esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamics of a 3-UPS-UPU-S Parallel Mechanism
## 2 A Novel 2-Rotational-DoF Parallel Mechanism (3 UPS-UPU-S) and Its DoF  
As is shown in Figure 1, a novel 2-rotational-DoF parallel mechanism as a simulator is proposed. This mechanism includes one moving platform, one base platform, and five kinematic chains. Three UPS kinematic chains consist of one universal joint, U, one intermediate prismatic joint, \(\underline{P}\), as the actuator, and one spherical joint, S. The fourth UPU kinematic chain consists of two universal joints, U, which are connected by one prismatic joint, \(\underline{P}\). The fifth S kinematic chain includes only one spherical joint, S, and the revolute center of spherical joint, S, and the geometrical center of the base coincide. The moving platform is directly built into the one with the S chain. Four kinematic subchains of 3 UPS-UPU are distributed around the spherical joint uniformly. Their primary universal joints are installed along the edge of the base, and the last four joints (universal joint or spherical joints) are assembled on the middle ring of the moving platform. The radii of the circumcircle of \(A_{1}A_{2}A_{3}A_{4}\) and \(C_{1}C_{2}C_{3}C_{4}\) are \(R\) and \(r\), respectively.  
For further analysis, the absolute coordinate \(O-xyz\) is set as shown in Figure 1. The origin \(O\) is chosen at the center of the base, and the \(z\)-axis is perpendicular to the base and points to the moving platform; the \(x\)-axis is along \(SA_{1}\) and then through the right-hand rule, the \(y\)-axis can be settled.
###### Abstract  
Local oscillatory-characteristic decomposition (LOD) is a relatively new self-adaptive time-frequency analysis methodology. The method, based on local oscillatory characteristics of the signal itself uses three mathematical operations such as differential, coordinate domain transform, and piecewise linear transform to decompose the multi-component signal into a series of mono-oscillation components (MOCs), which is very suitable for processing multi-component signals. However, in the LOD method, the computational efficiency and real-time processing performance of the algorithm can be significantly improved by the use of piecewise linear transformation, but the MOC component lacks smoothness, resulting in distortion. In order to overcome the disadvantages mentioned above, the rational spline function that spline shape can be adjusted and controlled is introduced into the LOD method instead of the piecewise linear transformation, and the rational spline-local oscillatory-characteristic decomposition (RS-LOD) method is proposed in this paper. Based on the detailed illustration of the principle of RS-LOD method, the RS-LOD, LOD, and empirical mode decomposition (EMD) are compared and analyzed by simulation signals. The results show that the RS-LOD method can significantly improve the problem of poor smoothness of the MOC component in the original LOD method. Moreover, the RS-LOD method is applied to the fault feature extraction of rotating machinery for the multi-component modulation characteristics of rotating machinery fault vibration signals. The analysis results of the rolling bearing and fan gearbox fault vibration signals show that the RS-LOD method can effectively extract the fault feature of the rotating mechanical vibration signals.  
