esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Total Soluble Solids, pH, and Titractable Acidity  
Bayberry juice was obtained through homogenization and filtration, followed by sterilization. The total soluble solids (TSS), pH, and tittatable acidity (TA) of the juice were measured immediately after sterilization and cooling. The ethanol content, residual sugar (RS), TA, and dry extract of the wine were determined after wine production was completed. These results are shown in Table 1.  
The flavor of IBW is largely determined by its raw material composition and recipe. Besides fresh bayberries and a base wine such as crude fermented wine, distilled alcoholic  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Composition of Wine**} \\ \cline{2-5}  & **Ethanol (\%)** & **RS (g/L)** & **TA (\%)** & **Dry Extract (g/L)** \\ \hline IBW & 12.81 \(\pm\) 0.41 \({}^{\mathrm{a}}\) & 4.92 \(\pm\) 0.06 \({}^{\mathrm{a}}\) & 1.44 \(\pm\) 0.11 \({}^{\mathrm{a}}\) & 20.48 \(\pm\) 0.31 \({}^{\mathrm{a}}\) \\ FBW & 10.51 \(\pm\) 0.18 \({}^{\mathrm{b}}\) & 3.81 \(\pm\) 0.18 \({}^{\mathrm{b}}\) & 1.27 \(\pm\) 0.06 \({}^{\mathrm{b}}\) & 19.31 \(\pm\) 0.44 \({}^{\mathrm{b}}\) \\ \hline TSS, Total soluble solids; TA, tittatable acidity; RS, residual sugar; data with significant differences (\(p<0.05\), \(t\)-test) in each row are represented by different superscript letters. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: General composition of Chinese bayberry wines.  
spirit, or edible alcohol, additional ingredients such as a color stabilizer, an acidity regulator, and preservatives are typically required. However, to minimize experimental interference, only fresh bayberries and base wine were used in this study.  
For FBW, the residual sugar mainly originated from bayberry juice and the sucrose added during processing, and its concentration was significantly lower than that in IBW. Additionally, the ethanol content in the FBW was much lower than that of the IBW, which is probably the greatest difference regarding the general composition between the two wines. For IBW, residual sugar mainly comes from the bayberry juice, with a small amount coming from the base wine. In this study, edible alcohol was used as the base wine so as to minimize the impact of the base wine on the flavor of wine. The RS in the IBW was slightly lower than the theoretical value, calculated based on the sugar concentration in the juice. On the other hand, the ethanol content in the IBW was slightly higher than the theoretical value, calculated based on the initial concentration of edible alcohol. This was likely due to the less strict pasteurization method used in this study. The surviving yeast underwent mild fermentation during the juice processing and wine preparation process, resulting in the conversion of part of the sugar into ethanol in the IBW.
## 4 In Situ AE Monitoring in Gold Mines
### Gold Mine Cook 4 in South Africa
The number of events in Box B in Figure 14a before relocation was about 10,688. The number of events successfully relocated as AE events was 10,337. Figure 15a,b show relocated AE events in projections onto the x-y plane and x-z plane, respectively. A local coordinate system aligned with the clear discernible plane is used. The x coordinate is along the strike direction (positive eastward), the y coordinate is in the normal direction to the plane (positive northward), and the z coordinate is along the dip direction (positive upward). Figure 15c shows projections onto the x-y plane for z coordinate between 13.5 m to 34.5 m in steps of 3 m thickness in the z direction. Blue dots are clustered AE events (approximately 4900) defined at the plane, which are situated in the red dashed frames in (a) and (c). Gray dots are other events, which belongs not to the cluster. The AE hypocenters projected onto x-y planes exhibit clear traces continuous over several meters or more. It was shown that the events of this cluster correspond to the Zebra fault, a local fault. The information gained from studying the characteristics of AE event on such structures revealed in-depth information useful for risk assessment and stope planning. For example, the formation of  
Figure 15: Close up of area marked by Box B in Figure 13. The relocated AE events in projections onto the x-y plane (**a**) and x-z plane (**b**) are shown. A local coordinate system aligned with clear discernible plane is used. x coordinate is along strike direction (positive eastward), y coordinate is in normal direction to the plane (positive northward), and z coordinate is along dip direction (positive upward). (**c**) Shows projections onto the x–y plane for z coordinate between 13.5 m to 34.5 m in steps of 3 m thickness in the z direction. Blue dots are clustered, which are located in the red dashed frames in (**a**,**c**). Gray dots are other events [84]. Ortlepp shear fractures [95; 96] due to the approaching stope front could be observed (see Figure 14b); AE activity picking up on the newly created formation already, when the mining front was still more than 20 m away [34]. At the same time, pre-existing discontinuities such as faults or dikes that get seismically activated by the stress re-distribution due to the excavation were also identified [34; 84]. Higher-order features such as branches and step-overs could be observed owing to the high localization certainty. Detailed analysis of AE event properties including a b-value study revealed that both quasi-static slip [85] as well as dynamic slip events is observed on faults [84]. Interestingly the frequent observation of repeating AE events on faults monitored during the SATRAPS project suggests that AE events on faults loaded by stress-redistribution due to mining undergo a similar process as tectonic faults subject to tectonic stresses [86]. Note that both Ortlepp shear fractures as well as the re-activation of faults cause on a regular basis violent rock burst events (M1 to M4), which are a significant threat to people working underground [96; 97; 98].
# Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer
## 5 Simulation Experiment and Analysis
### Path-Planning Simulation Experiment
#### 5.3.3 Stepped Simulation Experiment  
In order to verify the influence of environmental model and path generation operation improvement on the path planning of obstacle-crossing robots, the GSGWO (GSGWO1) without obstacle-crossing function, the GSGWO (GSGWO2) without node optimization and the GSGWO are compared in the environment shown in Figure 6a. The experimental results are shown in Figure 7.  
The total path cost of the GSGWO1 algorithm in the figure is 39.8236. The total path cost of the GSGWO2 algorithm is 53.6763. The total path cost of the GSGWO algorithm is 31.1093. Through comparative analysis, it can be seen that the GSGWO1 algorithm has great limitations because it cannot cross the obstacle-surmountable passage area, so the final path has great limitations. The path obtained by the GSGWO2 algorithm can only walk in a limited direction, resulting in problems such as a too-long path, too many inflection points and too large corners. At the same time, because of the large number of nodes, the convergence speed of the algorithm is also affected to a certain extent. The GSGWO algorithm can effectively combine the advantages of the two, and the generated path is shorter while the inflection point is less, which has higher trafficability.
## 5 Conclusions  
The rock-breaking effect of the combined long and short straight hole excavation blasting method was investigated through a synthesis of theoretical analysis, numerical simulation, and field experiments. The rock-breaking process of combined long and short straight hole excavation blasting was analyzed from the perspectives of stress wave theory, rock mass damage, and construction effect. The following conclusions were drawn under the aforementioned conditions.  
1. It was found that during the process of combined long and short straight hole excavation blasting, a deep hole without charge can act as an empty hole in shallow hole blasting. The concentration of stress, the reflection of stress waves, and the superposition of waves at the wall of the uncharged section will enhance the rock-breaking effect, which is affected by the aperture and distance.
2. During the process of excavation blasting involving the combined long and short holes and straight holes, the priority detonation of shallow holes can facilitate the creation of smaller resistance lines and free surface distances for deep hole detonation, thereby accelerating the fragmentation of the rock mass within the blasting area.
3. During the blasting process of combined long and short holes and straight holes, the resulting groove cavity is rectangular in shape and expands to both sides. By adjusting the spacing between each group of blasting holes in a reasonable manner, it is possible to achieve the greatest possible blasting energy.
4. When the total amount of explosives used is not significantly different, the combined long and short straight hole cutting method has a greater blasting depth, smaller over- and under-excavation, and lower explosive consumption per unit than traditional oblique hole cutting.  
Conceptualization, H.F. and Y.G.; methodology, Y.G.; formal analysis, Y.G.; writing--original draft preparation, Y.G.; writing--review and editing, H.F.; visualization, H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Joint Program National Natural Science Foundation of China (U1934210), the Scientific Research Project of Hunan Provincial Department of Education (Project Number: 24C0187), and the Service Safety and Intelligent Maintenance of Rail Transit Operation Tunnels (Project Number: 2022 JBXT007).  
Not applicable.  
The data used to support the findings of this study are included within this article.  
The authors are very grateful for the contributions made by [PERSON] in data curation, [PERSON] in software, [PERSON] in investigation, and [PERSON] and [PERSON] in validation.
