esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
An ML-based observer was successfully designed and implemented in a DSP-based sensorless PMSM-FOC process. A modified ENN algorithm, used as the ML model's basic architecture, was proven to overcome the speed transition problem. Data for training and validation were obtained from a simulated control process. The trained ML model combined with a PLL was the ML-based observer. The proposed observer was tested in a full-environment-control process simulation. An open-loop Volt/Hz startup strategy was used at the beginning of the control process, followed by the proposed sensorless control strategy. A hardware control platform based on DSP F28069M was used to experimentally implement the proposed observer. Compared to the actual rotor position, the rotor position estimated by the proposed ML-based observer has an average error of 0.0127 p.u-position on simulation and 0.0607 p.u-position on hardware implementation. Thus, the sensorless PMSM drive can be controlled well to follow the commanded speed and is robust to changes in load. Both the simulation and experimental results indicate that the proposed observer is effective as a sensorless PMSM-drive control system.  
Figure 18: Experimental results for speed and current responses with external load variations at 0.40 and 0.65 p.u-speed.  
**Author Contributions:** [PERSON] wrote this article, designed the control method, implemented the hardware platform, drew the figures, and performed the simulation as well as the experiment. [PERSON]-C.C. supervised the study, coordinated the investigations, and checked the manuscript's logical structure. [PERSON]-H.K. validated the simulation and hardware experimental data and reviewed the writing and editing. [PERSON] provided practical suggestions and evaluated the feasibility of the application. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from Imeier Green Technology Co., Ltd. with contract no. 14001110024.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Clustering-Based Modified Ant Colony Optimizer for Internet of Vehicles (CACOIOV)
## 3 Proposed CACOIOV Architecture  
In this part of the research, the architecture of the CACOIOV, as well as various mechanisms involved in its framework, are briefly presented. According to the literature, one of the best meta-heuristics is ACO, and the optimization of this algorithm was introduced by [PERSON] [40]. ACO is motivated by actual ant behavior and stimulates procedures by real ants to set up the shortest routes for searching food resources. They tend to pick channels that are characterized by the most tenacious concentration of pheromone and heuristic value. The algorithm of the ACO is an important system made based on natural behavior stimulant agents of ants, involving the adaptation and cooperation mechanisms. The ACO algorithm is made up of several iterations (cycles) that are of the construction of solution. In each of the cycles, a number of ants finish up solutions by using investigative information and the obtained experiences by the trail of pheromone, which is kept on the elements constituting a solution. A pheromone can be made on the connections and/or the components employed in an answer considering the problem. Randomly, each ant commences at a node and then pays visits to other ones based on the rule of transition. The process of learning is to repeatedly update the information of the pheromone. Also, ant tour quality can be affected by the evaporation ratio of pheromone (Section 3.2). The quality of ant tour/solution is obtained by calculating the heuristic value over the edges that exist between vertices in each tour. This evolutionary algorithm motivated us to increase the quality of ant tour/solution that results in the shortest path and optimal solutions by developing CACOIOV framework, which is ACO-based. CACIOV can offer significant improvement in cluster formation. Table 2 presents all notations that are used for analysis.
# Fast Optimization of the Installation Position of 5G-R Antenna on the Train Roof
## 4 Conclusions  
In order to improve the anti-interference performance of the 5G-R roof antenna, the electromagnetic coupling of the pantograph arcing to the roof antenna port is reduced. Taking the 5G-R roof antenna as an example, this paper quickly optimized the installation position of the antenna, and proposed a coupling coefficient prediction model based on the MMNN. The MMNN model based on BP, MEA-BP, ELM, and PSO-ELM is used to predict the coupling coefficient between the pantograph arcing and the port of the roof antenna. It is proven that the prediction model can replace the complicated electromagnetic simulation work accurately and efficiently through various prediction performance indices, so as to provide an effective prediction method for the rapid optimization of the installation position of the roof antenna.  
