esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 4 Examples and Application
### Bed Degradation  
The first example is based on the benchmark experiments conducted by [PERSON] and [PERSON] [9] to demonstrate the model's capability of handling bed degradation and armoring processes under clear-water conditions. The experimental flume was 20 m long and 0.8 m wide with a slope of 1%. On the flume bed, there were 12 size classes of sediment particles (Table 1) ranging from sand to gravel with the median size of 1.5 mm and the initial bed material thickness of 0.113 m. A steady flow of 0.0314 m\({}^{3}\)/s was imposed at the inlet, while the water level of 0.06 m was maintained at the outlet.  
In this simulation, the bed-load transport dominated, and the bed-load transport model was run on a 5 \(\times\) 100 rectangle mesh. Bed roughness was calibrated as 0.023 m\({}^{-1/3}\)s, and the time step was 1 s. For this bed degradation case, [PERSON] [20] investigated the effects of the mixing layer thickness (varying between \(d_{50}\) and \(2d_{50}\)) and the bed-load adaptation length (time dependent, water depth dependent, or constant) on the simulations using the one-dimensional model. In this study, the mixing-layer thickness was set as \(2d_{50}\) and the bed-load adaptation length was set as the averaged sand dune length (\(\approx\) 7.3 times the water depth).  
With the clear water condition, bed erosion occurred due to bed-load sediment transport. During the scouring process, a layer of coarse bed materials (called an armoring layer) may be formed, which could protect the bed from scouring, resulting in slowing or even stopping erosion (called armoring effects). In this bed degradation case, the armoring effects were significant. Figure 2 compares the measured erosion depth development with time at \(x=7\) m, 10 m, and 13 m to the simulation results. As can be seen, the erosion developed rapidly in the first 100 min and then became much milder afterward due to the armoring effects. The simulated erosion development profiles at three locations agreed well with the measured ones.  
The armoring effect is closely related to the bed sorting calculation. Figure 3 compares the simulated bed composition with the measured one at \(x=10\) m. The model succeeded in simulating the armoring effects. The initial \(d_{50}\) was 1.5 mm and the measured final \(d_{50}\) was 5.4 mm, while the simulated final \(d_{50}\) was 5.6 mm. However, the simulated armoring layer was formed earlier, resulting in a slightly coarser bed-armoring layer. In general, the bed-sorting calculation was satisfactory for this case.
# Sentence-Level Insights from the Martian Literature: A Natural Language Processing Approach
## 3 Results
### Sentiment Analysis
In the Martian atmosphere, recent exploration missions, notably MAVEN, have significantly advanced our understanding of the Martian atmospheric structure, ionospheric  
Figure 6: Sentiment-based clustering of Mars-related research statements. Panels (**a**) and (**b**) display the top ten themes associated with positive and negative sentiments, respectively, along with corresponding summaries generated by the GPT-4o model. In panel (**a**), light yellow clusters represent irrelevant content that was excluded from further analysis and is not included in the thematic interpretation. dynamics, and long-term gas escape mechanisms. These studies have identified diurnal variations and seasonal cycles in the Martian atmosphere, improved global climate models, and contributed to modeling potential mechanisms responsible for early warm and humid climates. However, notable contradictions between models and observations remain unresolved. Climate models based solely on CO\({}_{2}\) and H\({}_{2}\)O are insufficient to reproduce the warming conditions necessary for the stable existence of liquid water. Additionally, the sources and loss mechanisms of methane remain highly controversial, with conflicting results reported between the ExoMars Trace Gas Orbiter (TGO) and Curiosity rover, highlighting gaps in the current understanding of atmospheric chemical processes. These issues underscore the need for continued interdisciplinary collaboration to construct a more complete and coherent model of Martian atmospheric evolution. In the Martian space, the upper atmosphere and space plasma environment of Mars have been systematically observed in recent years through orbital missions such as MAVEN. These studies have revealed the coupling mechanisms between the solar wind and Martian crustal magnetic anomalies, advancing the understanding of Martian space weather and atmospheric escape processes. However, the absence of a global magnetic field and a dense atmosphere leaves the Martian surface exposed to prolonged bombardment by high-energy particles from the solar wind and cosmic rays. This radiation environment poses significant health risks to future crewed missions, including potential impacts such as Deoxyribonucleic acid (DNA) damage, cellular death, and increased cancer risk. Space radiation has been identified as one of the primary barriers to long-term human habitation on Mars, and the development of effective protective strategies remains an urgent research priority.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 6 Conclusions  
The first part of this paper summarizes earlier studies on PVDF-TrFE for controlling the shape of thin polymer shell reflectors. With proper annealing conditions, a piezoelectric coefficient close to \(d_{31}=15\) pC/N has been achieved. The electrode size to achieve an accurate surface figure correction is critically related to the thickness and the curvature of the shell.  
