esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamically Triggered Damage Around Rock Tunnels: An Experimental and Theoretical Investigation
## 1 Introduction
In addition, numerical methods provide a clear visual representation of the changes in physical quantities such as the stress, strain, and energy during the loading process. Various numerical methods, including finite element, discrete element, finite difference, and coupled numerical methods, are utilized to study the dynamic damage and stress distribution around the hole [3; 18; 19; 20]. Coupled initial stress and dynamic loading on the damage of an arched tunnel surrounding rock was also carried out by [PERSON] and [PERSON] [21], and the results showed when the lateral stress coefficients are 0.25 and 3, while the dynamic incident stress increases from 30 to 50 MPa, resulting in more severe damage to the surrounding rock, transitioning from spalling to rockburst. These numerical methods offer valuable insights into the behavior and effects of the cavity under different dynamic loading conditions. Despite the significant amount of research conducted on the dynamic response of tunnels through experimental, numerical simulation, and theoretical studies, most of the previous studies have focused on the dynamic response of the steady state. Investigations on the damage process, mechanism, and stress distribution of arch tunnels under dynamic impacts from different directions still need to be carried out in order to contribute to a comprehensive understanding of dynamically triggered failure in underground space engineering. In this study, a set of specimens containing arched holes with different inclinations was employed to carry out dynamic impact experiments combined with high-speed pho tography and DIC techniques to simulate the failure characteristics of tunnels. The strain evolution and damage patterns of the specimens were captured; furthermore, the transient dynamic stress distributions around the arched holes were theoretically calculated based on the complex function method and conformal mapping to reveal the effect of the incidence angle on the failure mechanism. The findings of this research will have significant implications for the design and construction of underground structures, ensuring their stability and the safety of surrounding areas, and they also provide a theoretical supplement for the transient-to-steady-state transition of stress wave scattering within the framework of complex variable functions.
## 2 Materials and Methods
### Experiment  
In the designed OWC lab setup, an ICL from Nanoplus Nanosystems and Technologies GmbH was used. The active region of this laser comprises six active stages that are embedded in 430 nm thick GaSb separate confinement layers on both sides. The epi side up mounted laser chip is not optimized for high power operation, has a cavity length of 900 \(\upmu\)m and a ridge width of 5.1 \(\upmu\)m to ensure lasing on the fundamental lateral mode. The back facet is coated with a high reflectivity coating, whereas the front facet is just covered with a thin dielectric layer to protect the emitting facet. The laser structure was mounted into the \"TO-66\" semiconductor package with a temperature Peltier module and a temperature sensor. In Figure 3, the spectral characteristic of the radiation at 4 \(\upmu\)m was shown (panel **a**) together with light-current-voltage (_LIV_) characteristics (panel **b**).  
The schematic diagram of the lab setup is presented in Figure 4. In the OWC transmitter, a programmable current pulse driver model MAX3967 supplies the ICL. This driver is dedicated to controlling fast LED sources, but the range of generated current and voltage signals effectively supplies such lasers. Also, a programmable biasing voltage from 400 mV to 925 mV was implemented. The laser temperature of 25 \({}^{\circ}\)C was stabilized using the Peltier temperature controller model Arroyo 5310. The laser beam was collimated using an off-axis mirror with a diameter of three inches and a focal length of two inches.  
Figure 3: (**a**) ICL radiation spectrum and (**b**) _LIV_ characteristics.  
Figure 2: OWC link range vs. scintillation level for two interesting wavelengths.  
The model of the OWC receiver consisted of a four-inches off-axis parabolic mirror, InAsSb photodetector (VIGO System S.A.), and readout electronics were used. The photodetector parameters are listed in Table 3.  
