esa-sceva/satcom-chunk-collection huggingface dataset:
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 2 Modeling of a Carbon Nanotube
### Continuum Modeling  
Continuum modeling includes all methods based on the representation of a CNT as a continuum structure with distributed properties, such as mass, stiffness, conductivity, and so on [14; 20]. The values attributed to such properties usually come from atomistic calculations or from empiric assessments. Continuum modeling overcomes time-scale and length-scale limitations of ab initio calculations. In fact, it neglects the discrete atomic structure of the CNT by replacing it with a continuum or an equivalent-continuum [21]. The analysis of such continuum is then performed according to truss, plate, shell or beam theory, also with the aid of Finite Element Modeling (FEM) techniques. However, it should be considered that FEM models are not suitable to represent structures with extreme aspect ratios. Continuum modeling can address the investigation of larger volumes than atomistic models (from nanometric to micrometric dimensions); the accuracy of simulations based on continuum modeling, though, must be evaluated very carefully, as neglecting the discrete nature of nanostructures can easily lead to inaccurate results when it comes to the assessment of fundamental properties of the material.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Description** & **Application** & **Length Scale** & **Strengths** & **Limitations** \\ \hline \multirow{4}{*}{Atomistic modeling} & Computation of the & \multirow{2}{*}{Extraction of the } & \multirow{2}{*}{nm} & \multirow{2}{*}{Very high accuracy} & Very high \\  & position of atoms in the & & & & computational burden \\  & nanostructure for the & & & & requires limiting the \\  & solution of the & & & & analysis to a few \\  & Schrodinger equation & & & & hundreds of atoms. \\ \hline \multirow{4}{*}{Continuum modeling} & Simulation of an & \multirow{2}{*}{Effective behavior of } & \multirow{2}{*}{nm-\(\mu\)m} & Allows multiphysical \\  & equivalent continuum & & & & simulation of the CNT \\  & structure with & & & & behavior \\ \cline{1-1}  & distributed effective & & & & structure. \\ \cline{1-1}  & material properties & & & & \\ \hline \multirow{4}{*}{Transmission line modeling} & Analysis of the electric & \multirow{2}{*}{\
\begin{tabular}{c} National propagation, \\ based on equivalent \\ circuit parameters \\ describing the CNT \\ resistive, capacitive, and \\ inductive properties \\ \end{tabular} } & Computation of the & \multirow{2}{*}{\(>\)\(\mu\)m} & \multirow{2}{*}{
\begin{tabular}{c} Fast simulation of the \\ electrical properties \\ \end{tabular} } & Application is limited to \\  & & describing the CNT & & & the electrical analysis. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between the different steps of the multiscale modeling of CNTs.  
Figure 1: Representation of three levels of the CNT modeling, arranged by increasing the length scale. The arrangement also follows the increase of the computational efforts required by the modeling steps.
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 3 Reward Functions  
The sensor trajectory scheduling is part of the underlying target tracking process, involving the computation of a sequence of future actions (sensor course corrections) based on the current target and sensor states. The key role in this process plays the criterion for measuring the reward of the future action. The reward functions used in the literature are based on the information theoretic measures. One criterion is the mutual information between the posterior probability density of the estimated target state and the measurement sequence, which we seek to maximize. A theoretic description is given in [18] and sub-optimal strategies are discussed in [19]. An alternative criterion is to maximize the information divergence between the prior and posterior probability densities of estimated target state [14]. As the ultimate goal of sensor trajectory optimisation is to improve target tracking accuracy, the posterior Cramer-Rao low bound [20] is also used as a cost function to take the estimator performance into account as well. In addition, we can also maximize the determinant or trace of the FIM [13]. All of these criteria are consistent in the sense that they maximize the Fisher information of the underlying system.  
In this work, we investigate two reward functions for the problem at hand, which can be derived analytically: the _Expected RID_ and the _Determinant of the FIM_. For the purpose of comparison, we also introduce the reward function based on the _Trace of the FIM_. The goal is to find a reward function which is both computationally efficient and reflects correctly the information change associated with various sensor trajectory hypotheses.
