esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of a Landslide on a Tunnel in Loess-Bedrock Ground
## 3 Results
### Lateral Deformation  
The lateral deformation of the slope was measured using a PIV system. The horizonal (H) and vertical (V) deformations at different time points are shown in Figure 8.  
Figure 7: Variation Schematic diagram of the variation of soil pressure with slope height.  
At 300 min, the soil deformation was obvious. There are three deformation zones on the horizontal contour: the tunnel roof, the tunnel bottom, and the loess-bedrock interface. These deformation zones determine the deformation of the entire slope. The maximum deformation in the horizontal direction was 8 cm, and the deformation in the vertical direction was 3.8 cm, indicating that the soil moved horizontally under the action of trailing edge pressure. The slope above the tunnel presented progressive failure characteristics, and the deformation decreased gradually from the back edge to the front.  
At 340 min, different changes can be observed in the three slide bands. The loess-bedrock interface continues to exhibit a linear pattern. The deformation of the tunnel roof and the tunnel bottom bands increases and the band sites change because of tunnel's deformation. The H deformation was 9.32 cm, and the V deformation was 4.48 cm.  
At the foot of the slope, the deformation occurs in the direction opposite to the direction of the force load. This phenomenon indicates that the frictional force between the soil and bedrock was greater than the load at the back edge of the slope. The slip zone (1) was completely cut through at 440 min, the deformation zone above and below the tunnel was most clearly defined near the end of the last loading phase, and the deformation of the soil  
Figure 8: Lateral displacement contour measured by PIV system. (Unit: cm).  
on both sides of the tunnel exhibited a clear difference. The soil above the tunnel slid as a whole and the bottom soil was compressed. The maximum displacement of the subsoil was 6 cm at the foot of the slope. After repeated loading, the soil above the tunnel showed obvious landslide characteristics: frontal uplift, a slip zone, soil loosening, and collapse. The landslide deformation was greatest in the upper part of the tunnel, with a maximum displacement of 18 cm, while the landslide deformation in the lower part of the tunnel was smaller, with a maximum displacement of 6 cm. The presence of this tunnel reduced the soil deformation threefold.  
The horizontal displacement field calculated by the numerical simulation exhibits good agreement with the images taken by the test PIV. The horizontal displacement of the side slope is divided into two parts by the tunnel structure at the top and bottom. Comparing Figure 8(a) with Figure 8(b), it can be observed that the existence of the tunnel structure resulted in significant differences across the slope displacement area. This difference was especially marked at the rear edge of the slope above the tunnel, which is the position where the initial velocity of the model was applied, and also the position where the displacement was the most obvious.
## 2 Results  
### Identification and Sequence Analysis of the MgPDI2 Gene from M. graminicola  
A 1573 bp cDNA of _MgPDI2_ (GenBank accession number MT517333) was cloned from _M. graminicola_. The _MgPDI2_ gene includes an open reading frame (ORF) of 1569 bp encoding a putative protein of 522 amino acids with a predicted molecular size of 58.7 kDa. According to the SignalP program, MgPDI2 contains a secretion signal peptide of 20 amino acids at its N-terminus, indicating that MgPDI2 might be secreted into the host tissue by _M. graminicola_.  
NCBI Conserved Domains search program analysis revealed that MgPDI2 contained four conserved thioredoxin domains (a, b, b\({}^{\prime}\), a\({}^{\prime}\)) (Figure 1). In the 3-D tertiary structure, MgPDI2 is predicted to have a shape of a twisted \"U\", in which the a and a\({}^{\prime}\) domains are found on the ends while the b and b\({}^{\prime}\) domains form the base of the U (Figure 1). In addition, the a and a\({}^{\prime}\) domains each contain one active site which is characterized by a Cys-Gly-His-Cys (CGHC) catalytic motif (Figure 1A). Phylogenetic analysis based on PDI-deduced amino acid sequences of different organisms revealed the distant relationship of two MgPDI proteins from _M. graminicola_. MgPDI and MgPDI2 are in two different clades. MgPDI2 grouped with PDIs from _P. parasitica_ and _Brugia malayi_ while MgPDI grouped with PDIs from _H. schachtii_ and _C. elegans_ (Figure 1B).  
Figure 1: Sequence analysis of MgPDI2. (**A**) The tertiary structure of MgPDI2. (**B**) Molecular phylogenetic analysis of protein disulfide isomerase (PDI) proteins from different organisms based on PDI-deduced amino acid sequences. The numbers indicated on the nodes are the bootstrap values for each cluster based on 1000 permutations.  