The detector electronics has a cascade configuration with the first stage based on a low noise transimpedance (TIA) and a voltage amplifier. In both, low-noise, high bandwidth opamps were used (OPA847 model). The designed electronics' current noise density and signal bandwidth were 20 pA/\(\sqrt{\text{Hz}}\) and 140 MHz, respectively. These characteristics define the theoretical limits of the data link range (noise power level of 532 nW) and its transfer rate (\(\sim\)180 Mb/s) for NRZ format coding.  
For the described configuration, the data range for different weather conditions was calculated. Finally, the laboratory tests of the OWC link were also performed using both RZ and NRZ coding formats. A pattern signal generator model 12,000 Picosecond controlled the laser pulses. The output signal from the receiver was registered and analyzed using an oscilloscope model MSO 6 Tektronix with a built-in eye diagram toolbox.
## 2 Materials and Methods
### ECG Processing and Generation of the HRV Signal  
According to the report yielded by CardioScan, all ECGs had less than 1% premature beats. The recordings were resampled to 256 samples/s using linear interpolation with MATLAB R2016 (MathWorks Inc., Natick, MA, USA) before filtering by moving averages and generating the HRV recording. To upsample the ECG-signal before R-wave detection is highly advisable [13], and a minimum of 250 samples/s is recommended to not compromise the high frequency content [36]. The three acquired channels were visually inspected to choose the one with less distortion for further processing. The R-wave detection, visual inspection and manual correction of misidentified peaks, the generation of the R-R tachyograms, and the regular resampling of the tachyograms to 4 samples/s using cubic spline interpolation were done with the Kubios HRV Analysis Software [37]. Regular sampling is mandatory in STFT given the irregular location of the detected R-waves. As an example of the generation of the HRV signal, Figure 1 shows a 10 s segment of the filtered ECG signal and the trace corresponding to the QRS complex detection (panel a), the identification and location of the corresponding R-waves (panel b), and the irregular R-R intervals obtained along altogether with the R-R tachogram resampled at 4 samples/s (panel c).  
Figure 1: Example of electrocardiogram (ECG) processing to obtain a heart rate variability (HRV)  
signal. Panel (**a**): a 10 s segment of the filtered ECG signal from one patient (solid black trace) and the corresponding detection of the QRS complex (solid red line). Panel (**b**) shows the same ECG segment and the location of the R-waves detected (red circles). In panel (**c**), the irregular R-waves detected (red trace) and the final R-R tabogram resamples at regular intervals (blue trace) are shown.
# Research on Intelligent Recognition for Plant Pests and Diseases Based on Improved YOLOv8 Model
## 1 Introduction
Firstly, the Global Attention Mechanism (GAM) [21] is introduced to enhance the interaction between channels and spatial dimensions by incorporating a mechanism to preserve information on top of channel and spatial attention mechanisms, leading to improved accuracy. Secondly, the Receptive-Field Attention Convolutional (RFA Conv) [22] method is used to replace standard convolution operations in the current neural networks, which significantly improves network performance without increasing computing costs. Finally, in terms of datasets, considering the differences and similarities in the presence of pests and diseases among different plants, a dataset is created in which rice and cotton are representatives for plants. It includes diseases like Blast, Blight, and Brownspot for rice [23] and Alternaria Leaf Spot, Curl Leaves, Foliar Disease, and Herbicide [24] for cotton, enriching the sample information and improving its applicability. The main contributions of this paper are summarized as follows:  
1. Add the GAM to improve the model accuracy without adding a calculational burden;
2. Use the RFA Conv to replace the standard convolution operation, reduce the waste of computational resources, and improve the accuracy;
3. Make representative datasets containing various pests and diseases to improve applicability;
4. Use the improved YOLOv8 model for practical application, apply it to the self-made datasets, and compare it to similar algorithms. The results show that the method of this paper yields the best performance in detection accuracy.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### System Operation and Optimization
#### 4.2.4 Overall Grid Optimization  
At the heart of system operation and optimization is the overarching goal of achieving overall grid optimization. Digital twins provide a comprehensive toolset for this purpose, enabling the simulation and analysis of the entire grid under various conditions. This holistic view allows for the identification of bottlenecks, vulnerabilities, and opportunities for efficiency improvements. By leveraging real-time data and predictive analytics, Digital Twins can guide decisions on infrastructure investments, maintenance schedules, and operational strategies, leading to a more resilient, efficient, and sustainable grid.
