esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
## 4 Real Operational Run & Excavation Results and Analysis
Various efforts were made to find particularities in the signals that could give hints to internal weld features. Two digs were initiated, one successful (GW 10340), one with nothing found (GW 27640). The review of all 10 verified features reported as linear indications in the UltraScan(tm) CCD inspection resulted in the following: \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**GW**} & \multicolumn{2}{c}{**Feature Type**} & \multicolumn{2}{c}{**Orientation [o’clock]**} & \multicolumn{2}{c}{**Depth [mm]**} & \multicolumn{2}{c}{**Length [mm]**} \\ \cline{2-9}  & **Field** & **ILI** & **Field** & **ILI** & **Field** & **ILI** & **Field** & **ILI** \\ \hline
10340 & Mid-wall & Internal & & 11:16 & 3.3 & \textless{}2.5 & 420 & 300 \\  & inclusion & Linear Indication & & & & & & \\  & External & & & & & & & \\
17720 & External gouge & Linear Indication & & & & & & \\
27640 & Nothing found & Internal & & 4:56 & & \textgreater{}2.5 & & 74 \\  & Linear Indication & & & & & & & \\
66850 & Inclusion & External & 7:00 & & & & & \\  & Indent in weld cap & Linear Indication & 6:20-9:15 & & & & & \\  & Indent in weld cap & & 6:00–7:10 & & & & & \\
67020 & Indent in weld cap & External & 7:45-9:00 & & & & & \\  & Misalignment & Linear Indication & Full circle & & & & & \\  & Dressing & & 6:20 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Excavation results of the second five digs. Figure 14: Picture of gouge in GW 17720 base metal. 1. Five features were found to be weld repairs;
2. One feature was found to be a linear imperfection (inclusion);
3. One feature was found to be an inclusion;
4. One feature was found to be a scratch;
5. One feature was found to be an indent of the weld cap;
6. One feature was not found in the field. The analysis of UltraScan(tm) CCD tool data for the 32\" pipeline and the review of the NDE field excavation data lead to the conclusion that there are still some uncertainties in the discrimination between reportable and non-reportable features. Possible ways to improve the tool performance shall be assessed, and would require additional excavations.
# Flexible Thermo-Optic Variable Attenuator based on Long-Range Surface Plasmon-Polariton Waveguides
## 5 Conclusions  
We demonstrated a flexible thermo-optic variable attenuator based on LRSPP waveguide that can be used to transmit and equalize high frequency microwave signals in microwave photonic systems. The controllable optical attenuation has been realized by applied different driving voltage to the thermo-optic VOA. The experimental results show that the maximum optical attenuation of the VOA is over 28 dB at a driving voltage of 4.17 V. The maximum attenuation of microwave signals is about 55 dB. The flexible thermo-optic VOA using LRSPP waveguide is promising for chip-scale interconnection in high-density photonic integrated circuits and data transmission, amplitude control, and equalization in microwave photonic systems.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON]-[PERSON] and [PERSON]-[PERSON] performed the experiments; [PERSON]-[PERSON] and [PERSON]-[PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents and materials; [PERSON] wrote the paper.  
This work is supported by Ministry of Science and Technology (MOST) under Grant Number 2017 YFA0205800, National Natural Science Foundation of China (NSFC) under grant numbers 11734005 and the Fundamental Research Funds for the Central Universities under grant numbers 2242018k1G020 and 2242015 KD006.  
