esa-sceva/satcom-chunk-collection huggingface dataset:
# A Safe Admittance Boundary Algorithm for Rehabilitation Robot Based on Space Classification Model
## 3 Experiments
### Results  
#### 3.2.1 Space Verification Experiments  
The point cloud for the space accessible by the human arm (Figure 10b) and by the robot is determined using DAS (Figure 10a) and the kinematic model with isodensification (Figure 10c). The four-dimensional combined point cloud is obtained using the space classification model (Figure 10d).  
Figure 9: Platform for the space verification: (**a**) non-powered linkage mechanism with 4 DOFs and low damping and six–dimensional force sensor (ROBOTIQ FT–300); (**b**) visualization of the robotic simulation system.  
Forward kinematics is used for the traversal and simulated generation of 5000 real-time moving points of the robot. Three spaces involved in HRI are defined using the space classification model. Figure 11a shows the dangerous space, (b) the boundary space, and (c) the HRI space.  
Based on the above, the side lengths \(r_{1}=r_{2}\) of the voxel grid for voxel center filtering are set to 0.02 m, 0.04 m, and 0.06 m, respectively, as shown in Table 1. The average judgment time is calculated for each, and the accuracy of each group of space classification experiments is verified by numerical inverse kinematics. The experimental results show that the maximum average judgment time is 11.6 ms and the lowest accuracy is 98.40%. Thus, the effectiveness and feasibility of the proposed method for defining the virtual boundaries based on HRI space analysis are demonstrated.  
Meanwhile, it is found that the side length of the voxel grid influences the number of midpoints of the point cloud. Moreover, due to the influence of the space classification model on the boundary, the smaller the side length of the voxel grid was, the greater was the number of midpoints of the point cloud and the longer was the average judgment time.  
\begin{table}
\begin{tabular}{c c c} \hline
**Side Length of the** & **Average Judgment Time** & **Success Rate** \\
**Voxel Grid (m)** & **(ms)** & **(\%)** \\ \hline \(r_{1}=r_{2}=0.02\) & \(11.60\) & 99.00 \\ \(r_{1}=r_{2}=0.04\) & 9.98 & 98.80 \\ \(r_{1}=r_{2}=0.06\) & 5.72 & 98.40 \\ \hline \end{tabular}
\end{table}
Table 1: Space verification results.  
Figure 11: Visualization of the space classification model based on DAS: (**a**) visualization of the dangerous space; (**b**) visualization of the boundary space; (**c**) visualization of the HRI space.  
Figure 10: Point cloud data obtained from DAS: (**a**) definition of DOF of the kinematic model for DAS; (**b**) point cloud of DAS’s workspace generated using the Monte Carlo method; (**c**) point cloud of DAS’s workspace obtained by voxel center filtering; (**d**) four-dimensional combined point cloud obtained by the space classification model.  
Nevertheless, the accuracy is higher in this case. In other words, the boundary and the space are more accurately represented.
# A Vision-Based Bolt Looseness Detection Method for a Multi-Bolt Connection
## 4 Results and Discussion  
### Bolt Loseness Detection  
The representative speckle fringe patterns under all 16 cases of looseness are illustrated in Figure 7, corresponding to the 16 looseness cases in Table 1. Certain differences with noticeable features can be identified in these representative images.  
Figure 6: Fringe patterns changing with the deformation.  
The dataset (named as Dataset 1) utilized in this part comprised 64,000 images (with 4000 images for each of the 16 cases), where 60% served as the training dataset, 20% as the test dataset, and 20% as the validation dataset. The experimental platform configuration for algorithm execution included the following: CPU--AMD 5800X; RAM--16 GB @ 3600 MHz; GPU--NVIDIA GeForce RTX 3070; experimental environment--Python 3.9.  
The RNN model was trained with the training and test datasets. Initially, the detection accuracy stood at only 6.25% (there are all 16 cases of looseness, i.e., \(6.25\%=\frac{100\%}{16}\)). After 30,000 iterations, the accuracy steadily increased to 99.07% on the test dataset, as illustrated in Figure 8.  
