esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of Influencing Factors of Crossing Cable
### Anchor Span Ratio of Crossing Cable  
A pair of crossing cables are added to the main span and set at different positions to calculate the displacement of the deck under the uniform load of 40 kN/m. The loading method of uniform load is shown in Figure 8. Figure 11 shows the maximum vertical deflection of the deck when the crossing cables are set at different positions.  
It can be seen from Figure 11 that when the main span length is 1000 m and 1500 m, the vertical deflection of the deck is the smallest when the anchor span ratio is 0.657 and 0.625. The maximum anchor span ratios of the crossing cable constraint stiffness given by the formula are 0.673 and 0.633, respectively. The relative errors between the theoretical value and the model value are 2.44% and 1.28%, respectively. One of the reasons for the error is that the influence of the bending stiffness of the deck is ignored in the formula, and assuming that the displacement of the side tower top is 0 also leads to some errors. The formula has high accuracy and can be used to determine the optimal anchorage position of the crossing cable in the long-span cable-stayed bridge.  
Figure 12 shows the bending moment of the mid-tower bottom under single-span loading when the crossing cable is anchored at different positions. It is noted that after adding a pair of crossing cables to the three-tower cable-stayed bridge with a main span of 1000 m, the anchor span ratio with the lowest bending moment at the tower bottom is 0.627. For the three-tower cable-stayed bridge with a main span of 1500 m, the vertical deflection of the deck and the bending moment at the tower bottom reach the minimum when the anchor span ratio is 0.625.  
Figure 11: The maximum vertical deflection of the deck when the crossing cables are set at different positions.
# Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications
## 3 Proposed Disaster Response Systems for Tunnel Accidents  
This section describes vision technology and network technology for detection of fires in tunnels, radar technology for detection of accident vehicles, robot technology for initial discovery, localization technology for rescue team tracing, and redundant search avoidance techniques. It also describes the control server technology that can control the whole system.
## 2 Background and Literature Review
### Sdn  
Conventional networking is infrastructure-based, with the control plane dispersed among several routers and other network gear. By logically isolating the fundamental network control mechanism from switches and routers to provide network control centralization, Software Defined Networking (SDN) makes network programming possible [12]. In comparison to conventional hardware-driven networking, SDN provides more network awareness and the capacity to design safe paths by using the controller's gathering of network status information [13]. SDN has three planes: the infrastructure plane, the control plane, and the application plane. Flexibility and programability are the main advantages that SDVN adds compared to conventional networks. In SDN, control is conceptually centralized, allowing any number of physical devices to interact via a single protocol. SDN has made it possible to perform new tasks and provide new services, such as traffic optimization, virtualization of networks and automation, cloud-based service coordination, etc. [14]. However, dependability is one of the major drawbacks of SDN, since the SDN controller often acts as one possible point of breakdown [15]. Additionally, SDN encounters difficulties integrating with traditional networks that cannot utilize the OpenFlow protocol, the centralized controller's being unable to independently control all traffic, the existence of only a handful of protocols for communication between the controller and applications, etc. [16].
# Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
###### Abstract  
The presented article is a review of the literature reports on the influence of magnetic and electric fields on the growth, yield, ripening, and durability of fruits and their quality. The article shows the potential application of MF and EF in agricultural production. Magnetic and electrical fields increase the shelf life of the fruit and improve its quality. Alternating magnetic fields (AMF) with a value of 0.1-200 mT and a power frequency of 50 Hz or 60 Hz improve plant growth parameters. MF cause an increase in firmness, the rate of maturation, the content of beta-carotene, lycopene, and fructose, sugar concentration, and a reduction in acidity and respiration. The most common is a high-voltage electric field (HVEF) of 2-3.61 kV/cm. These fields extend the shelf life and improve the quality of fruit by decreasing respiration rate and ethylene production. The presented methods seem to be a promising way to increase the quantity and quality of crops in agricultural and fruit production. They are suitable for extending the shelf life of fruit and vegetables during their storage. Further research is needed to develop an accessible and uncomplicated way of applying MF and AEF in agricultural and fruit production.  
