esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article presents a methodology for the monitoring of tall structures based on the joint use of a terrestrial laser scanner (TLS), configured in line scanner mode, and a ground-based real aperture radar (GB-RAR) interferometer. The methodology provides both natural frequencies and oscillation amplitudes of tall structures. Acquisitions of the surface of the tall structure are performed by the TLS with a high sampling rate: each line scan provides an instantaneous longitudinal section. By interpolating the points of each line, oscillation profiles are estimated with a much better precision than each single point. The amplitude and frequency of the main oscillation mode of the whole structure are derived from the TLS profiles. GB-RAR measurements are used to measure the vibration frequencies of higher oscillation modes which are not caught by the TLS due its lower precision in the measurement of displacements. In contrast, the high spatial resolution of TLS measurements provides an accurate description of oscillation amplitude along the tower, which cannot be caught by the GB-RAR, due to its poorer spatial resolution. TLS and GB-RAR acquisitions are simultaneous. The comparison with the analytical solution for oscillation modes demonstrates that the proposed methodology can provide useful information for structural health monitoring (SHM). The methodology does not require the use of targets on the structure and it can be applied during its normal use, even in presence of dynamic loads (wind, traffic vibrations, etc.). A test was carried out on a wind tower where the synergistic use of TLS and GB-RAR made it possible to fully describe the spectral properties of the tower and at the same time measure the amplitude of the first oscillation mode along the tower with a high spatial resolution.  
terrestrial laser scanner (TLS); ground-based real aperture radar (GB-RAR); line scanner; vibration frequency; spectral analysis; displacement; structural health monitoring (SHM)
## 2 Materials and Methods  
### Plant Materials  
Twenty-two turnip landraces were collected from the farmers of different regions. Seven landraces were collected from China (Mountain of Hengduanshan), five landraces were collected from QTP region, and ten landraces were collected from the Himalayas. The location information was listed (Supplementary Table S1). During the time of collection of the samples, data were recorded about the morphology of the plants along with the method of cultivation (use of manures or pesticides). The collection of seeds from the farmers was carried out during the time of harvesting (February-March). The seeds collected from the farmers were the representative samples of those particular agroclimatic environments. The geographical coordinates were recorded using a satnav (satellite navigation instrument) and altitude of each collection places was determined with help of a digital altimeter. The seeds were deposited in the germplasm bank of wild species--the Kunming Institute of Botany. Seeds were grown in the agricultural field of Ninglang, Yunnan province all at the same time to full maturity. Sandy loam soil of pH between 6 and 7 was used for the propagation of turnip. Separation between the seedlings was maintained 15 cm. During time of flowering, turnip flowers were netted to prevent introgression. Morphological observation was carried out from the  
Figure 1: Geographic distribution of landraces. Locations are not exact geographic locations because location data were given as country province. Landraces are represented by their accession numbers in this figure and detailed information of the collection sites are listed in Supplementary Table S1. The three environments are Henduanshan (KTRG-B-25, KTRG-B-33, KTRG-B-37, KTRG-B-54, KTRG-B-55, KTRG-B-61, and KTRG-B-65),Qinghai-Tibetan Plateau (KTRG-B-03, KTRG-B-05, KTRG-B-19, KTRG-B-49, and KTRG-B-57), and the Himalayas (KTRG-B-70, KTRG-B-77, KTRG-B-78, KTRG-B-79, KTRG-B-81, KTRG-B-82, KTRG-B-83, KTRG-B-84, KTRG-B-85, and KTRG-B-86). KTRG represents the Kunming Turnip Research Group.  
on-field data. The plant materials were photographed during the time of harvesting for their tuber dimension which was already documented in our previous study [29]. After transfer to the laboratory, seeds were stored at 15 \({}^{\circ}\)C and 15% relative humidity for 1 week to reduce moisture content to prevent mildew. Seeds harvested from landraces grown in the same agricultural field of Ninglang were used for measurement of seed characteristics and at least seeds from 15 individuals were mixed randomly and best seeds were selected for phenotypic measurements. Immature leaves were transferred to Kunming for GS investigation from Ninglang and all GS estimates were carried out within 5 days of arrival of the materials.
