esa-sceva/satcom-chunk-collection huggingface dataset:
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 4 Routing Algorithm Based on DCMST
A maximum degree constraint \(T^{\prime}\) must exist for node \(v_{0}\) of \(G\), and \(h_{T^{\prime}}(v_{0})=h_{G}(v_{0})\). \(h_{T^{\prime}}(v_{0})\) is the degree of \(v_{0}\) in \(T^{\prime}\), and \(h(v_{0})\) is the degree of \(v_{0}\) in \(G\). The DCMST model is as follows:  
\[minW=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}w_{ij}\tau_{ij} \tag{7}\]  
\[\left\{ \begin{array}{c}\sum\limits_{j=1}^{n}\tau_{ij}\leq h_{i},\;i\in V\\ \sum\limits_{i,j\in S}\tau_{ij}\leq|S|-1,\;\forall S\in V,\;S\
eq\varnothing\\ \sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\tau_{ij}=n-1\\ \tau_{ij}\in\{0,1\},\;i,\;j\in V\end{array} \right. \tag{8}\]  
If edge \((i,j)\) is in the optimal tree, \(\tau_{ij}=1\); otherwise, \(\tau_{ij}=0\). \(|S|\) is the number of nodes in \(G\) that are contained in the set \(S\). The steps of the DCMST Algorithm 1 are as follows:  
```
1.Input:\(V\) : Set of nodes; \(v_{k}\): Nodes; \(V_{G}(v_{k})\) : Set of nodes in tree \(G\); \(E_{G}(v_{k})\): Set of edges in tree \(G\);
2.Output:\(T_{k}\): DCMST of the network
3.G \(\leftarrow|V|-|V_{G}(v_{k})|-1\)
4.\(V_{G}(v_{k})=E_{G}(v_{k})=\varnothing\)
5.\(S_{0}=\{v_{0}\}\cup V_{G}(v_{0})\)
6.\(E_{0}=E_{G}(v_{0})\)
7.\(S_{0}^{\prime}=\frac{V}{\{v_{0}\}}\cup V_{G}(v_{0})\)
8.For\(k=1\); \(k\leq|V|\)do
9.While\(v_{k}\) neighbored \(v_{0}\)do
10.\(V_{G}(v_{k})=V_{G}(v_{k})\cup V_{G}(v_{0})\)
11.EndWhile
12.While\(v_{k}\) associated \(v_{0}\)do
13.\(E_{G}(v_{k})=E_{G}(v_{k})\cup E_{G}(v_{0})\)
14.EndWhile
15.\(S_{k}=S_{k-1}\cup\left\{v_{k-1}^{\prime}\right\}\)
16.\(E_{k}=E_{k-1}\cup\left\{\epsilon_{k-1}\right\}\)
17.\(S_{k}^{\prime}=\frac{S_{k-1}}{\left\{v_{k-1}^{\prime}\right\}}\)
18.If\(S_{k}=V\)then
19.\(T_{k}=(S_{k-1},\;E_{k-1})\)
20.Elsew(\(\epsilon_{k})=\min\limits_{e\in[0,\;\cup_{G},\;V_{k}^{\prime}]}w(e), \epsilon_{k}=v_{k}v_{k}^{\prime}\)
21.EndIf
22.If\(\left[S_{k},\;S_{k}^{\prime}\right]=\varnothing\)then
23.\(T_{k}=\varnothing\)
24.EndIf
25.EndFor
```  
**Algorithm 1** DCMST algorithm. Finally, \(T_{k}\) is the maximum degree constraint minimum degree spanning tree of the GCUM. Based on the edges that could be equivalent to routing paths, the clustering structure is introduced in the GCUM to reduce the energy consumption of the network and improve its longevity. In Section 4, minimizing the weights of routing paths based on the DCMST can effectively reduce consumption of energy of the transmission and increase the throughput rate in the network to achieve the goal of green communication.