However, the simulation model is a simplified model. The structure of the body of the carriage is equivalent to a rectangular structure in the model. In order to further improve the prediction accuracy, the relevant parameters in the simulation model should be modified by the measured data. Through the continuous cycle iteration of simulation analysis and actual measurement verification, the further optimization of the installation position of the 5G-R antenna is realized.  
Conceptualization, Y.W. and Y.B.; methodology, Y.B. and J.R.; software, Y.B.; validation, Y.B. and J.R.; formal analysis, Y.B.; investigation, Y.B.; resources, Y.B.; data curation, Y.B.; writing--original draft preparation, Y.B.; writing--review and editing, J.R. and Y.B.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (2022 YFB4301203-09).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Differential Transform Method as an Effective Tool for Investigating Fractional Dynamical Systems
## 4 Conclusions  
This study investigated the use of the multi-step differential transform method as a tool for integrating the classical autonomous Rossler system of a fractional order. In preliminary studies of the integer-order system, the quality of the DTM was assessed by comparing its results with those obtained with numerical tools widely used to integrate differential equations: the RK4 scheme and the Matlab ode45 function. The study has demonstrated that \(h=0.01\) is the optimal integration step value for the DTM. A comparison of different DTM cases for the chaotic fractional system with the adopted integration step of \(h=0.01\) helps determine the optimal order of the method (\(K=14\)) which should be  
Figure 6: Bifurcation diagrams obtained for the Rössler system using the G-L RK4 scheme and DTM for \(\alpha_{1}=0.96\).  
used in general. However, the same tests have shown that reducing the order to \(K=8\) does not lead to significant changes in the results of integration. These conclusions have been confirmed by the results of the investigation of the Rossler fractional system carried out for a wide range of changes in the bifurcation parameter (coefficient \(a\)). It has been found that the DTM results are very stable and resistant to changes for the system fractionality, ranging from \(\alpha_{1}=0.98\) to \(\alpha_{1}=0.96\). The same changes produce clear qualitative changes in the results obtained with the G-L RK4 method, which seems to be primarily caused by more intensive error propagation.  
As mentioned in the introduction, when considering topological conditions of a solution obtained via DTM processing of the FODE in the Caputo sense, we must take into account possible inaccuracies resulting from limiting the solution of a three-dimensional space. Therefore, more theoretical analyses and numerical experiments are needed to better assess which of the two integrating approaches of the fractional system under consideration gives results that are closer to true solutions.  
Conceptualization, A.R.; methodology, A.R.; software, A.R.; validation, M.G.; investigation, M.G.; writing--original draft preparation, A.R.; writing--review and editing, M.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).  
Not applicable.  
The research was financed in the framework of the project Lublin University of Technology-Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19).  
The authors declare no conflict of interest.
# Possibilities of Powering Military Equipment Based on Renewable Energy Sources
## 2 Literature Review
### Solar Electricity Generator
It consists of a set of six wind generators with a power of 1 kW each and photovoltaic panels generating up to 55 kW. It also has a battery system that allows the store of up to 75 kWh of electricity. The following set weighs about 10 tons and can be set up in 30 min. Presented below is the Canadian MOBISMART Hybrid Off-grid Wind and Solar Power Generator (Figure 4) [38]. This represents an example of smaller and cheaper solution that is also more interesting for the inhabitants of the northern hemisphere. Figure 4: Mobile solar-wind power plant by MOBISMART (source, https://mobis smart.ca/mobile-hybrid-wind-solar-system (accessed on 12 November 2021)). The kit is installed on an aluminum trailer that can be connected to any car. It allows one to generate up to 6 kWp of power and is equipped with an energy storage kit allowing up to 30 kWh (Lux model). An installed wind turbine generating up to 1 kWp (vertical axis) or 1.2 kWp (horizontal axis operation). The mobile energy management system (MEMS) from the German company PFISTERE integrates a conventional source of electricity in the form of a power generator with various renewable energy sources. The MEMS consists of two ZSEs with a power of 10 kW each, a wind turbine with a maximum capacity of 5 kWp, PV panels with a maximum capacity of 5 kWp, and an energy storage 60 kWh. Additionally, the system can be supplemented with Austrian Smartflower sets with a maximum power of 2 kWp and the PV MC66 system [2,20,39]. In the solutions related to the described systems, modular power supply systems play an important role, enabling the construction of their own structures dedicated to the specific needs of the user. They secure specific capacities in terms of electricity demand and offer solutions related to the mobility of structures (dimensions, shock resistance, etc.). The existing commercial offers of solutions in the field of small wind turbines, solar panels and is wide, but not every device is suitable for military applications. All of the autonomous hybrid generators presented above can be combined into larger structures, thus obtaining energy systems with greater powers. The MOBISMART company offers a solution consisting of connected generators giving a total of approx 200 kWp of electricity and dedicated to power a small container estate in Canada.
