esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Field Application Effect Evaluation  
Closed-cell aluminum foam is a kind of material with both fire prevention and sound absorption and noise reduction functions. The Haoshanyu Tunnel on the Qinglan Expressway has become the first expressway tunnel in China to complete the whole line laying of aluminum foam board due to the needs of noise reduction and fire prevention improvement. The field performance of closed-cell aluminum foam board in the tunnel before installation, during installation and after installation were shown in Figure 12. Before the installation, the concrete on both side walls of the tunnel lacked fire protection treatment, which can be seen from the on-site photos before installation. According to the relevant regulations in China's \"Code for Fire Protection of Building Design\" GB 50016-2014, it is known that the interior decoration in the tunnel should be made of non-combustible materials, and the temperature of the concrete surface should not exceed 380 \({}^{\circ}\)C. The closed-cell aluminum foam has a good effective thermal conductivity at high temperatures [27], and its installation in the tunnel perfectly compensates for the lack of fire protection and meets the national fire protection standards.  
Figure 12: _Cont._  
According to the relevant requirements in the \"Regulations of the People's Republic of China on the Prevention and Control of Environmental Noise Pollution\", \"Urban Area Environmental Noise Standards\" GB 3096-93, and \"External Noise Limits and Measurement Methods for Accelerated Vehicles\" GB 1495-2002, the maximum allowable noise level generated by trucks traveling in tunnels is 89 dB, which does not exceed 85 dB for small cars. According to the above field test results, before the aluminum foam boards were installed on both sides of the tunnel, the noise of vehicles in the tunnel is higher than the standard. After the installation of the aluminum foam board, the vehicle noise generated by small and medium-sized cars in the tunnel has been lower than the standard value. However, the noise generated by the large truck is still slightly higher than the standard value after noise reduction treatment, the possible reason is the influence of noise that is unavoidable in the test. Generally speaking, it has a good noise reduction effect.  
In summary, the laying of aluminum foam boards on the entire line of the Haoshanyu Tunnel of the Qinglan Expressway not only improves the fire prevention and noise reduction capabilities in the tunnel, but also Beautifies the tunnel environment, and play a very good effect in the field application. In addition, aluminum foam boards are more economical than fire-resistant panels and sound-absorbing panels. The aluminum foam boards have a service life of up to 50 years and can be recycled and reused with ultra-high value. Therefore, it has very good application prospects in tunnels.  
Figure 12: Comparison of the effect before and after installation of the al-foam board.
## 2 Vibration Signal Data Processing and Feature Enhancement
### Signal Analysis  
Western Reserve University (CWRU) bearing data acquisition tests were measured under different horsepower motor fan end and drive end bearing vibration data, of which the drive end bearing was for 6205-2 RS JEM SKF (produced by SKF, Gothenburg, Sweden) deep groove ball bearings, the fan end bearing was for 6203-2 RS JEM SKF deep groove ball bearings, and for which the SKF6025 bearings sampling frequency was 12 kHz and 48 kHz and the SKF6023 sampling frequency was 12 kHz. SKF6023 sampling frequency was 12 kHz. The experiment used EDM treatment to form a single point of failure on the inner and outer rings and balls of the bearings, and the bearings with different failure parts were operated at different speeds and frequencies. Finally, the vibration values of the inner ring, outer ring, rolling element, and normal state of the bearing were recorded, respectively.  
In this paper, the vibration signal data of the drive end bearing at a frequency of 12 kHz and a rotational speed of 1772 r/min are selected, and the specific data are shown in Table 2.
## 4 Experimentation and Results
### Computation Time and Running Time
#### 4.6.1 Average Detection and Tracking Time  
We assessed the average detection time per frame in several retail video sequences with varying crowd densities. With an average object detection time of 0.050 s per frame, the model's processing speed was 20 frames per second (fps). The detection model and the DeepSORT tracking algorithm processed each frame in 0.040 s, enabling the system to reach 25 frames per second throughput.
## 3 Results
### The Efficiency of the Spectrogram Mesh Surface Generation (SMSG) Algorithm  
In order to verify the efficiency of the proposed SMSG algorithm, two display algorithms were carried out by Visual C++ language, on a PC. The painter's algorithm and the SMSG algorithm were respectively employed to perform a 3D display under different mesh numbers. The elapsed time of the two algorithms is shown in Figure 12, where the consumed time is measured in milliseconds (ms).  
Figure 11: Back view of the spectrogram of the normal motor.  
