esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fluxgate Technology
### Fluxgate Basic Principle  
The working principle of a fluxgate is to use a saturable inductor to detect the magnetic field produced by an external current. This process exploits the non-linear relationship between the magnetic field (\(H\)) and the magnetic flux density (\(B\)) within a magnetic material [3]. The slope of the \(B\)-\(H\) magnetization curve is defined as the magnetic permeability \(\mu\), which depends on the material.  
\[\mu=\frac{\Delta B}{\Delta H}. \tag{1}\]  
Figure 1 shows a simple fluxgate sensor prototype. When the excitation alternating current \(I_{ex}\) flows through the excitation coil, it produces a magnetic field that periodically saturates the soft magnetic material of the sensor core in both directions. The device's name comes from this \"gating\" of the flux that occurs when the core is saturated [4]. The core saturates twice during a single excitation cycle; hence, the magnetic flux density \(B_{p}\) that is generated by the external current undergoes flux \"gating\" twice. According to [PERSON]'s electromagnetic induction principle, the induced voltage can be expressed as follows:  
\[V_{ind}=-NA\frac{d\left(B(t)+B_{p}\right)}{dt}, \tag{2}\]  
where \(N\) is the number of turns on the sensing winding and \(A\) is the cross-sectional area of the core. The second harmonic of \(V_{ind}\) can be used to derive the external measured current, which is known as the second harmonic detection method [23; 31].
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 1 Introduction
Without the request-grant procedure, MTDs will select the SCMA codebooks from the predefined limited codebook set randomly. If two or more MTDs choose the same codebook and they collide, it will cause codebook collisions which are destructive for SCMA decoding. The reason SCMA can achieve better performance in terms of BER is that multidimensional constellations of each codebook provide extra shaping gain [15]. However, codebook collisions eliminate the shaping gain, which makes the SCMA decoder hardly distinguish each codeword. Thus, to design an asynchronous SCMA scheme, we need to tackle the problem of codebook collisions first. Motivated by these issues, this paper introduces a novel uplink grant-free asynchronous flipped SCMA (AF-SCMA) scheme for the future SAT-IoT communication networks. The main contributions of this paper can be summarized as:  
* Propose a novel uplink grant-free asynchronous flipped SCMA (AF-SCMA) scheme for the future SAT-IoT networks. With the concept of flipped diversity, a packet and its flipped replica are transmitted simultaneously. Specifically, the extra flipped replica is encoded with a different SCMA codebook from the one that is chosen by the original packet. Different pilot sequences are added before the encoded symbols of each subcarrier, which are used to detect the signal, identify the codebook information, and estimate the channel state information. Taking advantages of asynchronous arriving packets, SIC combined with message passing algorithm (MPA) is exploited to decode the packets in a sliding detection window. With the help of the flipped replica, the probability of codebook collisions is reduced, which mitigates the negative effects of codebook collisions and improves the system performance. * Develop an analytical model of the network throughput for the proposed AF-SCMA scheme. We formulate a signal-to-interference-plus-noise ratio (SINR) estimator to identify the power of the received signal in an uplink land mobile satellite (LMS) channel and estimate multiple-access interference (MAI). The expressions for packet loss ratio (PLR) of the network are derived in a recursive way. * Investigate the performance of the proposed AF-SCMA scheme via both theoretical analysis and extensive simulations. We compare the proposed AF-SCMA with synchronous and asynchronous grant-free SCMA schemes in terms of PLR and throughput. Furthermore, the performance of different SCMA codebook sizes is also evaluated. Finally, the design of AF-SCMA pilot sequence is investigated and the pilot sequence missed detection rate is assessed. The rest of this paper is organized as follows. Section 2 introduces the system model. The transceiver design of AF-SCMA scheme is introduced in detail in Section 3. Section 4 derives the performance of the proposed AF-SCMA scheme. Simulation results are shown in Section 5. Finally, Section 6 concludes this paper.
