esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This study investigated the stability of a bedding slope in an open-pit mine and focused on estimating the shear strength parameters of rock joints. It used the least square method to conduct indoor direct shear tests and PFC-2D numerical simulation. Then, it analyzed the impact of roughness on the shear strength parameters of rock joints, both qualitatively and quantitatively. The study summarized empirical formulas for roughness coefficients and shear strength parameters. Furthermore, it applied them to optimize the slope design.  
(1) Analysis, physical testing, and numerical simulations of the shear strength parameters at various tooth heights indicated that the internal friction angle increased with tooth height while the cohesive force decreased as tooth height increased.  
(2) This study focuses on the influence of rock joint roughness on shear strength parameters by conducting numerical simulation using PFC-2D, which is calibrated and validated by physical tests. As a result, a generalized shear strength parameter of rock joint, sandstone from the project in Xinjiang, that couples the roughness and an equation for it were provided, and they were derived to account for the impact of roughness on shear strength parameters. The study also circumvented the issue of accuracy loss in replicating [PERSON]'s standard contour lines in physical testing.  
(3) GeoSMA-3D was used to analyze the slope stability of the dependent project, and the research region-established empirical formula was utilized to calibrate the shear strength parameters of the joints. The key block retrieval results indicated that the slope's design was highly safe, and the slope angle can be appropriately increased.  
Conceptualization and methodology, S.W. and P.V.; software, Z.G. and Q.Z. and Y.L.; test, H.Y.; original draft preparation, Z.G.; review and editing, Z.G. and H.Y.; supervision, project administration, and funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant No. U1602232), the Liaoning Province Science and technology plan, China (2019 JHz-10100035).  
Not applicable.  
The authors would like to thank the reviewers for their many constructive suggestions and comments that helped improve the quality of the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
JRCJoint Roughness Coefficient  
PFCParticle Flow Code  
UCSUniaxial Compressive Strength
## 3 Results and Discussion
### Antioxidant Properties of Bread and Pomace  
The content of polyphenols, anthocyanins, and antioxidant activity, as determined by the ABTS and DPPH methods, of the obtained gluten-free breads and raspberry pomace are presented in Table 5.  
The conducted ANOVA and the post-hoc test (\(p>0.05\)) showed that the average TAC and ABTS values were different for freeze-dried and convection-dried pomace. The ABTS value was 4.62% higher and the TAC value was 46.58% higher for freeze-dried pomace. [PERSON] et al. [74] indicated that the TPC value was 273.9 mg GAE/100 g f. w., TAC was 37.3 mg cyan-3-G/100 g f. w., and the ABTS value was 33.2 \(\pm\) 1.6 micromond TE/g f. w. for raspberry extracts, Polesie variety. [PERSON] et al. [68] reported that the ABTS value for red raspberry was 6.27 umol TE/g f. w., DPPH was EC\({}_{50}\) 4960.58 g f. w./g of DPPH, TPC was 357.83 mg GAE/100 g f. w., and TAC was 14.69 mg cyan-3-G/100 g f. w. [PERSON] et al. [75] freeze-dried (process parameters different from those in this study) raspberry fruits of the Polesie variety harvested in 2012 and 2013. They showed that the TPC value was 426.21, and in the following year, they found 229.60 mg chlorogenic acid per 100 g f. w., and the TAC value was 49.97. A year later, they found 45.21 mg cyan-3-G/100 g f. w, and the DPPH value, expressed as % of free radical scavenging, was 31.34, and in the following year, 41.23. [PERSON] et al. [76] freeze-dried raspberries (_Rubus ideaus_, cv. \"Meeker\") with process parameters other than those used in this work and showed that the TPC value was \(2209.86\pm 70.32\) mg GAE/100 g of freeze-dried raspberry, the TAC value was 144.55 \(\pm\) 0.39 mg CGE/100g of freeze-dried raspberry, and the EC\({}_{50}\)DPPH* value was 0.250 mg/mL. [PERSON] et al. [77] also freeze-dried raspberries with other process parameters