The second part of this paper is focused on the thermal response to a temperature gradient. It is shown numerically that, due to the very high CTE of the PVDF-TrFE, the unimorph configuration provides a poor design, very sensitive to thermal gradients; the thermal deformations are difficult to control with the array of piezoelectric actuators and they require large control voltages. This drawback can be alleviated by adding a passive layer of material to balance the thermal behavior of the shell (with sufficiently low surface roughness). The behavior can be further improved by using a low-CTE substrate.  
Finally, the paper examines the case of a petal reflector; the unimorph layer configuration is even more sensitive than the full reflector, but the behavior of the thermally balanced design turns out to be better than that of the full reflector. The petal reflector with thermal balancing layer and low CTE substrate results in a 160-fold reduction in residual error, and 500-fold reduction in control voltage as compared to the baseline design.  
Conceptualization, A.P., C.J.G.N., and K.W.; software, C.J.G.N., K.W. and D.T.; validation, C.J.G.N. and D.T.; writing--original draft preparation, A.P.; writing--review and editing, C.J.G.N.; visualization, C.J.G.N.; supervision, A.P.; All authors have read and agreed to the published version of the manuscript.  
Part of this research has been supported by ESA-ESTEC in the framework of the GSTP program, project Multilayer Adaptive Thin Shell Reflectors for Future Space Telescopes (MATS). RFQ 3-14210/14/PA/NL.  
Not applicable.  
The authors wish to thank [PERSON] and [PERSON] from ESA-ESTEC for their contributions to the earlier stages of this project. The demonstrators were manufactured by [PERSON] and [PERSON] from MateriaNova (Mons, Belgium) within the MATS project.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AO & Adaptive Optics \\ LIDAR & Light Detection and Ranging \\ PET & Polyethylene terephthalate \\ PVDF-TrFE & poly(vinylidene fluoride-co-trifluoroethylene) \\ RMS & Root Mean Square \\ \end{tabular}
# A Multi-Objective Degree-Based Network Anonymization Method
## 4 A Graph Realization Approach  
In the second phase of the anonymization procedure, the output graph \(G^{\prime}\) is constructed based on the target degree sequence \(D^{\prime}\). Recall that we aim to produce a graph that is \(k\)-anonymous by performing the least amount of perturbation to the topology and structure of \(G\). The common practice is to remove edges incident on vertices that have to decrease their degrees, and add new edges to vertices that have to increase their degrees, without taking into consideration the major effect of graph re-construction on the topology of the network. With this limitation in mind, we formally define \"the realization\" [15] as an optimization problem as follows:  
**Weighted Graph Realization**  
**Given:** A graph \(G\) and a function \(r:V(G)\rightarrow\mathbb{Z}\).  
**Question:** Is there a sequence of edge-editing operations that results in \(G^{\prime}=(V,E^{\prime})\), such that \(\forall v\in V(G^{\prime})\), \(degree_{G^{\prime}}(v)=degree_{G}(v)+r(v)\)?  
The realization procedure we propose begins by copying \(G\) into \(G^{\prime}\), on which edge edits are performed. Then, it computes the vector of changes \(\theta\) between \(D^{\prime}\) and \(D\), i.e., \(\theta=D^{\prime}-D\). This vector is used to detect which nodes have to introduce or remove incident edges. It can also be used to compute the anonymization cost. To further explicate, if \(\theta[v]\) is negative, then \(|\theta[v]|\) incident edges on \(v\) should be removed. However, if \(\theta[v]\) is positive, then \(\theta[v]\) edges should be added to \(v\). Each vertex will be labeled by its corresponding weight in \(\theta\).  