## 5 Future Prospects and Conclusions  
Integrated MFC systems are a promising technology to explore. This review described various integrated MFC systems along with their advantages and disadvantages over a stand-alone MFC. A large number of literatures describes the performance efficiency of an integrated MFC against a stand-alone MFC system. It was observed that the performance of an integrated MFC was much higher than that of a stand-alone MFC in terms of wastewater treatment and energy production. However, there are some bottlenecks related to integrated systems that need to be addressed. One of the major obstacles to overcome the upscale and commercialization of these integrated bioelectrochemical systems is the equipment and operational cost [109]. For that purpose, the costs of electrodes and membranes need to be reduced. The need for an external energy supply also has to be decreased through efficient cathode oxygenation. In conclusion, several technologies can be integrated with BESs, such as FO, DF, MBR, CDI, and aerobic bioreactors, to add various benefits and enhance the already-existing processes. The combined systems have many benefits, such as increased efficiency, lesser membrane biofouling, desalination, and better-quality effluent, along with the simultaneous generation of electricity. An integrated MFC system also has lower total capital costs than stand-alone systems, but upscaling increases the maintenance and processing costs. Hence, integration is the first step towards the final viable system. These integrations need to be refined and optimized to become viable. These systems need to be studied extensively, and their processes need to be understood deeply. Their fallacies have to be addressed and rectified. The systems should become economically viable and not complicated to scale up. With these obstacles cleared, the integrated systems of BESs have a promising potential to be commercialized for the simultaneous treatment of contaminated water and the production of electrical current.  
Conceptualization, S.P., D.A.J. and R.P.; writing--original draft preparation, N.S., S.B.P., S.P. and V.S.; writing--review, P.K.G., D.L. and R.R.R.; editing, A.S.M., R.P., A.K.R., V.K., and R.R.R.; supervision, S.P. and R.P. All authors have read and agreed to the published version of the manuscript.  
Authors duly acknowledge the grant received from Sharda University seed grant project (SUSF2001/01). Authors duly acknowledge the grant received from the Life Sciences Research Board, Defense Research and Development Organization (DRDO) (File No. LSRB/81/48222/LSRB-368/BTB/2020) and Department of Science and Technology (File No. CRD/2018/000022) for the accomplishment of this work.  
The authors declare no conflict of interest.
## 3 Database
### Correlation Analysis  
Correlation analysis, as a statistical approach [37], is used to evaluate the linear connection between two variables. The results are usually expressed in terms of correlation coefficient. By calculating the correlation coefficients between the parameters, the interactions and dependencies between the parameters can be understood, which in turn reduces the risk of model overfitting. In this study, the Pearson correlation coefficient is used to explore the correlation between model parameters, and the calculation formula is shown in Equation (37):  
\[r_{XY}=\frac{\sum\limits_{k=1}^{N}{(A_{k}-\overline{A})(B_{k}-\overline{B})}}{ \sqrt{\sum\limits_{k=1}^{N}{(A_{k}-\overline{A})^{2}\sum\limits_{k=1}^{N}{(B_{ k}-\overline{B})^{2}}}}} \tag{37}\]  
where \(\overline{A}\) and \(\overline{B}\) are the mean of \(N\) test values, respectively, and \(r_{XY}\in[0,1]\).  
Figure 5 shows the correlation between different indicators. Among them, the dam volume shows high correlation with the dam height, length, and width, with correlation sizes of 0.73, 0.62, and 0.68 respectively, indicating that as the dam height increases, the other geometrical dimensions of the dam usually increase accordingly. In addition, the correlation between the dam volume and the weir volume is more significant, reaching 0.54, showing that the water storage capacity of the dam is closely related to its size. The correlation between the upstream catchment area and the weir volume is 0.46, which indicates that the catchment size has some influence on the dam storage capacity. Overall, the weir life is jointly influenced by a variety of factors, among which the dam geometry has a high predictive value, while the trigger factors and dam materials have relatively little influence on the life. The results of the above analysis can provide an important feature selection basis for the subsequent weir dam life prediction model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Parameter** & **STD** & **Kurt** & **Max** & **Min** & **Mean** & **Median** & **Range** \\ \hline Dam height (m) & 84.32 & 52.90 & 1000 & 2 & 61.76 & 32 & 998 \\ Dam length (m) & 425.13 & 24.36 & 4000 & 5 & 367.50 & 250 & 3995 \\ Dam width (m) & 729.50 & 13.73 & 6000 & 20 & 612.44 & 350 & 5980 \\ Dam volume (\(10^{6}\) m\({}^{3}\)) & 216.24 & 125.91 & 3000 & 0.01 & 36.84 & 1.90 & 2999.99 \\ Dammed lake volume (\(10^{6}\) m\({}^{3}\)) & 187.16 & 19.89 & 1500 & 0 & 71.85 & 4.10 & 1500 \\ Upstream catchment area (km\({}^{2}\)) & 21,993.36 & 45.62 & 173,484 & 0.60 & 4298.30 & 98.70 & 173,483.40 \\ Triggers (earthquake) & 0.50 & \(-1.92\) & 1 & 0 & 0.43 & 0 & 1 \\ Triggers (rainfall and snowmelt) & 0.50 & \(-1.99\) & 1 & 0 & 0.48 & 0 & 1 \\ Triggers (others) & 0.28 & 6.52 & 1 & 0 & 0.09 & 0 & 1 \\ Dam material (rock) & 0.48 & \(-1.62\) & 1 & 0 & 0.35 & 0 & 1 \\ Dam material (debris) & 0.49 & \(-1.84\) & 1 & 0 & 0.40 & 0 & 1 \\ Dam material (earth) & 0.43 & \(-0.61\) & 1 & 0 & 0.25 & 0 & 1 \\ Longevity (day) & 86.94 & 4.12 & 370 & 0.01 & 53.80 & 16.00 & 369.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical characteristics of the datasets.
