esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 5 Evolution of Tunnel Seismic Resilience
### Classification of Resilience  
The resilience of shield tunnel structures under seismic loading is classified into four levels based on the high-, medium-, and low-performance cut-off points of the resilience index. When the ellipticity is used as the resilience index, the lining performance of 0-3%, 3-6%, 6-12%, and greater than 12% is divided into four grades: high performance, medium performance, low performance, and very low performance. Similarly, when the opening amount is taken as the resilience index, the opening amounts 0-1 mm, 1-2 mm, 2-4 mm, and greater than 4 are divided. However, due to the particularity of seismic load, there is no linear relationship between load and action time. Therefore, the structural performance curve under the influence of seismic load needs to be calculated by different types of seismic waves. However, the resilience index threshold generally needs to be determined based on the structural performance curve, so the threshold value of each performance grade in this study cannot be determined. In order to understand better, 0.9, 0.8, 0.6 are used as thresholds to divide each resilience and described [1; 14; 15; 16] as below:  
(1) High Resilience (Re \(\geq\) 0.9): The structural deformation does not exceed the specified warning value. The lining performance is overall within the high-performance range. The structural recovery performance is good, and the structure is in a safe state.  
(2) Medium Resilience (0.8 \(\leq\) Re \(<\) 0.9): The structural deformation has not exceeded the specified control value. The overall lining performance is within the high to medium performance range under seismic load, resulting in low structural safety risk. And generally, there is no need for structural reinforcement.  
(3) Low Resilience (0.6 \(\leq\) Re \(<\) 0.8): The structural deformation exceeds the specified control value. The overall lining performance is within the medium to low-performance range under seismic load. The recovery performance deteriorates, the ductility index decreases rapidly, and there is a high structural safety risk. Necessary measures for ground and structural reinforcement should be implemented as soon as possible.  
(4) None Resilience (Re \(<\) 0.6): The structural deformation significantly exceeds the specified control value, and the deformation rapidly progresses. The lining performance is in an extremely low-performance range under seismic load, with a substantial reduction, or even loss, of recovery capability. At this time, it is imperative to promptly implement reasonable comprehensive reinforcement and restoration measures considering the geological conditions and structural status.  
It is worth noting that the resilience index threshold used in this study is obtained through finite element calculations for various conditions based on structural performance curves. A more reasonable threshold value would require further refinement through extensive theoretical calculations and engineering case studies.
## 1 Introduction
Increasing computational power allows for virtually real-time handling of so-called \"big\" data (see, e.g., [18; 19]). This enables continuous Structural Health Monitoring (SHM) of large-scale civil engineering structures, e.g., bridges and high-rise buildings, or of supply infrastructure, e.g., pipelines, as well as Condition Monitoring (CM) of complex production facilities, e.g., power plants [20; 21; 22] or chemical production plants [23; 24; 25]. Digital tools also allow for data fusion from different types of monitoring systems and sensors and provide data almost in real time [26; 27]. All these developments make continuous process monitoring with AE and GUW, possibly in combination with other NDT, feasible and show high potential for real-time process control in the future [28; 29; 30]. Most applications of AE or GUW developed and standardized so far deal with either proof testing or periodic inspection that require mounting transducers on the test objects for durations between one and a few hours, e.g., for small LPG vessels inspected according to [31] up to a few days maximum, e.g., for large tanks or storage vessels [32]. Long-term continuous or intermittent SHM of structures or components and in-line process monitoring or process control, hence, pose challenging problems for transducer coupling and mounting. The same holds for the long-term durability of the transducers themselves as well as for the signal transmission and data acquisition systems if subject to complex service load spectra or exposed to rather severe operating environments. The successful implementation and use of AE and GUW for long-term SHM and process monitoring or process control, hence, not only requires respective solutions for coupling and mounting transducers or sensors but also suitable monitoring of the long-term performance of the entire measurement chain (transducers, signal transmission, data acquisition and analysis) as well as secure long-term data storage. Service providers of SHM and similar long-termmonitoring likely have gained some experience in these issues, but only scant information is publicly available at best. Hence, there is a need for understanding long-term behavior and durability of all components of the AE and GUW measurement systems and considering that in the design of the SHM or CM system. In any case, long-term SHM and CM require suitable equipment performance monitoring as well as defined maintenance and repair strategies before system design and implementation.
