esa-sceva/satcom-chunk-collection huggingface dataset:
# Human Exposure to Toxic Metals (Cd, Pb, Hg) and Nitrates (NO3-) from Seaweed Consumption
## 3 Results and Discussion
### Concentration and Dietary Intake of Toxic Metals (Cd, Pb, Hg)
The recommended consumption of 4 g/day, according to the manufacturer on the packaging, of hijiki and nori algae accounts for a percentage contribution to the TWI of Cd (2.5 ug/kg bw/week) of 19.6% and 16.4%, respectively (Table 4). Although this percentage does not exceed the TWI, it should be borne in mind that it is nearly a quarter of this maximum value and that the global diet may have a high total intake of Cd. The MoE value calculated by the NOAEL (0.01 mg/kg bw/day) [38] is 2.1 (hijiki algae) and 2.5 (nori algae). Considering this consumption and the MoE values, there is no risk. The consumption of wakame algae (4 g/day) is 1.10% of the BMDL of Pb set at 0.63 ug/kg bw/day (nephrotoxic effects) (Table 5) [16]. This percentage does not represent a significant contribution to the dietary intake of Pb, and therefore, adult consumers are not at risk. The MoE value calculated considering a BMDL\({}_{01}\) of 0.0015 mg/kg bw/day [16] is 3.15 (wakame algae consumption). This MoE value indicates low risk. The percentage contribution to Hg intake from the consumption of 4 g/day is 1.73% of the TWI of organic Hg (1.3 ug/kg bw/week for methylmercury) (Table 6) [18]. Therefore, this does not pose a risk to the health of adults. According to the NOAEL of 0.04 mg/kg bw/day of methylmercury [18], the MoE for the consumption of arame algae is 182.
## 3 Results and Discussion
### Effect of Inner Electrode Material  
Figure 14 shows the effect of electrode material on oxidation degree of NO\({}_{\text{X}}\) in the DBD reactor with 10-mm electrode diameter (gas gap of 2.5 mm). The results show that stainless-steel electrode has the best performance on NO oxidation and the highest energy efficiency. However, this experimental result is different from results of [PERSON] and [PERSON], which indicate that aluminum and tungsten electrodes have higher NO removal and energy efficiency than copper and stainless-steel electrodes due to the higher secondary electron emission coefficients \(\gamma\)[16,19]. \(\gamma\) is not a constant, which is related to the electron energy [40]. As a result, the performance of electrode material for NO treatment may also different under different E/N conditions.  
Therefore, we experimentally studied the influence of electrode materials on NO oxidation in the DBD reactor with 14-mm electrode diameter (gas gap of 0.5 mm), as shown in Figure 15. The experimental results show that copper electrode has a higher NO oxidation and energy efficiency than stainless-steel electrode. Obviously, the performance of electrode material for NO oxidation is  
Figure 14: Effect of electrode materials on oxidation degree of NOX (electrode length is 100 mm and inner electrode diameter is 10 mm).  
Figure 13: Distribution of E/N in gas gap of DBD reactor with different inner electrode diameter (the E/N of DBD reactor with different inner electrode diameter were calculated under their maximum oxidation degree of NO\({}_{\text{X}}\) conditions).  
different under different gas gap condition. There is obvious interaction between gas gap and electrode material. The E/N of the gas gap increases with the decrease of gas gap, which causes the increase of the mean electron energy [16]. Therefore, \(\gamma\) of electrode material will change under different gas gaps, resulting in the interaction between gas gap and electrode material.
## 3 Methodology
### The SEBS Model Sensitivity  
To compare model sensitivities and their dependencies on inputs, a sensitivity analysis was performed for all input maps (land surface temperature, NDVI, LAI, and Albedo) and meteorological inputs (air temperature, air pressure, wind speed, humidity, and incoming shortwave radiation).  
The sensitivity (\(S_{\mathrm{j}}\)) of the model to an input parameter (\(j\)) can be expressed as:  
\[S_{\mathrm{j}}\left(H_{\pm}\right)=\frac{H_{\pm}-H_{\mathrm{r}}}{H_{\mathrm{r }}} \tag{22}\]  
\(S_{\mathrm{j}}\) is calculated for a positive or for a negative deviation of an input (\(j\)) within the SEBS model. \(H_{+}\) and \(H_{-}\) correspond to the sensible heat fluxes simulated by the SEBS model when driven by the positive and negative deviation of the \(j\), respectively. \(H_{\mathrm{r}}\) is the result predicted by the reference variable, \(r\).  