## 5 Simulation Results and Discussion
### Simulations and Analysis Under Different Circumstances
#### 5.3.2 Compare APM and DCPM  
Figures 8 and 9 show a dynamic response diagram of the transmission power of Line 7-9 and the rotor speed of Generator 3 under APM and DCPM. It can be seen that the damping capacity provided by the dual-channel power modulation is nearly the same as the active modulation; the oscillation attenuation speed is fast, and the transient oscillation amplitude is small.  
Figure 8: _Cont._  
Figure 7: (a) Rotor speed of Generator 3 at Case A; (b) Rotor speed of Generator 3 at Case B.
## 3 Results and Discussion
### Starting Materials
The chromatograms obtained are presented in Figure 4. In the chromatograms of tung oil and isolated \(\alpha\)-eleostearic acid (Figure 4a,b, respectively), two signals are observed at 15-17 min. The existence of these signals in the chromatogram of tung oil (Figure 4a) is believed to relate to the various possible triglyceride structures of the oil based on its fatty acid composition (\(\sim\)83% of \(\alpha\)-eleostearic acid, 9% of linoleic acid, 4% of oleic acid, and 4%-6% of palmitic acid) [21], with a predominance of triglycerides bearing \(\alpha\)-eleostearic acid accounting for the major signal. A similar scenario is reflected in the chromatogram of isolated \(\alpha\)-eleostearic acid (Figure 4b). The absence of other relevant signals suggests that, besides the mixture of fatty acids, no other by-products are detected. For the methyl esters (Figure 4c), a better separation of the different methyl ester chains is achieved, most likely due to their independent structure when compared to triglycerides, and their lower polarity in comparison to free fatty acids. Three signals are indeed obtained in the 15-17 min. range with one major signal attributed to the methyl ester of \(\alpha\)-eleostearic acid (Figure 4c). When comparing the retention times of the major signal in each one of the chromatograms (Figure 4a-c), a clear and logical trend in retention times can be observed. On one hand, while tung oil's major signal displays a retention time of 16 min and 45 s (Figure 4a), the major signal for the fatty acid sample exhibits a slightly longer retention time (16 min and 54 s, Figure 4b) that can be attributed  
Figure 4: Chromatograms of (**a**) tung oil, (**b**) isolated \(\alpha\)-eleostearic acid, and (**c**) tung oil methyl esters. Figure 3: Raman spectra of (**a**) tung oil, (**b**) isolated \(\alpha\)-eleostearic acid, and (**c**) tung oil methyl ester. to its higher polarity and, therefore, better affinity for the GC column. On the other hand, the methyl ester's major signal exhibits a shorter retention time (16 min and 16 s, Figure 4c) in comparison to that of tung oil's major signal. Despite their similar polarities, independent methyl ester chains move more freely through the column than triglycerides, resulting in the observed shorter retention time. Based on the FT-IR, 1H NMR, Raman, and GC-MS results, it can be concluded that the preparation of methyl esters and the isolation of fatty acids from tung oil were successful.
# Initial Results from SQUID Sensor: Analysis and Modeling for the ELF/VLF Atmospheric Noise
## 3 Data Preprocessing
### Baseline Correction
It can be seen that the low frequency components caused by baseline wandering are suppressed efficiently by wavelet denoising and the harmonics are further suppressed after LS estimation, which validates the effectiveness of the proposed method. To suppress the harmonics efficiently, the data were divided into frames before LS estimation. As a result, the low frequency part of the spectrum after harmonics suppression increases slightly in Figure 5b. Figure 4: Waveform of the atmospheric noise before harmonic suppression.
###### Abstract  
In the industrial environment, the data transmission of Wireless Sensor Networks (WSNs) usually has strict deadline requirements. Improving the reliability and real-time performance of data transmission has become one of the critical issues in WSNs research. One of the main methods to improve the network performance of WSNs is to schedule the transmission process. An effective scheduling algorithm can meet the requirements of a strict industrial environment for network performance, which is of great research significance. Aiming at the problem of concurrent data transmission in WSNs, a real-time data transmission scheduling algorithm based on deep Q-learning is proposed. The algorithm comprehensively considers the influence of the remaining deadline, remaining hops, and unassigned time-slot nodes in the data transmission process, defines the reward function and action selection strategy of Q-learning, and guides the system state information transfer process. At the same time, deep learning and Q-learning are combined to solve the problem of disaster maintenance caused by the large scale of the system state. A multi-layer Stacked Auto Encoder (SAE) network model establishes the state-action mapping relationship, and the Q-learning algorithm updates it. Finally, according to the trained SAE network model, the data transmission scheduling strategy of the system in different states is obtained. The network performance of the proposed data transmission scheduling algorithm is analyzed and evaluated by simulation experiments. The simulation results show that compared with the commonly used heuristic algorithms, the proposed algorithm improves real-time performance and can better meet the data transmission requirements of high reliability and real-time WSNs.  
