esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis of Case Study
### The Determination of Installation Position for the Initial Support  
As an example, the admissible value of global safety factor is set to \([\overline{\mathbf{F}}]\) = 1. As shown in Figure 11, interpolating \(\overline{\mathbf{F}}\)= \([\overline{\mathbf{F}}]\) into the \(\mathbf{r}-\overline{\mathbf{F}}\) curve, the corresponding excavation load releasing ratio \(\mathbf{r}_{s}\) is 96.5%. Moreover, the corresponding global dimensionless displacement \(\overline{\lambda_{s}}\) is 0.918 by interpolating \(\mathbf{r}_{s}\) into the \(\mathbf{r}-\overline{\lambda}\) curve. Lastly, substituting the specific \(\overline{\lambda_{s}}\) into Equation (12), the installation location \(\mathbf{x}_{s}\) is calculated as 9.88, namely the initial ground support should be installed behind the tunnel face at a distance of no more than 9.88 m. Besides, according to the excavation speed, the timing of installation for the initial support can be determined by Equation (11) as 2.47 days after excavation.  
Similarly, if the admissible value of the global safety factor is set to \([\overline{\mathbf{F}}]\) = 1.2, the corresponding installation location will be behind the tunnel face no more than 2.79 m away, namely in 0.7 day after excavation, as shown the above process.  
Figure 10: Longitudinal displacement profile: (**a**) \(x-\lambda\) and \(x-\overline{\lambda}\), (**b**) the fitting result of \(x-\overline{\lambda}\).
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 4 Fractal Metasurfaces
### Fractal Metamaterial Absorbers
#### 4.1.1 Impact of Fractals on the Electromagnetic Performance of Metamaterial Absorbers  
In this subsection, the Minkowski fractal is used as an example to illustrate the impact of fractal shapes on the electromagnetic performance of metamaterial absorbers. Figure 15a displays the schematic of square, first-iteration Minkowski and second-iteration Minkowski fractal cells that are designed to operate in the UHF frequency band. Specifically, the square patch (n = 0) is designed for absorption at 880 MHz (Figure 15). Subsequently, the first (n = 1) and second (n = 2) fractal cells are constructed by considering two distinct cases in parallel, namely the cell with scaling factor S = 0.2 (Figure 15b) and the cell with scaling factor S = 0.25 (Figure 15c). The simulated absorption coefficients for both cases are shown in Figures 15b and 15c, respectively. It can be clearly seen that the main effect of the fractal cells is the reduction in the resonance frequency. As the order of the fractal geometry increases, the resonant frequency decreases compared to the square patch for all considered scaling factors S. Furthermore, the rate of frequency reduction increases as S increases. Thus the use of fractal geometries in microstrip element design enables a greater electrical length to be packed into a smaller area. In particular, the first and second Minkowski cells exhibit reductions of 14.3% and 21.4%, respectively, compared to the standard square patch when S = 0.2. Meanwhile, for S = 0.25, the percentage reductions are 35.7% and 40.7%, respectively. It is important to note that the first iteration of Minkowski is responsible for most of the size reduction.The justification for the aforementioned behaviour is described in Section 3.1.2 for Minkowski-based reflectarray cells. In this case, the resonant length of the \(n^{\text{th}}\) iterated patch is estimated to be \(L_{n}=(1+2S)^{n}\,L\), where \(n\) represents the number of iterations. The formula demonstrates that the electrical length of the fractal patch increases with both \(n\) and S, resulting in a lower resonant frequency. Therefore, to achieve resonance at the desired working frequency, the fractal element must be miniaturized.  
Thanks to their miniaturization capabilities, fractals have become widely used for designing compact and easily integrable microwave absorber structures. Furthermore, as discussed in the following sections, the self-similarity and compactness of fractal geometries  
Figure 15: Simulated absorption of fractal-based MAs: (**a**) fractal unit cells; (**b**) unit cell size and absorption coefficient for S = 0.2; (**c**) unit cell size and absorption coefficient for S = 0.25.  
allow for the implementation of multiband and broadband absorbers, thus saving space compared to conventional metamaterial absorbers.
