esa-sceva/satcom-chunk-collection huggingface dataset:
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 3 Case Study  
In order to verify the feasibility and effectiveness of the incomplete information processing and gross error detection methods proposed in this chapter, the deformation data of a concrete gravity dam is used in this analysis.  
Figure 3: Schematic diagram of BP neural network structure.  
This gravity dam is located at the junction of Yibin County, Sichuan Province, and Shuifu County, in Yunnan Province. The dam serves various purposes: power generation, improvement of navigation conditions, flood and sand control, and irrigation. The mountains on both sides of the dam toe incline slightly to the downstream. The bedrock surface of the dam (riverbed) is slightly inclined upstream, and there are coherent grooves on both sides. Bedrock lithology and lithofacies change abruptly, thus, the cross-stratification develops. Eleven small faults are found over the riverbed and dam foundation. The planform of the dam is shown in Figure 4.  
This dam is a concrete gravity dam with a normal water level of 380.0 m and a dead water level of 370.0 m. The dam water-retaining structures include the sluice section, the non-overflow dam section, the sand flushing hole dam section, the ship lift dam section, the powerhouse dam section, and the water release dam section. The dam crest elevation is 384.0 m (above sea level), with maximum dam height of 162.0 m, and dam crest length of 909.26 m. The upstream vertical view of the dam is shown in Figure 5.  
The average annual rainfall of the reservoir is 1000 mm, with the maximum level of the annual daily rainfall over 90 mm, or the medium annual daily rainfall level in the Sichuan Province. The upstream water level (recently) is 380 m and has remained high for a long time. The downstream water level is usually around 270 m.  
Figure 4: Planform of the dam.  
Figure 5: Upstream vertical view of the whole dam.  
In order to monitor the horizontal displacement of the dam, vertical lines are arranged in each important dam section. In this section, the monitoring data of each measuring point on the positive vertical line of the dam shuice Section 1 are taken as an example for analysis (Figure 6). The horizontal displacement process line of the six measuring points is shown in Figure 7. The frequency of measurement is once a day. It can be found that the horizontal displacement process line of these measuring points has strong correlation.
# An Investigation into the Influence of Sample Height on the Consolidation Behaviour of Dredged Silt
## 3 Test Results and Analysis
### Comparative Analysis of Permeability Coefficient between Dredged Silt and Marine Silt  
The permeability coefficient was determined based on the consolidation coefficients of the three silt samples. Figure 11 illustrates the permeability coefficient's variation in relation to the consolidation pressure. Two primary observations emerged:  
1. The permeability coefficient of dredged silt measured by different consolidation apparatuses in Shenzhen Qianwan and Shenzhen Airport had the same variation under the consolidation pressure. When the consolidation pressure \(p\leq\) 50 kPa, that is, the consolidation pressure was less than the structural yield stress, the permeability coefficient of dredged silt decreased significantly with the increase of the consolidation pressure. When the consolidation pressure increased, the permeability coefficient of dredged silt stabilised at a specific value. When the preloading load was 5-400 kPa, the permeability coefficient of dredged silt in Shenzhen Qianwan Bay decreased from 5.39 \(\times\) 10\({}^{-7}\) cm/s to 2.20 \(\times\) 10\({}^{-8}\) cm/s (it went down an order of magnitude). A constant permeability coefficient was used to predict the consolidation settlement process, and a significant error occurred.
2. Under the same consolidation pressure, the permeability coefficient of two kinds of dredged silt was found to be significantly greater than that of marine silt. The significance was particularly greater when the smaller consolidation pressure \(p\leq\) 50 kPa. When the consolidation pressure \(p=\) 25 kPa, the permeability coefficient of dredged silt was three times that of marine silt. When the consolidation pressure increased, the permeability coefficient of marine silt decreased and stabilised at a certain value. When the preloading load was 25-800 kPa, the permeability coeffi  
Figure 10: The variation of the consolidation coefficients of three kinds of silt [44; 45].  
cient of marine silt decreased from 6.77 \(\times\) 10\({}^{-8}\) cm/s to 3.80 \(\times\) 10\({}^{-9}\) cm/s. This value is nearly approximately one order of magnitude smaller than that of the dredged silt.  