Figure 10: Back view of the spectrogram of the fault motor.  
It can be seen from Figure 12 that the time growth of the SMSG algorithm was very stable, and the time increment was not obvious. With the growing number of meshes, the time of the painter's algorithm increased exponentially. The reason was that, in the painter's algorithm, a lot of time was needed to calculate, compare, and sort the depth value of each mesh surface. However, in the SMSG algorithm, this kind of work was not needed and the points were directly taken from the vertex table, which saved considerable time. So the SMSG algorithm was very fast, even for a large number of meshes.
## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.4 Adaptive Scheduling and Timetabling  
Figure 5 outlines an AI scheduling engine which integrates multiple inputs (e.g., course data, constraints, user preferences, maintenance status) to generate and maintain conflict-free timetables for classes, exams, and events. The scheduler continuously monitors real-time updates, applying minimal-perturbation algorithms for re-optimisation if disruptions occur, and dispatching notifications of any changes to relevant stakeholders.  
Scheduling in educational institutions is an NP-hard task, as it demands assignments which respect room capacities, instructor availability, and avoidance of course conflicts. Meta-heuristic approaches have demonstrated high efficacy for such complex timetabling problems [21]. The scheduler solves a binary-integer programme which minimises a weighted sum of room under-utilisation and student-conflict penalties. A Timefold Solver pipeline could be adopted: (i) a \"Strongest Fit Decreasing\" construction heuristic builds a feasible seed schedule; (ii) Local Search refines it with Hill-Climbing Late Acceptance followed by Tabu Search, an evolutionary meta-heuristic portfolio proven to scale on faculty-level data sets [56]. Consequently, the AI scheduler initially generates a timetable by combining constraint-solving with meta-heuristics, accommodating essential constraints (e.g., no double-booking, limited room capacity) and optimising optional preferences (e.g., optimal instructor distribution according to student personalised needs) [57].  
Adaptive rescheduling leverages real-time data to address unexpected changes. If an instructor is unavailable or critical equipment fails, the scheduler identifies these events and applies minimal-perturbation heuristics [58], modifying only the necessary assignments to maintain stability. Minor updates may be auto-deployed if they align with predetermined thresholds, minimising disruptions to the broader timetable.  
Figure 5 shows how the system continuously monitors feedback channels to update or refine schedules. Integration with resource data (e.g., projector downtime or lab equipment shared by other departments) further improves accuracy. Occupancy sensors offer insights to reassign rooms if actual attendance deviates substantially from enrollment. Instructors, departments, and students also provide scheduling preferences, which the system treats as soft constraints; it can run what-if scenarios to guide academic decision-making. Addi  
tionally, the system can incorporate forecasting models based on supervised (e.g., Random Forest, SVM) and unsupervised (e.g., k-means, hierarchical clustering) learning applied to social media data, enabling early detection of sentiment-driven events, such as health concerns or student mobilisations [59]. Sentiment analysis frameworks like the BERT-based model proposed in [60] further enhance this capability by classifying multilingual social media discourse in real time, allowing institutions to dynamically adjust timetables and get early alerts in response to shifts in public sentiments.
## 2 Materials and Methods  
### Model Validation  
To carry out the confidence in the study, a calibration process is always needed. For this purpose, experimental results were taken from a previous paper [14] and were compared with the same model results from FLOW 3D. The authors in [14] presented experimental results for the pressure profiles along the horizontal and vertical surfaces of three steps, i.e., step nos. 5, 9, and 13 of the stepped spillway model, along with water surface elevation over the stepped spillway model, for a flow rate of 0.027 m\({}^{3}\)/s. The same model geometry was constructed in SOLIDWORKS software package and was named as Model 1. The geometry file was imported to numerical solver software package FLOW 3D. Grid Sensitivity analysis was performed at step no. 13 (for which the results are discussed later). Different mesh sizes 20 mm, 15 mm, 10 mm, and 5 mm were opted to run the simulation. It was 5 mm mesh which gave the lowest error (less than 10%) when the values were compared to experimental results for step no. 13 taken from [14].
# A Local Discrete Feature Histogram for Point Cloud Feature Representation
## 5 Conclusions  
In this work, we propose a novel local feature descriptor, LDFH, which encodes the spatial and geometric features of the neighborhood point cloud around a given key point by constructing LRF. The method determines the LRF's \(z\)-axis direction using all neighboring points within the support radius, resolving the sign ambiguity. By using the LMA instead of the normal to encode the local geometric information, LDFH effectively improves robustness under various interference conditions, such as noise, varying mesh resolutions, and clutter, etc. Additionally, the proposed method discretizes point cloud features and introduces a weighted fusion strategy to comprehensively capture and encode  
Figure 6: The compactness of selected descriptors.  