Figure 7: Representative fringe patterns under 16 looseness cases.  
And the accuracy of the trained model on the validation dataset was calculated. The confusion matrix of the model depicted in Figure 9 offers a nuanced understanding of the detection precision of the methodology under investigation.  
Figure 8: The detection accuracy of the model increased during the training process.  
Figure 9: Confusion matrix of detection result of the trained RNN model on Dataset 1.  
In this matrix, each row represents the true label, and each column represents the predicted label. Each cell of the matrix then contains the number of samples under that true and predicted label. Each true label contains 800 images. The model, after undergoing rigorous training, exhibited an exemplary accuracy of 99.55% on the validation dataset of Dataset 1. Notably, the accuracy observed on the validation dataset marginally surpassed that of the test dataset. This phenomenon can be attributed to the presence of Dropout during the testing phase, which slightly influences the model's accuracy. Conversely, during the validation process, the Dropout component was deactivated, enabling the model to harness the collective strength of all the weak classifiers, thereby resulting in a marginal enhancement in accuracy.
## 1 Introduction
Phase information in real-time systems could have applications in brain- computer interfaces [23], closed-loop stimulator devices [24; 25; 26], and electrical stimulation of animal models based on a specific phase [18; 19]. There is substantial experimental and therapeutic potential in state-dependent brain stimulation as the participant or patient performs the task simultaneously. We are mainly interested in estimating the current instantaneous phase utilizing the visual alpha oscillation so that depending on the brain state, we can decide whether oscillatory phase-dependent stimulation should be given or not. This reflection of a closed-loop brain-state-dependent stimulation system can be seen as a new brain-computer interface (BCI) approach. Herein, we propose a novel approach for time-series forward prediction that was developed based on the conventional AR model [21; 27] with an extension of an adaptive least mean square (LMS)-based AR model. The AR model can be constructed using various algorithms to calculate model coefficients, each one of them fulfilling different purposes. These methods minimize the prediction error, either only forward prediction error or both backward and forward prediction errors. This study focuses on the forward prediction error and demonstrates that the LMS-based AR model minimizes the forward prediction error because its coefficients can be adjusted dynamically, and it performs the AR model better in real-time for long prediction lengths. Since adaptive methods can dynamically track the coefficients and allow more accurate prediction, we chose the LMS-based AR model.
## 2 Designed NISC-Based Architectures
### Equalizer Architecture
#### 2.1.2 \(EquaNlsc\) Module  
\(EquaNlsc\) is the principal module of the proposed architecture dedicated for MIMO MMSE linear turbo equalization. It is hierarchically composed of three units:  
1. Storage unit (SU)
2. Multiplexing unit (MU)
3. Computational unit (CU)  
Storage unit (SU):  
SU is responsible for saving data loaded from memory blocks and the results of intermediate computations. It is composed of three groups (H, V and G) of 16-bit registers that each can store one 4 \(\times\) 4 complex matrix. Inside the groups, registers are classified into couples such that each is proposed to store the real part and the imaginary part of a complex number. In addition to the register groups, four registers are instantiated to store the variance values.  
Multiplexing unit (MU):  
MU is responsible for arranging the data transfer in the \(EquaNlSC\) module between internal units (storage unit and computational unit). Furthermore, it manages the flow of input/output data and reformulates it in order to match the desired quantization. It is composed of multiplexers that construct a connecting chain between different components of the architecture. For each multiple-input component, a multiplexer is allocated to manage its input data flow coming from different sources.  
Computational unit (CU):  
The CU contains all hardware resources that perform all required computation operations in the MMSE equalization algorithm. It spreads over six pipeline stages and includes all hardware operators, which are utilized in the execution of the required algorithmic computations. It incorporates carefully-designed modules, which are capable of using the allocated resources efficiently for different system configurations. For the additional details about the structures of each unit, the reader can refer to [13].
