esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 2 Related Work
### Geometry-Constrained Dynamic SLAM  
Methods based on geometric constraints typically employ robust weighting functions or motion consistency constraints to eliminate dynamic targets, thereby improving localization accuracy. These methods usually rely on the motion trajectory or geometric relationships of feature points to detect dynamic objects [18; 19; 20]. [PERSON] et al. [21] eliminated outliers by estimating a non-parametric background model of the depth scene, achieving camera motion estimation on the basis of this model. [PERSON] et al. [22] proposed an RGB-D SLAM system based on keyframes, incorporating static weights of foreground edge points into the Intensity-Assisted Iterative Closest Point method to improve pose estimation accuracy. [PERSON] et al. [23] used optical flow to track dynamic targets, then constructed and updated foreground models based on RGB-D data to remove dynamic feature points. Additionally, StaticFusion [24] simultaneously estimates camera motion and probabilistic static segmentation for static background reconstruction. Overall, geometry-based approaches typically require the integration of additional geometric computation modules, which increases the system's implementation complexity. In contrast, the method proposed in this paper fully leverages the existing PnP optimization residuals within ORB-SLAM3.  
By matching outlier points between consecutive frames and analyzing their geometric consistency, it can effectively identify dynamic features without introducing additional geometric models or structural assumptions, thereby achieving lower computational cost and greater practical efficiency.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 3 Bloch Sphere Quantum Bee Colony Algorithm
### Solution Space Transformation and Food Sources Updating  
Apply solution space transformation to the chromosomes encoded with Bloch coordinates, and perform an update operation on the quantum chromosomes. The search space for each dimension of the QABC algorithm is [\(-1\),\(1\)]. In order to calculate the fit value of the artificial bee, the solution space needs to be transformed, and the corresponding transformation of the \(j\)th qubit of the \(i\)th food source \(R_{i}\) in the Bloch spherical coordinate system is  
\[\begin{cases}X_{ij}^{(1)}=\frac{1}{2}\big{[}b_{j}\big{(}1+\cos\varphi_{ij}\sin \theta_{ij}\big{)}+a_{j}\big{(}1-\cos\varphi_{ij}\sin\theta_{ij}\big{)}\big{]} \\ X_{ij}^{(2)}=\frac{1}{2}\big{[}b_{j}\big{(}1+\sin\varphi_{ij}\sin\theta_{ij} \big{)}+a_{j}\big{(}1-\sin\varphi_{ij}\sin\theta_{ij}\big{)}\big{]}\\ X_{ij}^{(3)}=\frac{1}{2}\big{[}b_{j}\big{(}1+\cos\theta_{ij}\big{)}+a_{j} \big{(}1-\cos\theta_{ij}\big{)}\big{]}\end{cases}, \tag{26}\]  
where \(i=1,2,\cdots,m\) and \(j=1,2,\cdots n\).  
In QABC, updating quantum chromosomes is usually finished by changing the quantum phase with revolving gates. Updating the location of the honey source essentially involves changing the angles between each Bloch coordinate.  
The honey sources have the capacity for evolution, that is, they gradually move towards the optimal solution at the same time. Consider adjusting for the change in \(\varphi\) by the cosine of the difference between \(\theta\) and \(\theta_{gbest}\) and for the change in \(\theta\) by the cosine of the difference between \(\varphi\) and \(\theta_{gbest}\).  
\[\begin{cases}\varphi_{ij}=\varphi_{ij}+\cos\Big{(}\theta_{ij}-\theta_{gbest} \Big{)}*\Big{(}\varphi_{ij}-\varphi_{gbest}\Big{)}\\ \theta_{ij}=\theta_{ij}+\cos\Big{(}\varphi_{ij}-\varphi_{gbest}\Big{)}*\Big{(} \theta_{ij}-\theta_{gbest}\Big{)}\end{cases}, \tag{27}\]  
where \(\varphi_{ij}\) and \(\theta_{ij}\) are the Bloch coordinate phase angles of the \(i\)th food source at the \(j\)th quantum position, and \(\varphi_{gbest}\) and \(\theta_{gbest}\) are the globally optimal Bloch coordinate phase angles in each generation.