The disparity in the permeability coefficients between dredged and marine silt arose from their respective particle compositions and pore structures. Particle size tests (as detailed in Table 6) revealed that dredged silt underwent hydraulic reshaping, gravity separation, and clayization during the dredger fill process. The aggregate of clayey and colloidal particles in dredged silt was lesser than in marine silt. Consequently, dredged silt manifested a higher permeability and a more considerable permeability coefficient. Additionally, dredged silt primarily exhibited interparticle pore development. Notably, with an increase in these interparticle pores, micro-layers and fractures emerged. The enhanced connectivity between these more abundant intergranular pores translated to a permeability coefficient that was significantly higher than that of marine silt.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 6 Communication Services
### Drone-to-Cloud (D2C) Communication  
The system supports two distinct ways of interacting with cloud services. The first method of interaction is via web services supported by protocols such as HTTP/REST or WebSockets. HTTP/REST is useful for communication that inherently needs a stateless information exchange, whereas WebSocket is a stateful protocol where communication happens over a reusable TCP connection. The other approach is to use the message-oriented middleware components in Robot Operating System (ROS). This approach makes use of a proxy function called ROSbridge. The ROSbridge protocol allows the sending of JSON-formatted data commands to a ROS system [43]. It provides a WebSocket transport layer among others. Both methods allow cloud services to be implemented as standard web services that can interact with the drone swarm system.
## 2 Modelling
### Operational Constraints  
In order to simulate the loop heat pipe operating as a heat switch, it is necessary to add operational constraint functions to the mathematical model. These functions must provide the five operating _states_ of the loop heat pipe:  
* The loop heat pipe must be in a stabilised condition, i.e., where the Wick (\(M_{7-1}\)), compensation chamber (\(M_{6-7}\)) and condenser (\(M_{4-5}\)) are primed with liquid;
* Heat power is applied to evaporator Node (8) causing an increase in condensation mass at the condenser (\(M_{4-5}\));
* The applied heat power is at the maximum capillarity limit, resulting in Wick dry-out;
* Heating or cooling of the compensation chamber cannister (\(M_{CC}\)) results in a reduction or increase in liquid in the compensation chamber (\(M_{6-7}\)), respectively.
* Applying heat in the evaporator (\(M_{8}\)) and compensation chamber cannister (\(M_{CC}\)) results in the reduction in liquid in the Wick (\(M_{7-1}\)) and the compensation chamber (\(M_{6-7}\)), resulting in shut-off of the loop heat pipe.  
The previously described operational States can be visualised in Figure 4, showing how the fluid in the liquid and vapour phases migrates internally between the different regions of the loop heat pipe.  
To integrate these five operational States in the loop heat pipe mathematical model, it is necessary to add the correction coefficients, \(f_{\dot{m}_{1}}\) and \(f_{G_{1-(1-2)}}\), as follows:  
\[f_{\dot{m}_{1}}=\begin{cases}\begin{cases}1,&\text{if }\dot{m}_{1}<\dot{m}_{4}.\\ n_{7},&\text{otherwise}.\end{cases},\quad\text{if }M_{7-1}<M_{(7-1)_{0}}.\\ 1,&\text{otherwise},\end{cases} \tag{38}\]  
\[f_{G_{1-(1-2)}}=\begin{cases}\begin{cases}1,&\text{if }P_{6-7}\leq P_{4-5}.\\ \begin{cases}\frac{n_{7}h_{fg}}{T_{1}-T_{1}-2},&\text{otherwise}.\end{cases}, &\text{otherwise}.\end{cases},\quad\text{if }M_{6-7}<M_{(6-7)_{0}}.\\ 1,&\text{otherwise},\end{cases}, \tag{39}\]  
where \(f_{\dot{m}_{1}}\) and \(f_{G_{1-(1-2)}}\) are correction coefficients used in \(\dot{m}_{1}\), from Equation (32), and \(G_{1-(1-2)}\), respectively.  
The red and blue arrows shown in Figure 4 indicate the boundary condition inputs for heating (red) and cooling (blue).
# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 6 Conclusions  
Aiming at the problem of multiple tramp material detection in raw coal, this paper proposed a new object detection framework based on feature fusion and dense networks named TMRC-SSD, to detect five types of tramp materials such as gangue, bolt, stick, iron sheet, and iron chain. In order to improve the detection accuracy, we have designed three modules for the detection of tramp materials in complex environments, especially the detection of small targets: MDCS, CLFF and AFM. Firstly, we designed a modified DenseNet as the backbone of the detector, using a four-stage feature extractor to down-sample the feature map stably. MDCS uses a multi-branch structure and dilation convolution to obtain abundant receptive fields while reducing computational cost. In the feature fusion mod  
Figure 10: Visualization results. In the sub-figure (**a**–**j**), the left is the original picture, and the right is the visualization result of the proposed network. The sub-figures (**a**,**b**) show the detection of gangue; the sub-figures(**c**,**d**) show the detection of bolt; the sub-figures (**e**,**f**) show the detection of stick; the sub-figures (**g**,**h**) show the detection of iron chain; the sub-figures (**i**,**j**) show the detection of iron sheet.  
ule, four CLFFs are used to fuse the shallow and deep feature maps to achieve semantic interaction between different feature maps. AFM is used to fuse adjacent CLFF feature layers. In the ablation experiment, the effectiveness of our proposed module is proved. In addition, we constructed a dataset containing 5 categories of tramp materials, including gangue, bolt, stick, iron sheet, and iron chain, to evaluate the performance of the detector. Experimental results show that our proposed TMRC-SSD network achieves 96.12% MAP at 24 FPS, which is the most advanced result compared with other existing methods. The TMRC-SSD network increases the AP of small objects by 4.1 to 95.57%.  
In the next work, we will further improve the performance of our network in the detection, speed and accuracy of tramp materials by expanding the dataset and improving the network.  
Conceptualization, D.L.; methodology, D.L.; software, D.L.; validation, D.L.; formal analysis, D.L. and Z.S.; investigation, G.M.; resources, G.M.; data curation, D.L.; writing--original draft preparation, D.L.; writing--review and editing, L.X.; visualization, D.L.; supervision, G.M.; project administration, G.M.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2016 YFC0600907.  
Not applicable.  
The authors would like to thank the editor and the anonymous reviewers for their constructive comments and suggestions, which improved the quality of this paper.  
The authors declare no conflict of interest.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 3 Data
### Reflectance-Based Approach (SDSU, UArizona, and GSFC)  
A summary of the satellite ephemeris for the Landsat 9 OLI at the SDSU sites near Brookings and the joint UArizona-GSFC field campaign at Ivanph Playa is shown in Table 3. It should be noted that the UArizona and GSFC work at Ivanph occurred during the tandem flight period of Landsat 8 and Landsat 9, and data are not collected on a routine basis at Ivanph following this field campaign. The Ivanph collection was considered successful, so the results are included in the overall analysis. SDSU had three successful Landsat 9 collects in June, July, and August 2022, and they are included in the overall results as well. The reason for an unsuccessful collection at SDSU is typically due to poor atmospheric conditions and water or snow on the site.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**RadCaTS (Railroad Valley, NV, USA)**} \\ \hline Group & UArizona \\ \hline Number of Collects & 12 \\ Collection Area (m) & 1000 \(\times\) 1000 \\ Time (UTC) & 18:21 \\ VZA (degrees) & 0.5 \\ VAA (degrees) & 103.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of the typical overpass time, satellite view zenith angle (VZA), and view azimuth angle (VAA) conditions for Landsat 9 at the RadCaTS. Note that the VAA defined here is measured from the ground site to the satellite.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Ground Site**} & **Brookings, SD, USA** & **Ivanph, CA, USA** \\ \hline Group & SDSU & UArizona \& GSFC \\ Number of Collects & 3 & 1 each \\  & \(150\times 250\) & \\ Collection Area(s) (m) & \(120\times 180\) & \\  & \(120\times 180\) & \\ Time (UTC) & 17:11 & 18:20 \\ VZA (degrees) & 0.6 & 3.8 \\ VAA (degrees) & 104.2 & 283.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: An overview of the overpass time, satellite view zenith angle (VZA), and view azimuth angle (VAA) conditions for Landsat 9 at the sites used by SDSU, UArizona, and GSFC. The VAA is defined as measured from the ground site to the satellite.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 4 Validation and Discussion
### Tuned Results  
The tuned harmonic response results are shown in Figure 8. It can be seen that only the excitation of the engine orders satisfying Equation (27) (e.g., EO13, EO4) aroused the resonances of the impeller. Moreover, the excitation with the engine order corresponding to the dominant harmonic component, EO13, aroused the larger resonant response of 0.0737 mm, while the response aroused by EO4 is only 0.00907 mm. The other excitations except EO13 and EO4 failed to arouse resonance with response amplitude under \(6\times 10^{-5}\) mm.  