# CFD Analysis of Subcooled Flow Boiling in 4 x 4 Rod Bundle
###### Abstract  
Rod bundle flow is an important research field related to reactor cooling in nuclear power plants. Owing to the rapid development of computerized performance assessments, interest in coolant flow analysis using computational fluid dynamics has garnered research interest. Rod bundle flow research data compared with experimental results under various conditions are thus needed. To address this, a boiling model verification study was conducted with reference to experiments. This study adopts the Reynolds-averaged Navier-Stokes equation, a practical analysis method compared to direct numerical simulation and large eddy simulation, including turbulence modeling, to predict the flow of coolant inside a rod bundle. This study also investigates void behavior in low-pressure subcooled flow boiling using a Eulerian approach (two-fluid model). The rod bundle has a length of 0.59 m and a hydraulic diameter of approximately 14.01 mm. At the cross-section at a height of 0.58 m, near the exit, numerical results were compared with the experimental values of the volume fraction of vapor and interfacial area concentration. The simulation results showed good agreement with the experimental data for six different initial conditions with constant density.  
subcooled flow boiling; two-fluid model; vapor volume fraction; rod bundle; STAR-CCM+
## 3 Circuit Design and Implementation
### Input Matching Network  
The input matching network adopts a C-L-L-C structure, with \(TL_{3}\) serving as the gate supply line for the first stage and also contributing to impedance matching. To improve the bandwidth of the input impedance matching network, the resistor \(R_{1}\) is placed in series below \(TL_{2}\). By adjusting \(R_{1}\), a trade-off between the amplifier's \(S_{11}\) and \(S_{21}\) can be achieved.
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 2 Materials and Methods  
### Catalyst Synthesis  
Commercial mesoporous silica (SBA-15) purchased from ACS Material was used as a support since its high surface area and hexagonal ordered structure allows better active phase dispersion than amorphous supports. CeO\({}_{2}\)-modified SBA-15 supports were synthesized following the incipient wetness impregnation technique using aqueous solutions of Ce(NO\({}_{3}\))\({}_{3}\)\(\cdot\)6H\({}_{2}\)O (99%, Sigma-Aldrich, St. Louis, MO, USA), as the Ce precursor, in different concentrations to obtain different Ce contents in the modified support: 5, 10, 20, and 30 wt.%. After that, materials were calcined in airflow at 550 \({}^{\circ}\)C for 5.5 h using a 1.8 \({}^{\circ}\)C/min heating rate.  
The active phase was incorporated according to the same procedure explained above, using Co(NO\({}_{3}\))\({}_{2}\cdot\)6H\({}_{2}\)O (>98%, Sigma-Aldrich, St. Louis, MO, USA) solutions to get 7 wt.% of Co in the synthesized catalysts. The synthesized catalysts have been denoted as Co/xCeO\({}_{2}\)-SBA-15, where x indicates the Ce theoretical loading (wt.%).