real-time; data transmission; deep Q-learning; Wireless Sensor Networks +
Footnote †: journal: 2022  
Accepted: 10 June 2022  
Published: 15 June 2022
# An Efficient RRT Algorithm for Motion Planning of Live-Line Maintenance Robots
## 4 Online Motion Planning
### Optimized Planning Path  
In order to fully wire in the plan space, the step size during expansion is short, which leads to more planned path segments. Therefore, the path needs to be smoothed to improve the execution efficiency of the robot. Smoothing is done by taking a small section of the path, testing whether the beginning and end points of the path can be directly connected, and removing the intermediate points to generate a new path. A path with fewer segments can be obtained by multiple operations, as shown in Figure 19. In the simulation, it is found that the sampling order has some influence on the generated smoothing results.  
In the simulation, it is found that the sampling order and sampling radius have an effect on the smoothing results. In Figure 19, the following three sampling orders are used: sequential sampling, maximum distance sampling, and minimum distance sampling. Sequential sampling rewires waypoints one by one in the order of the points on the path. The maximum distance sampling rewires the waypoints with a large safe distance first. The minimum distance sampling rewires the waypoints with a small safe distance first.  
To evaluate the sampling approach, we evaluate the smoothing results using three metrics: the average distance of obstacles, path length, and number of nodes. Fifteen different combinations of sampling methods are used to smooth the 20 planning paths. In Table 1, 20 planning paths are smoothed using 15 different combinations of sampling methods. In the table, a, b and c indicate the maximum distance sampling, minimum distance sampling and sequential sampling, respectively. Sampling method ab smooths the path with maximum distance sampling first and then minimum distance sampling. The simulation results are shown in Figure 20. The results show that: the number of nodes is less in scheme 10; the path length is smaller in scheme 11, but the average safety distance is smaller in this scheme; the average safety distance of the path is larger in scheme 2. Therefore, scheme 2 is usually selected for smoothing based on safety factors.  
Figure 19: Smoothing results of different sampling methods. ((**a**) maximum distance sampling, (**b**) minimum distance sampling, and (**c**) sequential sampling, and (**d**) the number of nodes and path length changes after smoothing).
# Optimized Deployment of Generalized OCDM in Deep-Sea Shadow-Zone Underwater Acoustic Channels
## 1 Introduction
Similar to OSDM, GOCDM possesses PAPR reduction capabilities while combining the advantages of both OSDM and OCDM. This paper applies GOCDM to deep-sea shadow-zone communication. The main contributions of this work are as follows:  
1. The impact of broadband Doppler effects on the OCDM system in underwater acoustic communication is analyzed. 2. A GOCDM-based underwater acoustic communication system, including transmitter and receiver design, is proposed. By deriving the system model, a theoretical analysis demonstrates that GOCDM inherits the full subcarrier frequency diversity and multi-path resilience advantages of OCDM. Simulations confirm GOCDM's effectiveness in mitigating multipath effects and reducing the PAPR, as well as evaluating the impact of the carrier frequency offset on these modulation schemes. 3. Deep-sea experiments were conducted to evaluate the performance of OFDM, OCDM, and GOCDM in shadow-zone communication. To the best of our knowledge, this is the first experimental demonstration of shadow-zone communication using the bottom-bounce method. The results demonstrate that GOCDM outperforms the other two schemes in low-SNR, complex multipath deep-sea environments. The remainder of this paper is organized as follows. Section 2 presents the fundamental model of the deep-sea shadow-zone channel and OCDM, analyzing the effects of multipath propagation and Doppler shifts on OCDM in practical underwater acoustic communication. Section 3 introduces the GOCDM system, examining its performance in an ideal multipath channel and detailing the transmitter and receiver architecture for underwater acoustic communication. Section 4 presents simulation results, while Section 5 provides deep-sea experimental results and performance evaluations. Section 6 discusses transmission rates and computational complexity. Notations: \((\cdot)^{\mathrm{H}},(\cdot)^{\mathrm{T}},(\cdot)^{-1}\), and \(\|\cdot\|\) denote the Hermitian transpose, transpose, matrix inverse, and \(\ell_{2}\) norm, respectively. \(\otimes\) denotes the Kronecker product, \(\mathbf{I}_{M}\) represents an \(M\)-dimensional identity matrix, and \(\mathbf{e}_{M}(m)\) indicates the \(m\)-th column of the \(M\)-dimensional identity matrix. \(\mathrm{eig}(\cdot)\) represents the eigenvalues of the matrix, \(\mathrm{mod}(a,b)\) denotes the remainder when \(a\) is divided by \(b\), and \(\mathrm{ceil}(\cdot)\) indicates the ceiling function.
