esa-sceva/satcom-chunk-collection huggingface dataset:
# Determination of Effective Parameters for Hydropower Plants' Energy Generation: A Case Study
## 4 Results and Discussion
### Correlation Development  
The generation capacity of a hydropower plant is contingent upon four key factors: flow rate, overall efficiency, ground acceleration, and net head, which pertains to the vertical distance that the water falls. The equation of the energy generation of the hydropower plant is given below [39,40].  
\[P=\eta\times\rho\times g\times Q\times H \tag{1}\]  
where the given parameters are \(P\), \(\eta\), \(\rho\), \(g\), \(Q\) and \(H\), which represent the produced power [kW], general efficiency, density of water [kg/m\({}^{3}\)], ground acceleration [m/s\({}^{2}\)], flow rate [m\({}^{3}\)/s], and net head [m], respectively. The values of the mentioned parameters in the given equation are nearly constant except for flow rate, which changes instantaneously. According to [PERSON] et al. [41], there is a clear relationship between hydropower energy generation and stream flow (flow rate) that can be accurately represented using a statistical model. From this point of view, a novel linear correlation with an R\({}^{2}\) value of 0.89 has been produced, given in Equation (2), using the long-term daily flow rate data and daily energy generation of the investigated hydropower plant. The only variable parameter is flow rate for the proposed equation, derived by implementing more than 1000 data points, covering 2016-2018, as shown in Figure 12.  
It should be noted that the flow rates that exceeded the value of the design flow rate of 31.24 m\({}^{3}\)/s have not been taken into account in the derivation of the correlation because the given design value determines the upper limit of energy generation.  
\[P=22.1\times Q+19 \tag{2}\]  
where \(P\) and \(Q\) are the energy generation [kW] and flow rate [m\({}^{3}\)/s], respectively. In Figure 13, the obtained energy generation values using the proposed correlation have been compared with the actual energy generation values of the same power plant for 2019, and it can be seen that the deviation band of the equation is in the range of \(\pm\)20%. The proposed correlation, derived using more than 1000 data points of daily flow rates and energy generation, can be implemented in cases where there are only data on flow rates to obtain an estimation of energy generation.  
Figure 10: Alteration in daily energy generation with respect to precipitation, (**a**) 2016, (**b**) 2017, (**c**) 2018, and (**d**) 2019.  
Figure 11: _Cont._  
Figure 11: Alteration in daily energy generation with respect to flow rate, (**a**) 2016, (**b**) 2017, (**c**) 2018, and (**d**) 2019.  
Figure 12: Alteration in daily energy generation with respect to flow rate, limited by design flow rate.  
Figure 13: Comparison of actual data with those of derived correlation.
Possible reasons for this include the different spatial distribution of the respective measurement data, as well as the number of measurements collected and the used evaluation methods. This shows that it is important to define guidelines on how to evaluate the validity of CNM data. However, little research was performed in this area to date. The only works that go towards validity of crowdsourced data are [1, 6]. The authors of [6] state that, for example, end device related issues, resource consumption, and privacy versus reliability are challenges that CNMs bring. In [1], the authors state that validity, reliability, and representativeness play an important role in all stages of a crowdsourcing campaign: in the design and methodology, the data capturing and storage, and the data analysis. Nevertheless, there is a lack of detailed discussion on the validity of crowdsourced data and, in particular, a lack of guidelines or metrics on how to test data for validity. Hence, the basics are needed to understand its importance, avoid errors, and carry out crowdsourced measurements in a meaningful way. To address this issue, in this paper a large-scale commercial CNM data set from July 2019 to December 2019 for France with 20.4 M crowdsourced mobile measurements throughout the country and its overseas territories is analyzed. We tackle the following aspect for analyzing the validity of crowdsourced mobile network measurements: we consider the precision of an evaluation, in particular, the precision of a certain metric such as the downlink throughput. We analyze the mean mobile downlink throughput for certain regions and derive the number of measurements required to achieve high precision. This paper is a full extension of [7] and, to the best of the authors' knowledge, shows for the first time the application and definition of a comparable score to quantify the precision of a statement from a CNM data set. The remainder of the paper is structured as follows. In Section 2, different ways of measuring mobile network quality are presented, and the methodology of crowdsourcednetwork measurements is introduced. Section 3 summarizes related work in the field of CNMs. Definitions for validity are summarized in Section 4, while an explanation of the used data set is given in Section 5. The aspect of precision, including the definition of the metric called CNM Precision Validity Score, is dealt with in Section 6. Section 7 illustrates the importance and the applicability of the CNM Precision Validity Score by showing some exemplary results based on the given data set. Section 8 concludes the paper and summarizes the findings.
