esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization of Scan Parameters
### Beam Divergence  
Beam divergence is another scanning parameter that can be adjusted to mitigate the impact of satellite vibrations and avoid creating scan gaps while scanning the uncertainty area [55]. By adaptively changing the beam divergence based on the magnitude of vibrations, the system can maintain alignment and minimize performance degradation. This approach, known as the Adaptive Beam Control (ABC) technique, has been extensively studied and demonstrated in various scenarios. ABC enables the dynamic adjustment of beam divergence at both the transmitter and receiver to respond to changing channel conditions, such as pointing errors and angle-of-arrival (AoA) fluctuations [56; 57].  
ABC can be implemented in two main forms: static ABC and dynamic ABC. Static ABC adjusts beam parameters based on pre-determined statistical information about the channel conditions, such as average pointing errors or AoA fluctuations and operates on slower timescales [58; 59; 60]. In contrast, dynamic ABC responds in real time to instantaneous feedback from rate sensors, making it more effective in mitigating rapid variations caused by satellite vibrations [61; 62]. Various techniques have been developed to implement ABC, each offering distinct mechanisms to optimize beam divergence and focus.  
One approach involves the use of optical switches to select between multiple emitters or detectors with predefined beam divergence angles or radii. While simple in concept, this method suffers from slow response times and limited adaptability, making it unsuitable for real-time dynamic adjustments [63]. Another method employs mechanically adjustable lenses, where the lens position relative to the emitter or detector is modified to control the beam's divergence or focus. Although this technique offers more flexibility than optical switches, it is prone to mechanical failure, slower adjustment speeds, and higher implementation costs [64].  
In contrast, the Variable Focus Lens (VFL) provides a more advanced solution for ABC [65; 66]. VFLs adjust the beam's focal length electronically using electrowetting or electromagnetic actuation, enabling precise, non-mechanical, and millisecond-scale adjustments [67; 68]. This approach is particularly advantageous for dynamic ABC systems, where real-time feedback from rate sensors is used to counteract pointing errors and AoA fluctuations. Additionally, the VFL-based technique can be enhanced by employing a double-lens configuration, which overcomes the aperture size limitations of VFLs and enables simultaneous beam optimization at both the transmitter and receiver.  
For the given parameters, i.e., overlap factor \(a\), satellite vibration standard deviation \(\sigma_{\text{vib}}\), and acquisition probability \(P_{c}\), the required beam divergence to avoid scan gaps can be calculated using Equation (78) as follows:  
\[\theta_{b}\geq\frac{2\sigma_{\text{vib}}\sqrt{-2\ln(1-P_{c})}}{a}. \tag{79}\]
# Electromagnetic Characteristics Analysis of a Tubular Moving Magnet Linear Generator System
## 3 Electromagnetic Analysis
### Force Characteristics  
By using the three-dimensional finite element model created in the last section, the static characteristic of the designed linear generator is analyzed firstly. Figure 6 shows the simulated electromagnetic force characteristics at the max current of 30 A and the rated current of 15 A. The max electromagnetic force is about 1670 N for the max current and 1230 N for the rated current, which is obtained at the 1/4 position of the stroke.  
The max cogging force of the linear generator is about 740 N. The cogging force is designed to have only one zero position. The cogging force will let the mover stop in the middle of the stroke after the running. This is used to increase the natural vibration frequency of the reciprocating motion and to increase the power density of the system. The average electromagnetic force coefficient of the linear generator is about 12 N/A at the max current and 14 N/A at the rated current.