## 4 Discussion of the Comparison Results
In example 1, the Support Vector Machine (SVM) classifier failed to effectively detect changes in the synthetic nonlinear signal but successfully detected changes in the synthetic signal. In the first example with the synthetic nonlinear signal, the features used for training the SVM classifier are the signal values themselves. Since the nonlinear signal does not exhibit clear patterns or distinct features associated with the changes, the SVM classifier may struggle to learn a discriminative decision boundary to separate the change and non-change regions. The synthetic nonlinear signal is a combination of sinusoidal components and a squared sinusoidal component. The changes introduced in this signal may not significantly alter its overall shape or characteristics, making them difficult for the SVM classifier to discern fromthe background signal fluctuations. In the first example, the SVM classifier is trained on the entire nonlinear signal without explicitly providing information about the location or timing of the changes. This lack of specific training data related to the changes may hinder the classifier's ability to learn meaningful patterns associated with the changes. In contrast, the synthetic signal in the second example exhibits abrupt changes introduced at random time points. These changes manifest as outliers or deviations from the underlying sinusoidal pattern, which can be discerned by the SVM classifier. Additionally, the SVM classifier is trained on features extracted from the EEG signal, which may capture characteristics indicative of the changes, such as sudden shifts in the amplitude or frequency content. The effectiveness of the SVM classifier for change detection depends on the characteristics of the signal, the representation of features, and the availability of informative training data. In scenarios where the changes are subtle or the signal dynamics are complex, alternative methods or feature representations may be more suitable for detecting changes effectively.
## 4 Conclusions  
This paper investigates the effect of DBD structure parameters on NO oxidation, including the effects of electrode length, electrode diameter, electrode material and electrode shape on discharge characteristics and NO oxidation. The experimental results show that there is an optimal electrode length for a certain flow of gas, which makes DBD reactor have the best performance on NO oxidation and energy efficiency. Too long electrode length cannot achieve higher NO\({}_{\text{X}}\) oxidation degree, but increases the energy consumption of DBD reactor. The oxidation degree of NO\({}_{\text{X}}\) increases first and then decreases with the increase of SED under oxygen-enriched condition, which is different from the experimental results that NO\({}_{\text{X}}\) removal efficiency increases with the increase of SED under the condition of no oxygen. Increasing the diameter of inner electrode not only improves the E/N, but also makes the distribution of E/N more concentrated in the gas gap, which promotes the oxidation and energy efficiency of DBD reactor. The performance of electrode material for NO oxidation is different under different gas gap condition, due to the change of E/N and secondary electron emission coefficient (\(\gamma\)). Compared with the rod electrode, the screw electrode has a higher electric field strength near the top of the screw, which promotes the generation of N radicals and inhibits the generation of O radicals. As a result, rod electrode has a higher NO oxidation and energy efficiency than screw electrode under oxygen-enriched condition.  
Conceptualization, Y.C. and L.L.; methodology, Y.C. and L.L.; software, Y.C.; validation, Y.C. and P.L.; investigation, Y.C.; resources, L.L.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Y.C., L.L. and P.L.; visualization, Y.C. and P.L.; supervision, L.L.; project administration, L.L.; and funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (NSFC), grant number 51679176.  
The authors are thankful to all the personnel who either provided technical support or helped with data collection. We also acknowledge all the reviewers for their useful comments and suggestions.  
The authors declare no conflict of interest.
# Video Super Resolution Using a Selective Edge Aggregation Network
## 3 Method  
Our model consists of a total of two stages, as shown in Figure 1. Stage 1 consists of progressive recurrent structure to restore \(LR_{t}\) to \(SR_{t}\) inspired by RBPN.  
In Stage 1, \(LR_{t}\) is the target frame and 2k neighboring frames are \(\{LR_{t-k},\ldots,LR_{t+k}\}\). The optical flow \(F_{t-k}\) is precomputed between \(LR_{t}\) and \(LR_{t-k}\) by Pyflow [18]. Edge map \(e_{t}\) of \(LR_{t}\) is generated using Canny edge detection, \(E_{t-k}\) is the edge map extracted through edge map extraction (Section 3.1). The size of \(LR_{t}\), \(F_{t-k}\), and \(E_{t-k}\) is w \(\times\) h, and the size on \(SR_{t}\) is W \(\times\) H (w \(<\) W, h \(<\) H).  
