esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Design of a 2 mN GIT

Table 9: Comparison between the characteristics and performances of NSTAR ion thruster (throttle level 15) [23] and the results of the tools. Molybdenum was selected for the grids, as is commonly done in the open literature [13]. A total weight of 0.3 kg, without cathodes and power lines, resulted. The data shown in Tables 10 and 11 fulfill the requirements of LUMIO and M-ARGO space missions for what concerns power, thrust, weight, and size. A study regarding life-time expectations has not been performed, but GITs, on average, have a good operative life, which satisfies the requirement of the two exploration missions [52]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Thrust (mN) & 2 \\ Input power (W) & 54 \\ Specific impulse (s) & 1771 \\ Total efficiency & 0.32 \\ Electrical efficiency & 0.72 \\ Mass utilization efficiency & 0.50 \\ Discharge voltage (V) & 28 \\ Discharge loss (W/A) & 361 \\ Total voltage between grids (V) & 1159 \\ Accel grid potential (V) & \(-\)232 \\ Propellant mass flow rate (mg/s) & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Main performances and working parameters of the 2 mN ion thruster. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Discharge chamber diameter (mm) & 50 \\ Discharge chamber length (mm) & 50 \\ Grids material & Molybdenum \\ Screen-grid thickness (mm) & 0.38 \\ Screen-grid holes diameter (mm) & 1.91 \\ Accel-grid thickness (mm) & 0.51 \\ Accel-grid holes diameter (mm) & 1.14 \\ Distance between grids (mm) & 0.66 \\ Number of holes & 433 \\ Number of injectors & 2 \\ Injector internal diameter (mm) & 0.83 \\ Total weigth without cathodes (kg) & 0.301 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: 2 mN Ion thruster characteristics. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(|B_{cusp}|\) (G) & 2600 \\ \(|B_{t}|\) (G) & 44 \\ Magnets material & SmCo 27 MGOe \\ \(Width_{1,2,3}\) (mm) & 1.5 \\ \(D_{int,1,2}\) (mm) & 50.64 \\ \(D_{ext,1,2}\) (mm) & 53.64 \\ \(D_{int,3}\) (mm) & 30 \\ \(D_{ext,3}\) (mm) & 33 \\ Distance between magnets (mm) & 25 \\ Anode material & Stainless Steel 304 \\ Anode thickness below the magnets (mm) & 0.32 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Magnets, anode material, and geometric parameters for the 2 mN ion thruster.
# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 3 Enhanced New Top-Hat Transform
In Figure 6, (a1)-(a3) are different sizes of the original image, which can show more details, and (a4) is a 3D display of (a3), which can show the nonuniformity of the background more intuitively; moreover, (b1)-(b4) are the processing results of the dilation operation showing that the targets are removed and their energy is transferred to the structural elements, (c1)-(c4) are the results of the background estimation after the erosion operation showing that the structural elements containing the target energy are removed, (d1)-(d4) are the results of preliminary background correction showing that there are still some residual background noise and hot pixels around the target, and (e1)-(e4) are the results of accurate background correction by using our NSE showing almost no noise residue around the target. In general, Figure 6 shows that our algorithm achieves accurate segmentation of the target while correcting the nonuniform background image. Figure 6: Background correction results of the proposed ENTHT method. (**a1–a4**) Original surveillance images, (**b1–b4**) Processing result of Equation (3), (**c1–c4**) Processing result of Equation (4), (**d1–d4**) Processing result of Equation (5), (**e1–e4**) Processing result of Equation (6).
## 4 Control Scenarios
### Total Flow Chart of Service
#### 4.2.3 **[Service Package #3]** AR-Based Fire Escape Guidelines Service  
Figure 10 and Table 7 show a package that provides safe evacuation and rescue guidelines to occupants and rescuers through fire status information detected by smart sensors. The following shows each step:  
1. **Occupant**: Provides safe evacuation location guidelines. After detecting the location of the fire from the smart sensor and transmitting it to the server, the server provides AR-based optimal guides to the occupants through smart phones and smart pads;
2. **Rescuer**: Provides the rescuer with the best rescue guidelines for the quick and safe rescue of the occupants. The location and status information of the occupants are collected from the smart sensor and transmitted to the server. The server informs the rescuer of the location of the occupants and provides the optimal rescue guidelines for safety.  
