esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments  
### Datasets and Experimental Details  
In this section, we perform experiments on two fine-grained image datasets, Stanford Cars [49] and FGVC-Aircraft [50]. The Stanford Cars dataset consists of 16,185 images in 196 categories. This dataset is split into 8144 training images and 8041 test images. The FGVC-Aircraft dataset consists of 10,000 images. They are divided into 100 categories. This dataset includes 6667 training images and 3333 test images. Figure 4 shows partial examples of these two datasets.  
The algorithms in this paper are implemented on Keras 2.2.4. The backend of this framework is Tensorflow 1.6.0. In addition, the programming language and version is Python 3.5.2 and the graphics card model is a GeForce GTX 1080. The learning rates of the models were 0.1, 0.01, and 0.001, at 0-150 epochs, 150-190 epochs, and 190-230 epochs. This paper uses convolutional networks with residual structures. Network structures, such as ResNet-50, are not used, mainly because their parameters are too large. This will result in some improved models, based on mainstream residual networks which are difficult to complete in existing experimental environments. The two-level attention and group attention residual network we propose can achieve more accurate feature learning, such that a large number of feature maps are not needed before feature classification. The parameters of Res-CNN and its various improved models are shown in Table 1.  
Res-CNN is the residual network model we defined. Table 1 briefly describes the convolution parameters of several networks based on Res-CNN. Res-CNN consists of six convolutional blocks, a global average pooling layer, and a dense layer. There are three convolution operations in one residual block, in each square bracket. The three parameters of the convolution kernel are the number  
Figure 4: Examples from Stanford Cars dataset (left), FGVC-Aircraft dataset (right).  
of feature channels after convolution, the sizes of the convolution kernels, and the strides. \(Conv1\) first convolves the input images through three different sizes of convolution kernels to provide feature maps of different fields of view. The upper-half convolution kernels have strides of 3 or 2, to reduce the size of the feature maps. The residual blocks, enclosed in blue square brackets in Table 1, need to be repeated 3 times. TA-CNN is a TA-block module added after each convolution block. GA-CNN is a group attention residual network, which adds a GA-block after \(Conv5\) and replaces \(Conv6\) with a GC-block. TGA-CNN is a residual network that introduces both TA-model and GA-model, where the GA-model consists of GA-block and GC-block. \(MFF\) refers to a multi-level feature fusion method.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 2 System Model  
The scenario shown in Figure 1, where the AP has a cylindrical array with \(N=N_{c}\times N_{v}\) antennas in total. The UEs are uniformly distributed following a homogeneous PPP with density \(\lambda\) that denotes the mean number of active UEs per square meter. The spatial channel  
Figure 1: Configuration of a \(N_{c}\times N_{v}\) uniform cylindrical array (UcylA) with line-of-sight (LOS) and non-line-of-sight (NLOS) links: \((\phi_{i,\ell}\theta_{i,\ell})\) are the azimuth and elevation angels; \(h\) is the height of the array; and the pointing directions are toward all the LOS (\(\ell=1\)) and NLOS (\(\ell=2,3,\cdots\)) links arriving from the user equipment (UE) over a propagation path \(R_{i,\ell}\).  
of the mmW and 6G sub-THz systems are purely directional (see, e.g., [37]), and the LOS (or NLOS) link is affected by the path-loss modeled in terms of UE-AP distance \(d_{LOS}\) (or \(d_{NLOS}\)), and faded amplitude \(\beta_{LOS}\) (or \(\beta_{NLOS}\)). The propagation attenuation model for LOS and NLOS is \(\beta/d^{b}\) with amplitude path-loss \(b\geq 1\). The array of antennas is uniformly cylindrical, the isotropic radiating antennas are arranged into a set of \(N_{v}\) UCAs with \(N_{c}\) antennas each, and antenna spacing is half the wavelength; that is, the two arrangements of antennas are such that the corresponding beamforming of UCAs reduce the interference angularly, and the vertical arrangement of the rings (acting as vertical ULAs), tilts the beam to improve the capability to reduce the near interferers when pointing toward far-away UEs. Note that here the UEs are considered on the ground, which means that the AP height is the height difference of UEs and AP (pseudo 3D or 2.5D geometry).
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Application Verifications  
After testing the network performance of a SUT, this experiment deploys vIMS as a cloud application to show the improvement of a virtual switch. Subsequently, the SIP stress is installed to benchmark the successful call rate (SCR) of the vIMS with different call rates. A higher successful call rate means better performance.  
