esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Solutions  
CYN was purified from fresh algae collected in Imperia (Italy) using the extraction protocol used by [PERSON] et al. (1996). Briefly, algae were air-dried and extracted with \(n\)-hexane-dichloro-methane, filtrated, and concentrated under pressure. The extracted oily liquid was solubilized in \(n\)-hexane and subjected to chromatography. The last fraction containing CYN was applied to exclusion chromatography. The fraction that was retained most was chromatographed on an open eluted silica column and subjected to two-step high-pressure liquid chromatography (HPLC). The yield of pure CYN was 0.16% on the dry weight of algae. Finally, CYN was dissolved in dimethylsulfoxide (DMSO), sonicated, and added to a saline solution (DMSO final concentration 0.1%).  
Krebs-Ringer-Hepes (KRH) solution (in mM) was as follows: NaCl 136, KCl 4.7, CaCl\({}_{2}\) 1.3, MgCl\({}_{2}\) 1.25, glucose 10, and HEPES 10, with the pH adjusted to 7.4 by sodium hydroxide (NaOH).
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 3 Adaptive Downward/Upward Protocol  
In this paper, we consider upward and downward routing in mobile scenarios. In these scenarios, we suppose that all nodes are free to move except the sink node. In upward routing, every node periodically generates data that are destined to the sink at a constant rate. Before sending packets, each node selects a next-hop from its neighbors based on RRD+. RRD+ helps to cope with the frequent-topology-changes problem in mobility. In downward routing, the sink periodically sends command packets to nodes. Due to the movement of nodes, routes from the sink to sensor nodes do not stay the same. If route information cannot be updated on time, packets may be lost. In what follows, we present our method, which is an extension of RRD+ that copes with downward routing in mobility.
## 2 System Architecture and Components
### Photovoltaic Model  
The PV panels are used to convert the sunlight to electricity. The position of the PV surface should be perpendicular to the sunlight. The output power during the day may be different based on solar irradiation, temperature and weather conditions. Equation (2) shows the output power of the PV system where \(\eta\) is the conversion efficiency of PV cells, S is the surface area of the PV surface, I is the solar radiation and \(T_{a}\) is the ambient temperature [29]. Figure 4 shows the predicted output power of PV panels considered in this work.  
\[P_{PV}=\eta\times\text{S}\times\text{I}\times(1-0.005\times(T_{a}-25)) \tag{2}\]
## 5 MTMD Applications on Taizhou Bridge
### Parametric Analysis on the MTMD Application
#### 5.4.3 Damping Ratio  
The control efficiency of the lateral and vertical MTMD systems is also investigated with different damping ratios, as shown in Figure 17. During the process, the frequency bandwidth ratio are kept the same as the one in the parametric analysis above, while the other parameters are updated as \(\mathbf{N}=13\), \(\mathbf{\mu}=0.03\) for lateral MTMD and \(\mathbf{N}=11\), \(\mathbf{\mu}=0.02\) for vertical MTMD.  
As illustrated in Figure 17, when the damping ratio changes from 0% to 10%, the control efficiencies for lateral and vertical MTMD vary only 1.41% and 3.44%, respectively, indicating that  
Figure 16: Influence of the mass ratio on the control efficiency of MTMD: (**a**) Lateral case; (**b**) Vertical case.  
Figure 17: Influence of the damping ratio on the control efficiency of MTMD: (**a**) Lateral case; (**b**) Vertical case.  
the damping ratio has less influence on the control efficiency. For the lateral MTMD system, with the augment of the damping ratio the control efficiency first increases and then gradually declines, which peaks at 20.27% with the damping ratio of 2%. This is mainly because increasing the damping is beneficial for the energy dissipation of the structure. However, when the damping ratio goes up to a certain level, the movement of the mass block will be suppressed, leading to the decline of the MTMD control effect. Hence, the preferred value of the damping ratio for the lateral MTMD is set as 2% in this round.  
For the vertical MTMD system, the control efficiency decreases with the augment of the damping ratio, which means only the suppression effect of damping is presented in the movement of the mass block. When the damping ratio is between 0% and 2%, the reduction of the control efficiency is unapparent, but the subsequent reduction is nearly in a big constant gradient. Since the un-damped TMD is very sensitive to the variation of the natural frequency of the main structure [27], the preferred value of the damping ratio for the vertical MTMD is also set as 2%.