Figure 7: The average computation time of descriptors under different support radii.  
geometric and spatial information. Compared with the existing methods, we compute three geometric features in each subspace, including the normal deviation angle, polar angle, and normal lateral angle, and perform feature discretization and weighted fusion. The experiments demonstrate that, compared with the existing descriptors, LDFH strikes an excellent balance between descriptiveness, robustness, compactness, and efficiency. Furthermore, the experimental results validate LDFH's effectiveness across various scenes and sensor data, with outstanding performance on the B3R dataset and the Kinect dataset, highlighting its broad applicability to diverse tasks.  
Future work can be explored in several directions. First, the construction method of the local reference frame can be further optimized by exploring more stable and efficient computational approaches to enhance the robustness of the LDFH in challenging environments such as high noise and occlusion. Second, we can try to integrate deep learning methods to further improve the performance of LDFH.  
Conceptualization, L.J. and X.L.; methodology, L.J. and C.W.; software, C.L. and G.X.; validation, G.X. and D.X.; formal analysis, X.L. and C.L.; writing--original draft preparation, L.J.; writing--review and editing, L.J. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are contained within the article.  
The manuscript was supported by the Xi'an Key Laboratory of Active Photoelectric Imaging Detection Technology.  
The authors declare no conflicts of interest.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals
## 2 Methods
### Scalar Tracking
#### 2.2.2 Optimizing the Discriminator
A key feature of these methods is their deviation from the standard coherent integration signals for frequency discrimination, instead using multiband or early-delayed coherent integration signals. However, for receiver ranging in these methods, pseudo-code ranging is typically used, which is less accurate than carrier phase ranging. Therefore, optimizing estimators for PLLs remains a crucial research area, particularly with respect to frequency estimators using multiband or early-delayed coherent integration signals. Table 3 provides a comparative summary of key discriminator optimization techniques discussed above. It highlights their respective strengths, challenges, and representative works in GNSS tracking research.
## Appendix A Proof of Lemma 1  
For a fixed \(h\in[H_{\text{min}},H_{\text{max}}]\), we obtain an optimal solution for \(\mathbf{X}\) by solving the following SDP problem:  
\[\mathbf{P}_{3}^{\mathbf{X}}:= \max_{\mathbf{X}}\;\text{Tr}\left(\mathbf{G}_{U}(h)\mathbf{X}\right)\] \[\text{s.t.}\;\text{Tr}(\mathbf{X})\leq P,\;\mathbf{X}\succeq \mathbf{0}. \tag{1}\]  
The problem \(P_{3}^{\mathbf{X}}\) is the well-known maximum eigenvalue problem. Thus, the optimal value and optimal solution for \(\mathbf{X}\) are obtained in closed form as follows [1; 2]:  
\[\text{Tr}\left(\mathbf{G}_{U}(h)\mathbf{X}^{\star}\right) = P\Lambda_{\text{max}}\big{(}\mathbf{G}_{U}(h)\big{)} \tag{2}\] \[\mathbf{X}^{\star} = P\mathbf{v}_{\text{max}}\big{(}\mathbf{G}_{U}(h)\big{)}\mathbf{ v}_{\text{max}}^{H}\big{(}\mathbf{G}_{U}(h)\big{)} \tag{3}\]  
where \(\Lambda_{\text{max}}\big{(}\mathbf{G}_{U}(h)\big{)}\) and \(\mathbf{v}_{\text{max}}\big{(}\mathbf{G}_{U}(h)\big{)}\) denote the maximum eigenvalue of \(\mathbf{G}_{u}(h)\) and its corresponding eigenvector, respectively.  
From (19), \(\mathbf{G}_{U}(h)\) can be rewritten by  
\[\mathbf{G}_{U}(h) =\] \[= \tag{4}\] \[= \tag{5}\]where \(\tilde{\mathbf{a}}_{U}=\mathbf{a}_{U}/\|\mathbf{a}_{U}\|\) and \(\mathbf{A}_{U}^{\perp}\in\mathbb{C}^{N\times(N-1)}\) are the orthonormal basis of the null space of \(\tilde{\mathbf{a}}_{U}\); (A4) follows from the fact that \(\mathbf{I}_{N}\) can be decomposed into \(\mathbf{I}_{N}=[\tilde{\mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}][\tilde{ \mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}]^{H}\). By denoting \(\mathbf{A}_{U}^{\perp}=[\tilde{\mathbf{a}}_{U,j}^{\perp}]_{j=1}^{U-1}\), (A5) is obtained from \((\mathbf{A}_{U}^{\perp})^{H}\mathbf{A}_{U}^{\perp}=\mathbf{I}_{N-1}\).  