We appreciate [PERSON] for the manuscript revision. We also appreciate [PERSON] at School of Information Science and Engineering, Southeast University for granting us access to their BER test system.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Initial Tests and Characterization of TITPS7414  
To elucidate the product profile of _TT_UPS7414, this was expressed in _Nicotiana benthamiana_. The resulting products were analyzed using HS-SPME and pentane extraction of the leaves. In the pentane extraction (-)-germacrene D (**1**) was found to be the major product and bicyclogermacrene (**7**) a minor product (Figure 2a). Thus, _TT_UPS7414 is a (-)-germacrene D synthase. This product profile was also observed with HS-SPME analysis when using a PDMS fiber and injection port temperature at 160 \({}^{\circ}\)C (Figure 2b). To enhance production levels, _TT_UPS7414 was co-expressed with _At_HMGR or _Cf_DXS, which are upstream precursor genes of the mevalonate (MVA) and the 2-C-methyl-d-erythitol 4-phosphate (MEP) pathway, respectively. HMGR, which is the third enzyme in the MVA pathway, is tightly regulated in plants and has been shown to be a rate-limiting step [31,32]. Cross-talk has been shown to occur between the MVA and MEP pathways. Therefore, DXS, which is a rate-limiting enzyme in the MEP pathway, was also tested [33,34]. The product profile and ratios were identical whether _At_HMGR or _Cf_DXS were used, and for both enzymes, the product levels were higher than without the co-expression of these upstream genes (Supplementary Figure 2). The data presented here are from co-expression of _TT_UPS7414 and _Cf_DXS. _Tp_GAS, _Tg_TPS1, and _Tg_TPS2 were also co-expressed with _Cf_DXS.
## 3 Circuit Level Perspectives of JCRS RF Hardware
### Antenna Implementation
#### 3.2.3 A Comparative Analysis and Steps for JCRS Antenna Enablement  
Numerous antennas, for both radar and communications, have been reported over the years. Similar design principles have been used in both applications. A comparison has been made between a few recently presented antenna types that can be used for radar and communications. The comparison is summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Application** & **Antenna Type** & **Frequency (GHz)** & **Isolation (dB)** & **Reference** \\ \hline \multirow{8}{*}{Radar} & Parabolic reflector & 20; 40 & — & [45] \\ \cline{2-5}  & Waveguide + two stacked & & & \\ \cline{1-1}  & printed circuit boards & & & \\ \cline{1-1}  & (PCBs) + metallic & & & \\ \cline{1-1}  & via + linear array & & & \\ \cline{1-1}  & Slotted radiator (Vivaldi) & 8.3–11 & — & [60] \\ \cline{1-1}  & Slotted radiator & & & \\ \cline{1-1}  & (rectangular loop) & & & \\ \cline{1-1}  & Slotted radiator & & & \\ \cline{1-1}  & (coplanar waveguide) & & & \\ \cline{1-1}  & Waveguide & 76–81 & 30 & [57] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different radar and communication antennas.  
The performance of a parabolic dish reflector-based antenna is good, but the milling and manufacturing of such antennas have a high production cost and labor-intensive procedure. Planar arrangements are thus a good choice, because they have relatively low production costs and quick processing. By utilizing various techniques, the desired antenna performance can be determined from planar layouts. Typically, slots are added to the ground plane or radiator to achieve a wide band performance, but with limited isolation properties. Waveguide-based antennas can produce high isolation characteristics, but at the expense of bandwidth. In order to meet the demands of the upcoming generations of communication standards, researching several MIMO antenna isolation augmentation approaches at the mmWave spectrum is necessary. In particular, different metasurface arrangements must be studied to attain EM properties that are unattainable with natural materials.
# Improved Design of LNG Marine Loading Arm Docking Method Based on TRIZ Theory
## 3 The Problem Analysis  
### Functional Models  
The establishment of functional models for functional analysis can clarify the useful functions, functional levels, and performance levels (normal, excessive, insufficient), as well as the harmful functions of each component in the system [14]. Furthermore, functional models can assist researchers in gaining a more detailed understanding of the interactions between the system's components [15].  
Currently, the process of docking the LNG marine loading arm is performed manually. Figure 2 depicts the working diagram of the LNG marine loading arm docking to the LNG ship. This docking process involves the LNG marine loading arm (along with the control system), the operator, and the LNG ship system. At least two or more operators are required to carry out the docking process. One operator is stationed on the LNG ship to guide the marine loading arm, while the other is stationed on land. The land-based operator needs to continuously control and adjust the LNG marine loading arm based on the feedback received from the shipboard operator and their observations so that the LNG marine loading arm can steadily approach the target and activate the terminal joint once the predetermined distance is reached. This completes the docking process. After analyzing and summarizing the functions of the above components, the functional grade and performance level of each component were determined. Consequently, a functional model of the current LNG marine loading arm docking scheme with the LNG ship was established, as shown in Figure 3.  