Keywords:local oscillatory-characteristic decomposition; rational spline interpolation; rotating machinery; vibration signal; fault diagnosis +
Footnote †: journal: Applied Sciences
## 3 Results
### Laser Irradiance (GW/cm\({}^{2}\)) and Spatial (mm) Dependence of Plasma Parameters  
In this section, we studied variation in plasma-parameters versus laser irradiance and distance (mm) between the plasma plume and lens. We optimized experimental conditions such as 80 mJ fixed laser energy. A distance of 3 mm from the plasma-plume to the light collecting lens was kept perpendicular to laser beam and the target sample was fixed on a rotating stage having a rotation speed of \(\sim\)12 rpm to ensure fresh surface for each laser pulse shot. Figure 5a shows the varying trend of electron density and excitation temperature versus laser irradiance covering the range from 0 to 20 GWcm\({}^{-2}\) at laser wavelength 532 nm. The solid (red-color) curve is showing the power law fitted pattern in electron density and excitation temperature data points. The measured electron temperature increases from 8300 to 8900 K. The electron temperature described by [PERSON] and [PERSON] [53] at 10.6 GW/cm\({}^{2}\) using a 532 nm laser shows a reasonable agreement with the present work. Likewise, the electron density versus the laser-irradiance ranges from 0 to 20 GW/cm\({}^{2}\) shows a rising curve from \(1.2\times 10^{17}\) to \(1.45\times 10^{17}\) cm\({}^{-3}\) at 532 nm. Figure 5b shows the spatial varying trend of excitation temperature and electron density. The power law fitting on the experimentally measured data points at 532 nm laser wavelength is depicted in Figure 5 (solid-red-line). The excitation temperature reduces from 9000 to 8000 K. The excitation temperature close to the target is high and reduces with rising distance from the target-material. The higher temperature near the sample occurs due to higher plasma growth and cooling rates as well. Likewise, the electron density spatial distribution demonstrated a similar decaying trend as in excitation temperature. The electron density versus distance (0.5 to 3 mm) reduced from \(1.7\times 10^{17}\) to \(1.1\times 10^{17}\) cm\({}^{-3}\).  
The calculated average elemental chemical composition for major, minor, and trace ingredients is presented in Table 2 along with the standard deviation (SD). The chemical composition for major and minor ingredients is given as wt.%, while the composition of the trace elements is provided in micrograms per gram (\(\upmu\)g/g). To calculate the relative errors in the composition, we calculated the SD of each element's composition exist in the target sample. The results obtained using CF-LIBS represents that C, Mg, and Ca are found as major ingredients, while K, Fe, Sr, Li, Na, and Si, are detected as minor ingredient elements. However, it is worth mentioning that Cu, Pb, and Zn are determined as traces in \(\upmu\)g/g. Zn is detected only in the sample-(\(\upmu\), \(\gamma\), and \(\Delta\)) due to the doping of Zn in these samples as presented in Table 1. The results corresponding to the Zn composition in the doped samples show reasonable agreement. After determining the composition of ingredients, the next task is to classify the various _P. harmala_ samples.
## 4 Experimental Results
### Evaluating the Strength of Our Proposal  
In order to examine the resiliency of the proposal-based RF model under various real-world circumstances, four different cybersecurity attacks have been incorporated, as elucidated in Table 3. The reason behind selecting the RF model for the testing is that the FR model provides better accuracy compared to the two other models, as shown previously in Table 2. The proposed technique showed excellent resiliency against spoofing attempts-based attacks in which the spoofing detection rate reached 96%, and this emphasized that the proposed techniques can successfully prohibit adversarial access-based attacks. Moreover, even when random noise has been inserted into the primary input dataset, the framework is illustrated to be strong against such noise insertion data-based attacks via accomplishing 90.8% accuracy. The proposal can also prevent environmental variations or conditions, including device orientation adjustments and lighting changes, by achieving 93.2% and 92.3% accuracies, respectively. Given the aforementioned experimental results, the proposal is shown to be resilient against different conditions and circumstances, and this makes it appropriate for real-world processing systems and applications.
## 3 Results  
### Combinatorial Characterization  
First, the features extracted by the CCA algorithm using 2 s data segments were analyzed. Dimensionality reduction was performed using the UMAP algorithm on the feature sets derived from both CCA and FBCCA, and the results are shown in Figure 3. Figure 3a presents the reduced features obtained from the FBCCA algorithm, while Figure 3b illustrates those from the standard CCA algorithm. As shown in Figure 3a, although a small number of features corresponding to the eight stimulation frequenciesand the idle state were confused, the FBCCA-derived features still exhibit relatively clear boundaries. In contrast, Figure 3b shows that the boundaries between features extracted by the CCA algorithm become more blurred, and the separation between different frequencies begins to fade. Overall, the features extracted by FBCCA demonstrate smaller intra-class distances and larger inter-class distances.  
