esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The relationships angle/torque and force/pressure were thoroughly investigated in the wide range of pressure. However, only the mathematical model was considered and verified in [20]. The low rate of desired trajectories was tracked in [21]. The mechanism-based model [22; 23] is another method in which the behaviour of PAMs was described based on their physical properties: length, diameter, and volume of PAMs, etc. However, as most of nonlinear models mentioned above, these types of models also require a complex procedure to derive the model parameters. To obtain the model of PAMs in a more simple way with a good enough accuracy, the linear mathematical model has recently been applied to approximate the characteristic of PAMs [24; 25; 26; 27; 28]. In these studies, the uncertain nonlinearity of PAM was considered as the system disturbance and solved by extended state observer (ESO) together with an active disturbance rejection controller (ADRC). The control performance is considerably improved with 0.5 Hz of sinusoidal reference signal frequency. In this research, a discrete-time fractional order integral sliding mode control (DFISMC) is employed to improve tracking performance of an antagonistic actuator driven by PAMs. First, a linear discrete-time second order plus dead time (SOPDT) model is chosen to describe the nonlinear dynamic behaviour of the antagonistic actuator. In this approximation, a nonlinear term in characteristics of PAM is considered as a disturbance. Then, the DFISMC is designed based on the fractional order integral (FOI) calculus and a disturbance observer (DSO) for the trajectory tracking purpose. Finally, the effectiveness of the proposed control technique is confirmed through multi-scenario experiments. The proposed method shows many advantages in both mathematical model and control technique. The linear discrete-time SOPDT can approximate the behaviour of antagonistic actuator at a good accuracy. Besides, the identification procedure is also simplified. By employing the FOI function together with a DSO, the DFISMC controller is able to reduce the \"chattering\" problem in sliding mode control systems. In addition, the proposed is designed in a discrete-time domain, therefore, it can be easily implemented by any digital control system.
## 2 Data and Methods  
### Introduction to the Wide-Field Camera Carried by BNU-1  
The wide-field camera uses sensor coating technology and complementary metal-oxide-semiconductor (CMOS) high-frame-rate digital time-delay integration (TDI) technology to achieve push-broom imaging in one panchromatic band and four multispectral bands (blue, green, red, and red edge). The camera's focal plane is composed of two CMOS sensors (pixel size of 4.25 um, effective pixel count of 5056 (H) \(\times\) 2968 (V)) stitched together to achieve wide coverage and bi-directional scanning capability, with adjacent CMOS sensors overlapping by 200 pixels, as shown in Figure 1. The camera uses the substrate of the main frame of the focal plane electronics box as the main load-bearing structure of the camera, which improves the overall stiffness of the focal plane electronics box, saves the weight and volume of the entire system, and achieves miniaturization. In addition to the standard operating mode suitable for routine tasks, the wide-field camera also has a high dynamic range (HDR) operating mode. This mode is suitable for observing the details of specific objects in high dynamic scenes. In this mode, the sensor automatically performs two long and short exposures of the same object, and the output fused image can effectively improve the imaging effect of high dynamic scenes where ice, snow, and land water coexist.  
Figure 1: BNU-1 wide-field camera focal plane layout diagram. The wide-field camera focal plane is composed of two CMOS sensors. Every CMOS can capture an image of 5056 \(\times\) 2968 pixels per shot. Two CMOSs can capture two images per shot. These two images have 200 overlapping pixels that represent the same object on the ground.
# An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems
## 2 Related Work
### Noise Filtering in Undersampling  
Data imbalance is caused by a significant difference in the number of objects between different classes in a data set. Undersampling, which is able to solve the problem of imbalance, discards certain data in the majority class and forms a new data set with balanced majority-class data and minority-class data [9; 15]. In addition, noise will affect the classification result. For example, when there is noise in the majority class and if the undersampling method incorporates noise into the balanced new data set, the accuracy of the classification will be greatly reduced. Although the ensemble learning method can be used alongside multiple sampling in order to weaken this effect, better classification results can be obtained if the noise is eliminated at the very beginning. Therefore, many researchers have introduced noise filtering into the process of undersampling.  
