esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For that, the full process of designing the electrical motor is brought in the early-design stage, where the characteristics (electromagnetic, motor losses, mode-shapes and natural frequencies) can be evaluated and predicted by the designer. Therefore, the designer can come to the best solution, identifying the system's capabilities and sensitivities in the concept phase, without involving experts from different domains. The proposed workflow is identified in Figure 1. The data, needed to build the multi-attribute model, are achieved by conducting both 2D electromagnetic and 3D structural FE analysis on a set of motor models generated by imposing multiple design parameters on the reference parametric model. After that, the resulting data is harnessed using machine learning algorithms, creating one machine learning model per physics involved. One harmonic model for the electromagnetic torque and back emf is created by applying a Fourier decomposition on the electromagnetic quantities, reducing the size and the complexity of the model, without a significant loss in accuracy [21]. Another two models are built, one for motor losses and one model to predict the natural frequencies of the mode-shapes. The most suitable machine learning model for each target is chosen by testing different machine learning algorithms applied on multiple dataset sizes. Based on their capability to predict electrical motors multi-physical characteristics, the most accurate machine learning model for each physics is selected. The paper is structured as follows: The process of building the stator parametric model, together with the most important design parameters and their interval of variation is presented in Section 2. Afterward, the multi-physical FE analysis, that include electromagnetic simulations, losses computations and structural modal analysis, performed on the parametric model for a set of imposed design variables are described in Section 3. Section 4 is dedicated to the multi-attribute machine learning model. Here, an evaluation of the prediction and fitting capabilities of different machine learning algorithms are investigated. Moreover, the impact of training sample size is discussed here. Section 5 deals with a comparison between machine learning and FEA computational cost. The final conclusions are drawn in Section 6.
# Co-Operative Binary Bat Optimizer with Rough Set Reducts for Text Feature Selection
## 5 Results and Discussion
### Results on Non-English Datasets
#### 5.3.2 Results of Arabic Dataset
These results indicate that the second group of classification results (i.e., the classification results obtained using the selected feature set by BBA\({}_{\text{CO}}\)) is significantly higher than the first group (i.e., the classification results obtained using the selected feature set by BBA\({}_{\text{LHS}}\)). These results demonstrate the efficiency of BBA\({}_{\text{CO}}\) to improve the resulting feature set, and therefore improve the classification performance by utilizing the coevolutionary technique as explained above in order to generating a better feature set. Observing the results of the five utilized datasets in this study, namely Reuters, WebKB, Mix-DS, Harian Metro dataset and Aljazeera news dataset, it is noticed that the proposed text-feature selection method has a similar effect to all datasets, regardless of the language. The reason behind that is mostly attributed to the nature of BBA-based TFS methods, which depend on the mathematical calculations and do not consider the semantic attributes. Therefore, the performance of the proposed methods is independent from the dataset language. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**NB-BBA\({}_{\text{LHS}}\)**} & \multicolumn{3}{c}{**SVM-BBA\({}_{\text{LHS}}\)**} & \multicolumn{3}{c}{**NB-BBA\({}_{\text{CO}}\)**} & \multicolumn{3}{c}{**SVM-BBA\({}_{\text{CO}}\)**} \\ \cline{2-13}  & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** & **P** & **R** & **F** \\ \hline Economy & 94.85 & 83.65 & 88.90 & 91.13 & 95.20 & 93.12 & 89.04 & 91.51 & 90.26 & 94.17 & 99.59 & 96.80 \\ Politics & 92.28 & 97.32 & 94.73 & 85.25 & 97.55 & 90.99 & 86.92 & 97.85 & 92.06 & 88.50 & 97.63 & 92.84 \\ Sport & 91.10 & 85.55 & 88.24 & 93.87 & 85.98 & 89.75 & 97.55 & 88.90 & 93.02 & 92.66 & 83.98 & 88.11 \\ Science & 90.94 & 89.11 & 90.02 & 86.30 & 87.50 & 86.90 & 91.05 & 86.72 & 88.83 & 86.44 & 90.50 & 88.42 \\ Art & 87.51 & 96.46 & 91.77 & 97.73 & 91.69 & 94.61 & 93.62 & 90.46 & 92.01 & 99.34 & 92.69 & 95.90 \\ Average & 91.34 & 90.42 & 90.73 & 90.86 & 91.58 & 91.07 & 91.64 & 91.09 & 91.24 & 92.22 & 92.88 & 92.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Classification results of Al-Jazeera news dataset using NB and SVM in terms of precision (P), recall (R) and F-measure (F). \begin{table}
\begin{tabular}{c c c} \hline \hline  & **BBA\({}_{\text{LHS}}\)** & **BBA\({}_{\text{CO}}\)** \\ \hline  & & NB & \\ \hline Mean & 90.78 & & 91.23 \\ \(p\)-Value & 0.20 & & 0.96 \\ \(t\) Stat & & \(-\)6.89 & \\ \(t\) Critical two-tail & & 2.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 24: The \(t\)-test of classification results of BBA\({}_{\text{LHS}}\) vs. BBA\({}_{\text{CO}}\) methods.