## 5 Computational Experiments
### Experimental Results
Therefore, the computational experiments demonstrate the effectiveness and efficiency of both the improved revised \(\epsilon\)-constraint method and the augmented \(\epsilon\)-constraint method for instances of different scales. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scale Type**} & \multirow{2}{*}{**Number of Nodes**} & \multirow{2}{*}{**ID**} & \multicolumn{4}{c}{**Basic \(\epsilon\)-Constraint Method**} & \multicolumn{4}{c}{**Revised \(\epsilon\)-Constraint Method**} \\ \cline{3-8}  & & & **NOSO** & **HR (\%)** & **CPU Time (s)** & **NOSO** & **HR (\%)** & **CPU Time (s)** \\ \hline \multirow{4}{*}{Small} & & 1 & 6 & 100.00 & 10.66 & 6 & 100.00 & 20.13 \\  & & 2 & 9 & 100.00 & 16.59 & 9 & 100.00 & 33.90 \\  & 60 & 3 & 5 & 100.00 & 8.08 & 5 & 100.00 & 15.53 \\  & & 4 & 8 & 100.00 & 21.47 & 8 & 100.00 & 51.26 \\  & & 5 & 9 & 100.00 & 24.23 & 9 & 100.00 & 48.62 \\ \hline \multirow{4}{*}{Medium} & & Avg. & 7.40 & 100.00 & 16.20 & 7.40 & 100.00 & 33.89 \\  & & 1 & 7 & 100.00 & 173.07 & 7 & 100.00 & 372.35 \\  & & 2 & 13 & 100.00 & 273.82 & 13 & 100.00 & 532.00 \\  & 200 & 3 & 6 & 100.00 & 123.80 & 6 & 100.00 & 306.41 \\  & & 4 & 11 & 100.00 & 375.81 & 11 & 100.00 & 661.75 \\  & & 5 & 13 & 100.00 & 219.51 & 13 & 100.00 & 598.17 \\ \hline \multirow{4}{*}{Large} & & Avg. & 10.00 & 100.00 & 233.20 & 10.00 & 100.00 & 494.14 \\ \cline{2-8}  & & 1 & 7 & 100.00 & 1122.57 & 7 & 100.00 & 1755.86 \\  & & 2 & 6 & 100.00 & 1120.02 & 3 & 94.58 & 1801.69 \\  & 500 & 3 & 5 & 100.00 & 1529.64 & 2 & 85.56 & 1805.72 \\  & & 4 & 6 & 100.00 & 1449.08 & 4 & 97.96 & 1804.32 \\  & & 5 & 7 & 100.00 & 739.79 & 7 & 100.00 & 1430.97 \\ \hline \multicolumn{8}{c}{Abbreviations: NOSO, number of solutions obtained; HR, hypervolume-based ratio.} \\ \end{tabular}
\end{table} 
Table 2: Experimental results of the basic \(\epsilon\)-constraint method and revised \(\epsilon\)-constraint method. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scale Type**} & \multirow{2}{*}{**Number of Nodes**} & \multirow{2}{*}{**ID**} & \multicolumn{4}{c}{**Improved Revised \(\epsilon\)-Constraint Method**} & \multicolumn{4}{c}{**Augmented \(\epsilon\)-Constraint Method**} \\ \cline{3-8}  & & & **NOSO** & **HR (\%)** & **CPU Time (s)** & **NOSO** & **HR (\%)** & **CPU Time (s)** \\ \hline \multirow{4}{*}{Small} & & 1 & 6 & 100.00 & 0.57 & 6 & 100.00 & 0.32 \\  & & 2 & 9 & 100.00 & 0.83 & 9 & 100.00 & 0.41 \\  & 60 & 3 & 5 & 100.00 & 0.48 & 5 & 100.00 & 0.27 \\  & & 4 & 8 & 100.00 & 1.02 & 8 & 100.00 & 0.55 \\  & & 5 & 9 & 100.00 & 0.83 & 9 & 100.00 & 0.47 \\ \hline \multirow{4}{*}{Medium} & & Avg. & 7.40 & 100.00 & 0.75 & 7.40 & 100.00 & 0.40 \\  & & 1 & 7 & 100.00 & 7.64 & 7 & 100.00 & 4.00 \\  & & 2 & 13 & 100.00 & 15.23 & 13 & 100.00 & 7.92 \\  & 200 & 3 & 6 & 100.00 & 9.66 & 6 & 100.00 & 4.70 \\  & & 4 & 11 & 100.00
