esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Lidar System Localization and Mapping with Online Calibration
## 2 Front-End Laser Odometry and Online Calibration
### Realization of Multi-Lidar Online Calibration  
#### 2.2.1 Online External Parameter Calibration  
In the process of multi-lidar calibration, point clouds in different coordinate systems are brought into alignment within a common coordinate system through point cloud registration [29; 30; 31; 32]. The assessment of point cloud registration accuracy generally employs RMSE as a quantitative metric that establishes corresponding points between two point clouds using a Kd-tree, subsequently determining the discrepancy between the Euclidean distance and the actual distance:  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\hat{x}_{i}\right)^{ 2}}{n}} \tag{4}\]  
where \(n\) is the number of corresponding points, \(x_{i}\) is the actual Euclidean distance between corresponding points, and \(\hat{x}_{i}\) is the Euclidean distance between corresponding points after registration. As inferred from the formula, a smaller RMSE indicates a lesser gap between the Euclidean distance and the actual distance of corresponding points after registration, signifying a higher degree of point cloud overlap. An RMSE of 0 implies complete coincidence of the two point clouds. However, this evaluation method holds certain shortcomings. Poor point cloud registration results may yield a small number of associated corresponding points and erroneous associations, thereby affecting the reliability of calculated results [33; 34]. Therefore, this paper proposes an improved RMSE index to evaluate point cloud registration accuracy. This improved index considers the number of corresponding points as a weight: the greater the count, the more dependable the index outcome, as expressed in the following:  
\[\text{RMSE}=\frac{1}{n^{2}}\sqrt{\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\hat{ x}_{i}\right)^{2}}{n}}\times 10^{6} \tag{5}\]During multi-lidar online external parameter calibration, the process begins by projecting the feature point cloud of the auxiliary laser radar onto the main laser radar's coordinate system using the current external parameters, as indicated in the following:  
\[\widetilde{X}_{k+1,i}^{L}=RX_{k+1,i}^{L}+t=G\left(X_{k+1,i}^{L},T_{k+1}^{L}\right) \tag{6}\]  
Within two-dimensional space, the modulus length of the cross product of two vectors equates to the area of the parallelogram formed by these vectors. Dividing the area of the parallelogram by the side length yields the distance from point \(i\) to line \(j_{l}\), thus obtaining the line feature optimization equation:  
\[d_{e}=\frac{\mid\left(X_{(k+1,i)}^{L}-X_{(k,j)}^{L}\right)\times\left(X_{(k+1, i)}^{L}-X_{(k,l)}^{L}\right)\mid}{\left|X_{(k,j)}^{L}-X_{(k,l)}^{L}\right|} \tag{7}\]  
In three-dimensional space, the volume of a three-dimensional object can be obtained through vector dot multiplication.
## 3 Methodology
### Depth Scale Alignment
The scale factor is learned during training and applied to the predicted depth map of the dynamic object as follows:  
\[D_{aligned}(o)=\beta_{o}D_{dyn}(o) \tag{17}\]  
where \(D_{dyn}\)_(o)_ is the initial depth prediction for the dynamic object \(o\), and \(D_{aligned}\)_(o)_ is the scale-aligned depth map for the object. The goal of this alignment is to ensure that the relative depth between the dynamic object and the static background is preserved, resolving the scale inconsistency introduced by the independent motion of the object. The DSA module ensures that the depth predictions for dynamic objects are consistent with the static background. This is performed by minimizing the difference between the depth of the static background \(D_{static}\) and the scaled depth of the dynamic object \(D_{aligned}\). The DSA module applies the following consistency condition:  
\[\left|D_{aligned}(o)-D_{static}\right|\to 0 \tag{18}\]  