## 3 Experimental Tests
### Specim Design
#### 3.2.2 Integral Abutment and Pile Specim Design  
Based on the scale ratio (1/10) and the flexural strength similarity relationship, an abutment specimen with a height (_h_) \(\times\) thickness (_t_) \(\times\) width (_b_) of 0.55 m \(\times\) 0.2 m \(\times\) 0.425 m was obtained, as shown in Figure 3. The XTRACT v3.0.5 software [39] was used to design the longitudinal rebars and stirrups for the abutment specimen. Twenty-two round rebars with a 6 mm diameter were arranged in a single row in the rectangular cross-section of the abutment, with nine rebars along each of the back and front sides and two rebars along each of the left and right sides, considering a 10 mm cover thickness. Twelve stirrups with a 6 mm diameter and 45 mm pitch were arranged along the height of the abutment, as shown in Figure 3. A pile cap with a height \(\times\) thickness \(\times\) width of 0.2 m \(\times\) 0.27 m \(\times\) 0.425 m was designed to connect the abutment to the pile.  
Based on the scale ratio (1/10) and the flexural strength similarity relationship, an RC pile specimen with a 2.5 m length (_l_) and a 150 mm diameter (_D_) were obtained, as shown in Figure 3. The XTRACT v3.0.5 software [39] was used to design the longitudinal rebars and hoops in the pile specimen. Twelve round rebars with a 6 mm diameter were arranged in the cross-section of the pile considering a 10 mm cover thickness. Thirty single-leg hoops with a 6 mm diameter and an 83 mm pitch were arranged along the pile's length, as shown in Figure 3. The rebar ratio was 1.7%, which is larger than the minimum rebar ratio of 1.6% for the RC pile in IABs [36].
## 5 Simulation Analysis  
### System Parameters for Fault Scenarios  
#### 5.1.1 Coefficient Setting and System Performance Analysis  
To illustrate the necessity of the FTC, a NT-DRAS with different gradual fault scenarios(single fault conditions and all faults in combination ones) is used for simulation study and analysis, where the system parameters are the same as in [25]. Then the system matrices in normal conditions can be determined. Typical gradual faults of EHA have been discussed in Section 2.2. Since these gradual faults are considered to be of limited range and for design of the baseline robust time-invariant fault-tolerant controller, different matrices changing bounds need to be determined. The variation ranges of the relative parameters are chosen based on their standard values and the rationality can be verified by using pole analysis method as shown in Table 3.  
As the upper bound of the gradual faults increases, pole positions under different fault conditions demonstrate tendency of moving towards imaginary axis (see Figure 6a), indicating that the system response is degraded by the gradual faults. Also, the circles indicate pole distribution areas under different fault conditions, thus demonstrating a tendency for convergence. It is necessary to point out that under the fault conditions, not only that the poles move towards the imaginary axis, but some of them are also moving away from the real axis, resulting in oscillatory system response. There is relationship between pole locations and the severity of the system fault (see Figure 6b). The dominant poles corresponding to system fault are obtained by the dominant pole analysis method. The third and fourth ones are taken as the dominant poles due to the relationship between the pole position and the system performance. The results are shown in Figure 6b, where arrows in the horizontal direction show the increase in the system fault, and the vertical arrows show the corresponding change in the position of the closed-loop poles. The four subfigures indicate that the dominant poles move towards the imaginary axis as the fault increases. In this process, the changing trajectory of \(\mathrm{E}_{\text{eha}}\) coefficient relevant dominant pole exhibits nonlinear characteristics, an inflection point occurs in the vicinity of value \(5.0\times 10^{8}\), and system performance before and after the inflection point should be analyzed and simulated.  
\begin{table}
\begin{tabular}{c c} \hline
**Fault Types** & **Variation Ranges of the Relative Parameters** \\ \hline Fault-1 & \(\mathrm{C}_{\text{eha}}:\ 0.1\times 10^{-10}\to 1.0\times 10^{-10}\) [(\(\mathrm{m}^{3}/\mathrm{s}\))/\(\mathrm{Pa}\)] \\ Fault-2 & \(\mathrm{B}_{\text{eha}}:\ 1.0\times 10^{4}\to 1.0\times 10^{5}\) [\(\mathrm{N}\mathrm{s}\)/\(\mathrm{m}\)] \\ Fault-3 & \(\mathrm{E}_{\text{eha}}:\ 8.0\times 10^{8}\to 5.0\times 10^{8}\to 8.0\times 10^{7}\) [\(\mathrm{Pa}\)] \\ Fault-4 & \(\mathrm{R}_{\text{e}}:\ 0.245\to 0.275\) [\(\Omega\)] \\ \hline \end{tabular}
\end{table}
Table 3: Parameter variation ranges.