# Quantitative Evaluation of the Effectiveness of Erbium Glass Laser Therapy for Acne Scars
## 4 Discussion
By reducing the depth of these folds, the therapy helps to achieve a more uniform surface, minimizing the appearance of rolling scars and improving the overall skin texture. Reduced pore volume and count post-treatment imply an improvement in skin texture. Enlarged pores can emphasize scarred areas, especially on acne-affected skin. The decrease in pore size contributes to a more refined, even skin texture, helping to mask the scars and creating a smoother surface. The decrease in the volume of skin depressions due to acne scars is a direct indicator of scar reduction. Smaller depressions reflect a more even skin surface where previously sunken areas are raised. This is likely due to the laser's collagen-stimulating effects, which help rebuild the skin's structure and lessen the depth of scars. Reduced roughness in skin texture after treatment suggests a smoother surface, which is essential for diminishing the appearance of acne scars. Acne scars often create a rough, uneven texture on the skin, and reducing this roughness leads to a more visually appealing, uniform surface. A decrease in maximum texture height implies the leveling of the highest and lowest points on the skin's surface. This is crucial for acne scars, where differences in height between normal skin and scar tissue can be significant. By reducing these differences, the treatment helps achieve a more consistent and smooth skin appearance. Reducing pigmentation levels, both mean and maximum, suggests an evening of skin tone, which is critical for reducing the visibility of acne scars. Scars can cause hyperpigmentation, and addressing this uneven pigmentation helps the skin appear more uniform, making scars less noticeable. These parameter improvements highlight how erbium glass laser therapy not only reduces the visibility of acne scars but also enhances the overall health and appearance of the skin.
# A Denoising Method for Seismic Data Based on SVD and Deep Learning
## 2 Method
### Denoising Method Based on SVD and Deep Learning
#### 2.2.4 Algorithm Program Steps  
The process of improving the SVD seismic denoising method is shown in detail in Algorithm 1. This pseudo-algorithm is performed in python.  
Figure 6: The trained MobilenetV2 network can predict the six right singular vectors as (**a**–**f**), respectively.  
```
1:\(D_{m,n}\): seismic data have m traces, n samples, window parameters were set to \(a\) traces, \(b\) samples, (\(a\) is an odd number and \(b\) is an even number), rank of matrix K = 4;
2:\(W_{m,n}\) : denoised seismic data;
3:\(W_{m,n}=D_{m,n}\):
4:for (\(a/2\)) + \(I<i\leq m-a/2+1\), \(i\) + \(a\)do
5:for (\(b\)-1)/2 + \(1<j\leq n-(b-1)/2\), \(j\) + \(1\)do
6: Set window = \(a^{*}b\) (\(a<m\), \(b<n\)), obtain \(d_{a,b}\) = \(D\left[i-\frac{a}{2}:i+\frac{a}{2}-1,j-\frac{b-1}{2}:j+\frac{b-1}{2}\right]\);
7: Compute SVD function USV = svd(d), obtain \(U_{a,a}\): left singular matrix,\(S_{a,b}\): singular value matrix,\(V_{b,b}\): right singular matrix;
8:for 0 < \(k\leq K\)do
9: Transform the kth RSV into 224*224 pictures;
10: Using a trained MobileNetV2 network to predict the kth RSV;
11:if V[:,k] = \"_effective_\" then
12:\(S[k]\) = \(S[k,k]\);
13: A five-point smoothing is applied to the kth RSV;
14:else V[:,k] = noise_\"
15:\(S[k,k]\) = 0;
16:endif
17:endfor
18:\(w_{a,b}\)= \(\sum\limits_{k=1}^{K}U_{k}S_{k}V_{k}^{T}\);
19: Extract the reconstruction matrix w[:, \(\frac{b-1}{2}+1\)] and insert it into the M[\(i-\frac{a}{2}:i+\frac{a}{2}-1,j\)] matrix data;
20:endfor
21:endfor
22:Return\(W_{m,n}\).