Subsequently, features extracted by the CSP algorithm using 2 s data segments were analyzed. UMAP was applied to both the CSP and FBCSP feature sets, and the results are shown in Figure 4. Figure 4 corresponds to the dimensionality-reduced features from the CSP, and Figure 4 corresponds to those from the FBCSP. As observed in Figure 4, both methods show some degree of boundary confusion between the idle and control states after dimensionality reduction. However, the FBCSP exhibits smaller intra-class distances and fewer instances of feature overlap, indicating better class separability compared to the CSP.  
The features extracted by FBCSP and FBCCA were then standardized separately and fused. UMAP was applied to the combined features, and the results are presented in Figure 5. Compared to Figure 4a, Figure 5 shows that the class separability between different states improved significantly after combining FBCCA and FBCSP features, with an increase in inter-class distances and a reduction in feature confusion. This indicates that the fusion of features from FBCCA and the FBCSP yields superior results in the low-dimensional space.  
Figure 4: CSP feature downscaling map.  
Figure 3: CCA feature downscaling map.  
This improvement can be attributed to the complementary nature of the two algorithms: the FBCSP emphasizes the spatial characteristics of EEG signals, while FBCCA focuses on frequency-specific information related to the stimulus response. Although the FBCSP can effectively distinguish between idle and control states based on spatial energy differences, it cannot explicitly determine whether these differences arise from stimulus-induced brain oscillations. In contrast, CCA can precisely capture correlations between EEG signals and target stimulation frequencies. Therefore, combining both algorithms leverages their respective strengths and results in better feature boundary formation in the dimensionality-reduced space than using either FBCCA or the FBCSP alone.
## 3 Methodology  
### Problem Statement: Creating a Reliable Deception Detector  
Given that human capabilities for detecting lies are very limited, machine learning models that have proven to be the best tool for predicting deception in computer-mediated communication were used. However, the first problem that arose in model use was the lack of labeled input data with truthful and deceptive statements. In a majority of prior work, data were collected using crowdsourcing platforms [4; 5; 8] while other research was done on \"real\" data collected through social experiments [2; 3] by analyzing public trials [6; 7] or by some other method.  
Another problem was the choice of text processing methods and machine learning models that gave the best prediction. By analyzing the choice of machine learning models in previous research, it could be concluded that Naive Bayes and SVM classifiers have proven to be very successful in solving this type of a problem [4; 5; 8], so they represent the choice of methods in this study as well. Logistic regression [2] and Random forest [6] were used somewhat less often, but they achieved acceptable results, so they were also included in the set of models for this study as additional methods. Due to the performance of computational linguistics methods used in previous research, in this study, data were analyzed using n-grams as opposed to POS analysis because it gave more precise results [4; 5]. Another reason for choosing n-gram analysis lay in the fact that it worked very well in combination with other methods. In a previous study, the highest precision was achieved by combining n-grams and deep syntax analysis when analyzing four different datasets [5] while or by combining LIWC analysis and bigrams [4]. LIWC has also served as a relatively good deception detector in other studies [3] because it provided insight into the psychological-lexical characteristics of words, which was not given with n-gram analysis. To increase model reliability, important LIWC features needed to be selected since the use of redundant features could greatly reduce the predictive power of machine learning models.  
Based on the aforementioned research, in this paper, it was decided to use a combination of analysis with n-grams and LIWC with individual approaches as verification. Deep syntax analysis was not examined in this paper because of the complexity of extracting important features from the data parsed using PCFG and the complex choice of production rules. The complete process of the methods applied in this study is given in Figure 1.
## 3 Crafting the Future: Unveiling 6G's Pinnacle Features, and
### Key Features of 6G  
3.1.1. Enhanced Security, Confidentiality, and Privacy  
In 6G, traditional encryption faces challenges, and new solutions like quantum key distribution and physical (PHY) layer security technologies are proposed. Blockchain technology aims to ensure complete anonymization, decentralization, and untraceability, addressing data privacy concerns [16].  