electrical fields; magnetic fields; high-voltage electric field; growth of fruits; ripening of fruits; shelf life of fruits +
Footnote †: journal: Applied sciences
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 5 Vertical Shear Failure of the Joint  
### Ultimate Shear Capacity  
Figure 13 shows the shear force-displacement curve of the joint in the compression-vertical shear destruction test, which can be divided into four stages. Similar to the above analysis, stage 1 is mainly the compression of the rubber bearings, which exhibits a non-linear development from the very beginning to a vertical shear force of 200 kN. Stage 2 represents a state where the shear resistance provided by the steel shear keys is gradually enhanced and finally occupies the main shear capacity of the joint, which exhibits a quasi-linear development from the vertical shear force of 200 to 450 kN. From the vertical shear force of 450 to 660 kN, stage 3 is a state where the shear resistance of the joint is  
Figure 12: Shear behaviors of the joint subjected to bidirectional shear load. (**a**) Shear force–displacement curves; (**b**) shear stiffness–shear force curves.  
mainly provided by the steel shear keys. From the vertical shear force of 660 to 690 kN, stage 4 is a short plastic failure state, which means that the joint reaches the yield state until the destruction state is quickly reached. After the vertical shear force reaches 690 kN, the shear force suddenly drops to 475 kN, and the jacks fail to apply larger vertical shear load. Hence, this situation is defined as the destruction state of the joint.  
It can be obtained from Figure 13 that the ultimate shear capacity of the joint is 690 kN, and the final vertical shear displacement is 21.3 mm. In the design, the shear capacity is assumed to be the sum of the shear capacities of all steel shear keys. However, the ultimate shear capacity of the joint (690 kN) obtained in this test is smaller than the sum of those of all steel shear keys (880 kN). Note that the obtained ultimate shear capacity of the joint is contributed by both the steel shear keys and the GINA rubber gasket. Hence, a conclusion can be made that not all the shear keys carry the shear force simultaneously, which is consistent with the conclusion reported in the literature [17]. Because of the deviations in installation and location, the steel shear keys in different sets do not contact each other simultaneously. Moreover, although the shear performance of the GINA rubber gasket can not be obtained in this test, the contribution of the gasket to the shear stiffness and the shear capacity of the joint should not be ignored.
# Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
## 1 Introduction
### Sleep/Wake Up Techniques  
Switching off (sleep mode) the non-active transceivers have become the ultimate approach towards the realization of EE in Information and communications technology, due to the fact that it can save large amount of energy. The philosophy behind the proposed approach is to exploit dense and redundant deployment of sensor nodes, leading to a small coverage area. The sensor nodes' off/on switching approach is more desirable for improving WSN EE and prolonging the battery lifetime of the wireless sensors. However, the coverage issue should be considered, and it should be guaranteed by the remaining active nodes. [PERSON] et al. [27] proposed a subset solution in which nodes with minimum overlap areas are activated and must be capable of reducing network energy. Meanwhile, [PERSON] et al. [28] modelled the EE issue as a subset selection problem of active connected sensors for correlated data payload gathering. Using spatial correlation, the sensor information of non-active sensor nodes can be obtained from those of active nodes, which makes it a good strategy. Equation (1) gives the average power consumption as the sleep power multiplied by the percentage of duration the system is in sleep mode plus the active power multiplied by the percentage of duration the system is in active mode all divided by 100. In a situation in which the system is designed to have bigger sleep energy comparable to the active energy, it is then feasible to engage power reduction strategy by tuning the sensor node to its lowest power mode. There are two scenarios in which the active power term can be larger than the sleep power term either (i) the power ratio per event is large or (ii) active power events have higher frequency.  
\[P_{\text{avg}}=\frac{(P_{\text{sleep}}\times\text{\%time\ asleep})+(P_{\text{ active}}\times\text{\%time\ active})}{100} \tag{1}\]  
On the other hand, one can exploit the duty cycling schemes to make a sensor node switched on/off based on network activity (traffic conditions). Duty cycling schemes can be classified into three categories: on-demand, asynchronous, and scheduled rendezvous. Meanwhile, duty cycle-based protocols are certainly the most EE [29; 30]. However, it should be taken into account that the low duty cycle has the capability to conserve a large big volume of energy but can lead to high communication delays. To reduce the delay, the protocol parameters can be tuned before deployment for ease, although it may result in inflexibility, or dynamical settings can be deployed to reflect the instantaneous traffic conditions. Moreover, the active period of nodes in order to optimise power consumption is a function of the traffic load, buffer overflows, delay requirements or harvested energy are discussed in [31].
