esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Procedures
### Real-Time PCR (qPCR)  
Six mock infected and six HCV infected Huh 7.5 culture specimens were used for further analysis of differentially expressed genes at each time point. Total RNA from infected and control hepatocytes were extracted using TRIzol. First-strand cDNA was synthesized using Moloney Murine Leukemia Virus reverse transcriptase (SuperScript III; Invitrogen) with 20 ng/mL of RNA at 55 \({}^{\circ}\)C (60 min) with random hexamer primers. Each qPCR reaction was carried out in a 384-well optical plate (Roche Applied Science) in a 10 \(\upmu\)L reaction buffer containing LightCycler 480 Probes Master Mix (100 mM Tris-HCl, 100 mM KCl, 400 mM of each dNTP (with dUTP instead of dTTP), 64 mM MgCl\({}_{2}\), FastStart Taq DNA Polymerase, 0.3 mM of each primer, 0.1 mM hydrolysis probe and approximately 50 ng of cDNA (done in triplicate)). Triplicate incubations without template were used as negative controls. Thermal cycling was done in a Roche LightCycler 480 System (Roche Applied Science). The qPCR thermo cycling was 95 \({}^{\circ}\)C for 5 min, 45 cycles at 95 \({}^{\circ}\)C for 10 s, 59 \({}^{\circ}\)C for 30 s and 72 \({}^{\circ}\)C for 1 second. The relative quantity of each RNA transcript was calculated with the comparative Ct (cycling threshold) method using the formula 2\(\Delta\)Ct. \(\Delta\)Ct represents the difference between target gene expression in mock-infected samples and target gene expression in HCV-infected samples. Reference genes (GAPDH and \(\beta\)-actin, ACTB) were used as controls and statistical significance was evaluated using the Student's t-test.
## 4 Selection of HSS Model Parameters
The value of \(\psi\) can be set according to [PERSON]'s [43] recommendation: for sandy soil, \(\psi=\varphi^{\prime}-30^{\circ}\), and when \(\varphi^{\prime}<30^{\circ}\), \(\psi\) is set to 0; for clayey soil, \(\psi\) is set to 0. Based on soil parameters in Shanghai, when the void ratio e is less than 1.0, \(R_{\rm f}\) is set to 0.95. However, [PERSON] [24] suggests that for loess, \(R_{\rm f}\) should be between 0.8 and 0.9. This article, based on the results of tests, set \(R_{\rm f}\) to 0.85. When there are no experimental data available, \(v_{\rm ur}\) can be set according to the recommended value in the PLAXIS (version, 20.1.0.98) manual, which is 0.2. \(\gamma_{0.7}\) can be set in the range \(1\times 10^{-4}\) to \(4\times 10^{-4}\)[44; 45]. This article used the value \(2\times 10^{-4}\).
## 4 Discussion
### Component Analysis  
Section 4.1 has demonstrated the efficacy of the DSTF-GAN fusion method compared with two other leading single-stage text fusion approaches. To gain deeper insight into the conceptual sketch generation process, we conducted ablation studies to assess the individual effectiveness of each component within the DSTF-GAN, namely the SF block, TF block, and the order of these two blocks. Three variations of DSTF were created: \"DSTF  
Figure 15: Examples of high-rise sketches synthesized by different text fusion methods.  
w/o SF block\", \"DSTF w/o TF block\", and \"DTSF\". These variations indicated the removal of the SF Block, the removal of the TF Block, and the swapping of the two blocks.  
To comprehensively analyze each component, was employed an additional metric, the LPIPS metric [38], in addition to the FID and CM precision. LPIPS leverages pre-trained networks to assess the diversity of the generated sketches. This was accomplished by quantifying the average feature distance between the feature maps of the synthetic images. A higher LPIPS value signifies a greater dissimilarity between pairs of synthetic sketches, indicating higher diversity. The calculation methods for the FID and CM precision follow the same procedure as described in Section 4.1. The comparative results of the ablation studies are listed in Table 2.  
Effect of the SF block. Compared with DSTF w/o SF block, DSTF enhanced the LPIPS value from 0.0588 to 0.2200, indicating that the SF block effectively increased the diversity of sketches generated from the same description by introducing sparsity to the feature maps. Furthermore, the incorporation of sparsity during the fusion process improved the CM precision from 30.83% to 36.08%. This demonstrated that integrating sparsity into the fusion process enhances the semantic consistency between the text and the generated sketches.  