## Appendix A
In addition, according to the SFT dataset, an opacity gray mask with 10-30% transparency is added to the predefined snow levels to simulate different snow densities. **Frost. ** Frost is composed of tiny ice crystals. Frost is mainly manifested in the image as local contrast reduction and texture changes caused by the surface being covered with a white crystalline layer and local blur caused by the scattering of light by ice crystals [55]. Therefore, opacity gray masks with 10%, 20%, and 30% transparency are added to the predefined frost levels to simulate light, moderate, and heavy frost. At the same time, the brightness is reduced by 10% to reflect the high reflectivity of ice crystals. **Dark. ** Under dark conditions, the decrease in light intensity leads to reduced brightness, a significant increase in sensor noise, and color distortion due to the decrease in saturation. According to the ExDark dataset [56], the brightness of low-light images is approximately 30-50% of normal. Considering the driving differences at different times of the night, the brightness is multiplied by 0.7, 0.5, and 0.3 to correspond to 30%, 50%, and 70% decreases in light intensity, respectively. Referring to the SID dataset [57], the Gaussian noise intensity is set to 5-20. Finally, based on the color statistical characteristics in the ExDark dataset [48], the hue shift and saturation adjustment ranges are set as (\(-\)10\(-\)+10), (\(-\)20\(-\)+20), and (\(-\)30\(-\)+30) according to severity. **Occlusion. ** The occlusion statistics in KITTI [31] show that the average occlusion ratio of vehicles in traffic scenes is 20-50%, and it can reach 70% in extreme cases. Therefore, we adopt random rectangular occlusion, and the size of the occluded area is 0.01-0.02% of the total image area to remove some features of the vehicle. The removed proportion of the total area is divided into 30%, 50%, and 70% according to severity. ### Basis for Parameter Selection of Synthesis Methods  
To comprehensively evaluate the robustness of vehicle detection models, we added the false alarm rate as a supplementary indicator and conducted analysis. A false alarm occurs when a non-vehicle object is misclassified as a vehicle, which reflects the reliability of models in distinguishing between vehicle and non-vehicle targets. Meanwhile, it can also reflect the robustness of models in complex environments, providing optimizationdirections for the reliable application of vehicle detection in real-world scenarios. We show FAR of YOLOv5 and YOLOv8 models on test data in Figure A1, with an IoU threshold of 0.5 for car, truck, and bus. \[FAR=\frac{FP}{FP+TN}\] (A1)  
Through the analysis, we reached conclusions similar to those previously. When YOLO predicts bounding boxes, it may generate some bounding boxes without objects, resulting in a relatively high FAR. Generally, the FAR of vehicle detection is negatively correlated with its baseline performance and the size of the model. Among various factors, as the severity increases, motion blur, zoom blur, and occlusion cause a sharp increase in FAR. This should be an important direction for the optimization of vehicle detection.
# State Observer Based on an Accelerometer for an Elastic Joint with Nonlinear Friction
## 1 Introduction  
The design of a robust controller for elastic joint manipulators has attracted much attention over the past several decades. The importance of the control applied to a system is increased when precise and rapid movement needs to be obtained. To obtain high control performance, the precise positioning problem of the elastic joint has been studied for a long time. Several effective control strategies have been devised in [1; 2; 3; 4]. The majority of previously proposed methods need information on the exact state of the joint (position, speed, etc.). However, the research on state estimation is extremely low. In particular, due to the nonlinear characteristics of elastic joints, it is not easy to find the states (e.g., position or velocity of link) of the link side under the constraint that only the states of the motor are known [5; 6].  