## 1 Introduction
CVT power splits are structured according to three-shaft configurations, known also as input-coupled (IC) and output-coupled (OC), or to four-shaft configurations, known as dual-stage and compound [30; 31]. Although a pure mechanical transmission with gearbox has higher efficiency than a power split transmission, it would make the use of the streetsweeper uncomfortable. For this reason, the transmission adopted for the streetsweeper under consideration follows the CVT power split input couple (IC) configuration. Considering that the use of the vehicle requires long periods of operation at almost constant speed, in order to improve the efficiency of the transmission, a mechanical gear engaged by lockup clutch was designed to bypass the CVT branch. The literature [32; 33] shows that the lockup clutch leads to an improvement of the efficiency and fuel economy of the system. The novelty of this study concerns the design of a CVT power split hydromechanical transmission for small machines, such as a mini street sweeper. In this study, a comparison was first made between a hydrostatic transmission commonly used on these vehicles, with a power split input-coupled transmission typically used on vehicles with high power density. The advantage of using the IC has been demonstrated from the point of view of emissions compared to the hydrostatic transmission. However, the street sweeper in the working phase moves in a speed range that does not allow the maintenance of the full mechanical point (FMP) and therefore of maximum efficiency. To overcome this problem, the authors designed a power split input-coupled transmission with the addition of a lockup clutch. The lockup clutch control system is designed to use a mechanical gear during the sweeper's operation and the CVT power split transmission during urban racing, improving overall efficiency and fuel consumption. The design of the transmission and the evaluation of the efficiency and fuel consumption were carried out using Simcenter AmesimTM simulation software. The first stage of the work was the design and comparison of the hydraulic transmission and the CVT split transmission. The simulation result shows greater efficiency for the power split CVT transmission, which justifies the use of the transmission for this application, despite the increased complexity of the system. Finally, a comparison was made between a power split CVT and a power split CVT with lockup clutch to highlight the advantage of using a mechanical gearbox during the working phase of the vehicle, in terms of efficiency and fuel consumption.
## 3 Results
### Artificial Neural Network (ANN) Models  
Figure 3 illustrates the R\({}^{2}\) values for the training, validation, and all datasets as the number of nodes in the hidden layer varied from 2 to 20. As shown in Figure 3, when the number of nodes in the hidden layer exceeded 10, the R\({}^{2}\) value of the training model remained high, exceeding 0.94. However, the R\({}^{2}\) values of the validation models consistently fell below 0.80, notably lower than when the number of nodes in the hidden layer was 10 or fewer.  
Consequently, in the range of 2-10 nodes in the hidden layer, the disparity between the R\({}^{2}\) values of the training and validation models was relatively small. The best performance is observed when the number of nodes is five; the R\({}^{2}\) values for the training and validation models are 0.94 and 0.86, respectively. To denote each model by considering a varying  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **MAPE (\%)** & **R\({}^{2}\)** \\ \hline Linear regression & 2.53 & 0.92 \\ Robust linear regression & 2.51 & 0.92 \\ Linear SVM & 2.51 & 0.92 \\ Squared exponential GPR & 2.37 & 0.92 \\ Matern 5/2 GPR & 2.37 & 0.92 \\ Rational quadratic GPR & 2.37 & 0.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of machine learning models.  
Figure 2: Prediction models using regression learner application.  
number of nodes in the hidden layer, the ANN model can be represented as follows: [number of nodes in the input layer = 10] \(\times\) [number of nodes in the hidden layer = 2-20] \(\times\) [number of nodes in the output layer = 1]. Therefore, when the number of nodes in the hidden layer is five, this can be succinctly expressed as 10 \(\times\) 5 \(\times\) 1.  
Among the training models developed using the 160 datasets, Figure 4 presents a comparison of the MAPEs and R\({}^{2}\) values of the MRA, ML, and ANN (10 \(\times\) 5 \(\times\) 1). The ANN model exhibited the highest R\({}^{2}\) value (0.94) and the lowest MAPE (2.29%), confirming its accuracy as the best prediction model compared to other models.  
Figure 4: R\({}^{2}\) and MAPE of the prediction models for the training dataset.  
Figure 3: R\({}^{2}\) values for ANN prediction models.  
A residual analysis was conducted for the models depicted in Figure 4 to evaluate the tendency of the model residuals. Figure 5 illustrates the residuals from the prediction models, revealing a random distribution without any discernible trends in the measured yield strength. The yield strength was determined through tensile testing, using round bar specimens (Figure 6), conducted in accordance with ASTM E8-16: Standard Test Methods for Tension Testing of Metallic Materials [47].  
Figure 5: Residuals plots for regression models: (**a**) multiple regression—Equation (1); (**b**) linear regression; (**c**) robust linear regression; (**d**) linear SVM; (**e**) Matern 5/2 GPR; and (**f**) ANN—10 \(\times\)\(5\times 1\) models.