Stage 2 is the method of refining \(SR_{t}\) to generate \(SR_{t}^{\prime}\), which is the final output of the model. Inspired by VDSR, the refinement module reduces the size of \(SR_{t}\) and then extends the size to generate \(SR_{t}^{\prime}\) that is W \(\times\) H.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 3 The Design of Simulation Environments and Simulation Tasks
### Simulation of Remote-Control System Faults  
(1) Simulation parameter settings when simulating remote control failure  
A remote controller is a tool for the operator to control the drone and send instructions to the drone. When the parameter SIM_RC_FAIL is set to 1, it means that the simulated Remote-Control System failure starts. Table 4 lists some parameters of the simulation environment when simulating a remote controller failure.  
(2) The Simulation of Remote-Control System Failure  
In the event of a remote controller failure, the UAV loses all communication with the remote controller. The UAV starts the RTL (Return to Launch) mode, ends the current mission, and returns to the take-off point immediately. However, the UAV in a normal state will completely perform the originally set flight mission. The planned flight trajectory of the UAV is shown in Figure 15a,b, and the flight trajectory when the remote controller fails is shown in Figure 15c,d. There was a remote failure between points A and B.  
In order to better understand the change in the state of the UAV when the remote controller fails, some information about the UAV is extracted after the flight. As shown in Figure 16a, the remote-control failure occurred when the TimeStamp was about 350,000 Ms. Since the UAV directly activated the RTL mode, its acceleration differed from that of a normal flight. As shown in Figure 16b, when the UAV was in RTL mode, it did not turn the course of the UAV but returned directly, so its pitch angle and roll angle was abnormal when the TimeStamp was about 350,000 Ms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline FS\_EKF\_THRESH & 0/1 & Disable/enable fail-safe mechanism \\ SIM\_RC\_FAIL & 0/1 & Clear/simulate remote control faults \\ SIM\_BATT\_VOLTAGE & 12.6 & Simulate ambient battery voltage \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings in the Remote-Control System failure simulation.  
Figure 14: UAV state changes when engine power is lost.
## 3 MJTAR Protocol  
### Hypothesis  
To discuss the limitations of the existing GyTAR protocol, let us consider the scenario depicted in Figure 2 below. In this illustration, the solid lines denote a two-lane road in both directions, with vehicles moving in the direction of each arrow. Here, S represents the source, D denotes the destination, and F signifies a forwarding vehicle responsible for relaying packets. Let us assume that the source vehicle located at junction \(J_{2}\) is tasked with forwarding packets to the destination.  
Dynamic junction-selection-based routing protocols, such as GyTAR [11], are designed to determine the next neighboring junction based on traffic density and the shortest distance to the destination. Consequently, a source vehicle employing this protocol would opt for \(J_{5}\) to forward a packet. However, upon receiving the packet, the forwarding vehicle stationed at \(J_{5}\) encounters an unforeseen situation. The forwarding vehicle at \(J_{5}\) intends to select \(J_{8}\) to relay the packet toward its destination. Unfortunately, the road between \(J_{5}\) and \(J_{8}\) is a closed, devoid of vehicular traffic. Consequently, encountering this closed road leads to a local maximum, resulting in heightened end-to-end delay or even packet loss. Conversely, the road between \(J_{5}\) and \(J_{6}\) similarly presents a closed road scenario. Ultimately, this limitation in the existing protocol may result in the selection of an inefficient routing path despite the optimal path (\(J_{2}\)-\(J_{3}\)-\(J_{6}\)-\(J_{9}\)). To address these challenges, we propose the MJTARprotocol. This dynamic multiple-junction-based routing protocol considers traffic density and the shortest distance to the destination up to two junctions at a time. This protocol aims to mitigate packet delay and enhance the packet delivery success ratio by assessing the vehicular traffic density between two junctions. However, our proposed MJTAR also exhibits certain limitations. If vehicles at junctions consider traffic from up to two junctions, it entails a higher computational effort than previous approaches. However, this compromise is necessary to forecast closed roads effectively. Extending the consideration of traffic to 3 or 4 junctions instead of 2 would result in an increased overhead for route discovery and a decline in network performance. Therefore, in this study, we constrain the consideration of junctions to a maximum of two.