In recent years, there have been several attempts to solve the problem of estimating the position and velocity in mechanical systems [7; 8; 9]. In particular, the authors in [9] present a robust state observer for elastic joints that contain nonlinear functional components in the stiffness part. The state observer was designed using the results of [10; 11; 12]. However, this study does not consider the effect of frictional forces. As depicted in Figure 1, elastic joints generally have a nonlinear friction torque that is approximated as affecting the motor side only. The nonlinear characteristics of its friction appear to be especially striking in the low-speed region of the motor [13].  
In this brief, we consider the nonlinear frictional force which was not considered in [9]. We present a state observer that overcomes the problem of the state estimation error not being eliminated due to the nonlinear friction. In general, the nonlinear friction model is discontinuous because of the signum function in coulomb friction [14; 15]. In order to avoid the discontinuities, the nonlinear friction of the motor, which includes the static, coulomb, and viscous terms, is considered a smooth function. The proposed state observer guarantees that the estimation error for the position and velocity of the link connected via an elasticjoint containing a nonlinear stiffness (elasticity) converges to zero. In addition, it is shown that the observer gain can be designed by LMI (linear matrix inequality) optimization. Finally, we conduct experiments on a two-inertia system consisting of two motors.  
The rest of this paper is organized as follows: We introduce the problem formulation and provide a design method of the state observer in Section 2. Next, Section 3 shows the stability and performance analysis of the proposed observer. In Section 4, we experimentally test the proposed observer on a servo drive system.
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Underwater Acoustic Communication
A standout solution to this bandwidth limitation is the integration of orthogonal frequency division multiplexing (OFDM) with subcarrier power modulation (SPM) [50]. This innovative two-dimensional modulation technique combines differential phase shift keying (DPSK) with subcarrier power modulation, effectively doubling the data transmission rate within the same bandwidth. At the receiver end, specialized equalizers are employed to mitigate channel variations, spreading loss, and multipath fading, thereby substantially enhancing throughput and reducing the bit error rate (BER). MATLAB (R2024b) simulations and laboratory experiments have confirmed the effectiveness of the OFDM-SPM system, underscoring its potential to surpass the limitations of traditional underwater acoustic communication systems [50]. In conclusion, while underwater acoustic communication remains indispensable for long-range marine applications, it continues to face persistent challenges like high noise levels, bandwidth limitations, and multipath interference. Recent advancements in denoising techniques, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Architecture** & **Input** & **Training Logic** &
\begin{tabular}{c} **Hyper-Parameters** \\ **Tuning** \\ \end{tabular} & **Reference** \\ \hline MLP and CNN & Original UAS & Unsupervised & Manually & [43] \\ CNN & Original UAS & Supervised & Manually & [44] \\ CNN and RNN & Original UAS & Unsupervised & Manually & [32] \\ RNN and Transformer & STFT & Unsupervised & Manually & [45] \\ MLP & Mel-frequency cepstral coefficients & Unsupervised & Manually & [46] \\ CNN and GAN & Original signal & Unsupervised & Manually & [20] \\ GAN & STFT & Unsupervised & Manually & [35] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of deep learning-based denoising approaches. spanning from conventional signal processing methods to cutting-edge deep learning approaches, coupled with advanced modulation schemes such as OFDM-SPM, are driving the development of more robust and efficient underwater communication systems.Looking to the future, research directions emphasize the synergistic integration of noise reduction, multipath mitigation, and multimodal communication strategies that combine acoustic, optical, and RF technologies. The ultimate goal is to achieve higher reliability, greater data rates, and broader applicability in the intricate and demanding underwater environment. In summary, traditional acoustic systems were constrained by high latency, narrow bandwidth, and noise sensitivity. Recent advances, such as OFDM-SPM modulation [50], GAN-based denoising [4; 32; 33], and adaptive equalizers [45], have demonstrably mitigated these issues, achieving up to 2 times higher data rates and significantly lower bit error rates in experimental settings.
# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 3 Experiments And Results
### Experimental Results and Analysis
#### 3.2.1 Experiments of the QARepNeXt Module  
To validate the effectiveness of the QARepNeXt module, several improved C3 modules based on mainstream lightweight networks, such as GhostNetV2, MobileNetV3, and FasterNet, were constructed to replace the original C3 module in YOLOv5. Table 3 presents the experimental results of the different models.  