## 3 Quantitative Analysis of Landslide Deformation Area
### Quantitative Analysis Methods
Landslide volume calculation: The image recorded by the trinocular camera not only contains two-dimensional information on the slope surface but also records the  
Figure 3: Schematic diagram of quantitative identification of deformation areas. (**a**) Schematic diagram of landslide area calculation. (**b**) Schematic diagram of landslide volume calculation. Figure 2: Landslide identification software. 1 Algorithm, 2 Slope angle, 3 Load T\({}_{0}\) moment image, 4 Load T\({}_{0}\) moment point cloud, 5 Load T\({}_{n}\) moment image, 6 Load T\({}_{n}\) moment point cloud, 7 Threshold, 8 Calculate, 9 Save, 10 Area, and 11 Volume. three-dimensional depth information of the slope, and the slope is reconstructed in three dimensions according to the Delaunay triangle network. As shown in Figure 3b, the surface of the slope before the experiment is set as the initial contrast surface, meshed into countless square grids of equal size, and the area of each grid is calculated according to the actual area of the slope. The slope surface at a selected moment is compared with the initial comparison slope surface for overlap, and the average depth difference between each mesh corresponding to the slope map and the initial comparison slope map at the selected time is calculated, respectively. The area with a mean difference greater than zero is a landslide depression area, the area with a mean difference less than zero is a slope protruding area, and the volume of the area with a mean difference in depth greater than zero is superimposed to be the volume of the slope failure area, as in Equation (2):  
\[\mathrm{V}=\sum_{0}^{n}\mathrm{h}_{n}\mathrm{S}_{1}(\mathrm{h}_{n}\geq 0) \tag{2}\]  
Formula: V is the volume of the slope failure area, n is the number of meshes in the failure area, h\({}_{n}\) is the average depth of the nth mesh, and S\({}_{1}\) is the area of each mesh. When calculating the area and volume of slope failure area, there is a problem that the initial slope surface is not an ideal plane, the initial slope before the start of the experiment is set as the initial contrast breaking surface, and the calculated slope failure area and volume minus the initial comparative slope calculated failure area and volume is the real damage area and volume of the slope due to rainfall.
## 2 Method
### The Super-Resolution BIFT Imaging by Analytic Extrapolation
Assuming the sampled point \((n,m)\) of \(U(f_{x},f_{y})\), the Equation (7) can be rewritten as  
\[U(n,m)=P(n,m){\sum\limits_{n_{0}}}{\sum\limits_{m_{0}}{h_{f}(n-n_{0},m-m_{0}) U_{0}(n_{0},m_{0})}}. \tag{9}\]  
After the discrete sampling, Equation (9) can be rewritten as simple matrix form as follows:  
\[\mathbf{y}\mathbf{=}\mathbf{Ax} \tag{10}\]  
where \(\mathbf{y}\) and \(\mathbf{x}\) are the vectorized spatial frequencies of \(U(n,m)\) and \(U_{0}(n_{0},m_{0})\), respectively, and the matrix \(\mathbf{A}\) is \(h_{f}(n,m)\). Considering the size \(\mathbf{y}\) and \(\mathbf{x}\) with the vector dimension of \(N\times 1\) and \(M\times 1\), respectively, and the analytic extrapolation range is normally larger than the known spatial frequency range (\(M>N\)), thus the dimension of \(\mathbf{x}\) will be much larger than \(\mathbf{y}\) and the Equation (10) is ill-posed. However, because we already know the object's spatial frequency in the finite aperture as \(U(n,m)\), we can separate \(U_{0}(n_{0},m_{0})\) into  
\[U_{0}(n_{0},m_{0})=\{ \begin{matrix}U(n_{0},m_{0}),&n_{0}^{2}+m_{0}^{2}\leq f_ {c}^{2}\\ t_{0}^{\prime}(n_{0},m_{0}),&n_{0}^{2}+m_{0}^{2}>f_{c}^{2}\end{matrix}  \tag{11}\]  