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 5 Experiments
### Experiments with Offline Keypoint-Map Tracking
#### 5.3.1 Point-Based Rendering  
The results for the offline tracking method using point-based rendering, shown in Table 3, present the performance of the AKAZE and ORB detectors in this configuration.  
AKAZE is shown to be effective, delivering consistent performance with high tracking rates and low ATE values across the tested datasets. In contrast, ORB exhibits considerable instability with this method. In several sequences, the tracking rate is very low, falling below 2% in four cases, indicating a general failure to maintain tracking. A particular case is the _Drive_ dataset, where ORB reports a low ATE. This result, however, corresponds to a tracking duration of only 0.3% of the sequence, making the ATE value unrepresentative of the overall performance.  
When comparing these findings to the online method's results in Table 1, a notable difference in the behavior of ORB can be observed. While ORB was a usable high-speed alternative in the online method, it does not provide the same level of reliability with this offline point-based rendering approach. This suggests that for the offline strategy presented here, AKAZE is the more dependable choice for achieving consistent results.  
Figure 3: Rendering example. (**a**) Three-dimensional reconstruction of the Garden sequence. (**b**) Real image camera image. (**c**) Image rendered using the point-based rendering approach. (**d**) Image rendered using M2 Mapping. (**e–g**) Zoom in on a specific area of the image. See text for more details.
# Fast Calculation of Transient Thermal Stress in Surge Lines with Thermal Stratification Region
## 4 Determination of Fluid Temperatures of Thermal Zones in Stratification Region
### Result and Verification of the Fluid Temperature
#### 4.4.2 Linear Heat Flux Distribution Assumption  
For the linear heat flux distribution assumption, the measured temperature and calculated temperature, which is the convolution result of the assumed fluid temperature and temperature Green's functions are compared in Figure 24 for the seven monitoring points of Section 3. It can be seen that all measured temperatures agree quite well with the calculated ones. The maximum error at the top monitoring point is 2.03%, the maximum error of the calculation result at the middle monitoring point is 1.07%, and the maximum error of the calculation result at the bottom monitoring point is 0.04%.  
Comparison of Thermal Stress Calculation Results with Different Number of Thermal Zones in Stratification Region  
In order to study the effect of the number of dividing thermal zone in the stratification region on the calculation result of thermal stress, the stable conditions after 20,000 s were used for comparison. When the thermal stratification region is divided into two zones, the bulk temperature of fluid in the upper zone is taken as the temperature of the fluid from the pressurizer, and the bulk temperature of the fluid in the lower zone is taken as that from the hot leg. The stress intensity contour is shown in Figure 25, the maximum stress intensity in Section A is 119 MPa, and the maximum stress intensity in Section B is 137 MPa.  
Figure 23: The comparison of calculation result for fixed heat flux and temperature of monitoring points. (**a**) Monitoring Point 31. (**b**) Monitoring Point 32. (**c**) Monitoring Point 33. (**d**) Monitoring Point 34. (**e**) Monitoring Point 35. (**f**) Monitoring Point 36. (**g**) Monitoring Point 37.  
Figure 24: _Cont._Figure 24: The comparison of the calculation result for the heat flux of the linear distribution law with the temperature of the monitoring points. (**a**) Monitoring Point 31. (**b**) Monitoring Point 32. (**c**) Monitoring Point 33. (**d**) Monitoring Point 34. (**e**) Monitoring Point 35. (**f**) Monitoring Point 36. (**g**) Monitoring Point 37.  
When the thermal stratification region is divided into six zones, the bulk temperature of the fluid in each thermal zone is taken as the calculation results in Section 4.2. The stress intensity contour is shown in Figure 26. The maximum stress intensity in Section A is 98 MPa, and the maximum stress intensity in Section B is 67 MPa. It can be seen that when the thermal stratification region is divided into two zones, the stress calculation result is too conservative, and the six thermal zones division yields more accurate results for the evaluation of thermal fatigue.