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 2 Research and Applications of RO in NWP  
This section reviews studies that applied RO to NWP. Section 2.1 details the progress made using RO in NWP, including the first assimilation of RO data in the assimilation systems of different institutes, and the sensitivities of the RO quality to height and region. Section 2.2 introduces a simulation study that focuses on the impact of the amount of RO data. Moreover, the total amount of RO data expected in the future is estimated.
# Thin Film Encapsulation for RF MEMS in 5G and Modern Telecommunication Systems
## 2 Outlook of 5G and RF Passives Requirements
In addition to these aspects, MEMS devices in general need to be encapsulated and protected. In fact, microsystems need a package due to their fragility and exposure to harmful environmental factors, including mechanical shocks, contamination, presence of dust particles, moisture, etc. [37]. When dealing with RF MEMS, additional aspects to be carefully tackled come into the equation. The application of a protective cap to RF passives means introducing various parasitic effects that can detune the electromagnetic performance of the devices and cause additional loss [38]. More in detail, applying a protective package to RF MEMS means building a housing for the device with a physical part (i.e., a dome) in the vicinity of the RF passive itself. Such a dome, therefore, will interact with the electromagnetic field around the device, thus introducing additional and unwanted capacitive couplings. Moreover, very often the packaging solution requires managing the redistribution of the electrical signals from the RF MEMS device to the external world. This can be done, for instance, by means of through-silicon vertical vias filled with conductive materials, or by means of signal underpasses [39; 40]. Regardless of the specific signal redistribution solution, additional resistive losses, discontinuities of the RF paths, series inductive, and shunt capacitive contributions are added, thus jeopardizing the characteristics of the initial RF MEMS device. This is the reason why the electromagnetic design of the package should be optimized with at least the same care spent for optimizing RF MEMS devices themselves [41; 42]. The packaging strategy proposed in this work and discussed in the following pages, relies on the thin film capping approach. This means that no additional paths for the RF signals are needed. However, the interaction of the capping dome can still represent a problem. This is the reason why electromagnetic simulations will be reported and discussed in the following sections. Our investigations at this stage refer to simple CPWs. This is because of a two-fold motivation. On one side, CPWs are easier and faster to fabricate as test structures for the subsequent application of the protective package. On the other hand, RF MEMS are always framed within CPW structures. Therefore, apart from switching and reconfigurability functions, the electromagnetic behavior of RF MEMS is very similar to that of standard CPWs.
# Review on Mechanisms and Kinetics for Supercritical Water Oxidation Processes
## 3 Kinetics Models and Their Development
### Empirical Model
#### 3.1.2 Multiple Nonlinear Regression Methods
With the maximum H\({}_{2}\) yield and the minimum yield of tar and char as constraints, the optimal operating parameters were 550 \({}^{\circ}\)C, 24.99 MPa, 851.28 s, and OC = 0.31. [PERSON] et al. [96] applied RSM combined with central composite design and regression analysis to optimize the operating parameters for the oxidation process of Lurgi coal-gasification wastewater in SCW. With TOC and NH3-N degradation rate as response variables and temperature, OR, pressure and residence time as explanatory variables, a regression model was then established, which showed the order for the effect degree of the parameters on the TOC and the NH\({}_{3}\)-N degradation rate: temperature \(>\) oxygen ratio \(>\) reaction time \(>\) pressure. [PERSON] et al. [97] studied the supercritical water oxidation of CI Reactive Orange 7 dye wastewaterusing RSM. The results showed that the influence of temperature, oxidant coefficient and reaction time was relatively sensitive in comparison with the pH value.
## 2 Experimental Section
### Test Methods
#### 2.2.2 Quantitative Phase Analysis and Atomic Site Occupancy Analysis  
Rietveld refinement was employed by TOPAS 4.2 software for quantitative analysis and atomic site occupancy analysis. The Rietveld refinement strategy consisted of emission profile, background, instrument factors and zero error.  
