esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Case Study  
Metro Manila, also known as the National Capital Region (NCR), was the location of the study. This region encompasses 619.57 square kilometers and consists of the sixteen cities and one municipality depicted in Figure 7. In the region, there are a total of 1690 Barangays. To collect the necessary data, two primary sources were consulted. First, reports on geotechnical investigations were gathered from both private companies and local government agencies. Second, seismic data were obtained from PHIVOLCS, which provided information on significant earthquake events, including their magnitude and epicenter location. In addition, the location of PGA stations where these events were recorded and the location of active faults in the region, including their head and tail latitude and longitude, were also recorded.
## 4 Experimental Validation
### Ground Simulation Facility
#### 4.1.1 Electron Counts Generated by TLE  
The TLE observation system is aimed at observing blue jets in the upper atmosphere. A blue jet is assumed to occur at an altitude of \(h_{\mathrm{TLE}}\) and generates a total of \(Q_{\mathrm{TLE}}\) photons during the \(T\) duration time [17; 36]. If the VisionCube orbits at an altitude of \(h_{\mathrm{Sat}}\) over the blue jet, the effective number of electrons \(e_{\mathrm{TLE}}\) that is produced by the photomultiplier during the sampling period of \(t_{\mathrm{s}}\) is calculated in Equation (1).  
\[e_{\mathrm{TLE}}=Q_{\mathrm{TLE}}\left(\frac{\pi\phi^{2}/4}{4\pi(h_{\mathrm{Sat }}-h_{\mathrm{TLE}})^{2}}\right)\left(\frac{t_{\mathrm{s}}}{T}\right)\psi G, \tag{1}\]  
where \(\psi\) is the quantum efficiency of the MaPMT at the wavelength of 405 nm. Using the parameters shown in Table 3, the maximum number of electrons \(e_{\mathrm{TLE}}\) generated by the MaPMT is approximately \(2.54\times 10^{6}\) when a blue jet occurs along the nadir direction at the closest distance of 535 km. On the other hand, considering the case where the TLE observation system detects blue jets near the limb of the Earth as depicted in Figure 10, the distance between the TLE and the VisionCube is computed as \(D_{\mathrm{limb}}=2674\) km by trigonometry. In this case, the effective electrons from the MaPMT is dramatically reduced to about \(e_{\mathrm{TLE}}=9.8\times 10^{4}\) using \(D_{\mathrm{limb}}\) instead of \((h_{\mathrm{Sat}}-h_{\mathrm{TLE}})\) in Equation (1). It should be noted that the electric charge is computed by multiplying the number of electrons by the elementary electron charge \(e_{v}=1.602\times 10^{-19}\) in coulomb, that is, \(q_{\mathrm{TLE}}=e_{\mathrm{TLE}}e_{v}\). The maximum and the minimum electric charge due to the TLEs will be utilized in the next section to determine the desired radiant power of the UV LED for the ground simulation experiments.
# A Person Re-Identification Scheme Using Local Multiscale Feature Embedding with Dual Pyramids
## 3 Method
### Scale Pyramid to Extract Multiscale Features  
In this section, we introduce the scale pyramid in more detail. This pyramid can convert the visual elements of various sizes and shapes in the input image into the features used to compose the feature embedding.  
The scale pyramid allows the person re-identification model to have receptive fields of various sizes including the rectangular shapes in addition to the square ones. The scale pyramid has a feature map sized (W, H, C) of (width, height, channel) as an input which is made by previous steps in the person re-identification model. It produces 6 outputs sized (W, H, C) from the receptive fields of different sizes and shapes as shown in (Figure 3). Looking first at the neural networks leading to the outputs Output_1 and Output_2, they share two consecutive convolutional layers with the kernels sized 1*1 and 3*3. Accordingly, the output feature map up to this point has a receptive field of 3*3 in size with respect to the input feature map, and since the two convolutional layers connected in parallel have the kernels sized 1*3 and 3*1, the receptive field of the output with vertically long rectangular shape (Output_1) of 3*9 and horizontally long rectangular shape (Output_2) of 9*3 can be accommodated with respect to the input feature map. The neural networks leading to the following Output_3 and Output_4 also share a convolutional layer with a kernel sized 1*1, and since the two convolutional layers connected in parallel have kernels sized 1*3 and 3*1, the receptive field of the output acquires a vertically long rectangular shape sized 1*3 (Output_3) and a horizontally long rectangular shape sized 3*1 (Output_4) with respect to the input feature map. Unlike the other parts, the neural network leading to Output_5 secures a square-shaped receptive field sized 3*3 through average pooling and learns the features of the corresponding region through a convolution layer with a kernel sized 1*1. Finally, the neural network leading to Output_6 uses a convolutional layer with a kernel sized 1*1 to secure the smallest 1*1 sized square-shaped receptive field.  