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Manipulation of Microbots  
The momentum property of light is often overlooked during its use. However, it can be very useful for the manipulation of microparticles due to momentum transfer from the scattering incident photons. This basic property of light has a significant implication. It extends the reach of robotics to the microworld with high accuracy and precision. Using the same principle of photon momentum, there are several ways of controlling microrobots. One of the very widely used trapping mechanisms is the optical tweezer. There are also some other types of manipulation methods using light, such as optoelectronic tweezers (OET), self-propelled micro-swimmers, or even independent autonomous actuation of the microbots as well.
# Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning
## 5 Conclusions  
In conclusion, this study proposes an innovative method for predicting the failure pressure of corroded pipelines by integrating empirical formulas into the loss function of a neural network model. The proposed approach effectively combines the strengths of empirical formulas and artificial neural networks, leveraging the instructive nature of empirical knowledge while benefiting from the powerful fitting capabilities of ANNs. The integration of an additional defect factor term in the loss function allows for the model to compensate for errors caused by varying defect conditions, thereby enhancing its adaptability and accuracy across diverse corrosion scenarios. The results demonstrate that the proposed method outperforms traditional empirical formulas and ANN models trained with standard loss functions, achieving a mean absolute percentage error (_MAPE_) of 2.52%, a root mean square error (_RMSE_) of 0.39 MPa, and a coefficient of determination (\(R^{2}\)) of 0.9886 on the validation set. This study highlights the importance of integrating empirical knowledge with data-driven models to improve prediction accuracy and robustness. The proposed method provides a simple yet effective solution for predicting the failure pressure of corroded pipelines, contributing to enhanced pipeline integrity assessment and safety management. Future work may focus on further optimizing the model architecture and exploring additional empirical formulas to further improve the prediction performance. However, the empirical formula used in the proposed method has a significant impact  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\(P_{F}\)} & \multicolumn{2}{c}{**D  
on prediction accuracy. Generally, the better the fit of the empirical formula to the data, the higher the accuracy of the model trained using the proposed method. Additionally, the proposed approach could be extended to other engineering applications where empirical formulas and data-driven models can be effectively combined to address complex prediction tasks.  
Conceptualization, X.M.; Methodology, H.L. and X.M.; Software, H.L.; Formal analysis, H.L.; Resources, H.L.; Writing--original draft, H.L.; Writing--review & editing, H.L.; Visualization, X.M.; Supervision, X.M.; Project administration, X.M. All authors have read and agreed to the published version of the manuscript.  
This research received no funding.  
The original contributions presented in this study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Shannxi Provincial Natural Gas Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 2 Related Work
However, the work did not present any model for quantifying the trustworthiness of an SA system. This work aims to fill in the gap in the literature pertaining to the security of SA and presents a model for the first time to quantify the trustworthiness of SA.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 6 Implementation Environment and Experimental Setup  
The system implementation used Python 3.11.1 within Windows 11 mobile devices with a 2.30 GHz Intel Core i7-12700H processor and 16.0 GB of RAM. The application utilized Tkinter and CustomTkinter libraries for interface design, while it used OpenCV for video processing and PyCryptodome for AES-based text and image encryption. LSB encoding functions within the red color channels enabled the masking system to be supported for extracted video images. The test used multiple MP4 videos starting from 640 \(\times\) 352 up to 3840 \(\times\) 2160 resolution, including video lengths spanning from 93 to 999 frames. The video assembly process relied on the FFV1 format in FFmpeg to achieve lossless compression. The experimental setup delivers efficient conditions for testing the proposed system because it accurately models actual implementation scenarios.
