esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper proposes a mine-microseismic-signal-identification method based on the combination of LMD, energy entropy and PNN. This method is used to identify and verify the types of microseismic-monitoring data in a coal mine, and the conclusions are as follows.  
1. There are regularities in the energy-entropy value of the PF component of the measured field microseismic signal. The rupture signals of coal-and-rock masses are mainly concentrated below 100 Hz, and the energy-entropy values of PF3-PF4 are mostly slightly larger than that of PF1-PF2, while the blasting-vibration signals are mainly concentrated above 100 Hz, which is exactly the opposite of the law of coal-and-rock-mass-rupture signals. The mine-microseismic-signal characteristics constructed by LMD and energy entropy can well characterize the time-frequency law of coal-and-rock-mass-rupture signals and blasting-vibration signals, and can effectively express the change characteristics of mine microseismic signals.
2. The PNN mine-microseismic-signal-identification method proposed in this paper shows a greater identification advantage compared to the BPNN and GRNN methods in terms of prediction accuracy and calculation time. Therefore, the LMD-PNN model is a new effective method for identifying microseismic signals in mines.  
Conceptualization, Q.L. and Y.L.; methodology, Y.L.; software, Y.L.; validation, Y.L. and Q.H.; formal analysis, Y.L. and Q.H.; investigation, Q.L. and Y.L.; resources, Q.L.; data curation, Q.L. and Y.L.; writing--original draft preparation, Q.L. and Y.L.; writing--review and editing, Y.L. and Q.H.; visualization, Y.L. and Q.H.; supervision, Q.L.; project administration, Q.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Comparison of calculation time of different input models and different classifiers.  
**Funding:** This research received no external funding.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
**Acknowledgments:** The technical staff of the Shaanxi Yanchang Petroleum (Group) Co., Ltd. are appreciated for their assistance.  
**Conflicts of Interest:** The authors declare they have no conflict of interest to this work.
###### Abstract  
When faults occur in rolling bearings, vibration signals exhibit sensitivity to periodic impact components, susceptibility to complex background noise, and non-stationary and nonlinear characteristics. Consequently, using traditional signal processing methods to effectively identify bearing faults presents significant challenges. To facilitate the accurate fault diagnosis of bearings in noisy conditions, we propose an intelligent fault diagnosis method using the Archimedes optimization algorithm (AOA), coupled with a one-dimensional multi-scale residual convolutional neural network (1D-MRCNN), to optimize the variational mode extraction (VME) parameters. First, we introduce a weighted correlated kurtosis (WCK) indicator, formulated using the correlation coefficient and correlated kurtosis as the objective function, to optimize the VME's center frequency \(\omega\) and penalty factor \(\alpha\), enabling targeted signal extraction. Second, deep learning techniques are employed to construct the 1D-MRCNN. The neural network then processes the extracted signal for feature extraction and automated fault-type identification. Our simulation results show that the WCK objective function effectively isolates impact components under fault conditions, and our experimental validation confirms that the proposed method accurately identifies diverse fault types across multiple noise levels.  
rolling bearing; fault diagnosis; Archimedes optimization algorithm; variational mode extraction; deep learning +
Footnote †: journal: Applied  
0
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# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 3 Results
### Deffuzzification to Crisp Sets  
Deffuzzification is the process of converting a fuzzified output into a single crisp value with respect to the fuzzy set in order to generate a readable output. The defuzzified process in the Fuzzy Inference System controller represents the action to be taken in controlling the process. There are various defuzzification methods, and each outputs a different result. A method is chosen depending on the nature of the problem that is being solved.  
Therefore, in this study, the Center Of Gravity (COG) method is used because of it being the most commonly used method among others, such as the mean of maxima method, maximum membership principle method and weighted average method. COG gives a more accurate result compared to the rest of the other methods because it finds the value that corresponds to the center of gravity of the curve obtained [29].  
