esa-sceva/satcom-chunk-collection huggingface dataset:
# A Metal-Insulator-Metal Deep Subwavelength Cavity Based on Cutoff Frequency Modulation
## 3 Results
### Gap Size-Modulated Cavity  
In the MIM waveguide, the effective index of the waveguide mode increased for thinner dielectric layers between the metal layers [24]. Therefore, as the thickness (\(t\)) of the dielectric layer increased, the dispersion curve moved upward due to the smaller effective index (caused by weaker plasmonic coupling). Indeed, the cutoff frequency increased from 1059 THz (1779 nm) to 1945 THz (1414 nm), as shown in the dispersion curves of Figure 4b for \(t\) = 10 nm (black) and \(t\) = 30 nm (red). Here, the waveguide width (\(w\)) and mirror length (\(L_{m}\)) were 240 nm and 1000 nm, respectively. The heights of the cavity and mirror waveguide silver strips were 100 nm and 80 nm, respectively. Because the height of the silver was much larger than the skin depth, changes in the height did not affect the properties of the cavity. Figure 4a shows the schematic of a cavity consisting of a cavity waveguide (\(t\) = 10 nm) and two mirror waveguides (\(t\) = 30 nm), where the thickness of the dielectric layer was modulated from 10 nm to 30 nm.  
Similarly to the waveguide width- or dielectric index-modulated cavities (Figures 2 and 3, respectively), in the dielectric thickness-modulated cavities, the fundamental and second-order cavity modes were observed for \(L_{c}\) = 220 nm (Figure 4c) and \(L_{c}\) = 780 nm (Figure 4d), respectively. Based on the cutoff frequency mirror modulation (similar to the width-modulated cavity (Figure 2) and the index-modulated cavity (Figure 4)), the fundamental and second-order cavity modes were observed at \(L_{c}\) = 220 nm (Figure 4c) and \(L_{c}\) = 780 nm (Figure 4d), respectively.  
Figure 4: \(Cont.\)  
Figure 3: (**a**) Schematic of a cavity consisting of a cavity region (\(n\) = 1.5, green color) and two mirror waveguides (\(n\) = 1.0). Here, \(w\) = 240 nm, \(h\) = 100 nm, \(t\) = 10 nm, \(L_{m}\) = 1000 nm, and the cavity length is \(L_{c}\); (**b**) Dispersion curves of first-order waveguide modes for dielectric materials (between the silver strips) with different refractive indices of \(n\) = 1.5 (blue) and \(n\) = 1.0 (red). The cutoff frequencies (wavelengths) are 2\(\pi\)\(t\) = 1059 THz (1779 nm) and 1493 THz (1261 nm), respectively. The horizontal blue line indicates a frequency of 1215 THz (1550 nm). The light line is indicated by the blue line; (**c,d**) Top and side views of the electric field profiles of fundamental (\(L_{c}\) = 300 nm) and second-order (\(L_{c}\) = 850 nm) cavity modes.
## 2 System Composition and Protocol Design
### PU Channel Access and Data Transmission  
As shown in Figure 3, we divide the channel access and data transmission process into five parts: the backoff phase, the BSR frame phase, the resource allocation phase, the PU data transmission phase, and the waiting for feedback phase.  
Figure 1: CR-OFDMA systems with \(n_{1}\) critical patients and \(n_{2}\) mild patients.  
Figure 2: The main idea of the CR-OFDMA.  
1. In the backoff phase, STAs that need to transmit packets listen to the \"busy/idle\" state of the channel and randomly select a number as the value of the backoff counter after the channel idles the Distributed Inter-Frame Spacing (DIFS). The value of the backoff counter is decreased by 1 for each idle slot until the value of the backoff counter is reduced to 0.
2. In the BSR frame phase, when the value of the backoff counter is 0, the user can randomly select a subchannel to send a BSR frame and inform the AP that data need to be transmitted. As shown in Figure 3, patients C1-C5 and M1-M6 have data that need to be transmitted. When the backoff is completed, these STAs randomly and independently select a subchannel to transmit the BSR frames.
