esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Diffuse Optics Emitter for High Visual Diffuse Transmission Density Measurement
## 1 Introduction  
Diffuse transmission optical density is an important optical property of many materials, such as exposed films, special filters, etc. [1; 2; 3; 4]. It is widely used for quality control in many advanced applications in diffuse optical spectroscopy [5; 6; 7; 8; 9; 10]. For example, it is used to evaluate photographs and achieve optical density (OD) calibration, including non-destructive testing (NDT), DNA microarrays in Biosensors and Bioelectronics, human bone image, fMRI cortical mapping, etc. [11; 12; 13; 14; 15; 16]. With the rapid development of Biosensors and Bioelectronics and fMRI, the precise request for OD measurement is changing. The measurement range has expanded and the precision has improved at the same time [17; 18; 19; 20; 21; 22]. In many requirements, it should be measurable up to 6.0. Many old diffuse optical instruments, including most national standard density instruments in many countries, cannot satisfy these new measurement demands. Therefore, how to expand the measurement range and decrease uncertainty at the same time is the new challenge in diffuse optical spectroscopy [23; 24; 25].  
According to the definition of visual diffuse transmission density \(D_{T}\) as shown in Equation (1), where \(\phi_{i}\) is the incident diffuse light flux and \(\phi_{z}\) is the transmission light flux [26], the key step of themethod to solve the question is how to improve the diffuse transmission property. Typically, there are only two methods to find the measurement. One is using a highly sensitive detector with low background noise, whereby a high transmission light flux \(\phi_{\mathrm{T}}\) can be achieved. But this method depends on the development of detector technology, and only reducing the background noise of measurement equipment does not have a significant effect. The other method is improving the incident light flux \(\phi_{i}\). Perhaps, it is relatively easy to use for a high power light source. But the challenges are that the incident flux should have a high diffuse coefficient \(\beta\) up to 9.1, according to the ISO 5 standard, and that the wide spectrum includes the visual scope at the same time.  
\[D_{\mathrm{T}}=\log_{10}(\phi_{\mathrm{T}}/\phi_{i}) \tag{1}\]  
How to comply with these three conditions is difficult in diffuse optical spectroscopy design. A new diffuse optics emitter (DOE) is designed to satisfy these three conditions in our research and is used in the new national reference densitometer of NIM (National Institute of Metrology, China) for visual diffuse transmission density. This new diffuse optics emitter enables the reference to improve the measurement range highly. And it can be used in other diffuse optical spectroscopy as a new light source with a high diffuse coefficient, high flux, and wide spectrum range.
## 2 Materials and Methods
### Characterization  
Characterization is divided into two parts; the first part is on the material characterization to understand the quality of the material and the second part is on the electrical characterization for device performance comparison. The surface morphology of the GNW was analyzed using field effect scanning electron microscopy (FESEM) analysis using JEOL JSM-7500F (JEOL Limited, Tokyo, Japan). A cross-sectional image was also taken to find the height of the GNW. Raman spectroscopy measurement was performed using Integra Spectra (NT-MDT Spectrum Instruments, Moscow, Russia) with a 514 nm laser wavelength to investigate the structural properties of the GNW.  
The current-voltage (I-V) measurement was done using a Keithley 236 SMU system and micromanipulator proper. As the TMBS diodes used in this experiment are designed for high breakdown voltage (above 60 V), a voltage range between \(-\)80 V to 10 V was used to study the device characteristics. I-V curve characteristics are crucial for this study to extract device parameters and analyze the device performance. The measurement was performed with regards to different temperature setting between 298 K and 473 K. Schottky barrier height (\(\varPhi_{B}\)), ideality factor (\(n\)), and series resistance (\(R_{s}\)) were extracted from the forward current and forward current density curves (I-V and J-V). Comparison of I-V curves and device parameters between the GNW-TMBS diode and the conventional TiSi\({}_{2}\)-TMBS diode were carried out to investigate the effect of vertically standing a few layers of graphene nanostructure on the device characteristics.  