# Opportunistic Sensor Data Collection with Bluetooth Low Energy
## 5 Evaluation
### Maximum Amount of Collected Data: Experimental Results
#### 5.5.2 Influence of Distance between the two BLE Devices on the Amount of Collected Data  
We next perform experiments to evaluate the effect of the distance between the two BLE modules on the amount of collected data. As aforementioned, to explore the practical limits of OSDC with BLE, we selected the BLE121 LR modules for the experiments because they support long range communication, i.e., an extended range in comparison with that of typical BLE platforms. According to the manufacturer data, the maximum range is 450 m [28]. We evaluate the achieved amount of collected data for different distances between the two BLE devices, in two different scenarios: (a) a university campus scenario (UPC Campus del Baix Llobregat, in Castelldefels, Spain); and (b) a beach scenario in the same city. For each considered distance between the two BLE devices, at least 25 individual experiments were carried out. _connInterval_ was set to 12.5 ms, as the maximum throughput results in the near-zero distance between the BLE devices were obtained with this value.  
Figure 25 depicts the average measured amount of collected data, and the corresponding standard deviation, for each considered distance between the two BLE devices. Measurements in the university campus show that in that scenario it is possible to achieve a range beyond the one reported by the manufacturer, probably due to a wave guide effect created by the buildings around the test area. On the other hand, while distance is up to 200 m, the amount of collected data is almost constant, and of similar values as the maximum throughput measured in the previous subsection. Beyond that distance, the amount of collected data initially exhibits a decrease, but from a distance of 300 m it oscillates, which is a characteristic of multipath propagation due to the buildings in the scenario.  
Behavior in the beach scenario offers visible differences with the university campus one. While the maximum achievable amount of collected data is the same in both scenarios, in the beach scenario it decreases monotonically for distances between the two BLE devices greater than 200 m, yielding an almost zero amount of collected data already at 437 m.  
Figure 24: Number of round trip exchanges measured per _connInterval_, as a function of _connInterval_.
## 4 Application of Fe-MOF in Advanced Oxidation Processes
### Electrochemical AOP  
In this process, the continuous and in situ production of hydroxyl radicals is based on the application of an electric field [191]. To obtain these radicals, a cathode, an anode, a catalyst, which is normally a metal catalyst and an air diffuser, which provides the system with oxygen, are required [192]. These four elements are essential to carry out the organic pollutant mineralization. Therefore, the importance of each of them is explained in the following.  
The main function of the cathode is the continuous production of hydrogen peroxide from the reduction of oxygen (O\({}_{2}\)) supplied by the air diffuser, as shown in Equation (25) [192]. Thus, once this hydrogen peroxide is produced, the Fenton reaction takes place, which allows the formation of hydroxyl radicals (Equation (27)). Thus, in the anode, the reaction of this radical with the organic pollutant, which is represented in Equation (26) as RH, takes place, and the degraded compound (R\(\bullet\)) and water are obtained at the end.  
