esa-sceva/satcom-chunk-collection huggingface dataset:
# Electromagnetic Induction Imaging with Atomic Magnetometers: Progress and Perspectives
## 4 Electromagnetic Induction Imaging with Atomic Magnetometers
### Algorithms for Image Reconstruction: The Machine Learning Approach  
An important aspect of electromagnetic induction imaging is the ability of reconstructing an image given a finite set of measurements of secondary magnetic field amplitude and phase at different locations. This is a key enabling factor of the tomographic capability of EMI. The standard approach relies on the numerical solution of the inverse problem of electromagnetism, a challenging task given that the problem is ill-posed. This is still an active area of research, and a thorough discussion goes beyond the present work, whose focus is the progress determined by the use of atomic magnetometers. However, it is worth mentioning that an alternative approach, based on machine learning, was demonstrated in the context of EMI-AM precisely to avoid the complications related to the inverse problem approach. While machine learning is routinely used with standard ray-optics images, it is a priori not obvious that it could be applied to EMI given the diffusive non-ray-optics nature of the images. In this context, a proof-of-concept validation of machine learning for EMI-AM was provided in 2018 [40] for the mapping of bidimensional objects. Localization and classification of objects of different shape and material was demonstrated. The validation of machine learning based alghoritms for EMI is of great interest, as it offers an alternative to the often cumbersome solution of the inverse problem of electromagnetism. Whether this approach can be extended to complex and cluttered scenes, and to 3D systems is still an open question, and forms the object of ongoing research.
## 2 Methodology
### Empirical Analysis  
The Q-system method was developed by [PERSON] et al. [30] in the Norwegian Geotechnical Institute (NGI) to classify rock masses. The method is based on about 200 case histories of caverns and tunnels. The Q-system has been updated and it is now based on 1260 case records [31]. [PERSON] carried out some changes and adapted it to give support recommendations due to the increasing use of steel fibre reinforced shotcrete (SFR) in underground excavation support [32]. The quality of rock masses (Q) is obtained by applying Equation (1).  
\[\text{Q}=\frac{RQD}{J_{n}}\frac{J_{r}}{J_{a}}\frac{J_{w}}{SRF} \tag{1}\]  
where \(RQD\) is the rock quality designation, \(J_{n}\) is the joint set number, \(J_{r}\) is the joint roughness, \(J_{a}\) is the joint alteration, \(J_{w}\) is the water reduction factor and \(SRF\) is the stress reduction factor. The Q value varies from 0.001 for exceptionally poor quality to 1000 for an exceptionally good quality rock mass. A stress-free form \(Q_{N}\) was defined by [PERSON] et al. [33], which is given by Equation (2).  
\[Q_{N}=\frac{RQD}{J_{n}}\frac{J_{r}}{J_{a}}J_{W} \tag{2}\]  
[PERSON] defined a new parameter \(Q_{C}\) (Equation (3)) to improve the correlation among the engineering parameters, where \(\sigma_{ci}\) is the strength of intact rock in MPa [32].  
\[Q_{CN}=\text{Q}\frac{\sigma_{ci}}{100} \tag{3}\]  
The bolt length (\(L_{b}\)) may be calculated in terms of the excavation width B, by the Equation (4), proposed by [PERSON] et al. [30].  
\[L_{b}=2+(0.15\text{ B}) \tag{4}\]
## 4 Flat-Panel Beam-Steering Antennas with Low-Profile Phase Shifters with Fast Response  
Among LC-based devices, the inverted microstrip line (IMSL) topology has been widely studied because of its relative high performance in the lower mm-wave bands. Moreover, particularly for ESAs, it enables low-profile phase shifters, which can be placed or integrated beneath any individual radiating element within an area of about \(\lambda_{0}\)/2 \(\times\)\(\lambda_{0}\)/2. Even the length for a 360\({}^{\circ}\)-differential phase shift is more than \(\lambda_{0}\)/2, this is feasible by spiraling/meandering or employing a specific feeding concept for a straight-line phase shifter or by miniaturization using, e.g., the slow-wave effect. In addition, it is possible to achieve high performances at mm-waves, combined with low response times, while reducing the manufacturing cost to an economical price point by using standard LCD processes. Its development toward the current state will be discussed in the following subsections.
# Tensile Specim Circular Grid Pattern and AI-Based Strain Calculation Method
## 5 Results and Discussion
### Discussion and Contributions  
The measurement error between the existing method and the newly proposed method was sufficiently effective at less than 5%, verifying the validity of the proposed method and confirming that it could yield meaningful values. The minimum size criteria for the proposed circular pattern were established. This paper proposes a laser marking method, but other methods can be applied according to the operator's needs. When predicting strain using laser marking and artificial intelligence, it takes approximately 1 min and 30 s. This is a significant reduction compared to the traditional DIC method, which takes around 1 h and 30 min for spray application/drying and calculation time. This demonstrates that the proposed method is more efficient in terms of time.  