where the \(U_{0}^{\prime}(n_{0},m_{0})\) is the spatial frequency to be recovered. And the responding matrix \(\mathbf{A}\) in Equation (10) can be split into two parts:  
\[\mathbf{y}=\mathbf{A}^{\prime}\mathbf{x}^{\prime}\mathbf{+}\mathbf{A}^{\prime}\mathbf{y}=\mathbf{A}^{ \prime}\mathbf{x}^{\prime}\mathbf{+}\mathbf{b}. \tag{12}\]  
In Equation (12), the \(\mathbf{x}^{\prime}\) is the vectored \(U_{0}^{\prime}(n_{0},m_{0})\) with the dimension of \((M-N)\times 1\) and the \(\mathbf{A}^{\prime}\) is the responding row of the matrix \(\mathbf{A}\) with the dimension of \(N\times(M-N)\). In Equation (11), \(\mathbf{x}^{\prime}\) can be solved by \(\mathbf{x}^{\prime}=\mathbf{A}^{-1}(\mathbf{y}-\mathbf{b})\). It is worth noting that, for Equation (12) to have the unique solution, the numbers of the equation must be larger than the number of unknowns, and matrix \(\mathbf{A}\) should be full-rank. Considering these constraints,the range of a single analytic extrapolation cannot be greater than twice the range of finite spatial frequency (\(M\leq 2N\)). When treating the case of \(M>2N\), an optimization problem \(\underset{\text{arg}}{\min}||\mathbf{y}-\mathbf{b}-\mathbf{A}\,\mathbf{x}||\) can be employed to search for an approximate solution, which increases the extrapolation range but also reduces reconstruction accuracy.
# Overview of Methods for Large-Scale RNA Synthesis
## 2 Methods of RNA Synthesis
### Ribozymes for RNA Production with Homogeneous Ends
#### 2.3.4 VS RNA Ribozyme  
The Varkud satellite RNA ribozyme was found in mitochondrial plasmids of certain strains of Neuroposora [104; 131]. It is the longest known small ribozyme (150 nucleotides), but its sequence can be reduced to approximately 120 nt without influencing its cleavage activity. The tertiary fold of the VS ribozyme is highly structured. It is composed of six double-stranded regions forming multihelix stacks, connected by three-way junctions and involving \"kissing-loop\" interactions [132]. Cleavage sites occur between A and G/A/U residues [133]. The VS ribozyme can act in cis and in trans, but only the trans system is suitable for the large-scale preparation of RNAs with homogeneous 3\({}^{\prime}\)-ends [110]. One of the examples is the preparation of the isotope-labelled RNA for NMR measurements [134].
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 4 Experiments and Results
### Searching for the Optimal Air Temperature Prediction Model
The graph justifies our hypothesis that the proposed prediction model can be used for the prediction of air temperature at micro-locations, because the PROP model (the blue line) predicted the temperature more reliably than the ICON-D2 reference model (the green line) compared with the real situation measured by the local meteorological station (the red line). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Start**} & \multicolumn{2}{c}{**End**} & \multicolumn{2}{c}{**\(T\)**} & \multicolumn{2}{c}{**MAE**} & \multirow{2}{*}{\(\boldsymbol{r}_{\text{Maximus}}\)} & \multirow{2}{*}{\(\boldsymbol{r}_{\text{ICON-D2}}\)} \\ \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**[h]**} & \multicolumn{1}{c}{**Maximus**} & \multicolumn{1}{c}{**ICON-D2**} & \\ \hline
11.1.2024 & 19:00 & 12.1.2024 & 6:00 & 12 & 0.4 & 0.8 & 0.8540 & \(-\)0.1679 \\
15.1.2024 & 22:00 & 16.1.2024 & 9:00 & 12 & 1.4 & 1.2 & 0.8624 & 0.7031 \\
17.1.2024 & 14:00 & 18.1.2024 & 1:00 & 12 & 4.6 & 2.9 & 0.8761 & 0.6522 \\