where \(D_{static}\) is the depth prediction for the static background at the location of the object, and \(D_{aligned}\)_(o)_ is the scale-aligned depth for the object. This condition encourages the depth scales of the dynamic object and the static background to be consistent, thereby resolving the scale ambiguity. The scale factor \(\beta_{o}\) for each dynamic object is learned during training using a scale-alignment loss. This loss ensures that the scale factor for each dynamic object is adjusted such that the depth predictions for dynamic and static regions are consistent. The scale-alignment loss \(L_{scale}\) penalizes discrepancies between the depth predictions for dynamic objects and the static background. The DSA module is integrated into the iterative elastic bin refinement, where the depth predictions are progressively refined over multiple iterations. At each iteration, the DSA module adjusts the depth predictions for dynamic objects by applying the learned scale factor. This iterative refinement process ensures that the depth predictions are accurate and consistent across both static and dynamic regions, improving overall depth accuracy. At each iteration, the network performs the depth prediction, which predicts the depth maps for both static and dynamic regions. Following this, the DSA module estimates the scale factor for each dynamic object and adjusts the corresponding depth prediction accordingly. Finally, the depth predictions are refined through the iterative elastic bin refinement process. At the same time, the DSA module ensures that the depth scales of dynamic and static regions remain consistent throughout the refinement. The DSA module is a critical component of the DI-MDE framework, enabling accurate and consistent depth predictions in dynamic scenes by resolving the scale ambiguity between moving objects and the static background. By learning a scale factor for each dynamic object and applying it to the depth predictions, the DSA module ensures that the depth maps are properly aligned, leading to improved depth estimation performance across a wide range of challenging scenarios.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 2 System of Remote-Controlled Robotic Arms
### The Type of Faults  
In this remote-controlled robotic arm system, the three joint angles of the leader, \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\), are transmitted to the follower through the controller designed in [30] to make corresponding joint angles of the follower, \(\theta_{1}^{\prime}\), \(\theta_{2}^{\prime}\), and \(\theta_{3}^{\prime}\), synchronize with \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\), respectively. Sensor faults, actuator faults, and communication faults are incorporated into the system as illustrated in Figure 1. \(\varphi_{i}\) and \(\varphi_{i}^{\prime}\), where \(i=1,\ 2,\ 3\), are real outputs of the leader and follower, respectively.  
In this paper, three typical faults of the remote-controlled robotic arm system are under consideration: sensor faults, actuator faults, and communication interruption (see Figure 1). Sensor faults consist of leader sensor \(f_{si}\) faults and follower sensor faults \(f_{si}^{\prime}\), which are modeled as follows:  
\[f_{si}=-\theta_{i}(i=1,2,3) \tag{1}\]  
\[f_{si}^{\prime}=-\theta_{i}^{\prime}(i=1,2,3) \tag{2}\]  
Figure 1: The remote-controlled robotic arm fault diagnosis system.  
An actuator fault is represented as follows:  
\[f_{a}=-u_{r}\cdot\tau \tag{3}\]  
where \(u_{r}\) is the input of the follower, and \(\tau\) is the rate of loss from 0 to 1. This type of fault is more representative of the robotic arm, such as actuator wear, energy supply issues, or transmission efficiency losses, where the output performance degrades in proportion to the input signal. Such faults are more commonly observed in robotic manipulator systems [31; 32; 33].  
The communication fault being investigated is classified as an interruption fault. When it occurs, the communication between the leader and the follower is disrupted, and they no longer work in coordination.  
In the rest of the paper, an online fault diagnosis scheme is designed to identify the abovementioned faults, only through the follower output, that is \(\varphi_{1}^{\prime}\), \(\varphi_{2}^{\prime}\), and \(\varphi_{3}^{\prime}\).
# Advances in Valveless Piezoelectric Pumps
## 4 Future Perspectives for the Valveless Piezoelectric Pump  
Valveless piezoelectric pumps are used more and more widely, especially in the biomedical field. This demands that piezoelectric pump materials have biocompatibility. In the future, valveless piezoelectric pumps can be used for artificial heart and living cell delivery. However, most of the valveless piezoelectric pumps have sharp protrusions in their interior, which would have a great impact on living sells. Therefore, the optimized design of structure is important. Further, it can also be helpful to boost the performance of piezoelectric pumps.  
Three-dimensional printing will be an important processing method for valveless piezoelectric pumps. The flow channel can be integrated into the pump body by using 3D printing technology, which can avoid liquid leakage. By 3D printing, the accuracy of piezoelectric pumps can further improve.  