**ILP Formulation**  
We formulate this particular graph realization problem as an Integer Linear Programming problem (ILP). For this purpose we use a binary variable \(x_{ij}\) for every pair of vertices \(i\) and \(j\). The interpretation is that \(x_{ij}=1\) if \(\{i,j\}\) was added or deleted, otherwise \(x_{ij}=0\). This gives the following ILP formulation:  
\[\text{Minimize}\sum_{u\in V,v\in V}x_{uv}\] \[\text{Subject to:}\] \[\sum_{ij\in E}x_{ij}-\sum_{ij\in E}x_{ij}\geq w(i)-t\] \[\sum_{ij\in E}x_{ij}-\sum_{ij\in E}x_{ij}\leq w(i)+t\]  
In other words, if the ILP solution assigns a value of \(1\) to \(x_{ij}\) and \(\{i,j\}\) is not an edge (initially), then we add the edge \(\{i,j\}\). On the other hand, if \(\{i,j\}\) was an edge and \(x_{ij}=1\), then we remove the edge. Here, \(w\) is the weight of the vertex representing the number of edges that should be added or deleted.
# Damage Detection of Insulators in Catenary Based on Deep Learning and Zernike Moment Algorithms
## 3 Insulator Damage Detection Based on Zernike Moment
### Calculation of Similarity Value and Similarity Standard Deviation  
For Hu moments, the seventh-order characteristic moments calculated according to Equations (6)-(12) can further calculate the similarity value between insulator pieces A and B according to Equation (18). The larger \(I\), the more dissimilar the two insulator pieces are.  
\[I_{A,B}=\sum\limits_{i=1}^{7}\left|{H_{i}^{A}-H_{i}^{B}}\right| \tag{18}\]  
where \(H_{i}^{A}\) represents the \(i\)-th order Hu moment of insulator piece \(A\) and \(H_{i}^{B}\) represents the \(i\)-th order Hu moment of insulator piece \(B\).  
For Zernike moments, the characteristic moment of any higher order can be calculated according to Equation (13). Assuming that the calculation reaches the 10 th order, a total of 36 Zernike moment eigenvalues, such as the eigenvalues of the zeroth order zeroth degree, the first order first degree, the second order zeroth degree, the second order second degree, and so on, can be obtained (as shown in Figure 7). The similarity value between insulator pieces \(A\) and \(B\) can be calculated by Equation (19). The larger \(I\), the more dissimilar the two insulator pieces are.  
\[I_{A,B}=\sum\limits_{i=1}^{36}\left|{Z_{i}^{A}-Z_{i}^{B}}\right| \tag{19}\]  
Assuming a catenary insulator has \(n\) pieces, the similarity value \(I_{i,N-1}\) between the \(i\)-th insulator piece and other (\(N-1\)) insulator pieces can be calculated according to Equation (18) or Equation (19). According to the calculation method of standard deviation, the similarity standard deviation of the \(i\)-th insulator piece can be derived:  
\[S_{i}=\sqrt{\frac{\sum\limits_{j=1,j\
eq i}^{N}{{(I_{i,j}-\overline{I}_{i})} }}{N-2}} \tag{20}\]where \(\overline{I}_{i}\) represents the average value of the similarity between the \(i\)-th insulator piece and the other (\(N-1\)) insulator pieces. When \(S_{i}\) exceeds the set threshold, it is judged that the \(i\)-th insulator piece is damaged.  