The results show that, compared to traditional tensile test methods, the equipment setup for the proposed method is easier, the preparation time is reduced, and the accuracy is similar to that of existing methods. As a result, tensile test automation is feasible, and it is expected that a process for real-time application and versatility across various materials can be developed.  
However, because this study was limited to steel materials and tensile tests, there are restrictions on applying it to various other materials and test methods. Additionally, noise was entirely removed from the dataset images for training, leading to reduced accuracy in noisy environments.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Compressive Strength  
As shown in Table 5 and Figure 6, when the proportion of GGBFS was 0% on the third day, the compressive strength of the control group was 29.6 MPa, and the compressive forces of groups with 1% to 4% waste PE were 28.3 MPa to 25 MPa (decreased by 5.5% to 16.9%, respectively). After 28 days, the strength of the control group with 0% GGBFS was 45.2 MPa; the compressive forces of groups with 1% to 4% waste PE were 42.8 MPa to 41.6 MPa (decreased by 5.4% to 8.1%). On the 91 st day, the strength of the control group was 53.3 MPa; the compressive forces of groups with 1% to 4% waste PE were 51.4 MPa to 48.2 MPa (decreased by 3.6% to 9.5%). We observed that as the waste PE increased, the compressive strength decreased. Due to waste PE having water absorbability and absorbing free water from the mortar, it led to an increased number of holes in the mortar.  
The 28-day compressive strength decreased with the PE aggregate content in the mixtures. The drop in compressive strength could be attributed to the poor bonding between the matrix and plastic aggregates [10].  
Figure 5: Setting time of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.  
strength of groups decreased by 6.6% to 10.5%. When the added waste PE was 1% to 2%, the decrease in strength was controlled to fall within 5%, which could achieve the function of removing wastes, reducing waste generation, and improving the economic efficiency of wastes for sustainable development of resources on Earth. After 91 days, the compressive strength of the control group was 53.3 MPa to 48.2 MPa, while that of the group with 10% added GGBFS was 56 MPa to 51.9 MPa (increased by 5.1% to 7.6%, respectively), and that of the group with 20% added GGBFS was 59.1 MPa to 53.8 MPa (increased by 10.8% to 11.5%, respectively). Since the added GGBFS produced pozzolanic reactions constantly during the late period, the strength of the cement mortar further improved.  
After 28 days, the proportion of the replaced volume of GGBFS was 10%, and the compressive strength of the groups decreased by 1.5% to 3.0% when the added waste PE increased from 1% to 2%. The strength of the groups was reduced by 6.5% to 9.3% when the added waste PE increased from 3% to 4%.  
After 56 days, the compressive strength of the groups decreased by 3.7% to 4.6% with the volume of 1% added PE to 2% to mortar, while that of the groups with 3% added PE to 4% decreased by 6.6% to 10.5%.
## 2 Background  
### Excavation and the Metro Line  
The hub of Nanjing metro line 2 has four tunnels including the test line, outgoing line, ingoing line, and the lead track line. The outgoing, ingoing, and lead track lines intersect at a small angle. The outgoing and lead track lines are connected by an additional passage. The tunnel size becomes larger at the intersections, where the test and lead track lines are straight and the outgoing and ingoing lines are curved. A large, irregularly shaped pit with a width of 82 m and a length of 225 m exists above four tunnels. The pit was excavated in three sequential sections. The plan layout of the pit and the tunnel is shown in Figure 1. As the pit is close to the tunnels, the soil displacement caused by the excavation is likely to influence the tunnels, so the deformation of the tunnel is monitored during the construction process. A total of 62 monitoring points were deployed to measure the vertical displacement, horizontal displacement, vertical convergence, and horizontal convergence of the tunnel lining. The excavation sequence of the pit is illustrated in Table 1. Sections A and C were excavated deeper than the basement of the main building, while section B was shallower because it is closest to the tunnels. The foundation pit used rotary grouted piles as a retaining structure, and the piles are 1100 mm in diameter and 1300 mm apart. The pile length is 8.73 m\(\sim\)20.38 m, the excavation depth is 5.73 m\(\sim\)15.38 m, and the tops of the piles are connected by crown beams.  