Figure 10: AR-based fire escape guideline service.  
Figure 9: Actuator control-based fire detection service package.
## 3 Results  
### Composition of UPM  
As shown in Figure 1b, UPM did not distribute equally and had different particle sizes and shapes. According to the scanning electron microscope image (SEM), a small amount of UPM had 10-15 \(\upmu\)m diameter, and a considerable amount of UPM had less than 3 \(\upmu\)m diameter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Primer** & **Sequence (5\({}^{\prime}\)\(\rightarrow\)3\({}^{\prime}\))** \\ \hline \multirow{2}{*}{GAPDH} & Forward & AAGGGTCATCATCTCTGCCC \\  & Reverse & GTGATGGCATGGACTGTG \\ \hline \multirow{2}{*}{iNOS} & Forward & ATGTCCGAAAGCAAACATCAC \\  & Reverse & TAATGCCAGGAAGTAGG \\ \hline \multirow{2}{*}{COX2} & Forward & CAGCAAATCTTGTGTCTC \\  & Reverse & TGGGAAAGAATGCAAACATC \\ \hline \multirow{2}{*}{IL-1\(\beta\)} & Forward & CAGGTAGGACATGACC \\  & Reverse & CTCTGCAGACTCAAACTTCCAC \\ \hline \multirow{2}{*}{IL-6} & Forward & GTACTCCAAGAACCAGAG \\  & Reverse & TGCTGGTACAACCACGGCC \\ \hline \multirow{2}{*}{TNF-\(\alpha\)} & Forward & TTGACCTCGCCTGAGTTG \\  & Reverse & CCTGTAGCCCACGCGTGTAGC \\ \hline \multirow{2}{*}{TLR2} & Forward & CAGCTGGAACACTCTGACC \\  & Reverse & CAAAGACCTGAAGTGGGAG \\ \hline \multirow{2}{*}{TLR4} & Forward & CAAACATCACCAGAAGGCC \\  & Reverse & GAAGCCGATACAATTCCACC \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sequences of the primers used to evaluate inflammation-related RNA expression levels.
## 2 System Model and Problem Formulation  
### Channel Model  
The paper considers a multiuser MISO downlink communication based on RIS, as shown in Figure 1. The messages are sent from one \(M\)-antenna BS to \(K\) users, and each user is equipped with a single antenna. The RIS in the system is equipped with \(N\) reflection elements to form a new communication path from the BS to the \(K\) users. We use \(\mathbf{h}_{d,k}\in\mathbb{C}^{M\times 1}\), \(\mathbf{G}\in\mathbb{C}^{M\times N}\), and \(\mathbf{h}_{r,k}\in\mathbb{C}^{N\times 1}\), \(k=1,\ldots,K\), to denote the channel vectors from the BS to user \(k\), from the BS to RIS, and from RIS to user \(k\), respectively.  
Figure 1: The reconfigurable intelligent surface (RIS)-aided multi-user multiple-input single-output (MISO) downlink system.  
The reflection operation, which is finished by the \(N\) RIS elements, directed by an intelligent controller in RIS, will change the incident signal by multiplying it with \(\theta_{n}\in\mathcal{F}\), where \(\mathcal{F}\) refers to the feasible set of reflection coefficient (RC).The RIS will then forward this composite signal just like transmitting it from a point source [15].  
Moreover, only the first-time-reflected signals are considered in the communication model considering substantially high path loss [18; 23]. We use a diagonal matrix \(\mathbf{\Theta}=\sqrt{\eta}\operatorname{diag}(\theta_{1},\dots,\theta_{n}, \dots,\theta_{N})\), where \(\eta\leq 1\) indicates the reflection efficiency, to denote the RIS phase-shift operation.
