esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 2 Sources of Magnetic Forces in PMSMs
### Magnetic Pressures on the Rotor
#### 2.2.1 Oscillating Magnetic Pressure Due to the Stator Slots  
In a slotless machine, the air gap reluctance seen in the magnets that are located in the rotor is constant. In this case, when the machine works in open-circuit mode, the flux density in the air gap is created only by the magnets; therefore, the flux density that crosses the surface of the rotor is constant. On the other hand, the flux density crossing the surface of the stator is not constant; it varies with the electrical frequency, which can be obtained multiplying the mechanical frequency by the number of pole pairs.  
Focusing on the rotor, as aforementioned, the magnetic flux crossing the surface of the rotor in a slotless machine in open-circuit mode is constant, so the magnetic pressure on the same surface is also constant. Nevertheless, in a slotted stator, the air-gap reluctance is not constant, producing a variation in the magnetic flux that crosses the surface of the rotor. The frequency of this variation is related to the number of slots and pole pairs, as described in (6).  
\[m=n\frac{Q_{s}}{p},n\in\mathbb{N} \tag{6}\]  
where \(Q_{s}\) is the number of slots. In Table 3, the relationship between harmonics of the magnetic flux density and harmonics of the magnetic pressure obtained by (2) is summarized. In this case, it can be deduced that the risky harmonics cannot be removed, but they can be displaced from the problematic frequencies by modifying the \(Q_{s}/p\) ratio.
###### Abstract
framework is proposed, where the \(k\) nearest neighbor (KNN) algorithm and support vector machine (SVM) classifier are adopted to predict the missing continuous and categorical variables in FR, respectively. A case study on the elevator customization is conducted to verify the proposed approach. KNN-SVM is compared with five single methods (KNN, GRNN, SVD, BPCA, and BBPCA) and five composite methods (KNN-GRNN, KNN-SVD, KNN-BBPCA, KNN-Bay, and KNN-Tree), with average reduction in root mean squared error (RMSE) of 39% and 21% against KNN and KNN-Tree, respectively. The computation time of the proposed approach is always less than two seconds. The proposed approach is helpful for managers to accurately predict the missing values in FR, is beneficial to obtain the complete requirements in design process, and can improve product design efficiency. This paper is structured as follows. Section 2 reviews the related work in requirements prediction and missing values imputation. Section 3 presents the problem description and framework formulation for missing values prediction. Following that, Section 4 details the proposed prediction approach, which integrates KNN and SVM into a multi-layer framework. A case study on the elevator customization is conducted in Section 5. Concluding remarks and future work in Section 6 end the paper.
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
## 4 Experiment
### Visualization  
Our algorithm can still accurately identify targets and calculate target frames in many complex scenes. Figure 8 shows the tracking results of our algorithm compared with mainstream algorithms such as the SiamRPN++ algorithm on some video sequences. The figure below shows that our algorithm outperforms the SiamRPN++ algorithm on all these video frames. The first four video sequences are taken from the OTB dataset. The last two video sequences are taken from the VOT dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **ECO**[31] & **SiamFC**[11] & **MDNet**[35] & **DaSiamRPN**[15] & **ATOM**[8] & **DSiam [22] & **SiamRPN++**[18] & **Ours** \\ \hline Success & 32.4 & 33.6 & 36.7 & 41.5 & **51.5** & 33.3 & 49.5 & 51.3 \\ Precision & 33.8 & 42.0 & 46.0 & 49.6 & 57.6 & 40.5 & 56.9 & **58.8** \\ \hline \end{tabular} Note: Success indicates accuracy, precision indicates precision.  
\end{table}
Table 4: Experimental results of our algorithm and other algorithms on the LaSOT dataset. The bolded values in the table are the best performance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Tracker** & **Accuracy \(\uparrow\)** & **Robust \(\downarrow\)** & **EAO \(\uparrow\)** \\ \hline C-COT [51] & 0.49 & 0.32 & 0.267 \\ ECO [31] & 0.48 & 0.28 & 0.276 \\ DaSiamRPN [15] & 0.57 & 0.33 & 0.326 \\ ATOM [8] & 0.59 & 0.20 & 0.401 \\ DiMP18 [10] & 0.59 & **0.17** & 0.402 \\ SiamMask [21] & 0.60 & 0.32 & 0.380 \\ SA-Siam [36] & 0.50 & 0.45 & 0.236 \\ SCSAtt [39] & 0.54 & 0.22 & 0.250 \\ SiamFRN [38] & 0.52 & 0.35 & 0.199 \\ SiamRPN++ [18] & 0.60 & 0.23 & **0.414** \\ \hline Ours & **0.60** & 0.22 & 0.395 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental results of our algorithm with other algorithms on the VOT2018 dataset. The bolded values in the table are the best performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Ours** & **ATOM**[8] & **SiamRPN++**[18] & **DiMP50 [10]** & **SiamMask [21]** & **ECO**[31] \\ \hline Speed(fps) & **47** & 30 & 48 & 18 & 56 & 4 \\ \hline \end{tabular} Note: Speed indicates the number of frames processed by the algorithm per second.  
