esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper investigates one kind of high performance control methods for Micro-Electro-Mechanical-System (MEMS) gyroscopes using adaptive sliding mode control (ASMC) scheme with prescribed performance. Prescribed performance control (PPC) method is combined with conventional ASMC method to provide quantitative analysis of gyroscope tracking error performances in terms of specified tracking error bound and specified error convergence rate. The new derived adaptive prescribed performance sliding mode control (APPSMC) can maintain a satisfactory control performance which guarantees system tracking error, at any time, to be within a predefined error bound and the error convergences faster than the error bound. Besides, adaptive control (AC) technique is integrated with PPC to online tune controller parameters, which will converge to their true values at last. The stability of the control system is proved in the Lyapunov stability framework and simulation results on a Z-axis MEMS gyroscope is conducted to validate the effectiveness of the proposed control approach.  
adaptive control; prescribed performance; sliding mode control; MEMS gyroscope Performance Approach
## 2 Materials and Methods
### Explainability  
In order to understand and explain the predictions made by our machine learning model, we used the SHAP library [28; 40]. SHAP values provide a unified measure of feature importance that can be used for both linear and non-linear models. SHAP is a powerful and unified measure for interpreting the output of machine learning models, offering a consistent approach to understanding the impact of features on model predictions. SHAP values are derived from cooperative game theory and provide an interpretable allocation of each feature's contribution to a prediction, while ensuring that the sum of all feature attributions equals the difference between the predicted outcome and the average baseline prediction. This approach allows for a fair distribution of each feature's influence on the prediction, accounting for potential interactions and dependencies among features. In our study, we employ SHAP as a post-hoc explainability tool to quantify the effects of each feature on the final outcome, helping us identify the key parameters that contribute to a team's overall scoring performance. For a given prediction, SHAP values attribute a contribution value to each feature, with positive values indicating that the feature pushed the prediction higher and negative values indicating that the feature pushed the prediction lower. This allows us to understand how each feature contributed to the final prediction and how they compare to one another. Overall, the use of SHAP values provides a detailed, accurate, and easily interpretable explanation of the inner workings of our regression model.  
In addition to the SHAP values employed in our study, there are other explainable AI methods, such as Local Interpretable Model-agnostic Explanations (LIME) [41], that can be used to provide insights into the importance of features in complex models. LIME is a popular technique that explains individual predictions by fitting a locally interpretable model around the specific data point. While both SHAP and LIME aim to increase the interpretability of machine learning models, they differ in their approach. SHAP values are grounded in cooperative game theory and provide a unified measure of feature importance that is both locally and globally accurate. In comparison, LIME focuses on local interpretability and may not provide the same level of global accuracy. Additionally, SHAP values maintain consistency, which means that the order of feature importance will remain the same across different models, while LIME does not guarantee this property. For our study, we chose to use SHAP values because they provide a more consistent and accurate measure of feature importance. However, future work could explore the use of LIME or other XAI methods to analyze football team performance and compare the resulting insights with our findings.
## 2 Materials and Methods  
The QMD simulations of dense Be were performed using the OFDFT to model the electronic component and MD method to drive ionic dynamics in the Born-Oppenheimer approximation. In addition, we performed the MD simulation of the Yukawa one-component plasma (Yukawa-MD). In the OFDFT simulations, the Wang-Teter (WT) kinetic energy functional [44] was used. The WT kinetic energy functional is fully non-local, takes into account density gradient effects, and also correctly describes the delocalised electronic structure characteristic for metals [45; 46]. The exchange-correlation functional was set to Perdew-Zunger local density approximation [47]. Simulations were performed in DFTpy [48] and ASE [49; 50] packages with a simulation step of 2 femtoseconds in MD. The electron-ion interaction was defined using optimized local pseudopotential [51]. The kinetic energy cutoff was set to 600 eV. The corresponding spacing separating the nearest real space grid points is 0.47 Bohr. The number of particles in the system is \(N=864\) for the main results. A self-written C++ code was used for the Yukawa-MD, and integration of the equations of motion was performed using the Velocity Verlet method. The Yukawa potential \(\Phi(r^{*})=\Gamma/r^{*}\exp(-sr^{*})\) is fully defined by the coupling parameter \(\Gamma\) and the screening parameter \(\kappa\) (here \(r^{*}=r/a\), with \(a\) being the mean interparticle distance). The Yukawa potential parameters \((\Gamma,\kappa)\) were chosen manually to match the OFDFT results. Periodic boundary conditions in MD simulations were applied. The simulations were carried out in two stages: first 200 steps in NVT ensemble with Langevin thermostat (with the friction parameter in ASE set to \(\gamma=0.5\)), then 300 steps in NVE ensemble without thermostat. Thermalization in the first step allowed the system to reach an equilibrium state and establish the correct particle velocity distribution. The transition to NVE ensured natural particle dynamics, eliminating the effect of the thermostat on transport properties such as diffusion and velocity autocorrelation. Test calculations with different numbers of particles were performed to ensure that the presented results are independent of system size. The parameter \(\gamma\) in the Langevin equation of motion is the friction coefficient, which determines the interaction force between the particle and the heat bath. At \(\gamma=0.5\), the balance between energy dissipation and random thermal perturbations is preserved, providing the correct establishment of the temperature in the system. To obtain statistically reliable values, 10 independent simulations were performed for each temperature value. All obtained data were averaged over these independent simulations.
