esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Establishment of Vehicle-Turnout Coupled Dynamic Model
### Wheel-Rail Contact Model  
[PERSON] and [PERSON] and [PERSON] (2018) proposed a wheel-rail non-elliptical multi-point contact algorithm to calculate creep forces. Let the \(x\)-axis direction be the longitudinal direction of wheel-rail contact and the positive direction be the rolling direction of the wheel-rail. The \(y\)-axis direction is the lateral direction of wheel-rail contact and is facing the inner side of the rail. The \(z\)-axis direction is the normal direction of wheel-rail contact, and the positive direction is the upward direction of the vertical rail surface. The Kik-Piotrowski model solves for the wheel-rail contact patch normal force \(N\) and the moment of the normal force \(M\), and the maximum pressure \(p_{0}\) can be calculated as follows:  
\[N=\frac{\pi E\delta_{0}}{2(1-\mu^{2})}\frac{\int_{y_{y}}^{y_{1}}\int_{-x_{(}y )}^{x_{(}y)}\sqrt{x_{l}(y)^{2}-x^{2}}dxdy}{\int_{y_{y}}^{y_{l}}\int_{-x_{(}y)}^{ x_{(}y)}\sqrt{\frac{x_{l}(y)^{2}-x^{2}}{x^{2}+y^{2}}}dxdy} \tag{2}\]  
\[M=\frac{p_{0}}{x_{l}(0)}\int_{y_{y}}^{y_{l}}\int_{-x_{(}y)}^{x_{(}y)}\sqrt{x_ {l}(y)^{2}-x^{2}}dxdy \tag{3}\]  
\[p_{0}=\frac{N\sqrt{2R\delta_{0}}}{\int_{y_{y}}^{y_{l}}\int_{-x_{(}y)}^{x_{(}y )}\sqrt{x_{l}(y)^{2}-x^{2}}dxdy} \tag{4}\]  
where \(\delta_{0}\) is the amount of penetration at the center of the contact patch; \(x_{l}(y)\) are the coordinates of the left edge of the contact area; \(E\), \(\mu\) are the elastic parameter of the material; and \(R\) is the principal radius of curvature of the wheel profile at the point of contact.  
After the normal contact force and clearance of wheel-rail contact are obtained, the creep force in the wheel-rail contact area is calculated by the FASTSIM algorithm. The FASTSIM algorithm is a general approach in the sense that its application area is not limited to the elliptical contact, but it can be used for an arbitrary area of contact. In such cases, there is difficulty in computing compliance coefficients since they are only well-defined for elliptic areas of contact. The modified FASTSIM algorithm was therefore used to solve for the tangential forces:  
\[\left\{\begin{array}{l}F_{x}=\int_{y_{y}}^{y_{l}}\int_{-x_{l}}^{x_{l}}q_{x}( x,y)dxdy=-\frac{4R}{L_{1}}\xi_{x}y_{y_{l}}^{y_{l}}g(y)dy+\frac{4R}{L_{3}}\psi \int_{y_{y}}^{y_{l}}yg(y)dy\\ F_{y}=\int_{y_{1}}^{y_{2}}\int_{-x_{l}}^{x_{l}}q_{y}(x,y)dxdy=-\frac{4R}{L_{2}} \xi_{y}y_{y_{l}}^{y_{l}}g(y)dy+\frac{4\sqrt{2}}{3L_{3}}\psi\int_{y_{y}}^{y_{l} }[Rg(y)]^{\frac{3}{2}}dy\end{array}\right. \tag{5}\]  
where \(\Psi\) is the spin, and \(\xi_{x}\), \(\xi_{y}\) are the longitudinal and lateral creepages. The interpenetration function \(g(y)\) is defined to be  
\[g(y)=\left\{\begin{array}{l}\delta_{0}-h(y),\ h(y)\leq\delta_{0}\\ 0\ \
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 4 Methodology in Reviewing Positioning Systems Applications Based on Positioning Systems
### The Categorization of the Researched Literature, According to the Main Research Objectives  
After filtering out the papers that did not meet our criteria, the next crucial step was to systematically categorize the remaining literature based on the type of positioning system (PS) employed and the specific citations or projects referenced. This categorization was essential for organizing the literature and facilitating a comprehensive discussion in Section 5, where we would delve into various types of papers and their applications.  
We focused on categorizing the papers according to their type, which is important for our analysis. This included distinguishing between journal articles, conference papers, and review papers. Additionally, we examined whether each paper employed Inertial Navigation Systems (INSs), Doppler Velocity Logs (DVLs), or other types of positioning systems and whether these systems were used in combination.