3. In the resource allocation phase, the AP allocates subchannels for the STAs according to the received BSR frames and informs each STA of the allocation result by sending a TF frame. To ensure fairness, as long as there is no collision and only one STA sends a BSR frame on a subchannel, the subchannel is assigned to the STA, regardless of whether the patient is critical or minor. If more than one STA in a subchannel sends BSR frames, the STA fails to compete for the subchannel. Therefore, among these r subchannels, some subchannels will not be successfully used by the STAs. To make full use of these remaining subchannels, we assign these remaining subchannels to critical patients who fail to compete, which guarantees QoS for high-priority patients.
4. In the PU data transmission phase, the critical patients and the mild patients who succeed in the competition transmit the packet on the assigned subchannel, and the critical patients who fail in the competition randomly select a remaining subchannel for packet transmission. As shown in Figure 3, the BSR frame sent by STA C1 collides with the BSR frame sent by STA M3, but C1 is a critical patient STA, so C1 can transmit the date in a randomly chosen remaining subchannel.
5. In the waiting for feedback phase, the AP sends an MBA frame to inform the STAs whether the data packets are successfully received.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 3 Related Work
### Approaches for Key Devices Positioning
They aimed to minimize the variation of hops between routers and gateways (VAR-MR-IG-Hop) of the routers (MR, Mesh Routers) to ensure that the gateways are properly positioned. In addition, the authors used the Dijkstra algorithm to calculate the shortest path between each router and all gateways in the network. The authors in [28] discussed gateway positioning based on graph clustering and the use of a repairing genetic algorithm (RGA) to work with such graphs, to repair unfeasible solutions. RGA differs from GA by detecting and repairing unfeasible solutions generated by crossover and mutation operations, in addition to being computationally efficient, with reduced processing time compared to GA.
## 3 Results
### Genome-Scale C. sativa Metabolic Reconstruction
#### 3.1.1 Functional Annotation  
The initial genome annotation reported by Grassa contains 31,170 genes, of which 25,296 are protein-coding genes (81%). A PlantSEED functional annotation was performed and complemented via BLASTKOALA to describe the metabolic capacity of _C. sativa_ leaves (Figure 4). A total of 10,636 _C. sativa_ genes were related to KO numbers. Most orthologous groups were observed in metabolic pathways related to primary plant metabolism (amino acid, carbohydrate, energy, cofactors and vitamins, and lipid metabolism). _C. sativa_ leaf metabolism reveals the complexity behind the biochemical reactions that occur in plant eukaryotic cells. A closer look at each of the modules (Figure 4) shows that energy acquisition, storage, and the utilization of stored energy are central processes in the overall control of plant metabolism [35]. Additionally, 6% of KO numbers were related to the biosynthesis of secondary cannabinoid and non-cannabinoid metabolites. These were important results that will be used to strengthen secondary metabolism in metabolic reconstruction.  
Some of the metabolic modules manually added are biosynthesis of flavanone, flavonoids, tryptophane, catecholamine, phenylalanine, proline, arginine, valine, leucine, cholesterol, cannabinoids, and fatty acids, among others.
## 3 Active Optics Correction of the 4m SiC Primary Mirror
### Surface Shape Correction of the 4m SiC Primary Mirror
#### 3.1.2 Influence Function and Bending Mode of the 4m SiC Primary Mirror
In addition, some bending modes exhibited similarities with specific Zernike modes. For example, the first-order bending mode was similar to the astigmatism in the Zernike mode. The third-order bending mode was similar to the trefoil aberration. The fifth-order surface was similar to the defocus, and the seventh-order surface was similar to the quadrafoil aberration. Figure 6 shows a schematic of the amplitude of the bending-mode force. The figure shows that as the order \(i\) of the bending mode increased, the amplitude of the bending-mode force generally increased. Figures 5 and 6 validate the viewpoint proposed in Section 3.1.1, that the stiffness and frequency of \(B_{i}\) gradually increased with increasing order. This indicated that the bending-mode method could eliminate high-frequency components in the surface error and reduce the amplitude of the correction force.