\[\begin{array}{l}0^{\text{th}}\;\text{order}:\;Z_{0,0}\\ 1^{\text{st}}\;\text{order}:\;Z_{1,1}\\ 2^{\text{nd}}\;\text{order}:\;Z_{2,0}\;\;Z_{2,2}\\ 3^{\text{rd}}\;\text{order}:\;Z_{3,1}\;\;Z_{3,3}\\ 4^{\text{th}}\;\text{order}:\;Z_{4,0}\;\;Z_{4,2}\;\;Z_{4,4}\\ 5^{\text{th}}\;\text{order}:\;Z_{5,1}\;\;Z_{5,3}\;\;Z_{5,5}\\ 6^{\text{th}}\;\text{order}:\;Z_{6,0}\;\;Z_{6,2}\;\;Z_{6,4}\;\;Z_{6,6}\\ 7^{\text{th}}\;\text{order}:\;Z_{7,1}\;\;Z_{7,3}\;\;Z_{7,5}\;\;Z_{7,7}\\ 8^{\text{th}}\;\text{order}:\;Z_{8,0}\;\;Z_{8,2}\;\;Z_{8,4}\;\;Z_{8,6}\;\;Z_{8,8}\\ 9^{\text{th}}\;\text{order}:\;Z_{9,1}\;\;Z_{9,3}\;\;Z_{9,5}\;\;Z_{9,7}\;\;Z_{9,9}\\ 10^{\text{th}}\;\text{order}:\;Z_{10,0}\;\;Z_{10,2}\;\;Z_{10,4}\;\;Z_{10,6}\;\;Z _{10,8}\;\;Z_{10,10}\\ \end{array}\]
## 5 Conclusions  
In this study, the MF technique was used to reconstruct 4D PET/CT images of patients to obtain PETMF images. We then employed the PETMF in the treatment plan for respiratory-gated radiotherapy, and compared the outcomes with those of traditional 3D radiotherapy planning and respiratory-gated 4D planning. Our results showed that the MF-Plan using PETMF images to define PTVs had smaller tumor volumes than the 3D-Plan and 4D-Plan; that PTMF was 40.6% and 5.6% lower than PTV3D and PTV4D (median values), respectively. Our study demonstrated that the MF technique can not only reduce PTVs, it can also reduce the dose received by OARs, such as the lung, heart, and spinal cord. However, in real cases, the conditions of respiratory motion are capable variations that suggest further research with phantom on the accuracy of radiation dose delivery and ITV volume coverage.  
Conceptualization, S.-N.Y. and W.-C.C.; Methodology, K.-T.C. and T.-C.H.; Software, G.Z. and T.-C.H.; Validation, S.-N.Y. and K.-T.C.; Formal Analysis, K.-T.C.; Investigation, S.-N.Y.; Resources, S.-N.Y.; Data Curation, K.-T.C.; Writing-Original Draft Preparation, T.-C.H.; Writing-Review & Editing, S.-N.Y., G.Z. and T.-C.H.; Visualization, S.-N.Y.; Supervision, T.-C.H.; Project Administration, T.-C.H.; Funding Acquisition, T.-C.H.  
This study was financially supported by China Medical University Hospital, Taiwan (DMR107-058) and Chang Bing Show Chwan Memorial Hospital, Changhua, Taiwan (RD107013). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
The authors declare no conflict of interest.
## 5 Experiments and Analysis
### Evaluation Metrics
#### 5.3.2 Ablation Experiment with DSDCN Module  
In this study, the DSDCN module was added to the network after the input layer and residual blocks to extract object features at the shallow and deep layers of the model. To  
Figure 10: Curve of accuracy and loss of the model. (**a**) Accuracy curve of the model. (**b**) Loss curve of the model.  
verify the performance of the model with the DSDCN module at different positions, the model proposed in Section 4.2 and denoted as ResNet_0 was used without the addition of the DSDCN module. For ResNet_1, the DSDCN module was only added after the input layer. For ResNet_2, the DSDCN module was only added after the residual blocks input layer. For ResNet_3, ResNet_4, and ResNet_5, the DSDCN module was added between several residual blocks. The experiments were conducted using the above models under the same conditions as those described in Section 5.3.1, and the classification results were compared with the methods in this study, as shown in Figure 11. The results show that the addition of DSDCN at the positions designed in this study resulted in better classification of the objects, with an accuracy of 95.5%, an average improvement of 5.1% compared to others. The reason may be that the deformable convolution after the input layer extracts mostly shallow information about the object, and the deep information can be obtained by the residual blocks. The further use of deformable convolution after the residual blocks could make full use of the deep and shallow features of the object and improve the classification accuracy.