Figure 1: Cross-sectional diagram of trench Schottky structure with (**a**) TiSi\({}_{2}\) and (**b**) GNW as Schottky barrier layer.
## 2 Materials and Methods
### Cell-Based In Vitro Assays
#### 2.6.2 Cell Viability Studies  
MT Assay  
Cell viability of TR146 cell line, after 24 h treatment with chitosan microparticles, guar-gum films, with or without peptide, were measured using the methylthiazolyldiphenyl-tetrazolium bromide conversation (MTT) assay [18].  
Cells were seeded in 96-well plates at 2 \(\times\) 10\({}^{5}\)/well in 300 uL culture medium and incubated for 24 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) environment. The medium was then changed, and the cells were treated with test samples (peptide, chitosan microparticles, chitosan microparticles with peptide; guar-gum films; guar-gum films with peptide; guar-gum films with chitosan microparticles with peptide or free) for 24 h. Each treatment was tested in eight individual wells. After 24 h, the supernatant was removed, and 200 uL of MTT solution (5 mg/mL in DMEM) was added to each well of 96-well plates and then incubated for 4 h at 37 \({}^{\circ}\)C to allow the formation of formazan crystal. The medium was then removed, and the blue formazan was eluted from cells by 150 uL of DMSO. The negative controlused was also DMSO. The plates were shaken on an orbital shaker to solubilize the crystals of formazan. The dark blue crystals were aspirated to another new 96-well microplate, and the optical density was measured directly in the microplate reader at 570 and 630 nm, read on a FLUOstar OPTIMA microplate reader (United Kingdom). All samples were tested for n= 5 experiments with comparable results.  
The cell viability of the tested delivery systems was calculated from the average optical density (\(OD\)) values, according to Equation (2).  
\[Cell\ viability\left(\%\right)=\frac{OD\ value\ of\ specimen\ suspension}{OD\ value\ of\ negative\ control\ suspension}\times 100 \tag{2}\]  
LDH assay measures the release of the intracellular enzyme LDH upon damage of the plasma membrane, and an increase in LDH release could be an index of cell damage. For measuring LDH activity, a colorimetric method was used. This method converted lactate to pyruvate in the presence of LDH with parallel reduction in NAD.  
Caco-2 cell lines were seeded in a 96-well plate at 2 \(\times\) 10\({}^{5}\)/well and incubated for 24 h at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) environment. After 24 h, test solution and controls (DMEM and DMSO) were added in triplicate to the cell culture.  
Cells were incubated with medium at 37 \({}^{\circ}\)C for 24 h. After this time, cells were washed twice with PBS, and the activity of LDH in the medium was determined using a commercially available kit from Sigma Diagnostics (LDL50). The absorbance was measured at 490 nm with background subtraction at 690 nm.
## 3 Signal Path Modeling and Analysis
### Backward Information Transfer Channel Analysis  
The signal backward transmission equivalent circuit is shown in Figure 6a. Backward transmission and forward transmission are considered symmetric structures. The backward signal transmission and forward signal transmission employ similar analysis methods. When analyzing backward information transmission, the reflected impedance of demodulation circuits on both sides cannot be ignored, but the reflected impedance of the primary modulation circuit will be substituted by \(L_{T3}\).  