## 2 Materials and Methods  
### Ecosystem Model for Crop GPP and NPP Simulation  
The Boreal Ecosystems Productivity Simulator (BEPS) is used to simulate crop GPP and NPP in this study [37; 38]. BEPS has been developed since 1997 and has evolved from a daily version [38] to the current hourly version [39], with process-based modules to simulate water, energy and carbon budgets [40; 41]. Although BEPS was initially developed for boreal ecosystems, it has been evolved and expanded to cover temperate and tropical ecosystems at regional and global scales [42; 43; 44; 45; 46] and has been re-structured for parallel computation and data assimilation [47; 48; 46]. BEPS has also been used for estimations of winter wheat yield in China [49] and cotton yield in the US [50], as C3 plants share the same enzymatic mechanism-based photosynthesis theory at the leaf-level, i.e., [PERSON]'s leaf-level biochemical model [51], and BEPS has a \"two-leaf\" approach [52] to upscale the ecosystem GPP to canopy-level for various ecosystems including crops [38]. NPP is then modeled as the difference between photosynthesis (GPP) and autotrophic respiration. BEPS has the mechanism to simulate CO\({}_{2}\) fertilization, using CO\({}_{2}\) concentration data from [[https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html](https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html)]([https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html](https://www.esrl.noaa.gov/gmd/ccgg/trends/global.html)) (accessed on 20 July 2019).  
The maximum rate of Rubisco carboxylation (V\({}_{\text{cmax}}\)) of crops varies with cultivars and nutrition condition. Although BEPS has the model structure to assimilate a dynamic variable V\({}_{\text{cmax}}\), the V\({}_{\text{cmax}}\) values are fixed at 120 umoL m\({}^{-2}\) s\({}^{-1}\) for different crops in this study, to be consistent with our previous crop modeling work [48], since pixel-level V\({}_{\text{cmax}}\) measurements are currently unavailable at the spatial resolution of Sentinel-2.  
The C4 plants have less photorespiration and are more efficient in photosynthesis although their leaf V\({}_{\text{cmax}}\) is low, e.g., at 40 \(\pm\) 1 umoL m\({}^{-2}\) s\({}^{-1}\)[53] for maize (corn). Since BEPS has no C4 module, we test if the increase in V\({}_{\text{cmax}}\) to 120 umoL m\({}^{-2}\) s\({}^{-1}\) for C4 corn using a C3 model could partially compensate the underestimation of GPP simulation due to the lack of a photorespiration module for the purpose of corn biomass mapping.
# Bridge Model Updating Based on Wavelet Neural Network and Wind-Driven Optimization
## 3 Wind-Driven Optimization Algorithm  
In this section, the fundamentals of the algorithm of wind-driven optimization (WDO) are introduced, the update equations are derived, and finally, the realization of WDO is demonstrated.  
WDO [42; 43; 44] is a novel-group iterative heuristic global optimization algorithm. The algorithm has the advantages of strong robustness and high optimization efficiency and is suitable for dealing with nonlinear, multi-extremal, and non-differentiable complex problems. It was first proposed in 2010 and has been rapidly developed and applied recently.
## 7 Conclusions  
This paper develops a device that uses supersonic high-pressure shockwaves to loosen the soil in the root zone of crops. It conducts CFD numerical simulation of shockwave loosening to analyze the transmission of shockwaves in the soil and quantify its impact on soil porosity and compaction. We conducted visual observation experiments on the multi-directional emission of shockwaves and pressure measurement experiments on shockwave impact. Through the analysis of these experimental results, we can draw the following conclusions: 1. The larger the storage tank volume of the shockwave generator, the farther the shockwave impacts the soil; the greater the storage tank pressure, the greater the shockwave impact pressure; therefore, users can set different storage tank volumes or pressures according to their needs to produce different shockwave-loosening effects. 2. By comparing the pressure measurement experiments with the numerical simulation of the time-dependent pressure variation, we found that \(p_{omax}=1036.3\) kPa and \(p_{5\,1 max}=345.4\) kPa in experiment 3. This pressure measurement result is consistent with the numerical simulation results which show the shock tube inlet pressure is 10 atm (about 1000 kPa) and the output hole pressure can reach 3.4 atm (about 340 kPa). 3. Through the visual observation experiments and pressure measurement experiments of shockwave impact, we can prove that the shockwaves propagate from top to bottom in the output tube and are emitted from the output holes in multiple directions in sequence to impact the soil outside the output tube to achieve the goal of soil loosening.  