## 4 Results and Discussion
### Grid Independence Study  
A grid convergence study was conducted using the above numerical method with three different grid resolutions (40,000, 60,000, and 80,000). The simulations were performed for Case 1. Figure 6 shows the velocity and pressure at the base of the piston over time for three grid resolutions. As shown in Figure 6, there are negligible differences for the piston base pressure and velocity between the three grid resolutions. Eventually, the resolution with 60000 grids was used in the present study.
# The Effects of 3D and 2D Imaging on Brain Wave Activity in Laparoscopic Training
## 3 Results  
### Performance  
#### 3.1.1 Peg Transfer Task  
There were significant differences in completion time (\(F_{1,11}=8.111\), \(p=0.016\)) and in error frequency (\(F_{1,11}=31.150\), \(p<0.001\)) between the vision systems. The 3D vision system was associated with a shorter time to completion and improved task precision. The mean completion times (Standard Deviation) for the 2D and 3D vision systems were 84.04 (20.71) and 68.37 (18.17) seconds. The mean number of times a ring was dropped during each transfer was 0.23 (0.14) in 3D, compared with 0.55 (0.24) in 2D.
## 3 Results and Discussions
### Overview of the Selected Studies  
As the studies extracted from SpringerLink and ScienceDirect in the candidate datasets consisted of more research databanks, the search source was restricted to four of the indexing sources in majority, namely Scopus, WoS, IEEE Explore, and with the least contribution from Google Scholar. A total of 56.6% of the selected papers were taken from Scopus and the next 38.5% was contributed by the articles taken from WoS for this study. The remaining 5% of the articles were found interesting from IEEE Explore and Google Scholar. The details are depicted in Table 3.  
As shown in Figure 3, the research questions were extended to 10 quality assessment questions based on which the scores of Table 4 are calculated.  
\begin{table}
\begin{tabular}{c  
The selected articles were taken into consideration to undergo quality score assessment. The scores were given on a scale of 1, 0.5, and 0 measuring a positive, partial, and negative response, respectively, to the quality assessment questionnaire that comprised 10 questions contributing to a total score of 10 for each selected article. Quality assessment attempts to weigh the studies and their importance to this survey. The scores were categorized as very high (9-10), high (7-8), medium (5-6), low (3-4), and very low (0-2). Each study under consideration could have a maximum score of 10 and a minimum score of 0.  
Hence, the 80 studies taken into consideration as shown in Table 2 were reduced to 56 final articles for a further analysis. The quality assessment questionnaire was prepared such that the answers were derived in relevance to the research questions.  
Table 5 shows the article distribution from the selected articles based on the quality score.
## 4 Simulation Results
### Performance Metrics  
We use four measures for the performance estimation of various models using different feature sets namely accuracy (\(Ac\)), precision (\(Pn\)), recall (\(Re\)), and F1-score (\(F1\)). The first measure is computed as a proportion of instances correctly identified. The proportions of relevant instances among all retrieved and relevant instances are measured by Pn and Re. F1 shows the harmonic mean related to Pn and Re, which implies the detection ability of relevant instances. \(Ac\), \(Pn\), \(Re\) and \(F1\) are calculated by  
\[Ac=\frac{TP+TN}{TP+TN+FP+FN}, \tag{4}\]  
\[Pn=\frac{TP}{TP+FP}, \tag{5}\]  
\[Re=\frac{TP}{TP+FN} \tag{6}\]  
and  
\[F1=\frac{2\times Re\times Pn}{Re+Pn}, \tag{7}\]  
respectively, where \(TP\), \(FP\), \(TN\), and \(FN\) are true positive, false positive, true negative, and false negative values.
###### Abstract  
As a key component to ensure the safe operation of the power grid, mechanical defect diagnosis technology of gas-insulated switchgear (GIS) during operation is often neglected. At present, GIS mechanical fault detection based on vibration information has not been developed. The main reason is that the excitation current is considerable but uncontrollable in the actual operation of GIS. It is difficult to eliminate the influence of excitation on the vibration amplitude and form an effective vibration feature description technology. Therefore, this paper proposes a unified feature-extraction method for GIS vibration information that reduces the influence of current amplitude for mechanical fault diagnosis. Starting from the GIS mechanical analysis, the periodicity of vibration excitation and the influence of amplitude are discussed. Then, combined with the non-linear characteristics of GIS systems and non-linear vibration theory, the multiplier frequency energy ratio (MFER) is designed to extract vibration-unified features of GIS for diagnosing the mechanical fault under different current levels. The diagnosis results of the experimental data with different feature-extraction methods show the applicability and superiority of the proposed method in the GIS's mechanical fault-detection field based on vibration information.  
