esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Erosion Minimization for a Slurry Pump Using Discrete Phase Model Simulations
## 3 CFD Analysis
### Analysis of the Simulation Results  
Figure 4 demonstrates the results of the simulation of the slurry particle velocity tracking and erosion damage in the slurry pump. The maximum velocity of the slurry particles was observed to be near the impeller and throat bush. Consequently, the slurry particles uniformly flowed into the pump casing, swirled around the impeller, and uniformly exited  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Conditions** \\ \hline Slurry condition & H\({}_{2}\)O + CaCO\({}_{3}\) + CaSO\({}_{4}\) \\ \hline Impeller speed & 600 rpm \\ \hline Turbulence model & SST \\ \hline Temperature & 50 \({}^{\circ}\)C \\ \hline Particle diameter & \(\leq\)325 mesh (44 \(\mu\)m) \\ \hline Slurry flow & 6800 m\({}^{3}\)/h \\ \hline Throat bush material & White cast iron \\ \hline pH of slurry & 5-6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary conditions.  
Figure 4: Gap between the impeller and the throat bush.  
the pump casing, as illustrated in Figure 5a. The maximum erosion damage occurred in the throat bush, as depicted in Figure 5b.  
A wear map for the entire throat bush was presented at angular intervals of 45\({}^{\circ}\) in the counterclockwise direction to identify erosion damage for each section of the throat bush, as shown in Figure 6.  
2.1 Analysis of the Erosion Rate Density (E) Based on the Gap between the Impeller and the Throat Bush (w)  
Figure 7 depicts the erosion rate density based on the gap between the impeller and the throat bush. In this instance, the slurry particle diameter and impeller rotation speed were fixed at 44 m and 600 rpm, respectively. However, the gap between the impeller and the throat bush (w) was varied from 1 to 1.2 to 1.4 mm.  
Figure 5: Simulation results: (**a**) slurry particles tracking and velocity streamlines; (**b**) erosion rate density.  
Figure 6: Wear map for different locations on an angular basis.  
As the distance between the two components increased, the erosion rate density decreased. The erosion rate density decreased more when the gap between the impeller and the throat bush increased from 1.2 to 1.4 mm compared with when the gap increased from 1 to 1.2 mm. If the gap between the impeller and the throat bush is increased, the sliding of the internal fluid and the surface is reduced and the wear due to impact is reduced [16]. Additionally, the simulation results showed that the highest erosion rate density occurred near \(\alpha=45^{\circ}\) and \(\alpha=315^{\circ}\), which were close to the outlet. The two parts close to the exit had a lot of erosion because the internal flow changed radically, despite the increase in the spacing between the impeller and the throat bush.
## 6 Challenges and Opportunities
### Future Research Directions
#### 6.4.5 Enhanced Security and Ethical Frameworks  
As IoPVT systems merge with essential urban infrastructure, the risks of a potential cyber attack increase [106; 132]. To maintain data integrity, researchers must develop robust multilayered security solutions, including real-time anomaly detection, low-power device-optimized encryption protocols, and distributed ledger technologies (e.g., blockchain). Zero-trust architectures, in which every device and service is continually authenticated and monitored, can further enhance the overall system's resilience.  
An emerging vital aspect of securing IoPVT data, especially as they transition between physical and virtual realms, is the establishment of verifiable anchors that authenticate the integrity of digital representations against real-world conditions [133; 134]. A promising approach involves using environmental fingerprints, such as distinctive patterns of noise, temperature variations, or even microchanges in barometric pressure as unique identifiers that link the digital environment to actual physical-world data [135; 136]. If environmental signatures are embedded in the data flow, then when someone attempts to manipulate the meter or spoof it and alter the readings, the system can identify that the presented data cannot be accurate due to discrepancies in the \"environmental fingerprints\" compared to what is expected. This next level of verification is crucial for digital twins and immersive platforms, where false or manipulated images or scenarios can ultimately lead to poor decisions or ineffective resource allocation. Cross-validation between virtual analytics and physical anchors thus helps ensure trust and coherence in IoPVT ecosystems.  
If not carefully audited, AI-driven decision making can inadvertently marginalize certain groups [137; 138]. Future research should investigate strategies to reduce algorithmic bias, such as using diverse and representative training datasets or implementing continual fairness checks throughout model lifecycles. As global and regional regulations around data privacy (e.g., General Data Protection Regulation (GDPR)) and responsible AI intensify, research must explore frameworks that harmonize these technologies with evolving legal landscapes.
# Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester
## 5 Conclusions  
In this study the performance evaluation of a robot combine harvester has been shown by analyzing the accuracies under static and dynamic conditions of the CLAS and MADOCA positioning augmentation systems. The evaluations under static conditions lead to conclude that both CLAS and MADOCA have relatively short activation times, but also lead to conclude that CLAS displays a better static accuracy than MADOCA.  