Since \([\tilde{\mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}]\in\mathbb{C}^{N\times N}\) is the orthonormal basis of the \(N\)-dimensional complex hypersphere, such as for \([\tilde{\mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}][\tilde{\mathbf{a}}_{U}, \mathbf{A}_{U}^{\perp}]^{H}=[\tilde{\mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}]^{ H}[\tilde{\mathbf{a}}_{U},\mathbf{A}_{U}^{\perp}]=\mathbf{I}_{N}\) and \(\mathcal{K}_{U}\|\mathbf{a}_{U}\|^{2}>0\) in (A5), the maximum eigenvalue of \(\mathbf{G}_{U}(h)\) and its corresponding eigenvector are obtained as  
\[\Lambda_{\max}\big{(}\mathbf{G}_{U}(h)\big{)} =\beta_{0}d_{U}^{-a_{U}}\frac{\mathcal{K}_{U}\|\mathbf{a}_{U}\|^{ 2}+1}{\mathcal{K}_{U}+1},\] (A6) \[\mathbf{v}_{\max}\big{(}\mathbf{G}_{U}(h)\big{)} =\tilde{\mathbf{a}}_{U}.\] (A7)  
Inserting (A6) and (A7) into (A2) and (A3), respectively, we obtain the optimal solution for the problem \(\mathbf{P}_{3}^{\mathbf{X}}\) as follows:  
\[\mathrm{Tr}\left(\mathbf{G}_{U}(h)\mathbf{X}^{\star}\right) =p\beta_{0}d_{U}^{-a_{U}}\frac{\mathcal{K}_{U}\|\mathbf{a}_{U}\|^ {2}+1}{\mathcal{K}_{U}+1},\] (A8) \[\mathbf{X}^{\star} =p\tilde{\mathbf{a}}_{U}\tilde{\mathbf{a}}_{U}^{H}.\] (A9)  
As shown in (A9), the optimal covariance matrix of \(\mathbf{X}^{\star}\) does not depend on the UAV's altitude \(h\). Thus, (A9) is the optimal solution for the problem \(\mathbf{P}_{3^{\prime}}\), regardless of the value of \(h\). This completes the proof.
# Itarare Group Sandstone as a Sustainable Alternative Material for Photon Radiation Shielding
## 3 Results
As expected, lead--being a high-Z and high-density material (11.34 g/cm\({}^{3}\))--exhibited the most effective photon attenuation across all energy levels. For example, at 0.015 MeV, Pb demonstrated MAC of c. 112 cm\({}^{2}\)/g and LAC of c. 1265 cm\({}^{-1}\). In contrast, Portland cement, with a density of 3.15 g/cm\({}^{3}\), displayed significantly lower values (MAC \(\approx\) 18 cm\({}^{2}\)/g; LAC \(\approx\) 58 cm\({}^{-1}\)), offering intermediate performance between lead and the sandstone samples we studied. This trend continues at higher photon energies. At 1.0 MeV, HVL and TVL for lead were c. 0.86 cm and c. 2.9 cm, respectively, while Portland cement required c. 3.5 cm (HVL) and c. 11.5 cm (TVL), confirming its lower, yet still practical, shielding performance. In comparison, the sandstone samples in our study exhibited lower attenuation capacity than both lead and cement. The values for the sandstone are closer to those of cement, with the differences primarily attributable to variations in the chemical composition of the samples, particularly their density. Notably, the density of the cement (Table 1) is nearly double that of the sandstone samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & \multicolumn{4}{c}{**E (MeV)**} \\ \cline{2-5}  & **0.015** & **0.1** & **1.0** & **10** \\ \hline MAC Pb & 112 & 5.6 & 0.07 & 0.05 \\ MAC PC & 18 & 0.22 & 0.06 & 0.03 \\ LAC Pb & 1265 & 70 & 0.81 & 0.56 \\ LAC PC & 58 & 0.68 & 0.20 & 0.08 \\ MFP Pb & 0.001 & 0.02 & 1.2 & 1.8 \\ MFP PC & 0.02 & 1.5 & 5.0 & 13 \\ HVL Pb & 0.001 & 0.01 & 0.86 & 1.2 \\ HVL PC & 0.01 & 1.0 & 3.5 & 8.8 \\ TVL Pb & 0.002 & 0.04 & 2.9 & 4.1 \\ TVL PC & 0.04 & 3.4 & 11.5 & 29.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of attenuation parameters for lead (Pb) and ASTM C150 Type I Portland cement (PC) at selected photon energies (0.015, 0.1, 1.0, and 10 MeV). Parameters include the mass attenuation coefficient (MAC, cm\({}^{2}\)/g), linear attenuation coefficient (LAC, cm\({}^{-1}\)), mean free path (MFP, cm), half-value layer (HVL, cm), and tenth-value layer (TVL, cm). The values illustrate the difference in shielding effectiveness between a high-Z material (Pb) and a conventional low-Z construction material (Portland cement) across a wide photon energy range. The parameter values shown are approximate after rounding. Figure 10: Average relative contribution (P, %) of photon interaction mechanisms—photoelectric absorption (blue), incoherent scattering (yellow), and pair production (red)—at different photon energies (0.015, 0.1, 1, and 10 MeV). Values represent the mean percentage for all rock samples studied, and the error bars indicate the standard deviation from the mean.
