esa-sceva/satcom-chunk-collection huggingface dataset:
# A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC
## 4 Simulation Validation and Analysis
### Scenario 3  
The setting of the scenario is shown in Figure 2c. The SV drives with 15 m/s while the speed of the obstacle vehicle is shown in Figure 14. Figure 15 shows the trajectories of different vehicles.  
Figures 15 and 16c vividly illustrate that the SV following the TMO strategy collides with OV1 at approximately t = 2.5 s. In contrast, the SV operating under the HMD approach successfully executes emergent obstacle avoidance. Additionally, Figure 16a,b show that  
Figure 14: Speed of obstacle vehicle. (**a**) Longitudinal speed. (**b**) Lateral speed.  
Figure 12: Acceleration, jerk and collision risk of HMD and TMO. (**a**) Longitudinal acceleration. (**b**) Lateral acceleration. (**c**) Jerk. (**d**) Collision risk.  
Figure 13: Fuel consumption and solving time of HMD and TMO. Evaluation results of different evaluation functions. (**a**) Accumulated fuel consumption. (**b**) Lateral acceleration. (**c**) Evaluation results.  
Figure 15: Trajectories of different vehicles based on HMD strategy and TMO in scenario 3.  
the HMD-based SV reacts more swiftly than its TMO-based counterpart. The reason behind this enhanced performance is attributed to the evaluation results of the risks of collision and loss of dynamic stability. These results become 1 when OV1 abruptly merges into the middle lane (as evident in Figure 17). Subsequently, the HMD-based SV promptly adjusts its desired position to the centerline of the right lane, temporarily prioritizing safety over comfort, fuel economy, and traffic rules. On the other hand, the TMO-based SV persists in tracking the centerline of the middle lane, striving for driving comfort and fuel economy. Moreover, the TMO retains the solid lane line constraint, which contributes to a delayed control input and constrained feasible regions, ultimately leading to the collision incident. During the implementation of the emergent obstacle avoidance task, the nonlinearity of the optimization problem escalates significantly. Consequently, the algorithm solving time substantially increases compared to normal situations (as observed in Figure 17d). The solving time of the TMO-based algorithm can exceed 5 s, indicating that its numerical stability is inferior to that of the HMD-based approach.
## 5 Experimental Evaluation
### Simulation of Real-Time Decision  
The newly generated tasks have to find the best computational nodes and allocate suitable resources through the decision model. The tasks generate queuing delay and decision response delay in the cache queue from generation until the decision information is derived. Through the LSTM prediction model, the next incoming task is predicted based on the historical data of the task and enters the decision model to propose an offloading scheme for its assignment. In the experiments, we select the first 500 tasks for comparison. The experimental results are shown in Figure 11. It can be seen that at the beginning of the experiment, the tasks can choose good compute nodes because the cache queue and the edge server system are relatively idle, but as time goes by, the number of tasks increases the task stacking in the cache queue, which leads to an increase in the queuing delay of the tasks. Based on the prediction model, when the task is generated, the actual value of the task is compared with the predicted value, and when the error meets the set threshold, the task can be directly offloaded for computation according to the proposed assignment, otherwise, it waits to enter the decision model for reassignment. In the long run, task based prediction can effectively reduce the decision latency of tasks.
## 6 Limitations  
As artificial intelligence continues to shape the landscape of higher education, it is essential to recognize the limitations of this study to ensure its ethical and effective integration into teaching and learning environments. This research primarily focuses on attitudes and perceptions, rather than actual outcomes of AI implementation in higher education. Consequently, it does not assess real-world effects of AI on the learning process.  
Another limitation is that while the study explores faculty attitudes, it does not deeply examine how AI influences instructional design, pedagogical strategies, or faculty workload. Additionally, the research does not measure whether AI exposure correlates with improvements in academic performance, critical thinking, or student engagement, important variables that warrant further investigation.