# A Precise Prediction of Tunnel Deformation Caused by Circular Foundation Pit Excavation
## 4 Result Interpretation  
### Tunnel Heave in Longitudinal Direction  
Figure 4 shows the measured tunnel heave computed by numerical analyses for both the square and the circular basement excavations. It can be seen that the computed tunnel heave was generally in agreement with the measured magnitude and distribution values. The tunnel heave in the existing tunnel was caused by stress relief, as shown in Figure 5. With the increase of the basement excavation depth, the tunnel heave increased. After the basement excavation, the measured tunnel heave reached 0.07% \(H_{\text{e}}\) (\(H_{\text{e}}\) is the basement depth) at a distance of 0.4 \(H_{\text{e}}\) from the basement center, and reduced to zero at a distance of 2.4 \(H_{\text{e}}\) from the basement center. The computed maximum value of the tunnel heave was 0.084% \(H_{\text{e}}\) and 0.099% \(H_{\text{e}}\) for the square and circular basement excavations, respectively, with a similar trend distribution. By comparing the computed tunnel heave caused by a different basement shape, it was found that the maximum tunnel heave caused by the unloading of the circular excavation was 1.18 times greater than that of the square excavation with the same excavation amount. According to the provisions of the Land Transport Authority [44], the allowable maximum tunnel heave value is 15 mm (i.e., 0.170% \(H_{\text{e}}\), as shown in Figure 4). The computed tunnel heave ranged in the standard allowable value and also in the measured value. From the above analysis, it was determined that the tunnel heave caused by the circular basement excavation was 18% larger than that caused by the square basement excavation. Additionally, if a circular basement is adopted, and assuming that the tunnel heave caused by the square basement is in a critical state, it is essential to take corresponding reinforcement measures.  
Figure 3: Flow chart of the numerical modelling procedures.
## 3 Results and Discussion
### Damage Pattern Recognition
#### 3.1.2 Clustering Results of the Density Peak Clustering Method  
Based on the introduction of the density peak clustering method provided previously, this method was adopted to process the experimental data of crumb rubber concrete. Similarly, six acoustic emission parameters--rise time, count, energy, duration, amplitude,  
Figure 6: Clustering results of common clustering methods: (**a**) k-means clustering; (**b**) FCM clustering; (**c**) GMM clustering; (**d**) SOM clustering; (**e**) hierarchical clustering.  
and peak frequency--were used as input data, and the peak density of each data point is shown in Figure 7. The 2210 th data point, the 5176 th data point and the 14,373 rd data point have significantly higher density peaks than the other data points. Based on the principle of density peak clustering, these three points were used as the density center. The cluster center of the peak cluster, and all the remaining data points were classified separately.  
After clustering by using the density peak method, the method described in this section also uses the average frequency and peak frequency to evaluate the clustering results. Based on Figure 8, the data points are well divided into three clusters, which can be clearly distinguished, and the average frequency between different clusters can be clearly discerned. The clustering performance is obviously better than the clustering performance of the five commonly used clustering methods noted above. Therefore, the density peak clustering method is the best clustering method for analyzing the acoustic emission data of crumb rubber concrete. This method can be used to perform deeper damage pattern recognition of crumb rubber concrete.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 4 \({}^{223}\)Radium-dichloride (Xofigo)
### Imaging for Patients' Selection and Follow-Up after \({}^{223}\)RaCl2
#### 4.1.2 \({}^{18}\)F-FDG PET  
As reported by [PERSON] et al., PET/CT with 18F-Fluorodeoxyglucose (\({}^{18}\)F-FDG) may improve the patients' selection for \({}^{223}\)Ra-Cl2 treatment. Patients with bone metastasis have a higher likelihood of being FDG avid: for this reason, \({}^{18}\)F-FDG PET may provide a prognostic stratification of patients at baseline [55]. Moreover, the authors demonstrated a longer overall survival in patients with partial metabolic response to \({}^{223}\)Ra-Cl2 treatment [55]. The same authors, in a retrospective analysis, highlighted the potential role of PET parameters for the prediction of overall survival: in particular, the metabolic tumor volume (MTV) of lesions at baseline was able to determinate patients with worse prognosis.
