esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
###### Abstract  
This study investigated the mechanical performance of the edge-closed honeycomb sandwich structure with face/core debonding under compressive load by experimental and numerical methods. Uniaxial compression tests of asymmetric sandwich structures with various debonding sizes between the carbon/epoxy face sheets and the honeycomb core were conducted. The experimental results showed that the failure of debonding specimens was mainly caused by the local buckling of face sheets at the debonding area. The failure zone of sandwich structures gradually translated from the edge-closed beveled area to the debonding area with the increase of debonding sizes correspondingly, Meanwhile, the stability and the load carrying capacity of sandwich structures were in a downtrend. Compared with the nondestructive specimens, the residual strength of two kinds of defective specimens decreased by 9.9% and 22.1%, respectively. The simulation of the developed numerical model based on the linear buckling theory and the continuum damage mechanics agreed well with the experimental data. The study has guide to assess on the damage tolerance of edge-closed honeycomb sandwich panels with face/core debonding.  
Keywords:honeycomb sandwich; edge-closed; debonding; buckling; failure analysis +
Footnote †: journal: Applied Sciences
# Study on Surface Damage Induced by High Heavy Layer Movement and Mining-Induced Earthquakes
## 2 Methodology
### Geological Overview and Monitoring Data of Mining-Induced Earthquakes
#### 2.1.2 Monitoring Data of Mining-Induced Earthquakes  
The mining operations of the 7202 working face began in June 2021 and concluded in August 2022. During this period, microseismic monitoring and statistical analysis recorded a total of 3193 mining-induced earthquakes. As shown in Table 1, mining-induced earthquakes in the 0-10\({}^{2}\) J energy range were rare and contributed minimal total energy. Mining-induced earthquakes in the 10\({}^{2}\)-10\({}^{3}\) J range were the most frequent but still accounted for relatively low total energy. In the 10\({}^{3}\)-10\({}^{5}\) J range, the frequency of mining-induced earthquakes decreased slightly, but this interval contributed the highest total energy. On 24 May 2022, at 21:46:08, the 7202 working face experienced its largest-energy recorded mining-induced earthquake, with an energy of 2.1 \(\times\) 10\({}^{5}\) J.  
Figure 3: Lithology column of the layers.  
Figure 2: Schematic diagram of the 7202 working face.  
In terms of energy distribution, low-energy mining-induced earthquakes (0-10\({}^{3}\) J) account for over 80% of occurrences, primarily corresponding to conventional strata movements such as immediate roof collapse and periodic fracturing of the main roof. Their spatial distribution exhibits a dense, scattered pattern, reflecting the progressive failure of shallow rock layers under mining-induced stress. In contrast, medium- and high-energy mining-induced earthquakes (10\({}^{3}\)-10\({}^{5}\) J) constitute less than 20% of occurrences but contribute over 90% of the total energy. Their spatial distribution follows a distinct pattern: events of 10\({}^{3}\)-10\({}^{5}\) J align in a banded formation along the strike of the working face. The highest energy seismic event had an epicenter approximately 227 m behind the working face, 180 m within the adjacent goaf roadway, and about 70 m above the working face, as illustrated in Figure 4.
# SambotII: A New Self-Assembly Modular Robot Platform Based on Sambot
## 2 Mechanical Structure of SambotII  
As displayed in Table 2, each SambotII is an independent mobile robot containing a control system, a vision module, a driving module, a power module, and a communication system.  