In this experiment, each component of vIMS has only 1 vCPU. As shown in Figure 19, the successful call rate declines along with the increment in the call rate. When the call rate is lower than 100, the workload is affordable for both OVS and OVS-DPDK. Therefore, the successful call rates are higher than 95%. In addition, the successful call rate is significantly improved from 72% to 86% by OVS-DPDK when the call rate is 120. That is because the components of vIMS with OVS-DPDK are enhanced by DPDK, CPU pinning, and hugepages. These technologies improve both networking and computing. Therefore, the successful call rate with OVS-DPDK performs better than OVS.  
When the call rate is higher than 120, the missing call rate of OVS ranges from 27% to 48%. The reason is that interactive video applications tend to use smaller packets [47]. When many small packets are transmitted to the application, the workload of packet processing is increased. Therefore, the missing call rate also increases when the virtual switch cannot afford the loading. While applying OVS-DPDK, the missing call rate is reduced by up to 14%. The next experiment further improves the performance of vIMS with OVS-DPDK in different VM sizes. Previous experiments illustrate that the VM size brings impacts on the performance of VNF. Therefore, this experiment increases the number of CPU cores for each component in vIMS. As shown in Figure 20, the vIMS performance is improved if the VM size is increased to 2 CPU cores. The successful call rate increases from 61% to 79% when the call rate is 140, which means that the enhancement is 18%.  
Figure 19: Successful call rate of vIMS with OVS and OVS-DPDK.  
Figure 20: Successful call rate of vIMS with different VM sizes using OVS-DPDK.
# Thermal Effusivity Tester (TET)--A New Device to Determine Thermal Effusivity of Textiles
## 2 Experimental
As the mass of the specimen is known, the mean specific heat capacity (\(c_{p}\)) can be calculated using Equation (2). \[c_{p}=\frac{\Delta Q}{m\cdot\Delta T_{AVG}} \tag{2}\]  
Here, \(\Delta Q\) represents the amount of heat energy absorbed by the material in Joules (J), \(m\) is the mass of the specimen in kilograms (kg) and \(\Delta T_{AVG}\) is the average temperature rise in Kelvin (K). The energy absorbed by the material can be determined by subtracting the lower heat flow from the upper heat flow and integrating this difference for a defined time period. Once the heat flow equilibrium within the specimen is reached, the thermal conductivity is determined using the steady-state heat flow and the temperature difference, following the Fourier conduction equation (Equation (3)). \[q=\lambda\cdot\frac{A}{L}\cdot\Delta T \tag{3}\]  
In this equation, \(q\) represents the heat flow into the fabric in Watts (W), \(\lambda\) is the thermal conductivity in Watts per meter-Kelvin (W/m\(\cdot\)K), \(A\) is the cross-sectional area in square meters (m\({}^{2}\)), \(L\) is the fabric thickness in meters (m) and \(\Delta T\) is the temperature difference. Rearranging the equation leads to Equation (4). \[\lambda=q\cdot\frac{L}{A}\cdot\frac{1}{\Delta T} \tag{4}\]  
According to the current protocol, each measurement is repeated at five different positions on the fabric, whereas five measurements are performed fully automated at each position and the average is calculated. Different measurement repetitions took place on different days to test reproducibility. The LABVIEW v.2016 software is employed for measurement automation, result processing and the user interface (UI). This software allows operation under preset device mechanics and temperature conditions, as well as variations in research conditions. The obtained data is exported as an Excel file for further analysis.
# Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation
## 4 Experimental Results and Analysis  
We acquired the data in three different scenarios:  
1. **Indoor scenario:** A silent environment inside an empty auditorium;
2. **Hall scenario:** A mixed indoor/outdoor environment at the same auditorium hall;
3. **Outdoor scenario:** Outside the auditorium.  
Investigations presented herein is organized as follows. Initially, we perform a time domain analysis of the signals to compare noise characteristics in the different scenarios. A power spectrum analysis is performed to verify the noise power level of each scenario. The impulsiveness is examined in the spectrum as well as its power level compared to signal power. Then, we show the Probability Density Function (PDF) fitting for Gaussian, GMM with two Gaussians and S\(\alpha\)S models. We use the Root Mean Squared Error (RMSE) to measure the quality of the PDF fitting. After that, an investigation about the estimation window length is conducted to assess what is the influence of the number of samples and the windowing strategy to the distribution fitting. Finally, as the acoustic noise could exhibit a non-stationary behavior, we analyze the stationarity of the measured signal to ensure that the fitting is reliable.