[PERSON] et al. [10] proposed the use of a threshold adjustment strategy in order to filter data noise. [PERSON] et al. [11] removed data noise by using an iterative integrated noise filter based on the SMOTE method. [PERSON] et al. [12] addressed the incorporation of a kNN filter into the undersampling method in order to exclude noise samples in the minority class. [PERSON] et al. [5] used the semantic relationship between the attributes of the problem itself to aid in the identification of noise. Indeed, kNN is a good preprocessing method for imbalance classification as long as the noise does not interfere significantly with the results.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 4 Keyword and Correlation Analysis
### Correlation Analysis  
#### 4.2.1 Multimodal Image Recognition  
This keyword is closely associated with concepts such as accuracy, comprehension, and challenge, indicating that remote teaching environments face difficulties in ensuring the precision of image recognition and effectively understanding students' needs. Its correlation with education and students underscores the direct role of multimodal image recognition in the learning process, emphasizing the necessity of optimizing student learning experiences as shown in Figure 3. Its connection to teachers shows that feedback mechanisms help educators improve their teaching strategies.  
The link between feedback and interaction highlights its key role in teaching, especially in providing personalized guidance to students. Its ties to context and interaction show the need to consider different instructional settings, such as personalized learning and adaptable curricula.  
Interaction is the core of remote teaching. It requires video, audio, chat functions, and AI-assisted tools to keep students engage in virtual classrooms. Its connection to models and systems suggests that structured frameworks and technological models are essential for effective remote teaching.
## 3 Modeling Procedures of Extended Finite Element Method (XFEM)-Based Multiscale Analysis
### Material Subroutines for Fiber Beam Elements and Their Verification  
A fiber beam element has been widely used in the numerical analysis for RC columns. As shown in Figure 5, the fiber beam element is employed for simulating the concrete and steel bars in the top half part of the two-scale model. The XFEM module has been implemented in ABAQUS. Moreover, the CDP constitutive law has been integrated into ABAQUS, which can be applied for 3D solid elements as well as 2D planar elements. For beam elements, there are no uniaxial hysteretic constitutive models available in ABAQUS. Therefore, the uniaxial hysteretic constitutive models for concrete and steel bars need to be developed for fiber beam elements employed in two-scale and multiscale models of RC column specimens in this study. The user-defined material subroutines have to be developed according to the interface requirements of material subroutine in ABAQUS. By referring to the hysteresis rule of Concrete 02 in Opensees [29], material subroutines for concrete are compiled in this study, as presented in Figure 6a. The hysteretic criterion for loading and unloading is defined according to the study performed by [PERSON] [29], which considers the stiffness degradation and hysteretic energy dissipation. To simulate the hysteretic behavior of steel bars, the bilinear model [29] is adopted as the constitutive law of steel reinforcement. In this model, the reverse loading curve directs to the point representing the maximum deformation during the loading history, as shown in Figure 6b. The Bauschinger effect is also considered in the compiled material subroutines.  
The verification of material subroutines and simulation results with different modeling techniques is summarized in Figure 7. It can be seen that the numerical results of fiber beam models using Vumat and Umat are almost identical, as presented in Figure 7a,b. Figure 7c indicates that the simulated reaction force-displacement curves of the fiber beam model and two-scale model subjected to cyclic  
Figure 6: Constitutive models and equivalent method for steel reinforcement.  
loading match well with that of solid beam element model and experimental result, which fully validates the accuracy of Umat and Vumat compiled in this study.