# Analysis and Design of Protection Device for Anchor Cable Pull-Out in High-Stress Roadways
## 2 Analysis of Tensile Test in Anchor Cables and Evaluation of Ejection Kinetics
### Case Analysis of Anchor Wire Breaking and Ejection  
Based on the strain energy density of the anchor wire in the unloading stage, combined with the on-site data, the kinetic energy and velocity at the time of anchor breakage ejection were calculated.  
In November 2017, anchor wire breaking occurred at the 1632 (3) roadway in Pansan Mine, with an average buried depth of 600 m and a roadway section of 5.2 m (width) \(\times\) 3.3 m (height). The roadway was supported by a bolt-mesh-anchor combined system. The anchors were 1860 MPa steel strands with a specification of \(\Phi\)22 mm \(\times\) 6.3 m (length). The lithologies of the roadway roof and floor are displayed in Figure 3.  
On the field, the anchor wire breaking and ejection length \(L_{1}\) was 0.3 m, the length of the anchor end \(L_{2}\) was 0.2 m, the total anchor length \(L_{3}\) was 6.3 m, the radius \(r\) of anchor wire breaking was 0.003 m, the ejection speed was \(v_{1}\), and the steel density \(\rho\) was 7870 kg/m\({}^{3}\). The locations of \(L_{1}\), \(L_{2}\), and \(L_{3}\) in the Formulas (8)-(10) are shown in Figure 4.  
As shown in Equation (6), the stored total strain energy in anchor wire breaking is expressed as follows:  
\[W_{x}=U_{x}\pi r^{2}(L_{3}-L_{2})=4745.44\;\text{J}, \tag{8}\]  
From Equation (7), the partial strain energy in anchor wire breaking can be obtained as follows:  
\[W_{\text{dx}}=W_{x}\frac{L_{1}-L_{2}}{L_{3}-L_{2}}=77.79\;\text{J}, \tag{9}\]  
From Equation (5), the initial anchor wire breaking and ejection speed is expressed as follows:  
\[v_{1}=\sqrt{\frac{2W_{\text{dx}}}{m}}=\sqrt{\frac{2W_{\text{dx}}}{\rho\pi r^{2 }L_{1}}}=48\;\text{m/s}, \tag{10}\]  
Figure 4: Diagram of the \(L_{1}\)\(L_{2}\)\(L_{3}\) position in the formula.  
Figure 3: Diagram of the roof lithology of the 1632 (3) roadway.
# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 4 Experiment
### Performance of the Defect Detection Algorithm under Different Settings
#### 4.2.5 Influence of Different Base Networks on Performance  
We compared our network with Resnet-50, VGG-19, and MobileNet [36]. The results are shown in Table 9 below, and the detection results are shown in Figure 10 below.  
As can be seen from the table, the best mAP was achieved using Resnet-50, but processing an image was more time consuming. VGG-19 achieved a mAP of 83.86% but took even longer to process a single image. MobileNet had a fairly high processing efficiency, but the mAP was the lowest among all the base networks. Our network achieved a better balance between the mAP and data processing efficiency due to less parameters and computation. Our mAP was close to that of using Resnet-50, and the detection time had a great advantage compared with Resnet-50 and VGG-19.  
It can be seen from Figure 11 below that the detection results using MobileNet deviated from the ground-truth label the most. Using our CNN model, the Resnet-50, or the VGG-19 as the feature extractor was more accurate.  