## 3 Results and Discussion
### Membrane Rejections  
Figure 4 shows rejection efficiencies of organics and phosphorus at the same operational conditions applied in Figure 3. Results in Figure 4A through D show that the rejection efficiency of organics and phosphorus are very high as 98.3 and 99.9%, respectively regardless of all operational conditions such as permeate recirculation velocity, mixing intensity, feed temperature and permeate temperature. The phosphate ions (PO\({}_{4}\)3-) can lead to the formation of inorganic precipitation which can be associated with magnesium and calcium ions such as Mg\({}_{3}\)(PO\({}_{4}\))\({}_{2}\), Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\) or NH\({}_{4}\)MgPO\({}_{4}\) and this can contribute to high phosphorus rejection efficiency [47; 48; 49]. However, the rejection of ammonia nitrogen was relatively low, yielding 80.6%-91.3% depending upon operational conditions. Figure 4C shows that increasing feed temperature reduces the rejection of ammonia significantly. A lower rejection of ammonia nitrogen is caused by more fraction of NH\({}_{3}\) gas formed in MD permeate at higher feed temperature due to its more vaporization through the membrane in the SDCMD process as discussed above [50; 51].  
Figure 4: Rejection efficiency of COD\({}_{\rm Cr}\), NH\({}_{3}\)-N and T-P with operational parameters of SDCMD process after 1 h of operational time with respect to permeate recirculation velocity (**A**), mixing intensity (**B**), feed temperature (**C**) and permeate temperature (**D**) (background operational condition; permeate recirculation velocity of 0.5 m/s, mixing intensity of 600 rpm, feed temperature of 55 \({}^{\circ}\)C and permeate temperature of 20 \({}^{\circ}\)C).
## 4 On-Site Monitoring Results Analysis
### Deep Surrounding Rock Convergence Deformation Characteristics  
Through the monitoring and measurement of the tunnel, the convergence curve of the deep surrounding rock was obtained (Figures 23 and 24). The deep displacement of surrounding rock increased with time in a manner of exponential decay, and the relationship is shown in Figures 23 and 24. The analysis of the convergence deformation curve showed that on the whole, the convergence deformation process of surrounding rock followed three stages: a rapid deformation period, a deformation stationary period, and a stable deformation period. In general, the convergence displacement value of surrounding rock increased rapidly in the early stages, and with the development of time, after the deformation reached a certain degree, it finally tended to stability. According to the calculations, the cumulative convergence values of the surrounding rock on the left and right sides of the section were 55.85 mm and 27.48 mm, respectively, which showed that the degree of deformation and fragmentation in the left surrounding rock was much larger than that in the right side. Compared with the cumulative convergence value of 300-800 mm before support improvement, the displacement was obviously reduced, and the support achieved good results. During the same period, the cumulative convergence values of surface displacement provided by the construction party were 79 mm (left side) and 46 mm (right side), which were greatly different from the data of the optical fiber displacement meter group. This might be due to the error-prone nature of, and lack of rigor in, manual operation. Locally, due to the interval arrangement of the 4 m NPR anchor cable and 10 m NPR anchor cable, the surrounding rock formed an overall layered plate structure at about 5 m and about 10 m, resulting in a large displacement fluctuation in the 5-10 m section. Thus, the convergence value of the 5-10 m section on the left and right sides was the largest. Because the 0-1 m section represented the loose fracture zone of the surrounding rock, the 0-1 m section also had large displacement. The prominent scatter points in the figure are the displacement fluctuations caused by blasting.  