The tuned harmonic response results confirm that the resonance of the impeller can be effectively identified by the proposed resonance conditions. Furthermore, the excitations of various engine orders can be further distinguished based on the spatial harmonic content, which is reflected from the amplitude of the \(h\)th harmonic component in the proposed resonance condition via Equation (27). Indeed, the resonance can also be detected by the conventional resonance conditions; however, these excitations cannot be distinguished in the conventional resonance conditions.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Interference Mitigation in D2D
#### 4.1.2 Power Control  
To manage D2D PT and minimize its interference with cellular users, 3 GPP specifies both open-loop and closed-loop POC methods [171]. Open-loop control adjusts power based on path loss, while closed-loop mechanisms allow finer adaptation using feedback from the network.
## 2 Materials and Methods  
**Ligand Dataset Preparation**  
Six terpenes--\(\alpha\)-pinene, \(\beta\)-pinene, menthol, camphor, limonene, and linalool--are selected based on their reported anti-inflammatory potential [47]. Arachidonic acid (AA) is included as a reference ligand for binding energy comparison. The 3D structures of all ligands are retrieved from the PubChem database in the SDF format.  
**Preparation of Compounds for Molecular Docking**  
Each ligand is imported into Avogadro [48], where its geometry is optimized using the \"Optimize Geometry\" function. The structures are then saved in the MOL2 format. Subsequently, the MOL2 files are loaded into AutoDock Tools [49], where polar hydrogens are added, Gasteiger charges are assigned, torsions are detected, and the ligands are saved in the PDBQT format for molecular docking.  
**Target Protein Preparation**  
The crystallographic structures of COX-1 (PDB ID: 6Y3C) [50], COX-2 (PDB ID: 3 HS5) [51], 5-LOX (PDB ID: 308Y) [52], and PLA\({}_{2}\) (PDB ID: 1 KVO) [53] are obtained from the RCSB Protein Data Bank [54]. Initial preprocessing is performed in UCSF ChimeraX, where all non-standard residues are removed. The structures are then imported into AutoDock 4.2 [55], where water molecules are deleted. Missing atoms are checked and repaired when necessary. Hydrogen atoms are added, with polar hydrogens retained. Kollman charges are assigned, atom types are set to AD4 specifications, and the proteins are saved in the PDBQT format for docking.  
**Molecular Docking Setup**  
Docking simulations are carried out using AutoDock 4.2 [49] with the Lamarckian Genetic Algorithm (LGA) [56]. A blind docking approach is applied by defining a grid box that includes all potential binding regions of the macromolecule. Docking parameters include 100 runs per ligand-enzyme pair, a maximum of 25,000,000 energy evaluations, a population size of 150, a mutation rate of 0.02, and a crossover rate of 0.8. The best binding pose is selected based on the lowest binding energy (\(\Delta\)G). Docked conformations are clustered using a root-mean-square deviation (RMSD) tolerance of 2.0 A to group similar poses, although higher RMSD values are expected in blind rigid-body docking due to the fixed protein conformation and the broad surface search space [57].  