\begin{table}
\begin{tabular}{c c} \hline
**Content** & **Parameters** \\ \hline Overall sizes & 120 mm \(\times\) 80 mm \(\times\) 80 mm \\ Weight & Approx. 355 g \\ DOFs & 4 (1 neck rotation + 2 wheels + 1 hook) \\ Connector & A pair of mechanical hooks \\ Torque of neck & 1.3 Nm (max) \\ Motion method & A pair of wheels \\ Power source & Inner 7.4V Lithium battery \\ Battery capacity & Approx. 1200 mAh (8.88 Wh) \\ Assistant peripherals & Laser tube, LEDs, switches and, etc. \\ Vision module & HD CMOS camera \\ \multirow{2}{*}{Camera resolution} & 640 \(\times\) 480 mode (currently used) or \\  & 1920 \(\times\) 1080 mode (maximum resolution) \\ Wireless system & Wi-Fi 24 G/5.8 G + Bluetooth 4.0 \\ \multirow{2}{*}{Coprocessor} & ARM Cortex-M3 STM32 \\ \multirow{2}{*}{Central Processing Unit} & Intel Atom dual-core x86 CPU \\ \hline \end{tabular}
\end{table}
Table 2: Main parameters of SambotII.  
Figure 2: SambotI and SambotII. (**a**) SambotI, the last generation; and, (**b**) SambotII: the left is active docking surface with camera and laser tube, the right is a passive docking surface with four LED lights.  
The mechanical structure of SambotII includes an autonomous mobile body and an active docking surface. They are connected by a neck (the green part in Figure 3). Each active docking surface has a pair of hooks.
## 5 Conclusions  
In order to solve the problem of how to optimize the deployment of a certain number of RNs in a TT-WSN to increase network life, this paper proposes an improved and enhanced Artificial Bee Colony algorithm (pdABC). In the pdABC algorithm, the search equation is improved in two aspects: first, that the global search part is adjusted according to the dimensions of the problem and the local search part is constructed according to the fitness value; second, in order to prevent the algorithm from premature convergence, a dynamic search balance strategy is proposed. This strategy is used to replace the traditional ABC scout bee stage. Before using the pdABC algorithm to solve the deployment problem of RNs in a TT-WSN, we first constructed an algorithm for using the RNs to form the upper communication backbone of the network. On this basis, the pdABC algorithm is used for optimizing RN deployment in the TT-WSN. To test the role of the pdABC algorithm in the optimization of RN deployment in a TT-WSN, the ABC, GA, and SA algorithms are compared to our proposed pdABC. The experimental results verify that the pdABC algorithm has strong global optimization ability compared to the other three algorithms, and the obtained RN deployment is more uniform. In terms of convergence speed and stability, the pdABC algorithm is better than the ABC and SA algorithms, although it is inferior to GA. In addition, we compare our algorithm against three current state-of-the-art algorithms, and the analysis results show that our algorithm is very competitive.  
In our future work, we hope to compare this algorithm with a variety of other state-of-the-art algorithms from multiple perspectives to further validate its performance. In addition, we aim to apply this proposed algorithm to real-world problems and extend it for multi-objective optimization problems.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{2}{c}{**6 RNs (100 \(\times\) 100)**} & \multicolumn{2}{c}{**22 RNs (200 \(\times\) 200)**} \\ \cline{2-5}  & **Ave.NL** & **Ave.CT** & **Ave.NL** & **Ave.CT** \\ \hline WOA & 1955 & 273 & 783 & 326 \\ SCA & 1824 & 358 & 681 & 378 \\ GWO & 1871 & 312 & 724 & 348 \\ dpABC & 1934 & 232 & 777 & 280 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TT-WSN lifetime optimization for different algorithms.  
**Funding:** This work was supported by the Science and Technology Planning Project of Sichuan Province (Grant No.2021 YFG0293), Major Science and Technology Special Projects of Sichuan Province (Grant No.2020 ZDZX0015), Sichuan Province Key Research and Development Plan Project (Grant No.2021 YFG0053), and the National Natural Science Foundation of China (Grant No.62172061).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to acknowledge the anonymous reviewers for their valuable comments and suggestions that helped to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Discussion
### Key Findings and Contributions  
1. Combining CNN and BiLSTM: By integrating the powerful feature extraction capabilities of CNN and BiLSTM with the advantages of Mean Teacher in semi-supervised learning, the accuracy of encrypted traffic identification is significantly improved. This integration reduces the model's reliance on labeled data, making it more practical for real-world applications where labeled data are scarce.  