## 2 Materials and Methods
### De Novo Assembly and Sequence Annotation  
Data filtering: using BGI's self-developed filtering software SOAPnuke (v1.4.0) [33], reads containing connectors (connector contamination), reads with an unknown base N content greater than 5%, and low-quality reads (defined as bases with a mass value less than 15 that account for more than 20% of the total base number of reads) were removed.  
De novo assembly and quality assessment: The experimental reference genome version is Rhododendron_lapponicum. Since there is no reference genome sequence of _R. chrysanthum_, the filtered high-quality clean reads were de novo assembled (removing PCR repeats to improve assembly efficiency) using Trinity (v2.0.6) [34], and then used CD-HIT (4.6) to cluster and de-redundantly cluster the assembled transcripts to obtain Unigene.  
The quality of the assembled transcripts was assessed via BUSCO, which improved the results of the assembly assessment via comparison with conserved genes, indicating, to some extent, the completeness of the transcriptome assembly.  
Gene Annotation: We annotated the assembled unigenes with seven major functional databases (KEGG, GO, NR, NT, SwissProt, Pfam, and KOG). Utilizing graphical representations, the KEGG database can depict various metabolic pathways and their interconnections. The KEGG Orthology (KO) system provided a cross-species annotation process by linking relevant information from molecular networks into the genome. By contrasting genes across various databases, we gain diverse insights into their structural and functional aspects.
# Computational Investigation of a Novel Box-Wing Aircraft Concept
## 3 Aero-Structural Characterization of CRM Aircraft
### Computational Set-Up
#### 3.2.1 CFD Computational Set-Up  
The problem being symmetric, only one half of the aircraft is considered (cf. Figure 4). The computational domain is defined such that it extends by approximately 64 \(\times\) 15 \(\times\) 16 semispan (\(b/2\)) in the axial, lateral and vertical directions, respectively. This domain is meshed with an unstructured grid, which is generated upon the CAD model of the CRM aircraft. The latter is available via the AIAA DPW repository site ([[https://aiaa-dpw.larc.nasa.gov/Workshop6/DPW6-geom.html](https://aiaa-dpw.larc.nasa.gov/Workshop6/DPW6-geom.html)]([https://aiaa-dpw.larc.nasa.gov/Workshop6/DPW6-geom.html](https://aiaa-dpw.larc.nasa.gov/Workshop6/DPW6-geom.html)), accessed on 24 February 2020).  
\begin{table}
\begin{tabular}{c c} \hline \hline Area (S) & \(\text{S}_{\text{ref}}=383.69\) m\({}^{2}\) \\ Mean Aerodynamic Chord (MAC) & \(c_{\text{ref}}=7\) m \\ Span (\(b\)) & \(b=58.76\) m \\ Aspect Ratio (AR) & 9 \\ Taper Ratio (\(\lambda\)) & 0.275 \\ Sweep (\(\Lambda\)) & \(35^{\circ}\) \\ Dihedral (\(\Gamma\)) & \(0^{\circ}\) (ground) \\  & \(5^{\circ}\) (cruise) \\ \hline \hline \end{tabular}
\end{table}
Table 1: CRM aircraft: Main characteristics of the planform [44, 45].  
Figure 3: Common Research Model (CRM) aircraft in its original wing-body-tail (WBT) configuration (i.e., with horizontal tail only). CAD model (**left**) and small-scale experiment ((**right**), reproduced from [47] with NASA courtesy).  
This grid is composed of polyhedral elements, whose optimal density was determined through a grid convergence study involving meshes of up to 1.8 million cells. The final mesh comprises approximately 600,000 polyhedral elements, 95% of which exhibit a skewness that is deemed to be acceptable (\(\sim\)0.05-0.6). The finer grid region is classically attached to the aircraft skin, being characterized by a maximum y+ of about 2200. Although this value may seem large, it is deemed to be acceptable given the modelling strategy adopted (wall function and very high Reynolds number flows, realizable k-e turbulence model). In turn, such a modest mesh is expected to entail light CPU consumption, thereby enabling the exploration of numerous flight conditions--as intended.