3.1.2. High Affordability and Full Customization  
Notably, 6G prioritizes affordability and customization to avoid increasing financial burdens on users. The goal is to provide cost-effective solutions without limiting users' options. The focus is on delivering high affordability, allowing users to choose service modes and customize preferences [1].  
3.1.3. Reduced Energy Usage and Extended Battery Duration  
Overcoming daily charging constraints, 6G aims for low energy consumption and long battery life. Energy-efficient technologies include offloading computing tasks to smart base stations, cooperative relay communications, and various energy harvesting methods [17].  
3.1.4. High Intelligence  
Additionally, 6G introduces high intelligence in three dimensions: operational intelligence for efficient network operations, environmental intelligence for adaptive wireless channels, and service intelligence for personalized communication services. AI [18], deep learning [19], and smart materials play crucial roles [20].  
3.1.5. Extremely Large Bandwidth  
Another feature worth noting is that 6G targets an extremely large bandwidth compared to 5G, utilizing the terahertz band (0.1 THz to 10 THz). Hybrid terahertz/free-space optical systems are envisioned, providing robust communication solutions. Terahertz transmission is anticipated to play a vital role, especially in uplink scenarios [21].  
3.2.2. Trade-Offs and Solutions  
3.2.1. Privacy Versus Intelligence  
A crucial trade-off exists between privacy and intelligence in 6G. Solutions involve introducing an intermediate agent to anonymize personal data, ensuring privacy is maintained while allowing AI algorithms to optimize network operations.  
3.2.2. Affordability Versus Intelligence  
High intelligence may increase system complexity and costs. To balance this, technological breakthroughs and new commercial strategies are proposed. Users exchanging anonymized data for lower prices is suggested, similar to the smart grid model.  
3.2.3. Customization Versus Intelligence  
Notably, 6G emphasizes customization over intelligence to preserve user preferences. Fundamental protocols should include prohibitive clauses to ensure that intelligent services operate within permissible boundaries.
# An Efficient Multi-Scale Focusing Attention Network for Person Re-Identification
## 2 Related Work
### Multi-Scale Feature Fusion Learning  
The way of capturing targets' features in a convolutional neural network usually adopts layer-by-layer extraction in the abstract. Over the past decade, most research in computer vision tasks, such as object detection, image segmentation, person re-identification (ReID), and so on, emphasized the effectiveness of multi-scale feature learning [27; 40; 41; 42], which is relevant to the receptive field. FPN [35] solves multi-scale problems by fusing multi-level features. For object detection tasks, refs. [42; 43; 44] carried out many investigations into the multi-scale target detection-based FPN to achieve higher accuracy. Based on previous studies of FPN, ref. [45] proposed an optimization strategy of bottom-up path augmentation to improve the use of low-level information and accelerate the dissemination efficiency of low-level details. NAS-FPN [40] employed neural architecture search with reinforcement learning to automatically generate a feature pyramid structure. [PERSON] et al. [41; 42] investigated the Bidirectional Feature Pyramid Network (BiFPN) to achieve multi-scale target classification and detection, which introduces learnable weights to the input to learn the importance of different input features, similar to the attention mechanism, and the weight is able to apply to all features or channels. Ref. [46] proposed an attention mechanism to learn the soft weight of the multi-scale features at each pixel location. Ref. [47] proposed a new semantic attention segmentation model that aggregates multi-scale and context information to re-predict the finer. Ref. [48] presented hierarchical multi-scale attention for semantic segmentation model, which reduces memory consumption and improves network training speed and prediction accuracy. For person ReID tasks, refs. [26; 49] studied a multi-layer structure to extract features that perform best under different convolution depths. OSNet [27] carried out quite different methods to obtain the features of multi-scale receptive fields, which adaptively aggregated four branches with varying numbers of lightweight blocks to get multi-scale features but aggregated features of different network layers. It dramatically reduces network parameters and computations. Overall, these studies highlight the importance and necessity of multi-scale feature learning. This paper's proposed MSFANet draws on the OSNet's architecture, which employs fewer branches and larger receptive field features to further improve ReID's performance.