## 2 Ground Motion Record Selection
### Record Selection Based on Scenario-Based Spectrum  
To illustrate the ability of the suggested model, the spectral shape and variability of target spectra derived from an available ground motion model (GMM) are utilized. For this aim, target spectra determined from the deterministic seismic hazard analysis (DSHA) problem [9] were considered. In the problem, magnitudes (\(M_{w}\)) were provided as 5.5, 7.0, and 6.8 for three different line sources, respectively. The style of faulting was assumed as strike-slip style. The shear wave velocity was taken (\(V_{s,30}\)) 300 m/s and the epicentral distance (\(R_{epi}\)) was used for the distance between the site and faults suitable for the problem. The controlling scenario earthquake was determined by a hypothetical earthquake with a magnitude of \(M_{w}\) = 7.0 and epicentral distance of \(R_{epi}\) = 16 km. Using the controlling parameters such as \(M_{w}\) and \(R_{epi}\), the target horizontal spectrum and its standard deviation were calculated by the [62] GMM, and their vertical counterparts were computed from the [63] GMM. Scenario-based target horizontal and vertical target spectra, and their standard deviations, are plotted in Figure 5. In this study, these spectra were utilized when scenario-based GM record selection was performed.  
Figure 4: Mean vertical spectral acceleration in database and target spectrum according to EC8 and the TBEC (**left**: EC8_B_TBEC_ZC, **right**: EC8_C-TBEC_ZD).  
Figure 5: Scenario-based target spectrum (**left**) and period-dependent standard deviation (**right**) for both horizontal and vertical directions.
# Position Accuracy Improvement by Implementing the DGNSS-CP Algorithm in Smartphones
## 3 Approach and Implementation
### DGNSS-CP Algorithm for Android Smartphone  
The DGNSS RS generates the Range-domain PRC (\(\delta\overrightarrow{\rho}\)) for each satellite as:  
\[\delta\rho^{i}=\hat{d}^{i}_{RS}+\hat{B}_{RS}-\hat{b}^{i}-\rho^{i}_{RS} \tag{2}\]  
where:  
\(\delta\rho^{i}\): Range-domain PRC for i-th satellite  
\(\hat{d}^{i}_{RS}\): estimated distance from RS to i-th satellite  
\(\hat{B}_{RS}\): estimated clock bias of RS  
\(\hat{b}^{i}\): estimated clock bias of i-th satellite  
\(\rho^{i}_{RS}\): pseudorange measurement for the i-th satellite received at RS  
After receiving a bundle of PRCs from the RS, the rover selects a PRC combination (\(\delta\overrightarrow{\rho}\)) according to its visible satellite set. After applying the selected PRC combination to the observables (\(\overrightarrow{\rho}\)), the rover calculates the error-mitigated position by the least-squares method as:  
Figure 4: Horizontal and vertical error of the Galaxy S5 ((a) Horizontal; (b) Vertical).  
Figure 3: Typical algorithm for improving the GPS SPP Accuracy.  