The major input maps (\(z_{0\mathrm{m}}\), NDVI, LAI, Albedo, and LST) and the meteorological inputs (air temperature (\(T_{\mathrm{a}}\)), air pressure (\(P\)), wind speed (\(u\)), humidity (\(H_{\mathrm{h}}\)), and incoming shortwave radiation (\(R_{\mathrm{s}}\))) were considered in this analysis.  
With the exception of LST and air temperature, the averages of additional inputs during the growing seasons of 2010 and 2011 at the EC site were used as reference data for \(r\) while \(r_{\pm 25}\) (\(\pm 25\%\times r\)) were used as the testing variables, respectively. For LST and air temperature, we used \(\pm 5\) K as the deviation because the 25% deviation exceeded the physical limits. The ranges of the above values can represent the majority of the local land surface and meteorological conditions at the site.
## 2 Basic Theory
### The First Stage on Damaged Edge Identification  
Assuming that damage to the structural sealant does not cause a change in the structural mass, the obtained displacement modes are orthogonally normalized with respect to the mass matrix. When the structural sealant is damaged, the modal displacement at the corresponding points of the damage location increases relatively, leading to changes in the BMC. Therefore, an indicator \(\gamma_{i}^{r}\) referred as to ACBMC is established to describe the differences to identify the damaged edge, as follows:  
\[\gamma_{i}^{r}=\frac{\left(\left\{C_{i}^{r}\right\}_{d}^{T}\{C_{i}^{r}\}_{u} \right)^{2}}{\left(\left\{C_{i}^{r}\right\}_{d}^{T}\{C_{i}^{r}\}_{d}\right) \left(\left\{C_{i}^{r}\right\}_{u}^{T}\{C_{i}^{r}\}_{u}\right)} \tag{3}\]  
where \(\left\{C_{i}^{r}\right\}\) is the BMC for mode \(i\) and edge \(r\). Subscripts \(u\) and \(d\) represent an undamaged and damaged edge, respectively. The ACBMC decreases with the increase in the damage severity of the edge \(r\). Therefore, the damaged edge can be identified by the ACBMC.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 6 Conclusions  
In this work, we summarize the linewidth characterization techniques of narrow-linewidth lasers. The characterization methods are classified into two categories based on the principle of linewidth measurement: those based on the beat frequency power spectrum and those based on phase noise analysis. The advantages and disadvantages of various testing schemes are analyzed. From the above research progress, it can be concluded that laser linewidth of different magnitudes can be characterized under different experimental conditions. Through comparative analysis, the fiber delay self-heterodyne method is widely used in the measurement schemes of narrow-linewidth lasers, while second-order Stokes light based on Brillouin scattering is used as a reference light source to drive heterodyne mixing, achieving linewidth characterization below the kHz level. In response to the shortcomings of existing research, it is proposed that the focus be on addressing the issues of phase noise jitter and beat frequency spectrum broadening caused by the \(1/f\) noise introduced by excessively long delay fibers, as well as the coherent envelope spectrum being submerged by the background noise caused by excessively short delay fibers, to reduce experimental costs and improve measurement accuracy. At the same time, future research trends in linewidth characterization methods are discussed.  
J.-Q.C.: methodology, investigation, writing--original draft; C.C.: conceptualization, methodology, validation, writing--original draft, writing--review and editing; J.-J.S.: formal analysis; J.-W.Z.: data curation; Z.-H.L.: formal analysis; L.Q.: supervision; Y.-Q.N.: project administration; L.-J.W.: resources. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research & Development Program of China (2023 YFB2805102), the National Natural Science Foundation of China (62374164, 62090061, 51672264, 62334009), the Science and Technology Development Project of Jilin Province (20220201063 GX), and the Changchun Science and Technology Development Plan Project (22 SH01).  
Not applicable.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflicts of interest.