# Pollution and Climatic Influence on Trees in the Siberian Arctic Wetlands
## 3 Results
### Climate Variables  
Within the study area, northwest winds prevailed during the summer and southeast ones prevailed in winter (Figure 3a). Summer and winter air temperatures have been increasing since 1990 (R\({}^{2}\) = 0.13-0.34; Figure 3b). Soil summer moisture has been decreasing since 2000 (R\({}^{2}\) = 0.63; Figure 3c). A strong decreasing trend experienced minimal values soil moisture in July, which indicated an acute drought frequency increase (R\({}^{2}\) = 0.81; Figure 3c). Terrestrial moisture content (GRACE-derived June values) has been decreasing since 2000 (R\({}^{2}\) = 0.20; Figure 3d). Meanwhile, precipitation values have stagnated.  
Maps of eco-climate variables within the study area show their spatial distribution within the study area (Figure 4). The annual temperature varied from c. \(-\)3 \({}^{\circ}\)C (in wetlands) to \(-\)15 \({}^{\circ}\)C (in mountains). Positive temperature trends were observed on the major part of the lowlands (Figure 4a,b). Annual precipitation varied from c. 600 mm/year (wetlands) to 1000 mm/year (mountains) (Figure 4c). Maximal values of atmospheric drought (indicated by drought index SPEI) were observed in the area southeast of Norilsk (Figure 4d). The highest soil temperatures are located around the Pyasino Lake and southeast of Norilsk (Figure 4e). Minimal soil moisture content was observed southward of Pyasino Lake (Figure 4f).  
Figure 3: Wind rose (**a**) and air temperature (**b**), soil (**c**), and terrain moisture content (**d**) anomalies. Trends are significant at \(p<0.05\).
# Resilient Formation Reconfiguration for Leader-Follower Multi-UAVs
## 5 Articulation of Formation Reconfiguration Strategies for Multi-UAVs with Variable Swarm Scale
### Design of Formation Reconfiguration Strategy for Multi-UAVs under Follower Disengagement  
During the formation flight of the leader-follower multi-UAVs, followers can suffer from accidents as well, including malfunction, battle damage and mission change, which leads to a reduced swarm size. In the case of follower departure, the first step for reconfiguring the formation is to restore the swarm communication topology for the smoothness of information interaction within the multi-UAVs. Then, renew the parameters of the multi-UAVs, including the amount of followers, the desired distance of each follower with respect to the leader and the number for each swarm member. Finally, the impaired multi-UAVs reform the intended geometry under the formation control law (30). The formation reconfiguration scheme in this case can be further summarized as the following steps with the restoring process of the swarm communication topology illustrated in Figure 5.  
**Step 1.** Determine if there are nodes that cannot be reached by node 0. If yes, find the node with the smallest number that node 0 cannot access and enter **Step 2**, otherwise go to **Step 3**.  
**Step 2.** Determine whether the disengaging follower is node 1 or 2. If yes, establish a directed connection from node 0 to the node found in **Step 1**. If not, establish a bidirectional connection between the node found in **Step 1** and the node whose number is two less than that of the disengaging follower.  
**Step 3.** Determine whether the length of the two paths starting at node 0 are the same. Disconnect the bidirectional connection between the terminating node of the longer path and its neighbor and establish a bidirectional connection between the node and the terminating node of the shorter path in the sense that the difference in length between the two paths is 2.  
**Step 4.** Update the amount of the followers inside the multi-UAVs, namely, N = N - 1.  
**Step 5.** Update the number of each follower from 1 to N.  
**Step 6.** Update the desired distance of each follower with respect to the leader and reform the intended formation under the control law (30).
## 4 Simulation Results and Security Evaluation
### Statistical Attack  
Security of the data depends on the states they are in, i.e., they can be data in motion, data at rest, or data in use. Most organizations prefer to transfer information in these states but mostly do so through insecure channels. Therefore, to maintain data security, a good and measurable mechanism must be established. The two mechanisms used to measure statistical attacks are the histogram and correlation coefficient analyses.