Effect of the TF block. The TF block improved the CM precision from 4.31% to 36.08% compared with DSTF w/o TF block. Additionally, the TF block contributed to a decrease in the FID from 32.43 to 25.73, indicating an improvement in the overall quality of the generated sketches. The TF block achieved this by incorporating channel- and position-level adjustments based on text semantics, effectively enhancing the fusion process and generating more accurate and visually appealing sketches.  
Swapping the order of the two blocks. A comparison between DSTF and DTSF revealed that the selected order of the two blocks had a notable impact on the performance of the model. DSTF achieved an improved CM precision of 36.08% compared with 32.01% for DTSF. Additionally, DSTF exhibits a decreased FID of 25.73 in contrast to 29.43 for DTSF. These results indicated that the order of combining sparsity and fusing text semantics in the sketch generation process plays a crucial role in generating high-rise building sketches that are more semantically consistent.
## 6 Verification and Results
### Episode 2: Resilience and Availability (req: v, vii)
#### 6.2.3 Key Distribution  
Due to the lack of a direct data plane communication between two SFs, we developed a new key exchange mechanism by using centralised key distribution (req: v). Our previous work [7] has shown that SD-IKE with a centralised key distribution and Authentication centre (AuC) makes key distribution more efficient in non-NFV environments. The SFC alternations in episode 1 and the SFC failover protection in SFC episode 2 (Section 6.2.1) provides a Proof-of-Concept demonstration for integrating this concept in NFV.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 3 Theoretical Analysis of Hydro-Pneumatic Spring
### Mathematical Derivation of Hydro-Pneumatic Spring
#### 3.2.5 Mathematical Model of Hydro-Pneumatic Spring  
To simplify the mathematical model of the hydro-pneumatic spring, the following assumptions are made: (1) The effective sealing of all parts of the hydro-pneumatic spring is assumed, with no leakage in any components or connecting pipelines. (2) The hydraulic fluid inside the hydro-pneumatic spring is considered to be pure fluid, neglecting its compressibility. (3) Pressure losses along the length and local pressure losses in all components of the hydro-pneumatic spring are neglected. (4) The impact of temperature changes on the hydro-pneumatic spring during operation is neglected. (5) Owing to the excellent lubricating effect of the hydraulic fluid in the cylinder, frictional forces are extremely small, and their impact on the output force of the hydro-pneumatic spring is neglected.  
According to the structure of the hydro-pneumatic spring, with the piston inner diameter denoted as \(D\), the accumulator inner diameter denoted as \(d\), the equivalent area of the piston denoted as \(S_{p}\), the equivalent area of the accumulator denoted as \(S_{g}\), and the initial height of the gas in the accumulator denoted as \(h_{c}\) when the piston rod is displaced by \(x\), the volume changes in the hydraulic cylinder and accumulator are equal. In this case, the floating piston displacement can be expressed as  
\[\Delta x=\frac{D^{2}}{d^{2}}x. \tag{9}\]  
From Equation (6), the following can be obtained:  
\[P=P_{gc}\frac{1}{(1-\frac{D^{2}x}{d^{2}h_{c}})^{\gamma}}. \tag{10}\]  
From this, the elastic force \(F_{k}\) of the hydro-pneumatic spring can be expressed as  
\[F_{k}=P\cdot S_{g}=\frac{P_{gc}\cdot S_{g}}{(1-\frac{D^{2}x}{d^{2}h_{c}})^{ \gamma}}. \tag{11}\]  
By taking the derivative of the elastic force \(F_{k}\), the stiffness coefficient \(K\) of the hydro-pneumatic spring can be expressed as  
\[K=\frac{dF_{k}}{dx}=\frac{D^{2}\cdot P_{gc}\cdot S_{g}\cdot\gamma}{d^{2}h_{c }\cdot(1-\frac{D^{2}x}{d^{2}h_{c}})^{\gamma+1}}. \tag{12}\]  
The damping force of the hydro-pneumatic spring generated by the hydraulic fluid passing through the constant-flow damping holes and the check damping holes in the damping valve system can be expressed as  
\[F_{c}=\frac{1}{2}\rho S_{p}\left[\frac{\dot{x}S_{p}}{C_{d}\cdot(S_{c}+S_{d} \frac{1+sign(\dot{x})}{2})}\right]^{2}sign(\dot{x}). \tag{13}\]Thus, the damping of the hydro-pneumatic spring can be expressed as  
\[C=\frac{dF_{c}}{dx}=\frac{S_{c}^{3}\dot{\rho}\dot{x}}{C_{d}^{2}\cdot\Big{(}S_{c}+S _{d}\frac{1+sign(\dot{x})}{2}\Big{)}^{2}}sign(\dot{x}). \tag{14}\]  
The output force of the hydro-pneumatic spring, which is the resultant force of the elastic force and the damping force, can be expressed as  
\[F=F_{k}+F_{c}. \tag{15}\]
# A New Concept of Reconfigurable Antenna Structure Based on an Array of RF-MEMS Switches
## 3 Development of a 7 \(\times\) 7 Reconfigurable Matrix Structure Prototype  
As a proof of concept in this section, a 7 \(\times\) 7 reconfigurable matrix structure is described, designed, fabricated, and numerically and experimentally assessed. As stated above, one of the main prototype keys of force is its monolithic structure. All the components are made with a single technology and in a single chip. This section is divided into two subsections: the first focuses on the MEMS switch, and the second on the whole matrix structure providing fabrication details.