and showed that the TAC content in the _Rubus ideaus_ L. variety was 0.05 mg cyan-3-G/g fresh matter and the EC\({}_{50}\)DPPH* value was 0.6 mg/mL. The higher contents of ABTS and TAC in the freeze-dried samples were consistent with the results obtained by other researchers of the fruit drying process under various conditions [78; 79; 80; 81]. TAC compounds are particularly sensitive to thermal drying conditions, the degradation of which occurs after the process temperature exceeds 45 \({}^{\circ}\)C. TPC and DPPH values did not differ statistically significantly. This is due to the greater tolerance of TPC to increased temperatures of processing processes, which only visibly reduce their share in the processed material after exceeding approximately 60 \({}^{\circ}\)C [81; 82]. No anthocyanins were detected in the breads we tested. In the available literature reports regarding fruit pomace in gluten-free bread, the TAC value was not determined using the spectrophotometric method. [PERSON] and others [55] analyzed the anthocyanin content in waffles with the addition of freeze-dried raspberry-blueberry pomace. Changes in the tested substances accounted for up to 75%. The authors showed that the highest anthocyanin content was in waffles with the highest pomace addition. However, it
## 3 Positioning Model Establishment and Solution
### Processing of Normal Data  
According to task 2, position the 5 groups of data without interference. For the two-dimensional space, the anchor points A0, A1, and A2 are the centers of the three circles in Figure 6. The intersection of the two will form 6 intersections in the plane. In the absence of interference, the three intersection points can approximately converge into a point (the target point).  
For the three-dimensional space, the anchor points A0, A1, A2, and A3 are the centers of the four spheres shown in Figure 6. In fact, in space, these four spheres intersect two by two, and they can indeed meet at one point, which is the target. Therefore, our goal is to find this point set in space by selecting the initial value of X, so that the distance to  
Figure 6: Schematic diagram of the positioning of normal data (The letters on the figure indicate the centers of the three circles or the points of intersection on the circles).  
these four anchor points has a high degree of agreement with the distance we measured. Then, we can pass these distances. As a function, the sum of squares is used to filter out the minimum value of this function, which means that the distance from this point to the four anchor points A0, A1, A2, and A3 is very close to the actual detected distance.  
According to the scene information given by task 2, the initial coordinate of X positions the mean value of the coordinates of the four anchor points, which is (2500, 2500, 1500), and defines the function \"fmincon\" to find the sum of the squares of the distances from the real target coordinates. The smallest intersection point coordinate; then, this point is the target point that needs to be obtained.  
For normal data, after the minimum value of this function is obtained, the corresponding values of X, Y, and Z are mathematically the coordinates of the point corresponding to the extreme value of this function. For this paper, at the physical level, the point is very close to the target position, which is in line with the accuracy of the real detected data. The coordinates of the 324 points that are very close to the target point are obtained by calculation, and the coordinates of these 324 points are compared with the Tag coordinate information given in annex 1, as shown in Figure 7. It can be found that 324 points are very similar to the coordinate results of Tag, so it can be approximated that the calculated points are the target points that need to be obtained in task 2.  
Due to some uncertainties in the process of the experiment, there is a slight error in the distance between the measured target point and the 4 anchor points. By calculating the distance between the Tag coordinate information provided in annex 1 and the 4 anchor points, it is found that the calculated distance value does have a slight deviation from the normal distance data provided in annex 1 (Figure 8), thus verifying the existence of the error.