The evaluation under dynamic conditions while using the RTK for navigation drew the conclusion that the overall accuracy of both CLAS and stabilized MADOCA is inferior to the RTK. Even though, the evaluation under dynamic conditions allowed to conclude that the performance of a robot combine harvester using the CLAS positioning augmentation systems is good enough for practical applications. In the other hand, the dynamic results allowed to conclude that utilization of the MADOCA is not suitable for the practical function of the robot combine harvester.  
Conceptualization, N.N.; methodology, N.N.; software, K.U.; validation, R.O. and N.N.; formal analysis, K.U.; investigation, K.U.; resources, N.N.; data curation, K.U.; writing--original draft preparation, K.U.; writing--review and editing, K.U., R.O., and N.N.; visualization, K.U.; supervision, Y.-J.K. and N.N.; project administration Y.-J.K. and N.N.; funding acquisition, N.N. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through the Advanced Production Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (318072-03).  
Not applicable.  
The authors declare no conflict of interest.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 3 Results
### Prediction Assessment: The Locust Egg Hatching
The region of interest is partitioned into hexagons, each of which is assigned a color according to the average of the \(\Delta_{d}\) values of the records within the area of the hexagon itself. bottom right, shows that nearly all instar 1 observations fall within a predicted egg hatching period between March and April. As a matter of fact, by adjusting the model with slightly different parameters, the prediction assessment relative to field observations can be notably improved. Mathematically, this indicates that the model we use is ill-conditioned, meaning small changes in the parameters can cause large changes in the results. This suggests that, on the one hand, a careful calibration of the model parameters is necessary, and on the other hand, the model would benefit from introducing some form of regularization, which would make the model more stable and robust.
## 3 Research Progress on Adsorption and Separation of Hydrocarbon Molecules
### Aromatic Hydrocarbons  
Aromatic hydrocarbons in petroleum mainly include polycyclic aromatic hydrocarbons (such as benzopyrene) and monocyclic aromatic hydrocarbons (benzene, toluene, xylene, etc.), while polycyclic aromatic hydrocarbons are organic compounds comprising two or more single ring molecules. Diesel produced by catalytic cracking is mostly inferior oil or heavy oil, therefore, recycling the aromatic hydrocarbons in diesel can not only improve the quality of diesel but also obtain the industrial raw material aromatics [41]. Commonly used methods for separating aromatic hydrocarbons include catalytic hydrogenation, adsorption separation, etc. At present, high-pressure hydrogenation is widely used in industry [42]. However, this method has problems such as high energy consumption, strict requirements, and poor product economy. The above problems can be effectively solved by the method of  
Figure 8: Pulse spectra of adsorbent Ca\({}^{2+}\) exchange degree [36] (**a**) 70.8% (**b**) 85.5% (**c**) 91.9% (**d**) 97.5%.  
adsorptive separation, which is mild, eco-friendly, and has high product quality. In addition, different adsorbents can be selected according to the different desired reactions, therefore, adsorptive separation is the most ideal separation mode. The currently applied adsorbents can be classified as follows:
## 4 Vibration and Acoustic Sensors in Non-Destructive Testing of Port Cranes
### Vibration Sensors in Port Crane Monitoring
#### 4.1.1 Working Principle of Vibration Sensors  
Vibration sensors detect and measure the vibrations generated by the dynamic responses of structures. When external forces act on port cranes, structural vibrations occur, reflecting their dynamic characteristics and integrity [78; 79]. Vibration sensors convert these mechanical vibrations into electrical signals for analysis. Common working principles include piezoelectric, capacitive, and strain gauge-based principles. Piezoelectric sensors generate electrical charges when they are subjected to mechanical stress. They exhibit high sensitivity and frequency response, making them suitable for dynamic vibration measurements. Capacitive sensors measure the capacitance changes caused by vibrations and offer high accuracy and stability. Strain-based sensors detect strains induced by vibrations and provide precise measurements of structural deformation. As [PERSON] et al. [48] noted, vibration sensors can effectively monitor the dynamic responses of crane structures, providing valuable data for assessing structural health [80; 81].
## 3 Results and Discussion
### Current Modulation and Temperature Difference Spectra  
Figure 6 shows the current modulation spectra near the interband transition edge of silver, together with the reflection spectrum. Similar to the plasmon modulation spectrum, the current modulated reflection spectra show such a change that corresponds to the interband transition edge becoming broader. As the current is increased, the reflectance change increases accompanied by a slight change  
Figure 4: Reflection spectra of Ag with a film thickness of about 50 nm in the Kretschmann arrangement for p- and s-polarized light at an incident angle of \(>\)45\({}^{\circ}\). The dip around 430 nm is due to the surface plasmon resonance appearing only in the reflection spectra for p-polarized light. The dip near 310 nm in the reflection spectra for both p- and s-polarized light is due to the interband transition.  