According to the equivalent circuit impedance analysis, the primary equivalent impedance \(Z_{ps}\) is:  
\[Z_{ps}(\omega)=j\omega L_{1}+\frac{1}{j\omega C_{1}}+R_{1}+j\omega L_{T3}+j \omega L_{k1}+R_{k1}+Z_{T1}(\omega) \tag{18}\]  
\(Z_{rp}(\omega)\) is the reflected impedance of the primary side to the secondary side:  
\[Z_{rp}(\omega)=\frac{\omega^{2}M^{2}}{Z_{ps}(\omega)} \tag{19}\]The secondary side equivalent impedance \(Z_{\text{ss}}\) is:  
\[Z_{\text{ss}}(\omega)=j\omega L_{2}+\frac{1}{j\omega C_{2}}+R_{2}+j\omega L_{k2}+ R_{k2}+Z_{T2}(\omega)+Z_{rp}(\omega) \tag{20}\]  
The gain of backward information demodulation cell is:  
\[G_{T1}(\omega)=\frac{V_{rs}}{V_{T1}}=\frac{1/(j\omega C_{d1}+1/R_{d1})}{j\omega L _{kfl1}+R_{kfl1}+1/(j\omega C_{d1}+1/R_{d1})} \tag{21}\]  
The transfer function of the tight coupled transform \(T_{4}\) is:  
\[G_{T4}(\omega)=\frac{V_{T4}}{V_{ds}}=\frac{1/(1/j\omega L_{md4}+1/R_{md4})}{j \omega L_{kld4}+R_{k4}+\frac{1}{j\omega C_{d4}}+\frac{1}{j\omega C_{md4}}+\frac {1}{j\omega C_{md4}}} \tag{22}\]  
So the backward information transmission gain is:  
\[G_{bl}(\omega)=\frac{V_{rs}}{V_{ds}}=\frac{Z_{T1}(\omega)}{Z_{\text{ss}}(\omega )}\cdot\frac{j\omega M}{Z_{p}(\omega)}\cdot G_{T1}(\omega)\cdot G_{T4}(\omega) \tag{23}\]  
The backward information transmission gain \(G_{bl}(\omega)\) is shown in Figure 6b. Obviously, there are also three extreme points. The lower frequency point A is the power carrier frequency. The frequencies of other two extreme points B and C are the forward and backward information transmission carrier frequencies, respectively. As shown in Figure 6b, the backward information transmission carrier frequency is 1 MHz. The transmission gain of point B is larger than that of point C, which is advantageous to backward information transmission and demodulation.
## 3 Improved CKF Localization Algorithm Based on Volume Criterion
### Precision Evaluation Methods  
In this study, some fixed reference checkpoints are selected to evaluate the performance of the positioning algorithm of the UWB system during the dynamic trajectory measurement of the positioning tags. The differences in the distance between the best valuation obtained by the localization algorithm and the base station location, as well as the distance between the reference checkpoint and the base station location are compared using the distance residual value.  
\[\delta_{l}=\sqrt{\frac{v_{i}-\hat{x}_{i}}{n}} \tag{23}\]  
where \(\delta_{l}\) represents the residual of the distance between the best valuation of the localization algorithm and the base station location, as well as that of the reference checkpoint and the base station location. \(v_{i}=\sqrt{I_{i}-x_{i}}\) represents the distance between the reference checkpoint and the base station location. \(\hat{x}_{i}=\sqrt{\hat{I}_{i}-x_{i}}\) represents the distance between the best valuation of the localization algorithm of the reference checkpoint and the base station location. \(I_{i}\) represents the true coordinates of the reference checkpoint. \(x_{i}\) represents the base station location coordinates. \(\hat{I}_{i}\) represents the best valuation of the location of the localization algorithm, and \(n\) represents the number of times the reference checkpoint has been localized, where \(i=1,2,3\ldots\).  
In this study, the Sigmoid-like adaptive function is used to update the measurement noise matrix \(R\) in real time. Based on the CKF algorithm, the estimation accuracy of the localization results is improved when the position error is large, and the stability of the algorithm is maintained when the position error is small to satisfy the parameter change requirements of the system. It improves the performance of the whole localization algorithm. The specific process is shown in Figure 2.