Keywords:diagnosis system; feature extraction; gas insulated switchgear; mechanical fault; vibration information +
Footnote †: journal: Applied Sciences
# Recent Advances in Artificial Intelligence-Assisted Ultrasound Scanning
## 3 Scanning Guidance with AI
### Human-Operated Systems  
As previously mentioned, reinforcement learning (RL) is one of the most successful AI methods. RL maximizes the rewards from the action the computer performs (called an agent in this context). In 2019, [PERSON] and [PERSON] [57] developed one of the first applications of deep reinforcement learning (DRL) to ultrasound guidance with a phantom containing only a single cuboid object of interest. The agent receives a 2D image corresponding to the current position and orientation of the transducer, and it can decide to change its lateral position, orientation, and focal-point depth. When the environment changes, the agent receives a new image and a reward according to the specific goal. They demonstrated the feasibility of using DRL for ultrasound guidance, although only in a simple case.  
Recently, [PERSON] et al. [58] have proposed a visual DRL architecture to suggest actions to guide inexperienced medical personnel to obtain clinically relevant cardiac ultrasound images of the parasternal long-axis view. The authors created RL environments by projecting a grid on subjects' chests and populating them with US frames collected from a set of volunteers. At inference time, the user receives motion recommendations from the RL policy.  
[PERSON] et al. [59] proposed a system that employed an NN that received the US video signal and the motion signal from an inertial measurement unit (IMU) attached to the probe. The goal was to provide guidance for fetal standard plane acquisition in freehand obstetric US scanning. The network is based partly on SonoNet [35], MobileNet V2, and a custom recurrent NN mechanism that tries to predict the move that an expert sonographer would make toward the standard plane position. Their behavioral cloning approach [60] is not based on DRL, although they have several points in common. The scenario used is more realistic than in other previous works. However, the indications in this work only refer to probe orientation, as the IMU does not provide accurate position or translation information. Their results showed that the algorithm is robust for guiding the examiner toward the target orientation from distant initial points. However, it does not provide an accurately predicted guidance signal when the probe is close to the desired standard plane due to inter- and intra-sonographer variations and sensor uncertainty.  
In a recent study, the DL-based software Caption Guidance [61] has shown its capacity to guide novices (in that case, nurses without prior ultrasonography experience) to acquire cardiac US images. The software provides real-time guidance during scanning to obtain anatomically correct images. Their results showed 92.5% to 98.8% agreement in the diagnostic assessments obtained from the scans performed by experienced cardiac sonographers and nurses guided by this AI tool.
## 4 Blind Schnorr Signatures--Results and Discussion
### Establishing a Shared Secret by \(\mathcal{S}\) and \(\mathcal{D}\)  
The first step of most of our attacks is to establish a shared secret (_anamorphic key_) ak between the Signer \(\mathcal{S}\) and the device \(\mathcal{D}\). The difficulty lies in the fact that no out-of-bound  
Figure 5: Blind Schnorr signature-creation procedure \(\langle\mathsf{SignS},\mathsf{SignU}\rangle\) and verification procedure Verify.  
channel can be used; the key agreement must be undetectable and well hidden in the regular interaction.  
The simplest way to establish a shared key is a static-static Diffie-Hellman (DH) protocol, where public keys \(\pk_{\mathcal{S}}\) and \(\pk_{\mathcal{D}}=g^{\sk_{\mathcal{D}}}\) are used to derive a shared secret \(\sk=\pk_{\mathcal{D}}^{\sk_{\mathcal{S}}}=\pk_{\mathcal{S}}^{\sk_{\mathcal{D}}}\). However, the problem is that the Auditor \(\mathcal{J}\) may ask for secret keys that are kept by \(\mathcal{S}\) and \(\mathcal{D}\) according to the system specification (the request to surrender the private keys may concern any keys, not only \(\sk_{\mathcal{S}}\)). In that case, \(\mathcal{J}\) would calculate the shared secret \(\sk\) derived as above. In that case, any information transferred between \(\mathcal{S}\) and \(\mathcal{D}\) and hidden with the key \(\sk\) would be readable for the Auditor \(\mathcal{J}\).  
For the same reason, the static-ephemeral Diffie-Hellman protocol for deriving the shared secret is not applicable in the attack. Indeed, the Auditor needs to request just one secret key to derive the shared secret. So, the only choice seems to be an ephemeral-ephemeral Diffie-Hellman key exchange.  