\[\text{Cathode:}\ O_{2}+2H^{+}+2e^{-}\to H_{2}\text{O}_{2} \tag{25}\] \[\text{Anode:}\ OH^{\bullet}+RH\to R^{\bullet}+H_{2}\text{O}\] (26) \[\equiv Fe^{2+}+H_{2}\text{O}_{2}+H^{+}\rightarrow\equiv F\phi^{ 3+}+OH^{\bullet}+H_{2}\text{O} \tag{27}\]  
Therefore, catalysts and cathodes are two major factors affecting the reaction rate and efficiency of the electro-Fenton system, so many studies have focused on these two components [193]. Moreover, the main drawback of this process is that Fe\({}^{2+}\) is unstable and can cause iron precipitation due to the pH. Therefore, alternative catalysts have been sought to avoid the formation of this precipitate and to avoid affecting the catalysis. Recently, MOFs have attracted attention for their application in electrochemical processes due to their catalytic properties, mechanical stability and good electrical conductivity [194]. Moreover, they are a possible solution to the problem of metal precipitate that occurred because those Fe-MOFs present an interconversion between Fe\({}^{3+}\) and Fe\({}^{2+}\) and can produce hydroxyl radicals at neutral pH [195].  
These Fe-MOFs can also be applied to electrochemical processes using two well-defined strategies: electrocatalyst (by Fe-MOF fixation into the cathode) and application of Fe-MOF in the bulk solution as heterogeneous catalyst.
## 7 Conclusions  
In this study, we proposed a network called DCMFN that employs an attention mechanism for pansharpening. The network first uses two independent extraction modules, PFEM and MFEM, to obtain the spatial and spectral features of the image respectively, and then uses CMFFM to achieve the fusion of the above cross-modality features, which enhances the spectral and spatial details of the image. Finally, the rich fusion features are reconstructed into the pansharpened image. Extensive experiments were conducted using QB, WV2, and WV4 remote sensing datasets. This paper provides a detailed comparison of objective performance indicators and subjective visual results, demonstrating the effectiveness of DCMFN in improving the quality of urban remote sensing images.  
Integrating pansharpening technology with remote sensing images provides rich, accurate data for environmental monitoring. This combination holds significant potential for advancing research in ecological and environmental monitoring, particularly in tracking changes in ecosystems, land use, and natural phenomena. High-resolution data are essential for effective environmental protection, resource management, and risk assessment, offering both substantial scientific value and practical implications for preserving biodiversity and assessing environmental health.  
Conceptualization, B.L.; methodology, B.L.; software, B.L.; validation, B.L.; formal analysis, B.L.; investigation, B.L.; resources, B.L.; data curation, B.L.; writing--original draft preparation, B.L.; writing--review and editing, B.L., Q.L. and H.Y.; visualization, B.L.; supervision, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sichuan University, grant number: 24 NSFSC2159.  
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.  
The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A New Rotor Position Measurement Method for Permanent Magnet Spherical Motors
## 3 Rotor Position Measurement for a PMSM
### Analysis of Rotor Motion in a PMSM
#### 3.3.1 Calculation of the Rotor Position in a PMSM  
The rotor position in a PMSM can be represented by three rotation angles: The tilting angle_\(\theta\), the yaw angle_\(\varphi\), and the spinning angle_\(\omega\). Three rotation angels can be calculated by computing the coordinate values of three OFPs in the ICF, which are captured by a high-speed camera. Figure 8 shows the procedure to calculate the three rotation angles.  
Figure 7: Rotor motion range of a PMSM.
## 4 Proposed Method
### Data Routing Stage
#### 4.2.2 Fitness Function of GJO  
The GJO method builds the fitness function using the following parameters in order to find the optimal route between CH and BS:  
1. Distance between CH and BS: The further the distance between the CH and the BS, the more energy is required to transmit the data. Therefore, the CH close to the BS is selected to form the best path. Consequently, minimizing the objective function \(f_{1}\) is our aim: \[f_{1}=1/K\sum_{(i=1)}^{K}D(CH_{i},BS)\] (41)2. The ratio of the distance between CH and BS to the inter-cluster head distance: The fitness function's second parameter will be this ratio. Here, maximizing the inter-cluster distance and minimizing the distance between \(CH_{i}\) and BS are the main objectives. Consequently, minimizing the objective function \(f_{2}\) is our aim: \[f_{2}=\left(\sum_{(i=1)}^{K}D(CH_{i},BS)\right)/\left(\sum_{(i=1,j=1)}^{K}D \big{(}CH_{i},CH_{j}\big{)}\right)\] (42)
3. Residual energy of CH: This is the fitness function's third parameter. Our goal is to prioritize the CH with more remaining energy as the next hop. Let \(E_{res}(CH_{i})\) denote the residual energy of CH; \(K\) denotes the number of CHs. Consequently, our goal is to maximize the objective function \(f_{3}\) as follows: \[f_{3}=\sum_{i=1}^{K}E_{res}(CH_{i})\] (43) The following three functions \(f_{1}\), \(f_{2}\), and \(f_{3}\), are to be minimized as the fitness function for our suggested GJO method. As a result, the following is the fitness function for our algorithm: \[f=\omega_{1}\times f_{1}+\omega_{2}\times f_{2}+\omega_{3}\times\frac{1}{f_{3}}\] (44) where \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\) are constants that give the objective functions weights. The total of these constants ought to equal 1. Minimizing the objective function is our aim.