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 4 Experimental Results
### Performance Evaluation of the LDFH Descriptor
#### 4.3.1 Descriptiveness and Robustness  
Verifying the descriptors' robustness to noise and variations in mesh resolution is the primary goal of testing on the B3R dataset. We generate RPC curves for each descriptor by following the procedure described in Section 3.2. The results for each descriptor are displayed in Figure 4. As can be seen from Figure 4a,b, there are significant differences in the performance of different descriptors under various noise levels. First, LDFH, LDFH-X, and LDFH-AZ demonstrate similar performances in noise robustness and exhibit a significant advantage. Second, SDASS also performs well, whereas FPFH shows the weakest robustness to noise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Descriptor** & **Support Radius (mr)** & **Dimensionality** & **Length** \\ \hline FPFH & 20 & \(3\times 11\) & 33 \\ SHOT & 20 & \(8\times 2\times 2\times 11\) & 352 \\ TOLDI & 20 & \(3\times 20\times 20\) & 1200 \\ SDASS & 20 & \(15\times 5\times 5\) & 345 \\ LDFH-AZ & 20 & \(7\times(12+11+5)\) & 196 \\ LDFH-X & 20 & \(8\times(9+13+2)\) & 192 \\ LDFH & 20 & \(8\times(9+14+2)\) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter settings for the seven feature descriptors.  
Regarding robustness to changes in mesh resolution, as displayed in Figure 4c,d, LDFH, LDFH-X, and LDFH-AZ outperform the other descriptors, followed by SDASS. The performance of descriptors obviously declines when the mesh resolution decreases, because geometric edges become less distinct when the point cloud becomes overly sparse. As displayed in Figure 4e,f, when both Gaussian noise and mesh decimation are present, the performance advantages of LDFH, LDFH-X, and LDFH-AZ become even more pronounced.  
The Kinect dataset has a relatively lower mesh quality compared to the other datasets. Figure 5 displays the RPC curves for this experiment. As can be seen, SHOT and LDFH perform the best, followed closely by LDFH-X and LDFH-AZ, which show a significant performance gap over TOLDI and SDASS. This indicates that the LDFH descriptor exhibits higher stability when handling cluttered and occluded data.  
Figure 4: The RPC results on the B3R dataset.  
In summary, the LDFH descriptor demonstrates strong robustness to noise, different mesh resolutions, and clutter. Additionally, LDFH achieves high RPC values across both datasets, indicating its high descriptiveness. This is because the method encodes spatial and geometric information while using the LMA instead of the normal, further improving its performance.
Management of Gingival Bleeding in Periodontal Patients with Domiciliary Use of Toothpastes Containing Hyaluronic Acid, Lactoferrin, or Paraprobiotics: A Randomized Controlled Clinical Trial  
[PERSON]  
1 Unit of Dental Hygiene, Section of Dentistry, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy; [EMAIL_ADDRESS] (A.B.); [EMAIL_ADDRESS] (C.M.); [PERSON]S] (C.P.); [PERSON].C.); [PERSON].E.)  
1 Unit of Orthodontics and Pediatric Chemistry, Section of Dentistry, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy  
2 Unit of Orthodontics and Pediatric Chemistry, Section of Dentistry, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy  
3C  
4C  
5C  
6C  
7C  
8C  
9C  
10C  
11 Unit of Dental Hygiene, Section of Dentistry, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy  
However, the most relevant drawback of SRP is the bacterial recolonization of dental pockets, occurring in a short time after the treatment [5]. It is thus fundamental that clinicians correctly instruct patients in the domiciliary maintenance of the results obtained after SRP and motivate them to adhere to at-home treatment [6]. One of the most common methods is represented by the use of toothpastes with specific components that promote gingival relief and healing thanks to their antibacterial and antiplaque effect. Several products have been commercialized with this scope, including toothpastes based on chlorhexidine, sodium bicarbonate, fluoride natural extracts, or the latest trend--'biotics'. This latter group includes prebiotics (food for bacteria able to stimulate their growth), probiotics (living bacteria), and more recently, paraprobiotics (heat-inactivated bacteria, i.e., tyndallization), lysates (bacterial fragments obtained through fermentation in culture broth), and post-biotics (concentrated bacterial active metabolites).  