To calculate the output crisp value in this work using the COG, assuming \"Z\" is \"C\", then the formula for the expression is formulated as follows, where Z is the final output KAQI:  
\[Z=\frac{\int\mu_{C}(Z)Z\,dz}{\int\mu_{C}(Z)\,dz} \tag{1}\]  
Therefore, the defuzzified value of the output for the input values of SO\({}_{2}\) = 30 ppb NO\({}_{2}\) = 50 ppb, and PM\({}_{2.5}\) = 20.9 \(\upmu\)g/m\({}^{3}\) is the predicted KAQI = 27 as shown in Figure 9.
# Predictive Control for Current Distortion Mitigation in Mining Power Grids
## 4 System Implementation and Results
### Case 1: Compensation of Selected Harmonics  
This scenario considered two periods as shown in Figure 5a. In the first period, the second and fourth current harmonics were compensated to zero references (Figure 5b); therefore, power supplied through the PCC was composed of a DC component and a 100 Hz component (Figure 5c). Note that, in Figure 4, the \(\alpha\) and \(\beta\) currents had different DC values, which was corrected in Figure 5c. This was because, according to Inequality (12), by bounding \(P_{1}\), current components \(I_{0}\) and \(I_{2}\) are controlled. However, this does not mean that unbalances are mitigated, as shown Figure 5c.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Proposed** & [32] & [33] \\ \hline Continuous-set MPC & ✓ & ✓ & ✓ \\ Harmonics mitigation & ✓ & ✓ & \(\times\) \\ Control level & Secondary & Primary & Secondary \\ Topology & Grid following & Grid following & Grid forming \\ Controlled Variable & Power & Current & Power and voltage \\ Optimized Power Quality & Harmonics and & & \\ Index & THD & Harmonics & Imbalances \\ Quadratic programming & ✓ & ✓ & ✓ \\ Reference Framework & \(\alpha\beta\) magnitudes & \(abc\) & \(abc\) \\ Implementation & HIL & Simulation & HIL \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison against State-of-the-Art Techniques.  
Figure 4: Power and \(\alpha\beta\) currents delivered by the PCC without compensation.  
The second period of case 1 considered the total compensation of harmonics in the PCC, as shown in Figure 5d,e. In this case, the power delivered through the PCC was close to a DC signal, which indicated that only active power was supplied from the utility to the load, whereas the DG supplied harmonic components. As shown in Figure 5a, an active power component (0.2 P.U.) was also supplied by the DG. Measured current values and the tuned weights of the MPC are shown in Table 3.
# A Novel Effective Multipath Routing Technique Providing High Availability in Wireless Networks
## 2 Related Works  
AODV is one of the most popular on-demand routing protocols in wireless networks. AODV uses the request-reply mechanism plus hop-by-hop routing to discover and establish a path between a source and destination in a wireless network. When a source needs to communicate with a destination for which it has no routing information in its route table, it initiates a route discovery process by sending a route request (RREQ) message and waiting for a route reply (RREP) message. When an intermediate node receives an RREQ, the node first sets up a reverse path to the source using the previous hop of the RREQ as the next hop on the reverse path. If the intermediate node has a valid route to the destination, it sends an RREP back to the source. Otherwise, the node re-broadcasts the RREQ. When the destination receives an RREQ, it generates an RREP. The RREP is sent back to the source via the reverse path. When the source receives the RREP, the forward path is established. AODV is designed for single path routing and does not support multipath routing. Several approaches have been proposed to provide multipath routing for wireless networks [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]. Out of the proposed multipath routing approaches, AOMDV, AODVM, and MP-AODV are multipath routing variants of AODV. AOMDV is a link-disjoint multipath routing approach that discovers link-disjoint paths whereas AODVM and MP-AODV can set up multiple node-disjoint paths in wireless networks.
# Evaluation of Multi-Beam Massive MIMO Considering MAC Layer Using IEEE802.11 ac and FDD-LTE
## 4 Computer Simulation
### Fdd-Ite
This verifies the effectiveness of multi-beam massive MIMO, which can effectively utilize the transmission rate in the PHY layer.
