is usually denoted as the series coif\(N\) (\(N\) = 1, 2, 3, 4, 5). The wavelet function \(\Psi(t)\) of a coiflet has zero moments of order 2\(N\), and the scaling function \(\varphi(t)\) has zero moments of order 2\(N-1\). \(\Psi(t)\) and \(\varphi(t)\) have a support length of 6\(N-1\). \(\Psi(t)\) and \(\varphi(t)\) of a coiflet have better symmetry than db\(N\). They are characterized by scaling functions and wavelets with the same number of vanishing terms. fk is short for Fejer-Korovkin and is usually denoted as fk\(N\) (\(N\) = 4, 6, 8, 14, 18, 22), with \(N\) denoting the filter length. A filter is constructed by minimizing the difference between an effective scaling filter and an ideal sine-pass filter, particularly useful in discrete (extracted and unextracted) wavelet packet transforms. At the same time, the number of decomposition levels is also crucial. In wavelet decomposition, the signal is decomposed level by level. The decomposition of the approximate component of one level can produce the approximate component (cA) and the detail component (cD) of the next level, as shown in Figure 2.  
Therefore, the more levels there are, the more obvious the distinction between information and noise is, and the better it is to distinguish between the two. However, the increased number of levels also leads to increased distortion of the reconstructed signal, which may affect the reconstruction of the signal.
_Applied_  
_sciences_  
_Article_  
**Process Parameter Optimization When Preparing**  
**Ti(C, N) Ceramic Coatings Using Laser Cladding**  
**Based on a Neural Network and Quantum-Behaved**  
**Particle Swarm Optimization Algorithm**  
[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2,*()  
1 College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China; [EMAIL_ADDRESS] (Z.D.); [EMAIL_ADDRESS] (T.C.); [EMAIL_ADDRESS] (H.W.); [EMAIL_ADDRESS] (S.L.)  
2 State Key Laboratory of High-Performance Complex Manufacturing, Changsha 410083, China  
*Correspondence: [EMAIL_ADDRESS]  
Received: 7 August 2020; Accepted: 7 September 2020; Published: 11 September 2020  
**Abstract:** The formation process of surface coatings fabricated with laser cladding is very complicated and coating quality is closely related to laser cladding process parameters. Generally, the optimization and control of process parameters play key roles when preparing high-quality ceramic coating. In this paper, three reasonable parameters were selected for each process parameter based on the preliminary experiment. The experiment of Ti(C, N) ceramic coating prepared with laser cladding was designed via the Taguchi method. The laser power, spot diameter, overlapping ratio, and scanning velocity were selected as the main process parameters, and their effects on coating micro-hardness were analyzed using the signal-to-noise (S/N) ratio and analysis of variance (ANOVA). Then, based on the back-propagation neural network (BPNN) and quantum-behaved particle swarm optimization (QPSO) algorithm, we created the prediction model of BPNN-QPSO neural network for laser cladding Ti(C, N) ceramic coating. The mapping of process parameters to the micro-hardness of the coating was obtained according to the model and we analyzed the influence of process parameters that interacted with the coating's micro-hardness. The results showed that the interaction of laser cladding process parameters had a significant effect on the micro-hardness of the coating. The established BPNN-QPSO neural network model was able to map the relationship between laser cladding process parameters and coating micro-hardness. The process parameters optimized by this model had similar results with ANOVA. This research provides guidance for the selection and control of ceramic coating process parameters Ti(C, N) prepared via laser cladding.  
**Keywords:** laser cladding; Taguchi method; back-propagation neural network; particle swarm optimization algorithm; prediction model; process optimization
## 4 Discussion
The high prediction precision of both MLR and GEP techniques confirms the results of previous investigations reporting the robustness of these algorithms in \(V_{s}\) estimation [46; 47; 48; 49; 50; 51]. To improve the predictability of these techniques, two innovative works can be carried out: the ensemble of the regression model [45; 66] and the coupling of deep learning with machine learning models [75]. Ensemble learning denotes a collection of methods employed to merge the outcomes of numerous foundational models, aiming for superior performance compared to any individual model within the ensemble [65]. The core principle of this approach lies in the amalgamation of outputs from multiple models, which effectively averages out the errors inherent in each base model. Several empirical investigations have consistently indicated that ensemble models frequently exhibit enhanced accuracy in comparison to their individual base models [66; 76; 77]. A judicious selection of \(V_{s}\) estimation methods, informed by empirical correlations or advanced data-driven models, empowers petroleum engineers to navigate the complexities of subsurface geology with confidence. This strategic approach not only aligns with cost-effectiveness but also plays a decisive role in minimizing non-productive time, thereby enhancing the overall success and sustainability of drilling endeavors [78].