The goal of this RCT was to test four different protocols of domiciliary oral hygiene management for periodontal patients based on the use of different toothpastes with different natural-extract components, among which were hyaluronic acid, lactoferrin, and paraprobiotics. The commercial products were chosen considering their recent introduction in the market and because of the fact that each of their active substances represented quite an innovative technology (especially the paraprobiotics), with respect to the control product traditionally used. The first null hypothesis of the study was that no intragroup difference would occur between the groups of patients assigned to the different kinds of toothpastes with regard to the effect on periodontal status. The second null hypothesis was that no intergroup difference would occur either.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 6 Conclusions
In addition, the influence of filter algorithms on the quality of the final snow cover product requires additional research. 9. The synergy of SAR with other sensors (e.g., optical and passive microwave) to improve the quality of snow cover classifications is still immature and requires further research. The synergic use with other sensors may also help develop and establish generally accepted validation strategies. * 10 Thanks to the characteristics of SAR which can penetrate through clouds and sense ground independently of solar illumination conditions, together with the recent prosperity of different SAR satellites and advancement of ML/DL algorithms, it is foreseeable that SAR-based SCE detection approaches can complement conventional optical sensor-based SCE detection approaches in the near future as SAR provides more snowpack condition information and can fundamentally solve the cloud coverage and polar darkness limitations. Conceptualization, Y.S.T., A.D. and C.K. ; Writing, Y.S.T. ; Review and Editing, A.D., C.K., and N.O. ; Supervision, A.D., C.K., and N.O. This research received no external funding. Support by the German Academic Exchange Service (DAAD) fellowship to [PERSON] is gratefully acknowledged. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Study Regions Type**} & \multicolumn{3}{c}{**Validation Dataset**} & \multicolumn{3}{c}{**Land Cover**} \\ \cline{4-10}  & & **Mountains** & **Forest** & **Clicer** & **Landsat** & **MODIS** & **AVNIR-2** & **Aerial** & **Meteo** & **Station** & \\ \hline \multirow{4}{*}{**Cover**} & COSOMO- & 134,135,137, & & & & & & & \\  & SkyMed & 186-188,220, & & & & & & & \\  & 226] & & & & & & & \\ \cline{2-10}  & TerraSAR-X & [86,136,209, 227] & [148] & [194,228,229] & & & & & \\ \hline \multirow{4}{*}{**Cover**} & ERS-1/2 & [85,126,135, 231, 232] & [138,139,230] & [126,232] & & & [121] & [121,139,230] \\ \cline{2-10}  & Radatast-1 & [106,131,133, 175,189,199, 200,235,296] & [104] & & [175,189,199, 200,235,296] & [103,133] & & [175,189,199, 200,235,296] \\ \cline{2-10}  & ENVSAT & [135,135,155, 237, 237, 237, 238, 239, 240,235,296] & [142,145,185, 206] & [68] & [102,151,153, 237] & [68] & [238,241,244] & [102,156,238, 241,244] \\ \cline{2-10}  & ENVSAT & [135,118,149, 201, 202,163, 207, 206,205] & [168] & [106,177,191] & [35,118,199, 200,163, 205] & & & [118,149,163, 205] \\ \cline{2-10}  & Radarsat-2 & [135,118,149, 201, 202,163, 207, 206,205] & [168] & [165,177,191] & [163,168,197, 205] & & & [118,149,163, 205] \\ \cline{2-10}  & Sentinel-1 & [140,143,198, 202,207, 208] & [143] & [96] & [140,143] & [143] & & [143] & \\ \hline \multirow{4}{*}{**Cover**} & ALOS-1 & [104,146, 201, 201,252] & [251] & [169] & & [30,169,170, 201] & & \\ \cline{2-10}  & ALOS-2 & [180] & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of included studies’ SCE monitoring strategy (studying region type, validation data and usage of land cover information).
## 6 Hardware Implementation  
The proposed joint communication jamming system was validated on a software-defined radio (SDR) (left image of Figure 17) in a controlled laboratory environment to acquire data and evaluate its radar and communications capabilities. The SDR consists of a single static radar that generates the LFM waveform and performs the basic jammed radar task and a detached communication receiver that simulates the demodulation of the pulse by a friendly communication object. The prototype is highly flexible since all parameters can be set, and the feasibility of the proposed waveform design has been demonstrated. The joint communication jamming platform was placed in the scene (right image in Figure 17) to generate the FBMC chirp waveform, send jamming signals, and perform communication tasks.  