In the future, the performance of the pump can be improved by combining different flow tubes or flow resistance structures. For example, the diffuser/nozzle tube and arc-shaped tube are combined to form the variable cross section arc-shaped tube, and the diffuser/nozzle tube and the Y-shape tube are combined to form the variable cross section Y-shape tube.  
Flow rate is the most important performance index of piezoelectric pumps. In order to further popularize the valveless piezoelectric pump, it is required that the piezoelectric pump has a stable flow rate. Driving voltage and driving frequency are the key factors affecting pump flow. We should provide a stable power supply to drive the piezoelectric pump. Simultaneously, the flow channel is another important factor affecting the pump flow. We should choose ultra-precision machining methods to process the flow channel. Moreover, the valveless piezoelectric pump realizes liquid flow by volume change, and cannot avoid flow fluctuation. Especially in the low-frequency valveless piezoelectric pumps, the flow fluctuation is more obvious. We can reduce this volatility by adding buffer mechanisms.  
Figure 8: Maximum flow rates for valveless piezoelectric pump: flow rates versus frequencies.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture
### GPS/INS Integrated Architecture  
In this paper, code and Doppler observations from GPS is combined with INS predicted observations in tightly coupled architecture. Figure 1 shows the block diagram of INS/GPS tightly-coupled integration with AIMM algorithm. The IMU outputs the angular rate and specific force provided by gyroscopes and accelerometers, respectively, which are corrected by the estimated states of sensor error, and now it is used to integrated navigation system and implements INS mechanization.  
In view of statistic uncertainties of process noise, up and down edges of process noise covariance are conducted in the IMM filter. Within any time stage, the mixture probabilistic in the IMM filter are given in the process of interaction step, then the state estimates of every filter are computed in the process of time updating stage of EKF.  
To mitigate the atmospheric errors, double differenced is used for the GPS code and Doppler observations. In the update of the measure data process, the updating methods can be executed by taking into consideration the measure values with the identical kind in every filtering stage with U-D filter; in other words, both code rate and Doppler measure values of GPS in diverse frequency channels are updated orderly.  
Every filter would implement the prediction and updating stages on condition that GPS observed value and INS provided observed value are given. When the measurement update is accomplished, the mode probability will be updated. A weighting group that the st  
Figure 1: Flowchart of INS/GPS tightly-coupled integration with AIMM algorithm.  
dual-model IMM filter is calculated, then the ultimate integrated navigation solution is fulfilled in the light of the hybrid formulations. Meanwhile, the measurement noise covariance can be updated by making use of the residual data. Ultimately, the error states vector of sensor, which are estimated in IMM filter, are fed into INS mechanization stage for the sake of offsetting inertial original outputs.
# A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation
## 5 Experiment and Result Analysis
### Analysis of Vessel Trajectory Restoration Results
#### 5.4.2 Analysis of Ablation Experiment Results  
To verify the effectiveness of the feature extraction module (Module A), the preliminary restoration module (Module B) and the Seq2 Seq-based restoration module (module C) in the vessel trajectory restoration model were used. In this study, the ablation tests of the two modules were conducted for straight and curved trajectories, and the results are shown in Table 6 and Figure 14.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Network**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} & \multirow{2}{*}{**FD**} & \multirow{2}{*}{**AED**} \\ \cline{2-2} \cline{5-6}  & **LAT** & & & & **LAN** & & \\ \hline R2R & 0.0044 & 0.0219 & 0.0053 & 0.0225 & 1.7565 & 1.2446 \\ L2L & 0.0018 & 0.0223 & 0.0026 & 0.0229 & 1.8000 & 1.2154 \\ G2G & 0.0051 & 0.0122 & 0.0055 & 0.0142 & 1.5501 & 0.7473 \\ BL2 BL & 0.0031 & 0.0061 & 0.0035 & 0.0067 & 0.7368 & 0.4057 \\ BG2 BG & 0.0021 & 0.0041 & 0.0025 & 0.0050 & 0.6964 & 0.2836 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of restoration indicators in robustness experiments.  