Figure 6: Convergence process of accuracy and loss on the ISCXVPN2016 dataset.  
Consistency Constraint: The use of consistency loss ensures that the student model's predictions are closely aligned with those of the teacher model. This constraint enhances the robustness and adaptability of the model, enabling effective identification and classification of encrypted traffic even when dealing with complex or anomalous patterns.
3. Theoretical and Practical Significance: Our approach has important theoretical significance and practical value for network security, traffic management, quality of service assurance, and other fields. It provides a reliable solution for recognizing and classifying encrypted traffic, which is critical for maintaining the integrity and security of network systems.
# Efficient Small-Object Detection in Underwater Images Using the Enhanced YOLOv8 Network
## 1 Introduction
To address these issues, we introduce a lightweight detector for underwater images based on YOLOv8, optimizing the detections on small and desensed underwater objects. Our contributions are summarized as follows:  
1. To achieve model lightweighting, we use FasterNet-T0 [11] to replace the backbone of YOLOv8, slightly reducing model accuracy in exchange for a faster training speed and fewer model parameters. 2. In order to enhance the accuracy of small-object detection, we first integrate a prediction head for small objects into YOLOv8 because underwater images often contain many small objects. The prediction head we add is generated from high-resolution feature maps, making it more sensitive to small objects. We also perform specific optimizations for the number of channels in different resolution feature maps. Second, we enhance the performance of detecting small objects and handling occlusions in dense underwater images by utilizing Deformable ConvNets v2 [12] and incorporating Coordinate Attention [13] to embed positional information into channel attention, which incurs almost no computational overhead but helps the network find regions of interest in images. With our lightweight model, we achieve 52.12% AP on the UTDAC2020 underwater dataset [6], surpassing the larger YOLOv8l model (AP 51.69%). When increasing the input resolution to 1280, the AP reach 53.18%. Additionally, we obtain outstanding results of mAP 84.4% on the Pascal VOC dataset, surpassing previous well-established detectors. These results demonstrate the effectiveness of our method in underwater environments and its generalization to common datasets.
## 3 Results and Discussion
### SERS Applications
#### 3.2.4 Discrimination of Propranolol Enantiomers by SERS with CD Decorated NPs
All these observations suggest that in the case of strong interaction between the propranolol and the \(\beta\)-CD molecules, the aliphatic chain vibrations dominate the SERS spectra, the naphthyl group being locked in the CD cavity. However, further studies are needed for elucidating all these experimental observations.
# Characteristics of Channel Incision Induced by Human Activity in a Wandering Reach in 20 Years
## 4 Results
### Changes in Water Stage  
The decrease in water stages at certain discharges can also be an indicator of river channel incision. The stage-discharge curves were plotted at three gauge stations from 1999 to 2018, and Figure 5a-c shows the variation in stage-discharge curves in 1999, 2009, and 2018. It indicates that the relationships between water stages and flow discharges changed dramatically at these stations, and the water stages decreased sharply at the similar discharges after the reservoir operation. The water stages at low flows and high flows were calculated between 1999 and 2018 based on fitted logarithmic functions of stage-discharge curves, as shown in Figure 5d-f. At the HYK station (Figure 5d), the water stage at low flows reduced from 93.3 m in 1999 to 90.0 m in 2018, and the water stage at high flows reduced by 2.1 m during the study period. At the JHT station (Figure 5e), the water stages at both low and high flows changed very little from 1999 to 2002. In 2003-2018, there was a significant decrease in water stages from 73.8 m and 74.9 m to 70.4 m and 71.9 m at low flows and high flows, respectively. At the GC station (Figure 5f), the water stages at both low and high flows also did not change much from 1999 to 2002. In 2003-2018, the water stages reduced from 62.0 m and 62.9 m to 58.7 m and 60.2 m at low and high flows, respectively. It revealed that the lowering of water stages at the JHT and GC stations show a delay response to the reservoir operation. Figure 5d-f also indicates that the water stages  
Figure 4: Variations in the annual river bed downcutting depths in three sub-reaches of the wandering reach from 2000 to 2018.  