## 3 Test of the Surrounding Rock  
### In Situ Rock Deformation Test of the Pressure Plate  
The deformation test of the pressure plate at the project site was carried out. The deformation modulus of surrounding rock under the water transfer and maximum test pressure is analyzed. A total of six tests were set up on the project site, with the test points placed on the side wall of a test pit. The location layout of the test site and the pressure plate test loading are shown in Figure 2. #1 to #6 represent test locations of the six pressure plate, respectively. The test pit's long side forms a 105\({}^{\circ}\) angle with the shield tunneling direction; its top elevation is roughly 25.5 m; its depth is approximately 2.5 m. The elevation of the test center point is about 27.2 m. Its elevation outside the shaft is nearly 3.7 m. The diameter circular rigid pressure plate of 0.30 m was imposed by using a 20 t jack. The maximum test pressure of 2.1 MPa is about three times the design pressure.  
Figure 1: Layout sketch of water resources allocation project.  
By measuring the surface deformation of the rock mass using a pressure plate test, the deformation modulus was calculated. And the deformation modulus of the borehole deformation test was compared. The test pit was located in a weakly weathered peilitic siltstone layer. The lithology of the six test points is relatively complete; weakly weathered peilitic siltstone without obvious fracture development. Under two stress levels of water delivery pressure of 0.7 MPa and a maximum test pressure of 2.1 MPa, the secant and fitting methods were used to compare and analyze the deformation modulus. The calculated deformation modulus at the highest point of the test pressure is shown in Table 1.  
The results indicate that the fitting and the secant method are nearly identical to each other. According to statistical fitting method data, the deformation modulus of each test point under stress close to the water transmission pressure of the engineering design is 1.16-8.84 GPa, and the average value is 4.71 GPa. The deformation modulus calculated by the maximum test pressure is 1.31-9.60 GPa, and the average value is 4.69 GPa. The fitted curve of test point No. six at the highest point of test pressure is shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Stress Level (Mpa)**} & \multirow{2}{*}{
\begin{tabular}{c} **Calculation** \\ **Method** \\ \end{tabular} } & \multicolumn{5}{c}{**Test Point (Gpa)**} & \multicolumn{5}{c}{**Average (Gpa)**} \\ \cline{3-8}  & & **1** & **2** & **3** & **4** & **5** & **6** & **Es** \\ \hline
2.1 & Secant line & 1.30 & 4.11 & 2.27 & 2.10 & 9.63 & 8.07 & 4.58 \\
2.1 & Fitting & 1.31 & 4.33 & 2.38 & 2.02 & 9.60 & 8.52 & 4.69 \\
0.7 & Fitting & 1.16 & 5.53 & 2.68 & 1.71 & 8.84 & 8.33 & 4.71 \\ \hline \end{tabular}
\end{table}
Table 1: Deformation modulus of each test point.  
Figure 2: In situ pressure plate test. (**a**) Site layout. (**b**) Field loading test.