# Optimized Sonar Broadband Focused Beamforming Algorithm
## 2 Broadband Focused Beamforming Algorithm
### Determination of Optimal Focusing Frequency  
Equation (4) shows that the error can be obtained as follows [11]:  
\[\begin{array}{l}\sum\limits_{j}^{J}\bigl{\|}A(f_{0},\theta)-T(f_{j})A(f_{j}, \theta)\bigr{\|}_{F}^{2}\\ =\sum\limits_{j}^{I}\bigl{(}\bigl{\|}A(f_{0},\theta)\bigr{\|}_{F}^{2}+\bigl{\|} A(f_{j},\theta)\bigr{\|}_{F}^{2}\\ -2\text{Re}\bigl{\{}tr\bigl{[}A(f_{0},\theta)A^{H}(f_{j},\theta)T^{H}(f_{j}) \bigr{]}\bigr{\}}\bigr{)}\end{array}. \tag{5}\]  
The direction matrix \(A(f_{j},\theta)\) of an arbitrary shape array should satisfy the following condition:  
\[\bigl{\|}A(f_{j},\theta)\bigr{\|}_{F}^{2}=\sum\limits_{i=1}^{P}\bigl{\|}a( \theta)\bigr{\|}_{F}^{2}=MP \tag{6}\]  
where \(i\) (\(i=1,2,\cdots,P\)) is the number of signals. Take Equation (6) into Equation (5), then:  
\[\begin{array}{l}\sum\limits_{j}^{J}\bigl{\|}A(f_{0},\theta)-T(f_{j})A(f_{j},\theta)\bigr{\|}_{F}^{2}\\ =2 JMP-\sum\limits_{j=1}^{I}\sum\limits_{i=1}^{P}\lambda_{i}\bigl{[}A(f_{0}, \theta)A^{H}(f_{j},\theta)\bigr{]}\end{array}, \tag{7}\]  
where \(\lambda_{i}\bigl{[}A(f_{0},\theta)A^{H}(f_{j},\theta)\bigr{]}\) is the first P maximum singular value of the matrix \(A(f_{0},\theta)A^{H}(f_{j},\theta)\). Therefore, the value of \(\sum\limits_{j=1}^{I}\sum\limits_{i=1}^{P}\lambda_{i}\bigl{[}A(f_{0},\theta)A^ {H}(f_{j},\theta)\bigr{]}\) must be maximized to minimize the focusing transform error.  
Let:  
\[F=\max\limits_{f_{0}}\biggl{[}\sum\limits_{j=1}^{J}\sum\limits_{i=1}^{P} \lambda_{i}\Bigl{[}A(f_{0},\theta)A^{H}(f_{j},\theta)\Bigr{]}\biggr{]}. \tag{8}\]  
The focusing fitting error is minimized when F is maximized. For the direction matrix of any array, if the number of signals P is less than the number of array elements M, we may obtain:  
\[\sum\limits_{i=1}^{P}\lambda_{i}\Bigl{[}A(f_{0},\theta)A^{H}(f_{j},\theta) \Bigr{]}\leq\sum\limits_{i=1}^{P}\lambda_{i}[A(f_{0},\theta)]\lambda_{i}\Bigl{[} A^{H}(f_{j},\theta)\Bigr{]}. \tag{9}\]  
Let:  
\[u_{i}=\sum\limits_{j=1}^{J}\lambda_{i}\bigl{[}A(f_{j},\theta)\bigr{]}. \tag{10}\]  
Then:  
\[F\leq\max\limits_{f_{0}}\biggl{\{}\sum\limits_{i=1}^{P}\lambda_{i}\bigl{[}A(f_ {0},\theta)\bigr{]}u_{i}\biggr{\}}. \tag{11}\]  
From Equation (11), the focusing frequency \(f_{0}\), which is the optimal focusing frequency, is obtained. The time complexity of this algorithm is \(O(n^{3})\).
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 1 Introduction
### Organization  
This manuscript is organized as follows: in Section 2, we survey the existing literature on emerging 5G technologies and similar SDR testbeds; in Section 3, we describe the system architecture and features of WISCANet; and in Section 4, we demonstrate several sub-6 and mmWave applications over-the-air.  
Figure 2: Example deployment of the WISCANet control architecture on several Ettus USRP devices controlled by Dell workstations and Intel NUC Small Form Factor PCs (SFFPCs). This deployment features 8 Ettus B210s and 5 Ettus X310s which operate in the typical sub-6 frequency bands. This implementation also features (2) 60 GHz phased array antennas for mmWave experiments. This deployment can be used for a variety of experiments in both mmWave and sub-6 GHz bands, enabling diverse, multi-band, multi-antenna experiments but also remaining accessible to users without advanced networking and programming expertise.