**Virtual Screening Protocol and Analysis**  
Ligand-enzyme interactions are analyzed using UCSF ChimeraX (Resource for Biocomputing, Visualization, and Informatics, University of California, San Francisco, CA, USA), focusing on the spatial localization of the ligand relative to catalytically important regions as reported in the literature [58]. Specific attention is given to hydrogen bonds, hydrophobic contacts, and interactions with residues implicated in enzymatic function. Three-dimensional interactions are visualized in ChimeraX, while two-dimensional interaction diagrams are generated using BIOVIA Discovery Studio Visualizer v21.1.0.20298 (Dassault Systemes, San Diego, CA, USA) to support residue-level interpretation [59].
## 3 Results and Discussion
### Static Linear Analysis
#### 3.2.2 Critical Position 2  
In this case, and unlike critical position 1, the maximum admissible pressure values for each piston are already available, so it is only necessary to confirm that these values also comply with this position, that is, that the safety factor is also greater than unity.  
Figure 17: Displacement distribution: isometric view (**left**) and front view (**right**).  
Figure 18: Safety factor distribution: rear isometric view (**left**) and front isometric view (**right**).  
Figure 19 shows the mesh convergence analysis, and Table 5 shows the mesh size value for each iteration in the most critical elements, the von Mises stress obtained in each iteration and the resulting relative error.  
On this occasion, and for this critical position, the convergence of the mesh was reached in the fourth iteration, obtaining a relative error of 7.53%, less than 10%. Therefore, it can be affirmed that the mesh converges.  
In Figure 20, the von Mises stress distribution is shown. As can be seen, the maximum von Mises stress is located in the high-pressure piston rod cap, with a value of 86.9 MPa.  
Figure 21 shows the distribution of displacements in mm, with an enlargement factor of 10 in order to observe the deformed position of the machine more clearly. As can be seen, the maximum displacement occurs in the plunger of the high-pressure piston, with a value of 4.851 \(\times\) 10\({}^{-3}\) mm, this being practically negligible.  
Finally, Figure 22 shows the distribution of the safety factor, noting that the minimum safety factor is located in the high-pressure piston rod cap, with a value of 2.102, which is greater than unity.
## 4 Implementation of Joint Plans
### Ufmc Modulation Principle
In the case of FBMC, which employs OQAM modulation, the complex signal is partitioned into two components, and a transmission delay of half a symbol period is introduced between the first two to achieve full-speed signal transmission. Unlike OFDM, FBMC operates without the necessity of a CP for ISI mitigation. Assuming the prototype filter overlap factor for FBMC is \(K\), the length of a single FBMC symbol is \(T_{FBMC}^{1}=K\cdot T_{D}+T_{D}/2\). Within the UFMC framework, sub-band filters create rising and falling regions at the start and end of a single UFMC symbol. The regions provide a'soft' ISI protection for UFMC symbols, eliminating the need for a CP. However, the use of filters introduces an additional time-domain extension for UFMC. Assuming the time-domain extension length of the filter is \(T_{F}\), the length of a single UFMC symbol is \(T_{UFMC}^{1}=T_{L}+T_{D}\). Note that \(T_{F}\) is related to the length of the FIR filter used in UFMC and that the FIR filter length can be adjusted to change \(T_{F}\). If \(T_{F}\) is adjusted to match \(T_{CP}\), the length of a single UFMC symbol becomes equal to the length of an OFDM symbol, i.e., \(T_{UFMC}^{1}=T_{OFDM}^{1}\). Despite  
Figure 3: Temporal comparison of symbols for OFDM, FBMC, and UFMC depicted in the time-domain: (**a**) CP-OFDM symbols; (**b**) FBMC-OQAM symbols; (**c**) UFMC symbols. this equivalence, the out-of-band leakage of the UFMC spectrum is considerably smaller than that of OFDM, underscoring its superior spectral efficiency. In general, the overlap factor \(K\) used in FBMC is typically set to 4, resulting in a single FBMC symbol length equal to \(T_{FBMC}^{1}=4.5\times T_{D}\). In the context of the 802.11 protocol, where the \(T_{CP}\) length is \(T_{D}/32\leq T_{CP}\leq T_{D}/2\), the length of a single UFMC and OFDM symbol is much smaller than the length of a single FBMC symbol. Additionally, due to the longer time sequence required to generate a single FBMC symbol, UFMC is more advantageous than FBMC for short-burst communication.