esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Congestion-Avoidance and Vehicle-Detection Modules of ACO-PT for Vehicles
### Automatic-Vehicle-Detection Module  
The automatic-vehicle-detection module is used to detect the presence and movement of vehicles in a specific area. The AVD module typically consists of several components, including sensors, cameras, and software that work together to detect and track vehicles. To meet the efficient-path condition, it is necessary to automatically detect the traffic. For this purpose, the vehicle-detection module consists of four stages, depicted in Figure 7, and the automatic vehicle detection is explained in Algorithm 2:  
Figure 6: Incoming- and outgoing-traffic-flow illustration.  
* Image preprocessing;
* Contour detection;
* Matching contours;
* Blob detection.  
**Algorithm 2**Automatic-vehicle-detection process  
Algorithm 2 demonstrates the vehicle-automation process. Step 1 initializes the variable for preprocessing, contour detection, and matching contours. Steps 2 and 3 show the input and output, respectively. Step 4 demonstrates the receiving process of the street-image frame given by matrix \((src_{i})\). Step 5 applies a threshold transformation to create a binary image \((dst_{i})\). Step 6 determines the direction of the steepest increase by the image gradient. Step 7 shows the detection process of the horizontal edges and the Sobel edge direction. Step 8 summarizes the edges to determine the approximate amplitude of the Sobel detector. Step 9 shows the image-noise reduction process by applying the Gaussian filter. Step 10 detects the contours of objects by applying the Laplace operator. Step 11 filters the contours, which are based on their area and aspect ratio, to remove small or irregular shapes. Steps 12 and 13 show the contour-matching process and compare the filtered contours to a set of predefined contour templates to determine if they match a vehicle, respectively.  
Steps 14 and 16 show the discard process for contours that do not match a vehicle. Step 17 initializes the variables for blob detection. Step 18 applies the first and second derivations of the Gaussian filter to brighten the color and determine the brightness and surrounding areas. Step 19 applies geometric transformations to discard and track vehicle position, width, and height. Step 20 shows the outcomes of the location for each detected vehicle in the image.
## 1 Introduction  
The arch cover method, developed from the traditional shallow tunneling method [1], is widely used to construct subway stations in the upper-soft lower-hard stratum. Due to the low self-stability of soft soil, an upper arch structure in the arch cover method is implemented to allow the bearing capacity of the underlying surrounding rock to be fully utilized. At present, some cities, such as Dalian, Chongqing, and Qingdao in China, have applied the arch cover method to station construction, including two different types of primary lining support and double lining support.  
Some scholars [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24;  
Nevertheless, the previous research has some drawbacks: (1) Few research study the dynamic responses of the surrounding rock and structures induced by blasting excavation in the arch cover method. (2) The effects of blasting excavation on the existing pipelines have rarely been investigated, considering the complex construction sequences. Although many researchers have widely considered the dynamic responses of blasting on the existing pipelines, few studies have focused on the effects of blasting excavation in the arch cover method.  
The dynamic responses of existing pipelines subjected to blasting excavation have been mainly analyzed by means of field tests [8; 9; 10], analytical [11; 12; 13], and numerical methods [14; 15; 16]. Some scholars mainly study the effects of surface blasting on non-pressurized pipelines [17; 18; 19]. Subsequently, other scholars began to investigate the dynamic responses of pressurized pipelines, such as gas, water, oil, and steel pipelines [20; 21; 22; 23; 24]. Moreover, the dynamic responses of the existing pipelines due to blasting during the construction of adjacent tunnels are studied because it is one of the most detrimental factors for existing pipelines [25]. However, the effects of blasting during different construction sequences of the arch cover method are rarely studied.  
This paper aims to study the dynamic responses of the existing pipelines subjected to blasting excavation by the arch cover method. A dynamic geotechnical software Flac3D.5.0 [26] is used to establish a numerical model. A triangular pattern describes the time history of the blast hole pressure. Using the Shi Kui Road Station project, No. 5 Line in Dalian, China, as a case study, our numerical simulation is verified from the monitoring data of the ground vibration in this project. The dynamic response characteristics of the existing pipelines induced by blasting pilot tunnels and the lower rock are analyzed.