# Effect Range of the Material Constraint in Different Strength Mismatched Laboratory Specimens
## 4 Conclusions  
In this study, different strength mismatched laboratory specimens that contain the CT, SENT, and CCT specimens with various specimen geometries, loading configurations, and initial cracks, and the effect range of the material constraint was systematically studied.  
1. Irrespective of the geometries and loading configurations, the center crack or interface crack, the shallow crack or deep crack, the effect range of material constraint exists in all the strength mismatched specimens and structures.  
2. The numerical value of the effect range is influenced by the geometry constraint. The high geometry constraint against the plastic deformation at crack tip reduces the effect range of material constraint. The specimen with deep crack has smaller effect range of material constraint than the specimen with shallow crack; the SENB and CT specimens have smaller effect ranges of material constraint than the SENT and CCT specimens.
3. When a material is located outside the effect range of material constraint, the fracture resistance curves, and crack propagation paths of the specimens and structures are no longer influenced by the mechanical properties of the material.
4. There exists an interaction between geometry constraint and material constraint. The high geometry constraints in the CT and SENB specimens strengthen the effect of material constraint. Under the low geometry constraint, the fracture resistance curve and crack propagation path are insensitive to the material constraint.  
Conceptualization, J.Y.; methodology, J.Y.; software, Y.D.; validation, Y.D., J.Y., and H.C.; writing--original draft preparation, Y.D.; writing--review and editing, J.Y. and H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [51975378].  
The authors declare no conflict of interest.
## 3 Field Experiments
### Experimental Results
#### 3.2.2 Coverage Measurement  
A schematic diagram of the experiment is shown in Figure 8b. Coverage was measured by varying the position of the ground user against a fixed-height UAV. This time, the altitude of the UAV was set at 30 m, and the UE's locations were set apart by 2.5 m steps from the center beneath the UAV. The green circles depicted in Figure 10 show the measurement results.  
The results show that the attenuation increases at a horizontal distance of 10 m. Since the physical length of the communication link is about 32 m when the horizontal distance is 10 m, it can be understood that the deterioration of communication quality is not due to distance attenuation but to the deterioration of antenna directivity due to the increase in the incident angle.  
Figure 8: Diagram of experiment scenario. (**a**) Direct experiment. (**b**) Coverage measurement. (**c**) Ground reflection experiment.  
Figure 9: Experimental results of the direct experiment.
## 2 Study Area Description  
### Location  
The Betwa River basin (BRB) is an active segment of the Ganges river system. It is spread between latitude 23\({}^{\circ}\)5\({}^{\prime}\) to 25\({}^{\circ}\)55\({}^{\prime}\) E and longitude 77\({}^{\circ}\)15\({}^{\prime}\) to 79\({}^{\circ}\)45\({}^{\prime}\) N in central India (Figure 1). It originates from Dehgaon Bamori Forest Range of the Raisen district in Madhya Pradesh, India. It is about 475 m above mean sea level (a.m.s.l.). It flows in a southwest to northeast direction for 590 km and drains into river Yamuna at about 106 m a.m.s.l. in district Hamirpur of Uttar Pradesh. It has a total drainage area of about 43,346 km\({}^{2}\), out of which 68.84% area is in Madhya Pradesh, and the rest of 32.16% area lies in Uttar Pradesh. The sub- tributaries, namely Kaliasot, Sagar, Halali, and Bina, are located upstream, Narain, Jamni, and Orr, are located midstream, and Dhasan and Birma are located downstream of the Betwa River. The strike ridges of Vindhyan hills and Malwa Plateau are situated, respectively, in the southwest and south margin of the basin, separating the Betwa River basin from Narmada valley. The urban settlements, hills, industries, and forests dominate the upstream regions of the basin, whereas the middle and downstream regions are covered by agricultural activities, rural settlements, stone mining, rocky outcrops and stone crushing industries, thermal power plants, and dams.  
Figure 1: Location map of the Betwa River basin shows the topographic variability, drainage network, and respective gauge stations.