# A Review on SIW and Its Applications to Microwave Components
## 5 Losses in Substrate-Integrated Waveguides
The mathematical formulation, as reported in [30], is given by Equation (5), where \(c_{0}\) is the free space speed of light and tan \(\delta\) is the loss tangent of the dielectric substrate material. \[\alpha_{d}=\frac{\pi f\sqrt{\epsilon_{r}}}{c_{0}\sqrt{1-{(f_{0}/f)}^{2}}}\tan\delta \tag{5}\]  
SIW radiation loss, as opposed to conductor loss and dielectric loss, is overtly connected to the substrate-integrated waveguide itself. This is because of the periodic gaps occurring in the middle of the metal vias, which are implanted in the dielectric substrate [30]. The radiation loss in substrate-integrated waveguide components is due to energy outflow out of these gaps. As reported in [4], the radiation loss in substrate-integrated waveguides may be overlooked when the metalised hole diameter \(d\) and the pitch \(p\) are selected in accordance with Equation (6), where \(\lambda_{g}\) is the guided wavelength in the substrate-integrated waveguide. The condition should guarantee that radiation loss is maintained at the lowest possible level that renders it insignificant. This implies that the substrate-integrated waveguide can be developed as a traditional dielectric-filled rectangular waveguide [4]. \[d<\lambda_{g}/5\quad\textit{ and }\quad p\leq 2d \tag{6}\]  
Although Equation (6) is the proposed bare minimum requirement for substrate integrated-waveguide design, the \(d\geq\lambda_{g}/5\) parameter value and/or the \(p>2d\) parameter value may still be employed with no devastating outcomes [4]. To achieve low radiation loss performance, [14] suggests that the ratio \(p/d\) ought to be maintained under 2.5, with an optimal substrate-integrated waveguide design having a parameter value of \(p=2d\). The radiation loss attenuation constant \(\alpha_{r}\) in substrate-integrated waveguide may be investigated based on Equation (7) [32], where \(w\) stands for the substrate-integrated waveguide cavity width and \(\lambda\) stands for the SIW propagation wavelength. Equation (7) is centred on the mathematical simplification of the basic (quasi \(TE_{10}\)) mode of the substrate-integrated waveguide into double plane waves. This is centred on the mathematical computation of the transmission coefficient of an unlimited grid of metallic vias [30;32]. \[\alpha_{r}=\frac{\frac{1}{w}\left(\frac{d}{w}\right)^{2.84}\left(\frac{s}{d}-1 \right)^{6.28}}{4.85\sqrt{\left(\frac{2w}{\lambda}\right)^{2}}-1} \tag{7}\]
###### Abstract  
In this study, we investigated the effects of additional ultraviolet radiation (UV) on the main growth fluorescent lamp light on pigment content and essential oil accumulation in sweet basil (_Ocimum basilicum_ L.). Three different UV light sources from light-emitting diodes and discharge lamps, which emit UV in the UV-A (315-400 nm), UV-B (280-315 nm) and UV-C (100-280 nm) ranges, were tested for basil plant growing. The plants, growing under additional UV-A and UV-B from mercury lamps, on the 60 th growing day were higher than control plants by 90% and 53%, respectively. The fresh leaf mass of the UV-A irradiated basil plants was 2.4-fold higher than the control plant mass. The dry mass/fresh mass ratio of the UV-A and UV-B irradiated plants was higher by 45% and 35% in comparison to the control plants. Leaf area was increased by 40% and 20%, respectively. UV-C affected the anthocyanin content most strongly, they increased by 50%, whereas only by 27% and 0% under UV-A and UV-B. Any UV addition did not affect the essential oil total contents but altered the essential oil compositions. UV-A and UV-B increased the linalool proportion from 10% to 20%, and to 25%, respectively, in contrast to UV-C, which reduced it to 3%. UV-C induced the eugend methyl ether accumulation (17%) and inhibited plant growth. Moreover, UV increased the proportion of \(\alpha\)-guaiene, \(\beta\)-cubebene and \(\alpha\)-bulnesene and decreased the proportion of sabinene and fenchone. Thus, we concluded that UV (except UV-C) used jointly with main light with PPFD 120 \(\pm\) 10 \(\upmu\)mol photons-m\({}^{-2}\)-s\({}^{-1}\) for sweet basil cultivation may be justified to stimulate basil growth and optimize the essential oil accumulation.  