20.1.2024 & 20:00 & 21.1.2024 & 7:00 & 12 & 0.7 & 1.3 & 0.9459 & 0.9099 \\ n/a & n/a & n/a & n/a & 12 & 1.8 & 1.6 & 0.8846 & 0.5243 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Overview of the metrics of the predictive model of Generation 4 in run 4. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Start**} & \multicolumn{2}{c}{**End**} & \multicolumn{2}{c}{**\(T\)**} & \multicolumn{2}{c}{**MAE**} & \multirow{2}{*}{\(\boldsymbol{r}_{\text{Maximus}}\)} & \multirow{2}{*}{\(\boldsymbol{r}_{\text{ICON-D2}}\)} \\ \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Time**} & \multicolumn{1}{c}{**[h]**} & \multicolumn{1}{c}{**Maximus**} & \multicolumn{1}{c}{**ICON-D2**} & \\ \hline
08.1.2024 & 18:00 & 09.1.2024 & 05:00 & 12 & 0.4 & 0.7 & 0.7051 & 0.7244 \\
09.1.2024 & 20:00 & 10.1.2024 & 07:00 & 12 & 1.2 & 1.9 & 0.5386 & 0.5159 \\
10.1.2024 & 22:00 & 11.1.2024 & 09:00 & 12 & 0.7 & 1.8 & 0.9765 & 0.8411 \\
11.1.2024 & 07:00 & 11.1.2024 & 18:00 & 12 & 0.8 & 1.5 & 0.9875 & 0.9781 \\ n/a & n/a & n/a & n/a & 12 & 0.8 & 1.5 & 0.8019 & 0.7649 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Overview of the metrics of the predictive model of Generation 2 obtained in run 1.
## 3 Theoretical Formulation of Turbulent Gas Flow Modeling
### Governing Equations  
Based on the above assumptions, the governing equations to be solved in this numerical simulation include mass, momentum, energy, and turbulent equations and are given as follows:  
Conservation of mass:  
\[\
abla\bullet(\rho\mathbf{u})=0 \tag{1}\]  
Conservation of momentum:  
\[\
abla\bullet(\rho\mathbf{u}\mathbf{u})=-\
abla p+\
abla\bullet\left[\mu \Big{(}\
abla\mathbf{u}+(\
abla\mathbf{u})^{T}\Big{)}\right]+\
abla\bullet \left[\lambda(\
abla\bullet\mathbf{u})\mathbf{I}\right] \tag{2}\]  
where the terms \(\frac{1}{2}\Big{(}\
abla\mathbf{u}+(\
abla\mathbf{u})^{T}\Big{)}\) and \((\
abla\bullet\mathbf{u})\) are the rate-of-strain tensor and the rate of expansion of the flow, respectively.  
Conservation of energy:  
\[\
abla\bullet\left[\rho\mathbf{u}\Big{(}\epsilon+\frac{1}{2}u^{2}\Big{)} \right]=\
abla\bullet(\kappa\
abla T)+\
abla\bullet[-p\mathbf{u}+\tau\bullet \mathbf{u}] \tag{3}\]Since in this study, we determined the working fluid as an ideal gas, the compressibility effect must follow the equation as follows:  
\[p=\rho RT \tag{4}\]
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Functional Integrity of the Brain  
Decades of studies have demonstrated that brain _functional_ characteristics (or _'software'_), such as memory performance, attention and processing speed, emotional regulation, individual capacity for information processing, cognitive preparedness, and others, including functional states of the brain, are readily reflected in qEEGs at all ages in both healthy individuals and individuals with neurological or psychiatric conditions [144, 21, 22, 24, 115, 129, 130, 141, 142, 143, 144, 145, 146, 147] (for a review, see [18]). This is so because qEEG oscillatory activity is generated by synchronous neural populations that mirror the firing rate of their constituent neurons [148]: for example, during arousal, task execution, and/or a behavioral act, the underlying neuronal populations will increase spiking with respect to baseline. These increased firing rates will engage non-linear feedback loops, effectively changing the system's response function and the specifics of its emergent oscillations. In contrast, during rest and states of quietness, the spiking activity decreases, which is also reflected by a decrease in oscillatory activity [148]. Further, qEEG oscillations are able to temporally coordinate and control neuronal firing and are proposed to be a basic principle of information processing in the human brain [149, 150].  