## 5 Conclusions  
In this paper, a tensor decomposition algorithm for the spotlight SAR imaging is developed based on Tucker model which formulizes SAR echo signal with Kronecker constraint. Under the common scenario in SAR imaging, the scatterers usually cluster together and the targets are sparsely located. This structured sparse feature provides a prerequisite for the proposed method. Not only has this method better robustness for signal reconstruction, but also produces less algorithm complexity  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (s)** & **SNR = 3 dB** & **SNR = 12 dB** & **SNR = 21 dB** & **SNR = 30 dB** \\ \hline SVA & 0.017192 & 0.017056 & 0.016684 & 0.016632 \\ OMP & 525.62 & 517.22 & 568.34 & 593.44 \\ CoSaMP & 1.0759 & 1.1573 & 1.2098 & 1.2882 \\ Proposed & 0.045192 & 0.05285 & 0.051329 & 0.048576 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average running time of each algorithm versus different SNRs.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time (s)** & \(\mathbf{N_{s}=20}\) & \(\mathbf{N_{s}=80}\) & \(\mathbf{N_{s}=140}\) & \(\mathbf{N_{s}=200}\) \\ \hline SVA & 0.016408 & 0.016871 & 0.017026 & 0.017111 \\ OMP & 459.18 & 460.22 & 450.89 & 464.71 \\ CoSaMP & 0.95787 & 0.97883 & 0.97839 & 1.0395 \\ Proposed & 0.033315 & 0.028116 & 0.03005 & 0.043741 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average running time of each algorithm versus different number of scatterers.  
and requires smaller computation resource as well, due to exploiting the Kronecker information in each mode sufficiently.  
In comparison with the existing SAR imaging methods, such as the SVA and classic matching pursuit algorithms, the proposed method performs much better with respect to robustness about noise and sidelobe disturbance. Numerical analysis has demonstrated that the recovery upper bound of the multiway sparse reconstruction based on structured sparsity is larger than that of the classic matching pursuit methods. Both the theoretical analysis and simulation results also indicate that the proposed algorithm requires much less computational resources than the reference methods, which is by far the most important in practical applications.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants (U1533125 and 61401069) and National Science and technology major project under Grant 2016 ZX03001022. The authors would like to thank the anonymous reviewers for their valuable comments that significantly improved the quality of this paper.  
[PERSON] conceived and verified the feasibility of SAR imaging by introducing tensor modeling, and proposed the multiway structured sparse target reconstruction algorithm based on tensor decomposition; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] contributed the design of numerical simulations; [PERSON] wrote this paper; [PERSON], [PERSON] and [PERSON] checked the manuscript and contributed to the rearrangement of the materials. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 4 Bio-Inspired MECS Sleep Control Method
### Algorithm Complexity  
The time complexity of our bio-inspired sleep decision algorithm presented in Algorithm 1 is analyzed as follows. At the beginning of each time slot, a MECS \(i\) collects \(\mu_{j}\) and \(d_{j}\) for all \(j\in N_{i}\) and calculates their averages (line 3 and line 4). Thus, the time complexity of the averaging operation becomes \(O(|N_{i}|)\). The lines from 7 to 11 determine the Delta activation level. Since it takes at most \(K\) iterations before the Delta activation level is decided, its time complexity is less than or equal to \(O(K)\). In [33], it is shown that the Delta-Notch signaling model converges to a stable state after a few rounds. To investigate the temporal behavior, we measure \(\delta_{d}=d_{i}^{\prime}(k+1)-d_{i}^{\prime}(k)\) (line 10) for different MECSs at different time slots under the default simulation environment presented in Section 5. We depict the result in Figure 2. We can observe that the Delta activation level converges (i.e., \(\delta_{d}\approx 0\)) exponentially fast (i.e., after 3 iterations). This behavior is consistent with that in [21] in that the Delta-Notch model stabilizes exponentially.  
We also note that \(K\) is a constant and not related to a system size such as the number of MECSs and the number of tasks. Since \(K\) can be configured as a small positive integer, we can say that the worst-case time complexity of the operation determining the Delta activation level (lines 7-11) is \(O(K)=O(1)\). The other lines in Algorithm 1 are assignment operations or comparison operations whose time complexity is \(O(1)\). Therefore, the worst-case time complexity of our bio-inspired sleep decision algorithm presented in Algorithm 1 is \(O(|N_{i}|)\).  
