esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Interfacial Adsorption  
#### 3.5.1 Interaction Energy Calculation  
Figures 7 and 8 (in order to facilitate the observation of the interaction between the corrosion inhibitor and Fe (110), the water in the system was removed from Figures 7 and 8) shows the adsorption position of the PCA-GLU molecules and carbon steel surfaces according to the molecular dynamic simulation. It is obvious that, according to the molecular dynamics simulation, the PCA-GLU molecules are more firmly adsorbed on the Q235 surface, which proves the adsorption mechanism of the molecules on the Q235 surface. The interaction value of the polymer and the Q235 surface can reflect the adsorption performance between both, and the interaction can be calculated using the formula as follows:  
\[E_{interaction}=E_{total}-\left(E_{surface}+E_{polymer}\right) \tag{11}\]  
In the formula, \(E_{interaction}\), \(E_{total}\), \(E_{surface}\), and \(E_{polymer}\), respectively, represent the interaction energy between carbon steel and the corrosion inhibitor, the total energy of the corrosion and the Q235 surface, and the energy and the energy of the Q235 surface.  
The smaller the negative value of the interaction, the greater the interaction force between the two, indicating better corrosion performance of the material. The final calculation of the interaction value between PCA-GLU and the CS surface is \(-224.23\) kcal/mol, and the interaction value between GLU and the carbon Q235 is \(-83.15\) kcal/mol. At the same time, the interaction between the PCA and CA and the CS surfaces is calculated, as shown in Table 4. The interaction value between the PCA and the carbon steel surfaces is \(-182.01\) kcal/mol, while the interaction value between CA and the carbon steel surfaces is \(-77.94\) kcal/mol.  
Figure 7: Side and top views of the PCA-GLU and GLU adsorbed on the Fe(110) surface in vacuum slabs.  
From Figures 7 and 8, it can be seen that polymer molecules are more likely to be adsorbed on the surface of Q235 than small molecules, which means that polymer molecules have better corrosion inhibitory effects on carbon steel than small molecules. Based on the interaction values of the four corrosion inhibitors, it can be found that the citric acid polymer modified with glutamic acid is more easily adsorbed on carbon steel than PCA. The results of the molecular dynamics simulation are the same as those of the quantum chemical calculation. The addition of glutamic acid can enhance the adsorption capacity of poly(citric acid) on the surface of Q235, thereby improving its corrosion performance.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 2 System Model  
This section briefly reviews the model of the downlink SCMA and introduces the cooperative downlink SCMA system.
## 2 Materials and Methods  
### Sampling Area and Field Study  
Field sampling was carried out during four cruises in June (Southwest monsoon period) and September (Southwest-Northeast monsoon transition period) in 2018, and March (Northeast monsoon period) and June (Southwest monsoon period) in 2019 around Kueishan Island, in I-Lan Bay off northeastern Taiwan (Figure 1). Samples were collected from 12 selected stations distributed in three seabed depth zones [shallow depth zone (<200 m), middle depth zone (200-400 m), deep depth zone (>400 m)] during each cruise (Table 1). In the past, the research methods of copepods in this water area were to collected samples from surface zone, such as [7; 55; 67]. Therefore, the present study collected samples from the surface zone for do comparison with the past studies. A conical net (45 cm in diameter, 200 m mesh size, and 180 cm length) was horizontally towed in surface waters (above the 10 m depth contour) for 10 min at each station to collect zooplankton samples. A flow-meter (Hydroios, Germany) was fixed at the center of the net mouth to calculate the respective filtered water volumes. Zooplankton samples were immediately preserved in 5-10% neutralized seawater-formalin for identifying and counting their abundance in the laboratory. The hydrographic parameters temperature and salinity were measured by Sea-bird CTD equipment at each station.  
Figure 1: Sampling stations in the area of investigation along the northeastern Taiwan coast.