###### Abstract  
The rapid growth of today's technological world has led us to connecting every electronic device worldwide together, which guides us towards the Internet of Things (IoT). Gathering the produced information based on a very tiny sensing devices under the umbrella of Wireless Sensor Networks (WSNs). The nature of these networks suffers from missing sharing among them in both hardware and software, which causes redundancy and more budget to be used. Thus, the appearance of Shared Sensor Networks (SSNs) provides a real modern revolution in it. Where it targets making a real change in its nature from domain specific networks to concurrent running domain networks. That happens by merging it with the technology of virtualization that enables the sharing feature over different levels of its hardware and software to provide the optimal utilization of the deployed infrastructure with a reduced cost. This article is concerned with surveying the idea of SSNs, the difference between it and the traditional WSNs, the requirements for its construction, challenges facing it, and the opportunities that are provided by it, then describing our proposed architectures. As a result of using virtualization technology as a basic block in building SSNs, using different types of virtualization will produce different types of SSNs that will give different usages to it. This article proposes a novel approach of taxonomy for SSNs that is based on the used virtualization techniques, and it describes the needs and usages of each one. It presents a wide array of previously proposed solutions comparing them to each other and a brief description of the issues addressed by each category of that taxonomy. Additionally, the shared sensor architecture and shared network architecture were depicted. Finally, some of its applications in some daily life fields are listed.  
IoT; shared sensor networks; virtualized wireless sensor networks (VSNs), sensor middleware 19 March 2019; _Sensor and Actuator Networks_
## 4 Proposed Sing-Ant Methodology
### Antenna Sectors  
The antenna sectors protrude linearly outward from the core and are adept at measuring the distance from the core. They are composed of antenna beams that are protruding outwards from the core and are displaced in a line. They are made up of columns of antenna beams. Through the pattern in Figure 3, the entire sectors in our target area and a single isolated sector are shown. The mathematical calculation remains the same as for the central core and antenna cluster, with the only variations being in the number of antenna beams used by each single sector. The sectors of all antennas and a single antenna are shown in Figure 3.  
Figure 1: Core antenna beams of Impiniy xArray reader.  
Figure 2: All antenna clusters (**left**) and a single antenna cluster (**right**).  
Figure 3 shows that the lines of the antennas are spreading straight upwards and hence this method allows for easier calculation of the \(y\)-axis spread as compared to clusters in which it is necessary to compare entire clusters in order to find the ideal point.  
When handling clusters, the main challenge is the lack of sequential activation; it can be seen that they have a rather arbitrary antenna beam selection count, and these sequences appear at inconsistent intervals for the sequential calculation of a tag location. Hence, when working with antenna sectors, the position of the target tag relative to the sectors being measured must be calculated.
## 3 Methodology
### Degradation Network  
We synthesize LR images using the high-order degradation model implemented in Real-ESRGAN [24]. In the high-order degradation model, downsampling is typically a fundamental operation for generating LR images. However, with our specific data type and pre-processing steps, the input image size is already reduced to \(256\times 256\) before the degradation process. As a result, the resizing step is omitted in this stage. Consequently, our degradation process focuses solely on applying blur effects and introducing noise. The detailed configurations for this procedure are described below.  
**Blur:** Blur degradation is formulated as a convolution process utilizing a blur kernel, with isotropic and anisotropic Gaussian filters commonly employed as standard approaches [25]. The selection between these filter types is determined probabilistically by comparing a randomly generated value against a predefined threshold at time \(t\). Here, if the value exceeds 0.5, an isotropic Gaussian blur is applied; otherwise, an anisotropic Gaussian blur is utilized [26]. In this study, the blur kernel size is randomly selected from the set of \(\{3\times 3;5\times 5;7\times 7;9\times 9;11\times 11\}\), while the isotropic and anisotropic Gaussian filters are characterized by values drawn from the range of \([0.1,2.8]\). To preserve spatial consistency, reflection padding is employed, ensuring that the dimensions of the blurred output remain unchanged.  
**Noise**: Gaussian noise and Poisson noise [27], two commonly applied noise types, were incorporated with probabilities of \([0.008,0.1]\) and \([0.05,1]\), respectively. Subsequently, the noisy images underwent JPEG compression, with the quality factor selected within the range of \([30,95]\).