## 4 Optimization Problem Modeling  
This section first models the system's cache queue and energy consumption then defines the system cost function based on the cache queue and energy consumption. Next, it considers constraints such as association and computation limitations to model the joint task offloading and resource scheduling problem as a long-term cost function minimization problem under these constraints.  
The dynamic nature of LEO networks requires a flexible approach to resource scheduling. As the number of IoT devices and their corresponding tasks vary, our system dynamically schedules the transmission and computation resources to ensure efficient processing and transmission. Continuous assessment of the operational status of satellites and ground stations allows our framework to respond promptly to changes in network conditions. The quality of communication channels is monitored to optimize data transmission paths, ensuring reliable connectivity. The relationships between satellites are considered, allowing for adaptive scheduling based on the current inter-satellite network topology.  
To effectively manage these dynamic factors, our resource scheduling strategy is designed to adapt in real time. By modeling the task offloading and computation scheduling processes as a Markov Decision Process (MDP), we enable our system to make informed decisions based on the current state of the network. This approach ensures that both the data transmission and computational tasks are scheduled efficiently, minimizing latency and maximizing throughput.
## 5 Optimization Control Strategies in Bems
### Uses of Optimization Algorithms in BEMS
#### 5.2.1 Ant Colony Optimization (ACO)  
ACO was motivated by observations of ant behavior [144]. This approach was originally created to handle discrete optimization problems before being expanded to include continuous variables. ACO for the continuous domain is the name of the extension (ACOR). ACO showed better performance in finding the best solution as compared to other benchmark algorithms: particle swarm optimization with inertia weight (PSOIW), hybrid particle swarm optimization and Hooke-Jeeves (PSO-H)), and the Nelder-Mead (NM) algorithm [142]. An ACO load scheduling strategy for a smart home was proposed. The goal was to achieve the best possible use of integrated renewable energy sources. This is accomplished by concentrating on the total electricity bill, TOU, and the overall improved quality of life (QoL) [145].
## 4 Results
### Synthetic Data
Figure 9: Synthetic sound pressure field reconstruction: (**a**) full angle (FA) sinogram; (**b**) limited angle (LA) sinogram; (**c**) neural network (NN) extrapolated sinogram; (**d**) full angle filtered back projection tomographic reconstruction; (**e**) limited angle filtered back projection tomographic reconstruction; (**f**) neural network extrapolated filtered back projection tomographic reconstruction; (**g**) error distribution of local reconstructions compared to full angle reconstruction; (**h**) local error of limited angle reconstruction compared to full angle reconstruction; (**i**) local error of neural network reconstruction compared to full angle reconstruction. In a further step, the impact of complexity in the synthetically processed pressure fields was investigated. Therefore, seven different pressure fields with different number of resonators were generated. After the neural network extrapolation and tomographic reconstruction, the relative error distribution to the full angle reconstruction is shown in Figure 10. For a better understanding, the used model field is shown above every error distribution plot. To give a better overview, the number of trained data sets with the same number of holes, meaning the same complexity distribution is shown as well. For a very simple model with only few resonators, the neural network reconstruction distribution error is a factor of 2 higher than reconstructions with higher complexity starting at nine holes. We assume that this effect is caused by a small number of trained data sets with a corresponding number of holes, because the focus of the training data is on a higher number of holes. At an average number of holes of 9 to 80 resonators, the neural network reconstruction distribution errors are very low and are below 4.5%. For 122 resonators and above, the neural network reconstruction distribution error increases again and is at a comparable level to that at low complexity. We assume that the resolution of the sinogram is to low in relation to its complexity. This results in averaging errors within a pixel and consequently in reconstruction errors compared to limited angle filtered back projection. The neural network always shows improvement over the limited angle reconstruction, even if only a limited number of training sets existed. However, the increasing uncertainty for lowly represented training sets indicates that network training always needs to be performed on structures similar to the specimen.