## 1 Introduction
Later, deep learning methods were also used for the detection and classification of particles [14; 16]. [PERSON] et al. [14] used a deep learning method with adaptive convolution and multiscale attention to locate and classify particles. Figure 1: Examples of foreign particles observed during the manufacturing process of fluid bags. Many studies show that machine learning methods in computer vision are used to classify the objects detected during the inspection. However, the main difficulty in machine learning is finding good features that can effectively discriminate between particles and bubbles. Various features have been proposed, and significant efforts have been made to develop good features through transformation and complex manipulation. Many experiments are necessary to identify good features and their combinations. Through these studies, all the features considered for inspection are grouped into the following two categories: the behavior and the shape of the objects. The features related to the behavior of the objects inside the moving fluid are observation position, moving direction, velocity, acceleration, patterns of trajectory, etc., and these are used to discriminate heavy particles such as metal or rubber from air bubbles and to show a good detection performance in real-time [10]. However, these features are not effective when the material of the particle is not heavy, as it often behaves like air bubbles. Thus, recent studies have focused more on the shape of the particles rather than their behavior. Features such as size, aspect ratio, colors, textures, brightness, shape patterns, and so forth are considered, but the issue of finding good descriptors or features that can discriminate between particles and bubbles still exists. Contrary to this machine learning-based approach, a deep learning-based approach does not need to find good features manually; instead, more data will guarantee better performance, which is advantageous for this application because various data on bubbles can be easily provided [16; 17]. If a good deep learning model is chosen, good features can be formed naturally through the deep learning training process. Thus, this paper focuses on deep learning technology and will explain more on its adaptation for particle detection.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 4 Experiments and Results
### Model Setting  
We constructed five models with different settings for preprocessing. Some used enlarged images, and others used sharpened images. Some used RGB images only, while others used both RGB and grayscale images.  
We then preset the parameters of RPN in the five models for blood cell detection on the images of the dataset. It is crucial to decide the scales and aspect ratios of the anchors in RPN for target-object detection. We exploited the coordinates of the bounding boxes in the annotation file. Table 2 displays the size information in the pixels of three types of blood cells.  
We select 42, 86, and 170 pixels as anchor scales for RPN to detect objects. As well, we employ 1:1, \(1/\sqrt{2}\):\(\sqrt{2}\), and \(\sqrt{2}\):\(1/\sqrt{2}\) as anchor aspect ratios. As a result, anchors in nine different sizes are used in RPN. Anchor scales have to be adjusted accordingly for models using enlarged images.  
Table 3 shows the settings of the five proposed models. Models 1, 2, and 3 differ in the size of the input images. In models 2 and 3, the input images are enlarged by 1.25 and 1.5 times, respectively. Together, they examine the impact of enlarging the input images. In model 4, the input images are not sharpened in the preprocessing step. Models 1 and 4 examine the effect of image enhancement by sharpening. Model 5 uses the input images  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training (80\%)** & **Validation (20\%)** \\ \hline Red Blood Cell (RBC) & 3310 & 843 \\ White Blood Cell (WBC) & 297 & 75 \\ Platelets & 301 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 1: BCCD dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Red Blood Cell**} & \multicolumn{3}{c}{**White Blood Cell**} & \multicolumn{3}{c}{**Platelets**} \\ \hline  & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** & **Min** & **Mean** & **Max** \\ \hline x-axis & 42 & 104 & 168 & 31 & 194 & 338 & 19 & 42 & 167 \\ y-axis & 39 & 100 & 166 & 27 & 174 & 286 & 23 & 40 & 135 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blood cell size information in pixels.  
in the RGB color space only. Models 1 and 5 investigate the potential of grayscale blood smear images.