## 1 Introduction
Advantages of this approach include a high quality of the obtained reflectance spectra, a high reproducibility with respect to the determination of the absorption features and a straightforward identification of signal artifacts, that also allows for their subsequent reduction. In what follows, we present our findings along with the optimized end-to-end instruction with a nanoscaled, pure dielectric powder of rutile-phase TiO\({}_{2}\) as an example. Section 2.1 is devoted to the preparation of pure, non-translucent powder pellets. The experimental requirements and procedures are presented in Section 2.2. Section 3 covers diffuse reflectance spectra obtained with rutile, potential artifacts and includes methods to avoid such. The obtained data enter theoretical considerations using the commonly applied Kubelka-Munk theory to derive a material's band gap energy by means of Tauc plots. Further considerations involving Mie theory are elaborated in Section 4 for the use of appropriate simulation methods via either iterative ab initio calculations or Monte Carlo simulations in order to extract \(\alpha\) as a function of wavelength from the DRS data. Subsequently, Section 5 summarizes the aforementioned and includes possibilities, e.g., for the integration of this approach within an industrial inline process. Concluding remarks are provided in Section 6.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 1 Introduction
Unlike most gateway deployment strategies that is either static done once or static but periodic where the timing of redeployment does not guarantee the full coverage of underwater nodes at all times, our PADP maximizes the average coverage, reduces end-to-end delay, controls the frequency of redeployment to reduce power consumption, ensures connectivity while satisfying budget constraints. PADP considers the mobility of underwater nodes for gateway redeployment decision by predicting their future positrons and allowing gateways relocation to prevent any coverage or connectivity loss during network lifetime compared to static/periodic deployment To achieve these objectives, PADP combines several algorithmic techniques, including the IMM (Interacting Multiple Model) for tracking, branch-and-cut to solve the underlying optimization problem, and a clustering algorithm based on thedisjoint-set data structure. Compared to static deployment, the deployment in PADP is determined by considering the positions of _virtual nodes_, which consist of both currents and predicted positions of sensor nodes. By considering the essential network performance while using currents and predicted positions of sensor nodes to find the optimal surface gateway redeployment position, PADP achieves a better coverage in time and space and can significantly reduce the frequency of redeployment and the consumed energy. The remainder of this paper is organized as follows. Related work is reviewed in Section 2. Section 3 introduces background concepts, while Section 4 describes the PADP Algorithm in detail. Simulation results are presented in Section 5. Section 6 offers conclusions and directions for future work.
# Flexible Null Broadening Robust Beamforming Based on JADE
## 4 Simulation
It is assumed that the gain-phase errors obey Gaussian distribution \(N(1,0.05^{2})\) and \(N\big{(}0,\big{(}5^{\circ}\big{)}^{2}\big{)}\), respectively. \(\text{SNR}=-10\text{ dB }\sim 30\text{ dB}\). The relationship between input signal-to-noise ratio (SNR) and output signal-to-interference-plus-noise ratio (SINR) of each algorithm is statistically compared in Figure 4 with 200 Monte Carlo experiments. From Figure 4, we can see that the output SINR is close to the ideal case, which shows that the proposed algorithm is robust to the gain-phase errors compared with CMT and INCM-RAB. The FCMT algorithm uses the eigenvalue decomposition method to perform better than CMT and INCM-RAB in the case of low SNR. The steering vector separated by JADE contains the information of gain-phase errors, which can eliminate the influence of gain-phase errors on the proposed algorithm. Thus, the proposed algorithm can suppress dynamic interference more thoroughly with the gain-phase errors. **Experiment 4:** Anti-interference ability verification--Relationship between interference mismatch angle and output SINR. Assuming \(\text{SNR}=0\text{ dB}\) and the broadened null is \(4^{\circ}\), we carry out 200 Monte Carlo experiments to simulate and analyze the variation trend of the output SINR and the output interference power when the interference deviation angle \(\Delta\theta\) is from \(-2^{\circ}\) to \(2^{\circ}\). We can see that the output SINR of INCM-RAB will decrease with the increase of the interference angle deviation from Figure 5, which indicates that the narrow null cannot suppress the dynamic interference. When the interference mismatch angle is less than the broadening angle, the broadening algorithm can effectively suppress dynamic interference. Compared with the similar CMT and FCMT, the proposed algorithm in this paper is closer to the optimal situation, because the designed matrix trap is more accurate. **Experiment 5:** The consumption DOFs of each algorithm--Comparison of large eigenvalues of covariance matrix. The simulation conditions are the same as in Experiment 1. Under the same broadening angle, the downward trend of the eigenvalue is compared, as shown in Figure 6.  
Figure 4: The relationship between output SINR and input SNR. The number of the covariance matrix's large eigenvalues of RAB is the least equal to the number of signal sources, which does not waste DOFs. CMT, the traditional null broadening algorithm, has the largest number of large eigenvalues. This algorithm treats all interferences as dynamic interference, which will cause the waste of DOFs. In this paper, blind source separation technology is used to separate dynamic interference and non-dynamic interference, and only the dynamic interference null is widened to achieve DOFs saving. However, FCMT uses the eigenvalue decomposition of the sample covariance matrix to distinguish the interference state, which will fail when the interference power is equal. Therefore, the algorithm in this paper is more universal.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 3 SD6 WSN Architecture
### SD6 WSN Nodes  
Each SD6 WSN node has at least two components: agent and control plane routing. At least one node must include a third component named border router. A sensing component, which is application dependent, can be included in any node.  