After analyzing the functional model, three main factors affecting the docking of LNG unloading were identified: (1) waves and wind can interfere with the docking process; (2) the land-based operator may not have sufficient information to guide the marine loading arm, relying only on observation and communication with the offshore operator; (3) the information received by the onshore operator may result in over- or under-operation of the LNG marine loading arm, which is not stable enough. To further study and find a solution to these problems, a causal analysis is needed.  
Figure 2: Working schematic diagram of LNG marine loading arm docking with LNG ship. 1—LNG marine loading arm control box; 2—LNG marine loading arm; 3—end joint of the LNG marine loading arm; 4—land operator; 5—wave; 6—LNG ship; 7—transportation pipe interface; 8—ship operator; 9—wind.
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
## 4 Discussion  
InSAR has many advantages in terrain height measurement and surface deformation measurement, but InSAR processing will be affected by many factors, especially in terms of phase unwrapping, which will directly affect the accuracy of the measurement results. Currently, existing phase unwrapping methods have certain flaws, and there is currently no perfect phase unwrapping method suitable for all situations. Especially when the phase  
Figure 8: The error maps resulting from true phase unwrapping. (**a**) LS phase unwrapping error map. (**b**) PCUA phase unwrapping error map. (**c**) RPUA phase unwrapping error map. (**d**) The proposed method phase unwrapping error map.  
noise is large and the phase gradient is steep, the mainstream phase unwrapping methods will produce large unwrapping errors.  
In recent years, many researchers have made improvements to the LS phase unwrapping method to make it suitable for high phase noise and steep phase gradient, such as PCUA and RPUA, which are experimentally compared in this paper. However, there will still be a large phase unwrapping error, and when the phase noise and phase gradient change, PCUA and RPUA are unstable, and the generated RMSE is sudden.  
Therefore, this paper makes further improvements based on LS phase unwrapping. In experiments, it was found that the factor that affects the accuracy of phase unwrapping is the phase gradient, and noise is also one of the factors that affect the change of the phase gradient. When the phase gradient is relatively flat, the LS phase unwrapping method can perform phase unwrapping quickly and accurately. However, when the phase gradient is steep, the LS phase unwrapping method can only perform phase unwrapping quickly and cannot accurately obtain phase unwrapping results. Therefore, this paper uses a low-pass filter to change the steep phase gradient into a flat phase gradient for phase unwrapping. Based on the properties of many low-pass filters, the Chebyshev low-pass filter is selected and improved, which is added in phase unwrapping. To maintain the phase characteristics, an iterative method is used to add all the phase unwrapped results after low-pass filtering to obtain the final unwrapped phase.
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
## 2 Engineering Background, Site Investigation, and Laboratory Test
### Landslide Stabilizing Measures  
Twenty anchors and twelve ordinary anti-slide piles were used to prevent and control the Zhenzilin landslide [2]. The piles were installed along the Sichuan-Tibet highway at an elevation of approximately 2100 m (refer to Figure 2b). The ordinary anti-slide piles were 23.5 m long (see Figure 2b); 2 m wide (b); and 3 m high (a). The anti-slide piles were installed 6 m apart. There existed four ordinary anti-slide piles for which the embedded section length (h) equaled 8.5 m in the AA' cross-section of the Zhenzilin landslide (see Figure 2b), and the pile length above the slip surface (h\({}_{1}\)) equaled 15 m. Anti-slide piles were installed at points where the bedrock comprised three rock types--the 1-m-deep upper mudstone (weak) layer, the limestone (hard) layer measuring approximately 7.5 m deep, and sliding mass containing backfill. The strength of the soil was observed in terms of the internal friction angle. The cohesion between soil particles was very small. To simplify calculations, the distribution form of the driving force behind a pile was taken as triangular [33]. The bases of some piles were located in the weak rock layer owing to the interbedding relationship between limestone and mudstone. Hence, free conditions were considered for such pile ends.
