esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Influence of Water Mineralization  
Water mineralization strongly influenced on the amount of produced oxidant. Higher mineralization increased the oxidant concentration, especially with concentrated substrate doses. The ratios between the lowest oxidant level, which corresponded to low mineralization, and the highest oxidant level, which corresponded to high mineralization, in the presence of half-dose, full-dose, and double-dose substrates were 1.23, 1.4, and 1.78, respectively, demonstrating that mineralization played a tremendous role in the presence of elevated substrate levels.  
The role of minerals has already been showed with ammonium sulfate, which allows horseradish and microbial peroxididases to work more effectively [42]. In particular, both stabilizing and activating effects were found with a cationic surfactant such as benzalkonium chloride [43]. Ammonium sulfate ((NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)) increased LPO activity, therefore positive charges are determining for enzyme activity [43]. The high-mineralized waters used in this study contained high levels of Ca\({}^{2+}\) and Mg\({}^{2+}\), which could explain why more oxidants were generated. The four waters had similar pH values and the pH was unlikely to play a major role in the differences in oxidant concentrations observed among waters through a modification of the reductant potential of Compound I/native LPO [44].  
The functionality of haem peroxididases is influenced by the heme Fe\({}^{3+}\)/Fe\({}^{2+}\) redox couple and by the protein-matrix interaction [45]. In fact, LPO is stabilized by iron-ligand binding interactions, by electrostatic interactions, by the protein environment, and by the solvent [45]. It is important to stabilize and optimize the redox functionality of lactoperoxidase when highly concentrated substrates are used, especially for H\({}_{2}\)O\({}_{2}\) whose redox couple is elevated [46] and it seems that concentrated minerals in the media optimize the catalytic reaction of LPO when elevated substrate levels are used. Better stabilization of the ferric heme could increase H\({}_{2}\)O\({}_{2}\) consumption and formation of LPO Compound I, which in turn could induce a higher consumption of iodide and thiocyanate and could finally generate a higher quantity of reaction products.
## 2 Numerical Methods and Preparations
### Finite-Temperature R-DMFT in the Lieb Lattices  
Figure 1a displays the structure of the Lieb lattices, where the unit cell consists of three sublattice sites marked as A, B, and C. We define \(L\times L\) lattices as the \(L\times L\) tiles of the unit cell, which includes \(N=3L^{2}\) sites in total. The origin \((x,y)=(0,0)\) of the harmonic trap potential \(V(x,y)\) is located at the central \(A\) site of the lattices. We set the spacing between the nearest neighboring sites to be one.  
We fix the temperature at \(T=0.05\) throughout our R-DMFT calculations in this paper. We use \(n_{s}=5\) for the number of the bath orbitals to construct the Anderson impurity model, which we solve by using the exact diagonalization. The frequency grids are set with 1000 Matsubara frequencies. The temperature \(T=0.05\) is low enough to exhibit the magnetic ordering as expected in the previous DMFT calculations at the half-filling in the Lieb lattice without a trap [53; 63]. Here, we mainly consider the interaction strength \(U=2\) where we do not see other orders, such as the strip order or the charge and spin density waves [59; 63] which may occur with finite doping at lower temperatures or at stronger interactions. Note that here we reserve \"R-DMFT\" exclusively for the calculations with the trap potential, while we use the term \"DMFT\" for the translation invariant system with the three sublattices which brings the Brillouin zone summation into Equation (2). Figure 2b presents our DMFT calculations of \(S_{z}\equiv\frac{1}{2}\sum_{\alpha=A,B,C}(n_{\alpha,\uparrow}-n_{\alpha, \downarrow})\), where \(n_{\alpha,\sigma}=\langle\hat{n}_{\alpha,\sigma}\rangle\), as a function of temperature \(T\) at the half-filling, verifying that the system at \(T=0.05\) exhibits \(S_{z}=1/2\) in agreement with the ground-state DMFT calculation of the flat-band ferromagnetism [53].