# The Role of an Industrial Alkaline Wastewater in the Alkali Activation of Biomass Fly Ash
## 2 Materials and Methods
### Alkali-Activated Material Preparation  
Fly ash and metakaolin were mixed and homogenised in a bag in the 70/30 mass proportion. The activator was prepared by mixing sodium silicate and NaOH (75/25 mass proportion). The reference composition was selected following previous investigations by the authors [27; 28]. Then, five additional formulations were prepared in which the NaOH solution was partially replaced by 5%, 10%, 15%, 20%, or 25% (volume) of the alkaline wastewater. The highest amount of effluent (25 vol.%) was selected considering preliminary tests that showed a reduction in the paste's workability as the amount of effluent increased (see discussion in Section 3.2). The NaOH or the NaOH/effluent solution was added to the sodium silicate and mixed for 10 min. Then, precursors and the activator were mixed during 5 min. Afterwards, the paste was transferred to metallic moulds of 2 cm \(\times\) 2 cm \(\times\) 4 cm, sealed with a plastic film and cured for 24 h at room temperature (23 \(\pm\) 2 \({}^{\circ}\)C). Then, the specimens were demoulded and cured at ambient conditions (23 \(\pm\) 2 \({}^{\circ}\)C) for an additional 27 days. To facilitate the reading, the following codes were used throughout the manuscript: reference (or 0_Ef), and then 5_Ef, 10_Ef, 15_Ef, 20_Ef, and 25_Ef for the compositions prepared with varying amounts of the alkaline effluent as NaOH replacement. Details of the compositions can be seen in Table 1. In the reference composition, the silica modulus (SiO\({}_{2}\)/Na\({}_{2}\)O) of the activating solution was 1.7.
# Coupling Denoising to Detection for SAR Imagery
## 2 Materials and Methods
### Proposed Methodology
Considering that there exist qualitative differences between the two sets of RoIs  
Figure 6: Overview of the proposed object detection framework: (1) connecting an unsupervised denoising network to an object detection network for dynamically extracting a denoised SAR image from a given noisy SAR image, and (2) forwarding an image pair of two SAR images to an object detection network and fusing region proposals from the two SAR images for complementarily integrating regional information. Figure 7: Overview of the traditional two-stage object detection network given a real or denoised (preprocessed) SAR image as input. derived real and synthetic SAR images, the real and synthetic SAR images are separately trained by the region proposal network. After fusing region proposals, we take the feature map from the real SAR image for preserving the global context information of the raw input SAR image. The proposed architecture is trained end-to-end with a multi-task loss which mainly consists of (1) unsupervised denoising loss, (2) region proposal loss, and (3) RoI loss for classification and bounding-box regression. Especially, the region proposal network is trained for both real and synthetic SAR image, and thus two distinctly losses are defined. The final loss function that we propose is a weighted summation of all losses as follows. \[\mathcal{L}(I_{real})=\lambda_{1}\mathcal{L}_{den}(I_{real})+\lambda_{2} \mathcal{L}_{rpn}^{real}(I_{real})+\lambda_{3}\mathcal{L}_{rpn}^{synth}(G(I_{ real}))+\lambda_{4}\mathcal{L}_{roi}(\mathbb{B}_{final}) \tag{1}\]  
where:  
\[I_{real} =\text{a real (noisy) image}\] \[G(I_{real}) =\text{a synthetic (denoised) image extracted from the denoising network }G\] \[\mathbb{B}_{final} =NMS(\mathbb{B}_{real}\cup\mathbb{B}_{synth})\text{, where }\mathbb{B}\text{. is set of RoIs from either }I_{real}\text{ or }G(I_{real})\]  
where \(\mathcal{L}_{den}\) denotes the unsupervised denoising loss. \(\mathcal{L}_{rpn}^{real}\) and \(\mathcal{L}_{rpn}^{synth}\) are the region proposal loss of RPN for \(I_{real}\) and \(G(I_{real})\), respectively. \(\mathcal{L}_{roi}\) refers to the loss summation of classification and bounding-box regression loss for all RoIs \(\mathbb{B}_{final}\). \(\lambda_{1:4}\) are the hyperparameters to balance the interplay between the losses and the all parameters are set to 1 in all our experiments.