## 4 Proposed Scheme
### Description of the Steps of Allocating Charging Task
First, the pair in set \(R\) with the longest match distance is given priority for selection. After selecting the longest matched pair \((u,v)\), the distances of node \(u\) and \(v\) to the base station are computed and compared. If node \(v\) is further from the base station than node \(u\), it is assigned to a drone carried by the vehicle, while node \(u\) is allocated to the vehicle, and node \(u\) is used as a flying stop for the assigned drone for charging node \(v\). Because node \(v\) is assigned a drone, it cannot be used as a flying stop; all matched pairs including node \(v\) should be removed from set \(R\). The above process is repeated until the maximum number of drones carried by the vehicle have been allocated or set \(R\) is empty. Figure 4 shows an example of this selection process; matched pair \((u_{4},v_{4})\) with the longest distance is sorted first, node \(u_{4}\) is allocated to the vehicle and \(v_{4}\) is allocated to a drone carried by the vehicle, then followed by \((u_{1},v_{2})\), \((u_{1},v_{1})\) and so on. The process for assigning nodes to drones or the vehicle is described in Algorithm 3. According to Algorithm 3, Step 1 takes \(O(n)\) time at most, while Step 2 and Step 3 take \(O(n)\) time. Thus, the total computational complexity of Algorithm 3 is \(O(n^{2})\). ```
Input: A match pair \(R\), the base station \(O_{1}\), the number of drones carried by the vehicle \(m\), \(num=0\); Output: \(S_{out\_d}\) and \(S_{v}\). Step 1: Select the match pair \(r=(u,v)\) with the longest match distance in \(R\) Step 2: Compute \(dist(u,O_{1})\) and \(dist(v,O_{2})\) Step 3: if \(dist(u,O_{1})<dist(v,O_{2})\) \(u\to s_{v}\) \(v\to S_{out\_d}\), \(num++\), \(R/r\) Remove the matched pairs, which include node \(v\) else \(v\to s_{v}\) \(u\to S_{out\_d}\), \(num++\) \(R/r\) Remove the matched pair, which includes node \(u\) Step 4: if \(num<m\) or \(R\
eq\varnothing\) Go to Step 1 Step 5: \(S_{v}=S_{v}\cup(S-S_{in\_d}-S_{out\_d}-S_{v})\) Step 6: Output \(S_{out\_d}\) and \(S_{v}\)
```  
**Algorithm 3**Assigning the nodes to the drones or vehicle
## 3 Experiments and Results
### Generalization
#### 3.4.1 Varying Degrees of Uncertainty Error  
First, the learned policies were evaluated on the 3D printed cuboid peg insertion task where there was a degree of error on the estimation of the goal position. To clearly compare the performances of the different methods with different degrees of estimation error, we added and offset of position or orientation about the x axis of the true goal pose. Nevertheless, for completeness we also evaluated the policies on goal poses with added random offset of translation, \([-1,\,1]\) millimeters, and orientation, \([-5^{\circ},5^{\circ}]\), in all directions. In each case, the policies were tested 20 times from random initial positions. Results are shown in Table 3.  
In all cases, the policy learned from the simulation with domain randomization and fine-tuned in the real robot gave the best results. If the difference between the physics dynamics in the simulation and the real world was too big, learning from scratch could yield better results than only transferring the policy from the simulation, as can be seen when the uncertainty error of orientation was too big  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Success Rate** & **Avg. Time Steps** & **Avg. Time (sec)** \\ \hline
**Scratch** & 100\% & 109.6 & 5.48 \\
**Sim2 real** & 95\% & 75.3 & 3.77 \\
**Ours** & 100\% & 65.6 & 3.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of learning from scratch, straightforward sim2 real, and sim2 real + retraining (ours). Test performed on a 3D printed cuboid peg insertion task assuming knowledge of the true goal position.  
Figure 7: Performance of learned policy (sim2 real + retrain) on 3D-printed cuboid-peg-insertion task. Insertion direction was aligned with the y axis of the robot’s coordinate system. Relative distance from the robot’s end effector to goal position and contact force are shown. The 24 policy actions besides the corresponding axis are also shown.  