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Working Principle
#### 2.2.3 Underwater Vision System Based on AI Edge Computing  
To dynamically monitor the growing condition of aquaculture species, this paper designs an automatic detection method based on YOLOv5. Compared to previous versions of YOLO, YOLOv5 offers faster computational speed and is considered the most suitable model for real-time object detection [34].  
U-Net is selected as the clarification network in this project to be used as the preprocessing of underwater aquatic images [35; 36; 37]. After processing with U-Net, the image clarity is significantly improved, the details are effectively enhanced, and the background noise is substantially reduced, providing high-quality image data support for subsequent water quality analysis and evaluation.  
After image preprocessing, target detection is performed using YOLOv5. First, mosaic data augmentation, adaptive anchor box calculation, and image scaling enrich the dataset, enhancing robustness and inference speed. The backbone extracts features through the focus structure and convolutional operations, generating a 304 \(\times\) 304 \(\times\) 32 feature map, and optimizes computational cost and accuracy using the CSP module. The neck introduces the SPP module to enhance feature fusion and expand the range of contextual features. Finally, the prediction module divides the image into different grids, predicting target bounding boxes, positions, classification results, and confidence levels to complete aquatic target detection. The YOLOv5 network structure is shown in Figure 9.
# Performance Analysis of ICA in Sensor Array
## 3 Performance of ICA _vs._ Practical Factors
### Condition Number vs. Practical Factors  
It can be seen from Equation (17) that under the white Gaussian noise assumption, the SNR at the input of the separator is only determined by the variances of the sources and the noise. That is to say, factors including the element spacing of the array, the frequencies and locations of the NB sources would not affect the signal-to-noise ratio, thus these factors may impact the separation performance via changing the condition number only. In this part, we attempt to analyze their relationships with the condition number in the analytical form and aim at reaching instructive conclusions. Moreover, special attention is paid to the case of singular mixtures.  
Without the loss of generality, suppose that in Equation (13) \(\mathbf{d}=\beta\lambda_{1}\), \(\lambda_{1}\leqslant\lambda_{2}\) and \(\lambda_{2}=D\lambda_{1}\). The parameter \(\beta\) represents the element spacing in a relative sense, \(D\) describes the divergence of wavelengths of the sources and the influence of the locations of sources will be depicted by the DOAs. With notations above, Equation (13) can be rearranged as:  
\[cond(\mathbf{A})=\frac{4}{\sqrt{2-2 cos[2\pi\left(\beta/D\right)\cdot\left(cos \theta_{2}cos\varphi_{2}-Dcos\theta_{1}cos\varphi_{1}\right)]}} \tag{22}\]
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
## 3 Time-Frequency Analysis Method Based on Wavelet Threshold Denoising and HHT
### Comparative Analysis of the Denoising Effect on Simulation Signals  
Several commonly used standard analogue signals, such as the blocks, bumps, Doppler, and heavy sine signal, were selected for a simulation analysis, which aimed to verify the performance of the proposed denoising method and examine the effective suppression of the modal aliasing phenomenon in the traditional EMD method. The denoising performance of the proposed method was compared with that of wavelet denoising and EMD methods. The two evaluation indicators were used to evaluate the denoising performance of the four methods quantitatively.  
Classic simulation signals were generated by the Wnoise function in MATLAB 2014b. These signals had the same sampling length and different SNR. Their waveforms (SNR were all 4 dB) are shown in Figure 3.  
Figure 3: Waveforms of the simulation signals containing noise.  
Table 1 indicates that each method performed well to a certain degree in terms of the denoising effect on the simulation signals. Overall, the denoising effect of the wavelet denoising method was not as good as that of EMD and was inferior to that of wavelet-EMD. The wavelet-EMD method performed well not only in the evaluation indicator SNR, but also in RMSE, thereby satisfying the demands mentioned in Section 3.2.  
To investigate this issue further, noisy signals with different SNRs (i.e., 6, 2 and \(-2\)dB) were analyzed in the same manner as above. The wavelet functions for the comparative analysis used the db8 function and the semi-soft threshold method. The waveforms of the denoising results from the different methods were omitted to save space; only the denoising effect indicators after processing are listed in Table 2.  