# GCS-YOLO: A Lightweight Detection Algorithm for Grape Leaf Diseases Based on Improved YOLOv8
## 2 Materials and Methods  
### Grape Leaf Dataset  
The original dataset used in this study is sourced from the publicly available Plant Village dataset, which contains three types of diseased grape leaves. The disease categories include black measles, black rot, and leaf blight, with a total of 3639 images. The original dataset was manually annotated using the LabelImg annotation tool to label the diseased grape leaves, drawing boundaries around the lesions and identifying the types of lesions. Examples of grape leaf diseases are shown in Figure 1.  
Data augmentation was performed on the original dataset in this study. First, the dataset was divided into training, validation, and test sets with a ratio of 7:2:1. The 7:2:1 split data allocation effectively evaluates the model's generalization ability by providing sufficient validation samples for parameter tuning to reduce bias, while also reserving adequate testing data for a reliable assessment of generalization. Then, to address the dual challenges of limited training samples and environmental variations in grape leaf images, this study applies data augmentation to the training set using four key techniques: horizontal mirroring, Gaussian blur, brightness/contrast adjustment, and image rotation. These transformations expand data diversity while preserving pathological features, thereby mitigating overfitting risks and enhancing model robustness against illumination shifts and viewpoint changes. After augmentation, a total of 6186 images were generated. Specific details are provided in Table 1.  
Figure 1: Grape leaf disease raw dataset and labeling.
# Two-Tier Cooperation Based High-Reliable and Lightweight Forwarding Strategy in Heterogeneous WBAN
## 2 Related Work
Therefore, data transmission in practical human body network applications can be achieved by cooperating with the energy cost of sensor relays [42]. In this cooperative data transmission, the energy consumption of relay sensors is very high, which will inevitably lead to the imbalance of sensor energy consumption, and the literatures [43; 44; 45; 46] provided some corresponding data forwarding measures for this problem. [PERSON] et al. [47] and [PERSON] et al. [48] designed WBAN forwarding methods according to the energy consumed by the link when sensor nodes transmitted data. However, due to the existence of too many assumptions and strong constraints, some subjective one-sidedness exists in their proposed methods. For the sake of objectivity, the literatures [49; 50; 51] have designed or improved data forwarding schemes based on factors such as the residual energy of sensor nodes. [PERSON] et al. [52] proposed an optimization scheme of WBAN routing protocol based on clustering using the genetic heuristic method, and used the genetic algorithm to determine network energy consumption, so as to prolong network service life. [PERSON] et al. [53] built a minimum cost function for WBAN to select the forwarding node with the largest residual energy and the shortest distance, and proposed an emergency data transmission protocol based on an enhanced energy-saving threshold. Although the above studies improved the performance of WBAN data transmission to some extent, they ignored a key premise, that is, WBAN sensors are heterogeneous, and their energy consumption rates are different [54; 55]. Therefore, only using initial energy and residual energy cannot accurately describe the heterogeneity of sensors. The main reason is that the importance of data collected by different sensors is also different, and the residual energy of important sensors must be maintained at a high enough level to deal with emergencies. Moreover, in the past, most energy-saving studies on WBAN data forwarding only focused on the performance improvement of the forwarding model, and obviously ignored the impact of de-redundancy processing on system energy consumption before forwarding, even though the data de-redundancy or compression technology has been very mature. That is to say, the design of the preprocessing and forwarding mechanism of data to be transmitted has not formed a systematic model in WBAN data forwarding and related studies. Therefore, for the dynamic WBAN environment, this paper will improve the efficiency of data transmission and reduce the energy consumption of the system by cutting down the amount of data to be transmitted and raising the forwarding performance.