_Article_  
Study on a Correlation-Based Anti-Islanding Method  
under Wider Frequency Trip Settings for  
Distributed Generation  
[PERSON](r)  
Kongju National University, Gongju-si 330-717, Korea; [EMAIL_ADDRESS]; Tel.: +82-41-521-9162  
Received: 29 April 2020; Accepted: 22 May 2020; Published: 24 May 2020  
**Featured Application: Grid Connected Inverter.**  
**Abstract: Islanding phenomenon of distributed generation (DG), such as photovoltaic (PV) generation, is undesirable because it causes safety issues for utility service personnel and power system equipment. Many anti-islanding methods have been studied since DG appeared in electric power systems (EPSs). Most anti-islanding methods focus on disconnecting DG from the grid using functionality to detect islanding under narrow frequency trip settings, because safety issues have a higher priority. However, as DG plays a key part of an EPS, a significant loss of DG due to a short disturbance could result in a reliability issue for the EPS. Corresponding to this matter, new international standards, such as IEEE standard 1547-2018, require more sophisticated and complex functionalities for grid-connected DGs by adopting ride-through technologies and wider voltage/frequency trip settings. Since most anti-islanding functions of inverter-based DG have been based on the frequency of the inverter voltage, it is more difficult to detect islanding under wider frequency trip settings. This paper presents a correlation-based anti-islanding method (AIM) without depending on the frequency trip of inverter-based DGs. Simulation results are provided to verify the performance of the correlation-based anti-islanding method. As a result, the proposed method detects islanding at 0.116 s under wider frequency trip setting by the IEEE Std. 1547-2018 test condition, while the popular active frequency drift method with positive feedback does not detect islanding using the same current disturbance.**  
**Keywords: anti-islanding; distributed generation; grid-connection; photovoltaic generation; frequency trip setting**
## 2 Analysis of Entropy Calculation for Special Terrains in Small Windows
Therefore, the calculation of the terrain entropy for (d) is as follows:  
\[H=-\frac{3}{9}\text{log}\frac{3}{9}-\frac{3}{9}\text{log}\frac{3}{9}-\frac{3}{9 }\text{log}\frac{3}{9}=0.918(bit) \tag{4}\]  
By calculating terrain entropy using elevation probabilities, it is evident that both terrain (b) and terrain (d) yield the same entropy value. Although the elevation values at different locations in the DEMs have changed, the number and types of distinct elevation values remain the same, resulting in identical terrain entropy values. Similarly, (e) also produces the same entropy value as (b,d). Since the calculation of slope entropy requires the computation of a slope value using a 3 \(\times\) 3 grid window, a 3 \(\times\) 3 slope window was first calculated, based on which the slope terrain entropy was then calculated. The results of the slope calculation are shown in Figure 3.  