Figure 2 shows three typical profiles in construction, profiles 1-1 and 2-2 show the layout of the eastern and western parts of the foundation pit respectively. The tunnel profile is irregularly circular, with a minimum width of 6.3 m and a maximum width of 10.4 m, a minimum height of 6.6 m, and a maximum height of 7.8 m. The shortest distance from the tunnel to the support piles and the pit bottom is 14.56 m and 19.76 m, respectively. A layer of reinforced concrete support exists on top of some of the support piles, and profile 3-3 shows the relationship between this support, pit, and tunnels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sequence** & **Excavation Section** & **Depth of Piles (m)** &
\begin{tabular}{c} **Depth of** \\ **Excavation (m)** \\ \end{tabular} & **Date (dd/mm/yy)** & **Days (d)** \\ \hline
1 & Section B & 8.73–11.68 & 5.73–8.68 & 3 March 2021–2 July 2021 & 121 \\
2 & Section A & 14.83–18.68 & 9.83–12.78 & 21 June 2021–22 October 2021 & 123 \\
3 & Section C & 17.43–20.38 & 12.43–15.38 & 12 October 2021–25 January 2022 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Excavation information.
# A Subjective Study on User Perception Aspects in Virtual Reality
## 3 Study Description and Experiment
### Factors and QoE Aspects
#### 3.1.2 Encoding Parameter  
It is well-known that QoE of traditional video is strongly affected by encoding parameters such as QP, frame rate, and resolution. In this work, a subjective experiment is carried out to study the effects of two encoding parameters of resolution and QP on the QoE aspects of 360 video. In particular, we investigate 20 combinations of five QP values and four resolutions as shown in Table 3. An investigation of other parameters is reserved for future work.
# Performance of Tilting Pad Journal Bearings with the Same Sommerfeld Number
## 2 Numerical Modeling of Tilting Pad Journal Bearing
### Bearing-Journal Model  
Figure 2 shows the schematic of the workflow of TEHD lubrication model. The fluid film thickness (\(h\)) is fed into the Reynolds equation to calculate the fluid film force (\(Fn\)) and fluid velocity (\(u\)). Then, the fluid velocity (\(u\)) is used as an input for the Energy equation to estimate the lubricant temperature and viscosity which are fed back to the Reynolds equation. Hence, it is important to first define the fluid film thickness of the TPJB. The fluid film thickness is defined in Equation (12). The thermal expansions of the journal and bearing pad are considered as \(h_{TEJ}\) and \(h_{TEP}\), respectively. Figure 3 shows a schematic of the flexible pad film thickness model. The bearing film thickness (\(h_{n}\)) is estimated with respect to the nodal point of the tilting pad surface (\(p_{n}\)).  
\[\begin{array}{ll}h(\theta,z)=CL_{P}-&\left(e_{x}+z\theta_{x}-p_{pyt}cos \big{(}\theta_{p}\big{)}\right)cos(\theta)-\left(e_{y}+z\theta_{y}-p_{pyt}sin \big{(}\theta_{p}\big{)}\right)sin(\theta)\\ &-\left(CL_{P}-CL_{B}\right)cos\big{(}\theta-\theta_{p}\big{)}-\delta_{tilt} R_{s}sin\big{(}\theta-\theta_{p}\big{)}-h_{TEJ}(\theta,z)\\ &-h_{TEP}(\theta,z)\\ \end{array} \tag{12}\]  
Figure 2: Thermo-hydro-dynamic lubrication model.  
The fluid film thickness account for pad thermal/elastic deformation and pivot deformation at each time step during the numerical analysis. At each time step, the nodal film thickness (\(h_{n}\)) is estimated at n-th nodal point on the pad (\(p_{n}\)) as  
\[h_{n}=\sqrt{\left(x_{n}-x_{s}-z\theta_{x}\right)^{2}+\left(y_{n}-y_{s}-z\theta_{ y}\right)^{2}}-R_{s}-h_{TEJ}(\theta,z)-h_{TEP}(\theta,z) \tag{13}\]  
The misalignment of the bearing is not considered in the current analysis. See reference [23] for the effect of misalignment in the TPJB performance.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 4 Experimental Configuration
### On-Board Unit Installation  
We created on-board units (OBUs) for GiV2V communications using the Tensorcom IEEE 802.11 ad modules and laptops (Intel Core i7 7700 HQ, 4 GB memory, graphics processing unit) as host PCs. For a connection between the IEEE 802.11 ad module and the host laptop, the Tensorcom 802.11 ad module supports USB 3.0 interface; its physical data rate was 5 Gbps; however, the actual data rate measured on the packet level was less than 3 Gbps due to USB driver and Linux kernel overhead. The IEEE 802.11 ad module processes physical and MAC protocol stacks for mmWave communications and the host PC deals with the upper layer protocols from an IP to identify the user datagram protocol (UDP)/transmission control protocol (TCP). The IEEE 802.11 ad modules were installed on the vehicle roof and connected to Linux laptops via USB cables, as shown in Figure 4. In the host PC, an evaluation software was installed that generated UDP/IP packets to be sent to the IEEE 802.11 ad module using the GNU USB library, and then the IEEE 802.11 ad module encapsulated the MAC frames over those packets. Similarly, the 802.11 ad module decapsulated the received MAC frames and forwarded the packets to the host. Additionally, the evaluation software calculated instantaneous throughput with the received packets and also allowed users to configure the beam directions, MCS levels and modes of intra-structure or ad hoc of the IEEE 802.11 ad module. To collect information only available in the IEEE 802.11 ad module such as SNR, receive signal strength (RSS), etc., control messages, such as SNR request and response, were defined to be exchanged between the IEEE 802.11 ad module and host PC through the GNU USB library.  