The parameters were set as follows: wavelength \(\lambda\) = 3 cm, bandwidth \(B\) = 50 MHz, pulse duration \(\tau\) = 100 \(\upmu\)s, \(N\) = 3 information bits, \(G\) = 3 guard bits per subcarrier, PRF = 1 KHz, and OQAM was the selected modulation scheme. The jamming capability of the joint system was evaluated by calculating the real-time MTD of the pulse received by the SDR. In the acquisition process, when the target moves towards and away from the radar, its range and Doppler characteristics can be observed from the real-time MTD spectrogram (see Figure 18). According to the horizontal coordinate Doppler channel index of 106 in Figure 18a, there were 128 Doppler channels; the target speed was calculated to be 12.422 m/s, and the corresponding speed was 44.719 km/h.  
The vertical coordinate indicates the time axis. With the time index of 1.212 \(\upmu\)s and the blind area time of 2 \(\upmu\)s for the equipment, the moving target position after the near-field blind was about 481.8 m. Considering the near-field blinding error and measurement error, the detection distance error of the SDR was about 3%. As shown in Figure 18b, the MTD result with jamming has completely obscured the moving target.  
Figure 16: Trade-off between the jamming signal ratio and the number of false targets; SNR = 12 dB [24].  
Furthermore, the SAR imaging simulation of the curve trajectory was completed at the personal computer (PC) end. The jamming echo data received by SDR were superimposed on the SAR imaging echo at the PC end, and the results are shown in Figure 19. Under the influence of jamming, the image's contrast is decreased and the detail information is covered by jamming, leading to multiple false targets in the range dimension.  
Figure 17: Experimental platform: SDR and spatial position of the experiment.  
Figure 18: MTD results of target tracking: (**a**) without jamming; (**b**) with jamming.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
###### Abstract  
This paper reports on the effect of structural materials on heat loss-associated propulsion performance degradation of monopropellant thrusters in the micro scale. In order to address the effect of fabrication materials on heat loss, propellant flow characteristics, and propulsion performance, a conjugate heat transfer numerical study has been conducted considering several practical substrate candidates for microthrusters. The results were analyzed with respect to the thermal diffusivity of the materials, which revealed different propulsion performance characteristics and inner nozzle flow characteristics due to varying amounts of heat loss, depending on the microfabrication materials used and propellant enthalpies. Regardless of propellant enthalpies, however, there was a dramatic degradation in the amount of the thrust produced with respect to thermal diffusivity, particularly in the range of low thermal diffusivity. Glass, among the material types compatible with fabrication processes in regard to microthrusters, exhibited a 4% degradation in thrust performance for the 50 mN class microthruster considered, with the least degradation, while copper, with 7% degradation, exhibited the greatest amount of degradation among the materials considered. With varying chamber pressure and Mach number at the nozzle exit depending on structural materials, the results also indicated the necessity of heat loss consideration in a microthruster design process.  
micro-propulsion; heat loss; thrust degradation; fabrication materials; thrust sensitivity 2023
## 3 B-Mode Ultrasound Velocimetry Based on Optical Flow
### Implementation of the Proposed Algorithm
#### 3.4.2 Tracking Pixels through Pyramid Images  
The purpose of image point tracking is to find the matching pixel point \(N\) in the image to be matched for the pixel point \(M\) in the specified reference image \(I\), or to calculate the optical flow \(V\) of the point \(M\), where \(V=(u,v)^{T}\). Assuming that the coordinates of the point \(M\) in the original image \(I\) are \((M_{x},M_{y})\), and \(M_{L}=(M_{x}^{L},M_{y}^{L})\) is the value of the coordinates of the point \(M\) in the \(L\)th level pyramid image, \(M^{L}=M/2^{L}\), which is known from the principle of image pyramid formation [17].  
Let the optical flow prediction in layer \(L\) be \(g^{L}=[g_{x}^{L},g_{y}^{L}]^{T}\), and remaining optical flow value, optical flow residual displacement vector, be \(d^{L}=[d_{x}^{L},d_{v}^{L}]^{T}\). The relationship between the optical flow vector in layer \(L\) and the optical flow vector prediction in layer \(L-1\) can be expressed as follows:  
\[g^{L-1}=2(g^{L}+d^{L}) \tag{10}\]  
Considering the top layer, layer \(L_{m}\), has no reliable optical flow prediction, set \(g^{L_{m}}=[0,0]^{T}\), so the optical flow of original image, layer 0, can inferred as follows:  
\[\begin{array}{rcl}d&=g^{0}+d^{0}\\ &=2(g^{1}+d^{1})+d^{0}\\ &=\cdots\\ &=\sum\limits_{L=0}^{L_{m}}2^{L}d^{L}\end{array} \tag{11}\]  
As can be seen from Equation (11), there is an obvious advantage on the pixel tracking of pyramid images, that the residual displacement vector at every level, \(d^{L}\), is always a very small value when calculating the optical flow vector.