Figure 22: FBG principle of sensing system.
## 4 Results and Discussions
### Evaluation Indicators  
To verify the effectiveness of each part of DRFENet, we used a controlled trial to test the performance of different modules of DRFENet. To improve the information exchange between the channels and broaden the receptive field of the model, we used DEB and RDB to test skip connections at different positions. We tested four configurations: the skip connection before dilated convolution, the RDB without skip connection, DRFENet without DEB, DRFENet without RDB, and the full DRFENet. We took the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) as targets for assessing the image denoising effect.  
PSNR is the most common and widely used objective measurement method to evaluate picture quality. The formula is shown in Equation (12):  
\[\text{PSNR}=10\cdot\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right), \tag{12}\]  
where \(MAX_{I}^{2}\) is the maximum pixel value of the image: if the pixel value is represented by K-bit binary, then \(MAX_{I}=2^{K}-1\). The SSIM formula is based on three comparative measures between two samples: luminance, contrast, and structure, as shown in Equation (13):  
\[\text{SSIM(x,y)}=\frac{\left(2\mu_{x}\mu_{y}+c_{1}\right)\left(2\sigma_{xy}+c _{2}\right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+c_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+c_{2}\right)}, \tag{13}\]  
where \(x\) and \(y\) are two different photos, \(\mu_{x}\) and \(\mu_{y}\) are the mean of \(x\) and \(y\), \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), and \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variance of \(x\) and \(y\), respectively.  
Finally, we found a more practical scheme to build the 23-layer neural network, which not only had an excellent overall denoising performance but also showed a better effect on the recovery of local features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device Name** & **Unit Type** & **Amount** \\ \hline \multirow{2}{*}{CPU} & Intel\({}^{\beta}\) Core\({}^{\text{TM}}\) & \multirow{2}{*}{1} \\  & Processor \\ \hline Mainboard & ASUS PRIME Z490-P & 1 \\ \hline GPU & Nvidia Geforce RTX 3090 & 1 \\ \hline Memory & 8 GB(DDR4 3200 MHz) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation details.
## 3 Proposed Frequency-Dependent Amplitude Panning for Stereophonic Image Enhancement  
### Overview  
In this section, we propose an FDAP-SIE method applied to audio recording with two closely spaced omni-directional microphones and illustrate the block diagram of the proposed method in Figure 3. First, the left- and right-channel input signals, respectively designated \(x_{L}(n)\) and \(x_{R}(n)\), are each segmented into a sequence of frames of 2048 samples by applying a Hanning window, and each frame is overlapped with 1024 samples of the previous frame. Then, a 2048-point STFT is applied to each segment to obtain \(X_{L}(k)\) and \(X_{R}(k)\). Next, A-F analysis is carried out to estimate the direction of the sound sources in each frequency bin. After that, frequency-dependent amplitude panning is applied to \(X_{L}(k)\) and \(X_{R}(k)\) according to the estimated direction for the \(k\)-th frequency bin. Finally, an inverse STFT followed by an overlap-add method is applied to obtain the enhanced stereophonic signal.  
Figure 3: Block diagram of the proposed frequency-dependent amplitude panning for stereophonic image enhancement.  
Figure 2: Directivity patterns of the DS beamformer (\(l=3\) cm, direction \(=90^{\circ}\)) at (a) 2.5 kHz and (b) 4 kHz.