Figure 2: Network architecture of the proposed person re-identification model.  
Therefore, the scale pyramid can secure the receptive fields of 6 different sizes and shapes that include the square shapes sized 3*3 and 1*1 as well as the rectangular shapes sized 3*9, 9*3, 1*3, and 3*1 with respect to each arbitrary point of the (8, 24, 512) sized feature map, which originated from the output feature map of the backbone as shown in the example of (Figure 4). Regarding the input image, the scale pyramid can extract large and small, square and rectangular-shaped visual elements for all regions of the image as the features to reflect in the embedding. Accordingly, the proposed person re-identification model can utilize the visual feature elements of different sizes and shapes that exist at various locations on the input image during the formation of the feature embedding.
## 5 Discussion
### Impact of Mixed-Type Generators  
To demonstrate the adaptability of the proposed method, additional simulations were conducted using a mixed type of generators in the IEEE 9-bus system. In this analysis, we considered three scenarios: (i) all generators operating as VSGs (VSG\({}_{1}\), VSG\({}_{2}\), VSG\({}_{3}\)), (ii) one SG (SG\({}_{1}\)) combined with two VSGs (VSG\({}_{2}\), VSG\({}_{3}\)), (iii) two SGs (SG\({}_{1}\), SG\({}_{2}\)) combined with one VSG (VSG\({}_{3}\)). In the case of synchronous generators, the inertia constant \(H_{i}\) and damping coefficient \(D_{i}\) are physical machine parameters and were therefore fixed in the analysis. The results, summarized in Table 4, include the \(J_{1}\) and \(J_{2}\) objective function values for both the point closest to the origin and the knee point solutions, with damping  
Figure 10: Monte Carlo simulations for five Gramian-based controllability metrics: (**a**) EV\({}_{\rm min}\); (**b**) EV\({}_{\rm 2 min}\); (**c**) EV\({}_{\rm max}\); (**d**) TR; and (**e**) DET.  
ratio constraints included. The TR metric consistently gives the lowest \(J_{1}\) values for controllability across all configurations. The EV\({}_{\text{max}}\) metric achieves the lowest \(J_{2}\) values in cases (i) and (ii), and the DET metric gives the lowest \(J_{2}\) values in case (iii). Furthermore, the results show that the \(J_{1}\) and \(J_{2}\) values are lower when more VSGs are used, i.e., more parameters, \(H_{i}\) and \(D_{i}\), are optimized. Thus, the mixed configurations confirm that the proposed method works not only when all generators are VSGs, but also when integrating traditional synchronous generators or potentially other renewable-based generation.
## 2 Study Area and Data  
### Study Area  
The Heihe River Basin is the second-largest inland basin in the region of Northwest China (97.12\({}^{\circ}\)E-102.08\({}^{\circ}\)E, 37.73\({}^{\circ}\)N-42.74\({}^{\circ}\)N), with hot summers and cold winters. As is characteristic of arid and semi-arid environments, the region has little and irregularly distributed precipitation, primarily in the summer. Due to human interference and the local drought-prone climate, the ecological balance is delicate. The vast regional coverage, differing climate, and terraced terrain, extending from the upstream to the downstream, combine to create the rich and varied natural landscape of the Heihe River Basin. The upstream is dominated by alpine meadows, the midstream is primarily made up of artificial oases and irrigated areas, and the downstream is mostly covered by the Gobi Desert, except for a small stretch of riparian forests along the Heihe River. The Heihe River Basin is heavily dependent on water resources because of predominantly irrigated agriculture, and as cities and industries expand, so does the demand for water resources. The development of the Heihe River is time-honored, and human activities have significantly affected its ecological environment. The abovementioned characteristics make the Heihe River Basin an ideal region for conducting integrated watershed studies. The land cover and the locations of the flux towers selected for the evaluation are shown in Figure 1.  