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
## 3 Implementation
### Deec-K-nn
#### 3.2.3 Performance Results  
The implementation results of the DEEC-K-NN protocol, as shown in Table 4, show that the proposed protocols significantly outperformed the original DEEC in several critical aspects. Most notably, the time until the first node dies was extended by 12%, indicating improved network longevity. The total number of data packets successfully delivered nearly doubled, reflecting more efficient and reliable communication. The adaptive nature of the clustering process resulted in fewer but more effective clusters on average, contributing to better energy utilisation and load balancing among cluster heads.  
A visual analysis of network performance plots further supports these findings. As shown in Figure 3, the node death curve was more gradual, indicating a slower and more controlled depletion of network resources. Packet delivery was higher and more consistent throughout the network's lifetime, and energy consumption was more evenly distributed among nodes. The spatial distribution of cluster heads was also improved, leading to better coverage and network stability.  
Integrating K-NN-based clustering into the DEEC protocol led to a more energy-efficient, stable, and long-lasting Wireless Sensor Network compared with the original protocol, as shown in Table 5. The proposed method achieved superior network lifetime, energy balance, and communication reliability compared with the original DEEC protocol. These results highlight the potential of ML techniques, such as K-NN, to enhance the organisation and efficiency of resource-constrained sensor networks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No** & **Metric** & **Original DEEC** \\ \hline
1 & Total Nodes & 100 \\
2 & Initial Energy per Node & 0.5 J \\
3 & First Node Death at Round & 1131 \\
4 & Last Node Death at Round & 1400 \\
5 & Total Packets Sent to BS & 11,865 \\
6 & Remaining Network Energy & 0.0989 J \\
7 & Energy Consumption Rate & 0.025018 J/round \\
8 & Average Number of Clusters & 5.93 \\
9 & Network Lifetime (70\% alive) & 1204 rounds \\ \hline \hline \end{tabular}
\end{table}
Table 4: DEEC-K-NN protocol simulation results summary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Metric** & **Original DEEC** & **DEEC-K-NN** & **Improvement** \\ \hline
1 & First Node Death (FND) & 1010 rounds & 1131 rounds & +12.0\% \\
2 & Total Packet Delivery & 5982 packets & 11,865 packets & +98.3\% \\
3 & Average Cluster Formation & 10 (static) & 5.93 (dynamic) & Enhanced efficiency \\
4 & Network Stability Period & Not quantified & 1155 rounds & Superior stability \\
5 & Energy Consumption Rate & 0.024997 J/round & 0.024988 J/round & +0.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of DEEC and DEEC-K-NN protocols.
## 3 Results and Discussion
### Preference Selection Index Result  
From the method of preference selection, Table 9 displays the normalized matrix, and in Table 10, the results of divergence, weight, and ranking. In this case, the results present a mixture between the before mentioned methods. Where an environmental aspect is considered as the most important followed by the density and lastly the flexural modulus.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Normalized Matrix \(P_{ij}\)**} \\ \hline
0.36 & 0.07 & 0.10 & 0.26 & 0.11 & 0.41 & 0.21 \\
0.05 & 0.12 & 0.01 & 0.03 & 0.02 & 0.10 & 0.12 \\
0.04 & 0.16 & 0.17 & 0.17 & 0.17 & 0.06 & 0.16 \\
0.23 & 0.17 & 0.23 & 0.21 & 0.23 & 0.08 & 0.09 \\
0.08 & 0.13 & 0.17 & 0.06 & 0.16 & 0.07 & 0.10 \\
0.14 & 0.19 & 0.14 & 0.07 & 0.14 & 0.21 & 0.15 \\
0.10 & 0.16 & 0.17 & 0.21 & 0.17 & 0.07 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Normalized matrix entropy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Criteria** & **Entropy** & **Weight** & **Rank** \\ \hline Price & 0.87 & 0.25 & 2 \\ Density & 0.98 & 0.03 & 7 \\ Young’s Modulus & 0.93 & 0.13 & 4 \\ Yield Strength & 0.90 & 0.18 & 3 \\ Flexural Modulus & 0.94 & 0.11 & 5 \\ Embodied Energy & 0.86 & 0.26 & 1 \\ CO\({}_{2}\) Footprint & 0.98 & 0.04 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Entropy weight.  