From the slope calculation results, it can be observed that both terrains (a,b) have only one slope value, and when substituted into the formula, the terrain entropy values are calculated as 0. In terrains (c,d), the slope calculations result in two distinct slope values, with one slope occurring in six grids and the other in three grids. As a result, the calculated terrain entropy values are identical. The underlying reason for this is the same as the  
Figure 2: DEM examples of five special terrain types in a small window: (a) flat terrain, (b) single slope, (c) symmetrical slope, (d) asymmetrical slope, and (e) complex terrain. example of elevation probability terrain entropy mentioned earlier and thus will not be repeated here. The terrain entropy calculation results of the four methods for the above-mentioned five special terrain types are shown in Table 1. It is evident that the four common methods fail to accurately calculate the terrain entropy of these five special terrains. Slope entropy is the most widely used method, but it still achieved erroneous results, such as identical terrain entropy values between the flat terrain and the simple slope, and between symmetric and asymmetric slopes. Therefore, an improved method is needed to effectively calculate the terrain entropy of special terrains in small windows.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 10 Multefire  
MulteFire is an LTE-based technology built on elements of the 3 GPP Release 13 and 3 GPP Release 14 specifications for LAA and Enhanced LAA (eLAA). MulteFire operates in standalone mode within the unlicensed spectrum. The main advantage of MulteFire is that it allows anyone to deploy and operate their private network, which can revolutionize the industrial Internet-of-things (IoT). In MultiFire 1.1 release [104], it has been mentioned that it is designed to coexist efficiently with other technologies like Wi-Fi, Licensed Assisted Access (LAA), and Listen-Before-Talk (LBT). In [105; 106], an overview of coexistence has been discussed for wireless technology, commercial medical devices, and performance evaluation of a standalone LTE network in an unlicensed band that has been tested in [107]. The Markov-Chain model for the LBT mechanism of LTE-LAA according to the 3 GPP release 13 and 14 that has been proposed in [108] applies to MulteFire. However, Multefire supports new bands such as 1.9 GHz for Japan and other regional band ranges in 800-900 MHz. Moreover, MulteFire has introduced two key features such as an Autonomous UE Mobility (AUM) and Self-Organizing Networks (SON), in order to complement the typical eNB controlled handover procedure and the network optimization of stand-alone networks operating in unlicensed spectrum and networks deployed with the Neutral Host Network (NHN) architecture, respectively. Additionally, MultiFire focuses on IoT and Expanded IoT services to fulfill their specific requirements for performance, security, latency, and mobility with low power wide area support. In [109; 110; 111], a performance comparison of MulteFire with previous technologies has been presented with numerical analysis and system-level simulations. The results have shown that MulteFire can double the capacity and provide more than twice the coverage of Wi-Fi. However, resource allocation for standalone operation of cellular technologies like MulteFire needs more attention from the research community. In [85], a time and frequency division algorithm has been proposed to eliminate the control signaling collisions in standalone LTE-U systems. Next, we discuss the existing implementation/field trial results for cellular operation in unlicensed spectrum.
## 5 Experiments and Results
### Implement Details of Instance Level Data Augmentation  
In order to make the description of the algorithm more intuitive, the object that algorithm currently operates on is called the current box/object, and the object around it is called another box/object.  
Since the overlap of objects in street scene images is common, when transforming a single box, it will somehow affect the content of the surrounding boxes. For example, if the current box is transformed, and the overlapped area with another box is too large, it will inevitably damage content in another box. When another box is completely in the current box, its annotations will be invalidated. Of course, we can simultaneously transform another box, but this will make programming  
Figure 3: Examples of the results of instance level data augmentation before rotation. Green lines indicate annotations after transformation. (**a**) and (**b**) are augmentation examples for bicycles, (**c**) and (**d**) are augmentation examples for persons.  
more complicated. Because of the complex spatial relationship between objects, the results of the augmentation will be affected to some extent. Therefore, it is necessary to quantitatively measure the spatial relationship among objects in algorithm. In this paper, two ratios (IOU1, IOU2) are proposed to take different roles in algorithm.  
Firstly, IOU1 is the ratio of the common part of two boxes to the current box, as seen in Figure 4a. For the current object, if the content of another object occupies a large proportion (IOU1 > 0.4) of its box, deleting the common part will lead to a huge loss of current box content, and, to a certain extent, false positive examples will be introduced. Therefore, we don't delete the common part, as seen in Figure 4b. If IOU1 is less than 0.4, deleting the common part is equivalent to a cropping operation. Secondly, IOU2 is the ratio of the area of the common part to the area of another box, as seen in Figure 4c. For another box, if most of its content is in the current box (IOU2 > 0.35), the transformation of the current box will inevitably lead to serious loss of another box's content. Therefore, the algorithm needs to delete another object, otherwise false positive examples will be introduced into the dataset, as seen in Figure 4d. If the proportion is less than 0.35, it is equivalent to the cropping another object. The setting of relevant parameters was based on experiment experience.