# Design of Indoor Visible Light Communication PAM4 System
## 1 Introduction
The field programmable gate array (FPGA) development board with EP4 CE10 FPGA chip as the core is used to verify the communication system, which verifies the feasibility of the system scheme. In addition, the system has high programmability and is easy to debug and develop with subsequent system upgrades.
## 4 Proposed Model
### User-Based Similarity Measure Using Item Genre Information
Accordingly, the UGSM includes an intersection process among the first K-NN in all genres to find \(a\)'s closest neighbors, similar to \(a\) in all preferences. Each \(a\) will have several neighbors, which differ from other \(a\)s. The reason for this is that the intersection process will keep users who are similar to \(a\) in all genres. Thus, the results of the intersection process for users vary from one \(a\) to another. For example, the first \(a\) might get 40 _NNf_, while the second \(a\) might get only 35. If the similarity values among \(a\) and many neighbors are equal, to form the \(a_{NNf}\) these neighbors are arranged in descending order according to the number of common genres shared with \(a\). To maintain the most significant number of neighbors sharing one or more genre with the active user, the proposed model includes the following process:  
\[\text{If }a_{kNN,g1}\ \cap\ \ a_{kNN,gj}=\{\},\ \text{then, }a_{NNf}=a_{kNN,g1}\ \cap\ \ a_{kNN,gj-1}\text{else }a_{NNf}=a_{kNN,g1}.\]
* Step 5: The active user rating prediction process. To recommend the correct item to the active user, the final step in the proposed model is active user rating prediction. The proposed model relies on the prediction model proposed in [48], which is based on the first available rating from the closest neighbors, as illustrated in Section 3.2. Then, the final neighbors' average aggregate rating differences are ordered in descending order to establish the proper neighborhood's ranking.
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Markovian Model  
Consider the following discrete-time MJLS \(\mathcal{G}\), on the probabilistic space \((\Omega,\mathcal{F},\{\mathcal{F}_{k}\},\Gamma)\), described by a set of difference equations given by:  
\[\mathcal{G}\triangleq\left\{\begin{aligned} x(k+1)& =A(\theta_{k})x(k)+B(\theta_{k})u(k)+E(\theta_{k})w(k)\\ y(k)&=C_{y}(\theta_{k})x(k)+E_{y}(\theta)w(k),\\ z(k)&=C_{z}(\theta_{k})x(k)+D_{z}(\theta_{k})u(k)+E_{ z}(\theta_{k})w(k),\end{aligned}\right. \tag{1}\]  
where \(x(k)\in\mathbb{R}^{n_{x}}\) is the state vector, \(u(k)\in\mathbb{R}^{n_{u}}\) is the control input and \(w(k)\in\mathbb{R}^{n_{w}}\) is the noisy input. Vector \(y(k)\in\mathbb{R}^{n_{y}}\) is the measurement output and \(z(k)\in\mathbb{R}^{n_{z}}\) is the controlled output. To ease the notation, whenever \(\theta_{k}=i\), one writes \(A(\theta_{k})=A_{i}\), \(B(\theta_{k})=B_{i}\) and so forth, \(\forall i\in\mathbf{K}\).  
To design \(\mathcal{H}_{\infty}\) stabilizing controllers for system (1), firstly it is necessary to present the concept of stability for MJLS. This definition, named as _mean square stability_ (MSS) [38], ensures that \(\mathcal{E}\left[\|x(k)\|\right]\to 0\) as \(k\to\infty\) for any initial condition \(x_{0}\in\mathbb{R}^{n_{x}},\ \theta_{0}\in\mathbf{K}\).