Figure 13: Comparison of trajectory restoration results in robustness experiments: (**a**) restoration results of curved trajectory-3; (**b**) restoration results of curved trajectory-4; (**c**) Restoration results of curved trajectory-5; (**d**) restoration results of curved trajectory-6.
## 1 Introduction  
In recent years, the study of Micro Air Vehicles (MAVs) driven by electric propellers has gained considerable momentum. Some substantial progress has been made towards designing, building and test-flying remotely piloted high-performance MAVs, and there exist many commercially available MAVs as well as laboratory prototypes [1; 2; 3]. Such MAVs are intended to lower the total system cost, and are easy enough to operate with minimal training. Moreover, the electric propellers can also reduce the noise signature in practical use.  
In general, the thrust force of a MAV largely depends on the rotational speed of the electric propeller while the MAV performs low-speed flight missions [4], and the characteristics of the rotational speed system strongly affect the flight dynamics, stability and reliability of the MAV. Therefore, it is essential to develop a reliable control scheme to ensure an accurate rotational speed of the electric propeller, which can contribute to the significant improvement in the flight performance of the MAV, and new applications for both military and civilian markets.  
The studies have been conducted for electric MAVs, as well as other propeller-based propulsion systems, mainly including multidisciplinary design optimization [5; 6], system modeling [7; 8; 9], and control design [10; 11; 12; 13; 14; 15]. Although the optimization of the electric propulsion systems of such MAVs is extremely crucial, most of the previous investigations were limited to either aerodynamic and structural analyses of propellers [16; 17; 18], or the optimization of electric motors [19; 20]. Until recent times the rotational speed of the electric propeller was mainly regulated according to the altitude control signal without a closed loop speed control [21; 22] or it was based on the linearized model around an operation point [23], thus resulting in degrading the flight performance of the MAV, such as the positioning accuracy and stability. Although the conventional method can guarantee the stability of the test plants with moderate flight performance, it largely depends on a lot of information and knowledge derived from the plants, and easily fails to achieve a satisfactory flight performance in the presence of parametric uncertainties.  
The purpose of this study is to present a nonlinear adaptive control scheme for the electric propulsion system of a certain MAV, based on which the significant improvement in the dynamic tracking accuracy of the rotational speed can be easily achieved by further using linear control techniques. The control performance of the nonlinear adaptive controller is to be verified by a series of experimental tests.  
This paper describes the rotational speed control problem of the propeller of the prototype electric MAV in Section 2. The modeling of the rotational speed system of the propeller is presented in Sections 3 and 4 provides the nonlinear adaptive rotational speed control of the propeller. The experimental results are shown in Section 5. Finally, conclusions are drawn in Section 6.
## 3 Results and Discussion
### Tilt Angle and Filling Ratio  
Figure 12a,b represent the variation of the cumulative energy absorption from the collector with the tilt angle and filling ratio of the evacuated tubes in the 15 th and 16 th August, 2017 with the same solar irradiance profiles. As can be seen in Figure 12a, with an increment in the tilt angle of the collector, the cumulative energy absorption initially increased and then was suppressed with boosting the tilt angle of the collector. The highest cumulative thermal energy absorption belonged to the collector at tilt angle of 30\({}^{\circ}\) (the blue rectangular), while the lowest thermal energy was absorbed with the collector at tilt angle of 45\({}^{\circ}\) and higher. As can be seen in Figure 12b, with a growth in the filling ratio of the HP from 10% to 60%, the cumulative thermal energy absorbed by the collector increased from 14.2 MJ/day to 15.1 MJ/day, and suddenly decreased to 14.5 MJ/day at filling ratio of 70%. This behavior can be attributed to the trade-off between the space available inside the HP and also the heat capacity of the carrying fluid (\(m\times C_{p}\)). With the filling ratio increment of the working fluid, the total heat capacity of the working fluid increases, while also the space available for transporting the vapor inside the HP significantly decreases. Hence, there is an optimum value for the filling ratio in which the thermal transport within the HP is maximized, while the heat capacity of the working fluid is plausible as well. Notably, for the tilt angle value, there is another trade-off in between the gravity forces, residence time of the carrying fluid and also the area of the collector exposed to solar insolation. With tilt angle increment, the gravity force facilitates the return of the carrying fluid to the evaporator section while  
Figure 11: Variation of the solar irradiance and hourly ambient temperature with time.  
in turn decreases the area exposed to the solar insolation and also decreases the residence time of the carrying fluid in the condenser region. Hence for both parameters, depending on the type of the carrying fluid, the optimum value should be identified.
# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
## 3 Results
### Problem-Solving in Older Adults  
The application of AI chatbots in health promotion interventions has been also explored in studies targeting problem-solving for older adults. [PERSON] et al. (2017) explored the use of web-based conversational agents to facilitate problem-solving among older adults. The study compared the usability, helpfulness, and effectiveness of two conversational AI chatbots (MYLO based on the method of levels therapy and ELIZA based on Rogerian counseling) for problem-solving and reducing distress in a sample of 112 older adults without mental health disorders. The AI chatbots were offered as a standalone intervention in this study. Participants were randomly assigned to interact with either the MYLO or ELIZA chatbot and did not receive any other form of therapy or intervention as part of the study. Both chatbots enabled significant reductions in problem distress and depression/ anxiety/stress from baseline to 2-week follow-up, with MYLO showing greater reductions in problem distress at follow-up compared to ELIZA. Participants rated MYLO as significantly more helpful and were more willing to use it again compared to ELIZA. MYLO had higher correlations between system usability ratings and perceived helpfulness, willingness to use again, and problem resolution. The overall system usability scores were below the acceptable threshold for both chatbots, highlighting the importance of optimizing usability for this application in older adults. The promising but mixed results suggest further research is needed on integrating chatbot support systems into clinical care pathways.
## 4 Results and Discussion
### Farm-to-Retailer Environmental Profile of Malted, Dehulled, and Split Lentils  
Table 9 summarizes the effective, normalized, and weighted values across key impact categories, including the PEF scores for producing, processing, and distributing malted, dehulled, and split yellow lentils in 25 kg paper bags. Due to the malting process, which requires more water and energy and generates additional waste (like cuticles and rootlets), the PEF scores for malted lentils (i.e., MDSML and MDSOL) increased by approximately 50% and 55%, respectively, compared to dry lentils. In terms of global warming potential, the malting process increased greenhouse gas (GHG) emissions to about 4.5 kg CO\({}_{\text{2e}}\) per kg for MDSML and 3.3 kg CO\({}_{\text{2e}}\) per kg for MDSOL, representing a 41-52% increase over untreated dry grains. Land use (LU) remained the largest contributor to the PEF, making up around 50% for MDSOLs and 39% for MDSOLs. Climate change (CC) was the second largest contributor, accounting for 17% of the PEF for MDSOLs and 21% for MDSOLs. Freshwater ecotoxicity (ETFW) contributed 9.5% for MDSOLs and 8.9% for MDSOLs, while particulate matter (PM) contributed 5.4% and 8.5%, respectively. The Sankey diagrams in Figure S4 illustrate the allocation of input resources and their impact on the PEF for both types of malted yellow lentils. Even in this case, the agricultural phase, primarily driven by factors such as fertilizer and seeding use, accounted for the majority of the PEF, contributing 89.6% and 88.9% for ML and OL grains, respectively. The contribution of the malting process ranged from 2 to 3% of the PEF. Transportation and energy consumption contributed 6.3% and 3.5%, and 9.6% and 5.5%, respectively. Despite the higher environmental impact, it is worth noting that one potential environmental benefit of malting for the use phase is its ability to reduce the cooking time of Onano lentils from 30 to 15 min and their cooking energy consumption from 0.98 to 0.85 kWh/kg [43], while simultaneously improving their nutritional value by lowering the phytic acid content to 56% and 73% and the raffinose content to 5% and 21% of the original levels in malted ML and OL grains, respectively [10].