Since the objective of this research is to find an alternative material that can resist seismic events, it is considered that the priority has to be laid in the mechanical properties, for this reason among the three methods, the standard deviation weights were selected as the best suited to be used in the following multicriteria decision methods.
# Physically-Data Driven Approach for Predicting Formation Leakage Pressure: A Dual-Drive Method
## 3 Selection, Processing, and Correlation Analysis of Training Samples
### Selection of Input Layer Data  
For this study, Well M-1 is chosen as the training well to supply sample data for model learning, while Well M-2 serves as the validation well to predict and assess the accuracy and generalization of the trained model. The quality of the sample data has a direct impact on the accuracy of the prediction results. This study establishes an intelligent prediction model for pressure loss by selecting specific logging data from Well X-1, guided by the fundamental principles of formation pressure loss and existing research findings. The input layer data of the prediction model comprise well diameter (CAL), formation density  
Figure 4: Stratigraphic comparison of the M-1 well and M-2 well.  
(DEN), sonic travel time (DT), natural gamma (GR), shale content (VSH), and equivalent pore pressure density (Pp). This selection is grounded in the capability of logging data to directly capture essential information about the formations, along with its attributes of continuity, accuracy, and cost-effectiveness, which render it a crucial foundation for subsequent developmental endeavors.  
The logging data selected as input parameters are visualized using a violin plot, as depicted in Figure 5. Within the violin plot, the median is represented by a central dot, the boxplot illustrates the data spanning from the lower quartile to the upper quartile, the black lines indicate data points within 1.5 times the interquartile range, and the outer shape depicts the kernel density estimate. Figure 5 reveals that the distribution of each input parameter does not conform to a standard normal distribution. Hence, subsequent correlation analysis should utilize Spearman correlation analysis. Additionally, the absence or scarcity of outliers in the data suggests the rational construction of the dataset.
# Protection of Buried Pipelines from High-Speed Train Vibrations with Expanded Polystyrene Geofoam
###### Abstract  
High-speed railway (HSR) lines commonly operate over hundreds of kilometers, crossing several other large-scale infrastructures, such as highways, tunnels, bridges, and pipelines. This fact makes adjacent infrastructure more vulnerable to high-speed train (HST)-induced vibrations; thus, their potential distress should be carefully examined. The current study aims to assess the level of traffic-induced vibrations on the surface of buried pipelines vertically crossing under an HSR line. Firstly, the necessity to reduce high vibration levels is highlighted, utilizing a three-dimensional (3D) finite element model in conjunction with the moving load approach. Subsequently, an efficient mitigation measure is proposed to minimize these vibrations. For this purpose, a low-weight, high-performance geosynthetic fill material, i.e., expanded polystyrene (EPS) geofoam blocks, has been implemented between the HSR line and the buried pipeline to minimize the impact of vibrations. In this manner, HST-induced vibrations are reflected on EPS blocks, preventing them from reaching the pipeline surface. Based on this detailed parametric study, useful conclusions are drawn regarding the mechanical properties and geometry of the EPS protection layer.  