Figure 1: Location of the Heihe River Basin and four EC flux towers at Arou, Huazhaizi, Daman, and Sidaoqiao. The background shows the land cover of the Heihe River Basin and its surrounding areas.
# A Direction-Sensitive Microwave Sensor for Metal Crack Detection
## 4 Experimental Study and Discussion
Additionally, the proposed sensor is compared with other references in terms of the working frequency, sensitivity of crack width and depth, and ability of crack direction detection. The details are illustrated in Table 3.  
Through the comparison, we can find that the sensitivity of the proposed sensor for horizontal crack width detection is the highest among listed references, and the sensitivity for vertical crack width detection is higher than that in [10,11]. It means that a larger resonant frequency shift will be contributed by the sensors with a higher sensitivity for crack detection with the same dimensions. In addition, the proposed sensor is also able to detect crack depth, though its sensitivity of crack depth is lower than sensors in [8,10]. Especially, the designed sensor has a unique ability to realize metal crack orientation detection, which other sensors cannot realize. With the ability to detect crack directions, the proposed sensor is able to avoid the blind region in crack detection and predict the crack \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Reference Frequency**} & **Sensitivity of Crack Width (MHz/mm)** & **Sensitivity of Crack Depth (MHz/mm)** & **Sensitivity of Crack Direction Detection (MHz/5 Degrees)** \\ \hline Simulation results & 2.430/1.950 & 102/56 & 16/10 & 3.78/2.56 \\ (Port 1/Port 2) & 2.485/2.000 & 100/63.3 & 20/14 & 6.07/1.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of results of simulation and experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **This Work** & **Reference [8]** & **Reference [10]** & **Reference [11]** \\ \hline Frequency (GHz) & 2.485/2.000 & 4.91 & 4.35 & 2.25/3.12 \\ \hline Sensitivity of crack width (MHz/mm) & 100/63.3 & 77.50 & 50 & 36.82 \\ \hline Sensitivity of crack depth (MHz/mm) & 20/14 & 57.50 & 50 & NULL \\ \hline Ability of crack direction detection & YES & NO & NO & NO \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of different kinds of metal crack detection sensor. Figure 17: Measured reflection coefficient results of fabricated sensor for crack with different directions. (**a**) Port 1. (**b**) Port 2.  
growth direction compared with single-port sensors. In summary, the proposed sensor has the ability to detect the width, depth and direction of metal crack, and has a better comprehensive performance in crack detection than other referred sensors.
# Assessment of Bone Aging--A Comparison of Different Methods for Evaluating Bone Tissue
## 3 Results
### Statistical Analysis  
The correlation analysis performed on approximately 300 textural features calculated for each manually annotated ROI revealed several significant relationships with patient age. The Pearson, Spearman, and Kendall correlation coefficients were computed depending on the feature distribution, as described in Section 2.5.  
The results are summarized in Table 1. The strongest correlations were observed using Pearson's coefficient. The best-performing ROIs were the right femoral shaft (R05) and the left femoral shaft (L05), with correlation coefficients of 0.49 and 0.46, respectively. Next, a meaningful relationship was found for the greater trochanter (R03 and L03). The analysis confirmed that a moderate correlation strength (r = 0.3-0.5) was observed for several ROIs, with R05 achieving the highest correlation. All correlation coefficients presented in Table 1 are statistically significant with \(p\leq 0.001\). Figure 2 illustrates the Pearson correlation coefficients across all evaluated ROIs visually.  
One of our goals was to check the impact of preprocessing methods, similar to what we did in our previous work [12]. Therefore, we conducted several experiments both without preprocessing and with operations such as HEQ, CLAHE, their combination (CLAHE and HEQ), and SDA. The results indicated that preprocessing did not provide a significant improvement. Specifically, there was no significant improvement in correlation for the most critical ROI, ROI 05 (R05: none-0.49, CLAHE/HEQ--0.49, SDA--0.497; L05: none-0.46, CLAHE/HEQ--0.46, SDA--0.46). However, this correlation improved for the second ROI, ROI 03 (R03: none-0.38, CLAHE/HEQ--0.43, SDA--0.44; L03: none-0.38, CLAHE/HEQ--0.39, SDA--0.41). For the remaining ROIs, a positive change was noted, but in these cases, the correlation value did not exceed 0.3. Therefore, we limited the presentation to the results obtained without preprocessing. Additionally, we assessed whether patient sex influenced the correlation between textural features and age. The most age-dependent ROIs (R05 and R03, L05 and L03) were not related to sex. However, a slight impact of sex was noted. In the male group, there was a noticeably higher correlation with age for L01, L02, and R01, as well as a higher correlation for R02 (although lower than for R/L05 and R/L03, it was not high enough to be considered significant). These results were not observed in the female group. This is consistent with previous findings [12].  