\end{table}
Table 3: Experimental results of the speed of our algorithm running with other algorithms on the VOT2018 dataset. The bolded values in the table are the best performance.
# Parameter Identification of Lithium Battery Model Based on Chaotic Quantum Sparrow Search Algorithm
## 5 Battery Model Parameter Identification Based on Improved Sparrow Search Optimization Algorithm
### Experimental Results
#### 5.2.4 Discussion  
From Table 6, we can see that using the model parameters identified by the proposed QSSA algorithm can achieve the smallest MAE, RMSE and MaAE under the HPPC test. It implies that the battery model identified by QSSA agrees better with the actual battery. Furthermore, it can be seen that the parameters obtained by QSSA also perform best under the pulsed discharge test and UDDS test from Tables 7 and 8. It shows the QSSA-based battery model has better adaptability to different operating conditions.  
For the convergence characteristics of the algorithms shown in Figure 6, QSSA has a better solution at the beginning of the iteration since it uses a Tent chaotic mapping to improve population diversity. Furthermore, the improvements of Gaussian variation mixed with quantum behavior enables it to overstep the local optima and obtain better convergence accuracy than PSO, GA, GWO, DOA and SSA.  
To sum up, the advantages and disadvantages of QSSA algorithm can be analyzed by the performance of QSSA and other five algorithms in benchmark functions and practical applications. The main advantage of the proposed algorithm is the high optimization accuracy and the ability of jump out of local optimal solutions due to combination with the Tent chaotic mapping and quantum behavior strategy. Especially when solving problems with multiple local optimal solutions, the superiority of the QSSA algorithm becomes more prominent. Moreover, the proposed algorithm has a clear structure and few parameters. It  
Figure 8: The comparison of estimated and measured voltages under UDDS test. (**a**) The comparison of measured and estimated terminal voltages. (**b**) The comparison of terminal voltage errors of six algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **PSO** & **GA** & **GWO** & **DOA** & **SSA** & **CQSSA** \\ \hline MAE/V & 6.8785 & 6.7375 & 7.8769 & 7.4498 & 7.8005 & **6.5561** \\ RMSE/V & 9.3589 & 9.3657 & 10.2224 & 9.8503 & 10.1413 & **9.0941** \\ MaAE/mV & 92.0834 & 93.0041 & **90.0701** & 90.7653 & 91.7841 & 91.5958 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Terminal voltage errors with six different groups of FOM parameters under UDDS test.  
is easy to apply in practical engineering to solve different optimization problems. However, the complexity of the algorithm is relatively higher than that of the other algorithms.