We have completed the pressure measurement experiments of shockwave impact in a water tank. In the future, we will conduct shockwave emission and soil-loosening measurement experiments in a pectin tank and actual soil to verify the effectiveness of soil loosening by high-pressure shockwaves. In addition, since the soil is the world's third-largest carbon storage reservoir, traditional farming uses rotary plows to till the soil. Although this can improve soil aeration, each tillage will emit about 30% of carbon, which is not conducive to promoting net-zero carbon emissions goals. The shockwave-loosening mechanism proposed in this study does not damage the soil surface during the loosening process, thus preventing the soil from being disturbed by large-scale tillage, which will reduce the emission of carbon stored in the soil into the atmosphere.
## 2 Theoretical Model of a System in the Presence of Gaussian Noise
### Single-Correlator Receiver
 \tag{12}\]  
According to the CLT the density function of variable \(w_{1}\) is Gaussian and can be expressed as  
\[f_{W1}(w_{1})=\frac{1}{\sqrt{2\pi\sigma_{w1}^{2}}}e^{\left(-w_{1}^{2}/2\sigma _{w1}^{2}\right)}. \tag{13}\]  
Then, the probability of error can be calculated according to this expression  
\[p_{be}=\frac{1}{2}erfc\left(\sqrt{\frac{\eta_{w1}^{2}}{2\sigma_{w1}^{2}}} \right). \tag{14}\]  
By inserting Equations (10) and (13) into (14) we may find the expression for the probability of bit error in this closed form  
\[p_{be}=\frac{1}{2}erfc\left(\sqrt{\frac{[2\beta/E_{c}E\{c_{i1}^{2}\}]^{2}}{2 \beta E_{c}[E\{c_{i1}^{4}\}-E^{2}\{c_{i1}^{2}\}]+2\beta E_{N}\sigma_{ni}^{2}E \{c_{i1}^{2}\}}}\right), \tag{15}\]  
which can be simplified to  
\[p_{be}=\frac{1}{2}erfc\left(\frac{\
u-1}{\beta}+\frac{E_{b}}{N_{0}}^{-1} \right)^{-1/2}, \tag{16}\]  
where expression \(\
u=E\{c_{i1}^{4}\}/E^{2}\{c_{i1}^{2}\}\) represents random variability of the spreading sequence. The energy of the noise is equivalent to the power spectral density of the two sided noise spectrum. As it was said before, this system is analyzed assuming that the signals are generated in discrete time domain. Each chip and related noise sample are generated once for each chip interval and then repeated (interpolated) \(M\) times in that chip interval to allow the discrete time carrier modulation. For \(M\) repeated samples of noise in a chip interval the energy is \(E_{N}=M\sigma^{2}\). For variance \(\sigma^{2}=BN_{0}\) and the bandwidth \(B\)=1/2\(T_{c}\)=1/2\(M\), the energy is calculated to be \(E_{N}=N_{0}\)/2. If the source generates binary bits and the spreading sequence is in binary form, we may have  
\[p_{be}=\frac{1}{2}erfc\left(\sqrt{\frac{E_{b}}{N_{0}}}\right) \tag{17}\]  
where the energy of a bit is related to the energy of a chip as \(E_{b}=2\beta E_{c}\).
## 2 Materials and Methods
### Mapping Units and Geo-Environmental Predictors  
According to the aim of the research, starting with high-resolution (0.25 m) LIDAR (Light Detection and Ranging) images taken in 2012 by ARTA (Assessorato Regionale al Territorio e all'Ambiente), five grid cells of different resolutions (5, 8, 10, 16 and 32 m) were adopted as mapping units. By overlapping the grid cells with the landslide inventories, the unstable status of each pixel was then assigned with respect to each landslide type investigated, depending on whether it hosted at least one LIP.  
Based on the expected influence of geo-environmental variables in landsl and according to the literature (e.g., [29; 30; 38; 39; 40]), 10 variables were exploited as potential predictors for landslide susceptibility modelling.  
Elevation (ELE) is considered a proxy of the mean annual which typically reflects the altitude [29]; steepness (SLO) is linked to the inclination of potential underlying rupture surfaces, and it controls the speed of water [29; 38]; aspects, expressed here in terms of northernness (N) and easternness (E), can be used to determine the seasonal wet/dry cycles of soils [40]; topographic curvatures, expressed in terms of plan (PLC) and profile curvature (PRF), are assumed to express the direction of flow and shallow mechanical stresses [29; 38; 41]; the topographic wetness index (TWI) is a good proxy for estimating the potential infiltration [31] or saturated soil thickness; the stream power index (SPI) expresses the intensity of surface water erosion [29; 42]; landform classification (LCL) directly differentiates the morphological setting of the study area [29]; the outcropping lithology (LITO) is assumed to be a good proxy for the physical-mechanical properties of rocks at the basin scale [29]; finally, soil use, obtained here via CORINE 2018, is directly related to the potential hydrological and surface water disturbances induced by erosion [29; 42].  