## 2 Materials and Methods
### Data Pre-Processing and Performance Evaluation Metrics  
The experimental data, capturing the average maximum temperatures recorded during bone drilling at three distinct parameter levels, were imported into Google Colab using the Pandas Python library (version 3.10.12). Pandas is widely used for its robust capabilities in data manipulation, enabling the efficient handling and analysis of complex datasets.  
Figure 4: Illustration of random forest construction.  
The SVM and RF models' performance was evaluated using standard metrics for model assessment, namely the mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (\(R^{2}\)), described by the following equations:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline{ y})^{2}}, \tag{4}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{|y_{i}-\hat{y}_{i}|}{y_{i}}\times 1 00, \tag{5}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y})^{2}, \tag{6}\]  
where \(n\) denotes the quantity of data points, \(y_{i}\) represents the observed values, \(\hat{y}\) stands for the predicted values, and \(\overline{y}\) denotes the mean value of \(y\).
## 3 Experimental Results and Analyses
### AE Count/Energy Characteristics  
The aforementioned experiments described the evolution of the macroscopic fracturing of the argillaceous shale, but they could not characterize the internal structural changes or crack extensions that occurred during the process of the rock loading. The AE characteristics effectively monitored the rock deformation, crack extension, and rock damage processes, and provided information for understanding the fracture processes undertaken by the rocks [26; 27; 28; 29; 30; 31].
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 4 System Design
### Preprocessing
#### 4.2.2 Signal Selection and Event Detection  
A sliding window selects signal samples from the signal stream. The window size is 2048 samples. The sliding window shifts 64 samples each time. The data of the four sensors share the same sliding window.  
We used the first-order second-moment method [38] to detect the beginning of a vibration event. By analyzing the change of Gaussianity, this method can be used to differentiate the vibration event from Gaussian noise. The first-order second-moment is defined in Equation (1), where \(N\) is the window length of the first-order second-moment method, \(\mu\) is the mean of the values in this window, \(x_{i}\) is each value in the window. Empirically we determined a window size of 64 samples, which equals 32 milliseconds of sampling. We set the variance of ambient background noise as the threshold. When the \(m_{2}\) values are larger than the threshold, the system detected a vibration event. If a vibration event is detected, the window will shift 2048 samples. If the number of data values in the shift window is less than 2048, all the data in the window will be dropped.  
\[m_{2}=\frac{1}{N}\sum_{i=1}^{N}{(x_{i}-\mu)^{2}} \tag{1}\]  
We used the signal of Sensor 1 to detect vibration events. The layout of sensor deployment in Figure 1 guarantees that any sensor can detect even the weakest signal generated in its area. Regarding the isolation of step events, the choice of the reference sensor does not make any difference regarding the detection of vibration events. When a vibration event is detected, the data of four sensors in the sliding window will be recorded.  
Figure 7 shows an example of vibration event segmentation from the signal stream. The data values between each solid black line and red line will be extracted and packaged as input samples. The solid black lines denote the beginnings of an input sample, and the  
red lines the corresponding ends. After each shift of the window, all the data in the shift window will be packaged as an input sample to the neural network for pedestrian counting.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 4 Use of Doped ZnO NPs in the Biomedical Field  
Zinc oxide has been part of many applications in the biomedical field. Many of them are related to its antimicrobial activity [216], but other applications also prove the use ZnO as a therapeutic agent against cancer cells [217; 218] or as a tool for tissue engineering [70; 78]. However, despite pure ZnO showing many interesting properties, it still presents many factors that need to be further optimized for its further exploitation in the biomedical filed in general, and in nanomedicine in particular.  
A first important aspect to be considered is its dissolution behavior in aqueous media. Actually, zinc oxide can dissolve in water generating free Zn\({}^{2+}\) cations. This can more easily occur in presence of high surface area nanoparticles because of their superior surface reactivity [216]. Zn\({}^{2+}\) cations are successfully exploited to increase the bactericidal activity of ZnO [219]. However, this aspect may also result in an increased cytotoxicity that could prevent its use in the human body.  