## 9 Stokes Space Polarization and Field Recovery Principle
### Stokes Space Field Recovery
#### 9.2.1 1D Direct Detection-Based Field Recovery  
As previously mentioned, field recovery is of paramount importance for system performance improvement. The complex signal field can be recovered merely from its intensity by modulating it with the SSB. In this regard, the optical carrier, \(C\), is normally inserted along with the complex signal \(S(t)\). Though it can be introduced away from the signal spectrum, to maximize the SE, it is usually located at the edge of the signal spectrum. Based on this, the SSB signal, \(U(t)=S(t)+C\), \(I/Q\) parts are related by the Hilbert transform, \(\mathcal{H}\) as [13]:  
\[U_{Q}(t)=\mathcal{H}[U_{I}(t)] \tag{55}\]  
It is worth noting that the main associated limitation of the transformed signal is that it is challenging for the square law detection to recover a pure \(I\) part of the SSB signal. This is due to the second-order SSBN that contaminates the signal. This can be inhibited through the employment of a high CSPR, as well as several digital SSBN cancellation techniques [13; 40]. Moreover, the relationship between the intensity, \(I(t)=|U(t)|^{2}\), and the phase, \(\varphi(t)\), can be expressed in polar coordinates as [13]:  
\[\varphi(t)=1/2\cdot\mathcal{H}[\log I(t)]. \tag{56}\]  
In addition, the 1D direct detection is also capable of recovering a DSB complex signal when a time domain redundancy is employed. This can be realized by means of the blockwise phase switching scheme discussed in Section 7.3.1. This approach is known as a carrier-assisted dual-polarized (CA-DP) modulation system with the SV-DD. In the CA-DP SV-DD system, two consecutive time domain signal blocks, \(S\) and \(iS\), are forwarded by the blockwise phase switching transmitter [41]. Therefore, the carrier consecutively beats with the signal blocks for the \(I/Q\) parts, respectively, yielding [13; 41]:  
\[I=|S+C|^{2}=|S|^{2}+2\Re(SC^{*})+|C|^{2} \tag{57a}\] \[Q=|iS+C|^{2}=|S|^{2}+2\Im(SC^{*})+|C|^{2}, \tag{57b}\]  
where \(i\) denotes a \(90^{\circ}\) phase shift (switched).  
It is noteworthy that the SSBN cancellation algorithm is still required by the blockwise phase switching receiver for the appropriate removal of the second-order terms. In relation to the SSB-FR, the BPS-FR can realize the same receiver electrical spectrum efficiency, but halves the optical spectrum efficiency as the SSB-FR [13].
## 3 Efficient Superframe Structures with Adaptive Duty Cycle \((Ess_{ADC})\)
### Nodes Selection  
PAN coordinator after computing the new SO and BO, recomputes the number of CFP slots \((GTS^{new})\) for all \(node_{GTS}\) to transmit their data based on the new parameter value of SO. It scrutinizes and allocates GTS to all the \(node_{GTS}\) with their \(GTS^{new}\) by applying \(SJF\), that is, by allowing nodes with less GTS requests to send their data before the other nodes. This helps more nodes to complete their transmission earlier as compared to the standard, resulting in reduced network delay at the cost of minute fairness.  
In this work, similar to the standard, the duration of CAP and CFP depends solely on the value of SO. However, each slot in CFP has been halved to increase its capacity to 14 instead of 7 as mentioned in ESS. This increases the maximum capacity of GTS from 7 to 14 within a superframe structure. Data capacity of each CFP slot \((CFP^{SLOT})\) can be determined by a node from the following equation.  
\[CFP^{SLOT}=960\times 2^{\text{SO}-3}(bits) \tag{15}\]
## 3 Review of Anifrolumab Mechanism of Action and Clinical Trials
### Clinical Trial Data
#### 3.2.1 Early Phase I and Phase II Trials  
Interestingly, ANI, then termed MEDI-546, was first tested in a phase I clinical trial in Systemic Sclerosis (SSC) patients [15]. In this phase I trial, 34 subjects received MEDI-546 in a dose-escalation fashion for 12 weeks. A total of 68.9% of subjects experienced mild adverse events (AEs), and 27.7% experienced moderate AEs. In addition, there were four serious AEs (skin ulcer, osteomyelitisitis, vertigo, and chronic myelogenous leukemia). Only the latter was judged as possibly treatment-related [15].  
Since interferon signaling pathways involved in the pathogenesis of SSc and SLE share similarities [16], MEDI-546, later renamed ANI, was further investigated in a phase IIb trial in non-renal SLE [17]. In this trial, 305 participants with moderate-to-severely active SLE were randomized to receive one of two doses of ANI (300 vs. 1000 mg every four weeks for 48 weeks) or a placebo (PBO). Patients were randomized based on disease activity (SLE disease activity index-2000 [SLEDAI-2K] >10 vs. <10), glucocorticoid (GC) dose (>10 vs. \(\leq\)10 mg/day), and type I interferon gene expression (high vs. low). The SRI4 endpoint was met by more patients treated with ANI (34.3% of 99 patients for 300 mg and 28.8% of 104 for 1000 mg) compared to PBO (17.6% of 102 patients) (Table 1). With these encouraging results, two phase III trials were performed subsequently.