## 4 Conclusions  
The powder produced by doping lanthanum in BaFe\({}_{12}\)O\({}_{19}\) was examined by XRD to demonstrate that La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) was successfully prepared. The behavior of the La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) ceramic was examined in an electric field at room temperature, and a double \(P\)-\(E\) hysteresis loops diagram without macroscopic polarization in the AFE region was successfully obtained. The dielectric loss of La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) was measured from \(-190\)\({}^{\circ}\)C to 400 \({}^{\circ}\)C and the frequency varied from 10 H to 10 MHz. We successfully mea  
Figure 17: The trends of \(MR\) with the magnetic field strength at frequencies of 10 Hz, 100 Hz, 1 kHz, 10 kHz.  
sured the two phases transition peaks of La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) at the frequency of 200 kHz. The \(T_{F-A}\) was located at about \(-184\)\({}^{\circ}\)C, and \(T_{A-P}\) was about 214 \({}^{\circ}\)C. Thus, it was concluded that La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) was in the AFE phase at room temperature, which was highly consistent with the \(P-E\) hysteresis diagram measured at room temperature, and this evidence proves that doping lanthanum into BaFe\({}_{12}\)O\({}_{19}\) can shift its \(T_{F-A}\) and \(T_{A-P}\) towards lower temperatures, and that the doping of lanthanum makes the FE phase of BaFe\({}_{12}\)O\({}_{19}\) less stable and its AFE phase more stable. This broadens the scope of the study of AFE materials at room temperature. The UV-vis-NIR optical spectrum of La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) proved it to be a direct bandgap semiconductor with an \(E_{g}\) of 1.753 eV. It is also observed that La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) has a distinct \(M\)-\(H\) hysteresis loop with a remanent magnetization of 23.6 emu/g and a coercivity of 1063.9 Oe. In addition, the compound possesses strong ME coupling and a GMR effect. The maximum \(MR\) ratio reached \(-110.6\%\) when the magnetic field was 1.1 T. In summary, La\({}_{0.75}\)Ba\({}_{0.25}\)Fe\({}_{12}\)O\({}_{19}\) is a novel type of multiferroic candidate which combines AFE and FM at room temperature, demonstrating magnetic semiconducting properties, strong ME coupling, and a large GMR effect.  
Writing--original draft preparation, G.-L.T.; writing--review and editing, Z.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant No. 11774276.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 4 Numerical Simulation Analysis
### Determining the Step Height Parameters Based on the NSGA-II Multi-Objective Optimization Algorithm  
#### 4.2.1 Step Height Parameter Optimization Problem  
Considering the large deformation and significant stress concentration during full-face excavation, we used the step-by-step excavation method. In this study, the term \"step-by-step excavation\" refers to a staged excavation method in which the tunnel cross-section is divided into two parts: the upper step and the lower step (as in Figure 10). The excavation is first carried out in the upper part (pilot step) to a certain length, followed by the excavation of the lower part (completion step). This method is commonly used in fractured and weak surrounding rock zones, as it helps reduce deformation concentration, control face stability, and improve construction safety.  
Figure 8: Full-face excavation tunnel surrounding rock stress diagram at a 40 m excavation.  
Figure 7: Full-face tunnel surrounding rock stress diagram at a 20 m excavation.  
Figure 9: Full-face excavation tunnel surrounding rock stress diagram at a 60 m excavation.  
Let the lower step height be denoted as x; then, the upper step excavation height is \(4.86-\mathrm{x}\). The lower step height x is the variable to be solved in this optimization problem. During the excavation process, the upper step must be excavated first, followed by the lower step. Considering the influence of deformation, the tunnel deformation after 20 m, 40 m, and 60 m of lower step excavation is set as an objective function. It can be observed that there are three objective functions; therefore, the NSGA-II multi-objective optimization algorithm was selected in this paper to solve for the optimal step height design parameters.
# A Spatial Prediction-Based Motion-Compensated Frame Rate Up-Conversion
## 2 Proposed SP-MCFI Algorithm
### Block Classification  
The key to spatial prediction technology is based on the effective use of spatial correlation, and the premise of applying this technique lies in the block classification. Figure 2 shows the block classification model in the spatial prediction technique. The gray blocks represent basic blocks, and the remaining blocks are absent blocks. The two kinds of blocks alternate in horizontal and vertical directions. The MVs of the basic blocks are accurately obtained through BME algorithm, and then the MV of each absent block is calculated by effectively using the motion information of basic blocks adjacent to it.