# Super-Twisting Sliding Mode Control with SVR Disturbance Observer for PMSM Speed Regulation
## 2 Sliding Mode Control for PMSM
### SMC Controller Design for PMSM  
The primary goal of this study is speed tracking, which ensures that motor speed \(\omega\) follows the desired speed command asymptotically. When the system trajectory reaches and remains on the sliding surface, SMC becomes less sensitive and more robust to varying parameters and disturbances. However, chattering, which causes vibration in the control input prevents system stabilization. Hence, it is important to design an SMC controller to reduce such chattering. We introduced a modified reaching law that addresses the chattering problem. Figure 1 shows the configuration of the PMSM control system. We set \(\omega_{ref}\) as the reference speed signal and represent the speed error state variable and its derivative as follows:  
\[\begin{split} e&=\omega_{ref}-\omega,\\ \dot{e}&=-\dot{\omega},\end{split} \tag{5}\]  
where \(\dot{\omega}_{ref}=0\) due to the reference speed is supposed to be constant. The SMC design consists of two steps. The first step is to decide the sliding surface and the second step is to determine a control input. The control input ensures that the system trajectory remains near the sliding surface and forces the system to achieve the sliding reaching condition. According to the Lyapunov stability theorem with a Lyapunov candidate function of \(V=(1/2)\cdot s^{2}\), the following condition must be satisfied:  
\[\dot{V}=s\cdot s<0, \tag{6}\]  
where \(s(t)\) is the sliding surface. We choose the sliding surface as follows:  
\[s(t)=e. \tag{7}\]  
Using the motor dynamic equation given in Equation (3), the equivalent reaching law can be determined as follows:  
\[\dot{s}=-\frac{3n_{p}\psi f}{2J}i_{q}+\frac{T_{L}}{J}+\frac{B}{J}\omega. \tag{8}\]
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 1 Introduction
SegNet and UNet are clearly structured and are easy to understand. DeepLab employs \"Atrous Convolution\" to process more detailed images. RefineNet employs a structure termed multi-path refinement to combine coarse high-level features with fine low-level features in order to produce high-resolution semantic segmentation images. Per-pixel-label models based on CNN have yielded remarkable results in the field of remote sensing image segmentation, and conditional random fields have often been used to further refine the segmentation results [52; 53; 54; 55]. Researchers have also established a number of new per-pixel-label models based on CNN, such as the Multi-Scale FCN [56], patch-based CNN [57], Two-Branch CNN [58], CloudNet [59], DCNNGF [60], Multilayer Prediction-CNN [61], patch-wise CNN [62], Cascade CNN [63], and ResFCN [64], to adapt the characteristics of remote sensing images. Convolution-based per-pixel-label models have also enabled the effective extraction of agricultural information from remote sensing images including the extraction of crop plant information [65; 66], land-use [67], roads [68], and water [69]. These models have also served in the targeted detection of vehicles [70], ships [56], buildings [71], trees [72; 73], needs [74], and diseases [75]. The remote sensing index method and the SVM method focus on extracting features from the spectral information of pixels themselves, but cannot express the spatial relationship between pixels. Texture features have the ability to express spatial relationships between pixels that are statistically significant. The advantage of CNN is that it can extract the spatial correlation between pixels; however,the features showing this spatial correlation are obtained through training, and the extraction quality of the features greatly varies by sample. In addition, differences exist between the feature values of the inner pixel and those of the edge pixel for the same object; the edges obtained using CNN are often coarse. The fusion of low-level fine features and high-level rough features to form new features is effective technology for refining these edges. In crop planting areas in Gaofen 2 (GF-2) images, a pixel covers areas containing dozens of crops, pixel contents are often similar, and image texture is relatively smooth. Based on these characteristics, this paper proposes an improved CNN structure, which we call the crop extraction model (CEM). We used this CEM to extract crop spatial distribution information from GF-2 remote sensing imagery and compared the results with those from the comparison models.
## 3 Conventional Detection Schemes
### Conventional Scheme A  
In [12], the blind IFO estimation scheme was proposed using the power difference between data and null subcarriers. In this approach, the power-based objective metric is given by  
\[\Omega_{a}(m)=\sum_{k\in\mathcal{D}}|R_{l}(k+m)|^{2}-\sum_{k\in\mathcal{N}}|R _{l}(k+m)|^{2}, \tag{6}\]  
where \(m\) is the trial value for offset-matching, \(\mathcal{D}\) is the set of non-zero data subcarrier indices including \(N_{u}\) members, \(\mathcal{N}\) is the set of NSC indices including \(N-N_{u}\) members, and \(|\cdot|\) is the magnitude operation. The IFO can be estimated by  
\[\hat{\epsilon}_{i}=\underset{|m|\leq M}{\arg\max}\ \Omega_{a}(m), \tag{7}\]  
where \(M\) is the maximum allowable range of \(\epsilon_{i}\), which is determined by the stability between the oscillators at the transmitting and receiving ends. The performance of such a blind method depends on the ratio between the number of data subcarriers, \(N_{u}\), and the number of null subcarriers, \(N-N_{u}\).