(5\({}^{\circ}\)); where the friction with the environment makes the task much harder, such contact dynamics are difficult to simulate.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
###### Abstract  
To study the static and dynamic response of micropile-reinforced slopes, static model tests and shaking table tests were performed. The failure modes, the pile-slope interaction, the displacement, and the static/dynamic earth pressure distributions were analyzed based on static and dynamic model tests with a prescribed sliding surface. The test results indicated: (1) The micropile failure mode is mainly bending failure under both loading conditions. As far as the damage to the pile body is concerned, under static loading, the rear row piles showed more damage than the middle row piles followed by the front row piles. Under dynamic loading, the damage of the rear row piles was approximately the same as the middle row piles, which was greater than the front row piles; (2) The earth pressures in front of and behind each row of micropiles and the axial force of the pile body distributed triangularly for both loading conditions, with the bending moment of the pile body distributed in an \"S\" shape; (3) The landslide thrust experienced by the micropiles has a relatively large group effect. The group effect or shear ratio parameters are recommended for each loading case; (4) The interaction between the micropiles and the soil landslide presents evident progressive failure and load transfer between the rows.  
micropiles; soil landslide; static model test; shaking table test; group effect +
Footnote †: journal: Applied Sciences
## 4 Results
### Analysis of Evaluation Metrics
#### 4.2.1 Chatbot Usability: CUQ
The analysis showed that the hybrid-based interaction mode performed strongly across multiple dimensions, particularly with respect to effectiveness (mean = 85.56) and error handling (mean = 76.9). These results indicate that the hybrid-based mode supports accurate task completion and manages errors effectively, thereby providing a balanced user experience. Therefore, the hybrid-based mode is a reliable and adaptable interface that is suitable for users who prioritize problem-solving and precision. The text-based interaction mode also demonstrated robust performance, especially in personality (mean = 69.77), onboarding (mean = 74.9), error handling (mean = 75.67), and learnability (mean = 91.20). This indicates that users found the text-based mode to be approachable, easy to navigate, and intuitive to learn. Its high score for onboarding suggests a smooth entry experience, where users quickly understand how to use the interface, and its superior learnability reflects simplicity and intuitiveness. However, while the text-based mode's efficiency was high (mean = 76.03), it did not surpass that of the menu-based mode (mean = 59.1), indicating that there may be areas where the text-based mode can be further streamlined. The menu-based interaction mode excelled primarily in efficiency, achieving the highest score for simple questions (93.0) and the worst for complex questions (36). This suggests that users can complete tasks quickly using this interface, highlighting its strength in straightforward and time-efficient interactions. However, it showed notable weaknesses in personality (mean = 50.33) and error handling (mean = 60.33), especially as the tasks became more challenging. Figure 3: CUQ performance over sub-dimensions. Therefore, the menu-based mode facilitates fast task execution but struggles to create a realistic, engaging experience that handles user errors. Therefore, each interaction mode has distinct strengths: the hybrid-based mode excels in effectiveness and error handling, offering balanced, versatile performance for precise and reliable task completion; the text-based mode is ideal for ease of use and learnability, providing a user-friendly experience, although it may struggle with more complex tasks compared to the hybrid mode; the menu-based mode is efficient for quick and straightforward tasks but requires improvements in error handling and personality to support more complex use cases and enhance the overall user experience.
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 3 Computational Time Reversal for Structures
### The Time Reversal or Backward Step  
The time histories for recorded DOFs \(\mathbf{u}_{\mathbf{r}}\) are time-reversed and retransmitted into the medium as excitation (sources).  
\[\mathbf{M}\ddot{\mathbf{u}}(t)+\mathbf{C}\dot{\mathbf{u}}(t)+\mathbf{K}\ddot {\mathbf{u}}(t)=\hat{f}(t)\mathbf{\hat{f}} \tag{20}\]  
Following previous work [33; 36], our choice here is to assume \(\mathbf{\hat{f}}\) of zero values components or units when it corresponds to some \(\mathbf{u}_{\mathbf{r}}\) component. Evolution \(\hat{f}(t)=u(t^{*})\) is the reversed in time component of vector \(\mathbf{u}\), actually being the solution of the forward problem of Equation (18). For the case of structural irregularities, i.e., damage localization, \(\mathbf{u}_{\mathbf{r}}\) contains the scattered field response.  