## 4 Environmental Impact on Temperature Distribution of Thin-Walled Concrete
### Correlation between Temperature Distribution and Environmental Factors
#### 4.2.2 Concrete Gradient Temperature and Solar Radiation  
Since both the extreme gradient temperature patterns \(T_{\rm g}^{+}\) and \(T_{\rm g}^{-}\) could be described via \(T_{\rm gb}\) and \(T_{\rm gt}\), the \(T_{\rm gb}\) and \(T_{\rm gt}\) values were both chosen as the typical cases to be analyzed (Figure 12).  
For the bottom surface, \(T_{\text{gb}}^{\text{min}}\) and \(T_{\text{gb}}^{\text{avg}}\) were both negatively and linearly correlated with the average solar radiation, and the correlation ratios (\({}^{\circ}\)C/kW) were \(-11.4\) and \(-5.37\), respectively. Negative correlation ratios showed that strong sunshine increased the temperature of the upper part of the concrete more effectively, so the temperature of the lower part of the concrete increased more slowly compared to the mean temperature. Moreover, there was no correlation between \(T_{\text{gb}}^{\text{max}}\) and average solar radiation, so an average \(T_{\text{gb}}^{\text{max}}\) value of \(0.3\)\({}^{\circ}\)C was more representative.  
Meanwhile, opposite to the bottom surface, \(T_{\text{gt}}^{\text{max}}\) and \(T_{\text{gt}}^{\text{avg}}\) both had a positive linear correlation with the average solar radiation, and the correlation ratios (\({}^{\circ}\)C/kW) were \(35.0\) and \(9.6\). Apparently, compared to the bottom surface, the gradient temperature of the top surface was more significantly impacted by solar radiation due to direct irradiation. In addition, \(T_{\text{gt}}^{\text{min}}\) was not correlated with the average solar radiation. Therefore, an average \(T_{\text{gt}}^{\text{min}}\) value of \(-1.8\)\({}^{\circ}\)C was recommended for different solar radiation conditions.  
Figure 11: Correlation between the concrete mean temperature and air temperature.  
Figure 12: Correlation between the concrete gradient temperature and solar radiation: (**a**) bottom elevation; (**b**) top elevation.
## 6 Congestion Management
In large power systems, the load-ability increased with the increase in the number of FACTS devices. 9. There are four different optimization techniques presented in the literature (previously discussed). However, meta-heuristics optimization algorithms are widely adopted and preferably considered in power systems. Out of the various approaches, the improved harmonic search algorithm, imperialistic competitive algorithm, and whale optimization algorithm are the more popular meta-heuristic approaches adopted. 10. Classical optimization techniques (mixed integer linear programming and mixed integer non-linear programming optimization) are widely used to optimally place FACTS devices in deregulated networks because such techniques can handle both discrete and continuous variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**FACTS Devices**} & \multicolumn{2}{c}{**Methods/ Optimization Algorithm**} & \multirow{2}{*}{**Findings**} & \multicolumn{2}{c}{**Limitations/Drawbacks/ Future Directions**} \\ \hline \hline \multirow{5}{*}{[194]} & \multirow{5}{*}{TCSC, SSSC} & \multirow{5}{*}{Power flow model} & Reduced congestion in the IEEE-40 bus system by optimal placement of FACTS devices & The authors reduced congestion in terms of improving voltage profile and power transfer capability. However, the best transmission line in the IEEE-40 bus system is presented but not direct comparison about selection of these two FACTS devices is presented. In addition, no simultaneous effect of TCSCs and SSSCs is evaluated \\ \hline \hline \multirow{5}{*}{[195]} & \multirow{5}{*}{TCSC} & \multirow{5}{*}{PSO} & Alleviate congestion on IEEE-30, IEEE-118, and 33 bus Indian bus network using TCSCs. Therefore, “load flow sensitivity factor” is proposed to optimally place TCSCs. Moreover, robustness of the PSO is also analyzed based on mean time of execution, number of fitness evaluations, success rate, etc. \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: FACTS devices and their role in congestion management.