## 4 Results, Discussions and Achievements  
In prior studies, uniform linear arrays and plenty of heuristic algorithms were used for DOA estimation. Still, in this work, we implemented a novel approach of the coprime array with a cuckoo search algorithm for DOA estimation. In this part of the paper, a series of experimental simulations are conducted to confirm the performance of the proposed scheme for DOA estimation. In our first experiment, we analyze the estimation accuracy of the proposed algorithm by comparing the results with PSO. After this analysis, the performance of root means square error (RMSE) by signal to noise ratio (SNR) and the number of snapshots is examined. Moreover, variation analysis, cumulative distribution function analysis and histogram analysis are also performed. The experimental analyses are categorized into three portions, six, nine and twelve sources estimations to validate our results and DOF.
## 3 Results and Discussion
### Concerning the Mechanical Test and GOM ARAMIS Measurement Series
#### 3.1.2 Observations During and After Drying of Concrete with GOM ATOS  
The specimens were observed with GOM ATOS during and after the drying cycle (28 days) of the concrete structures. Before scanning measurements were started, the specimens were dried for 12 h, as the system is only capable of detecting a mate, gloss-free surface. The test specimens were scanned twice on days 21 and 28 of the drying cycle.  
During the underwater treatment of the specimens, the thinnest concrete layer, which coated the reinforcing PLA patterns, partially flaked off, leaving them exposed. Therefore, these surface parts with severe defects were not taken into account during the ATOS scanning measurements, so the extent of their surface deviation was not taken into account. This procedure was used to compare the initial CAD model with the cast test specimen that had been under water for 21 days and to compare the 21-day test condition with the 28-day test condition. Table 5 represents the deviation values of the test specimens.  
Figure 13: White material precipitated on the surfaces of the HI and GYP specimens.  
As summarized in Table 5, the sum of the deviations did not reach 0.5 mm in any of the cases, even though the maximum deviation was only 0.45 mm for the ABS specimens. The deviation values suggest that the specimens shrunk in the longitudinal direction and also shrunk in the transverse direction, excluding ENG and HI specimens. The test specimens were again stored in an underwater medium after the measurement on day 21, and the measurement was repeated on day 28.  
At the end of the ATOS measurements, the residue of the flaked concrete covering the reinforcement had to be cleaned off for the GOM ARAMIS measurements. The painted measurement points would have acted as erroneous, incomplete measurement data if they had not been flaked off.  
It is important to note that the specimens had to be dried before starting the ARAMIS measurements and the load tests, as the residual moisture content would have affected the measurement results. This was verified by hourly weighing of the specimens after 24 h of drying at room temperature. When the standard deviation of the weight values was minimal, the authors started to execute the ARAMIS measurements.
# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 2 Data Sampling Platforms
This sophisticated platform guarantees an automatic and highly efficient data acquisition process with a communication latency less than 10 ms.
## Appendix B Detail Procedure of Qualitative Evaluation by Radiologists  
The detail procedure of qualitative evaluation was as follows. Two board-certified radiologists (17 and 10 years of experience, respectively) independently performed the visual evaluation as readers. The patient IDs of TCGA-OV in 50 test cases were sorted in alphabetical and numerical order, and the two radiologists interpreted the CT images in this order. All the 50 sets of denoised CT images with deeper U-net were evaluated first, then followed by those with RED-CNN. Image quality evaluation of the entire CT and the local normal lesions were performed by comparing the original image and the denoised image on 3D Slicer (version 4.10.2, [[https://www.slicer.org/](https://www.slicer.org/)]([https://www.slicer.org/](https://www.slicer.org/))) [24], and then the abnormal lesions were evaluated. Two other board-certified radiologists (6 and 14 years of experience, respectively) determined the abnormal lesions on the original image by consensus, and recorded the locations of the abnormal lesions on a file. In evaluating the abnormal lesions, the two readers referred to the file for the locations of abnormal lesions. At the time of interpretation, the two readers were informed of patient's age, and blind to all other clinical data. The image quality differed greatly between the two models, therefore the readers could easily determine the deep learning model with which the given CT images were denoised. Therefore, the interpretation order of the denoised images with deeper U-net and RED-CNN was not randomized. It was presumed that bias in evaluation of denoised CT images was inevitable even if interpretation order of deeper U-net and RED-CNN was randomized or the evaluations of CT images denoised with the two models were performed separately at long interval.