For our testbed, two vehicles were used to measure inter-vehicle connectivity of a mmWave link in the convoy model, as shown in Figure 1a. Thus, the front car had the WiGig module attached to the back end of the roof while the trailing car had another module attached to the front end of the roof, as shown in Figure 5. The front car (model: Tucsan ix) was a sport utility vehicle with a height 1.55 m and the trailing car (model: Elandra) was a sedan with a height 1.4 m, while their widths were similar. This installation nearly guaranteed LOS for the mmWave link while those two vehicles moved in the same lane.  
Figure 3: Tensorcom 802.11 ad module for evaluation kit. [57].  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline Pathloss exponent (\(\alpha\)) & 2.0 & 2.1 & 2.2 & 2.3 & 2.4 & 2.5 & 2.6 & 2.7 & 2.8 & 2.9 \\ \hline Distance (m) & 10.01 & 9.96 & 8.11 & 7.41 & 6.81 & 6.31 & 5.88 & 5.50 & 5.18 & 4.89 \\ \hline \end{tabular}
\end{table}
Table 3: Reachable distances according to pathloss exponents at MCS1.
## 2 Preliminary Description of a Multirotor UAV
### EPU Components
#### 2.3.3 Esc  
ESC is an integrated power inverter that converts supplied DC voltage into appropriate AC phase voltages to drive the motor. The ESC consists of a microcontroller that processes (interprets) the input PWM signal and switching transistors (most commonly MOS-FET). The switching sequence of the transistors is determined by commutation and depends on the position of the rotor, which can be determined using a sensor or sensorless method [23].  
In other words, ESC implements the proper sequence to energize particular phases of a motor, in order to achieve continuous rotation and thus desired RPM set by the input signal from the flight controller. The digital input signal (throttle) defines the switching rate of transistors and consequently voltage fed to phases. With a higher duty cycle of the PWM, the phase voltage is increased and thus the angular velocity of the motor.  
The main parameter for selecting the ESC is the maximum allowed current of the controller and the operating voltages expressed in the number of battery cells. The maximum allowable current must be 20-50% higher than the maximum motor current to avoid overheating or failure. The factory settings can be changed using the ESC programmer, such as the possibility of dynamic braking. Recently, several communication protocols have been developed between the ESC and the flight controller (Oneshot, Multishot, and Dshot) that offer certain advantages over the PWM signal, such as faster communication.
## 3 Results
### Water Consumption, Water Use Efficiency and Water Productivity  
Plants' cumulative and daily average ETs are reported in Table 3.  
In _Tetragonia_, only H-SA-treated plants consumed significantly less water compared to the control; in lettuce, the same significant reduction was observed for both high-salinity treatments, whereas both moderate-stress treatments did not show any significant difference compared to the control.  