Considering that different qEEG oscillations reflect functionally different components of information processing acting on various temporal scales [140, 141], it is possible to map qEEG oscillations onto specific mental and/or behavioral states [151]. qEEG oscillations from the same frequency band may express different functions depending on the conditions they are involved in [152]. This seems biologically plausible: qEEG oscillatory functional diversity creates a rich repertoire of brain activity that can meet the complex computational and communicational demands of the brain during healthy and pathological conditions.  
In this context, qEEG measures can provide independent evidence of variations in alertness, attentiveness, memory, emotional regulation, or mental effort. Incorporating them into tests of cognitive function might lead to more sensitive and less ambiguous clinical assessment tools [153, 154].  
Since information-processing modes depend on the functional integrity of the brain, which, in turn, depends on the orchestrated oscillatory activity of neuronal pools (reflected in the characteristic qEEG rhythms); functional coupling between qEEG oscillations, cognitive functions, and vegetative processes is important.
# Design of a 90 GHz SOI Fin Electro-Optic Modulator for High-Speed Applications
## 4 Comparison with Other Devices  
Many modulator structures have been proposed in the literature. The most important of which are summarized in Table 2.  
Looking into the previous performance metrics, the proposed modulator in this work features the simplest fabrication process, especially if compared to complicated PN junction designs in [20] and [46]. The reason is that this modulator is based on the 'fin' structure which is a standard structure in the electronics industry. This feature enables easy and inexpensive fabrication of the proposed modulator through, for example, the multi-project-wafer (MPW) prototyping service from IMEC [62]. 'Modulator B' features a very high bandwidth of 90 GHz which can accommodate up to 180 Gbps of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Modulator** & **Main Feature** & **V\(\pi\)\(\pm\)\(\pm\)\(\pm\)\(\pm\)\(\pm\)** (Volt.cm)** & **Voltage (Volt)** & **L (mm)** & **3 dB Bandwidth (GHz)** & **IL (dB)** & **ER (dB)** \\ \hline A & Low \(IL\) and high \(ER\) & 9 & 30 & 3 & 26 & 1.35 & 29.5 \\ B & High speed & 6.4 & 40 & 0.8 & 90 & 4 & 3 \\ C & Low voltage & 6.6 & 2 & 7.5 & 10 & 8.1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the proposed structures.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Structure** & **V\(\pi\)\(\pm\)\(\pm\)\(\pm\)\(\pm\)\(\pm\)** (Volt.cm)** & **V (Volt)** & **L (mm)** & **Speed \(\star\) (Gbps)** & **\(IL\) (dB)** & **\(ER\) (dB)** \\ \hline
[51] & MOS & – & 25 & 0.11–0.15 & – & 5–10 & – \\
[52] & MOS & 8 & 3.5 & 2.5 & 1 & – & – \\
[53] & MOS & 8 & 3.5 & 10 & 1 & 6.7 & 16 \\
[26] & MOS & – & 30 & 14 & 19 & 9–25 & – \\
[46] & MOS & 3.3 & 1.4 & 3.45 & 10 & 10 & 3.8 \\
[54] & MOS & 24 & 30 & 8 & 0.02 & – & 0.6 \\
[55] & MOS & 0.3 & 1.5 & 0.2 & 15 & 2.2 & 3.6 \\
[56] & MOS & 0.3 & 2.5 & – & 10 & 4 & 5 \\
[57] & MOS & 0.5 & 2 & 2.5 & – & 5 & – \\
[50] & MOS & 1.53 & 6 & 0.5 & 25 & 26 & 3.65 \\
[58] & PN Junction & 4 & 6 & 1 & 40 & 4 & 1.1 \\
[48] & PN Junction & 2.7 & 4 & 3.5 & 40 & 15 & 10 \\
[59] & Wrapped around PN Junction & 11 & 6 & 1.3 & 40 & 25 & 6.5 \\
[22] & PIPN Junction & 3.5 & 7 & 4.7 & 20 & 6 & 6.6 \\
[60] & PN Junction - slow light structure & 0.85 & 1 & 0.5 & 40 & 11 & 8.5 \\
[61] & PN Junction & 2 & 2.7 & 0.75 & 60 & 3.5 & 4.4 \\ \hline \hline \end{tabular} – – Data is not available. * The speed is related to the 3 dB bandwidth using the relation Speed \(=2\times(3\) dB bandwidth), where the non-return-to-zero modulation is assumed.  