MgPDI2 Is Expressed in the Subventral Esophageal Glands and Was Up-Regulated during the Early Parasitic Stage of M. graminicola  
In order to determine the tissue localization of _MgPDI2_ in pre-parasitic _M. graminicola_ juveniles, _in situ_ hybridization was performed. Strong green fluorescent signals were observed in the subventral gland cells of the pre-parasitic second-stage juveniles (J2s) after the Digoxin (DIG)-labelled antisense probe of _MgPDI2_ specifically hybridized with transcripts, while no signal was detected when using the labeled sense probe (Figure 2A). qRT-PCR was used to further analyze the transcriptional expression pattern of _MgPDI2_ during different developmental stages of _M. graminicola_. The transcription levels in sedentary stages of nematode were relatively high, reaching its maximum level at 3 dpi relative to actin gene expression (Figure 2B).
## 2 Related Work  
There has been a lot of research on the difficulties that come with deploying UAVs while taking into account a number of factors. Examples include optimizing response time and bandwidth efficiency [19], maximizing the number of high-priority ground nodes (GNs) [20], minimizing the number of mobile base stations (MBSs) to provide coverage to a group of ground terminals (GTs) [21], maximizing system throughput [22], improving spectral efficiency [26], and optimizing quality of experience (QoE) [27]. Next, we provide a brief introduction to these aspects.
# The CIPCA-BPNN Failure Prediction Method Based on Interval Data Compression and Dimension Reduction
## 5 Results  
### Data Compression  
The original data included 123 sorties of flight data, with a total of 253,885 sample points. We used the interval method to compress the original flight data of each sortie. After finishing, there were a total of 35,091 samples, including 2973 fault samples and 32,118 normal samples. After intervalidation, the sample size was 13.82% of the original data. The effect of data compression is obvious. It can reduce the number of samples, retain the interval information of the original data, and alleviate the problem of data imbalance to a certain extent (Table 1).
## 2 WSNs Enabled Smart Cities Applications  
To deal with the expanding demography of urban centers, the development of smart cities is becoming more and more critical. These cities will indeed improve the quality of life of citizens by optimizing their daily lives (e.g., urban transportation planning), their safety (e.g., rapid response in emergency situations), while offering them value-added services (e.g., remote monitoring and control of their homes). On the other hand, these cities are expected to integrate intelligence, in order to automate their operation, and should foster energetic sobriety, for a more sustainable and resilient world [9].  
The deployment of heterogeneous wireless network technologies will be necessary to enable these advances. Long-range wireless networks, such as 5G or Beyond 5G (B5G) networks, will definitely be needed to serve bandwidth-intensive or low-latency applications. These cities will also require sensor networks, whether short-range, such as WSNs, or long-range, such as Low Power Wide Area Networks (LPWANs), which could serve as a base station (or sink) to aggregate and report sensors readings.  
Without being exhaustive, WSNs can be considered in several use cases in smart cities. Their use within smart road infrastructure and integration with Connected Autonomous Vehicles (CAVs) will help preventing accidents while improving traffic flow fluidity. Their use in buildings can reduce energy consumption when combined with actuators. They can trigger alerts, faster, during fires, and allow to adapt, for example, the frequency of the means of transportation according to the needs, enabling a significant reduction in carbon footprint. The number of applications and practical use cases are extremely numerous and are only limited by the boundaries of the human imagination [10].  
By organizing themselves in networks, WSNs potentially cover large areas, even inside buildings [11]. However, the viability of these sensors is very much linked to their lifetime which should be of several years. To maximize the operating lifetime of these wireless sensors, several strategies have been proposed in the literature. These techniques can be broadly divided into three categories: (i) Strategies based on energy harvesting from the environment (stemming from different sources such as solar, mechanical, or electromagnetic energy) [12], (ii) strategies optimizing the idle time of the devices [13], and (iii) techniques aiming to optimize the wireless devices' operation [14]. These different strategies can be found in different scenarios for smart cities. They are in no way contradictory, and can even be leveraged by the same wireless sensors.  
In this paper, we focus on the operational optimization of sensors, and particularly on clustering techniques. Thus, our contribution may concern the various introduced use cases, in which clustering strategies are central for enhanced energy savings. Please note that all notations used in the rest of the manuscript are summarized and explained in Table 1.