# Estimation of Dose Enhancement for Inhomogeneous Distribution of Nanoparticles: A Monte Carlo Study
## 2 Materials and Methods
### Beam and Physics Modeling  
A directional 2 \(\times\) 2 cm\({}^{2}\) monoenergetic photon beam was used with the following energies: 50, 100, 150, 200, 300, 400, 600, 800, 1000, 1500, 2000 and 4000 keV. The source to skin distance \"SSD\" was set to 100 cm. The wide range of photon beam energies was intended to study the approximate energy range where NPs could be considered most useful due to the higher interaction probabilities at these energies.  
The emstandard_opt4 physics package was used, as it provides a well-validated physics model for medical applications and covers the energy range considered in this study [56; 57]. Both fluorescence and Auger electrons processes were enabled. To achieve low statistical uncertainty, 10\({}^{9}\) particles were used. The secondary production threshold is defined in GATE as a range cut-off. GATE uses the provided range cut-offs for each region to determine the energy production thresholds. In this study, the cut-off was set to 0.2 mm for gamma, electrons, and positrons.
# Bearing Fault Diagnosis Approach under Data Quality Issues
## 3 Materials and Methods
### Crossover Phenomenon  
The existence of two linear scaling regions in the log-log plot of the fluctuation function is called the crossover. This phenomenon was first discovered in the heartbeat time series [34]. Subsequently, the existence of crossover phenomenon was confirmed in several real-world time series in diverse research fields. In [35], the authors showed that the crossover in the DFA method could be a result of periodic trend in the time series.  
In a correlated time series with additive periodic sinusoidal trend with a period of T, the fluctuation function, \(F(n)\), can be written as the superposition of the fluctuation function, \(F_{S}(n)\), of the time series and the fluctuation function, \(F_{T}(n)\), of the sinusoidal trend, as shown in Equation (3).  
\[|F(n)|^{2}\ =\ |F_{S}(n)|^{2}+|F_{T}(n)|^{2} \tag{3}\]  
The fluctuation function, \(F(n)\), exhibits a crossover phenomenon with two linear regions. It can be shown that crossover occurs at the period of the sinusoidal trend (\(n\ =\ T\)). As shown in Figure 3, at the short-time scale (\(n\ <\ T\)), the effect of the periodic trend was dominant compared to the correlated series. Moreover, the fluctuation function of the correlated series was dominant at the long-time scale (\(n\ >\ T\)) as the fluctuation function of the periodic sinusoidal trend remained bounded at higher time scales.
## 2 Control and Modeling of the OSWF System
### MMC HVDC Outer Loop and Energy Control  
The basic outer controller schemes were well expressed in [22]. The on-shore side converter operated as an inverter providing power to the grid, while the off-shore side converter operated as  
Figure 1: Off-shore doubly-fed induction generator (DFIG) wind farm with MMC.  
a grid former, which synthesizes the AC voltage by a predetermined frequency for Off-shore Wind Farm. There were studies [23; 24] that varied the grid-forming parameters for the intended control purposes, but the basis was to generate a fixed frequency and voltage for grid synchronization. The off-shore side converter acted as an infinite voltage source to absorb generated power from wind turbines. Furthermore, it established the off-shore frequency and AC voltage of the grid.  
Because this paper deals with DC voltage as the energy storage, capacitor voltage and its stored energy should be defined. In Figure 2, the overall MMC converter structure and submodule configurations are displayed. If the capacitors are well balanced and sized identically, each MMC arm can be represented as an aggregated equivalent capacitor [25]. To calculate energy stored in a DC capacitor, an equivalent capacitor and measured capacitor voltage sum are required. The average voltage of arm capacitors can be expressed as:  
\[\hat{v}_{c\Sigma}=\frac{1}{3}\sum_{i=1,2,3}\frac{v_{c\Sigma u_{i}}+v_{c\Sigma l _{i}}}{2} \tag{1}\]  
where \(\hat{v}_{c\Sigma}\), \(v_{c\Sigma u_{i}}\) and \(v_{c\Sigma l_{i}}\) represent the average arm voltage and the upper and lower arm average voltage. The total equivalent capacitance is then derived to calculate total stored energy in the MMC:  
\[W_{\Sigma}=\frac{1}{2}C_{eq}\hat{v}_{c\Sigma}^{2}=3C_{tot}\hat{v}_{c\Sigma}^{2 },\;C_{tot}=\frac{C}{N} \tag{2}\]  
where \(C_{eq},C_{tot},C,\text{and}\;N\) represent equivalent capacitance, total arm capacitance, submodule capacitance, and submodule number. Assuming no losses in the MMC, the variation of the total energy in the converter can be expressed as a power difference:  
\[\frac{dW_{\Sigma}}{dt}=P_{in}-P_{out} \tag{3}\]  
Figure 2: MMC HVDC converter configuration.