Among the texture features, the most frequently best correlating textural features were the Teta 1 and Sigma AutoRegressive Model (ARM) features, as well as the Local Binary Pattern (LBP) group features.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis
### Performance Analysis
#### 3.3.4 Comparing with Related Works  
This paper used all the sub-datasets in the NASA C-MAPSS dataset. Some literature has only reported results on the FD001 dataset in terms of RMSE, but in order to fully evaluate the effectiveness of the model, the results of all four sub-datasets were given. In this section, the performance of the proposed method is compared to other state-of-the-art methods reported in the literature.  
As shown in Table 7, the proposed models outperformed the existing prediction models in the datasets except for FD003. There are two failure models of FD003, therefore the network proposed in this paper had relatively weak learning ability in this sub-dataset, but it was still better than the results in References [23; 28]. FD002 and FD004 correspond to complex simulation conditions, and have more practical significance. The method proposed in this paper achieved better results than the previous methods on these two datasets. The RUL prediction was based on health indicators in References [17; 19], and these two methods were only validated on FD001. Compared with these two methods, the proposed network reduced the score on FD001 by 17.64 and 0.63, respectively. A similar  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Optimization** & **Learning Rate** & **Epoch Number** & **RMSE (Mean)** & **RMSE (Std)** & **GPU Time (s)** \\ \hline Adam & 0.001 & 40 & 12.18 & 0.31 & 72.56 \\ Adagrad & 0.001 & 150 & 12.22 & 0.24 & 303.49 \\ Adadelta & 0.01 & 300 & 12.34 & 0.12 & 621.15 \\ RMSpro & 0.001 & 50 & 13.89 & 0.69 & 102.16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Computational cost and result.  
study, which was published in Reference [27] and achieved the best results on the C-MAPSS dataset in published papers, were selected to be compared with our method. However, the shortage of this method was that it selected a specific kernel artificially. Therefore, the performance of this method was worse than the proposed method in terms of FD001, FD002 and FD004 datasets. In addition, the proposed network showed its superiority over the other time window-based methods in References [22; 23; 28].
# Realistic Simulation for Body Area and Body-To-Body Networks
## 3 Proposed Simulator
### Semi-Deterministic (Stochastic) Radio Link and Mobility Modeling
#### 3.1.1 Mobility Model  
The mobility model is a mixture of group mobility (inter-BAN) and entity mobility (intra-BAN). The inter-BAN mobility model is based on the reference point group mobility model (RPGM) in which the logical center describes the group movement, while the nodes are moving around this logical center. Each logical center or BAN position could follow any kind of mobility model. In our simulations, we have arbitrarily defined a random way point model in which each BAN moves to a newly-chosen destination with a newly-chosen speed and keeps confined in the two rooms. The mobility model provides for each iteration some important deterministic information (_i.e._, positions, direction, speeds), which is treated by the BAN environment to be the defined link condition, the input parameters of body-to-body and on-body channel models to generate realistic traces.  
Concerning the intra-BAN mobility, the human walking movement is directly taken into account in the channel model through the auto-regressive model implementation for time correlation and through the Cholesky model implementation for inter-link correlation. Nevertheless, in order to be in accordance with the inter-BAN mobility model, each sensor on the BAN will use the logical center position and a position-specific drift, and the channel model has been adapted to take the speed into consideration.