## 2 Materials and Methods
### Evaluation Criteria
#### 2.5.4 PCM Layer Thickness  
PCM layer thickness is considered a particularly important factor for adjusting diurnal temperature variation inside the buildings. Therefore, finding the optimal thickness for the PCM layer inside the external wall is crucial. In this regard, the evaluation process includes investigating the external wall with different PCM layer thicknesses while fixing all other measures to focus on finding the optimal PCM sheet thickness that will achieve the best results for the specific application. However, the PCM products, which were previously specified in Section 2.5.2 come in macro-encapsulated panels that have specific thicknesses. For instance, BioPCM(r) comes in different sizes of macro-encapsulated PCM panels with three available thicknesses of 11.2 mm, 20.8 mm, and 37.1 mm. These panels can even be installed within the wall assembly during the construction phase or can be directly attached to the wall during the retrofitting phase using adhesive or fasteners, depending on the manufacturer's recommendations.  
Figure 16: _Cont._  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**SPECCS**} & \multirow{2}{*}{**PCM TYPE**} & \multirow{2}{*}{**BioPCM®**} & \multirow{2}{*}{**InfiniteR PCM**} & \multirow{2}{*}{**WinCo Enerciel PCM**} \\ \cline{2-2} \cline{4-5}  & & & & \\ \hline Source & & Organics & Inorganics & Eutectic mixture \\ \hline Melting point [\({}^{\circ}\)C] & 21, 23, 25, 27, 29 & 18, 21, 23, 25, 27, 29 & 21, 23, 29 \\ \hline Latent heat of fusion []/g] & 250 & 200 & 175 \\ \hline \multirow{2}{*}{Specific heat []/kg-K]} & Liquid & 1970 & 2000 & 2200 \\ \cline{2-5}  & Solid & 4500 & 3140 & 2500 \\ \hline Thermal conductivity [W/m-K] & Liquid & 0.15 & 0.54 & 0.148 \\ \cline{2-5}  & Solid & 2.5 & 1.09 & 1.8 \\ \hline \multirow{2}{*}{Relative density [kg/m\({}^{3}\)]} & Liquid & 850 & 929 & 832 \\ \cline{2-5}  & Solid & 1400 & 1540 & 1170 \\ \hline Sheet thickness [cm] & & 1, 2.1, 3.7 & 2, 4 & 0.3 \\ \hline \multirow{2}{*}{Containment} & \multirow{2}{*}{Macro-encapsulated} & \multirow{2}{*}{Macro-encapsulated} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \cline{2-5}  & & & \\ \end{tabular}
\end{table}
Table 5: Specifications and thermal properties of the studied PCM types, adapted from [54].
## 4 Results and Discussion  
This section presents the results and discussion on the proposed DR-UAVIoT routing solution. The results are evaluated on different performance parameters. The results showed the performance of the proposed solution as compared with existing solutions. For the design and development phase, we used the most preferable and feasible routing metrics which were extracted from the detailed literature review. The routing decisions are always a challenge for high mobility networks including transportation systems or UAVs based networks [7]. The node's mobility is difficult to handle, especially when using UAV  
Figure 5: RREP Process at UAV-to-UAV.  
networks in traditional simulators such as NS-2.34. Mobility model is used in simulation like SUMO or MOVE to check the mobility of the UAV nodes. These mobility models provide the mobility patterns schemes to facilitate the basic simulation.  
The NS-2.34 is stable and more feasible for UAVs and IoT networks. It has technical capabilities and is equipped with a back-end programming platform. The nodes in UAVs and IoT networks need control stations, mobility, network size, and other important elements for running the protocols. The NS-2 simulation is used to evaluate the performance of the proposed solution in different scenarios. The SUMO is used to provide mobility features and is integrated with simulation. SUMO and MOVE are editor and mobility models usually used to test the mobile nodes' performance. The mobility traces are exported from a SUMO traffic simulator and fed to NS-2.34 for further analysis.  
The cellular network nodes', macro cells', and UAVs' settings are set by using the editor. For UAV nodes' mobility, Random Walk mobility is used to set the altitude up to 200 to 500 Feet. The performance parameters to test the proposed solution are Packet Delivery Ratio (PDR), end-to-end delay, network overhead, and network overhead. The NS-2 simulator uses C and Object-oriented Tool Command Language (OTcl) languages. Table 1 shows the simulation parameters used to test the proposed solutions.