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
## 4 Simulation Verification
### YNa0d11 Autotransformer One-Phase and Two-Phase Disconnection Test  
To verify the low-voltage measurement method of the three-winding autotransformer disconnection test proposed in this paper, a simulation model of a YNa0d11 autotransformer  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Standard Test** & \multicolumn{2}{c}{**Two-Phase Disconnection Test**} \\  & **Measured Value** & **High-Voltage** & **Low-Voltage** \\ \hline \(V_{0}\) & 1443 V & - & - \\ \(3l_{0}\) & 33.58 A & - & - \\ \(V_{ab}\) & - & 358.3 V & 402.7 V \\ \(V_{bc}\) & - & 400 V & 0 V \\ \(V_{ca}\) & - & 361.3 V & 390.3 V \\ \(I_{a}\) & - & 17.66 A & 17.64 A \\ \(l_{b}\) & - & 0.522 A & 0 A \\ \(l_{c}\) & - & 0.522 A & 0 A \\ \(Z_{\text{H(L|g)}-0}\) (\%) & 9.58 & 9.65 & 9.69 \\ error & - & 0.73\% & 1.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Two-phase disconnection test of double-winding autotransformer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Capacity/kVA** & **No-Load Loss/kW** & **Load Loss/kW** & **Short Circuit Impedance \%** \\ \hline YNa0 & 31,500 & 45 & 157 & 13 \\ autotransformer & & & 9 (High-Medium) \\ YNa0d11 & 63,000 & 30 & 132 & 28 (High-Low) \\ autotransformer & & & & 20 (Medium-Low) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transformer parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Standard Test** & \multicolumn{2}{c}{**One-Phase Disconnection Test**} \\  & **Measured Value** & **High-Voltage** & **Low-Voltage** \\ \hline \(V_{0}\) & 1443 V & - & - \\ \(3l_{0}\) & 33.58 A & - & - \\ \(V_{ab}\) & - & 379.4 V & 399.8 V \\ \(V_{bc}\) & - & 350.9 V & 414.6 V \\ \(V_{ca}\) & - & 385.6 V & 385.1 V \\ \(I_{ab}\) & - & 13.51 A & 13.78 A \\ \(I_{bc}\) & - & 22.98 A & 23.65 A \\ \(I_{ca}\) & - & 13.88 A & 13.95 A \\ \(Z_{\text{H(L|g)}-0}\) (\%) & 9.58 & 9.76 & 9.38 \\ error & - & 1.88\% & \(-\)2.01\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: One-phase disconnection test of double-winding autotransformer.  
(63 MVA 220/121/36 KV) was built in Simulink. The positive-sequence short circuit impedance is 28% for \(Z_{\rm HL-1}\), 19% for \(Z_{\rm TL-1}\) and 9% for \(Z_{\rm HT-1}\). Tables 5 and 6 show the measurement results of the different wire disconnection tests.  
The theoretical analysis and simulation validation involve model simplifications and equivalence measures to streamline calculations. While these measures may introduce some errors into the final simulation results, the accuracy of the proposed method's measurements is found to be within 3%, based on the conclusive results. The observed error is sufficiently small, making the method suitable for power system protection adjustment and short circuit current calculation.
## 4 Evaluation of the Developed Unit
### Tuning the Friction  
This section presents the evaluation of the effects of increasing or decreasing friction on the surface contact area of the device. The experimental setup used for these experiments is shown in Figure 9. The maximum static and kinetic friction forces were measured through placing the developed device on top of a bioskin plate (Beaulax H077-002), a model for the human skin, and a load \(f_{n}\) of 130 g on top of the device; subsequently, the bioskin plate and load were pulled using a force gauge, as shown in Figure 9. Measurements were taken after injecting a liquid (deionized water) into the contact area for 1 min to evaluate its ability to increase friction. Subsequently, the frictional force (pulling force) was measured while blowing dry air to evaluate the capability of the device to reduce friction. The pulling speed of the force gauge was set at 1 mm/s. For comparison, the friction force was also measured while the developed device was OFF; this is referred to as the \"nominal case.\" Each experiment was repeated three times under the experimental conditions listed in Table 4. The representative results of these experiments are shown in Figure 10. The maximum static frictional force, \(f_{max,st}\), was defined as the initial peak value. The kinetic frictional force, \(f_{kn}\), was defined as the mean value of the friction force for 1 s (1 mm in terms of the pulling displacement) at a steady state after the initial peak. If the steady state time was less than 1 s, the average value was calculated for the entire steady state. Using the measured pulling force (\(f_{max,st}\) and \(f_{kn}\)) and load applied to the device (\(f_{n}\)), the static and kinetic friction coefficients, \(\mu_{st}\) and \(\mu_{kn}\), were derived as follows.  