## 3 Simulation Analysis
### Influence of the Half Cone Angle  
Based on Equations (5) and (6), the key structural parameters of the on/off valve would also affect the dynamic response characteristics. Taking the half cone angle \(\alpha\) of on/off valve spool as an example, when changing the value of the half cone angle \(\alpha\) from 20\({}^{\circ}\) to 70\({}^{\circ}\) (\(u\) = 240 V and \(P_{s}\) = 8 MPa), it is achieved in Figure 8 that the bigger half cone angle \(\alpha\) could slightly increase the opening response time of VCM-DHPV. The explanation of this is that as the half cone angle of the spool enlarged, the transient and steady gas flow forces would be gradually increased. However, it is also shown in Figure 8 that the maximum difference of VCM-DHPV opening response time under different half cone angle is very small (0.31 ms). This indicated that increasing the half cone angle cannot effectively improve the dynamic response of VCM-DHPV. In addition, the 45\({}^{\circ}\) angle is convenient for manufacturing and obtaining an appropriate sealing ability. Therefore, the half cone angle of 45\({}^{\circ}\) should be applied to the design of VCM-DHPV prototype.  
Figure 7: Dynamic response curves under different supply pressures (\(u\) = 240 V, \(\alpha\) = 45\({}^{\circ}\)).
# Ellipsoidal Path Planning for Unmanned Aerial Vehicles
## 3 Teaching-Learning Based Optimization
### Teacher Phase  
A good teacher will try to increase the knowledge of the students/learners based on his or her own knowledge over time/iterations. But no matter how good a teacher is, because of many factors this can only be done to some extent in a classroom composed of \(n\) students. It can be said that the mean of the new knowledge of the class will be moved in some extent towards the teacher's knowledge, but it will also depend on the capabilities of the class [21].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \(\mathbf{R^{2}}\)**-Score** & **MAE (Meters)** & **MedianAE (Meters)** \\ \hline Train set & 0.9928 & 0.0302 & 0.0238 \\ Test set & 0.9922 & 0.0308 & 0.0241 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Neural Network training results.  
Figure 6: Distance map. The warmer colors represent greater distances (20 random ellipsoids).  
The following equation shows the intent of the teacher \(X_{T}\) to influence, to some degree, each individual \(X_{i}\) (composed of the drones \((x,y,z,yaw,pitch,roll)\) values) with the help of the mean of knowledge of the whole class \(\bar{x}\):  
\[X_{i}^{\prime}=X_{i}+r(X_{T}-T_{f}\bar{x}), \tag{4}\]  
where \(T_{f}\in\{1,2\}\) is a random value of only two possible values, named the teaching factor; and \(r\in[0,1]\) is a random number. If \(f(X_{i}^{\prime})\) provides a better solution than \(f(X_{i})\) (where \(f\) is a fitness function), \(X_{i}^{\prime}\) replaces \(X_{i}\) as a solution.
# Automated Detection of Multi-Rotor UAVs Using a Machine-Learning Approach
## 2 Existing Methods for Detection and Identification of Objects in an Image
### TensorFlow (TF)  
TensorFlow (TF) is an open-source machine learning platform that is used in a wide range of applications [17, 18]. A tensor is a generalization of vectors and matrices to potentially higher dimensions. TF represents tensors as n-dimensional coordinates of basic data types [19]. This system is based on artificial intelligence and it was published by Google (for free use) in 2015. TF uses the dataflow graph method to represent the calculations. Units of calculations are represented by graph nodes. The edges of the graph transmit tensors (multidimensional arrays) between nodes [17, 18] and represent the data consumed or produced by the calculation [20]. The advantage of this system is its architecture which by its flexibility allows easy and affordable deployment of computing on various platforms such as multicore processors, graphics cards, or Tensor processing units (a computer system also developed by Google designed primarily for machine learning). As mentioned in [17], training a neural network to classify and recognize objects requires a large number of calculations. The features of TF allow its users to perform computationally complex tasks such as classification in a relatively reasonable time.