\[\left[\begin{array}{c}\stackrel{{\rightarrow}}{{x}}_{DGPS}\\ B\end{array}\right]=\left(H_{GPS}^{T}H_{GPS}\right)^{-1}H_{GPS}^{T}\left[ \begin{array}{c}\vdots\\ LOS_{ecef}^{i}\stackrel{{\rightarrow}}{{R}}-\left(\rho^{i}+ \delta\rho^{i}\right)\\ \vdots\end{array}\right] \tag{3}\]  
where:  
\(\stackrel{{\rightarrow}}{  
section, we assumed that both devices obtain the time offset between the GNSS as one parameter of their navigation solution. Thus, to calculate the position-domain correction for the application, \(H_{multiGNSS}\) was used instead of as the observation matrix \(H_{GPS}\) in Equation (4):  
\[H_{multiGNSS}=\left[\begin{array}{cccc}E_{GPS}&-1&0&0\\ E_{GLONASS}&0&-1&0\\ E_{Beidou}&0&0&-1\end{array}\right] \tag{8}\]  
Finally, we assumed that the GPS chipset in the smartphone uses the atmospheric model to improve its standalone position accuracy, as many commercial receivers do. Thus, it should be possible to restore the error mitigation in its pseudorange measurement. Using Equations (1) and (2), the compensated PRC (\(\delta\rho_{c}^{i}\)) was calculated according to Equation (9), and this quantity was substituted for \(\delta\rho^{i}\) in Equation (4):  
\[\delta\rho_{c}^{i}=\delta\rho^{i}+\hat{t}_{lob}^{i}+\hat{T}_{sas}^{i} \tag{9}\]
## 3 Problem Formulation
### Objective Function  
The objective is to minimize the whole day's active power loss at the same time ensuring that no constraint violations occur.  
\[\begin{array}{c}\min f(\textbf{u})=\min\sum\limits_{h=1}^{24}P_{Loss}^{h}\\ P_{Loss}^{h}=\sum\limits_{(i,j)\in B}g_{ij}\left\{\left(V_{i}^{h}\right)^{2}+ \left(V_{j}^{h}\right)^{2}-2V_{i}^{h}V_{j}^{h}\text{cos}\theta_{ij}\right\} \end{array} \tag{11}\]  
where \(P_{Loss}^{h}\) is the power loss at time \(h\), \(B\) represents the set of branches, and \((i,j)\in B\) denotes the two nodes of one branch. \(V_{i}^{h}\) and \(V_{j}^{h}\) are voltage magnitudes of two nodes \(i\) and \(j\) at time \(h\), respectively. \(g_{ij}\) is the conductance value between nodes \(i\) and \(j\). \(\theta_{ij}\) is the phase angles difference of \(\theta_{i}\) and \(\theta_{j}\). **u** is the vector containing all the control variables which is expressed as follow:  
\[\textbf{u}=\left\{\textbf{u}^{1},\textbf{u}^{2},\cdot\cdot,\textbf{u}^{h}, \cdot\cdot\cdot,\textbf{u}^{24}\right\} \tag{12}\]  
where \(\textbf{u}^{h}\) is the vector of RPO control variables at time \(h\), which is expressed as follow:  
\[\textbf{u}^{h}=(Q_{c1}^{h},Q_{c2}^{h},\cdot\cdot,Q_{cN_{c}}^{h},T_{r1}^{h},T_{ r2}^{h},\cdot\cdot,T_{rN_{r}}^{h}) \tag{13}\]  
where \(Q_{ci}^{h}\) and \(T_{ri}^{h}\) are the compensation capacity of reactive power capacitor and the tap setting of regulating transformer at time \(h\), respectively. \(N_{c}\) is the number of the compensator capacitors including substation capacitors and feeder capacitors. \(N_{r}\) is the number of regulating transformers. The vector of state variables \(\boldsymbol{x}\) is expressed as follow:  
\[\textbf{x}=\left\{\textbf{x}^{1},\textbf{x}^{2},\cdot\cdot,\textbf{x}^{h}, \cdot\cdot\cdot,\textbf{x}^{24}\right\} \tag{14}\]  
where \(\textbf{x}^{h}\) is the vector of state variables at time \(h\), which is expressed as follow:  
\[\textbf{x}^{h}=(V_{1}^{h},V_{2}^{h},\cdot\cdot\cdot,V_{N}^{h},\theta_{1}^{h}, \theta_{2}^{h},\cdot\cdot\cdot,\theta_{N}^{h}) \tag{15}\]  
where \(N\) is the total number of nodes.