# Research Progress of Gas Sensor Based on Graphene and Its Derivatives: A Review
## 4 Graphene Gas Sensor
### Gas Sensors Based on Defective and Functionalized Graphene Materials
It is observed by the Raman spectroscopy that the defects are introduced into the monolayer graphene [73]. The FG based gas sensor exhibited fast response/recovery behavior and high sensitivity that the detection limit of NH\({}_{3}\) can reach 2 ppm and the response of 100 ppm NH\({}_{3}\) can reach 3.8% in 30 s at room temperature. In 2017, [PERSON] et al. from Delft University of Technology contrasted the response of mechanical peel graphene (ME-Gr), CVD prepared graphene (CVD-Gr), and liquid peeling graphene (LPE-Gr) toward NO\({}_{2}\)[74], as shown in Figure 11. LPE-Gr (green line) showed no obvious recovery. ME-Gr (black line) showed significant growth after passing gas, and significant decline in the current recovery process. CVD-Gr (red line) trend between the first two. The studies showed that CVD-Gr and LPE-Gr had more obvious defects than ME-Gr. CVD-Gr had many sites on the plane that are prone to gas molecule binding due to its structural diversity. At the same time, LPE-Gr had high energy binding sites, such as defect sites, resulting in its corresponding slow rate. Figure 10: (**a**) Ratio of conductance to initial conductance response of CVD-grown defective graphene, CVD graphene microribbon, and 5 \(\upmu\)m wide pristine (exfoliated) graphene sensors to 10\({}^{14}\) molecules of toluene; and (**b**) CVD-defective graphene, CVD graphene microstrip and a 5 \(\upmu\)m conductivity change of 10\({}^{15}\) 1,2-Dichlorobenzene molecules [69]. In the process of preparing graphene, defects are inevitable. The initial theoretical study found that defects can enhance the graphene adsorption effect on gas, which aroused widespread concern of researchers [58]. Subsequent experiments also verify this conclusion [60]. Inspired by this, researchers artificially introduced defects into graphene and doped graphene with other elements. The resulting graphene has a very good gas sensitivity and opens up a new gas sensing gap for graphene [63; 64; 65].
## 3 Results and Discussion
### Analyzing Submitted Jobs after Integration  
#### 3.2.1 Job Submission Trends  
In the same way that the 2018 job statistics were compared, the trend patterns of the job submitted by each group in 2019 and the level of overlap between the two groups were checked. Comparing Figures 2 and 6 shows that the timing of job submissions changed.  
Similarly, we also compared the trends of queued jobs. In the case of Group A, the ratio in the queue during the submission process increased to 32.97%, and in Group B the ratio in the queue during the submission process decreased to 46.98%. In fact, the overlapping intervals of the jobs stacked in the queue are shown in Figure 7--15.79% of the total in Group A and 1.68% of the total in Group B. Both groups increased compared to before the integration. This part appears to be a natural change due to the increase and decrease in the amount of job processing and wall time of Groups A and B. In particular, users in Group A submitted larger jobs than in 2018 as the total slots grew. In fact, the utilization rate of Group A increased significantly to 70.36%, and the utilization rate of Group B decreased to 15.33%. In conclusion, the utilization rate of the entire cluster was 25.06%, which was 16.64% before the integration, that is higher than before despite the side effect due to integration.  
Figure 6: Submitted and queued jobs in 2019.
# A Survey of Secure Time Synchronization
###### Abstract  
Today, the use of wireless sensor networks has grown rapidly; however, wireless sensor networks are prone to receiving cyber-physical attacks. Time synchronization is a fundamental requirement for protocols in wired and wireless sensor network applications; hence, secure time synchronization is also crucial. This paper presents an introduction to time synchronization, including the concepts, challenges, and requirements of time synchronization protocols. The scope of the paper includes both software- and hardware-based protocols. Then, different time synchronization methods are analyzed. Moreover, research progress in secure time synchronization is reviewed. The survey also discusses the weaknesses of current secure time synchronization protocols and provides suggestions for future research directions. This survey aims to highlight research progress and trends in time synchronization and secure time synchronization.  
time synchronization; security; wireless sensor networks; wired networks +
Footnote †: journal: Accepted: 14 March 2023
###### Abstract  
The non-ideal mask absorber can cause an increase in critical dimension error (CDE) and decrease in process window (PW). However, the random mask absorber errors induced during mask fabricating and measuring are not considered in computational lithography. The problem cannot be neglected as the continuous scaling of lithography technology node. In this work, for the first time to our knowledge, a source, numerical aperture (NA), and process parameters co-optimization (SNPCO) method is developed to reduce the CDE induced by absorber errors and improve the PW. First, the source is represented by Zernike polynomials to balance computational burden and flexibility of source. Then a weighted cost function containing CDE and PW that incorporates the influences of absorber errors is created. Finally, a statistical optimization method is used to optimize the lithographic system parameters. Simulations of 1D mask pattern show that for the system with extreme absorber errors, the pattern errors of the proposed method are reduced by 62.1% and 58.9%, and the PWs are increased by 40.3% and 36.4%, respectively. The results illustrate that this method is effective in mitigating the CDE caused absorber errors and improving process robustness.  