###### Abstract  
Background: The purpose of this in vitro study was to compare the implant axis' spatial position and orientation by using laboratory scanner versus intra-oral scanner with three different scan abutments. Methods: A 3D model was printed with an internal hex implant analog in the place of teeth 35#. Three standard scan abutments were used: MIS (two-piece titanium), AB (two-piece PEEK and titanium base) and ZZ (one-piece PEEK). Each scan abutment was scanned 30 times by TRIOS E3 (laboratory scanner) and 30 times by Omnicam (intra-oral scanner). For each scan, an STL (stereolithography) file was created, and the spatial characterization of each scan abutment was measured in the X, Y, Z coordinates, and rotational and longitudinal angles. The comparison between all the scans was conducted by superimposition of the STL files, using a 3D software. A \(t\)-test and Wilcoxon signed-rank test were used. (\(p<0.05\)) Results: Only the MIS scan abutment showed no statistical difference in the X and Z axes. (\(p<0.05\)). All other scan abutments showed a statistical difference in all axes. The rotational angle of the AB scan abutment was twice the angle of the MIS and ZZ scan abutments. Conclusions: All three scan abutments showed a rotational deviation of the implant axis between the laboratory scanner and the intra-oral scanner. The AB scan abutment showed the greatest deviation (1.04 degrees) while the other two abutments showed deviations of about half a degree in relation to the laboratory scan abutment. There is a need for further studies which will examine the influence of geometry, material, and scan abutment parts on the accuracy of the scan obtained.  
CAD-CAM; scan abutment; implant axis; laboratory scanner; intra-oral scanner; TRIOS; CEREC +
Footnote †: journal: Medical Sciences
## 4 Discussion
### Research Limitations and Further Studies  
The result of this study shows only a prototype system that tracks the weight of heavy objects carried by construction workers using safety shoes with FSR sensors. The main scope of this study focuses mainly on determining whether construction workers lift heavy objects above the safety guidelines. In order to practically apply this system to construction sites, several limiting factors of this study should be improved through future studies from at least the following points of view. First, further research is needed on the durability of the sensor, the durability of the battery, and the convenience of wearing safety shoes under various construction work environments in order to practically use the safety shoes with FSR sensor. Second, continuous research efforts are needed to improve the accuracy of the weight tracking system. For this, experiments and analyzes need to be conducted under more diverse conditions, such as the different physical conditions of the construction workers, the grounding condition of the shoes, the use of other types of sensors, etc. Third, the experiment scenario adopted in this research was limited to 20 kg because the physical safety of the experimenter was considered. However, it may be necessary to keep analyzing the impact of various weights over 20 kg to see what those results can add to the study. Recently, the introduction of IT/ICT application technology has been newly attempted at construction sites, but the reality is that it is still insufficient. Therefore, this research result is expected to be one of the efforts to overcome this.
## 2 Methodology of SSI-COV
### Extraction of Modal Characteristics  
Matrices \(A\) and \(C\) represent the system dynamics in the measurement space and capture the system's modal characteristics. Although the output matrix \(C\) describes how the internal state of the system translates to the observed output \(y_{k}\) its exact value is not always directly known due to the presence of noise \(\theta_{k}\). In this context, the Hankel matrix plays a fundamental role, as it provides a comprehensive representation of the system dynamics based on the observation \(y_{k}\).  