## 3 Results
### Results of a Squall Line and a Hailstorm from CINRAD C-Band Radars  
Nearly half of the radars in China's CINRAD network are C-band Doppler radar systems, which are mainly installed in the northeast and midwest of China. These C-band radars present particular challenges for dealiasing algorithms due to their small Nyquist velocity values. To examine the capability of ADDA in dealing with aliased velocities from C-band radars, we ran ADDA for two intense convective cases. The first was a squall line system initiated in the northwest part of North China close to 05:00 (UTC), 23 July 2009, which developed to its maximum intensity when the storm passed through Beijing at 09:00 (UTC). Six CINRAD radar systems, including the C-band Zhangbei radar, observed the event. The Nyquist velocity of the Zhangbei radar was only 13.55 m/s, resulting in a considerable amount of folded velocity data. Figure 5a displays the original radial velocity observation at the 3.3\({}^{\circ}\) elevation angle of 3.3\({}^{\circ}\) by the Zhangbei radar at 07:05 (UTC), 23 July 2009. The dealiasing result shows that more than 95% of the aliased data were correctly dealiased at this elevation, and the only error appears in the eastern area of the radar field between the 100 and 200 km ranges shown by the narrow arc-shaped blue band (within the white ellipse) in Figure 5b. Because some remaining noisy gates and large observations of the missing area led to insufficient velocity gates with the same distance from the radar, the quadratic least squares error check in the azimuth direction could not determine accurate coefficients for some ranges.  
Figure 4: Radar radial velocity at elevation angle of 4.3\({}^{\circ}\) from the CINRAD S-band radar system in Guangxi province of China at 20:28 (UTC), 30 Mar 2014. (**a**) Raw radial velocity, (**b**) flag 3 showing the real shear regions and the first reference radial shown by the yellow line, (**c**) dealiased result after the first two steps in module 4, (**d**) dealiased result after the last two steps in module 4, and (**e**) dealiased result after module 5. The Nyquist velocity for the Guangxi radar is 27.12 m s\({}^{-1}\). The spatial scales of the pairs of images are different.  
The second case was a hailstorm event observed by the Datong C-band radar in the Shanxi province of China on 27 June, 2008. The large wind speeds, i.e., those exceeding 25 m/s, that occurred during this event caused many aliased velocity observations since the Nyquist velocity of the Datong radar was only 13.99 m/s. Figure 5c shows the raw radial velocity field at the 4.3\({}^{\circ}\) elevation angle from the Datong radar at 08:06 (UTC), 27 June 2008. The aliased data (blue shade), including the isolated echoes, were completely corrected, as shown in Figure 5d.  
Due to the small Nyquist velocities and the higher frequencies of occurrence of the patchy observations from the CINRAD C-band radar, ADDA has a lower success rate for the two C-band cases than for the other S-band cases (see Table 2).
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### VMH Zoning System
In arriving at a value for \(t_{ho}\), a successful broadcast of the handover request packet (hrp), \(p_{s}\), depends on two probabilities: (i) a successful carrier sense, \(p_{cs}\); and (ii) no collision occurred during packet transmission, \(p_{coll}\):  
\[p_{cs}=\left(1-\left(1-p_{cf}\right)\right)^{k+1} \tag{31}\]  
where \(k\) is the number of re-sensing allowed for a specific transmission and \(p_{cf}\), represented below, is the probability of sensing the channel as free:  
\[p_{cf}=e^{-\lambda_{nat}\left(t_{cs}+t_{pg}\right)} \tag{32}\]  
\(t_{cs}\) is the period for carrier sensing. A collision can only occur when a second node tries transmitting during \(t_{cs}\). Thus, the probability of experiencing no collision can be computed as:  
\[p_{coll}=e^{-\lambda_{nat}t_{cs}} \tag{33}\]  
\(\lambda_{net}\) represents the overall traffic generated by all nodes inside the VC (which is a summation of the original traffic, \(\lambda\), and the traffic generated due to retransmissions). \[\lambda_{net}=\lambda\frac{p_{cs}}{p_{pg}}\left(1-\left(1-p_{pg}\right)^{L+1}\right) \tag{34}\]  
\(L\) is the number of allowed retransmission, while \(p_{pg}\) represents the probability that the node sending the hrp packet can successfully access the channel. This can be computed by solving \(p_{cs}p_{coll}\). Thus, if \(t_{pg}\) is the propagation for the hrp packet to travel in air from the LMR node to all eligible nodes in the VC, then \(t_{s,hp}=t_{pg}(p_{cs}p_{coll})^{2}\), \(t_{s,hp}=t_{pg}\left(1-\left(p_{cs}p_{coll}\right)^{2}\right)\) and \(t_{w,rp}=t_{pg}\left\{\sum\limits_{i=1}^{G}\left[\left(\sum_{k=1}^{i-1}CW_{k} \right)+\frac{CW_{\text{max}}}{2}\right](p_{cs}p_{coll})^{2}\right\}\). Figure 7 illustrates the full network model for MROR. Figure 7: Full network illustration for a mobile reliable opportunistic routing (MROR)-based cognitive radio sensor network (CRSN).
# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 6 Data Fusion
### Cross-Space Data Fusion through Correlation  
Cross-space data fusion has taken the form of statistical methods being applied to calculate correlation between (usually) numerical data streams derived from the physical and social space. These include utilizing the data generated by citizens in social networking platforms in conjunction with data from sensor installations to build a model of the city's dynamics. These mainly utilize location-based social sensing services. The research by [PERSON] et al. [57] analyzed Foursquare check-in data and its correlation with diurnal pollutant levels and traffic volume in Patras, Greece. The correlation and its \(p\)-value was calculated between the diurnal cumulative pollutant level averages for NOx and CO and diurnal Foursquare check-ins, with the check-in data closely following the pollution patterns. [PERSON] et al. [63] correlated the traffic behavior with temperature in the city of Santander, discovering a fine-grained correlation in the evolution of both flows.
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 4 Performance Evaluation
### Network Overhead  
Figure 12 compares the average network overhead for the RDSP system and UF scheme. The UF overhead was very high because of the periodic exchange of entire routing tables to maintain the whole network information at each relay node. In contrast, the RDSP system overhead was much lower, due to the absence of the exchange of routing tables. Thus, the overhead in the RDSP system was related to the transmission of hello messages and acknowledgment messages. As a result, the average network overhead for the RDSP was about 33% lower than that for the UF scheme.  
Figure 11: Message delivery ratio.  
Figure 12: Network overhead.
# Breeding Tomato Hybrids for Flavour: Comparison of GWAS Results Obtained on Lines and F1 Hybrids
###### Abstract  
Tomato flavour is an important goal for breeders. Volatile organic compounds (VOCs) are major determinants of tomato flavour. Although most tomato varieties for fresh market are F1 hybrids, most studies on the genetic control of flavour-related traits are performed on lines. We quantified 46 VOCs in a panel of 121 small fruited lines and in a test cross panel of 165 hybrids (the previous panel plus 44 elite cherry tomato lines crossed with a common line). High and consistent heritabilities were assessed for most VOCs in the two panels, and 65% of VOC contents were strongly correlated between lines and hybrids. Additivity was observed for most VOCs. We performed genome wide association studies (GWAS) on the two panels separately, along with a third GWAS on the test cross subset carrying only F1 hybrids corresponding to the line panel. We identified 205, 183 and 138 associations, respectively. We identified numerous overlapping associations for VOCs belonging to the same metabolic pathway within each panel; we focused on seven chromosome regions with clusters of associations simultaneously involved in several key VOCs for tomato aroma. The study highlighted the benefit of testcross panels to create tasty F1 hybrid varieties.  
tomato; breeding; flavour; volatiles; GWAS +
Footnote †: journal: Genetics
# Dynamic Vertical Mapping with Crowdsourced Smartphone Sensor Data
## 4 Evaluation
### Different Phone Calibration  
In this section, we discuss the use of our algorithm for two different smartphones. As can be seen in Figure 12, there is an offset between the sensor readings of the two phones. This implies that there cannot be a single point of reference for both sensors and highlights the need for calibration between the two phones. However, as can be seen, the offset between the two sensors is almost stable. As a result, this effect demonstrates the need of self-reference that our approach offers. Hence, considering the fact that each phone will extract reference pressure from its own sensor and the fact that the offset between different phones is stable, our proposed approach will work for any given barometric sensor calibrated under any given circumstances.  