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Antenna Manufacture  
The modified antenna was manufactured on flexible polyimide material with 18 um copper from both sides using standard PCB manufacturing processes. A standard SMA connector was attached to the antenna as a feeder. The connector has four ground pins and is suitable for a PCB thickness of 1.6 mm. To overcome this issue, two ground pins were removed from the connector, and the connector was assembled diagonally so that it fit without bending the antenna. Figure 15 shows the top and bottom sides of the  
Figure 14: Effects of Changing Parasitic Element Gap.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Citation** & **Type** & **Radiation** & \begin{tabular}{c} **Size** \\ **mm3** \\ \end{tabular} & \begin{tabular}{c} **Band** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Fc** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **BW (GHz)** \\ \end{tabular} &
\begin{tabular}{c} **Gain** \\ **(dBi)** \\ \end{tabular} \\ \hline
[3] & Vivaldi & Directional & \(40\times 40\times 1.6\) & 2.5–11 & NA & 8.5 & 7.2 \\ \hline
[7] & \
\begin{tabular}{c} Flexible \\ Monopole \\ \end{tabular} &
\begin{tabular}{c} Omni- \\ directional \\ \end{tabular} & \(20\times 20\times 0.05\) & 2–4 & 3 & 2 & NA \\ \hline
[30] & Vivaldi & Directional & \(36\times 36\times 1.6\) & 3–12 & 7.2 & 9 & 8.2 \\ \hline
[31] & Vivaldi & Directional & \(48\times 46\times 0.8\) & 3.1–10.6 & 7.8 & 7.5 & 8.25 \\ \hline
[32] & Vivaldi & Directional & \(57\times 41\times 1.6\) & 3–9 & 3.8 & 6 & NA \\ \hline
[33] & Vivaldi & Directional & \(51\times 42\times 0.05\) & 2.8–7 & 5 & 4.2 & 7.5 \\ \hline Proposed 1 & \multirow{2}{*}{\begin{tabular}{c} Vivaldi \\ With two resonant \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Directional \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 3.8–4.8 \\ and 9–10 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 4.0 and 9.5 \\ and 9–10 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 1.0 and1.0 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 2.24 and 2.7 \\ \end{tabular} } \\ \hline Proposed 2 & \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ With one resonant \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Directional \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 25\times 20\times 0.1 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 4.0–5.2 \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} 4.4 \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} 1.2 \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} 2.33 \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Proposed Antennas and others reported in the Literature.
## 2 Wavelet Packet Transform
### Frequency Band Energy Feature Extraction  
Suppose the highest frequency of the time signal \(x(t)\) is \(f_{\text{max}}\), and perform \(J\)-layer WPT decomposition on \(x(t)\) to get \(2^{j}\) groups of WPT coefficients, respectively \(w_{j}\big{(}i=0,1,\cdots,2^{j}-1\big{)}\). The frequency band corresponding to these \(2^{j}\) coefficients is \(\big{[}\frac{j}{2^{j}}f_{\text{max}},\frac{j+1}{2^{j}}f_{\text{max}}\big{]}\).  
Take the three-layer WPT as an example. First, find the signal energy of each frequency band. Suppose the corresponding energy of \(w_{3i}(i=0,\ 1,\cdots,7)\) is \(E_{3i}(i=0,\ 1,\cdots,7)\), then:  
\[E_{3i}=\|w_{3i}\|^{2}=\int_{-\infty}^{+\infty}|w_{3i}|^{2}dt \tag{3}\]  
Secondly, the feature vector is constructed with \(E_{3i}\) as the element:  
\[T=[E_{30},E_{31},\cdots,E_{37}] \tag{4}\]  
When the signal energy is large, \(E_{3i}(i=0,\ 1,\ \cdots,\ 7)\) represents a large value, so it will bring certain difficulties in signal processing. Therefore, the feature vector \(T\) will be normalized:  
\[E=\left|\sum_{i=0}^{7}|E_{3i}|^{2}\right|^{\frac{1}{2}} \tag{5}\]  
Finally, get the normalized vector \(R=\ [E_{30}/E,E_{31}/E,\ \cdots,E_{37}/E]\).