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Q-Learning Dynamic Almost Blank Subframe
#### 3.3.2 Q-Learning-Based Dynamic ABS (QD-ABS) Scheme
Then Q-value is updated as follows:  
\[Q(s_{t},\ a_{t})=\ (1-\rho)Q(s_{t},\ a_{t})+\ \rho\bigg{[}c+\min_{a_{t+1}} \{Q(s_{t+1},\ a_{t+1})\}\bigg{]} \tag{21}\]  
where \(s_{t+1}\) is the next state after taking the action \(a_{t}\), and \(\rho\) is the learning rate, which denotes the willing to learn from the environment. Eventually, a Q-table will be built as shown in Table 4. Once these values in a Q-table have been learned and converged, the optimal action at each state is the one with the lowest Q-value. The detailed pseudo-code is given as follows: \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Dynamic ABS Ratio**} \\ \hline  & & \(a_{1}\) & \(a_{2}\) & \(a_{3}\) & \(\ldots\) & \(a_{n}\) \\ \cline{2-6}  & \(<25\%\) & Q(\(s_{1},a_{1}\)) & Q(\(s_{1},a_{2}\)) & Q(\(s_{1},a_{3}\)) & \(\ldots\) & Q(\(s_{1},a_{n}\)) \\
**PUE satisfaction ratio** & 25\(\sim\)50\% & Q(\(s_{2},a_{1}\)) & Q(\(s_{2},a_{2}\)) & Q(\(s_{2},a_{3}\)) & \(\ldots\) & Q(\(s_{2},a_{n}\)) \\  & 50\(\sim\)75\% & Q(\(s_{3},a_{1}\)) & Q(\(s_{3},a_{2}\)) & Q(\(s_{3},a_{3}\)) & \(\ldots\) & Q(\(s_{3},a_{n}\)) \\  & \(>75\%\) & Q(\(s_{4},a_{1}\)) & Q(\(s_{4},a_{2}\)) & Q(\(s_{4},a_{3}\)) & \(\ldots\) & Q(\(s_{4},a_{n}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Q-table in Q-Learning-Based Dynamic ABS (QD-ABS) scheme.
# Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding
## 2 Research Significance  
As an efficient parameter optimization algorithm, the DE algorithm has been widely used in various optimization problems by researchers in various fields around the world. However, research that integrates differential evolution algorithms into the field of video coding is relatively rare. In the process of video coding, there are many coding parameters involved, such as CU split depth and prediction mode for intra-frame prediction, motion vectors for inter-frame prediction, and quantization parameters of the quantization process. Therefore, how to use the differential evolution algorithm to solve the problem of finding optimal coding parameters in each module of video coding needs to be studied. Quantization parameters are important parameters in video encoding. However, in the current encoding standard, under the default encoding configuration, the quantization parameters of each CTU are fixed and do not change adaptively according to the video content or scene complexity. There is still room for improvement in compression ratio. Since the differential evolution algorithm has the characteristics of easy implementation and good robustness, the differential evolution algorithm can be used outside the H.265/HEVC encoder and combined with the data characteristics of the panoramic video to quantify the CTU of each frame of the 360-degree panoramic video. Parameters are selected to ensure reasonable allocation of code rates and improvement of rate-distortion performance.
## 5 Results and Discussion
### Evaluation Criteria  
To measure the performance of the proposed approach, a set of evaluation criteria was employed to assess the achieved results. These criteria included the normalized correlation coefficient (\(NCC\)), image fidelity (\(IF\)), and peak-signal-to-noise ratio (\(PSNR\)). The formulas used to calculate these criteria are presented in the following:  
\[NCC=\frac{\sum_{u=-w}^{w}\sum_{v=-w}^{w}[I_{uv}-\bar{I}]*[I_{uv}^{\prime}-\bar{ I}^{\prime}]}{(2w+1)^{2}\sigma(I)*\sigma(I^{\prime})} \tag{25}\]  
where \(I_{uv}\) is the first image, \(I_{uv}^{\prime}\) is the second image, \(\bar{I}\) and \(\bar{I}^{\prime}\) are the means of the images, and \(\sigma(I)\) and \(\sigma(I^{\prime})\) are the standard deviations of all of the points in \(I\) and \(I^{\prime}\), respectively.  
\[IF=1-\frac{\sum_{u,v}(I_{uv}-I_{uv}^{\prime})^{2}}{\sum_{u,v}I_{uv}\times I_{ uv}^{\prime}} \tag{26}\]  
where \(I_{uv}\) is the pixel at position \((u,v)\), and \(I^{\prime}(u,v)\) is the stego image at the same position.  
\[PSNR=10.log_{10}\bigg{(}\frac{Signal}{Noise}\bigg{)} \tag{27}\]
## 2 Theory of RNN Utilization in Battery Models
### Classification of Battery Models  
Battery voltage behavior models can be divided into the following categories:  
* Analytical models
* Electrochemical models
* Equivalent circuit models
* Data-based models  
Mathematical models describe the electric behavior of a battery cell in an analytical way. Three main equations--the Sheperd, Nernst and Peukert equations--are applied in this approach. These models are parameterized with test data, including input values for the SOC, voltage and current. Consequently, a previous SOC estimation is obligatory and a temperature dependency is not included. Physical-based models can achieve a high accuracy by modeling the dynamic behavior with equations derived from physical and electrochemical laws. Therefore, it is necessary to solve a large number of partial differential equations in real-time and, thus, they are typically excluded from industrial applications. Common approaches use the Butler-Volmer equation [26] and can achieve high accuracy.  