\begin{table}
\begin{tabular}{c c c} \hline
**Resolution** & **mAP(\%)** & **Detection Time** \\ \hline
288 \(\times\) 288 & 74.06 & 13.9 ms \\
320 \(\times\) 320 & 77.25 & 16.9 ms \\
352 \(\times\) 352 & 79.79 & 21.7 ms \\
384 \(\times\) 384 & 82.14 & 25.6 ms \\
416 \(\times\) 416 & 84.89 & 29.4 ms \\ \hline \end{tabular}
\end{table}
Table 7: Influence of different resolutions on performance.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Pre-Trained** & **Not Pre-Trained** \\ \hline mAP (\%) & 84.42 & 76.78 \\ \hline \end{tabular}
\end{table}
Table 8: Influence of model pre-training on performance.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Base networks** & **Resnet-50** & **VGG-19** & **MobileNet** & **Our CNN Model** \\ \hline mAP (\%) & 85.65 & 83.86 & 78.45 & 84.19 \\ Detection Time & 83.3 ms & 142.9 ms & 11.2 ms & 28.6 ms \\ \hline \end{tabular}
\end{table}
Table 9: Influence of different base networks on performance.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 3 Background
### Preliminaries
#### 3.1.2 Counting Bloom Filter  
The counting Bloom filter [24] is an improved version of the Bloom filter, which can support elemental addition and deletion operations. Before introducing the counting Bloom filter, it is necessary to explain the Bloom filter. The structure of a Bloom filter is a bit array of \(m\) bits, where each bit is set to 0 initially. The Bloom filter is usually used to determine whether an element is in or not in certain sets. Suppose that there is a set \(S=\{s_{1},s_{2},\ldots,s_{t}\}\) of \(t\) elements. To enable an element, \(s\in S\) can be represented in the Bloom filter; it needs to use \(l\) independent hash functions \(\{h_{1},h_{2},\ldots,h_{l}\}\) to hash \(s\) to obtain \(l\) different positions in the Bloom filter, and these hash functions are with the same output range \([0,m-1]\). Then these \(l\) different positions in the structure of the Bloom filter are set to be 1. To decide whether \(x\in S\), it needs to examine whether all the positions in the structure of the Bloom filter corresponding to \(h_{i}(x)\) are equal to 1, where \(1\leq i\leq l\). Therefore, if one of these corresponding positions is 0, \(x\
otin S\). If all the corresponding positions are 1, then \(x\in S\) or a false positive. To minimize the impact of the false positive, our scheme adopts the same way as the paper [9]. That is, the parameter \(l\) is set to be \(\frac{m*ln2}{l}\), which will result in the minimum probability of the false positive (i.e., \(2^{-l}\)). However, since each position of the standard Bloom filter only represents a single bit, it does not support element addition and deletion operations. Thus, the counting Bloom filter uses fixed size counters to represent an element instead of single bits. In this case, the corresponding counters are added by 1 when an element is inserted and the corresponding counters are decreased by 1 when an element is deleted.
# Spectrum Based Power Management for Congested IoT Networks
## 1 Introduction
As the data packets reach the channel coordinator (green box in Figure 1), the control data are forwarded to their destination. The coordinator node cannot manage resources because it does not have channel condition information and follows the default way of resource management which does not include any power or spectrum management mechanism. Using Figure 2, our proposed spectrum based power management architecture for an IoT is explained. The FREDEVPLA is used only to manage spectrum related information between each CN and its IoT devices, such as: when to do the channel scan, which coordinator can do the scan, select a channel with low interference, identify level of congestion based on received LQI, store child table, and decide when to switch a channel for a specific CN. In our proposed solution, we scan channel conditions when queue delay time is above the limited estimated threshold time and allocate the least congested channel for data communication between IoT device and its CN. The major motive behind this work is that extending IoT device battery life by reducing retransmission created due to busy channel and manage IoT device configurations from a central FREDVPLA device. It effectively manages channel and reduced retransmission which lead to less power consumption. We use a FREDEVPLA device for managing channel status. While managing congestion and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and physical layer of IoT devices such that it provides the outstanding power utilization compared to the basic IoT standard in active state. Next, we will present some related works in Section 2. Detail functionality of our proposal will be explained in Section 3. In Sections 4 and 5 we will go through our test set up and the test result analysis, respectively. Result comparison is presented in Section 6, and finally in Section 7, we conclude our research.