Of crucial importance is the choice of quantity to be monitored in order to observe refocusing during the backward step. Such refocusing on appropriate discrete time step [33] or time averaged [37] will indicate the spatial location of original source point of the forward step. As for monitoring quantity, we consider here the value of some DOF, its time derivative, some appropriate norm (e.g., Euclidean) or even some energetic quantity, as would be the density of elastic, kinetic or total energy.
## 4 Results and Discussion
### Influence of Initial Tracking Position of an SDC System under Azimuth Axis Tilt Error
In the azimuth axis tangential angle \(\phi_{1}\) = 0\({}^{\circ}\) (due south) or \(\phi_{1}\) = 180\({}^{\circ}\) (due north), the azimuth axis tilt error has the most influence on tracking performance. Similarly, it can be seen in Figure 7b that when \(\gamma_{2}\) > 0\({}^{\circ}\), the azimuth axis tilt error affects the tracking performance the least at the azimuth axis tangential angle \(\phi_{1}\) = 75\({}^{\circ}\) (i.e., 75\({}^{\circ}\) south by west) or \(\phi_{1}\) = 225\({}^{\circ}\) (i.e., 75\({}^{\circ}\) north by east). In the azimuth axis tangential angle \(\phi_{1}\) = 345\({}^{\circ}\) (i.e., 15\({}^{\circ}\) south by east) or \(\phi_{1}\) = 165\({}^{\circ}\) (i.e., 15\({}^{\circ}\) north by west), the azimuth axis tilt error affects the tracking performance the most. Figure 8 shows the variation of the daily average effective tracking factor \(\overline{\sigma}_{\rm d}\) and SD\({}_{\rm d}\) with the initial tracking position offset for an SDC system. When the initial tracking position offset is less than 0\({}^{\circ}\), the average tracking performance of the initial position of the SDC system in the horizontal plane is better than that in the vertical plane. When the initial tracking position offset is greater than 0, the average tracking performance of the initial position of the SDC system in the vertical plane is better than that in the horizontal plane. At the same initial position offset, the tracking performance stability of the SDC system initial position in the horizontal plane is better than that in vertical plane. However, compared with the initial tracking position due east, the deviation of the initial tracking position of the SDC system in the horizontal or vertical plane will reduce its tracking performance and tracking performance stability. Figure 7: Variation of the effective tracking factor \(\sigma\) of an SDC system with different initial tracking positions under the azimuth axis tilt error. **(a)** The initial tracking position varies in the horizontal plane (\(\gamma_{2}\) = 0\({}^{\circ}\)). **(b)** The initial tracking position varies in the vertical plane (\(\gamma_{1}\) = 0\({}^{\circ}\)).
## 4 Discussion
### Recommendations
#### 4.2.3 Factors/Framework Related  
Authors in [17] suggested that the analysis of additional characteristics such as electrical conductivity, free residual chlorine, nitrates, and dissolved oxygen in the water is also advised as part of their proposed system reliability check. Authors in [30] used an IoT device and decision tree algorithm to construct a water quality evaluation system specifically for the aquaculture of Penaeus monodon. However, the system can still be improvised by including an evaluation of other aquaculture to provide more assistance to fish farmers. The creation of a mobile application as the primary graphical user interface for the user is also advised for future researchers to make the system more direct and user-friendly than having users access it through a web browser.  
Authors in [38] proposed that their designed system might be used to check the water quality for an entire town or city, depending on where the water supply is coming from, as well as for individual households. The appropriate period for cleaning the city water tank can be determined using this framework. Sooner rather than later, a cross-city or cross-town quality monitoring framework can be developed using a combination of different sensors. The researcher in [54] suggests changing the method to track how many heavy metals are added, removed, and reduced in the soil and water studies.  