The WUE and the WP of the tested species are reported in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{_Tetragonia_} & \multicolumn{2}{c}{**Lettuce**} \\ \cline{2-5}  & **Cumulative** & **Daily Average** & **Cumulative** & **Daily Average** \\  & **(L)** & **(L day\({}^{-1}\))** & **(L)** & **(L day\({}^{-1}\))** \\ \hline C & 5.3 \(\pm\) 0.6 \({}^{\text{a}}\) & 0.12 \(\pm\) 0.01 \({}^{\text{a}}\) & 3.9 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.12 \(\pm\) 0.003 \({}^{\text{a}}\) \\ M-SW & 4.6 \(\pm\) 0.3 \({}^{\text{a}}\) & 0.11 \(\pm\) 0.01 \({}^{\text{a}}\) & 4.2 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.13 \(\pm\) 0.003 \({}^{\text{a}}\) \\ M-SA & 4.5 \(\pm\) 0.2 \({}^{\text{a}}\) & 0.10 \(\pm\) 0.01 \({}^{\text{a}}\) & 4 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.13 \(\pm\) 0.002 \({}^{\text{a}}\) \\ H-SW & 4 \(\pm\) 0.8 \({}^{\text{a}}\) & 0.09 \(\pm\) 0.02 \({}^{\text{a}}\) & 3 \(\pm\) 0.1 \({}^{\text{b}}\) & 0.09 \(\pm\) 0.003 \({}^{\text{b}}\) \\ H-SA & 2.8 \(\pm\) 0.2 \({}^{\text{b}}\) & 0.07 \(\pm\) 0.004 \({}^{\text{b}}\) & 3 \(\pm\) 0.1 \({}^{\text{b}}\) & 0.09 \(\pm\) 0.005 \({}^{\text{b}}\) \\ \hline \hline \end{tabular} Values are single plants’ consumption means (\(n\) = 4) \(\pm\) SEM expressed in liters. Different letters in the same column indicate significant differences assessed at \(p\)\(<\) 0.05 ([PERSON]’s test).  
\end{table}
Table 3: Cumulative and daily average ETs.  
Figure 2: The _Tetragonia_ (**a**) and lettuce (**b**) plants’ yield. Values are single plants’ shoot weight means (\(n\) = 4) \(\pm\) SEM expressed in grams. Different letters indicate significant differences assessed at \(p\)\(<\) 0.05 ([PERSON]’s test).  
_Tetragonia_ and lettuce responded differently, with the halophyte significantly enhancing its WUE and WP in both M-SW and M-SA compared to the control. In fact, the WUE increased by 80.4% and of 77.8% and, similarly, an increase in WP of 82% and of 89.4% was recorded under M-SW and M-SA, respectively. By contrast, lettuce showed for both seawater treatments a WUE comparable to the control, whereas WP decreased in all salt-treated plants compared to the control. Moreover, whereas no significant differences were assessed in lettuce WUE and WP between the two moderate stresses (i.e., M-SW vs. M-SA), the H-SA treatment significantly reduced both WUE and WP compared to the EC-comparable H-SW.
# Analysis of Carrier-Based Aircraft Catapult Launching Based on Variable Topology Dynamics
## 5 Conclusions  
In this paper, the catapult dynamics model of a carrier-based aircraft was established, and the dynamics model variable topology solution method was deduced. In addition, the dynamic calculation of the catapult and take-off process of a certain carrier-based aircraft was carried out, and the following conclusions were obtained:  
1. The catapult dynamics model and the variable topology solution method of the aircraft established in this paper can realize the variable topology solution of the separation of the restraining rod and the nose gear and the separation of the catapult rod and the catapult trolley during the catapulting process of the carrier-based aircraft.
2. Before and after the tie rod and the nose gear were separated, due to the change in the force transmission path of the catapult load of the nose gear, the catapulting force was transmitted to the rear strut, which instantly increased the load of the rear strut by 238.5 kN.
3. After the catapult started, the 2.867 s carrier aircraft reached the end of the catapult's stroke, the ejector rod was separated from the ejector tow shuttle, the ejector rod was unloaded, and the load of the rear strut was reduced from 486.2 kN to \(-20.3\) kN.
4. Due to the oblique downward traction of the catapult rod, the carrier-based aircraft was always in a state of bowing during the dragging and rolling process. After the catapult rod and the catapult tow shuttle were separated, the aircraft quickly turned into a head-up state under the protruding effect of the nose gear.  
It is worth emphasizing that the methods and models adopted in this paper can be improved in terms of time cost and model accuracy. In future research, researchers could also use higher precision algorithms and more detailed models to analyze the dynamic response of the ejection and take-off processes.  
Figure 11: Time versus pitch angle of carrier-based aircraft.  
**Author Contributions:** Conceptualization, X.F. and H.C.; methodology, X.F.; software, H.C.; validation, H.N.; formal analysis, H.C.; investigation, H.C.; resources, X.F.; data curation, H.N.; writing--original draft preparation, H.C.; writing--review and editing, H.C.; visualization, H.C.; supervision, H.N.; project administration, H.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and code generated or used during the study appear in the submitted article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(q\)Generalized coordinate array of the system  
\(M\)Generalized mass matrix  
\(N\)Number of mass matrices  
\(C_{q}\)System kinematics constraint matrix  
\(F_{p}\)Generalized coupled inertial force vector  
\(F_{e}\)Generalized active force vector  
\(\Lambda\)Lagrangian multiplier  
\(F_{r}\)System constraint reaction force vector