\[\begin{array}{l}\mu_{st}=f_{max,st}/f_{n},\\ \mu_{kn}=f_{kn}/f_{n}.\end{array} \tag{3}\]  
To assess how much friction has changed before and after the friction change, the respective ratios of \(\mu_{st}\) and \(\mu_{kn}\) before and after friction change were derived. Figure 11 summarizes the results obtained for all the experiments under different environmental conditions. The ratio of the maximum static and kinetic coefficients of friction after the friction change was compared with that of the nominal case. As expected, the results confirmed that under different environmental conditions, injecting water increased friction, whereas blowing dry air reduced it.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **t20h30** & **t20h50** & **t20h70** & **t30h30** & **t30h50** & **t30h70** \\ \hline After injecting the liquid [\%] & +47 & +25 & +6.3 & +41 & +23 & +8 \\ (Standard deviation) & (2.1) & (9.5) & (2.9) & (10) & (3.8) & (2.0) \\ \hline After blowing dry air [\%] & \(-2\) & \(-24\) & \(-40\) & \(-10\) & \(-31\) & \(-48\) \\ (Standard deviation) & (1.0) & (1.0) & (2.3) & (2.5) & (2.0) & (1.5) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Result of the function for changing the relative humidity.  
Figure 9: Experimental setup to measure the surface friction coefficient.
## 2 System Description and Mathematical Modeling
### Parrot Mambo Sensors and Actuators  
The Parrot Mambo drone is a versatile quadcopter equipped with a comprehensive set of sensors and actuators that enhance its dynamic capabilities. The propulsion system consists of four propellers, with two of them rotating clockwise (CW) and the other two rotating counterclockwise (CCW). Each propeller is directly connected to a motor, enabling precise control over thrust and orientation during flight. The Parrot Mambo's material constituents are introduced in [11]; these are shown in Figure 1.  
The onboard inertial measurement unit (IMU) of the Parrot Mambo drone includes the MPU6050, manufactured by InvenSense Inc., San Jose, CA, USA integrates a three-axis gyroscope and a three-axis accelerometer, providing essential data for attitude estimation and regulation. This sensor suite ensures that the drone maintains the desired orientation and responds promptly to movement changes. Additionally, ultrasonic sensors contribute to altitude control by measuring the distance between the drone and the ground, facilitating stable vertical positioning. The inclusion of an optical flow sensor enhances the Parrot Mambo's ability to detect ground movement, enabling precise horizontal positioning. Connectivity options include Bluetooth and a USB connector, allowing wireless communication and data transfer. A lithium polymer battery powers the drone, offering an optimal power-to-weight ratio (PWR).  
The Parrot Mambo drone's sensors, including the MPU6050, ultrasonic sensors, and optical flow sensors, combined with its quadcopter configuration and connectivity options, collectively form a robust platform for experimentation and application in various drone scenarios.  
Figure 1: Parrot Mambo’s material constituents.  
An additional feature that enhances the Parrot Mambo's capabilities is the inclusion of a camera extension. This component serves as a central unit, establishing a WiFi connection with Matlab to facilitate real-time data generation during flight. While Bluetooth connectivity alone cannot retrieve flight data, the camera extension significantly improves the drone's communication capabilities. By integrating this extension, the drone ensures seamless wireless communication with Matlab, providing continuous and immediate data generation throughout the flight. This feature not only broadens the scope of the Parrot Mambo's applications but also increases its value in research and experimentation, offering users a comprehensive and dynamic platform for data-driven analysis.  
The physical parameters of the Parrot Mambo model are summarized in Table 1 below.
## 5 Conclusions  
In this paper, a novel coordinated control algorithm of distributed BESSs is proposed, which regulates the voltage and frequency deviation of the power distribution system by coordinated control of charging and discharging of multiple BESSs. The proposed algorithm regulates the maximum voltage of the power system from 1.136 p.u. to 1.0995 p.u., and regulates the maximum frequency of the power system from 50.3653 Hz to 50.1598 Hz. In comparison with Lee algorithm, the total number of charging and discharging cycles and total power of BESSs are reduced. The results show that the proposed algorithm improves the life time of BESSs by reducing the total number of charging and discharging cycles. The BESSs can be better utilized for the voltage and frequency deviation regulation in the distribution system.  
Figure 14: Total charge and discharge power comparison.  
Figure 13: Comparison of charge and discharge times.  