high-speed railway; high-speed trains; train-induced vibrations; pipelines; mitigation measures; expanded polystyrene
# Dual-Band Shared-Aperture Antenna with Pattern and Polarization Diversity
## 2 Antenna Design and Analysis  
The proposed antenna is shown in Figure 1, which consists of a hollow metal cone, a metal Vivaldi antenna placed at the bottom of the metal cone, a reflective ground, and a dielectric circular polarization converter [26; 27; 28; 29; 30]. Compared with a solid cone, the hollow cone is used to achieve a lightweight without affecting the radiation performance. The reflective ground is based on the substrate FR-4 with a thickness of 1 mm and a dielectric constant of 4.3. The metal cone antenna and Vivaldi antenna are fed separately. The metal cone is fed by the coaxial cable from the bottom, while the Vivaldi antenna is fed by the lateral coaxial cable. The medium circular polarization converter is placed directly above the metal cone, and the converter's bottom surface is flush with the top surface of the metal cone. As Figure 1 shows, the dielectric circular polarization converter consists of a series of slabs in a circular shape. The total size of the designed antenna is 0.76 \(\times\) 0.76 \(\times\) 0.37 \(\lambda_{0}\)\({}^{3}\), where \(\lambda_{0}\) is the free space wavelength of the lowest operating frequency of 2.09 GHz. The final design parameters are shown in Table 1.
## 3 Results and Discussions
### No\({}^{+}\) Reagent Ion Measurements with Propofol  
The same two product ions found for the reactions of O\({}_{2}\)\({}^{+\bullet}\) with propofol result from NO\({}^{+}\), although the fragment ion, C\({}_{11}\)H\({}_{15}\)O\({}^{+}\), is significantly less pronounced and only becomes the dominant product ion at a high _E/N_ value of about 180 Td. The less pronounced fragmentation compared to the O\({}_{2}\)\({}^{+\bullet}\) reactions at all _E/N_ values must be a result of the smaller internal energy resulting from the transfer of charge. This result is in good agreement with the SIFT-MS study reported by [PERSON] et al. [6], for which the reaction of NO\({}^{+}\) with propofol resulted only in non-dissociative charge transfer. The lack of a reaction of propofol with NO\({}^{+}\) via a charge transfer process provides an upper limit for its ionisation potential, namely that it is less than that of NO, which is at 9.3 eV [35].  
H\({}_{3}\)O\({}^{+}\), H\({}_{3}\)O\({}^{+}\).H\({}_{2}\)O, O\({}_{2}\)\({}^{+\bullet}\) and NO\({}^{+}\) Reagent Ion Measurements with 2,6-Disopropyl-1,4-benzoquinone  
No reaction of O\({}_{2}\)\({}^{+\bullet}\) or NO\({}^{+}\) with 2,6-disopropyl-1,4-benzoquinone was observed. No values for the ionisation energy of this molecule are available in the literature. However, the lack of any charge transfer suggests that the ionisation energy of the metabolite is higher than that of O\({}_{2}\), which is 12.07 eV [32]. Similarly, the PA and GB of 2,6-disopropyl-1,4-benzoquinone are unavailable. Therefore, these values have been calculated using the same method as described for propofol. In the case of 2,6-disopropyl-1,4-benzoquinone, there are two sites for protonation (positions 1 and 4). For position 1, the proton affinity and gas-phase basicity are calculated to be 829 kJ mol\({}^{-1}\) and 797 kJ mol\({}^{-1}\), respectively. For position 4, the proton affinity and gas-phase basicity are determined to be 865 kJ mol\({}^{-1}\) and 834 kJ mol\({}^{-1}\), respectively. These values are in agreement with our observations that both H\({}_{3}\)O\({}^{+}\) (calculated PA(H\({}_{2}\)O) 684 kJ mol\({}^{-1}\) and GB(H\({}_{2}\)O) 653 kJ mol\({}^{-1}\)) and H\({}_{3}\)O\({}^{+}\).H\({}_{2}\)O (calculated PA((H\({}_{2}\)O)\({}_{2}\)) 842 kJ mol\({}^{-1}\) and GB((H\({}_{2}\)O)\({}_{2}\)) 777 kJ mol\({}^{-1}\))) reagent ions react efficiently with the metabolite. Only one product ion at _m/z_ 193.124 was observed at any _E/N_ value, corresponding to the protonated metabolite.