Figure 5: Plasmon modulation spectrum \(\Delta\)R/R, which was taken around the interband transition edge from the air side of the prism, while the plasmon resonance dip around 410 nm was excited by a 408-nm laser.  
in the spectral shape such that the zero-crossing point is red-shifted with increasing magnitude of the current. Figure 7 shows the normalized temperature difference spectra. In marked contrast with the current modulation spectra, the zero-crossing point is fixed throughout the change in temperature from +10 K\(\rightarrow\)50 K, indicating that any change in the spectral shape with temperature is negligibly small.  
Figure 6: Change in (**a**) the current modulation spectra and (**b**) the normalized current modulation spectra with the increase in the current.  
Figure 7: Temperature difference spectra normalized by the reflection spectrum at room temperature. The temperature is increased from room temperature in steps of +10 K.
## 5 Experiment Results and Analysis
### Experiment for the Generalization and Robustness in Noisy Conditions (Answer to RQ4)
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**AWARE**} & \multicolumn{4}{c}{**BFRU**} \\ \hline \multirow{2}{*}{**Noise Proportion**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**F1-Score (\%)**} \\ \cline{2-9}  & **SOTA** & **Ours** & **SOTA** & **Ours** & **SOTA** & **Ours** & **SOTA** & **Ours** \\ \hline
0\% & 80.84 & 82.18 & 78.66 & 80.05 & 76.32 & 77.28 & 84.06 & 85.18 \\
10\% & 79.68 & 81.70 & 77.72 & 79.76 & 75.89 & 77.04 & 83.35 & 85.05 \\
20\% & 79.06 & 80.21 & 76.08 & 78.38 & 74.84 & 76.08 & 82.95 & 84.17 \\
30\% & 67.23 & 69.93 & 65.04 & 66.74 & 73.64 & 75.33 & 81.01 & 83.59 \\
40\% & 54.12 & 56.92 & 46.79 & 51.30 & 62.44 & 68.57 & 69.74 & 75.56 \\ \hline Average drop rate & 13.38\% & 12.16\% & 15.58\% & 13.75\% & 6.05\% & 3.91\% & 5.71\% & 3.62\% \\ \hline Improvement rate & - & **9.12\%** & - & **11.75\%** & - & **35.37\%** & - & **36.60\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average performances of SOTA and LePB-SA4 RE on the benchmark dataset and untrained datasets. Figure 8: Confidence interval bands at 95% critical value on AWARE dataset. needed for the evolving requirements elicitation process and offering valuable potential for modern software development practices.
## Materials and Methods
### Model Validation  
To get insight into the model performance, we compared inverted in situ spectra, sampled at the calibration points, with in situ measurements. The model was inverted for a single parameter at a time. This provided insight into the effects of a single parameter on an in situ spectrum. To exclude mixed pixel effects, we only used plots dominated by _Spartina_ (cover > = 95%: n = 22). The spectra used for the validation process were not used for calibration. In addition we inverted spectra from a RapidEye satellite image (see 2.8) and compared the estimated values based on this inversion with in situ values collected at completely independent plots in the same study site.  
The relative advantage of using a more advanced physical model when quantifying vegetation characteristics is tested by comparing the results of ProSail with a simple correlation based approach using NDVI. The NDVI was calculated using band 3 (red) and 5 (NIR) of the RapidEye satellite and compared with the modeled vegetation characteristics.
# Object-Level Semantic Map Construction for Dynamic Scenes
## 3 Methodology
### Object Motion Detection through 3D Tracking Based on Object Model Database
#### 3.3.2 Photometric Error Term
Similar to the method used for camera pose estimation, the pose change \(T_{m}^{\Delta}\) of object \(m\) against the camera pose \(T_{rou}\) is estimated by minimizing an energy that combines geometric error with photometric error between the data in the current frame and \(m\) transferred using pose \(T_{m}\):  
\[E_{track}^{m}=\frac{min}{T_{th}}\left(W_{icp}E_{icp}^{m}+E_{rgb}^{m}\right) \tag{10}\]  
where \(E_{icp}^{m}\) and \(E_{rgb}^{m}\) are the object geometric error and photometric consistency error, respectively, against the current frame data. \(T_{m}^{\Delta}\) can be obtained using the Gauss-Newton approach in a three-level coarse-to-fine scheme. If \(T_{m}^{\Delta}>\epsilon\) (translation threshold 0.2, rotation angle threshold 0.1), which indicates that object moves independently from the camera in the current frame, then the object is treated as a dynamic object and inserted into the dynamic object database. Otherwise, the object is treated as a static object and inserted into the static object database.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 4 Numerical Model for Validation  
### Seal Geometry and Operating Conditions  
To validate the orbit decomposition method, the results of the present method are compared with those of the transient CFD method. Considering that the time of transient computation is very long, a short seal with five teeth is taken as the research object. Figure 8 shows the seal geometry. Five seal teeth are installed on the stator. The rotor is smooth. The detailed geometry parameters of the labyrinth seal and operating conditions used for calculations are listed in Table 1.