In the literature, ECM are widely discussed using RC circuits [27], in comparison to math models [28] and various ECM [29]. [PERSON] et al. [30] showed the applicability of ECM for LTO application. ECMs model the macroscopic effects of the electrochemical processes that occur in a battery cell during charging and discharging. Voltage polarization arising from non-linear effects caused by diffusion, charge transfer, and the electrochemical double layer is modeled using one or more RC circuits. A valid SOC model is elementary for building an ECM. Furthermore, precise measurements have to be carried out and the parameters must be fitted.  
Data-based models have emerged over the past few years as a promising approach for modeling batteries, due to advancements in computational power and machine learning algorithms. Fitting models to training data is computationally intensive, whereas predicting profiles with a trained model is less so and, thus, capable of operating in real time. Training with large datasets allows us to model effects that current battery knowledge cannot explain and to fit highly non-linear correlations such as aging [31]. Furthermore, no expert battery knowledge is necessary to model the behavior.  
This paper investigates the use of ML algorithms to detect the battery behavior. Therefore, only physically measured parameters are used for fitting and, hence, no state variables, such as SOC or state-of-power (SOP), are needed. This ensures the ease of implementation of the training algorithm, as well as high accuracy. Additional data often improve the accuracy but overfitting can be effectively applied in some parameter areas, if the amount of data is lacking. Hence, every dataset can be processed into a valid training dataset without a need for specifying the measured current or voltage profiles.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 3 Types and Characteristics of UAVs  
Different types of UAVs are important for various applications due to their unique capabilities and features. The choice of any particular drone depends on the purpose of such a drone. Studying the types of UAVs regarding their structure is important for understanding their applications in society. The structure of a UAV can greatly impact its capabilities, performance, and suitability for different tasks [28]. Different types of UAVs have varying designs, such as fixed-wing, rotary-wing (e.g., helicopters), and hybrid configurations as mentioned in Figure 5. Each type has its own advantages and limitations, making them suitable for specific applications. For example, fixed-wing UAVs are known for their long endurance and high-speed capabilities, making them suitable for tasks such as aerial mapping, surveillance, and cargo delivery over long distances. Rotary-wing UAVs, on the other hand, offer vertical takeoff and landing capabilities, making them ideal for tasks that require hovering, maneuverability, and close-range operations, such as aerial photography, search and rescue, and inspection of infrastructure [30]. By studying the structure of UAVs, researchers, and engineers can better understand the capabilities and limitations of different types of UAVs, enabling them to design and develop UAVs that are optimized for specific applications. This knowledge is crucial for advancing the field of UAV technology and maximizing their potential benefits in various sectors, including agriculture, transportation, environmental monitoring, disaster management, and more. A short overview of UAVs based on their types is provided in Table 1. It is important to note that UAV technology continues to evolve rapidly, and new features and characteristics are constantly being developed and integrated into these systems. UAVs are typically operated and controlled by a remote pilot on the ground or can even operate autonomously using pre-programmed flight paths [31]. The remote pilot uses a control station to maneuver the drone and make decisions regarding its flight. UAVs do not carry any human pilots onboard, which makes them autonomous and capable of operating in environments that may be dangerous or inaccessible for manned aircraft. Studying the types of UAVs regarding their characteristics is highly important for understanding their capabilities, limitations, and applications. The characteristics of UAVs encompass various aspects that influence their performance, functionality, and suitability for specific tasks [32]. By taking concentration on the characteristics of UAVs, researchers, engineers, and operators can make informed decisions about which type of UAV is best suited for a particular application. This knowledge helps optimize UAV selection, design, and operation, leading to more efficient and effective use of UAV technology in various industries, including agriculture, construction, environmental monitoring, and public safety [33]. The characteristics of UAVs are elaborated in Table 2.