caputational lithography; mask absorber; co-optimization; process window (PW) +
Footnote †: journal: Applied Sciences
# Remaining Useful Life Estimation of Bearings Using Data-Driven Ridge Regression
## 3 RUL Prediction Method  
Figure 1 illustrates two main components of the prognosis workflow to predict RUL of degraded bearings, namely health indicator construction and RUL estimation. The prognosis framework predicts when a failure will occur based on the historical and current machine data, including temperature, pressure, or vibration measurements. The health indicator construction module consists of feature extraction and feature postprocessing to construct a reliable and predictable health indicator as a system degrades. Potential indicators include mean, standard deviation, kurtosis, and peak-to-peak value to quantify the chaotic behavior of a signal. Feature postprocessing techniques identify suitable features to serve as the health indicator in the offline phase. In our approach, feature importance ranking and PCA reduce the feature dimension by eliminating irrelevant features.  
In the next step, the RUL estimation module predicts the future value of the extracted health indicator based on the degradation model to estimate the RUL value in the online phase. The proposed prognostics integrate machine learning and mathematical degradation model of the time series data of the health indicator. The proposed feature importance ranking and PCA computes the health indicator based on the time series data in the online phase. We predict the time to cross the specific threshold value of the health indicator using the degradation model. An adaptive sliding window method then optimizes the model parameters of the degradation model by using the ridge regression of the time series data via the sliding window in the online phase.
## 3 Wireless Localization Technology
### Range-Based Techniques  
Range-based techniques involve estimating a device's location by considering physical factors (such as signal strength, time, and angle) of transmitted and received signals, along with geometric and linear algebraic methods like trilateration and triangulation between the device and anchor/AP/base stations (referred to as anchor).  
First, the distance information between the device and the anchor is obtained using the physical factors of the signal, and then the location of the device is estimated using trilateration or multilateration. In this case, trilateration or multilateration refers to calculating the device's location as the intersection point of circles (or spheres) centered at each anchor, with each circle's radius being the distance from the anchor to the device, using at least \(n+1\) anchors in \(n\)-dimensional space, as shown in Figure 2.  
The distance between a device and an anchor is measured by the change in the physical factors of radio waves during transmission and reception. Thus, by using signal strength and propagation time, received signal strength (RSS) and time of arrival (ToA) can be used to estimate the distance between the transceivers.
## 4 The Pressure-Based Coupled Solution Procedure
### Discretized Mass Conservation Equation  
The pressure equation appearing in Equation (12) can be written as  
\[\mathbf{a}_{\text{C}}^{pv}\mathbf{v}_{i}+\sum_{F,j}\mathbf{a}_{F}^{pv}\mathbf{ v}_{j}+a_{\text{C}}^{pp}p_{i}+\sum_{F,j}a_{F}^{pp}p_{j}=b_{\text{C}}^{p} \tag{15}\]  
and the coefficients are defined as follows:  
\[\mathbf{a}_{\text{C}}^{pv}=\sum_{ip}\,\frac{\rho_{ip}^{*}\,N_{i} \big{|}_{ip}\mathbf{S}_{ip}}{\text{mass divergence}} \tag{16}\] \[\mathbf{a}_{F}^{pv}=\underbrace{\frac{\rho_{ip}^{*}\,N_{j}}{\Delta t }}_{\text{mass divergence}}\] \[a_{\text{C}}^{pp}=\underbrace{\frac{\left(\frac{\partial p}{ \partial p}\right)_{p_{i}^{*}}V_{\text{C}}}{\Delta t}}_{\text{transient}}+\sum_{ ip}||\underbrace{\frac{\dot{m}_{ip}^{*}}{\rho_{ip}^{*}},0||\,N_{i}|_{ip}\left(\frac{ \partial p}{\partial p}\right)_{p_{i}^{*}}}_{\text{advection-like}}-\sum_{ip} \underbrace{\rho_{ip}^{*}\,\overline{\mathbf{D}_{ip}^{\text{\bf v}}}\,\