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 6 Conclusions  
Through the study of the fault characteristics of centrifugal pump water hammer, the following conclusions can be drawn:  
1. In response to the difficulty in monitoring water hammer in centrifugal pumps, the proposed water hammer fault monitoring method and diagnostic model can achieve timely capture, early warning and rapid response to water hammer events.
2. In view of the problem that water hammer events are difficult to deal with in time, this paper adds edge computing to the data acquisition module, which makes full use of the computing power of the edge layer, reduces the computing pressure of the server and improves the data transmission speed and the processing efficiency of water hammer events.
3. After experimental testing, the water hammer monitoring and diagnosis system designed in this article can effectively collect operational data of centrifugal pumps and intelligently identify faults based on the characteristics of centrifugal pump water hammer faults.  
In summary, this project overcomes the difficulties of water hammer monitoring, fills the research gap in the field of centrifugal pump water hammer fault monitoring and intelligent diagnosis, expands the scope of intelligent fault diagnosis and can more comprehensively monitor the operating status of centrifugal pumps, improving the reliability of centrifugal pump operation. In the future, research will be conducted on how to further improve the accuracy of cloud-server-based diagnosis in order to better apply it to practical production.  
**Author Contributions:** Conceptualization, L.C. and Z.L.; Investigation, L.C. and Z.L.; Supervision, L.C.; Writing--original draft preparation, Z.L. and W.S.; Writing--review and editing, W.S. and W.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is partially supported by the National Natural Science Foundation of China (51775515), the Key Scientific and Technological Projects in Henan Province (182102210016) and the National Key Research and Development Project of China (2016 YFF0203104-5).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Acoustic Applications of a Foamed Geopolymeric-Architected Metamaterial
## 1 Introduction
Blowing agents, including hydrogen peroxide, metallic Al, and Si powders, can be used [44; 45; 46]. Using this synthesis approach [47; 48], porous materials with pore sizes ranging from nanometres to a few millimetres and total porosity up to 90% can be obtained without the use of high-temperature treatments such as burnout of organics and sintering required for the production of traditional porous ceramics [49]. Processing of geopolymer-based materials enables the development of porous materials with targeted thermal and acoustic conductivity properties. Still, the presence of pores and the highly heterogeneous structure unavoidably affect their mechanical properties, resulting in low compressive strengths (around 1 MPa or less, depending on the corresponding apparent density) [46]. Sodium or potassium silicate alkali-activated kaolin geopolymers [49; 50] foaming with funnel silica or silicon powder [47] resulted in consolidated foams with thermal conductivity values of 0.15 Wm\({}^{-1}\)K\({}^{-1}\) or less. The ability of such systems to generate porosity by releasing molecular hydrogen and achieve significant percentages of pore volume (>60%) has already been demonstrated [51]. Porous geopolymer from fly ash precursors with hydrogen peroxide [52] or sodium perborate [53] as foaming agents resulted in consolidated foams with an 80% porosity, a thermal conductivity of 0.08 Wm\({}^{-1}\)K\({}^{-1}\), and compressive strengths ranging from 0.80 to 0.40 MPa. Nevertheless, fly ash-based geopolymer foams with similar bulk densities varying from 400 to 800 kg/m\({}^{3}\) obtained higher compressive strengths using aluminium powders as a blowing agent [53]. Adding aluminium powder to the alkaline-activated metakaolin mixture improved the geopolymer's mechanical properties [54; 55]. Due to these improved connectivity and microstructural properties, we aim to develop aluminium-foamed geopolymers with still lower apparent densities of 180-200 Kg/m\({}^{3}\), which could further strengthen their applications as thermal and acoustic insulating materials [56; 57; 58; 59; 60; 61]. Noise impacts the psychology and physiology of animals and humans, leading to accidents and health problems [58; 59; 60; 61]. When sound strikes a material with visible porosity (exposed pores), the acoustic waves are absorbed, their energy is converted to heat, and the sound is dispersed (acoustic absorption) [61]. Several modern acoustic materials need to be more sustainable regarding energy consumption and greenhouse gas emissions; others may be hazardous to human health. Mineral woods are commonly used for thermal and sound insulation due to their high performance and low cost, but their fibres can settle in the lung alveoli when breathed, causing skin discomfort. As a result, they may be unsuitable for interior noise reduction applications [59].