```  
**Algorithm 1** Pseudocode of Seismic Denoising Algorithm
## 5 Experimental Results and Discussion
### Discussion  
From the simulation experiments, we can obtain the synthetic motion trajectories of the proposed two-arm synergistic method, which can achieve similar motion results and better flexibility with respect to the single-arm motion trajectories. Further, two scenarios of two-arm cooperative sanding experiments were conducted, and we recorded the trajectories of each of the two robotic arms in different directions for each scenario.  
In the two-arm longitudinal collaboration experiments, we recorded the motion of the two robotic arms in their respective designed motion directions. From the calculated motion errors, the average error basically stays at 1-4 mm, which is still some difference from the desired trajectory, and the error of the decomposition motion will obviously affect the collaboration effect. However, under the whole length of motion, the synthesized motion basically conforms to the shape of the workpiece, and only produces an overall offset in a certain direction. Considering the final overall effect, this is a more reasonable result. For the rotational cooperative motion, it can be seen from the results that the motion accuracy in the rotational direction is slightly higher than the translational motion. Moreover, the difficulty of motion planning and trajectory tracking in this case is higher in practical situations, but the actual motion results can still be obtained with good results, which essentially proves the effectiveness of the proposed method.  
Figure 11: The Z-direction movement and error of manipulator 2.  
Figure 10: The Y-direction movement and error of manipulator 2.
## 4 Results
### Compression Ratio Results  
To evaluate the compression performance of the proposed algorithm, the LZW [28; 29] and Huffman [30] algorithms, the most widely used compression algorithms, were first used for comparison. The compression ratio (\(CR\)) was calculated using the following Equation (1), where _US_ is the uncompressed size and \(CS\) is the compressed size:  
\[CR=US/CS \tag{1}\]  
A comparison of the \(CR\) with those of other widely used algorithms is shown in Table 5. LZW and Huffman showed \(CRs\) of 1.87 and 1.90, respectively, compared to 3.61 for the proposed algorithm, thus exhibiting a significant difference. Therefore, the proposed algorithm yielded a more efficient compression.  
For a further subjective evaluation of the performance of the proposed algorithm, a comparison with prior studies was conducted. Few studies of lossless compression of EMG signals of maternal subjects are extant, but recent researches similar to this study were used. Note that the percentage residual difference (\(PRD\)) is calculated according to Equation (2).  
\[PRD=\sqrt{\frac{\sum_{n=0}^{K-1}(x[n]-\hat{x}[n])^{2}}{\sum_{n=0}^{K-1}x^{2}[n ]}}\times 100\% \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CR Values** & **LZW** & **Huffman** & **Proposed Algorithm** \\ \hline Average \(\pm\) Standard Deviation & 1.87 \(\pm\) 0.03 & 1.90 \(\pm\) 0.01 & 3.61 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of \(CR\) with those of other algorithms.  
In Equation (2), \(x\) is the uncompressed original signal and \(\hat{x}\) is the reconstructed signal after compression. In addition, \(K\) is the total sample length of the signal. For instance, the length of \(K\) is 2000 in the proposed experiment. A comparison of the \(PRD\) and \(CR\) of the proposed algorithm and previous researches is shown in Table 6.  
For an objective comparison, a \(CR\) under similar circumstances to \(PRD\) was compared, because the proposed algorithm was lossy (\(PRD\) of 0). The lower the \(PRD\), the lower the loss rate. [PERSON] reported a \(CR\) of 2.00, and [PERSON] a \(CR\) of 1.69. [PERSON], [PERSON], [PERSON], and [PERSON] reported \(CRs\) of 3.33, but different \(PRD\) values. The \(PRD\) values of [PERSON], [PERSON], [PERSON], and [PERSON] were 3.90, 1.79, 1.21, and 2.12, respectively.  
The proposed algorithm was more efficient (\(CR\), 3.61) than those in previous studies. Additionally, [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] had markedly higher \(PRD\) values than the proposed algorithm. Although not proposed in their research, their compression performances will be far lower in close-to-zero \(PRD\) situations.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 6 Conclusions  
With the development of SDN technology and the improvement of service quality, more and more attention has been paid to the construction of constrained SDN path pre-planning. Besides, mobile devices are widely applied, and wireless multi-hop networks are also known as a research hotspot. The combination of the two points provides a favorable network environment for multiple users to transmit data. This paper mainly studies the data transmission path with the lowest global energy consumption for multiple users under the delay constraint.  