Despite the high quality of ZnO as an antimicrobial agent, its performance may be even enhanced if the corresponding photocatalytic activity is further optimized. ZnO is a semiconductor able to generate highly cytotoxic/ antimicrobial ROS [220]. Upon interaction with electromagnetic radiation, semiconducting ZnO materials are able to promote electrons from the valence band to the conduction band, generating free electrons which are able to migrate up to the outermost ZnO surface and react with the external aqueous environment, leading to the generation of ROS [92; 221]. Band-gap engineering of ZnO may be a valuable tool to increase the efficiency of this process, reducing or increasing the energy required to generate free electrons and controlling the efficiency in ROS generation. This ability in generating ROS can be exploited in photodynamic therapy, transforming ZnO nanoparticles into an interesting weapon against cancer.  
Finally, ZnO on its own lacks intrinsic imaging properties which are very useful for various biomedical applications, such as in theranostics. Tuning its dimensions and energy band-gaps, the corresponding optical properties can be properly optimized and interesting luminescent properties can be gathered as in the case of ZnO quantum dots [163; 222]. Moreover, as already stated, the inclusion of transition metals in the crystal lattice confers on ZnO a magnetic behavior, paving the way to its exploitation as a contrast agent in magnetic resonance imaging [118].  
All these aspects can be fulfilled by doping ZnO with specific elements. In the following, the main findings in this sense will be described.
## Materials and Methods
### Determination of Oxidative Status  
For oxidative status analysis, the oil phase of each mayonnaise was separated based on the procedure used by [PERSON] et al. [2]. In brief, mayonnaises were frozen at \(-\)18 \({}^{\circ}\)C for 12 h and then thawed and centrifuged for 5 min. Each oil phase, separated from the emulsion residue, was stored in a closed glass flask in the refrigerator at 4 \({}^{\circ}\)C until further analysis.  
The primary and secondary oxidation products in the oil phase of mayonnaise samples were determined and expressed as peroxide value (PV) and anisidine value (AnV), respectively.  
The PV of each oil phase was assessed using the official procedure ISO 3960:2017 [25], whereas the AnV was carried out spectrophotometrically in accordance with the ISO 6885:2016 method [26].  
The overall oxidation state of the oil phase was calculated as a TOTOX value based on the PV and AnV using the formula: (TOTOX = 2 PV + AnV).  
The conjugated dienes (CD) and conjugated trienes (CT) values were measured according to [27] as the absorbance of 1% solution of each oil from oil phase in hexane at 233 and 268 nm, respectively. Hexane was used as the blank sample.  
The determination of acid value (AV) evaluating the amount of free fatty acids liberated by hydrolysis was performed according to the ISO 660:2020 [28].  
Figure 2: The appearance of the prepared egg-less mayonnaise samples: commercial plant-based mayonnaise (CPBM), control aquafaba-based mayonnaise (CM), aquafaba-based mayonnaise with 0.1% of black cumin cake extract (BCCM0.1%), aquafaba-based mayonnaise with 3% of black cumin cake extract (BCCM3%), aquafaba-based mayonnaise with 0.1% of octyl caffeine (OCM), and aquafaba-based mayonnaise with active film incorporating black cumin cake extract (AFM).
# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 3 Methodology and Analytical Approach
### Chain Building Threshold Analysis
\[\varphi_{1}=arcsin(sin(i)\times sin(\beta)) \tag{8}\]  
\[\lambda_{1}=\lambda_{0}+arctan(cos(i)\times tan(\beta))-\zeta\times t+C \tag{9}\]  
\[\beta=\omega\times t+\tau \tag{10}\]  
where \(i\) is the orbital inclination, \(\beta\) is the angular distance between the satellite \(S_{1}\) and the ascending node, \(\lambda_{0}\) is the longitude of the ascending node, \(\zeta\) is the angular velocity of the Earth's rotation, \(t\) is the time of the satellite, \(\omega\) is the angular velocity of the satellite, which is generally determined by the satellite period, \(\tau\) is the initial phase of the satellite, which is calculated by Equation (1). When \(C\) is a constant, \(\beta\in[-\pi,-\pi/2)\), \(C=-\pi\); when \(\beta\in[-\pi/2,\pi/2)\), \(C=0\); when \(\beta\in[\pi/2,\pi)\), \(C=\pi\). As shown in Figure 5, the instantaneous elevation angles of the satellites \(S_{1}\) and are \(S_{2}\), \(\delta_{1}\), and \(\delta_{2}\), respectively; and the elevation angle is the angle between the tangent line of the satellite's orbit and the LISLs. Based on the geocentric angle \(\theta\), the instantaneous interplanetary link distance \(d_{12}\), and the instantaneous elevation angles \(\delta_{1}\) and \(\delta_{2}\) can be calculated from the satellites \(S_{1}\) and \(S_{2}\), as shown in Equations (11)-(13). \[d_{12}=\sqrt{R_{1}^{2}+R_{2}^{2}-2\times R_{1}\times R_{2}\times cos\theta} \tag{11}\]  