abla N _{i}\big{|}_{ip}\cdot\mathbf{S}_{ip}}_{\text{diffusion-like}}\] \[a_{F}^{pp}=-||-\frac{\dot{m}_{ip}^{*}}{\rho_{ip}^{*}},0||\,N_{j} \big{|}_{ip}\left(\frac{\partial p}{\partial p}\right)_{p_{i}^{*}}-\underbrace{ \rho_{ip}^{*}\,\overline{\mathbf{D}_{ip}^{\text{\bf v}}}\,\
abla N_{j}\big{|} _{ip}\cdot\mathbf{S}_{ip}}_{\text{diffusion-like}}\] \[b_{\text{C}}^{p}=\underbrace{\frac{\rho_{i}^{\circ}V_{\text{C}} }{\Delta t}}_{\text{transient}}\]  
For an ideal gas, the partial derivative \(\frac{\partial p}{\partial p}\) is given by  
\[\frac{\partial p}{\partial p}=\frac{1}{RT} \tag{17}\]  
\(\overline{\mathbf{D}_{ip}^{\text{\bf v}}}\) is an adaptive tensor interpolated to an integration point from the straddling nodal values:  
\[\overline{\mathbf{D}_{ip}^{\text{\bf v}}}=\sum_{k=1}^{n}N_{k}|_{ip}\mathbf{D}_ {k}^{\text{\bf v}} \tag{18}\]and for a node of index \(i\), \(\mathbf{D}_{i}^{\mathbf{v}}\) can be expressed such as:  
\[\mathbf{D}_{i}^{\mathbf{v}}=\begin{bmatrix}D_{i}^{v^{x}}&0&0\\ 0&D_{i}^{xy^{y}}&0\\ 0&0&D_{i}^{v^{z}}\end{bmatrix}=\begin{bmatrix}\frac{V_{C}}{a_{C}^{v^{x}}}&0&0\\ 0&\frac{V_{C}}{a_{C}^{v^{y}}}&0\\ 0&0&\frac{V_{C}}{a_{C}^{v^{z}}}\end{bmatrix} \tag{19}\]  
\(v^{x}\), \(v^{y}\) and \(v^{z}\) are the Cartesian components of \(\mathbf{v}\).
## 4 Discussion
The data of Figure 6 are summarized in Figure 7 where the deviations of \(\overline{\text{RBE}}_{\text{emp}}\), \(\text{RBE}_{\text{TDRA}}\) and \(\text{RBE}_{\text{MKM}}\) from the fixed value of RBE = 1.1 in each region of the SOBP for each dose fraction and for each cell type are presented. Although the deviations from \(\text{RBE}=1.1\) in the proximal and central regions of the SOBP are relatively small (up to \(\sim\)10%), they can be significant (up to \(\sim\)20-40%) at the distal SOBP. Empirical models seems to be in good agreement with the experimental findings in the literature which, for the conventional dose fraction (\(\sim\)2 Gy), report an RBE\(-1.1\) in the entrance region of the SOBP, an RBE\(-1.15\) in the central region of the SOBP, and an RBE\(-1.35\) in the distal SOBP (with the RBE decreasing with increasing dose fraction, in all cases) as mean values over several cell lines [1]. Similar values were found by [PERSON] et al. [75], who studied proton RBE in a clinical scenario for two cell lines with \((\alpha/\beta)_{R}=2\) and 10 Gy utilizing the RMF model. Specifically, they found RBE values of 1.03 in the entrance region and 1.27-1.30 at the distal edge of the SOBP with an average RBE value of 1.10 in the whole SOBP. [PERSON] et al. [76] also examined proton RBE with some of the studied empirical models (i.e., [PERSON]'s model and [PERSON]'s model) and the Local Effect Model (LEM) in clinical scenarios of two brain tumor cases with \((\alpha/\beta)_{R}=2\) Gy and \((\alpha/\beta)_{R}=10\) Gy and found RBE values between 1.07 and 1.18 at the entrance, 1.21 and 1.29 in the middle, and 1.6 and 1.9 at the distal edge of the Planning Target Volume (PTV). The examined biophysical models overall predict slightly lower RBE values than the empirical models. Current studies on the biological effectiveness of protons focus on conventional treatment protocols including dose rate. However, there is growing interest in FLASH radiotherapy in which ultra-high dose rates (UHDRs) (greater than 40 Gy/s as opposed to conventional dose rates of \(<\)0.1 Gy/s) are used to spare healthy tissues, as normal tissues appear to recover better with UHDRs compared to tumors [77; 78]. Although the current methodology is applicable to conventional dose rates, their application to FLASH radiation therapy (RT) needs further consideration, given the limited RBE models available for FLASH RT [79]. RBE for FLASH experiments is beyond the scope of the present work.
# Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites
## 1 Introduction
They employed selected features such as normalized amplitude, correlation coefficient and cross-correlation to characterize the fatigue damage and established a regression model to relate them with stiffness degradation [33]. [PERSON] and [PERSON] found that time-of-flight (TOF), amplitude, and power spectral density (PSD) of the guided waves were sensitive features to fatigue induced matrix cracks and delamination. They defined damage indices (DIs) with these features to characterize the growth of matrix cracks and selected the PSD as the most sensitive feature to correlate with the matrix cracks [34]. In addition to linear waves, nonlinear guided waves and their corresponding features were also employed for fatigue damage detection [35; 36]. For example, [PERSON] et al. investigated the second harmonic generation of guided wave in composite laminates, and the correlation between the acoustic nonlinearity and the degradation of composite laminate was studied, demonstrating that the extracted nonlinear feature can characterize damage in an early stage [36]. However, most of these works extracted only a univariate feature in the time or frequency domain, while deterministic approaches were employed for characterizing fatigue damage without consideration of uncertainties from environmental effects and measurement noise. This paper presents experimental studies on detecting and monitoring of evolution of fatigue damage in glass fiber/epoxy (GF/EP) composites under cyclic loads by using guided waves. Following feature extraction of signal energies in multiple frequency bands by wavelet packet transform (WPT), a statistical multivariate outlier analysis is performed to determine whether fatigue damage exist in the specimens and to characterize the evolution of fatigue damage using Mahalanobis squared distance (MSD). The rest of this paper is structured as follows. Section 2 describes the experimental fatigue tests of the composite specimens and the monitoring process. Sections 3 and 4 present the WPT-based signal processing and feature extraction, and statistical multivariate outlier analysis, respectively. Experimental results are given in Section 5 to verify the effectiveness and applicability of the proposed method. Finally, conclusions are summarized in Section 6.
## 2 The Proposed Pulse-OTFS Signal
### Signal Model  
The principle of OTFS modulation/demodulation is equivalent to adding an inverse symplectic fast Fourier transform (ISFFT) operation before OFDM modulation and a symplectic fast Fourier transform (SFFT) operation after OFDM demodulation, as shown in Figure 1. These two-dimensional transformations convert modulated symbols between the time-frequency domain and the delay-Doppler domain. The main signal parameters include the delay bins \(M\), Doppler bins \(N\), and bandwidth \(B\), which are equal to the subcarrier number, symbols number, and bandwidth of OFDM, respectively.  
The delay-Doppler transmitted symbol \(\mathbf{X}_{\text{dd}}[m,n]\) (\(m=0,\cdots,M-1,n=0,\cdots,N-1\)), is transformed to the time-frequency symbol by ISEFT:  
\[\mathbf{X}_{\text{tf}}[k,l]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M-1} \mathbf{X}_{\text{dd}}[m,n]e^{j2\pi\Big{(}\frac{nl}{N}-\frac{mk}{M}\Big{)}} \tag{1}\]  
where \(k=0,\cdots,M-1,l=0,\cdots,N-1\). Conversely, the time-frequency transmitted symbol \(\mathbf{X}_{\text{tf}}\) is transformed to the delay-Doppler symbol by SFFT:  
\[\mathbf{X}_{\text{dd}}[m,n]=\frac{1}{\sqrt{NM}}\sum_{l=0}^{N-1}\sum_{k=0}^{M- 1}\mathbf{X}_{\text{tf}}[k,l]e^{-j2\pi\Big{(}\frac{nl}{N}-\frac{mk}{M}\Big{)}} \tag{2}\]  
The received symbols \(\mathbf{Y}_{\text{dd}}[m,n]\) and \(\mathbf{Y}_{\text{tf}}[k,l]\) can also be converted between the delay-Doppler domain and time-frequency domain using the above equations.  
The time-domain baseband signal can be expressed as:  
\[s(t)=\frac{1}{\sqrt{M}}\sum_{l=0}^{N-1}\sum_{k=0}^{M-1}\mathbf{X}_{\text{tf}}[ k,l]\Pi(t-lT)e^{j2\pi f_{k}(t-lT)} \tag{3}\]  
where \(\mathbf{X}_{\text{tf}}\) is defined in (1), \(T\) denotes the symbol period, \(f_{k}\) is the frequency of the \(k\)-th subcarrier, and \(f_{k}=k\Delta f\), \(\Delta f\) is the subcarrier spacing and \(\Delta f=1/T\), and \(\Pi(t)\) is a rectangular pulse defined as:  
\[\Pi(t)=\begin{cases}1&0\leq t\leq T\\ 0&\text{otherwise}\end{cases} \tag{4}\]
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## 3 Research Analysis
### Traffic Management Methodologies Consisiting of Primary Goals
#### 3.2.5 Ecology, Passenger Comfort, and Safety  
One article on traffic management [48] discussed three objectives--ecology, passenger comfort, and safety--and proposed a decentralized optimum control system to reduce fuel consumption and passenger discomfort while maintaining safety. The study's findings [33] suggested that the suggested strategy is appropriate for online deployment.