In the Rietveld refinement process, \(R_{\mathrm{wp}}\) (radiation work permit) was used to evaluate the fitting results as shown in Equation (1). Generally, when \(R_{\mathrm{wp}}\) is less than 15%, the result could be considered as reliable [16].  
\[R_{\mathrm{wp}}=\sum\{W_{i}[(\mathcal{V}_{i}(\mathrm{obs})-\mathcal{V}_{i}( \mathrm{cal}))^{2}]/\sum w_{i}\;[\mathcal{V}_{i}(\mathrm{obs})^{2}]\}^{1/2} \tag{1}\]  
where, \(R_{\mathrm{wp}}\) indicates radiation work permit; \(\mathcal{V}_{i}(\mathrm{obs})\) is the measured diffraction strength at the point 20\({}_{i}\) in the diffraction pattern; \(\mathcal{V}_{i}(\mathrm{cal})\) presents the calculated diffraction strength at the point 20\({}_{i}\) in the diffraction pattern and \(w_{i}\) is weighting factor.  
Lattice parameters, atomic coordinates, atomic site occupancy and temperature factors were considered in the refinement process.  
Replacement rate (\(P\)) of Ba ions was introduced as Equation (2).  
\[P=\left[\frac{\mathrm{Ba}_{\mathrm{c}}}{\mathrm{Ba}_{\mathrm{c}}+\mathrm{Ca}_{ \mathrm{c}}}\right]\times W_{c}+\left[\frac{\mathrm{Ba}_{\mathrm{o}}}{ \mathrm{Ba}_{\mathrm{o}}+\mathrm{Ca}_{\mathrm{o}}}\right]\times W_{o} \tag{2}\]  
where, \(P\) presents the replacement rate of Ba ions; \(\mathrm{Ba}\) and \(\mathrm{Ca}_{\mathrm{c}}\) are the occupancy rate of Ba ions and Ca ions in cubic yeelimite; \(\mathrm{Ba}_{\mathrm{o}}\) and \(\mathrm{Ca}_{\mathrm{o}}\) are the occupancy rate of Ba ions and Ca ions in orthorhombic yeelimite; \(W_{\mathrm{c}}\) and \(W_{\mathrm{o}}\) are the percentages of cubic yeelimite and orthorhombic yeelimite, respectively.  
The replacement rate \(R\) (%) is defined as Equation (3).  
\[R=(P\times 4)\times 100\% \tag{3}\]  
where, \(R\) presents the replacement rate of Ba ion; \(P\) is the ionic site occupancy of Ba ion; 4 and 1 present the total cation and the number of Ba ion in theoretical \(\mathrm{Ca}\mathrm{Ba}\mathrm{Al}_{6}\mathrm{SO}_{16}\).
## 5 Parametric Studies
### Slope Angle and Flow Depth  
To explore the influences of the flow depth \(H\) and slope angle \(\theta\) on the transformation rate \(E\)[59], more cases were simulated with a slope angle \(\theta\) of \(1^{\circ}\) and flow depths \(H\) of 0.3 m and 0.8 m, respectively. In each of these cases, different values of the yield strength of the slurry, including 7 Pa, 20 Pa, 95 Pa, 290 Pa and 750 Pa, are considered. The simulation results show that the transformation process of the debris flow is slightly affected by the flow depth and the slope angle. The transformation rates at the initial moments are shown in Table 1. With all the data collected from these cases, Figures 11 and 12 were re-drawn and the equations were updated as follows:  
\[\tau_{0}=5.84\ \times\ 10^{-5}\mathrm{s}_{u0} \tag{15}\]  
Figure 11: Determination of the activation shear stress. (**a**) Transformation at initial moments; (**b**) Best-fit of the activation shear stress.  
Figure 12: Relationship between the erosion rate and the flow velocity.  
\[E=2.26\bigg{(}\frac{\tau-\tau_{0}}{\tau_{0}}\bigg{)}^{0.22} \tag{16}\]  
Equations (15) and (16) were utilized to evaluate the rates of transformation of debris flows into turbidity currents.