## 5 Conclusions and Future Work  
In this study, an enhanced YOLOv5-s model is proposed for detecting vehicle targets in traffic surveillance. To address the issues of a large number of model parameters and a slow estimate speed, we replaced the CSP structure in the original model with the GhostNet structure and used the SPPF structure to replace SPP, resulting in a reduction in redundant feature parameters and a faster inference speed. Furthermore, we used DIoU-NMS non-great suppression instead of the original NMS to improve localization accuracy and effectively reduce missed and false detections. Additionally, three attention mechanisms were proposed, and comparison experiments were conducted to test the effects of CA attention mechanisms at different locations on detection performance. Finally, the FFD-ViBe algorithm and MD-SILBP operator were combined to extract vehicle features, and the feature maps were used to expand the dataset, enhancing feature extraction. Experimental results show that the proposed method improved the accuracy of the original YOLOv5 by 3.63%, the recall rate by 2.4%, and the average accuracy by 2.2%, indicating that using traditional foreground target extraction to assist the YOLOv5 model for training can effectively improve the detection effect in peak, flat, low, rainy, and night scenarios. Visualization results showed that the proposed algorithm can effectively reduce the rate of missed detections and false detections. Nonetheless, future work will focus on improving the detection of small target vehicles in dense scenes and enhancing the foreground extraction method to obtain more complete vehicle appearance features.  
Conceptualization, Q.Z. and W.M.; methodology, W.M.; software, W.M., C.Z. and L.L.; validation, Q.Z., W.M. and C.Z.; investigation, L.L.; writing--original draft preparation, L.L. and C.Z.; writing--review and editing, Q.Z.; visualization, W.M. and L.L.; supervision, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (GrantNo. 51804248), the Shaanxi Provincial Science and Technology Department Industrial Research Project (Grant No. 2022 GY-115), the Beilin District Applied Technology R&D Project (Grant No. GX2114), and the Shaanxi Provincial Education Department Service to Local Enterprises (No. 22 JC050).  
Not applicable.  
Data Availability Statement: Data are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Efficient High-Dimensional Kernel k-Means++ with Random Projection
## 5 Discussions
### Reliability, Stability and Robustness
#### 5.3.2 Robustness  
For algorithm robustness, it usually represents the comparison between the training error and the test error for supervised learning. In our case of clustering, we are assessing the robustness by considering the noise associated with the random projection. In our experiments, when the projected dimensionality is small, the noise induced by the random projection is theoretically higher than that of higher projected dimensionality. In our experiments with six datasets, when \(d>200\), percentage changes in WCSS are less than 1% in most of the cases. In other words, the proposed method remains robust when the projected dimensionality \(d\) is greater than 200.
## 1 Introduction
Although affordable, further refinement is needed to ensure its reliability in clinical settings [38]. The Microsoft Kinect(Microsoft, Redmond, WA, USA)sensor, employing depth camera technology, offers a more robust alternative able to reliably track head movements across various angles [39; 40] and support real-time, non-invasive three-dimensional pose estimation [41]. Smartphone applications have also emerged as promising tools for measuring AHP, including forward head posture. Leveraging built-in sensors like accelerometers and gyroscopes, these applications deliver reliable assessments of cranivertebral angles and other postural parameters; also, providing real-time visual feedback can significantly improve cranivertebral alignment and reduce muscle fatigue [42]. Validation studies demonstrate the clinical utility of smartphone-based tools, showing high sensitivity and specificity for accurately measuring cranivertebral angles [43]. Their affordability, ease of use, and potential for integration into telemedicine platforms make smartphone applications a valuable addition to the array of tools for assessing and managing AHP. Finally, 3D optoelectronic motion capture (MoCap) systems represent the gold standard in terms of precision and accuracy in assessing movement kinematics [44; 45; 46]. A MoCap system uses multiple infrared cameras to detect the position of reflective markers attached to a subject's body. These markers reflect infrared light emitted by the cameras, allowing the system to track their precise three-dimensional position in space. The cameras capture the markers' movements from different views, and specialized software triangulates the data to calculate the exact location of each marker. By tracking multiple markers, the system can capture the full motion of the subject, reconstructing it into a digital model. However, the technical complexity and high costs make these tools more suitable for research settings than for routine clinical use. Despite the availability of various measurement instruments, each presents distinct advantages and limitations. Many of these tools are hindered by unresolved issues, such as inadequate precision or the requirement for expensive equipment that may not be accessible in many clinical settings. In this context, the aim of this study was twofold. First, it evaluated the accuracy of tools commonly used by ophthalmologists to measure AHP by comparing their measurements with those obtained from a gold-standard MoCap system in a pediatric population with AHP. Second, it aimed to detect and quantify the presence of potential limitations in head movements in children with AHP, comparing their motion profiles to those of a control group in terms of the range of motion (ROM).
# Place Classification Algorithm Based on Semantic Segmented Objects
## 4 Conclusions  
In this work, we have proposed an efficient scene and place classification scheme using background objects and the designed weighting matrix. We designed this weighting matrix based on the open dataset which is widely used in the scene and object classifications. Also, we evaluated the proposed classification scheme which was based on semantic segmentation comparing to the existing image classification methods such as VGG [15], Inception [16; 17], ResNet [18], ResNeXt [19], Wide-ResNet [20], DenseNet [21], and MnasNet [22]. The proposed scheme is the first approach of object-based classification that can classify outdoor categories as well. We have built a custom dataset of 500 images for testing which can help researchers who are dealing with scene classification. We crawled frames from Korean movies and labeled each image manually. The images were labeled as three major scene categories (i.e., indoor, nature, and city).  