All the DTM-derived variables passed the test for multicollinearity.
## 3 Results and Discussion
### Methylene Blue Removal  
The initial experiments in the MB dye adsorption tests exaimned how several parameters impacted on the adsorption process. The impact of particle size, pH of the MB solution, adsorption temperature, and initial MB concentrations were investigated to assess the suitability of each biosorbent for such applications and identify their optimal MB removal. Subsequently, adsorption kinetic, equilibrium models, and thermodynamics were developed for the selected biosorbents.
## 3 A Fast Prediction Model for the Coupling Coefficient
### Verification  
In [20], researchers conducted on-site measurements of the throughput of LTE-R systems based on the File Transfer Protocol (FTP) under high-speed conditions. The equipment connection of the measurement system is shown in Figure 13. FTP transmits 100 MB of files between the onboard subsystem and the ground subsystem as the train travels through the LTE-R network coverage area at high speed. The control part of the terminal side calculates the peak downlink throughput, which is the maximum amount of data that can be transmitted per second, to obtain the relationship between downlink throughput and SNR. The simulation data are compared with the field measurement data in Figure 14.  
Figure 14: Scatter plot of the downlink throughput and SNR.  
Figure 13: Equipment connections for the throughput measurement system.  
It can be seen that the field measurements are in good agreement with the simulations. The downlink throughput increases as the SNR increases. When the SNR is within the range of 0 dB-25 dB, the maximum throughput can reach 19.9 Mbps, which is 53.2% lower than the theoretical value of the 42.5 Mb/s downlink peak rate calculated in [21].
## 6 Conclusions
Most of the reviewed studies use either the interferometric coherence, the SAR intensity correlation or both correlations as input for the damage assessment (see Section 3). The reported change detection techniques range from e.g., visual interpretation of the coherence and visual interpretation of RGB combinations of pre- and post-event intensity correlation and backscatter images, to more automated procedures e.g., applying the normalized differences of the coherence and the intensity correlation or coherence based change indexes. Other studies make use of more sophisticated change detection approaches such as neural network classification based on the coherence, or neuro-fuzzy per-pixel and Markov random field spatial model classifiers. Especially the differentiation between undamaged and slightly damaged buildings using current remote sensing sensors (even with very high spatial resolution optical sensors) is still not satisfactorily solved so far. Time is a very crucial consideration in disaster response. Therefore, the processing time of the damage mapping procedure is as important as its accuracy. Consequently, future research in this field should also address the development of faster and more automated damage assessment procedures. Time-consuming manual interaction may be reduced by focusing on unsupervised classifiers. Moreover, to enable the worldwide applicability of the damage mapping procedures at an operational level, easy-to-use methodologies are required. Up to today, the multi-temporal SAR data approach for damage assessment can only be applied for very limited case studies, where suitable pre-event SAR imagery is available (see Section 4). Current SAR missions (e.g., TerraSAR-X, Radarsat-2 and COSMO-SkyMed) are not able to provide worldwide coverage within a short time frame. However, damage detection using only post-disaster data (e.g., employing SAR simulation and analysis of SAR texture statistics) is much more complicated and its accuracy is not as high compared to multi-temporal data methodologies (Section 3.6). The upcoming European SAR mission Sentinel-1 is a promising solution of the aforementioned challenge (II), which states the worldwide availability of useful pre-event SAR data recorded shortly before the event. Sentinel-1, with its two-satellite-constellation, will be characterized by a high swath width of 250 km (Interferometric Wide swath--IW, standard mode over land), fast repeat cycle (12 days with one satellite/6 days with two satellites) and relatively high spatial resolution. The SAR imagery of Sentinel-1 will provide a very good opportunity for a successful worldwide application of the aforementioned SAR interferometric damage assessment techniques (see Section 5). However, the spatial resolution of _ca._ 5 m \(\times\) 20 m for the Sentinel-1 IW mode limits the application of damage assessment to the block level.