We strived to find a fast backbone network to meet the need for real-time strawberry detection. Among various metrics, the actual detection speed, that is, FPS, served as our  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Backbone** & \(mAP_{0.5}\) **(\%)** & \(mAP_{0.75}\) **(\%)** & \(mAP_{0.50.95}\) **(\%)** & **Params (M)** & **FLOPs (G)** & **FPS** \\ \hline YOLOv5 & 86.55 & 62.08 & 55.61 & 7.03 & 16.0 & 122 \\ GhostNetV2 & 87.05 & 58.53 & 53.95 & **4.90** & 10.6 & 114 \\ MobileNetV3 & 86.77 & 58.74 & 54.63 & 6.44 & 14.1 & 122 \\ ShuffleNetV2 & 85.52 & 61.39 & 53.72 & 4.85 & **10.3** & 97 \\ InceptionNet & 87.10 & 63.72 & 55.61 & 5.37 & 11.7 & 94 \\ FasterNet & **87.34** & 64.82 & **57.44** & 5.72 & 12.6 & 120 \\ QARepNeXt & 86.58 & **65.46** & 56.87 & 13.00 & 33.6 & **154** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance, complexity, and analysis speed of various backbones.  
primary selection criterion for the backbone. According to the results in Figure 3, these lightweight models effectively reduced the model's parameter count and FLOPs. However, these improved models, except for QARepNeXt, did not demonstrate performance improvements that matched the reduction in FLOPs. The detection speed was determined by both FLOPs and FLOPS (floating point operations per second) [28], which is captured by  
\[\text{FPS}=\frac{\text{FLOPs}}{\text{FLOPS}} \tag{12}\]  
It is important to achieve higher FLOPS beyond simply reducing FLOPs for faster neural networks. While many attempts have been made to reduce FLOPs, such as reducing the parameters, they seldom consider optimizing FLOPS simultaneously to achieve truly low latency. The QARepNeXt converts to a single-path structure during inference, while other models all have complex multi-branch designs. The single-path structure is a hardware-efficient architecture that can utilize computing ability and memory bandwidth more effectively [26], which results in higher FLOPS. Despite having higher parameters and FLOPs, the QARepNeXt achieved the best detection speed. In conclusion, we selected the QARepNeXt as the primary module for the backbone and neck, which efficiently improved the detection speed without compromising accuracy.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 2 Background  
In this section, an overview of UAVs and their potential network services, as well as the energy issues associated with them--including batteries, recharging methods, the utilization of renewable sources, and the tethered UAV system--will be presented.
## 2 Materials and Methods  
According to the design equations derived from the theory of thick-walled pressure cylinders [9; 10], the contact pressure \(p\) in an interference fit considering a hub and shaft made of the same material can be obtained as follows:  
\[p\ =\ \frac{E\delta}{2R}\bigg{(}1-\frac{R^{2}}{r_{o}^{2}}\bigg{)} \tag{1}\]  
where \(E\) is the Young's modulus, \(r_{o}\) is the outer radius of the hub, \(\delta\) is the radial interference, and \(R\) is the radius at the shaft-hub interface.  
The contact pressure causes a stress distribution in both components. According to theory, the radial stress is equal to the contact pressure, the axial stress is null, and the tangential stress can be obtained from the following equation [9; 10]:  
\[\sigma_{r,o}\ =\ -p \tag{2}\]  
\[\sigma_{t,o}\ =\ \frac{pR^{2}}{r_{o}^{2}-R^{2}}\bigg{(}1+\frac{r_{o}^{2}}{R^{2}} \bigg{)} \tag{3}\]  
These equations are obtained considering that (i) both the shaft and hub have the same length and (ii) the hypothesis of the plane strain case of the two-dimensional theory of elasticity is satisfied. From the contact pressure, \(p\), the maximum transmitted torque, \(T\), can be obtained as follows [9; 10]:  
\[T\ =\ 2\pi R^{2}\mu lp \tag{4}\]  
where \(\mu\) is the friction coefficient and \(l\) is the hub contact length.  
When the elements fitted by radial interference have different lengths, the discontinuity at the contact point generates a local stress concentration. Thus, the assumption of uniformly distributed contact pressure \(p\) and, consequently, of the stress tensor's components, given by the theory of pressure cylinders (Equations (1)-(3)), becomes unrealistic. Indeed, the radial stress component, compressive for both parts, exhibit huge stress concentrations at the edges of the shaft-hub interface.
# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 4 Proposed Multi-Frame Blind Super-Resolution Algorithm
### Blur Kernel Estimation  
\(\mathbf{F}\) and \(\mathbf{x}\) are fixed in this blur kernel estimation step, the blur matrix \(\mathbf{K}\) can be determined as follows  
\[\dot{\mathbf{K}}=\underset{\mathbf{K}}{argmax}p(\mathbf{y}_{1},\mathbf{y}_{2},\ldots,\mathbf{y}_{ N}|\mathbf{K},\mathbf{F},\mathbf{x})p(\mathbf{K}), \tag{12}\]  
We estimate the blur kernel by using the estimated HR image \(\mathbf{x}\) and motion estimation \(\mathbf{F}\), the blur matrix \(\mathbf{K}_{n}\) of the \(nth\) LR image can be expounded as  
\[\dot{\mathbf{K}_{n}}=\underset{\mathbf{K}_{n}}{argmin}\{\mathbf{\beta}_{n}\cdot\parallel \mathbf{D}\mathbf{K}_{n}\mathbf{F}_{n}\mathbf{x}-\mathbf{y}_{n}\parallel_{1}+\mathbbm{2} \parallel\
abla\mathbf{K}_{n}\parallel_{1}\}, \tag{13}\]  
where \(\mathbf{\beta}_{n}\), which is the confidence weight of the \(nth\) LR image, is calculated in the image reconstruction stage. We set  
\[\mathbf{A}_{n}=\mathbf{F}_{n}\mathbf{x}, \tag{14}\]  
Equation (13) can be rewritten as  
\[\dot{\mathbf{K}_{n}}=\underset{\mathbf{K}_{n}}{argmin}\{\mathbf{\beta}_{n}\cdot\parallel \mathbf{D}\mathbf{K}_{n}\mathbf{A}_{n}-\mathbf{y}_{n}\parallel_{1}+\mathbbm{2}\parallel \
abla\mathbf{K}_{n}\parallel_{1}\}, \tag{15}\]  
Equation (15) can be worked out through solving the following linear system by the Iterated Reweighted least squares (IRLS) method [28] iteratively:  
\[\left[\mathbf{A}_{n}^{T}\mathbf{D}^{T}\mathbf{\beta}_{n}\mathbf{D}\mathbf{A}_{n}+\mathbbm{2} \left(\mathbf{I}_{x}^{T}\mathbf{W}_{s}\mathbf{I}_{x}+\mathbf{I}_{y}^{T}\mathbf{W}_{s}\mathbf{I}_{y} \right)\right]\mathbf{K}_{n}=\mathbf{A}_{n}^{T}\mathbf{D}^{T}\mathbf{\beta}_{n}\mathbf{y}_{n}, \tag{16}\]  
where the matrices \(\mathbf{I}_{x}\) and \(\mathbf{I}_{y}\) represent the \(x\)- and \(y\)- derivative filters, respectively. the diagonal weight matrix \(\mathbf{W}_{s}\) can be expressed as  
\[\mathbf{W}_{s}=diag\{[(\
abla\mathbf{K}_{n})^{2}+\epsilon]^{-\frac{1}{2}}\}, \tag{17}\]  
where \(\epsilon\) is a tuning parameter, which is set as \(\epsilon=0.0001\).  
The corrected blur matrix and motion matrix are used to return to the image reconstruction stage to calculate the super-resolved HR image iteratively. The number of iterations is set to five in this paper. The final algorithm of multi-frame blind super-resolution is demonstrated in Algorithm 1.
## 2 Numerical Modeling  
### The Physical Model  
The physical model is a tube with internal rectangular transverse ribs, as shown in Figure 1. The zigzag configuration consists of a set of two neighboring ribs with different heights, and the set is repeated along the tube length. The higher rib has a height of e\({}_{1}\), while that of the shorter rib is denoted e\({}_{2}\). Four ratios of e\({}_{2}\)/e\({}_{1}\) (0.25, 0.5, 0.75, and 1.0) were tested. The e\({}_{1}\)/d ratio was kept fixed at a value of 0.1. The pitch between the ribs, p, and the rib width, w, were held fixed throughout the current study. The values for the pitch-to-diameter (p/d) and width-to-diameter (w/d) ratios were 1.0 and 0.05, respectively.