On the side of the Signer, one can attempt to use \(R\) as a share for Diffie-Hellman key exchange. The Auditor cannot ask for \(r\), as \(\mathcal{S}\) can claim that it has been erased after the protocol execution for security reasons (or just to conserve space). However, it does not help if \(\mathcal{S}\) finalizes the protocol execution with \(s=r+c\cdot\sk_{\mathcal{S}}\bmod q\): after gaining \(\sk_{\mathcal{S}}\), the Auditor would be able to derive \(r\) from this equality.  
We have to conclude that to hide manipulations in the protocol, the secret key \(\sk\) has to be established from the Diffie-Hellman shares, where \(\mathcal{S}\) and \(\mathcal{D}\) either forget the discrete logarithms effectively or claim not to know them at any time after the protocol is completed.  
Our plan is to create shares of ephemeral-ephemeral Diffie-Hellman key exchange using exclusively the messages exchanged during the creation of blind signatures. The hidden key agreement will require a few steps, as presented below.
## 4 Lunar Antenna Types
### Antennas for Low-Frequency Radio Astronomy
#### 4.3.1 Lunar Gravitational-Wave Antennas (LGWAs)  
The Lunar Gravitational-Wave Antenna (LGWA) project aims to create a lunar network to monitor gravitational wave-induced vibrations, paving the way for groundbreaking scientific exploration on the moon. By observing gravitational waves in the decihertz range, LGWA enhances our cosmic understanding and requires advanced components, including cryogenic sensors and noise-cancellation arrays, to minimize interference. Multiple stations with LGWA-grade arrays are crucial for effective noise reduction and local data analysis on the moon [27].  
In [27], the authors found that LGWA has several unique characteristics compared to other detectors, which require specific data analysis techniques and give it distinct capabilities. LGWA stands out due to its evolving antenna pattern linked to the moon's 27.3-day rotation, while its dispersed array across the lunar surface offers precise gravitational wave polarization measurements and extended gravity theory testing. With resonant bar antenna attributes up to 10 mHz, LGWA transitions into a wideband detector beyond this range. Notably, LGWA boasts unparalleled sensitivity in the 0.1-1 Hz frequency span among gravitational wave detectors, pending the emergence of new detection technologies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Antenna Type** & **Antenna Name** & **Ref** & **Frequency Band** & **Mission/Research** & **Application** \\ \hline \multirow{3}{*}{Reflector} & Inflatable Impulse-Radiating & \multirow{3}{*}{[57]} & VHF-S & Research & Radar \\  & Antenna (inf-IRA) & & & & \\ \cline{2-5}  & Backfire (all-metal) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison between reflector lunar antennas in terms of application.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Size (Diameter in m)** & **Dielectric Materials Used (If Any)** & **e\({}_{\tau}\)** & **Software** \\ \hline
[57] & 0.3 & Mylar and Kapton & 2.8–3.7 &
\begin{tabular}{c} FEKO ([[http://www.feko.info/](http://www.feko.info/)]([http://www.feko.info/](http://www.feko.info/)), \\ accessed on 4 October 2023) \\ \end{tabular} \\ \hline
[60] & 0.07 & n/a & n/a & Ansys HFSS \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison between reflector lunar antennas in terms of simulation parameters.
## 4 Simulation Results and Discussion
### Experiments with CPNRA
#### 4.2.2 Resource Utilization  
Next, we used our simulator to study the utilization of each resource independently. The requests were generated in such a manner, that the CPU was dominant for 40% of the cases, the memory was dominant for 30% of the cases and the disk was dominant in 30% of the cases and the number of users ranged between 50 and 1000. In all the simulation sets, the duration period was 360 s. As the time proceeded and resources were being consumed, fewer jobs were generated and the overall resource utilization decreased, but it never dropped below 90%. As can be seen in Figure 8, the CPU utilization begins dropping after about 160 s while the utilization of memory and disk seems to be dropping in a smoother way and at a later time (200 and 240 s, respectively). The peaks seen in this graph represent the cases where some more resources become available and return to the pool, either due to the allocation policy or due to returns from finished jobs that return the resources back to the pool.  
In our last set of experiments, we averaged the utilization of all the resources under our policy using a small number of users (up to 20), to fairly compare the utilization provided by our policy to the utilization provided by a new algorithm DRBF [33], where the authors reported their results for only a few users. The results are displayed in Figure 9. Again, our simulator verified that our resource allocation strategy outperforms the DRBF strategy and achieves utilization of about 98-99% while the changes are very small (notice that the line is rather smooth). The DRBF policy achieves a utilization of about 94-98%, with some peaks where the utilization drops off in a non-smooth fashion. Also, note that our strategy was found to achieve a utilization of over 90%, even for larger number of users, which is not proven for the DRBF scheme.  