In our task transfer algorithm presented in Algorithm 2, \(N_{i}^{ON}\) is determined by examining \(\beta_{j}\)s sent from all the neighboring MECSs (\(j\in N_{i}\)). Therefore, its time complexity becomes \(O(|N_{i}|)\). In lines 4 to 8 in Algorithm 2, tasks are transferred from a MECS deciding to sleep to its active neighbors. Therefore, its time complexity is \(O(|N_{i}^{ON}|)\). Since \(N_{i}^{ON}\subseteq N_{i}\), the worst-case time complexity of our task transfer algorithm becomes \(O(|N_{i}|)\).  
In our MECS activation process, an active MECS compares its queue length with a given threshold to determine whether to wake up its sleeping neighbors or not. This comparison operation takes \(O(1)\). Once a MECS \(i\) decides to wake up its sleeping neighbors, it wakes up all of the sleeping neighbors and fairly distributes the tasks in its task queue to each sleeping neighbor. If we denote the set of sleeping neighbors of an active MECS \(i\) as \(N_{i}^{s}\), the task distribution operation takes \(O(|N_{i}^{s}|)\). Since \(N_{i}^{s}\subseteq N_{i}\), the worst-case time complexity of our MECS activation algorithm becomes \(O(|N_{i}|)\). Therefore, the worst-case time complexity of our sleep control mechanisms is \(O(|N_{i}|)\).
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 2 Multi-Access Channel and Quantum Detection
### Multi-User Detection Based on Quantum Measurement
\[E_{j}=\frac{P_{j}}{4K^{2}}\sum_{k,k^{\prime}=0}^{2K-1}c_{k^{\prime}}^{\ast-1} c_{k}^{-1}e^{\frac{\pi i(k-k^{\prime})}{K}}|\gamma_{k^{\prime}}\rangle\langle \gamma_{k}|,(0\leq j\leq 2K-1), \tag{13a}\] \[\big{|}\alpha_{j+1}\big{\rangle}=U\big{|}\alpha_{j}\big{\rangle}=U^{j}\big{|} \alpha_{0}\big{\rangle}, \tag{7a}\]0  
In Equation (13), \(P_{j}\) represents the probability of obtaining result \(j\) after measurement when the measured state is \(\big{|}\alpha_{j}\big{\rangle}\). Considering the existence of noise in the channel, it is assumed that the transmitted signal is pure state \(\big{|}\alpha_{j}\big{\rangle}\), and the pure state signal is correspondingly transformed into \(\rho_{j}\) after passing through the channel (\(\rho_{j}\) is the mixed-state density operator). Then, the \(j\)th user prepares the signal \(l\) in advance, and after the measurement, the probability of obtaining the result \(k\) is  
\[\big{|}\alpha_{j+1}\big{\rangle}=U\big{|}\alpha_{j}\big{\rangle}=U^{j}\big{|} \alpha_{0}\big{\rangle}, \tag{7a}\]1  
In the above equation, \(k=0,1,\cdots,2K\), \(j=0,1,\cdots,K-1\), \(l\in\{0,1\}\). Substituting Equation (14) into Equation (6), it can be concluded that under the premise of using the USD measurement, the probability of detection error between users is  
\[P_{e}^{\text{USD}}=\frac{1}{2K}\sum_{j=0}^{K-1}\sum_{ \begin{subarray}{c}k=0,k \
eq j\\ k\
eq j+K\end{subarray} }^{2K-1}\big{[}tr\big{(}E_{k}\big{(}\rho_{j}+\rho_{j+K} \big{)}\big{)}\big{]}. \tag{15}\]On the other hand, if the channel contains no noise background, the measurement results are transformed into  
\[P\Big{(}k\Big{|}H_{l}^{j}\Big{)}=tr\Big{(}E_{k}\Big{|}\alpha_{j+lK}\Big{\rangle} \Big{\langle}\alpha_{j+lK}\Big{|}\Big{)}. \tag{16}\]  
This is easy to verify based on the nature of USD measurements:  
\[P\Big{(}k\Big{|}H_{l}^{j}\Big{)}=0,(k\
eq j+lK). \tag{17}\]  
Combining the above equation and Equation (15), it can be inferred that the detection error probability between users without noise background is  
\[P_{e}^{\text{USD}}=0. \tag{18}\]  
From a theoretical point of view, this means that if the effects of noise are ignored, USD measurements can perfectly detect the signals sent by different users from multi-access channels. Accordingly, MED measurements can only vaguely detect the signals of different users because its orthogonal measurement mechanism will always maintain a certain detection error probability.