The agent is a CoAP server that communicates with the controller through SD6 WSNP messages. It changes the local operating system packet transmission procedure (data plane forwarding) and interacts with the local operating system to read and modify specific more information. The agent's main functions are: (i) send information to the controller node; (ii) manage the entries in the flows table; (iii) inspect data plane packet headers; (iv) send or discard data plane packets according to matching entries in the flow table; (v) send packet-in notifications to the controller when there is no matching entry; and (v) interact with the operating system to change more transmission parameters, such as transmission channel and RF power. SD6 WSN modifies package forwarding at the operating system as follows. It inspects packets headers and checks if the incoming packet is an RPL message, and if so, it is routed through the control plane, that is it follows standard 6 LoWPAN-RPL routing. Otherwise, if the packet contains an SD6 WSNP message, it is handled by the agent. Otherwise, the packet is delivered to the local sensing application (see Figure 6).  
Figure 5: Flowchart for handling incoming packets in a node.  
The border router extends standard 6 LBR with three functions: (i) notify the controller when a node is detected or removed in 6 LoWPAN; (ii) perform the routing between the IPv6 and 6 LoWPAN network; (iii) identify the packets belonging to SD6 WSN flows and handle them properly. Nodes with the border routing function perform data plane forwarding as described in Figure 6.
# Secure and Transparent Space Exploration Data Management Using a Hybrid Blockchain Model
## 2 Related Work  
### Hybrid Blockchain  
A hybrid blockchain combines the advantages of both public and private blockchains, balancing data accessibility and security. Publicly accessible data are stored on the public blockchain to ensure transparency and verifiability, while confidential data are secured on the private blockchain. The use of ZKP techniques ensures data integrity without exposing sensitive information.
# Velocity Control of a Multi-Motion Mode Spherical Probe Robot Based on Reinforcement Learning
## 4 Simulations
### Velocities  
The results of the MMSPR with different initial velocities and different controllers are shown in Figure 12. As Torqu_RL cannot solve the control problem, only the results of the other three algorithms are displayed. As shown in Figure 12, although the velocity errors finally converge successfully with PID, there are obvious jitters during the error reduction, and the convergence time is longer than ARLPID. RLPID is the algorithm with the shortest convergence time, but small error fluctuations occur in the follow-up timesteps, which cannot be completely stable. The reason may be that the _tanh_ function is used in the reward function. When the error is small, the reward function tends to be saturated and stable, so it cannot continue to guide the policy update.  
Figure 12: The results of different controllers: (**a**) initial speed is 0.08 m/s; (**b**) initial speed is 0.16 m/s; (**c**) initial speed is 0.24 m/s.  
With ARLPID proposed in this paper, the velocity errors converge rapidly and remain stable, which outperforms other controllers. The error convergence time of each algorithm is shown in Table 5.  
In summary, ARLPID performs best in maintaining motion stability and achieving rapid braking among the three control methods. It has a fast convergence speed, a small displacement offset, and stable control performance. This method can solve the problems of motion stability during the rolling and rapid braking after jumping to avoid obstacles, relying on its high adaptability and robustness.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 4 Predictive Modeling
### Data Preprocessing  
In this study, the results of numerical simulation tests were used as the dataset, and the data needed to be pre-processed before the model was built. The input characteristic variables selected in the prediction model of the mechanical performance index of the steel bridge deck pavement system are the elastic modulus of the upper layer of the pavement \(EI\), the elastic modulus of the lower layer of the pavement \(E2\), the thickness of the upper layer of the pavement \(HI\), the thickness of the lower layer of the pavement \(H2\), the thickness of the steel bridge panel \(T\), and the spacing of the transverse spacer \(D\). The output characteristic variables are the maximum transverse tensile stress on the surface of the pavement \(A1\)(MPa), the maximum longitudinal tensile stress on the surface of the pavement \(A2\)(MPa), the maximum transverse shear stress between the pavement and steel deck \(B1\)(MPa), the maximum longitudinal shear stress between the pavement and steel deck \(B2\)(MPa), and the maximum vertical displacement of pavement \(C\)(mm). A simple statistical analysis of each characteristic variable is shown in Table 4. The model was built by dividing the dataset into a training set and a test set, using the training set to train the model and the test set to test the performance of the built model. In this study, 70% of the data in the dataset are used as the training set, and 30% of the data are used as the test set.