Table 2 together with Table 1 show that the denoising effect of the different methods was verified again. Several conditions were inapplicable to the blocks signal. The denoising effect varied with the SNR of the noisy signal. The wavelet-EMD method exhibited good adaptability, and its denoising effect had little correlation with the noise intensity of the noisy signal. In particular, even when the SNR of the noisy signal was relatively low, the denoising result from the wavelet-EMD method was smooth, and the method could still denoise effectively. The superiority of the proposed algorithm was proven again.
## 1 Introduction
Following the actual landing of a flight, an automated SC is executed to compensate orders that exceed the stipulated delay time. By utilizing SCs for automated compensation, the cumbersome processes of manual submission, verification, and approval are eliminated. Consequently, insurance companies are unable to deceive consumers, thereby safeguarding the trust between insurance companies and users. Fizzy [4] is an insurance service that covers flight delays and aims to provide a decentralized insurance solution settled on the Ethereum blockchain via open-source SCs. This study extracts the  
Figure 1: Multi-party interaction scenario: purchasing flight delay insurance and obtaining compensation. key multi-party interaction scenarios in purchasing flight delay insurance and obtaining compensation. This case will be used in this paper to illustrate the method proposed to generate SCs automatically. Since SCs are interdisciplinary in nature, different practitioners, such as lawyers, computer engineers, business and finance experts, and others from different fields, can collaborate to design, propose, and implement SCs [5]. At present, the mainstream method used to write SCs is technology-related. The current, most popular SC languages (such as Solidity [6] and Vyper [7]) are mainly intended for technical personnel and are not easy to understand, which makes them difficult for non-IT professionals to use. According to the CoinGeco survey [8], only 40% of users of decentralized finance can read and understand the source code of smart contracts. Therefore, it is difficult for these people to participate in the development process of SCs. Professional developers of SCs may also fail to understand the requirements of the domain, resulting in the development of SCs with requirement errors. In addition, the manual development method will also lead to problems such as low development efficiency. To address these issues, we devise three research questions. * **RQ1**: How can we model the requirement scenarios and interaction relationships of SCs so that they can be easily understood and used by both business personnel and developers? * **RQ2**: How can we automatically generate SC codes based on the process and interaction models in a reusable way? * **RQ3**: How can reusable libraries be maintained in an efficient, low-barrier way to support RQ2's automatic code generation? The main contributions of this paper are summarized as follows.
# Tunable and Switchable Erbium-Doped Fiber Laser Using a Multimode-Fiber Based Filter
## 3 Experimental Results and Discussions
The wavelength fluctuation of the 1 st and 2 nd peak is 0.04 nm and 0.03 nm, and their power fluctuations are 0.46 and 0.32 dB, respectively, which shows good power uniformity and stability. The stable dual-wavelength operation could benefit from a polarization hole burning effect [11] and a deeply saturated spectral hole burning effect in the EDF [24] to effectively suppress the mode competition from the homogenous line broadening and cross-gain saturation in the EDF. It is worth noting that the laser is tunable once the pump power is slightly greater than threshold. With increasing pump power, the tuning range and stability can be improved. When the pump power is greater than 120 mW, the tuning range reaches maximum and remains stable for both single and dual wavelength modes, and thus the pump power of 130 mW was used for the tests. Figure 4: Tunable dual-wavelength laser spectra.
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 3 Methodology
### Identification of Metamorphic Relations
#### 3.2.1 Counter-Clockwise Rotation at 90 Degrees  
The mathematical property of MR\({}_{1}\) is:  
\[\text{MR}_{1}\text{:}\text{C}(\text{E}(\text{Im}))=\text{E}(\text{C}(\text{Im }))\]  
where Im is the input Image, C(.) is the counter-clockwise rotation at 90 degrees, and E is the edge detection. The image output of counter-clockwise rotation followed by edge detection should be like the image output of edge detection followed by counter-clockwise rotation.