\[\delta_{1}=arccos(R_{2}\times sin(\theta)/d_{12}) \tag{12}\]  
\[\delta_{2}=arccos(R_{1}\times sin(\theta)/d_{12}) \tag{13}\]  
Figure 3:0 Scenario of establishing LISLs between the same orbit. The altitude of the LISLs established by the two satellites above the surface \(h^{\prime}\) is shown in Equation (14). Figure 3:1  
Since the radius \(R_{i}\) of the satellites in the same orbital are identical, which is \(R_{i}=R+h\), the instantaneous inter-satellite link between the two satellites \(d\) is therefore presented in Equation (15). Figure 3:2  
The maximum visible distance \(x\) between the two satellites is shown in Equation (16). Figure 3:3  
When \(h^{\prime}>R+H\) or \(d<x\), an interplanetary link can be established between the satellites \(S_{1}\) and \(S_{2}\), but not vice versa, so the set of visible satellites of the satellite \(S_{1}\) can be expressed as \(Y=\left\{S_{1}|x\geq d_{li}\right\}\).
## 4 Discussion
### Challenges and Potential Solutions  
When a glacier transfers from a clean-surface condition to a debris-covered condition, it is very important to capture this transfer signal at its initial stage, which is also a challenging subject. From field investigation and high-resolution UAV images, we find an emerging signal of englacial debris on the Qiyi Glacier, and we can conjecture that such a clean surface glacier may switch into a heavy debris-covered one, but this bold speculation needs more observations if it is to be validated. For instance, based on a more detailed measurement of englacial debris using ground penetrating radar (GPR) combined with the glacier dynamic models, the development of the englacial debris of the Qiyi Glacier might be analyzed in order to obtain more solid results. Nevertheless, it is undeniable that the continuing climate warming has been substantially influencing the fate of the Qiyi glacier. According to our field observations over the past few years, although the percentage of the debris area is still low, the debris cover at the glacier terminal has been expanding upwards to the interior tongue part of the glacier since 2005, and if this change trend continues, it is probable that the Qiyi Glacier will change to a debris-covered condition eventually.  
On the other hand, despite the relatively higher uncertainties in the SEC results over the proglacial region due to the absence of control points, its anormal positive changes still deserve further investigation. Theoretically, the positive elevation change can be explained as a result of the extrusion of the moving glacier: when the glacier shrinks as a result of  
Figure 5: Conceptual model of the Qiyi Glacier showing the transformation from a clean-surface to debris-covered glacier due to climate warming (**a**), and its effect on glacial runoff (**b**).  
climate warming, the ice above the ground vanishes and its englacial debris is exposed, but there is still abundant hidden ice covered by thick debris. The debris cover can protect the buried ice from further melting (i.e., there is no evident decrease in the surface elevation), whereas the extrusion of the moving glacier might force the surface of the hidden ice to rise (i.e., there is an increase in the surface elevation). In our future field work, we plan to set control points with more accurate RTK in order to verify whether the SEC in the proglacial region has increased or not. If we obtain a positive answer, this would be direct evidence of the existence of hidden ice in the proglacial region, and would be a breakthrough in studies of ice loss estimation during the process of glacier shrinkage.
## 4 Experiments and Results
### Experiments
#### 4.4.3 Comparison with Fine-Tuned Models on Computational Resources  
In order to assess the advantages of the method proposed in this study in terms of resource utilization, an experiment was conducted to compare it with the model fine-tuning method in terms of time, hardware resources, and model generalization capabilities. The results of the experiment are presented in Table 6.  