# Competing Failure Modeling for Systems under Classified Random Shocks and Degradation
## 3 Modeling for Risk Modes
### Modeling for Soft Failure
The cumulative distribution function (CDF) of degradation measurement \(X_{s}(t)\) can be deduced as:  
\[ \begin{array}{ll}F_{X}(x,t)&=P(X_{S}(t)<x)\\ &=\sum\limits_{n_{0}=0}^{n-n_{2}}\sum\limits_{n_{2}=0}^{n}\sum\limits_{n=0}^{ \infty}P(X(t)+S(t)<x|(N(t)=n,N_{2}(t)=n_{2},N_{3}(t)=n_{3}))\\ &\times P(N(t)=n)P(N_{2}(t)=n_{2})P(N_{3}(t)=n_{3})\\ &=\sum\limits_{n_{3}=0}^{n-n_{2}}\sum\limits_{n_{2}=0}^{n}\sum\limits_{n=0}^{ \infty}P\left(X(t)+\sum\limits_{j=1}^{n_{2}}B_{j}+\sum\limits_{k=1}^{n_{3}}C_{ k}<x|(N(t)=n,N_{2}(t)=n_{2},N_{3}(t)=n_{3})\right)\\ &\times P(N(t)=n)P(N_{2}(t)=n_{2})P(N_{3}(t)=n_{3})\\ &=\sum\limits_{n_{0}=0}^{n-n_{2}}\sum\limits_{n_{2}=0}^{n}\sum\limits_{n=0}^{ \infty}\left(\int_{0}^{x}\int_{0}^{u}G(x-u,t)f_{\rm{B}}{}^{<m>}(u-u_{1})f_{\rm {C}}{}^{<m>}(u_{1})du_{1}du\right)\\ &\times\frac{\exp(-\lambda t)(\lambda t)^{n}}{n!}\frac{\exp(-\lambda_{2}t)( \lambda_{2}t)^{n_{2}}}{n_{2}!}\frac{\exp(-\lambda_{3}t)(\lambda_{3}t)^{n_{3}}} {n_{3}!}\end{array}  \tag{4}\]  
where G(x,t) denotes the CDF of \(x(t)\), and \(f_{\rm{B}}{}^{<m>}(u),f_{\rm{C}}{}^{<m>}(u)\) means the PDF of the sum of m i.i.d. \(B_{j}\) and n \(C_{k}\). Moreover, when the instantaneous increments are normally distributed i.i.d., \(B_{j}\sim N(\mu_{B},\sigma_{\rm{B}}{}^{2})\), \(C_{k}\sim N(\mu_{C},\sigma_{\rm{C}}{}^{2})\), and the degradation path is linear with a constant parameter \(\varphi\) and a normal-distributed \(\beta\sim N(\mu_{\beta},\sigma_{\rm{B}}{}^{2})\), the expression will be more specific:  
\[ \begin{array}{ll}F_{X}(x,t)&=\sum\limits_{n_{3}=0}^{n-n_{2}}\sum\limits_{n_ {2}=0}^{n}\sum\limits_{n=0}^{\infty}\Phi\left(\frac{x-(\mu_{\beta}t+\varphi+ n_{2}\mu_{B}+n_{3}n_{C})}{\sqrt{\sigma_{\rm{B}}{}^{2}t^{2}+n_{2}\mu_{B}+n_{3} \sigma_{\rm{C}}{}^{2}}}\right)\\ &\times\frac{\exp(-\lambda t)(\lambda t)^{n}}{n!}\frac{\exp(-\lambda_{2}t)( \lambda_{2}t)^{n_{2}}}{n_{2}!}\frac{\exp(-\lambda_{3}t)(\lambda_{3}t)^{n_{3}} }{n_{3}!}\end{array}  \tag{5}\]  
where the \(\Phi()\) is the CDF of a standard normal random variable.
## 4 Numerical Simulation Results Analysis
### Impact of Excavation at Individual Stages on the Retaining Wall
#### 4.1.1 Surface Subsidence in the Excavation Area
From the analysis results, it can be seen that the surface subsidence curves on both sides of the diaphragm wall were groove-shaped curves, which were consistent with the research results of most of the deep excavation cases collected by [PERSON] et al. (2019). Further observation of Figure 18 shows that the figure could be divided into two regions, with a distance of 15 m from the diaphragm wall as the boundary. The surface subsidence curve of the first area (0-15 m) had a concave shape, which was presumed to be mainly affected by the excavation of the arrival well. The surface subsidence curve of the second area (15-35 m) was also in the shape of a depression. It was speculated that it was mainly affected by the excavation support of steel sheet piles. The excavation area was small, but caused a large amount of soil deformation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**South Side/Location**} & \multicolumn{3}{c}{**After the Completion of Pipe Roof**} & \multicolumn{3}{c}{**After the Completion of Underpass**} \\ \cline{2-7}  & **Analysis Value** & **Actual Value** & **Difference** & **Analysis Value** & **Actual Value** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline One meter away from the diaphragm wall & \(-6.68\) & \(-5.90\) & \(0.78\) & \(-7.06\) & - \\ Five meters away from the diaphragm wall & \(-10.02\) & \(-9.64\) & \(0.38\) & \(-10.78\) & - \\ Sixteen meters away from the diaphragm wall & \(-1.05\) & \(-0.75\) & \(0.30\) & \(-1.46\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of the numerical analysis and actual values of surface subsidence on the south side of the diaphragm wall. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**North Side/Location**} & \multicolumn{3}{c}{**After the Completion of Pipe Roof**} & \multicolumn{3}{c}{**After the Completion of Underpass**} \\ \cline{2-6}  & **Analysis Value** & **Actual Value** & **Difference** & **Analysis Value** & **Actual Value** \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
2.5 m away from the diaphragm wall & \(-4.68\) & \(-5.28\) & \(0.60\) & \(-6.01\) & - \\
6.0 m away from the diaphragm wall & \(-6.05\) & \(-6.82\) & \(0.77\) & \(-7.36\) & - \\