_Article_  
Structural Health Monitoring and Time-Dependent  
Effects Analysis of Self-Anchored Suspension Bridge  
with Extra-Wide Concrete Girder  
[PERSON] 1, [PERSON] 1,2,*, [PERSON] 3 and [PERSON] 3  
1 School of Civil Engineering, Southeast University, No. 2 Sipailou Road, Nanjing 210096, China; [EMAIL_ADDRESS]  
2 Beijing Advanced Innovation Center for Future Urban Design, Beijing University of Civil Engineering and Architecture, No. 1 Zhanlanguan Road, Beijing 100044, China  
3 School of Civil Engineering, Nanjing Forestry University, No. 159 Longpan Road, Nanjing 210037, China; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (M.D.)
* Correspondence: [EMAIL_ADDRESS]  
Received: 8 December 2017; Accepted: 13 January 2018; Published: 15 January 2018  
**Abstract:** The present work is aimed at studying the structural health status of Hunan Road Bridge, which is currently the widest concrete self-anchored suspension bridge in China. The monitoring data included the structural deformations, internal forces, and vibration characteristics from April 2015 to April 2016 were analyzed to evaluate the structural changes and safety. The influences brought by the ambient temperature changes and the dual effects composed of concrete shrinkage & creep (S&C) and seasonal temperature changes were analyzed based on the measured data. The long-time effects of concrete S&C were predicted using the CEB-FIP 90 model and the age-adjusted effective modulus method based on the ANSYS beam finite element model. The measured data showed that the transverse displacements of towers were more significant than the longitudinal ones. The spatial effect of the extra-wide girder is significant, which performs as the longitudinal stresses change unevenly along the transverse direction. The seasonal ambient warming caused overall increases in girder compressive stresses, and the cooling resulted in decreases along with significant temperature gradient effects. The prediction results show that the cable anchoring positions at girder ends and tower tops will move towards the mid-span affected by concrete S&C. In terms of the middle region of mid-span girder, significant increases in longitudinal stresses of top plate and decreases in the ones of bottom plate will be caused by the significant deflection. Comprehensively, the increases in the girder compressive stresses of side-span bottom plate and mid-span top plate are worthy of attention when confronted with extreme high temperature during the bridge service life cycle.  
**Keywords:** self-anchored suspension bridge; extra-wide concrete girder; concrete shrinkage & creep; ambient temperature; safety evaluation
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 5 Verification of Frame Experiment
### Damage Identification  
Based on the experimental frequency values of the damage structures, the OMP method and the two types of IOMP methods were used for damage identification. The identification results were as follows: substructures 2 and 9 were actual damaged, and the damage factors were 0.67 and 0.5, respectively.  
The damage factors obtained using the three methods are listed in Table 5. The damage factors of substructures 2 and 9 identified using the OMP method were 0.685 and 0.475, respectively, close to the actual damage (Figure 11). These results indicated that the OMP method exhibited high accuracy in damage identification but misjudged the undamaged substructure 4. The damage factor identification values based on the two IOMP methods for damage substructures 2 and 9 were 0.666 and 0.47, respectively, consistent with the actual damage results. These findings show that the IOMP damage identification method based on additional virtual mass can precisely identify the damage to this frame model.  
When the OMP and IOMP methods were used for damage identification, it was necessary to determine the number of substructures preliminarily that could be damaged. After calculations, a broken line chart of the damage residual norm with the number of damaged substructures was plotted in Figure 12. When the number of damaged substructures was two, the damage residual norm was approximately 0.249. Because of the high accuracy of the experimental data, the OMP and IOMP methods both showed good performance  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Substructure** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** \\ \hline Mean & 108.27 & 107.13 & 108.27 & 107.13 & 108.27 & 107.13 & 108.27 & 107.13 & 107.13 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Selected fourth-order frequency means (Hz).  
Figure 11: Damage identification results.  
in determining the number of damaged substructures. However, when the number of identified damage substructures were equal, the residual norm of the IOMP method was always smaller than that of the OMP method, indicating that the IOMP method was more accurate than the OMP method.