## 2 Analytical Model of the MMM Signals  
This section describes the MMM signals of a rectangular defect of a ferromagnetic material considering the 2D magnetic dipole model. Figure 1 shows a rectangular defect that contains \(2a\) width, \(b\) depth and a magnetic charge density \(\pm Sm\) around the defect edges. A 2D magnetic dipole model is employed to predict MMM signals around the rectangular defect. This model can determine the MMM signals of defects with simple geometries [23; 24]. [PERSON] [25; 26; 27] developed a theoretical modeling and experimentation of the MMM signals of discontinuities on test specimens. In addition, [PERSON] and [PERSON] [28; 29] proposed analytical models for the tangential and normal MMM signals around a 2D rectangular defect, considering a constant pole density on its surfaces. These models were modified by [PERSON] and [PERSON] [30] to evaluate the magnetic field (\(S_{m}\)) inside the gap of a rectangular defect.  
Thus, the tangential (\(Hy\)) and normal (\(Hz\)) MMM signals for a rectangular defect on a ferromagnetic pipe can be calculated by [27; 28]:  
\[H_{y}=\frac{H_{0}\pi n(\mu-1)}{2\tan^{-1}n(n+\mu)}\left\{\tan^{-1}\left(\frac{b(y +a)}{(y+a)^{2}+z(z+b)}\right)-\tan^{-1}\left(\frac{b(y-a)}{(y-a)^{2}+z(z+b)} \right)\right\} \tag{1}\]  
\[H_{z}=\frac{H_{0}\pi n(\mu-1)}{4\tan^{-1}n(n+\mu)}\ln\left\{\frac{\left[(y+a)^ {2}+(z+b)^{2}\right]\left[(y-a)^{2}+z^{2}\right]}{\left[(y+a)^{2}+(z)^{2} \right]\left[(y-a)^{2}+(z+b)^{2}\right]}\right\} \tag{2}\]  
where \(n=b/a\), \(H_{0}\) is the uniform magnetic field along the y-axis (i.e., axial axis) of the ferromagnetic pipe, \(b\) and \(2a\) are the depth and width of the rectangular defect, respectively, \(y\) and \(z\) are the horizontal and vertical axes, respectively, and \(\mu\) is the relative permeability of the material.  
The magnetic field density inside the defect (\(S_{m}\)) was determined by [PERSON] and [PERSON] [30] as:  
\[S_{m}=\frac{\mu_{0}\mu H_{0}\pi n(\mu-1)}{\tan^{-1}n(n+\mu)} \tag{3}\]  
where \(\mu_{0}\) is the magnetic permeability of free space.  
Equations (1)-(3) consider a small size for each rectangular defect in comparison with the diameter of the pipe (i.e., the curvature of the pipe wall is neglected). Figure 2 depicts the behaviour of the tangential (\(Hz\)) and normal (\(Hy\)) MMM signals of a rectangular defect on a ferromagnetic sample using Equations (1) and (2). For this case, we use the following data: \(\mu=180\), \(H_{0}=0.7958\) A/m, \(a=2.25\) mm and \(b=3.5\) mm. For \(Hz\) component, the maximum value occurs at the defect center. With respect to the \(Hy\) component, it presents a zero value at the defect center and two peaks with maximum and minimum values on both sides of the rectangular defect.  
Figure 1: Schematic of a rectangular defect with magnetic charge density \(\pm\)\(S_{m}\).  
Figure 2: Variation of the normal (\(Hy\)) and tangential (\(Hz\)) metal magnetic memory (MMM) signals for a rectangular defect of a ferromagnetic sample with magnetic charge density \(\pm\)\(S_{m}\).