## 4 Results and Discussion
### Comparison between Beam-Based Model and ABAQUS Structural Model
Given these caveats, the result of the comparison between the beam-based model and ABAQUS indicate that beam-based models show significant promise for the prediction of deformations and residual stresses in large, 3D-printed polymeric parts. Figure 14: Comparison of stress after cool down.
# Folded Heterogeneous Silicon and Lithium Niobate Mach-Zehnder Modulators with Low Drive Voltage
## 4 Conclusions  
In this paper, we have demonstrated folded MZMs based on a heterogeneous integrated silicon and lithium niobate platform using meandering optical waveguides and TWEs. The devices show reduced device length while maintaining the overall performance (half-wave voltage and bandwidth) of the non-folded counterparts. Since both of the optical waveguides and TWEs undergo two U-turns, the device length is reduced to approximately two-fifths of the original value. A similar folded thin-film lithium niobate modulator had been demonstrated based on the poling process [22]. By employing more U-turns, the length can be future compressed at the cost of increase in width. This architecture provides great design flexibility especially for building sophisticated Si/LN photonic integration circuits, because most silicon photonic devices are very compact in footprint. Moreover, the low-drive-voltage operation demonstrated in the devices could open opportunities not only for future compact and low power consumption optical transceivers but also for the direct integration of CMOS integrated circuits with high-speed modulators and optical switches.  
The following are available online at [[https://www.mdpi.com/article/10.3390/mi12070823/s1](https://www.mdpi.com/article/10.3390/mi12070823/s1)]([https://www.mdpi.com/article/10.3390/mi12070823/s1](https://www.mdpi.com/article/10.3390/mi12070823/s1)). Section S1: Characteristic of Si waveguide crossing, Section S2: Design of microwave U-turns, Section S3: Design of microwave delay line, Section S4: High-speed experiments of 21 mm folded MZM.  
Figure 3: High-speed transmission experiments. (**a,b**) Measured optical eye diagrams for OOK modulation at date rates of 84 Gb s\({}^{-1}\) and 100 Gb s\({}^{-1}\). (**c,d**) Measured optical eye diagrams for PAM-4 modulation at data rates of 56 Gbaud (112 Gb s\({}^{-1}\)) and 64 Gbaud (128 Gb s\({}^{-1}\)). (**e**) Measured back-to-back BER curves versus the received optical power for 56 Gbaud (112 Gb s\({}^{-1}\)) and 64 Gbaud (128 Gb s\({}^{-1}\)) PAM-4 signal. (**f**) Measured optical eye diagram for OOK signal at date rate of 32 Gb s\({}^{-1}\) without any RF amplifier.  
**Author Contributions:** Conceptualization, S.S. and X.C.; validation, S.Y. and X.C.; investigation, S.S., M.X., M.H., S.G., X.Z., L.Z. and L.L.; writing--original draft preparation, S.S.; writing--review and editing, S.S. and X.C.; supervision, S.Y. and X.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by National Natural Science Foundation of China (11690031, 61575224, 61622510); Key R&D Program of Guangdong Province (2018B030325002); Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program (2017 BT01X121).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.