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 6 Review of Recent Progress of MSs Based Polarization Conversion
### Reconfigurable Transmissive MS Based Converters  
Polarization conversion from MSs can be achieved by designing a pattern providing a certain distribution of electric and magnetic fields. Therefore, these operations are highly dependent on the pattern/structure of the unit cell, dielectric substrate and metallic layers material. Once fabricated, it becomes difficult to adjust the resonance frequency or properties. Single-band MS operations for LP-to-CP conversions present limitations in terms of the bandwidth and frequency of operation. This is because the intrinsic properties of resonating cells (LC resonators) vary as the frequency and bandwidth of operations are changed. For multiple frequency band operations, controllable MSs become essential. This not only helps in the reconfigurability of MS-based LP-to-CP converters but also can integrate multiple functionalities into a single MS. Tunability can be introduced by some external stimulus control mechanisms, such as controllable circuit elements, state-changing and structural changing.  
Control through controllable circuit elements mostly requires varactor diodes [95; 96; 97]. Tuning methods using structural change and state change can cover microwave to optical regions. However, the third method is only applicable to the microwave region. State change control can be employed by using substrates, such as ferrites [98], semiconductors [99] or phase changing materials [100]. Structural change can exist by using microfluidic [101], gravitational field [102] or microelectromechanical (MEMS) systems [103]. Next, we will discuss each controlling method one by one.
# MEBN-RM: A Mapping between Multi-Entity Bayesian Network and Relational Model
## 4 Experiment for MEBN-RM
### Experiment
#### 4.2.1 Mapping Time  
Table 4 shows 10 relational databases with the name, domain, and number of attributes/relations. Each relational database had different attribute and relation features, so the following factors were used to investigate the mapping time: (1) the number of relations and (2) the number of attributes. Table 4 also shows the experiment results for the mapping time over the different number of attributes and relations in each of the 10 real-world relational databases. The correlation coefficient for the mapping time over the number of attributes was \(-\)0.033, while the correlation coefficient for the mapping time over the number of relations was 0.97 (see Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**\#**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Domain**} & \multicolumn{2}{c}{**\# of RS**} & \multicolumn{2}{c}{**\# of ERS**} & \multicolumn{2}{c}{**\# of RRS**} & \multicolumn{2}{c}{**\# of**} & \multirow{2}{*}{**Mapping**} \\  & & & **(Definition 5)** & **(Definition 12)** & **(Definition 13)** & **Attributes** & & **(Definition 8)** & **Time (Second)** \\ \hline
1 & Stats & Education & 8 & 8 & 0 & 71 & 8 & 0.0597 \\
2 & Financial & Financial & 8 & 8 & 0 & 55 & 8 & 0.0498 \\
3 & MovieLens & Entertainment & 7 & 4 & 3 & 24 & 10 & 0.0445 \\
4 & LegalActs & Government & 5 & 2 & 3 & 33 & 7 & 0.0334 \\
5 & SAT & Industry & 36 & 3 & 33 & 69 & 37 & 0.1656 \\
6 & Dmur & Kinship & 17 & 1 & 16 & 34 & 33 & 0.0726 \\
7 & Eli & Kinship & 11 & 1 & 10 & 22 & 21 & 0.0503 \\
8 & Bupa & Medicine & 9 & 2 & 7 & 16 & 9 & 0.0383 \\
9 & Pima & Medicine & 9 & 1 & 8 & 18 & 9 & 0.0417 \\
10 & Facebook & Social & 2 & 1 & 1 & 265 & 3 & 0.0359 \\ \hline \hline \end{tabular}
\end{table}
Table 4: 10 Real-World Relational Databases with Experiment Results for the Mapping Time.  
The increase in mapping time was linear in the number of relations. However, the acceptable speed of the algorithm alone is not sufficient. The accuracy for the mapped results is also essential.