\begin{table}
\begin{tabular}{l r r r r r r} \hline \hline
**Date \& Time** & **T A** & **T G** & **H A** & **H B** & **P A** & **ID** \\ \hline May 10, 10:20 & 9 & 10 & 70 & 74 & 1011 & 1 \\ May 10, 21:40 & 11 & 13 & 61 & 45 & 1006 & 1 \\ May 12, 18:20 & 21 & 19 & 40 & 52 & 1004 & 1 \\ May 9, 17:00 & 10 & 9 & 49 & 52 & 1016 & 1 \\ May 9, 10:40 & 8 & 9 & 75 & 72 & 1017 & 2 \\ May 9, 17:30 & 10 & 11 & 49 & 55 & 1016 & 2 \\ May 10, 22:00 & 11 & 11 & 61 & 65 & 1006 & 2 \\ May 12, 18:30 & 21 & 19 & 40 & 45 & 1004 & 2 \\ May 9, 10:10 & 8 & 9 & 75 & 60 & 1017 & 3 \\ May 9, 16:40 & 10 & 9 & 49 & 59 & 1016 & 3 \\ May 10, 10:00 & 9 & 8 & 70 & 40 & 1011 & 3 \\ May 12, 17:50 & 21 & 19 & 40 & 43 & 1004 & 3 \\ Feb 11, 14:30 & 6 & 2 & 70 & 72 & 1019 & 3 \\ Feb 12, 19:00 & 0 & 1 & 87 & 80 & 1028 & 3 \\ Feb 21, 21:30 & 7 & 0 & 93 & 83 & 1017 & 3 \\ Mar 21, 13:30 & 13 & 8 & 58 & 64 & 1010 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Collected Data used for evaluation. The table shows the date of collecting the data, the time, the indicated temperature from AccuWeather (T A) and Google (T A) (unit: \({}^{\circ}\)C), the relative humidity from the same two sources (H A) and (H G), and the ambient pressure from AccuWeather (P A) (unit: Pa). The buildings belong to the Technical University of Munich (TUM) main campus area and are (1) Agness 27, (2) Adelheid 13A, (3) Agness 33 and (4) TUM main campus.
# Multiple Current Reversals Using Superimposed Driven Lattices
## 4 Discussion
### Directed Transport in Background Lattice  
First, we discuss the directed transport of particles in the positive \(x\)-direction in the presence of only the lattice \(V_{B}\) and the driving force. To do so, it is necessary to understand the phase space underlying our setup I by taking stroboscopic snapshots of particle trajectories \(x(t),v_{x}(t)\) at \(t=nT(n\in\mathbb{N})\) with each particle having different initial conditions. This leads to the 2D Poincare surface of sections (PSOS): \(\{x(nT)\ mod\ L,\ v_{x}(nT)\}\), which provide a representative overview of the structure of the complete 3D phase space (Figure 2a). Due to the broken \(P_{x}\) and \(S_{t}\) symmetries, the PSOS do not possess any reflection symmetry about \(v_{x}=0\). The PSOS is characterized by a single chaotic manifold or 'chaotic sea' bounded by the two first invariant spanning curves (FISC) at \(v_{x}\simeq 10\) and \(v_{x}\simeq-6\). The chaotic sea correspond to trajectories undergoing diffusive motion through the lattice. The large regular island embedded in the chaotic sea denotes trapped particles oscillating near the potential minima of the lattice. The particles with speed \(|v_{x}|\) higher than the speed of the respective FISC at positive and negative velocities correspond to ballistic unidirectional motion through the lattice along positive or negative \(x\)-directions.  
The low energy initial coordinates of our particle ensemble correspond to trajectories in the chaotic sea. Hence in the course of their time evolution, they ergodically populate the entire chaotic sea. This can be observed from (Figure 2b), where we project the snapshot of the ensemble population  
Figure 2: **(a)** The stroboscopic 2D Poincaré surface of sections (PSOS) in the \((x,v_{x})\) plane corresponding to the driven background lattice \(V_{B}\). The regular islands and invariant curves (in black) denote trapped oscillations and ballistic motion respectively. The chaotic sea (in green) denotes diffusive motion. **(b)** The asymptotic particle distribution as a function of position \(x\) mod \(L\) and \(v_{x}\) (in colormap) of all the \(N=10^{4}\) particles propagating in the setup I superimposed on the PSOS shown in Figure 2a. The parameters are the same as in Figure 1.  
distribution as a function of the particle coordinates \((x,v_{x})\) at time \(t=t_{f}\) onto the PSOS. This leads to a converged value of the ensemble velocity which is equal to the transport velocity of the chaotic manifold [16]. Physically this signifies that these particles undergo diffusive motion through the lattice which is however asymmetric about \(v_{x}=0\) due to the broken symmetries. Hence, the asymptotic average velocity of the ensemble is non-zero and the particles exhibit directed transport along \(x\)-direction with \(\bar{v}_{x}\simeq 1.3\) as observed in Figure 1b.