In this paper, the network model was established by [PERSON]'s theorem to simulate the routing and links. The energy consumption matrix and time delay matrix were obtained from the initial topology and edge weight dataset. Next, the alternative paths between two points were selected by the K-LARAC algorithm. In the end, the alternative paths of each user were combined by the improved GA to develop the routing strategy that satisfied the global minimum energy consumption and ensured that each intermediate node could only forward data once.  
For the QoS routing optimization problem based on SDMWN, it will take much time from theoretical research to technical implementation and practical application, particularly for the research of multi-user service in a large-scale network topology. From now on, the study of the QoS routing algorithm will focus on low computational complexity and high precision in order to better plan the path.  
**Author Contributions:** Conceptualization, Z.L. and G.X.; data curation, Z.L.; formal analysis, Z.L., G.X., P.L., X.F., and Y.L.; investigation, P.L.; methodology, Z.L. and P.L.; resources, G.X.; software, Z.L.; supervision, G.X. and P.L.; validation, Z.L., G.X., P.L., X.F., and Y.L.; visualization, Z.L.; writing, original draft preparation, Z.L.; writing, review and editing, G.X., P.L., X.F., and Y.L.  
**Funding:** This work is funded by: the Jilin Industrial Innovation Special Fund Project (No. 2017C028-4) and the National Key R&D Plan of China (No. 2017 YFA0604500).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 5: User number vs. energy consumption.
# Laser Welding on Cerclage Wire Joining in Femur Surgery
## 4 Experimented Results and Discussion  
### Effect of Laser Parameters on Weld Bead Width  
In regard with the charge voltage at 80 V, the weld result in Figure 8a shows the example of oxidation region at a high pulse frequency of 15 Hz with pulse width of 3 ms. A good weld appearance with nice ripples is shown in Figure 8b in which the pulse frequency was set at the acceptable range of 6 Hz with pulse width of 3 ms. In the case of low charge voltage at 70 V, the result in Figure 8c shows the phenomena for lacking of fusion. Based on the experimental results, the weld appearance, including the weld shape and the bead width, were affected by increasing charge voltage and pulse frequency.  
The weld appearance was measured in the experiments and the results were used to investigate the connection between the weld bead width and the set values of charge voltage and pulse frequency. Figure 9 shows the weld bead width obtained at the charge voltage of 80, 90, 100, and 110 V, while the pulse frequency was set at 6 Hz and the pulse width was set at 3 ms. The weld bead width obtained at pulse frequency of 6, 9, and 12 Hz, is shown in Figure 10. The charge voltage was set at 80 V and the pulse width was set at 3 ms. It can be concluded that the increase of charge voltage and pulse frequency will result in increased bead width because the heat for metal melting is also increased. When the bead width was increased, the sound bead was also increased because the gap area was reduced. The reduction of gap area was due to the increasing of pulse frequency of welding. Based on the results shown in Figure 9, the charge voltage at 110 V could result in the highest weld bead width which is referred to as the soundest bead.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & & & **Level** & \\ \hline Charge voltage (V) & 80 & 90 & 100 & 110 \\ Pulse frequency (Hz) & 6 & 9 & 12 \\ Welding length (mm) & 5 & 10 & 15 \\ \hline \hline \end{tabular} Note: pulse width is set at constant of 3 ms  
\end{table}
Table 2: Variables for testing the tensile strength.  
Figure 7: Schematic view of the relationship between shear loading and weld joint.  
Figure 8: Weld appearance (**a**) Oxidation, (**b**) Good appearance with nice ripples, and (**c**) Lack of fusion.  
Figure 10: Effect of pulse frequency on weld bead width.  
Figure 9: Effect of charge voltage on weld bead width.
## 4 Simulating Array Acquisitions and Array Imaging
### Calculating Delay Laws with a Ray Model  
Including material information in ultrasonic imaging requires calculating travel times between array elements and all points in the imaged domain. In this study, we used a shortest ray path model developed for the ADVISE project. The model is based on the Fermat principle, stating that the ultrasound takes the path which results in the shortest propagation time, analogously to previously reported implementations [30] above.  