The fine-tuning method uses Lora [32] and the amount of data involved in the experiment is 1000.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Task** & **llama-3-8b-Instruct** & **llama-3-Chinese-8b-Instruct-v3** & **RL–Fusion LLM** \\ \hline stem & 67.9\% & 63.2\% & 65.2\% \\ \hline average(Hard) & 59.8\% & 57.4\% & **60.7\%** \\ \hline Social Science & 82.8\% & 84.5\% & **85.0\%** \\ \hline Humanity & 78.4\% & 82.2\% & **83.9\%** \\ \hline Other & 70.8\% & 71.1\% & **72.3\%** \\ \hline Average & 75.1\% & 77.2\% & **78.6\%** \\ \hline \end{tabular} Bolded content is the experimental results of this paper.  
\end{table}
Table 4: The overall results of RL–Fusion LLM and the baseline for the C-eval inference evaluation.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Task** & **llama-3-8b-Instruct** & **llama-3-Chinese-8b-Instruct-v3** & **RL–Fusion LLM** \\ \hline NER & 1.8 & 2.1 & **2.5** \\ \hline \end{tabular}
\end{table}
Table 5: The overall results of RL–Fusion LLM and the baseline for the Yayi NER data inference evaluation.  
The experiment comparing RL-Fusion and fine-tuning highlights RL-Fusion's advantages in resource efficiency and performance. RL-Fusion completes tasks in just 15 min using only CPU resources, while fine-tuning takes 60 min and requires an RTX4090 GPU. Despite the fine-tuning method being trained on a Chinese dataset, which improves its C-Eval performance (79.2%), RL-Fusion outperforms it on MMLU (70.8% vs. 68.5%) and remains competitive on C-Eval (78.6%). These results demonstrate that RL-Fusion offers a more time- and resource-efficient approach, achieving comparable or even slightly better performance in key benchmarks.
# Complex Network Modeling and Analysis of Microfracture Activity in Rock Mechanics
## 2 Experimental Methods and Testing Procedures
### Test Results and Preliminary Analysis  
We can obtain the stress-strain curve and cumulative quantity curve of the acoustic emission of granite according to the test results (Figure 5). This paper studies the stage before the peak stress of granite (\(\sigma_{\text{p}}\) = 205.96 MPa) and focuses on the microfracture of rock before failure; for example, the elastic modulus of granite is \(E\) = 5.40 \(\times\) 10\({}^{4}\) MPa, and Poisson's ratio is \(\mu\) = 0.120. Then, the volumetric strain and elastic volumetric strain of the rock can be calculated by the following:  
\[\varepsilon_{v}=\varepsilon_{v}^{e}+\varepsilon_{v}^{c} \tag{1}\]  
\[\varepsilon_{v}=\varepsilon_{x}+\varepsilon_{y}+\varepsilon_{z}\varepsilon_{1 }+\varepsilon_{3} \tag{2}\]  
where \(\varepsilon_{v}\) is the volumetric strain, \(\varepsilon_{v}^{e}\) is the elastic volumetric strain, and \(\varepsilon_{v}^{c}\) is the crack volumetric strain. The volumetric strain consists of elastic volumetric strain and crack volumetric strain. The crack volumetric strain is caused by crack closure, initiation, and expansion. \(\varepsilon_{1}\) is the axial strain, and \(\varepsilon_{3}\) is the circumferential strain; in the test, \(\varepsilon_{2}=\varepsilon_{3}\). According to the generalized Hooke's law, the crack volumetric strain can be calculated as follows:  
\[\varepsilon_{v}^{c}=\varepsilon_{v}-\frac{1-2\mu}{E}(\sigma_{1}+2\sigma_{3}) \tag{3}\]  
where \(E\) is the elastic modulus of the granite and \(\mu\) is [PERSON]'s ratio in this test. According to the calculation results, we obtain the relationship between the axial strain and other parameters.  
The stress-strain curve before reaching the peak stress can be divided into several main stages according to the volumetric strain and crack volumetric strain: crack compaction, linear elastic deformation, stable crack growth, and unstable crack growth. The statistical results of the cumulative AE events reveal that the microfractures in the first two stages are relatively calm, but the number of microfractures in the fourth stage is very large. In particular, the number of microfractures increases sharply when the rock is near the peak stress before failure. At present, this feature is generally regarded as the precursor of rock fracture. The general characteristics of the AE or microfractures are shown in Figure 6. However, the behavior of microfractures cannot be statistically analyzed from the perspective of the stress-strain relationship and AE parameters.