**Author Contributions:** Conceptualization, Y.H.; methodology, Y.H.; formal analysis, X.S.; investigation, X.S.; data curation, Y.H.; writing--original draft preparation, Y.H.; writing--review and editing, Q.X. and Y.D.; supervision, Q.X.; project administration, Q.X.; funding acquisition, Q.X.  
**Funding:** This research was funded by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ18E070002.  
**Conflicts of Interest:** The authors declare that there are no conflicts of interest regarding the publication of this paper.
## 2 Materials and Methods  
The deposition of coatings was carried out by RF magnetron sputtering of a ceramic target of the composition BaTiO\({}_{0.8}\)Sn\({}_{0.2}\)O\({}_{3}\) on alumina substrates (polycrystalline aluminum oxide Al\({}_{2}\)O\({}_{3}\)). The temperature of the substrate \(T_{8}\) was varied in the range of 650-850 \({}^{\circ}\)C. A mixture of Ar/O\({}_{2}\) was used as the working gas, the oxygen content in the mixture was varied from 0 to 40%. The working gas pressure was 2 Pa. The thickness of the films was 400 nm and 800 nm. After deposition, the films were annealed at a temperature of 1100 \({}^{\circ}\)C in air for 2 h.  
The crystal structure and phase composition of the films were studied by X-ray diffraction using a DRON-6 diffractometer on the CuKa1 emission spectral line (\(\lambda\) = 1.5406 A). The parameters of the crystal lattice of BTS films were calculated based on the angular positions of X-ray reflexes, using reflections from the substrate as a reference. The surface morphology was studied by atomic force microscopy (AFM) on a scanning probe microscope Solver-P47 Pro from NT-MDT in a semi-contact mode. The scanning resolution was 512 \(\times\) 512 pixels. Cantilevers of the NSG01 type with an average resonant frequency of 150 kHz and an average radius of curvature of the tip of 6 nm were used. Before the measurements the device was calibrated using NT-MDT TGS1 test grids.  
For electrical research, planar capacitors were formed on the basis of BTS films. The upper electrodes of the capacitors were made by thermal evaporation of a Cu film (1 \(\upmu\)m) with an adhesive chromium sublayer, followed by lithography and chemical etching. The capacitance C- and Q-factor \(Q\) = 1/tan \(\delta\) of the capacitors were measured at a frequency of 2 GHz using a half-wave strip resonator [37] and HP 8719C vector analyzer. The resonator provides an unloaded Q-factor of 1000 (the accuracy of measuring the capacitance and Q-factor is 1 and 5%, respectively), as well as the possibility of supplying a control voltage up to 1000 V. The tunability of the capacitors was calculated as the ratio of capacitances at zero and maximum applied control voltage \(n\) = \(C\) (0 V)/\(C\) (\(U_{\rm{max}}\)), and additionally as \(n\) = (\(C_{\rm{max}}\) - \(C_{\rm{min}}\))/\(C_{\rm{max}}\) in order to compare results with other works.
## 4 Comparison of Pseudo-Dynamic Test Results and Non-Linear Dynamic Analysis Results
### Overview of Non-linear Dynamic Analysis
#### 4.1.1 Determination of Flexural Restoring Force Characteristics  
\[M_{c}=0.63\sqrt{F_{c}}Z\phi\ (\mathrm{Beam}) \tag{5}\]  
\[M_{c}=0.63\sqrt{F_{c}}Z+ND/6\ (\mathrm{Column}) \tag{6}\]  
\[M_{u}=0.9a_{t}\sigma_{y}d\ (\mathrm{Beam}) \tag{7}\]  
\[M_{u}=0.8a_{t}\sigma_{y}D+0.5 ND\bigg{(}1-\frac{N}{bDF_{c}}\bigg{)}\ (\mathrm{Column}) \tag{8}\]  
\[k_{B}=6 EI/1\ (\mathrm{Both\ beam\ and\ column}) \tag{9}\]  
where, \(M_{c}\): flexural crack moment (N\(\cdot\)mm), \(M_{u}\): ultimate flexural moment (N\(\cdot\)mm), \(k_{B}\): initial flexural stiffness (N/mm), \(F_{c}\): compressive strength of concrete (N/mm\({}^{2}\)), \(Z\): section modulus (mm\({}^{3}\)), \(\phi\): shape factor of the beam, \(N\): axial force (N), \(D\): column depth (mm), \(a_{t}\): total cross-sectional area of the tensile reinforcing bars (mm\({}^{2}\)), \(\sigma_{y}\): yield strength of the reinforcing bars (N/mm\({}^{2}\)), and d: effective depth (mm).