The present model uses a macroscale weld map description of the orientations. The edges of each square cell are seeded with a chosen number of nodes which helps to define an allowable number of paths the ultrasound can take while passing through each cell. The solver uses classical shortest path finding algorithms (here we use the scipy implementation) with local travel times across cells calculated from the group slowness of the rotated transversely isotropic material. A generic diagram behind this model (not limited to welds) is shown in Figure 7. Thanks to the cells having the same structure and using the same base group slowness for the weld and constant wavespeed for any isotropic material, the algorithm is quick for a large number of sources and target positions. The outputs of this calculation are the times of flight between all transducer positions and all points in the imaging grid.  
Figure 6: Schematic diagram of the ultrasound FE model setup with defect scenarios considered: (**a**) the model domain; (**b**) element excitation.
# Numerical Simulation and Optimization of the Airflow Field of a Forage Drum Mower
## 4 Results and Discussion
### Optimal Parameters and Regression Models
Factor C, i.e., disc tilt angle, has a significant effect on the velocity of the lifting and feeding airflows rather than the gathering airflow. Thus, only its effect on the velocity of lifting and feeding airflows is considered. The range analysis reveals that the superior level for the velocity of the disc tilt angle on lifting and feeding airflows is 2, so the optimal parameter of the disc tilt angle is determined to be 4\({}^{\circ}\). For factor D, i.e., the number of guide plates, its effect on the feeding airflow velocity reaches a significant extent, but its effect on the velocity of the lifting and gathering airflows is insignificant. Therefore, only its effect on the feeding airflow is taken into consideration. According to the results of the range analysis, the superior level is 2 for the number of guide plates on the velocity of feeding airflow. Thus, the optimal number of guide plates is determined as 2. In summary, to obtain the maximum lifting, feeding, and gathering airflows, the optimal structure and operating parameters of the double-drum cutter are determined as follows: a disc speed of 2000 r/min, a blade twist angle of 8\({}^{\circ}\), a blade tilt angle of 4\({}^{\circ}\), and 2 guide plates. The orthogonal Latin square arrangement was adopted in the above experiments, which satisfied the orthogonal conditions of orthogonal polynomial regression. The model of airflow velocity prediction can be constructed by performing multiple linear regression based on the results of the orthogonal test so that the parameters of airflow field characteristics can be predicted according to the structure and operation parameters of the double-drum cutter. For the velocity \(v_{g}\) of the gathering airflow, the regression equation is established as follows:  
\[v_{g}=2.4094+0.5351A+0.056B-0.2271C+0.3036D\]  
\[N=9\;R=0.9345\;F=14.278\;S=0.2133\]  
For the velocity \(v_{l}\) of the lifting airflow, the regression equation is established as follows:  
\[v_{l}=2.7325+0.9063A+0.0943B+0.6493C-0.1955D\]  
\[N=9\;R=0.7152\;F=2.5113\;S=0.8777\]  
For the velocity \(v_{f}\) of the feeding airflow, the regression equation is established as follows:  
\[v_{f}=5.6383+1.4228A-0.0246B+0.2955C+0.2172D\]  
\[N=9\;R=0.9442\;F=12.9269\;S=0.4374\]
## 3 The SRL-Based Control Strategy for CACC
### The SRL Control Algorithm
#### 3.2.3 The Supervisor  
Driver behavior can be modeled with parametric models, such as the SUMO model and the Intelligent Driver Model, and non-parametric models, such as the Gaussian Mixture Regression model and artificial neural network model [42]. In [43,44], a neural network-based approach for modelling driver behavior is investigated. In this part, the driver behavior is modeled by a feed-forward neural network with the same structure as the actor network. The driver's operation data in a real vehicle-following scenario can be collected to form a dataset \(\mathbf{D}=\{e_{d}(t)\), \(v_{r}(t)\), \(a_{r}(t)\), \(a_{des}(t)\}\). Note that here we use the desired acceleration \(a_{des}(t)\) as the driver's command instead of the accelerator pedal signal \(\alpha\) and braking pressure \(P_{b}\). The supervisor network can be trained with dataset \(\mathbf{D}\) to predict the driver's command \(a_{des}(t)\) according to a given state \([e_{d}(t)\), \(v_{r}(t)\), \(a_{r}(t)]\). The weights are updated by prediction error back-propagation, and the Levenberg-Marquardt method is employed to train the network until the weights converge.