Experimental results showed that the proposed classification model outperformed several well-known CNNs mainly used for image classification. In the experiment, our model achieved 90.8% of verification accuracy and improved over 2.8% of the accuracy when comparing to the existing CNNs.  
The Future work is to widen the scene classes to classify not just indoor (library, bedroom, kit) and outdoor (city, nature), but also more subcategories. It would be helpful for searching in videos with such semantic information.  
Conceptualization, B.-G.K.; methodology and formal analysis, W.-H.Y.; validation, Y.-J.C. and Y.-J.H.; writing-original draft preparation, W.-H.Y. and Y.-J.C.; writing-review and editing, B.-G.K.; supervision, B.-G.K. All authors have read and agreed to the published version of the manuscript.  
This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright Commission in 2020.  
Authors thank for all reviewers who helped to improve this manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l} CNN & Convolutional Neural Network \\ BN & Batch Normalization \\ \end{tabular}
# Versatile Video Coding-Post Processing Feature Fusion:
## 3 Proposed Method
### Feature Fusion  
The learned representations of CNNs, known as deep features, retain vital details and intrinsic patterns from input images, effectively summarizing their visual content. The CNN feature extraction process unfolds in a hierarchical manner, with initial layers detecting primitive visual cues such as edges and lines, and subsequent layers building upon these to recognize more sophisticated patterns, including shapes, textures, and objects.  
In CNNs, a distinction exists between the features extracted by early layers and deep layers, which are shaped by the varying intensity of pixels in an image. The early layers focus on low-level details, such as edges and textures, which are sensitive to local pixel intensity changes. In contrast, deep layers capture higher-level features (e.g., objects and patterns), which are more robust to pixel intensity variations. This disparity in feature extraction arises from the different ways early and deep layers process visual information, with early layers emphasizing local details and deep layers recognizing more global structures. As a result, the features extracted by early layers are more localized, detailed, and sensitive to pixel intensity variations, whereas the features extracted by deep layers are more global, abstract, and robust to pixel intensity variations.  
By merging early and deep layer features, the model can create a more complete and nuanced representation of the input image, blending fine-grained details with broader contextual patterns. The sensitivity of early layer features to pixel intensity fluctuations is compensated for by the robustness of deep layer features, mitigating the impact of intensity changes on feature extraction. This fusion yields a more diverse and informative set of features, enabling the model to more accurately differentiate between objects, scenes, and patterns. By harnessing the complementary strengths of early and deep layer features, the model can reduce the disparity between them, resulting in a more appropriate and effective feature extraction process.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 3 Research Progress
### Linewidth Testing Method Based on Noise Analysis  
#### 3.5.1 Frequency Discrimination Method  
In 2019, [PERSON] et al. used frequency discrimination to measure frequency noise [105]. The setup is shown in Figure 10a. Using a frequency discriminator, such as a nonequilibrium MZI with a sub-coherent delay difference, the fluctuation in the laser frequency is converted into the output amplitude fluctuation of the interferometer recorded by a photodiode, and the laser frequency power spectral density is obtained. The system is more complex, but it can observe all the noise characteristics of the laser [106]. The use of higher sensitivity frequency discriminators can achieve greater noise suppression.
## 2 Preliminaries
### Public Key Encryption  
In a public key cryptosystem [19], each party is in possession of a private-public key pair. The private key is kept secret while the public one can be made worldwide available. A message encrypted under the public key can only be decrypted by a party in possession of the private one.
# Recent Advances in Non-Traditional Elastic Wave Manipulation by Macroscopic Artificial Structures
###### Abstract  
Metamaterials are composed of arrays of subwavelength-sized artificial structures; these architectures give rise to novel characteristics that can be exploited to manipulate electromagnetic waves and acoustic waves. They have been also used to manipulate elastic waves, but such waves have a coupling property, so metamaterials for elastic waves uses a different method than for electromagnetic and acoustic waves. Since researches on this type of metamaterials is sparse, this paper reviews studies that used elastic materials to manipulate elastic waves, and introduces applications using extraordinary characteristics induced by metamaterials. Bragg scattering and local resonances have been exploited to introduce a locally resonant elastic metamaterial, a gradient-index lens, a hyperlens, and elastic cloaking. The principles and applications of metasurfaces that can overcome the disadvantages of bulky elastic metamaterials are discussed.  
elastic metamaterial; phononic crystals; gradient-index lens; elastic hyperlens; elastic cloaking; elastic metasurface +
Footnote †: journal: Applied Sciences