Figure 3: Cumulative river bed downcutting depths at 28 sedimentation sections in 1999–2018.  
at low flows decreased more than those at high flows at the three hydrometric stations from 1999 to 2018. At low flows, the water stage at the GC station decreased most after the reservoir operation, with the magnitude of 3.5 m. At high flows, the water stage at the JHT station decreased most, with a value of 2.8 m. The observed lowering of water stages as a result of channel incision was also documented in various rivers. It is reported that the lowering of flood water stage in the lower Pearl River was about 1.5 m in the late 1990s and about 2.0 m in the 2000s when compared with the floods of similar water discharge pre-1990s or in the early 1990s [10]. In the Rivers of the Polish Carpathians, the lowering of the minimum annual water stages was approximately 0.5-3.8 m over the twentieth century [12].
## 3 Methodology
### Experimental Environment: NaviMoon Spaceborne Receiver
#### 3.3.2 Space Segment and Control Segment  
The same GNSS antenna patterns, ephemerides and user ground truths as for the Monte Carlo simulations were used and injected in a GNSS RFCS to create representative signals for a user in lunar orbit. The space segment of the simulated scenario is run by the ESA Radio Navigation Laboratory's RFCS. No tropospheric nor ionospheric errors were applied since the percentage of Earth GNSS signals acquired at the Moon that passes through the Earth's atmosphere is actually negligible. The GNSS transmit power levels were calibrated to match the \(C/N_{0}\) levels obtained via STK, see Figure 7. The control segment communicates with the NaviMoon receiver using Telemetry and Telecommand (TM/TC) to provide the receiver with the necessary aiding and to retrieve and store the GNSS observable measurements. Finally, the post-processing block runs a positioning algorithm based on the receiver raw measurements logs and tests the various glsGNSS ranging techniques.  
Figure 9: Test bench block diagram.
## 2 Theoretical Analysis of Moon-Based Earth Observational Angles
### Definition and Calculation of Observational Angles
#### 2.2.3 Observational Angular Combination Performance Index  
The purpose of this study was to evaluate the effect of the time sampling interval on the characteristics of the angular combination. Therefore, we must define several parametersto describe the characteristics of the angular combination. In this subsection, we introduce the definition and calculation method of the ACNI proposed in this paper.  
The ACNI refers to the maximum coverage of the three-angle combination range over a specific time sampling interval during the observation period. When the angular combination is completely covered, the ACNI reaches a maximum value; conversely, the ACNI reaches a minimum value. Note that the ACNI has no units. In practical applications, we first must discretize the range of the three-angle combination to facilitate the calculation. The required parameters are the size and number of discrete bins for the three angles.  
The calculation of the ACNI consists of three main steps. In the first step, the observation period and time sampling interval are defined. In the second step, the ranges of the three angles are discretized based on the input parameters. It is assumed that the viewing zenith angle is divided into \(p\) angular bins, solar zenith angle has \(q\) angular bins, and relative azimuth angle has \(m\) angular bins. In the last step, all observation samplings are traversed to calculate the maximum coverage. First, we can use Equations (21)-(27) to calculate the three observational angles for each observation. Subsequently, we can identify the corresponding angle bin and record it in a three-dimensional Boolean matrix \(R\):  
\[R_{ijk}=\left\{\begin{array}{l}1,\;\delta_{ijk}>0\\ 0,\delta_{ijk}\leq 0\end{array}\right., \tag{28}\]  
where \(R_{ijk}\) reflects if the \(i\)th viewing zenith angle bin, \(j\)th solar zenith angle bin, and \(k\)th relative azimuth angle bin are covered. If the value is 1, this angle bin is covered; otherwise, it is uncovered. The parameter \(\delta_{ijk}\) refers to the angular sampling times corresponding to the \(i\)th viewing zenith angle bin, \(j\)th solar zenith angle bin, and \(k\)th relative azimuth angle bin.  