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 2 Results and Discussion
### UV/TiO\({}_{2}\) Photocatalysis of Real Poultry Atmosphere
#### 2.2.1 Potential Effectiveness of VOCs Removal  
Specific VOCs were selected for presentation if they were observed in multiple experiments and based on their detection quality (see Materials and Methods). Three preliminary experiments were conducted with no light energy to evaluate possible VOC removal through adsorption to the photoreactor walls and to the TiO\({}_{2}\) coated brush. These experiments showed large variability with either increased or decreased peak areas at the exit of the photoreactor. Yet, on average, compound conversion without light source was minimal (average of \(R\) was nearly zero for the collective data). Then, when light energy was provided, all VOCs conversions were more consistent, and \(R\) was always positive (i.e., signifying the net reduction of the VOCs), except for acetic acid (discussed below).  
The potential effect of energy dose on VOCs conversion rates was demonstrated either by (1) manipulating treatment time (applying airflow rates between 24 and 85 L min\({}^{-1}\), which was equivalent to treatment time of 1.8 to 6.1 s; all 24 UV lamps were in use; Figure 5A) or by (2) manipulating light intensities (using a varying number of lamps at a fixed treatment time of 5.5-6.3 s; Figure 5B.  
The results show the effective removal of key malodor compounds in poultry manure (dimethyl trisulfide (DMTS) and dimethyl disulfide (DMDS), \(p\)-cresol, phenol, and 4-ethyl phenol) in short treatment times. Decane (which appeared in most samples but is not considered as a key odorant) showed more dependence on energy dose, either at the shorter treatment times or lower light intensities. The results obtained for acetic acid (Figure 5A; inset) were not consistent and varied from positive (i.e., net removal) to negative (net formation) \(R\), with no clear dependence on energy dose. These observations were consistent with the experiments done with synthetic VOCs mixture (where acetic acid was generated under certain conditions).
## 2 Human-Machine Coupling Model
### Large Deflection Analysis of Cantilever Beam  
The design of stiffness (change in thickness) and bio-imitability of flexible hinges is an important topic in this paper to meet the requirements of stretching force and avoid interference between the hinge and finger. [PERSON] [26] determined the large deflection of a single cantilever beam through an established pseudo-rigid-body (PRB) 3R model. However, to realize the bio-imitability of the flexible hinge, the cantilever beams with different stiffness should be connected in series into the VSFH, so that each segment can realize synchronous flexion with the corresponding finger joints. For multi-hinge, the PRB model will produce too many characteristic radius factors, which aggravates the analysis of the model, so that the classical Bernoulli beam formula is more convenient and effective to establish the model of the VSFH [27].  
As the basic flexion unit, the model of a single flexible hinge shows in Figure 2. One end of the initially straight cantilever beam is fixed and the other end is subjected to a combined force \(F_{0}\) and moment \(M_{0}\). The tip slope angle and the direction of the end force are \(\theta_{0}\) and \(\phi\). Parameters \(a\) and \(b\) are the horizontal and vertical tip deflections.  
Refer to (2) and (3), which are the curvature of a beam in cartesian coordinates and Bernoulli-Euler, respectively, the curvature of cantilever beam \(k\) can be calculated by  
\[k=\frac{1}{\rho}=\frac{d\theta}{ds}=\frac{d^{2}y/dx^{2}}{\left[1+\left(dy/dx \right)^{2}\right]^{1/2}} \tag{2}\]  
\[\frac{M}{EI}=\frac{d\theta}{ds} \tag{3}\]  
Figure 1: Basic design principle of hand exoskeleton.  
\[k=\sqrt{\frac{2F_{0}}{EI}[\cos(\phi-\theta_{0})-\cos(\phi-\theta)]+\left(\frac{M_{0 }}{EI}\right)^{2}} \tag{4}\]  
where \(E\) and \(I\) are the Young's modulus and the inertia moment, respectively.  