## 3 Proposed Optimum Mother Wavelet Selection Method
### Scheme of the Proposed Method  
The wavelet shrinkage scheme is composed of a novel optimum mother wavelet selection method and a decomposition level determination method, which is detailed as follows:  
1. Prepare a series of candidate wavelets \(\{\psi(i)\}\) to be selected, where \(i\)\(\in\)[1, 2,..., \(n_{w}\)], and \(n_{w}\) is the number of candidate wavelets. In this paper, the wavelets db2-db20, coif1-coif5 and sym2-sym8 are adopted.
2. Initialize the current decomposition scale \(j=1\). The signal to be analyzed at the first scale is original noised signal s.
3. Perform the \(j\)th scale wavelet decomposition on the signal s with candidate wavelets \(\psi(i)\) for \(i=1,2,\)..., \(n_{w}\).
4. Calculate the scale dependent threshold with (3) for \(i=1,2,\)..., \(n_{w}\).
5. Calculate the SNR value of approximation and detail coefficients and record as SNR(\(j\), \(i\)) according to Equations (4)-(14).
6. Find the maximum value of SNR(\(j\), \(i\)), i = 1, 2,..., \(n_{w}\), and the corresponding wavelet is selected as the optimum mother wavelet of \(j\)th scale.
7. Calculate the \(p_{A_{j}}\) and \(SNR_{A_{j}}\) according to Equations (15) and (16) with respect to the optimum mother wavelet.
8. If \(p_{A_{j}}<5\%\) or \(SNR_{A_{j}}>20\) dB, no further decomposition will be performed, otherwise, decomposition of next scale will be performed by taking the approximation coefficients obtained by the optimum mother wavelet of \(j\)th scale as the signal s, and repeat steps (3)-(7) with \(j=j+1\). The flowchart is shown in Figure 2.  
Figure 2: Flowchart of the proposed wavelet shrinkage scheme for PD signal denoising.
# Application of Three-Dimensional Porous Aerogel as Adsorbent for Removal of Textile Dyes from Water
## 2 Materials and Methods
### Batch Adsorption Tests  
Adsorption studies under static conditions were terminated after the predetermined adsorption time by separating the aerogel capsules from the prepared aqueous solutions of the dyes Congo red, Naphthol green B, Rhodamine B and Methylene blue that are used in the textile industry by filtering them through a glass-fiber filter. The separated aerogel capsules were not reused in further stages of the study.  
Aerogel capsules are composed of small cellulose fibers that can be seen with the naked eye. One type of aerogel capsule with the cellulose content of 5% was used in this study. When synthesizing aerogel capsules of different composition, there are no restrictions regarding the amount of cellulose in them. Aerogel capsules with cellulose content below or above 5% were not used due to the fact that these capsules have lower values of adsorption capacity.
## 5 Discussion
### Readily Available Leaf Age  
Leaf age, an important indicator of crop physiological characteristics, is usually relatively easy to obtain. The emergence date can be directly observed in the field, allowing for straightforward counting to determine leaf age [62]. Additionally, remote sensing technologies offer a powerful tool for estimating leaf age across large areas of farmland [86; 87]. By calculating vegetation indices from remote sensing images, we can indirectly assess the growth stage of leaves. These indices exhibit specific patterns of change as the leaves progress from young to mature and eventually to senescent stages. By analyzing these patterns, we can accurately infer leaf age.
## 5 Experimental Verification
### Hunting  
If the yaw suspensions are all disconnected, and the IRW is accelerated to the wheel rotational speed of 895 r/min, a unique phenomenon similar to the hunting motion on the SW will also appear on the IRW, as shown in Figure 27. The experimental results show that the lateral displacement \(y\) and yaw angle \(\psi\) oscillate alternately. However, the differential wheel rotational speed \(\Delta\omega\) also varies with \(y\), which differs from the SW. This phenomenon demonstrates that although the IRW rides without any yaw suspension, if the rotational speeds of the wheels are high enough, the derailment tendency will be conquered, and the self-guidance ability will be regained. The experimental results are in accordance with the simulation results in Sections 3 and 4.1, and the phenomenon can be interpreted by the gyroscopic effects discussed in Section 2.3.  