## 3 Results and Discussion
### Conversion Pathway of VOCs in Pear  
Most VOCs in food are generated by chemical reactions between substrates [26]. In this study, a series of flavor compounds with similar structure or synthesis route were detected,such as 2-octen-1-ol, octanol, octyl acetate, 2-octenal, 2-hexenyl acetate, hexyl acetate, 2-hexenal, hexanal, and hexanol. Those compounds could be formed from hydrogenation or dehydrogenation of other compounds.  
According to [PERSON] et al. (2003), volatile esters are generated by esterification of alcohols, and the reaction is catalyzed by the enzyme alcohol o-acyltransferase (AAT) [35]. As shown in Table 2, an increased content of octyl acetate (from 0.40 to 0.53 mg/kg FW) was associated with a decrease in 2-octen-1-ol (from 0.59 to undetectable) and 1-octanol (from 0.43 to 0.19 mg/kg FW) after 14 days storage. Furthermore, [PERSON] pear presented a reduced 2-octenal production after storage at 4 \({}^{\circ}\)C (from 0.75 to 0.31 mg/kg FW). Volatile alcohols are formed during the catalytic reactions of aldehydes and ADH [35]. Based on the results from the present study, it is possible that alcohols were the intermediates for conversion of aldehydes to esters (Figure S4a). However, this assumption was not confirmed due to the limitation of the experimental method used.  
Similarly, pear fruit showed a rapid increase of hexyl acetate content (from 19.91 to 45.97 mg/kg FW) during storage. Meanwhile, 2-hexenal exhibited a decreasing trend (from 0.23 to 0.15 mg/kg FW). This result suggests that hexyl acetate was the 2-hexenal derived ester, and 1-hexanol was the intermediate (Figure S4b). Probably due to the intense ADH activity [32], 1-hexanol showed a slight increase during storage.
# Inkjet-Printed and Paper-Based Electrochemical Sensors
## 2 Fabrication Methods  
For modern electronic components and systems, an abundant number of fabrication methods exist allowing for incredible control over the process and device parameters. Depending on the needs for a particular device or system, a number of fabrication methods are considered. Parameters of interest when considering various fabrication methods include materials, deposition thickness and uniformity, device dimensions and tolerances, space limitations, and of course cost. Among the existing fabrication techniques, well-established semiconductor fabrication methods provide precise control over device dimensions, accept the usage of a large range of materials, and enable fine control over doping and other parameters. Common semiconductor fabrication methods include sputtering, evaporation, chemical vapor deposition, and photolithography. Although the knowledge and experience surrounding standard semiconductor manufacturing techniques is vast, the cost to use these techniques can be particularly high. The equipment alone is quite expensive, but many techniques require specialized facilities like cleanrooms to operate properly and produce functioning devices. Furthermore, substantial floor space is also required for many of these pieces of equipment. As a result of these requirements, it is becoming more and more common to investigate other existing fabrication methods to reduce cost.  
As an alternative to standard semiconductor manufacturing techniques, a number of other methods have been explored in order to drive down manufacturing time and cost. In terms of electrode fabrication, various groups have demonstrated the use of inkjet printing [13; 14; 15; 16; 17; 18; 19], screen printing [11; 12; 20; 21; 22; 23; 24; 25], and microwire placement [38] on flexible or paper-based substrates. In addition to electrode fabrication, some groups have also utilized various techniques for patterning fluid channels on paper using hydrophobic materials. Channel fabrication has been achieved using photolithography [8], plasma treatment [10], wax printing [11; 29], inkjet printing [13], and screen printing [20]. Among these techniques, both screen printing and inkjet printing are the most prominent techniques for patterning electrodes and accompanying hydrophobic barriers. Although this review is intended to focus on inkjet-printed sensors, it is important to thoroughly review screen-printed sensors as a comparison. Both of these printing techniques are incredibly adaptable and provide a low-cost manufacturing process. However, it is the authors' opinion that inkjet printing is a superior process due to its individual advantages, which are discussed in detail in Section 2.2.