By applying the chain rule and the coupling to (2), the length of the hinge \(l\) and the deflections of the tip can be obtained by  
\[l=\int_{0}^{\theta_{0}}\frac{d\theta}{k} \tag{5}\]  
\[a=\int\limits_{0}^{\theta_{0}}\frac{\cos\theta}{k}d\theta \tag{6}\]  
\[b=\int\limits_{0}^{\theta_{0}}\frac{\sin\theta}{k}d\theta \tag{7}\]
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.4. Osmotic Adjustment through Osmolytes  
Halophytes synthesize osmolytes in response to low external water potential to adjust osmosis and maintain positive turgor pressure. Some examples of osmolytes are proline, aquaporin, and glycine betaine. _Salicornia_ and _Sarcomodia_ species have a long history of human consumption and are ideal models for developing halophyte crops. Osmotic adjustment in both species is related to sequestration of Na\({}^{+1}\), Cl\({}^{-1}\), Ca\({}^{+2}\) in the shoot that might be achieved via production of high levels of glycine betaine [37]. [PERSON] et al. [38] reported that amino acids, sugars, organic acids, quinidine acid, kaempfer, and melatonin contents were increased under elevated stress conditions (500 mM NaCl and 5% polyethylene glycol) for 24 h.
###### Abstract  
In view of the inconsistency of guided wave energy in distributed acoustic sensing coal mine maps and the difficulty in distinguishing the vibration levels of coal mines, which leads to the poor sensitivity and accuracy of microseism detection, a coal mine microseism detection method based on time-space characteristics and a support vector regression algorithm is proposed to ensure the safety of coal mine operations. The spatiotemporal sliding window was used to collect the coal mine data in real-time, and the continuous attribute discretization algorithm based on entropy was used to discretize the coal mine data, then the data were mapped to different state spaces to build a Markov chain; by calculating the state transition probability matrix and the cross-state probability transition matrix, respectively, the temporal and spatial characteristics of the coal mine microseisms at the target node were extracted. The extracted spatiotemporal characteristics of the coal mine microseisms were used as the input to the particle-swarm-optimization-improved support vector regression model, and the regression solution results of the coal mine microseism detection signals were output. The error penalty factor and kernel function parameters were improved, and the particle swarm optimization algorithm was introduced to optimize the detection results of microseisms in coal mines. The experimental results showed that this method can accurately and detect in real-time the microseisms in coal mines in the mining area, can effectively control the rate of missing detections in the detection process, and can ensure the stability of the overall detection operation. When the inertia weight was set at 0.9 and the number of particles was 45, this method had the highest sensitivity and the best-detection accuracy for microseisms in coal mines.  
coal mine microseism; spatiotemporal characteristics; feature extraction; Markov chain; support vector regression algorithm; particle swarm optimization +
Footnote †: journal: Applied sciences
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
## 4 Simulation  
In this section, simulations are demonstrated to verify the proposed method based on unsteady aerostat platform. The scenario for the simulation is shown in Figure 1. An X-band MSCI radar system with carrier frequency of 10 GHz is considered. The randomly radiating radar array with 25 transmitters and 1 receiver is raised to 350 m height by a tethered aerostat. The main system simulation parameters are given in Table 1, and the target model is shown in Figure 4. In simulations, the measurement errors of position and altitude angles are assumed to be independent and subject to Gauss distribution with zero mean and 1 mm standard deviation for position and 0.05\({}^{\circ}\) standard deviation for altitude angles.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation parameter** & **Value** \\ \hline Aperture of antenna array & 1.5 m \(\times\) 1.5 m \\ Number of transmitting antenna & 25 \\ Height of array center & 350 m \\ Slanting angle & \(\theta_{0}=45^{\circ}\) \\ The overall observation area & 120 m \(\times\) 120 m \\ Beam coverage area by single pulse & 105 m \(\times\) 105 m \\ Number of grid & 40 \(\times\) 40 \\ Grid spacing & 3 m \\ Signal form & Random frequency hopping \\ Pulse repetition interval & 5 \(\upmu\)m \\ Pulse width & 600 ns \\ Bandwidth & 500 MHz \\ Carrier frequency & 10 GHz \\ Total imaging time & 10 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameter.  
To illustrate the effectiveness of the proposed method, the trajectories in Figure 5 are used as the three-dimensional translations and the rotational components of the antenna array caused by unsteady platform.
# VFLD: Voxelized Fractal Local Descriptor
## 2 Related Works
### Deep Learning-Based Descriptors  
In recent years, deep learning methods have emerged for scene registration. There are several types, but we only focus on correspondence-based methods, as they are similar to the previous traditional methods [1].  
3 DMatch [15] uses a 3D CNN to map from a volumetric 3D patch to a local region descriptor. For each keypoint, a 3D volumetric representation is extracted for the local surrounding region, converted into a volumetric voxel grid, and fed into the 3D CNN to construct the final descriptor. This representation has certain drawbacks, such as resolution constraining, data sparsity and computational cost.  