ultraviolet radiation; _Ocimum basilicum_ L.; plant pigments; anthocyanin; essential oil +
Footnote †: journal: Applied Sciences
# Differential Privacy in Federated Learning: An Evolutionary Game Analysis
## 1 Introduction
In reality, individual strategy choices play a crucial role in determining the performance of the global model in federated learning and have a direct impact on the actual overall incentives. Given the decentralized nature of federated learning, where each client has the autonomy to choose strategies that balance privacy protection and model performance, understanding these choices and their collective impact is essential. Traditional game theory, while powerful, often focuses on individual strategy optimization and may not fully capture the dynamic and evolving nature of client behavior in federated settings. In contrast, evolutionary game theory provides a more comprehensive framework to analyze how individual strategies evolve over time and how these changes affect the overall system dynamics. This study leverages the strengths of evolutionary game theory to address the challenges posed by individual strategy choices in federated learning and makes several key contributions:  
1. Evolutionary game theory analysis: We employ evolutionary game theory to analyze the differential privacy protection problem in federated learning. Unlike traditional game theory, which focuses on individual strategy choices, our approach examines the dynamics of population shares, capturing how the collective behavior of participants evolves over time. This perspective provides a novel framework for understanding the interplay between privacy preferences and system performance. 2. Impact of differential privacy mechanisms: We quantify the impact of differential privacy mechanisms on global model performance and reflect these impacts in the incentive pool. Additionally, we characterize privacy leakage to understand its effects on user utility, providing a comprehensive analysis of the trade-offs between privacy protection and model performance. 3. Differential privacy federated learning model (DPFLM): We constructed DPFLM, a robust model that integrates differential privacy into federated learning. We also propose an algorithm to compute the utility of each strategy, enabling systematic analysis of the incentives associated with different privacy mechanisms. 4. Existence and stability analysis: We conducted a thorough analysis of the existence of solutions in evolutionary game theory. For the two-strategy case, we derived closed-form results for the evolutionarily stable strategies (ESSs). For more general cases, we validate our theoretical findings through extensive simulations, demonstrating the practical applicability of our approach.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Modified-Graphene-Based ECG Electrodes
#### 2.5.1 Chemically Modified Graphene-CNT ECG Electrode  
Chemical modification and doping are good methods to improve the electrical properties of graphene electrodes, and if the two can be combined, better results may be achieved. Based on chemically modified graphene and functionalized multiwalled carbon nanotubes (MWCNT), [PERSON] et al. developed a paper-based flexible ECG electrode [68]. Compared to CVD growth of graphene, the use of chemically modified GO (CG) is a cheaper and more efficient way to obtain graphene materials. To improve electrical conductivity between CG flakes, a conductive network is formed by MWCNT. Since CG has a lot of oxygen groups on its surface, by using carboxylic groups to functionalize the MWCNT, the modified MWCNT  
Figure 3: Laser-induced/scribed graphene ECG electrodes. **(a)** Schematic illustration of the preparation process of the LIG ECG electrode. Reprinted with permission from Ref. [64]. Copyright 2020, Elsevier. **(b)** Preparation of the polyaziridine-encapsulated phosphorene composite **(above)** and the schematic diagram of the ECG electrode, immunosensor, etc. based on 3D porous LIG **(below)**. Reprinted with permission from Ref. [65]. Copyright 2021, Wiley. **(c)** Schematics of the fabrication process and photos of the LIG-SEBS-based multifunctional sensors. Reprinted with permission from Ref. [67]. Copyright 2022, Springer Nature.  
binds more tightly to CG. CG acts as the carrier of the MWCNT, and the CNTs anchor the CG nanoflakes. The modified surface groups make them difficult to aggregate, which forms a more uniform conductive work (Figure 4a). By transferring the CG-MWCNT composite to the paper substrate, the flexible ECG electrode shows low sheet resistance (75 \(\Omega\)/square) and low contact impedance (45.12 k\(\Omega\) at 100 Hz).