Figure 7: The DSQ core.
## 4 Minimum Acceleration Combination Technique: Rationale and Applications
In particular, the COSMO-SkyMed dataset is the one exploited in [149], consisting of 61 SAR scenes (descending passes, HH polarization, with a side-looking angle of about 29\({}^{\circ}\) and a satellite heading angle of about 8\({}^{\circ}\)) collected from December 2013 to March 2016. The second dataset, acquired through the Sentinel-1 mission from February 2015 to April 2017, consisted of 33 SAR scenes (ascending passes, VV polarization, with a side-looking angle of about 39\({}^{\circ}\) and a satellite heading angle of about 12\({}^{\circ}\)). Sentinel-1 data were available in the single-look-complex (SLC) format and acquired through the Interferometric Wide Swath (IW) mode by employing the Terrain Observation by Progressive Scans (TOPS) acquisition mode [150]. Figure 21: E-W DinSAR results relevant to Big Island of Hawaii, U.S. (**a**) false colour mean displacement velocity map superimposed on a grey-scale SAR amplitude image of the area. (**b–g**) Plots of the E-W time-series (black triangles) compared to GPS-driven E-W time-series of displacement (red stars). GPS locations are highlighted in (**a**). (Adapted with permission from [32], IEEE, 2016). Figure 23: Maps of E-W (**left**) and U-D (**right**) mean displacement velocities in the Shanghai’s ocean-reclaimed lands obtained using MinA algorithm. (Adapted with permission from [151], MDPII, 2017). Figure 22: N-S DinSAR results relevant to Big Island of Hawaii, U.S. (**a**) false colour mean displacement velocity map superimposed on a grey-scale SAR amplitude image of the area. (**b–e**) Plots of the N-S time-series (black triangles) compared to GPS-driven N-S time-series of displacement (red stars). GPS locations are highlighted in (**a**). (Adapted with permission from [32], IEEE, 2016).
# A Phased Array Antenna with New Elements Designed Using Source Transformations
## 1 Introduction
The \"pinwheel\" transformation shows that with the approach of the transformation electromagnetic/optics, it is possible to design electromagnetic structures and devices of many different complex and arbitrary geometries. Antennas designed in this way have many advantages compared to standard dipole antennas. A \"pinwheel\"-shaped antenna can be embedded into transformed regions to avoid interference, such as the cloak. The \"pinwheel\"-shaped antenna may also make use of the inherent properties of metamaterials to meet unique design parameters. For instance, a transformation designed antenna such as a \"pinwheel\"-shaped antenna may have less overall metal than a standard dipole antenna. The natural dispersion of metamaterials may then result in an antenna that interferes only very weakly at frequencies away from its frequency of operation. Under certain circumstances, the overall weight of the antenna may also be reduced compared to standard dipole antennas.
# Effect of Post-Weld Heat Treatment on the Solid-State Diffusion Bonding of 6061 Aluminum Alloy
###### Abstract  
The precipitation-hardenable aluminum alloy 6061 (AA 6061) is favored for aerospace components and automotive parts. However, the tenacious oxide layer on the surface greatly limits the quality and applicability of joining AA 6061. In this study, the joining method of solid-state diffusion bonding was implemented for AA 6061 plates, and the effects of post-weld heat treatment (PWHT) on the joint interface were investigated. The bonding temperatures were within the range of 500-530 \({}^{\circ}\)C, and the time periods varied from 30 to 240 min under a static pressure of 5 MPa in a vacuum. The diffusion bonded specimens were subjected to T4- and T6-PWHT to improve the bonding quality. The interfacial microstructure of the joints was analyzed by scanning electron microscopy, and the mechanical properties were evaluated with shear tests. The experimental results showed that the shear strength of the diffusion bonded joint could reach around 71.2 MPa, which was highly dependent on bonding temperature and holding time, and T6-PWHT further enhanced it to over 100 MPa. The effects of PWHT on the diffusion bonded AA 6061 joint were investigated, and the fractography on the sheared surfaces indicated that PWHT-T6 played an important role in enhancing joint strength, which was consistent with the measured shear strength. The sequential PWHT for AA 6061 after diffusion bonding was proven to be feasible for bonding of AA 6061 parts, and the joint strength was sufficient for industrial needs.  
aluminum alloy 6061; diffusion bonding; post-weld heat treatment; fractography +
Footnote †: journal: Applied Sciences