# Magnetic Properties of Nanocomposites
## 2 A Survey of Magnetic Measurements and Typical Magnetic Parameters of Nanocomposite Systems
### Typical Magnetic Parameters and Quantities in Nanoparticle Systems
The temperature that divides both regimes is called the blocking temperature \(T_{B}\) (see Section 2.1). The superparamagnetic theory is based on many (over-)simplifying assumptions. For instance, even well-dispered, not touching magnetic NPs in a nanocomposite interact via a non-contact interaction (the magnetic dipolar interaction), a fact that is not taken into account by the standard superparamagnetic model. When the strength of the dipolar interaction is small but non negligible, the interacting superparamagnetic (ISP) model explains very well the measured anomalies in the magnetic response with respect to the predictions of the simple superparamagnetic theory [17]. _(v) Verwey transition temperature_. Magnetic NPs are one of the most popular fillers in magnetic nanocomposites. Bulk magnetite is characterized by a low-temperature phase transition occurring near \(T_{V}=125\) K, associated with changes in magnetic, electrical, and thermal properties. Upon warming, through the Verwey transition temperature, the magnetite crystal lattice changes from a monoclinic structure to the cubic inverse spinel structure that persists at room temperature. Although the most important changes between \(T<T_{V}\) and \(T>T_{V}\) are associated with the material's electrical properties, the change in structure entails a change in crystal anisotropy with measurable consequences on the shape of hysteresis loops. The presence and relevance of the magnetic Verwey transition in magnetite NPs is still debated (see Section 3.1).
# Color Mutations Alter the Biochemical Composition in the San Marzano Tomato Fruit
## 3 Results
### Bioinformatics to Investigate Metabolite-Metabolite Relationships  
Bioinformatic approaches, including Venn diagram visualization and correlation network analysis, were used in order to achieve a deeper understanding of the biochemical perturbations and relationships occurring in the SM mutants under study. Venn diagrams showed the degree of overlap for VOC, NP and P metabolites in each double mutant and its two parental single mutants. For line-specific metabolites in each group, all lines, except \(y\_gf\) and \(y\_r\), showed a higher number of significantly up-regulated metabolites (Figure 7). When the overlaps between single mutants and the respective double combinations were considered, the epistasis of \(r\) over \(gf\) (29 metabolites were in common between \(r\) and \(r\_gf\)) and \(y\) (28 metabolites in common between \(r\) and \(y\_r\)) and of \(y\) over \(gf\) (21 metabolites in common between \(y\) and \(y\_gf\)) were found (Figure 7).  
Furthermore, we used a correlation network approach to investigate mutation-induced alteration at VOC, NP and P metabolome levels. To this purpose, three networks were built by integrating all differentially accumulated metabolites in, at least, each single mutant and the corresponding double mutants (Figure 8). Overall, the three force-directed networks allowed the achievement of specific topologies according to the distribution of the significant correlation networks existing in each metabolite-metabolite interaction; notably, a high extent of conservation was observed in each network, either in the direct (PHE in the \(y\)- and CAR in the \(r\)-yielding networks) or not direct (L volatiles in \(r\), \(y\) and \(r\_y\); and AA in \(r\), \(gf\) and \(r\_gf\)) targets of the mutations. In order to evidence the more robust and strongest correlations, the MCODE Cytoscape plugin was applied to each of the three networks (Figures S2-S4). In this way, it was possible to identify the highly interconnected regions, which resulted particularly abundant in the \(r\)/\(gf\) mutants, with a lower number of greatly dense clusters including primary (SAP, AA) and secondary (isoprenoids as CAR, CHL, QUI, T and PHE) compounds; on the contrary, the \(y\)/\(r\) mutants were characterized by a higher number of clusters with lower density, and with a high representation of amino and organic acids, besides CAR and PHE.  
Figure 7: Venn diagrams for (**a**) \(gf\), \(r\) and \(r\_gf\); (**b**) \(gf\), \(y\) and \(y\_gf\); (**c**) \(r\), \(y\) and \(y\_r\). Red and black numbers correspond to up- and down-regulated metabolites, respectively.