13.5 m away from the diaphragm wall & \(-3.82\) & \(-3.13\) & \(0.42\) & \(-5.04\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of the numerical analysis and actual values of surface subsidence on the north side of the diaphragm wall. Figure 19: Surface subsidence curves on the north side of the diaphragm wall: (**a**) three-dimensional schematic diagram; (**b**) plan schematic diagram. Figure 17: Schematic diagram of the positions of section A-A and section B-B. Figure 18: Surface subsidence curves on the south side of the diaphragm wall: (**a**) three-dimensional schematic diagram; (**b**) plan schematic diagram.
###### Abstract  
Many years ago, it seemed inconceivable that our cars could drive autonomously or communicate with each other to form a self-organizing convoy or platoon. By 2023, however, technological advances have taken us to the point where most of these goals will be achieved. In the time of what was initially known as Day 1, single-channel Intelligent Transport System (ITS) devices fully met the requirements for safe communication. The trends show that with the rapid development and the emergence of new, more robust Vehicle-to-Everything (V2X) applications, which require higher bandwidth (collectively called Day 2), the current single-channel medium access method in the available ITS bands will no longer achieve the desired capacities. The main reason is that Day 2 and beyond V2X information dissemination protocols introduce increasing packet sizes and sending frequencies. To complete a resource-friendly and more efficient operation with Day 2 or other advanced V2X services, ITS standards present the Multi-Channel Operation (MCO) constellation as a potential solution. In the case of MCO, we use two or more channels simultaneously, thus preventing the radio medium from saturating its capacity. However, there are still several pending questions about MCO applicability, practical usage, configuration, and deployment, to name a few. The primary purpose of this article is to present a dynamic channel selection framework design and implementation capable of modeling and simulating advanced multi-channel communication use cases. We used this framework to investigate Channel Busy Ratio (CBR) based dynamic channel switching within the Artery/OMNeT++ simulation environment.  
dynamic channel selection; Multi-Channel Operation (MCO); CBR; congestion; Artery/OMNeT++ +
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 4 Wearable Orthoses
### Mechanism Underpinning the Efficacy of Wearable Orthoses in Tremor Suppression  
Although some of the underlying causes of tremors remain unknown, several putative interactive factors contributing to the motor expression of tremor have been hypothesized. These include the oscillating tendencies of the joint and muscular mechanical systems, short- and long-loop reflexes of the spinal cord and the brainstem, and the closed-loop feedback systems of higher motor centers such as the cerebellum [124]. Unlike electrical stimulation systems, wearable orthoses target the clinical manifestations of tremors. By generating an opposite force of equal magnitude, these devices attempt to mechanically counteract the involuntary movements.
## 1 Introduction
### Eruption History  
The main evidence of volcanic hazards at the islands stems from the abundant occurrence of volcanic deposits. As there are no historical records of eruptive activity, all information about the eruption history, frequency, and distribution has come from the study of these deposits. Pyroclastic ashfall, pyroclastic flow, and pyroclastic surge deposits are all present [2] suggesting an explosive nature to past eruptions. The age of the most recent eruption at both volcanoes remains debated mainly because of the paucity of charcoal suitable for dating in the most recently deposited bedsets. For The Quill, [PERSON] and [PERSON] [2] consider a date of 1700 \(\pm\) 90 year B.P., circa 250 CE, to be the most likely date for the last eruption, based on archaeological dating of the posthole backfill. One charcoal sample from a pit in The Bottom on Saba (Figure 2) was dated at 310 \(\pm\) 80 year B.P., circa 1640 CE [2] and matched to the most recent eruption of Mt. Scenery. The uncertainty in the dates of the most recent eruptions on the two islands and the lack of data to complete the geochronology complicate the understanding of the characteristics of the volcanoes. This affects the hazard assessment of the islands as well as the choice of locations for (temporary) monitoring instruments. Especially on Saba, which consists of multiple domes, knowledge of the eruption history is critical to ensure that the most recently active domes are monitored accurately.  