It is necessary to re-install the yaw suspensions and let the IRW still run at a high speed. The experimental results are shown in Figure 28. The critical oscillation velocity of the IRW with yaw suspensions is 800 r/min. It demonstrates a counterintuitive phenomenon that if yaw suspensions with relatively low yaw damping are installed, the critical oscillation velocity of the IRW is lower than that without yaw suspensions. Furthermore, the frequency of the oscillation is higher. It is analyzed that the function of the yaw spring on the IRW is not to suppress the oscillation but to resist the derailment tendency caused by the gravitational restoring moment. It is the yaw damper that is the main factor contributing to oscillation suppression. When the derailment tendency is resisted or partially resisted, the slight self-guidance effects caused by the gyroscopic moment dominate the lateral motion. Then, the oscillation is prone to appear. If the longitudinal speed decreases, the oscillation is suppressed by the yaw dampers, as shown in Figure 29. Due to the limitation of the suppression ability of the yaw dampers on the scaled IRW in this paper, the oscillation appears when the speed is high enough.  
Figure 26: Experiment results of the IRW with yaw suspensions at average rotational speed 200 r/min. (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram.  
Figure 28: Experiment results of the IRW with yaw suspensions at average rotational speed, 800 r/min. (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram.  
Figure 27: Experiment results of the IRW without any yaw suspension at average rotational speed 895 r/min. (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram.
# Research on the Reliability Allocation Method of Smart Meters Based on DEA and DBN
###### Abstract  
Reliability allocation can reasonably determine the reliability index of each unit in the system to ensure product quality in design, manufacturing, testing and acceptance. In the design process of the smart meter, the preliminary reliability allocation results may be unreasonable, so in the middle and later stages of the design stage, the reliability needs to be reallocated. The traditional allocation method has some limitations, such as strong subjectivity, large amount of calculation and too much reliance on expert judgment. In order to solve these problems, this paper presents a multi-methoded fusion method of reliability allocation. First, this paper uses the goal-oriented methodology (GO methodology) to integrate dynamic Bayesian networks (DBNs) to predict the reliability of smart meters. Second, a data envelopment analysis (DEA) reliability allocation model is established, the posterior probability obtained by DBN reasoning together with the failure rate and structural complexity of each unit are used as the output indicators of this model. Finally, the reliability allocation weight is calculated by using the efficiency value obtained from the DEA reliability allocation model. The validity and accuracy of this method is verified by an accelerated life test. This provides a new idea for reliability reallocation of smart meters.  
reliability allocation; smart meter; data envelopment analysis; dynamic Bayesian networks; GO methodology +
Footnote †: journal: Applied  
12 June 2021  
Accepted: 20 July 2021  
Published: 27 July 2021  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Published: maps and institutional affiliations.**  
==WARNING: Truncated because of repetitions==
998334 & 0.998294 & 0.999634 & 0.99933 \\
4 & 0.997714 & 0.997751 & 0.997557 & 0.997498 & 0.999464 & 0.999017 \\
5 & 0.996882 & 0.996933 & 0.996669 & 0.996588 & 0.999269 & 0.998659 \\
6 & 0.995947 & 0.996013 & 0.99567 & 0.995564 & 0.999049 & 0.998257 \\
7 & 0.994908 & 0.994991 & 0.994559 & 0.994427 & 0.998806 & 0.99781 \\
8 & 0.993764 & 0.993866 & 0.993338 & 0.993175 & 0.998538 & 0.997319 \\
9 & 0.992517 & 0.992639 & 0.992005 & 0.99181 & 0.998245 & 0.996783 \\
10 & 0.991166 & 0.99131 & 0.990562 & 0.990332 & 0.997928 & 0.996202 \\ \hline \hline \end{tabular} \end{table}
Table 9: Dynamic reliability of each unit after allocation.
+++