## 2 Numerical Modeling
### Solution Method and Computational Grid  
In the present study, a commercial computational fluid dynamics solver ANSYS CFX is utilized to carry out numerical simulations. It solves 3D viscous Reynolds-averaged Navier-Stokes (RANS) equations in generalized coordinates through the finite volume method. High-resolution second-order central-difference scheme with implicit method is used for the spatial discretization. Investigations [21; 22] revealed the capability of shear-stress transport (SST) k-\(\omega\) turbulence model in capturing complex flow in the turbomachinery. In addition, the studies by [PERSON] et al. [23] and [PERSON] et al. [10] showed that the steady RANS method can effectively and reliably capture the reserved flow caused by the vortex breakdown in the tip-leakage vortex core. Accordingly, the steady RANS method coupled with the SST k-\(\omega\) turbulence model is used in the present study for all simulations.  
The computational grid is generated with ICEM-CFD, where the topology of the grid is based on O-grid and H-grid. H-grid is adopted in the inlet region, outlet region and cascade passage, while O-grid is used surrounding the blade surface. The non-dimensional distance \(y^{+}\) of the first mesh node near the blade wall, blade tip wall, casing wall and hub wall is set to about 1. In the tip clearance, O-grid is combined with unstructured hexahedral mesh, while unstructured hexahedral meshes with a uniform spread are adopted in the rotor passage outlet at the trailing edge region of the suction surface. Figure 2 illustrates the casing mesh in the trailing edge region of the rotor. It is observed that the mesh has good adaptability to ensure the mesh orthogonality in the tip clearance.  
Since the tip-leakage flow is the most concerning phenomenon and the flow has a high local velocity gradient in the blade tip clearance, it is necessary to consider an appropriate grid number in the tip clearance along the span-wise and pitch-wise directions. Figure 3 shows the mesh independence analysis in the tip clearance. The obtained results show that 23 and 40 layers can capture the same relative velocity distributions at 0.75 \(C_{x}\). [PERSON] et al. [24] set 20 layers for 2 mm tip clearance height.  
According to the performed grid sensitivity analysis, the pressure loss coefficient of the passage varies less as the total grid number reaches 1.18 million [10]. In order to clearly capture the flow details in the tip-leakage vortex, grids of the outlet region of rotor blade (especially the tip-leakage vortex area) should be refined. Accordingly, 25 grid layers are set in the tip clearance within 1% blade height and 174 grid layers are set in the span-wise direction of the blade. The total number of grid nodes is about 9.2 million.  
Figure 2: Casing mesh in the trailing region of rotor.
## 4 Materials and Methods
### Validation of Gene Expression Data by qRT-PCR  
Quantitative reverse-transcription PCR (qRT-PCR) was performed to validate the gene expression levels of _RcTPS23_ in eight tissues of _R. chinensis_ 'Old Blush' (petals at four stages: FB_GP, FB_CP, FB_PP, OF_PP, stamen, leaf, stem, and root tissues). A Plant RNA Isolation Kit was used to extract total RNA from each sample. A PrimeScript RT Reagent Kit with gDNA Eraser was used to synthesize first-strand cDNA from 1.0 \(\upmu\)g total RNA. The reaction mixture contained 10 \(\upmu\)L SYBR Premix Ex Taq, 0.4 \(\upmu\)L 10 \(\upmu\)M forward and reverse transcript-specific primers (forward primer: 5\({}^{\prime}\)-TTTAGAGAAGACAATACACAGGTC-3\({}^{\prime}\), reverse primer: 5\({}^{\prime}\)-CAGGGAAGCCTTGTGTCTTA-3\({}^{\prime}\)), 2 \(\upmu\)L cDNA, and 7.2 \(\upmu\)L sterile distilled water. The qRT-PCR was performed using a CFX96\({}^{\upmu\text{M}}\) Real-Time PCR system with the following program: 95 \({}^{\circ}\)C for 30 s; 40 cycles of 95 \({}^{\circ}\)C for 5 s and 60 \({}^{\circ}\)C for 30 s; and a final melting curve analysis of 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 1 min, and 95 \({}^{\circ}\)C for 15 s. The relative gene expression levels were normalized against the expression level of the endogenous reference gene _RcActin_[81] and calculated using the 2\({}^{-\Delta\Delta\text{Ct}}\) method [82]. Each sample was examined with three biological replicates. The histograms were generated using the Origin9 program (OriginLab, Northampton, MA, USA). All commercial reagents and kits used in this section are detailed in Section 4.6 (Reagents and Materials).