In 3 DSmoothNet [16], the authors proposed a feature extractor based on the smoothed density value (SDV) voxelization. First, it generates a local reference frame with the neighbors of the keypoint. The SDV voxel grid is then computed using a Gaussian smoothing kernel. Finally, this representation is fed into stacked convolutional layers and batch normalization layers to generate local feature descriptors. In this case, the local reference frame is obtained using a learning strategy.  
In the case of DeepVCP [17], it uses the same structure as 3D point cloud recognition networks, such as PointNet++ [18] and mini-PointNet [19] to learn the point descriptors. First, PointNet++ is applied to generate a feature descriptor. Mini-PointNet is then applied with the output of the previous method to summarize the local information and generate a more informative feature.  
3 DFeat-Net [20] uses a similar approach to PointNet++, but only relies on one set abstraction structure because of its great complexity. The sampling layers generate a set of points, which are the clustering centers. The grouping layer then generates clusters of points around these centers. Finally, a max-pooling strategy is applied to obtain a cluster feature that is concatenated with the point features to provide the ensemble with contextual information.  
Predator [21] is a deep model designed for pairwise registration of point clouds with low overlap. The core of the model is an overlap attention module that allows for the early exchange of information between the latent encodings of point clouds, in order to infer which of their points are likely to be in their overlap region. Since it uses fully convolutional point cloud encoders, it requires a reasonable number of superpoints, which can be a limitation in cases where the point density is very uneven.  
SpinNet [22] presents a transformer-based neural architecture to extract local features that are rotationally invariant. First, a spatial point transformer is introduced to map the input local surface into a cylindrical space in order to optimize an equivariant representation. Next, a neural feature extractor is used that takes advantage of the three-dimensional, point-based, cylindrical convolutional neural layers to obtain a compact and representative descriptor for matching.
# Simulation of AEBS Applicability by Changing Radar Detection Angle
## 2 Preparation of Simulation
### AEBS Simulation Scenario  
Before applying the actual traffic accident database and AEBS, a pre-simulation was performed to check the suitability of PC-Crash's Active Safety function. Specifically, as shown in Figures 5-7, simulations were conducted using a total of three situations to meet Euro NCAP's AEBS scenario conditions [12; 13]. Figure 5 was a low-speed test scenario, named as an AEBS city scenario, in which the test vehicle approached the speed of V\({}_{0}\) to the black target vehicle stopped on the right side.  
Figure 6 shows the AEBS Inter-urban scenario, which is further classified into three categories. First, CCRs (Car to Car Real stationary) is the same as the AEBS City test, where the target vehicle is stationary, and the test vehicle's speed runs at medium and high speeds. Second, CCRm (Car to Car Real moving) is a condition where the target vehicle proceeds at a constant speed of 20 km/h. Third, in the case of CCRb (Car to Car Rear braking), the distance between the test vehicle and the target vehicle is fixed at 12 m and 40 m, and the two vehicles proceed at the same speed of 50 km/h, and the target vehicle decelerates at 2 m/s\({}^{2}\) and 6 m/s\({}^{2}\). It is a condition for braking on the road.  
Figure 7 is a summary of the pedestrian test scenario (VRU-Pe; VRU stands for Vulnerable Road User) and is further classified into three categories. First, the CVFA (Car to VRU Far side Adult) is a scenario in which an adult pedestrian dummy collides with the center of a test vehicle at 50% of the width while approaching at a speed of 8 km/h from the opposite lane. Second, CVNA (Car to VRU Nearside Adult) 25% and 75% are scenarios in which an adult pedestrian dummy approaches 5 km/h in the direction of the test vehicle and crashes at 25% and 75% of the vehicle width. Third, CVNC (Car to VRU Nearside Child) is a scenario where a child's pedestrian dummy approaches the vehicle at a speed of 5 km/h and collides with the center of the vehicle width by 50% after suddenly appearing from around two vehicles stopped in front of the test vehicle.  
The dimensions of the adult dummy and the child dummy are shown in Figures 8 and 9. Here, the specification of the pedestrian dummy was based on the Euro NCAP standard [13], and the test vehicle was simulated with the specifications of Hyundai Motor Co.'s Sonata, the most sold vehicle in Korea in 2016.
# Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications
## 4 Proposed Hybrid PSOGA  
In this section, the routing algorithm for WSN that employs PSO and GA is discussed. This PSOGA method works in two steps. During the first phase, it obtains the population for a fixed number of epochs and holds the fittest \(M\) individuals while eliminating the rest of the individuals. During the second phase, various GA operators, namely selection, crossover, and mutation, generate the number of individuals equal to the ones omitted during the previous step. Further, these M individuals, in addition to the newly generated individuals, are used to generate the population of the next generation, thus integrating the advantages of PSO and GA. As a result, the proposed approach achieves a high convergence rate and global optima due to the PSO and GA, respectively. The proposed approach gradually increases the number of fittest individuals over each generation. Ahead of discussing the proposed hybrid approach, we discuss the PSO and GA approaches for further understanding.