# An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta
## 3 Materials and Methods
### MODIS Data Products
#### 3.3.2 MOD11A1 Product  
The MOD11A1 product offers daily measurements of land-surface temperature (LST), emissivity values, and corresponding quality indicator layers at a resolution of 1 km [28]. In  
Figure 5: The MODIS MCD19A2 product. The yellow box shows the study area Dong Thap Province.  
areas above 30 degrees latitude, certain pixels may contain multiple observations that meet the criteria for clear-sky conditions. In such cases, the pixel value is calculated as the average of all qualifying observations. LST can be computed from the brightness temperature \(T_{b}\) in combination with the global atmospheric correction model, e.g., LST images of the MOD1A1 product in the level-3 data processing directly derived from the MODIS Band 31 and 32 thermal emissive data. The brightness temperature is a measurement of the radiance of emission traveling upward from the top of the atmosphere to the satellite, in Kelvin scale temperature unit (K). The brightness temperature \(T_{b}\) detected by a thermal sensor is determined by [PERSON]'s Equation (2), where \(L_{\lambda}\) (W/m\({}^{2}\).sr.m) is the spectral radiation, \(h=6.62\times 10^{-34}\) (J.s) is [PERSON]'s constant, \(c=3\times 10^{8}\) (m/s) is the speed of light, \(k=1.38\times 10^{-23}\) (J/K) is [PERSON]'s constant, and \(\lambda\) (um) is the central wavelength. \(K_{1}\) and \(K_{2}\) (W/m\({}^{2}\).sr.m) are calibration coefficients, for band 31: \(K_{1}=730.01\), \(K_{2}=1305.84\), and band 32: \(K_{1}=474.99\), \(K_{2}=1198.29\)[29].  
\[T_{b}=\frac{\frac{hc}{K_{1}}}{ln(\frac{2\pi bc^{2}\lambda^{-5}}{L_{\lambda}}+ 1)}=\frac{K_{2}}{ln(\frac{K_{1}}{L_{\lambda}}+1)} \tag{2}\]  
Additionally, LST can be estimated from the emissivity values in combination with the vegetative cover using an experimental model, which is presented in Section 4.1--LST computation.
## 3 Results and Discussion
### Characterisation
#### 3.2.5 Sem  
SEM images revealed the surface morphology of UiO-66, UiO-66-MTX, UiO-66-NH\({}_{2}\), and UiO-66-NH\({}_{2}\)-MTX as shown in Figure 6. The morphology of UiO-66 and UiO-66-NH\({}_{2}\) is in accordance with the published results [46; 54; 56]. UiO-66 was synthesised by an ageing process to obtain a nano size, and the difference is obvious in SEM images' appearance (Figure 6A). The size of the UiO-66 crystals is much smaller (also observed from DLS) and tends to form aggregates, compared to UiO-66-NH2 crystals, which are more well dispersed and uniform. After MTX loading, a slight change was observed for UiO-66-MTX with more diffused agglomeration. However, in the case of UiO-66-NH\({}_{2}\)-MTX, there was no significant change in morphology, and the well-dispersed cubic structure was maintained.  
Figure 5: FTIR spectra at the wavelength range of 650–4000 cm\({}^{-1}\) for (**A**) MTX, UiO-66, and UiO-66-MTX with the characteristic peaks of MTX found between 980 cm\({}^{-1}\), 1391 cm\({}^{-1}\), and 1640 cm\({}^{-1}\) of the UiO-66-MTX spectra and (**B**) MTX, UiO-66-NH\({}_{2}\), and UiO-66-NH\({}_{2}\)-MTX with the characteristic peaks of MTX found between 1607 cm\({}^{-1}\) and 1640 cm\({}^{-1}\) of the UiO-66-NH\({}_{2}\)-MTX spectra.