# A Recursive Least-Squares Algorithm for the Identification of Trilinear Forms
## 5 Conclusions and Future Works  
In this paper, we explored the identification of trilinear forms using the RLS algorithm. The approach was developed in the framework of an MISO system identification problem, based on the decomposition and modelling of third-order tensors. The resulting RLS-TF algorithm was tailored for the identification of such trilinear forms in a more efficient way as compared to the conventional RLS algorithm. Moreover, the proposed RLS-TF algorithm outperformed its previously developed NLMS-TF counterpart in terms of the main performance criteria, providing a faster convergence rate and tracking, together with low misalignment.  
The ideas presented in this paper could be further exploited in an extended framework, aiming to identify more general forms of global impulse responses, which cannot be decomposed as rank-\(1\) tensors. Several preliminary results can be found in [30; 31; 32], but they are applicable only in the bilinear context (i.e., second-order tensors). The extension to the trilinear case represents a very challenging problem, since finding (and approximating) the rank of a higher order tensor is a much more sensitive task. Furthermore, it would be interesting to extend other versions of the NLMS and RLS algorithms (e.g., based on variable step-sizes [33] and variable forgetting factors [34], respectively) to the trilinear forms.  
Finally, it would be useful to evaluate how the proposed algorithm could benefit (in terms of implementation) from the current technology of the tensor processing unit (TPU) and the TensorFlow software [35]. This aspect could bring additional advantages, especially in the framework of specific applications related to machine learning/vision, neural networks, and artificial intelligence.  
Conceptualization, C.E.-I; methodology, L.-M.D.; investigation, C.P.; validation, J.B.; software, A.-A.E.; formal analysis, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by UEFISCDI grant number PN-III-P1-1.1-PD-2019-0340.  
The authors declare no conflict of interest.  
Figure 7: Normalized misalignment (NM) evaluated based on (58), in dB, for the identification of the global impulse response \(\mathbf{h}\) (of length \(L=2048\)) from Figure 2d, using the conventional RLS algorithm (with different values of the forgetting factor \(\lambda=1-1/(KL)\), varying the value of \(K\)) and the RLS-TF algorithm (with the forgetting factors \(\lambda_{i}=1-1/(KL_{i}),\ i=1,2,3\), where \(K=20\)).
## 4 Results and Discussion
### Divergence of Attributes
#### 4.3.3 Hands-Free Mode  
Hands-free mode is another attribute that showed divergence among papers. Having one's hands free during an AR call is a benefit mentioned by five papers [13; 31; 32; 35; 38]; these papers implemented AR for communication in different environments (both controlled and uncontrolled) and for different types of projects or applications (i.e., infrastructure and academic research). Conversely, [PERSON] and [PERSON] (2019) stated that AR modules could not be used in hands-free mode [45]. The literature provides a variation in the results. The five papers that reported a benefit used head-mounted devices supporting AR, whereas [PERSON] and [PERSON] (2019) used a tablet [45]. Therefore, and to no surprise here, there is a consensus among the papers that the type of the device in use (head-mounted versus tablet) is related to considering this item a benefit or a challenge, supporting the premise of this literature analysis paper in which we are analyzing thefindings of various studies around this same topic and identifying the contexts in which these findings are made.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### ETX-Based Solutions
#### 4.2.4 Modified RPL  
A vehicular ad hoc network (VANET) refers to ad hoc wireless networking between vehicles. It is seen that RPL has a slow response to topology changes due to delays in ETX probing, unreachable destinations, and loops in the network. Authors [45] have proposed a modified RPL VANET where ETX probing is performed immediately upon new neighbor discovery rather than following a periodic strategy so that new neighbors can be immediately considered as preferred parents. Loops in the network can result if the node selects a neighbor whose parent is the node itself as the rank of the neighboring node is less than the current node's rank which is set to infinity. To avoid looping, a parent ID is sent in the DIO message so that if the parent ID matches the node ID, the node can discard the DIO messages, preventing loop formation. In addition, DIO and DAO messages are sent immediately to notify the new rank and parent routes to children ignoring Trickle timer delays. Simulation results show that, upon modifications, the modified RPL shows a better PDR of 90% for 10 nodes as compared to 80% shown by RPL. Similarly, they observe a throughput of 19 kbps for both basic and Modified RPLs, furthermore, an overall delay of 1.3 ms is observed in the modified RPL, compared to a 1.2 ms delay for RPL.