# Static and Dynamic Response of Micropiles Used for Reinforcing Slopes
## 1 Introduction
[19] researched the dynamic behavior of micropile under horizontal load in a seismic area, and the results of ambient vibration tests and horizontal impact load tests on the two micropiles reveal that high pressure injections visibly modify the dynamic response at small strain, influencing the fundamental frequencies and the directional behavior of the system. [PERSON] et al. [20] conducted research on the performance of micropiles under earthquake loading and determined the relationship between the average resistance of micropiles and the pile spacing under different conditions through experiments. The pile group effect on pile side resistance was studied. [PERSON] et al. [21] designed a pile-soil dynamic interaction shaking table test in order to study the influence of the input wave frequency on the vibration response characteristics of micropiles. The study found that the deformation of the micropile and the stress distribution of the pile body are closely related to the relative magnitude of the input wave frequency and the natural frequency of the system. [PERSON] [22] used \"herringbone\"-shaped micropiles in large-scale shaking table tests to study the deformation and internal force distribution characteristics of this system under earthquakes. The results showed that this kind of micropile system has a better seismic bearing capacity. [PERSON] et al. [23] utilized dynamic centrifuge tests to study the dynamic response characteristics of the lateral deformation and acceleration of micropiles and the bending moment of the pile at different depths. The researchers provided insights for the seismic design of micropiles in liquefiable sites. Although the previous researchers have advanced the understanding of the micropile behavior under loading, the research focused on the performance of micropiles under static or dynamic action alone. Few researchers have compared the reinforcing performance of micropile-reinforced slopes under static and dynamic loading. Therefore, this research conducted a static model test and a shaking table test to investigate the interaction between micropiles and landslides. Through systematic research and comparison, the reinforcing mechanism, seismic response mechanism, and failure modes of the micropiles are discussed to provide a more reliable basis for the design of the micropiles in landslide prevention and slope stabilization design.
## 5 Bioactivity of Secondary Metabolites in the _Leontopodium_ Taxa
### Anti-Metabolic Disorder Activity  
_L. leontopodioides_, known as common Edelweiss in China, was reported for the first time to have a hypoglycaemic effect in alloxan or adrenaline-induced diabetic Kunming mice [91]. The authors investigated the effects of treatments with two doses of \(L\). _leontopodioides_ decoction on blood glucose concentrations. Their results showed that administration for 10 consecutive days of the 30 \(\mathrm{\SIUnitSymbolMicro g}\)/kg dose generally decreased the concentration of blood glucose in normal mice in glucose-treated mice but also in alloxan-induced diabetic mice and in mice with increased blood glucose levels caused by the exogenous administration of adrenaline. Moreover, the decoction extract prepared from whole plants could significantly reduce the levels of blood glucose, cholesterol and triglycerides and increase the insulin level in serum of rats with type 2 diabetes, using as control Sitagliptin, an anti-diabetic drug [125]. Another study claimed, also, that the ethanolic extract from this species can remarkably reduce the blood glucose level in hyperglycaemic mice [126]. In fact, hypoglycaemic and hypocholesterolemomic effects were further demonstrated to be due to some chemical constituents with inhibitory activity on \(\alpha\)-glucosidase and lipase. Thus, two acyl flavone derivates from luteolin 4-O-\(\beta\)-D-glucopyranoside and one lignin glucoside were identified as potent inhibitors for \(\alpha\)-glucosidase [85], and four compounds (nebrodenside A, pungenin, betulalbuside A and geranyl \(O\)-\(\beta\)-D-glucopyranoside) exhibited lipase inhibitory activity, which suggested participation in the reductive effect of this herb on triglyceride absorption [86].  
Other studies showed an increased glucose uptake effect of the different extracts and individual compounds isolated from _L. leontopodioides_ whole plants in the cytoplasm of human hepatoma cells (HepG2 line) [87]. The aqueous and hydroalcoholic eluates and certain compounds (leontoaerialoside D and E) had an insulin-like effect with enhanced glucose uptake (>11%) at a concentration of 30 uM.  
Chemoprotective activity was described in a study investigating the hepatotoxicity effect of an aqueous extract of _L. leontopodioides_ against D-galactosamine (D-GalN)-induced hepatocyte injury using in vitro primary cultures of neonatal mice and HL-7702 hepatocytes [127]. The in vivo protective effect of this extract on D-GalN-induced liver injury was evaluated in mice by examining some parameters characteristic to hepatocyte injury, such as serum aspartate aminotransferase (AST), alanine aminotransferase (ALT) and histopathological alterations [127].