Authors in [63] proposed that future water quality estimate methods will take into account on the factors such as conductivity, hardness, chloride, smelling salts, press, fluoride, and others. These factors are used to assess the water's cleanliness for uses like drinking water and daily needs. Authors in [53] clarified that their future work will focus on the framework's applicability to pond areas between 2 and 5 acres in size as well as its deployment in the western Godavari region. Additionally, efforts are being made to create new ergonomic and cost-effective designs with the assistance of the industry in preparation for the product's commercialization for use by aquaculture farmers. Authors in [31] discussed the concentration of model optimization by merging the various water quality characteristics, to increase the model's accuracy by using multi-dimensional input datasets to forecast the target values.
## 4 Discussion
[71] also found excellent contributions of the VARI in their models, highlighting the predictive ability even in shadier conditions, thus denoting the capability of these indexes even in the most adverse situations inherent to remote sensing data collection. Unlike the relationships presented by [72] evaluating the severity of diseases on leaves under controlled conditions in image taking, few indexes showed a strong relationship with the adjusted parameters, demonstrating the influence of environmental and physical conditions imposed during image taking, either by meteorological issues, the inclination of the sun, or even by the complexity of the production field. In any case, the satisfactory ratio shown by NGRDI and VARI may be related to the greater focus, of their respective formulas, on the red and green bands, as they have a higher quantum yield and a higher reflection rate, respectively [73; 74; 75]. The linear regression models, with a model that is easier to reproduce, and the other models generated, although they showed a worse performance than CART, can still be used. According to [76], even under conditions where precision and accuracy are not obtained, the methods can still be recommended due to the cost, the time-consuming and destructive measurements, and the producer's experience in their area. The models developed presented satisfactory predictive performance with considerably high accuracy and precision, given the various factors that influence the estimation process, such as environmental conditions, the complexity of vegetation, equipment, and the quality of the data survey [77]. The better performance of the CART method, as per the calculated values of CCC (0.94) and R\({}^{2}\) (0.89), is due to factors such as the ability to leverage information through learning, being robust to the outlier, non-parametric, and, most importantly, being less sensitive to learning data [78]. It is important to highlight that unfavorable weather conditions have resulted in insufficient yields of cowpea, corn, and cotton crops, limiting the consideration of their impact on the system during the modeling process. Therefore, it is important to conduct research that takes these factors into account to make significant advances in estimative methods in AFS. Furthermore, the process of removing components from orthomosaics through threshold values or cutting masks is still performed manually, which can lead to a prolonged process in large-scale applications, particularly in the removal of trees involving variable structures. Therefore, it is crucial to develop automated algorithms for such tasks, considering precision and process optimization.
# A New Constrained State Estimation Method Based on Unscented Hoo Filtering
###### Abstract  
The unscented Kalman filter (UKF) is widely used in many fields. When the unscented Kalman filter is combined with the Hoo filter (HF), the obtained unscented Hoo filtering (UHF) is very suitable for state estimation of nonlinear non-Gaussian systems. However, the application of state estimation is often limited by physical laws and mathematical models on some occasions. The standard unscented Hoo filtering always performs poorly under this situation. To solve this problem, this paper improves the UHF algorithm based on state constraints and studies the UHF algorithm based on the projection method. The standard UHF sigma points that violate the state constraints are projected onto the constraint boundary. Firstly, the paper gives a broad overview of Hoo filtering and unscented Hoo filtering, then addresses the issue of how to add constraints using the UHF approach, and finally, the new method is tested and evaluated by the gas-phase reversible reaction and the State of Charge (SOC) estimation examples. Simulation results show the validity and feasibility of the state-constrained UHF algorithm.  
unscented Hoo filtering; state constraints; projection 2020