\end{table}
Table 2: Summary of previously published designs.  
NRZ signal. Also, it possesses a competitive phase shifter length and an acceptable insertion loss and extinction ratio, compared to the modulators in Table 2. However, with the high voltage of operation, this modulator is more suitable for backbone circuits. For the end user device, the alternative is given as 'Modulator C'.
## 5 Conclusions  
A three-dimensional large-deformation thermo-elastic-plastic FE model and experimental study for the oblique cutting of the turning of 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites in PCD tooling were performed in order to investigate the microscale mechanism of edge defect formation at the workpiece exit end, as well as the influence of the machining process on the size of edge defects. Good agreement was found between the experimental and calculated results. The numerical results on the dependence of edge defects on the machining parameters were also compared to those of the turning experimental data, and a good agreement was attained. The two main conclusions are as follows:  
(1) As the tool gradually approaches the exit end, a great many micro cracks occur near the tool nose because of the dislocation pileup and wear support, and when these micro cracks grow into the negative shear deformation zone, the micro voids evolved from micro cracks grow along a new fracture, satisfying the minimum fracture energy release, and these micro cracks/voids start propagating when they arrive at a critical state, followed by the coalescence of micro cracks/voids. The crack initiation and propagation along the negative shear deformation plane results in edge defects. The negative shear angle and stress levels caused by chip formation and weak support at the workpiece exit end are very important evidence for judging edge defect formation and optimizing the machining process.  
(2) The brittle characteristics of 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites were very obvious, and brittle SiC particles in the Al matrix led to very rough fracture morphologies. The length and height of edge defects increased with the depth of cut. While the depth of cut was below 2.5 mm, the size of micro edge defects showed a slow increase. As the feed rate increased, the size of micro edge defects at the exit end significantly increased, and there was a linear increasing relation between edge defect sizes and feed rate, while the spindle speed had a lesser influence on the size of the exit edge defects. Therefore, to achieve high-quality turning operation with less edge defects and high dimensional accuracy during the turning of 17 vol.% SiC\({}_{\text{p}}\)/2009 Al composites, as high a spindle speed as possible under a depth of cut below 2.5 mm should be employed, while smaller depth of cut and feed rate should be used.  
Methodology and experimental design, L.Z.; experiment and writing--original draft preparation, J.X. ([PERSON]); writing--review and editing, J.Y.; experiment and data curation, P.G and J.X. ([PERSON]).  
This research was funded by the Natural Science Foundation of Shandong Province, grant NO. ZR2019 MEE074, National Natural Science Foundation of China, grant NO. 51175353, Fundamental Research Funds for the Central Universities, grant NO. FRF-TP-18-007A1, Doctoral Research Fund of Shandong Jianzhu University grant No. XNBS1809.  
The authors declare no conflict of interest.