## 2 ML Methodologies
### Supervised Learning
#### 2.1.2 Regression
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Algorithm** & **Description** & **Applications** & **Ref.** \\ \hline Regression and water resources management & Polynomial regression & Fits a polynomial equation to the data, allowing for curved relationships between variables. & Time-series forecasting, hydrological modeling. & [99] \\ \hline Hydrological modeling and prediction & Support vector regression (SVR) & Uses support vector principles to find a hyperplane that best fits the data in a higher-dimensional space. & Groundwater level prediction, water quality modeling. & [100] \\ \hline Water quality analysis and prediction & Decision tree regression & Uses a tree-like model to represent decisions based on feature values, suitable for nonlinear relationships. & River flow forecasting, water resource optimization. & [101] \\ \hline Hydrological modeling and risk assessment & Random forest regression & Ensemble of decision trees to improve prediction accuracy and reduce overfitting. & Land use change prediction, flood risk assessment. & [102] \\ \hline Streamflow forecasting and hydrology & Gradient boosting regression & Builds multiple decision trees sequentially, each correcting the errors of the previous one. & Streamflow modeling, feature selection in hydrology. & [103] \\ \hline Hydrological data modeling and analysis & Neural network regression & Utilizes artificial neural networks to model complex relationships between inputs and outputs. & Rainfall-runoff modeling, water demand forecasting. & [104] \\ \hline Water resource allocation and prediction & K-nearest neighbor (KNN) regression & Predicts values based on the average of its k-nearest neighbors in the training dataset. & Water quality prediction, aquifer characterization. & [105] \\ \hline Environmental data analysis and modeling & Gaussian process regression & Models the relationship between variables as a distribution, allowing for uncertainty quantification. & Climate modeling, uncertainty analysis. & [106] \\ \hline Water resource management and assessment & Bayesian regression & Uses Bayesian framework to estimate model parameters and uncertainty in predictions. & Flood risk assessment, climate change impact modeling. & [107] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._
## 3 Preparation of EEG and EMG Signals for Classification: Pre-Classification Part
### Presentation of Signal Integration Method- EMG  
The MyoWare Muscle Sensor (EMG) device is a separate kit. As a result, it is not fully synchronized with the Emotiv EPOC Flex Gel (EEG) kit. This in turn affects the sampling time used, which was chosen arbitrarily by the system designers. It is worth noting that the EMG measurement device does not have to give a signal that is identical to the one sampled by the electroencephalograph. A longer signal analysis is also not essential here. It is therefore important to efficiently and quickly detect muscle stimulation visualized by the measured signal.  
\[h=\frac{N}{k}. \tag{3}\]  
It was assumed that the method of splitting the collected samples string would be in accordance with the Formula (3). Thus, having a 10 s EEG signal of \(k=10\) and an EMG signal of \(N=100\) samples, it can be calculated that for each second of the electroencephalographic signal there will be \(h=10\) samples of the EMG waveform.  
\[L^{(1\_k)}=\begin{cases}1,&\text{for }E_{(1\_k)\_m}^{EMG}\left(N-h\_m\_N\right) \geq V_{max}\\ 0,&\text{for }E_{(1\_k)\_m}^{EMG}\left(N-h\_m\_N\right)<V_{max}.\end{cases} \tag{4}\]  
As mentioned in Section 5, the verification of the electromyographic signal will be binary. This is represented by the Formula (4). The \(L\) variable will be the 0-1 string, which means no muscle movement (\(E^{EMG}<V_{max}\)) and their activity (\(E^{EMG}\geq V_{max}\)). The \(V_{max}\) variable stands for the maximum voltage, selected on the basis of the observation of the system for which the arm muscles rest. It is \(4.5V\). An example can be given assuming that if at least one of the samples of the 70-80 range of the EMG signal takes a value greater than the voltage mentioned, i.e., \(E_{(70\_80)}^{EMG}\geq V_{max}\), then \(L^{(8)}=1\). This in turn will absolutely interrupt any robot movement command, even if it meets the conditions in the form of imagining the movement of the arm.  
The approach used has its advantages. The main one is that it makes the functioning of the system independent of the sampling of the EMG device. The approach used allows for arbitrary sampling of the MyoWare Muscle Sensor, as it will depend on the \(k\) variable, which will affect the range of \(h\) samples per 1 s of the EEG signal.  
Section 4 will present examples of the signals prepared for classification.