To achieve this representation, a time estimation spanning \(n\) steps is considered, where \(n>2i\). The Hankel matrix is organized into blocks of past output vectors \((Y_{0},\ Y_{1},\...,\ Y_{n})\), and future output vectors \((Y_{i+1},\...,\ Y_{2i})\), ensuring that the system's dynamics are fully captured. The Hankel matrix is defined as follows:  
\[H=\begin{bmatrix}\Delta_{(1,1)}&\Delta_{(1,2)}&\cdots&\Delta_{(1,3)}\\ \Delta_{(2,1)}&\Delta_{(2,2)}&\cdots&\Delta_{(2,3)}\\ \vdots&\vdots&\cdots&\vdots\\ \Delta_{(i,1)}&\Delta_{(i,2)}&\cdots&\Delta_{(ij)}\end{bmatrix} \tag{6}\]  
where each block \(\Delta(a,b)\) is a temporal correlation matrix defined as  
\[\Delta(a,b)=E\Big{(}y_{k+a}\ \cdot\ y_{k+b}\mathit{refT}\Big{)} \tag{7}\]  
and represents how the system outputs are correlated at different time instants. The reference output \(y_{k+b}\mathit{refT}\) is used as a benchmark to evaluate the interactions between the observed data \(y_{k+a}\) and the reference output. This correlation captures the dynamic dependencies among the system's outputs and is pivotal for constructing the Hankel matrix, which is central to identifying the modal characteristics of the system.
## 3 Design Equations of Key Components in the Presented LED Tube Lamp Driver  
### Design of Coupled Inductors \(L_{pfc1}\) and \(L_{pfc2}\)  
The design equation of the coupled inductor \(L_{pfc1}\) (\(L_{pfc2}\)) is expressed as [22]  
\[L_{pfc1}=\frac{\eta{v_{AC-rms}}^{2}D^{2}}{2P_{lamp}f_{S}}=L_{pfc2} \tag{4}\]  
where \(\eta\) is the estimated circuit efficiency, \(P_{lamp}\) is the rated power of the LED tube lamp, and \(f_{S}\) is the switching frequency.  
With a \(\eta\) of 0.9, a \(v_{AC-rms}\) of 110 V and a \(P_{lamp}\) of 18 W, an\(f_{S}\) of 55 kHz and a \(D\) of 0.5, the coupled inductors \(L_{pfc1}\) and \(L_{pfc2}\) are given by:  
\[L_{pfc1}=L_{pfc2}=\frac{0.9\times 110^{2}\times 0.5^{2}}{2\times 18\times 55,0 00}=1.34\text{ mH}.\]
## 5 Results and Discussion
### Simulation Results  
This section is dedicated to discuss results of MITM attack in VANET. We simulated MITM attackers according to above two scenarios (distributed attackers and fleet of attackers) and evaluated network efficiency based on the evaluation metric defined above. Further, each simulation scenario is carried out twenty five times with random seed value to ensure unique initial vehicle assignment within the network every time. Moreover, the simulation results presented below are the average of twenty five runs for each simulation scenario.
## 4 On-Site Test Results and Analysis
### Vault Settlement  
The vault settlement was continuously monitored after the steel arch support at the up-step was established in the left and right caverns. The value of the vault settlement and its sinking rate are shown in Figure 6.  
1. The process of vault settlement can be divided into three stages: the rapid development stage within 15 d after the construction of up-step, the continuous development stage before the application of the invert, and the gradual stabilization stage after the application of the invert. The vault settlement at each stage accounts for about 50%, 30%, and 20% of the total settlement. After the application of the invert, the sinking rate gradually tends to be stable.
2. The stabilization periods of vault settlement in the left and right caverns are different. It takes 42 d and 30 d for vault settlement of the left and right caverns to reach stability, respectively. At the section of 30.5 m to working face, the settlement value of the vault at the left cavern reaches about 80% of the total settlement. This result is consistent with the observation of [PERSON] et al. [34]. Since this study relies on an open-buried double-arch tunnel, there is no surrounding rock above the open cavern. This prompts the time for the vault settlement to reach stability, and reduces the distance from the working face between the former cavern and the later cavern.
3. The maximum vault settlement position and the sinking rate in each cavern are different. The maximum vault settlement and sinking rate of the left cavern occur at  
Figure 6: Development of the settlement and sinking rate of the vault. (**a**) Vault settlement; (**b**) the sinking rate of vault settlement.  
the VM, while they occur at the RM in the right cavern. The minimum settlement and sinking rates of each cavern occur in the measurement point near the mid-partition wall. The reason is that the deflecting retaining wall and the mid-partition wall effectively suppress the overall sinking of the primary lining at the measurement points. The maximum vault settlement of the left and right caverns is 55.5 mm and 49.0 mm, respectively, which are about 1/3 of the admissible value (150 mm). The open-excavation and concealed construction method is effective to control the settlement of the tunnel during the construction of the tunnel.