Finally, all \(p\) viewing zenith angle bins, \(q\) solar zenith angle bins, and \(m\) relative azimuth angle bins are traversed to calculate the ACNI.  
\[\cup=\sum_{i=1}^{p}\sum_{j=1}^{q}\sum_{k=1}^{m}R_{ijk} \tag{29}\]  
The maximum value of ACNI is \(p\times q\times m\), and the minimum value is zero. Based on ACNI, we can calculate the angular coverage ratio using the following equation:  
\[\gamma=\frac{\cup}{p\times q\times m}, \tag{30}\]  
where \(\gamma\) is the ratio of the ACNI, which has a maximum value of 1 and a minimum value of 0. Note that this term is also unitless.
# Miniaturized Compact Reconfigurable Half-Mode SIW Phase Shifter with PIN Diodes
## Appendix B  
As PIN diodes are nonlinear elements, their equivalent circuit models can be varied by altering the supply voltage and bias source [47]. It is possible to use a precise DC source and a fast switch key to change the state of a PIN diode with more consistency. As another option, the proposed phase shifter can be applied in a receiving section using a lower amount of RF power signal (a small signal). This will improve the stability of the diode.  
A parametric analysis was performed here with the purpose of applying a 20% variation to some equivalent circuits of the applied PIN diode in simulation software. The components to be studied are the resistor and inductance values in the ON state and the capacitance in the OFF state (\(C_{\text{off}}\) = 0.10704, 0.1338, and 0.16056 PF).  
To perform parametric studies, it is beneficial to use circuit software, such as ADS or AWR, rather than working with full-wave CST or ANSYS HFSS. This is because circuit software is here to tune equivalent circuit elements. First, the structure is simulated without PIN diodes in CST. To replace the diode component, discrete ports are added that connect to the ground layer. Then, there would be eight different ports, including the two main input and output ports in the case of the proposed fabricated structure. In the next step, scattering parameters can be exported in s8p format with a normalized 50 \(\Omega\) impedance. Then a s8p file can be applied to an 8-port scattering module in ADS software. Finally, it is an easy and quick way to tune the equivalent parameters of the diode in circuit software to achieve the desired scattering parameters.  
Figure 11 is the parametric study for the OFF state on the scattering parameters. Similarly, Figure 12 presents the variation of the OFF state capacitance in the transmission phase. Changing the ON state parameters does not affect either the magnitude or phase of the scattering parameters in a sensible way.  
The parametric analysis shows that with a lower-capacitance PIN diode, it is possible to increase the operating frequency band of the phase shifter.
## 1 Introduction
In addition, as a test system, some beamforming technologies with better performance were not applied, and the advantages of multi-channel sounding system were also not been fully demonstrated. The processing effect of the beamformer on the oblique backscatter signals were also not analyzed in detail. In this paper, a new dedicated multi-channel oblique backscatter sounder with a miniaturized L-shaped antenna array is described. Relaying on the high pulse compression gain of m-sequence, a 500 W transmission power can meet the sounding requirement. Some experiences were carried out to test the performance of the present system with adaptive beamforming algorithms. Compared with existing systems or sounding methods, the main innovations of this paper are as follows:  
1. The system realized miniaturization design and is dedicated to ionospheric oblique backscatter sounding, which can provide real-time ionospheric state information. 2. The system adopted two-dimensional directional receiving, which is more flexible and easier to adjust. Compared with the beamforming method of the transmitter, it has better anti-jamming ability at the receiver. 3. The 2-D adaptive beamformer effectively suppressed the vertical echoes and other RF interferences. 4. After adaptive beamforming processing, the leading edge of the oblique backscatter ionogram is clearer and steeper. It is of great significance for the trace extraction, which may further affect the inversion accuracy and improve the effective observation range.