# Superpixel-Based Singular Spectrum Analysis for Effective Spatial-Spectral Feature Extraction
## 3 Results and Discussion
### Experimental Result and Analysis
#### 3.4.3 Results from the Salinas Dataset  
The visual classification maps and quantitative results obtained by various classifiers on the Salinas dataset are shown in Figure 13 and Table 4, respectively. In the table, the best results are shown in bold. Based on the visual quality as well as objective metrics, it can be observed that the proposed SP-SSA method outperformed other competing approaches. In addition, compared with the 2D-SSA method that globally reconstructs the image using fixed-size embedded windows, the SP-SSA method considers the local spatial information by reconstructing each superpixel individually, which helps in further reducing the disturbances and improving the class assignment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Class** & **Samples** & **SVM** & **EPF** & **SCMK** & **R2 MK** & **ASMGSKK** & **MsuperPCA** & **2 DSSA** & **2 DMSSA** & **SP-SSA** \\ \hline \hline \multirow{2}{*}{Brocoli-green-weeds-1} & 2009 & 99.74 & 98.43 & 99.44 & 99.85 & 99.36 & 99.39 & 98.83 & **100** & 99.64 \\  & 3726 & **100** & 99.92 & **100** & 98.62 & **100** & 99.82 & **100** & **100** & 99.96 \\  & 3976 & 93.34 & 99.31 & 99.9 & **100** & 99.71 & 99.89 & 99.64 & 99.64 & **100** \\  & 394 & 96.37 & 97.28 & 98.52 & 98 & 98.26 & 98.56 & 97.38 & 97.54 & **98.67** \\  & 3678 & 91.19 & 99.06 & 97.77 & 99.42 & 98.72 & 99.75 & 98.42 & 99.25 & **99.63** \\  & 3959 & **100** & 99.92 & 98.54 & 99.92 & **100** & **100** & **100** & **100** & **100** \\  & 3579 & 99.18 & 99.65 & 99.14 & 99.54 & 99.8 & 99.78 & **100** & 99.88 & 99.96 \\  & 31,271 & 91.38 & 92.49 & 94.6 & 95.2 & 96.77 & 95.76 & 95 & 96.31 & **97.91** \\  & 6203 & 97.34 & 99.88 & 99.65 & 99.98 & 99.82 & 99.96 & 99.66 & 99.95 & 100 \\  & 3278 & 93.96 & 97.37 & **98.96** & 97.42 & 98.47 & 98.24 & 96.73 & 98.3 & 98.82 \\  & 1068 & 81.66 & 96.55 & 96.81 & 97.78 & 97.99 & 97.92 & 97.24 & 97.86 & **99.87** \\  & 1927 & 98.03 & **100** & **100** & **100** & **100** & **100** & **100** & **100** & **100** \\  & 916 & 98.85 & 97.01 & 97.41 & 99.55 & 99.69 & **99.76** & 99.74 & 99.69 & 99.69 \\  & 1070 & 92.92 & 98.23 & 99.13 & 98.17 & 99.07 & 98.86 & **99.78** & 99.47 & 99.73 \\  & 7268 & 82.69 & 90.41 & 91.32 & 92.28 & 95.89 & 92.98 & 94.21 & 95.58 & **97.7** \\  & 1807 & 95.63 & 99.13 & 90.3 & 99.83 & 99.6 & 99.82 & 98.57 & 99.29 & **99.92** \\ \hline  & 93.64 & 96.56 & 96.82 & 97.5 & 98.43 & 97.89 & 97.63 & 98.33 & **99.1** \\  & **AAz** & & 94.52 & 97.79 & 97.59 & 98.47 & 98.95 & 98.78 & 98.45 & 98.92 & **99.47** \\  & 6x & 92.91 & 96.17 & 96.46 & 97.21 & 98.25 & 97.65 & 97.36 & 98.14 & 99 \\ \hline \end{tabular}
\end{table}
Table 4: Classification Results for the Salinas Dataset with 1% training for SVM, EPF, SCMK, R2 MK, ASMGSK, MsuperPCA, 2D-SSA, 2D-MSSA, and SP-SSA algorithms  
Figure 12: (**a**) Ground Truth Image, Classification Maps of (**b**) SVM (**c**) EPF (**d**) SCMK (**e**) R2 MK (**f**) ASMGSK (**g**) MsuperPCA (**h**) 2D-SSA (**i**) 2D-MSSA (**j**) SP-SSA for the Indian Pines dataset.