Figure 2: Geophysical monitoring network of the Royal Netherlands Meteorological Institute (KNMI) at Saba (**left**) and St. Eustiatius (**right**): permanent sites with colocated seismometer and GNSS sensor (blue triangle), seismometer sites (red triangles), submarine hotsprings (white squares), and a temporary site with two temperature sensors (yellow triangle). SRTM Digital Elevation Model in the background with elevation contours in meters.
## 4 Results and Discussion
### The Comparative Analysis of SASW Modulus and Compressive Modulus Results  
Theoretically, although the elastic modulus calculated by SASW is much more reliable, it still needs to be compared with the results obtained in laboratory tests to illustrate its persuasiveness. Table 2 summarizes the concrete slab modulus obtained through the SASW method and laboratory testing of field cores at a total of 16 measurement locations in Section I and Section II. It is obvious that the static compressive modulus values are much smaller than those measured by the SASW method. This is due to different ways of loading applications (static vs. dynamic) and different boundary conditions at the measurement locations (restricted vs. unrestricted).  
A linear regression analysis of these two types of moduli is performed to obtain the correlation between the SASW modulus and the compressive modulus, as shown in Figure 4. It was found that these two types of moduli are highly correlated, with a correlation coefficient R\({}^{2}\) of 0.7622 and a Pearson coefficient of 0.8731. These findings align well with prior research that similarly undertook a comparison between the SASW modulus and laboratory compressive modulus, yielding a notable correlation coefficient R\({}^{2}\) of 0.8553 [39]. Given the reliability of laboratory tests, it can be seen that the SASW method is feasible as a means of detecting the modulus of the concrete layer.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Pavement Section** & **Measurement Location** & **SASW (MPa)** & **Compressive Modulus (MPa)** \\ \hline \multirow{8}{*}{Section I} & X1 & 43,291 & 28,571 \\  & X2 & 34,075 & 21,814 \\  & X3 & 34,958 & 22,118 \\  & Y1 & 49,675 & 30,571 \\  & Y2 & 46,224 & 28,773 \\  & Y3 & 46,996 & 28,201 \\  & Z1 & 46,433 & 26,523 \\  & Z2 & 47,363 & 28,302 \\  & Z3 & 42,591 & 24,521 \\ \cline{2-4}  & M & 45,881 & 26,715 \\  & L & 44,956 & 26,667 \\  & N & 48,131 & 29,759 \\ \hline \multirow{8}{*}{Section II} & AA & 40,861 & 26,715 \\  & BA & 39,218 & 26,467 \\ \cline{1-1}  & CA & 39,131 & 25,881 \\ \cline{1-1}  & DA & 40,254 & 26,344 \\ \hline \end{tabular}
\end{table}
Table 2: Modulus of elasticity and compressive modules of concrete samples from the SASW method and compressive strength test, respectively.
# Analysis of the Interaction Effects of Shield Structure Oblique Passing under an Existing Tunnel
## 5 Deformation Characteristics of Existing Tunnels
### Torsional Deformation
For this paper, the rotation range of the existing tunnel caused by the shield underpass is \(-\)0.70 \(\times\) 10\({}^{-6}\)-1.80 \(\times\) 10\({}^{-6}\). For the shield oblique underpassing the existing tunnel, the deformation mode of the existing tunnel is not only limited to shear and bending deformation but will also include local torsional deformation. If various reinforcement measures are not taken in advance, the existing tunnel may experience large torsional deformation, which will lead to lining cracking, causing groundwater leakage, sidewall deformation, ballastless track faster shear damage, and ballastless track bed and back arch separation, and which may even affect the normal operation of the existing tunnel. Many researchers have conducted a huge amount of work on the shear and bending deformation of tunnels, with many conclusions being reached, but little attention has been paid to the local torsional deformation of tunnels. When the spatial location of the existing tunnel and the new tunnel involves oblique intersections, the designers and constructors must consider not only the shear and bending deformation of the existing tunnel but also its torsional deformation.