esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Rigid-Flexible Coupling Dynamic Model of Cylindrical Roller Bearings
### Pocket Dimensional Deviation Characterization
#### 2.2.3 Contact Stiffness between the Rollers and the Ring Raceway  
During operation, an oil film will be formed between the roller and the raceway. In this paper, the stiffness between the roller and the ring includes the contact stiffness between the roller and the raceway and the oil film stiffness between the roller and the raceway [18].  
When the bearing is loaded, the normal stiffness of the roller unit and the inner and outer ring raceway units is the series of normal contact stiffness and oil film stiffness, which is expressed as follows:  
\[K_{i}=\frac{K_{ri}K_{oi}}{K_{ri}+K_{oi}} \tag{7}\]  
\[K_{e}=\frac{K_{re}K_{oe}}{K_{re}+K_{oe}} \tag{8}\]  
where \(K_{ri}\) and \(K_{re}\) are the normal contact stiffness of the roller and the inner and outer ring raceways, respectively. \(K_{oi}\) and \(K_{oe}\) are expressed as the normal oil film stiffness of the roller and the inner and outer ring raceways, respectively.  
(1) Contact stiffness between roller and raceway.  
The cylindrical roller bearing only bears the radial load. The contact stiffness between the roller and the raceway is the normal contact stiffness caused by the radial load. The normal contact stiffness of the cylindrical roller bearing is expressed as follows:  
\[K_{ri}=\frac{dQ_{i}}{d\delta_{ri}} \tag{9}\]  
\[K_{re}=\frac{dQ_{e}}{d\delta_{re}} \tag{10}\]  
where \(Q_{i}\) and \(Q_{e}\) are, respectively, expressed as the normal contact load between the roller and the inner and outer ring raceways; \(\delta_{ri}\) and \(\delta_{re}\) are expressed as the normal elastic deformation of the contact between the roller and the inner and outer ring raceways, respectively.  
(2) Oil film stiffness of roller and raceway.  
The partial derivative of the contact load between the roller and the raceway and the minimum oil film thickness is the oil film stiffness. According to the theory of elastic fluid lubrication, the oil film stiffness of the cylindrical roller bearing is as follows:  
\[K_{oi}=\frac{dQ_{i}}{dh_{\min}}=-13860.986\times\eta_{0}^{5.3846}u^{5.3846} \alpha^{4.1538}E^{-0.2307}R^{3.3076}h_{\min}^{-8.6923} \tag{11}\]  
\[K_{oe}=\frac{dQ_{e}}{dh_{\min}}=-13860.986\times\eta_{0}^{5.3846}u^{5.3846} \alpha^{4.1538}E^{-0.2307}R^{3.3076}h_{\min}^{-8.6923} \tag{12}\]  
where \(\eta_{0}\) represents the dynamic viscosity at an atmospheric pressure of 20 \({}^{\circ}\)C; \(u\) is the fluid velocity of the roller and the raceway inlet; \(\alpha\) represents the coefficient in the Barau viscosity-pressure formula of base oil contained in lubricating oil or grease; \(E\) is the equivalent elastic modulus of contact between the roller and the inner and outer ring raceways; \(R\) is the equivalent radius of curvature perpendicular to the moving direction of the rolling element; and \(h_{\min}\) is the minimum oil film thickness between the roller and the inner and outer ring raceways [19].
# An Efficient Rice Virus-Induced Gene Silencing System Mediated by Wheat Dwarf Virus
## 3 Results
### Silence OsPi2I by the WDV-VIGS System to Enhances Rice Blast Resistance  
It was reported that the wild type Pi2I slowed down the defense response of rice, while the plants carrying the resistant pi2I allele of the natural mutant race enhanced the resistance response to rice blast. The difference between the two alleles was that there were 21 bp and 48 bp deletions in the pi2I, respectively [25,27]. Seven days after inoculation with rice blast disease, some _OsPi2 Ii_ rice leaves had fusiform lesions with brown edges, were gray-white in the middle, and had yellow halos on the periphery, while the leaves of the control group had more lesions, which were irregular and large, with dark brown edges (Figure 2b,c).  
Meanwhile, the relative _OsPi21_ gene expression level and fungal biomass was significantly reduced in _OsPi21_ rice (Figure 2**d**,**e**). Using the different disease grades of rice leaf phenotypes as a reference, the gene-silenced rice with pCambia1300-WDV-_OsPi21i_ mainly exhibits leaf blast symptoms at levels 4-5 (Figure 2**f**), while the wild-type rice primarily shows leaf blast symptoms at levels 8-9. Based on the overall count of plants with varying degrees of disease, the _OsPi21i_ rice shows significantly milder leaf blast symptoms compared to the wild-type rice. These results indicate that the _OsPi21_ gene  
Figure 2: _Magnaporthe oryzae_ inoculation and infection of WDV-VIGS in ZH11 rice, followed by phenotypic scoring and quantitative detection. (**a**) Rice leaves after the _OsPDS_ gene and _OsPi21_ were silenced by WDV-VIGS; each group has two replicates. (**b**) Comparison of the lesion size of rice inoculated with _Magnaporthe oryzae_ in vitro. Rice leaves with gene silencing are inoculated with the rice blast fungus RB2. After seven days, the phenotypes were photographed; each group has two replicates. (**c**) Phenotypes corresponding to leaf blast grading criteria for rice blast. The numbers 1–9 represent the disease grades. (**d**) The relative gene expression level after gene silencing. The significance of the difference was determined using ANOVA. “\({}^{**}\)” represents significant differences (\(p<0.05\)). (**e**) Comparison of fungal biomass after gene silencing. The significance of the difference was determined using ANOVA followed by [PERSON]’s HSD test for multiple comparisons (\(p<0.05\)), and different lowercase letters indicates significant difference between the two groups. (**f**) Percentage of wild-type rice and Pi21 silent rice leaf blast levels.  
could also be silenced by the WDV-VIGS system. And this system was recommended to be an effective tool to study the gene function in rice biology. However, the genetic effects of _OsPi21_ silencing using VIGS technology in the progeny lines, specifically whether the low expression level of _OsPi21_ can be maintained and the blast resistance can be sustained, need further experimental study.
## 4 Modeling and Results
### Results  
In order to comprehensively evaluate the effectiveness of the developed model and its training algorithms, a series of experimental studies was conducted. These experiments involved using the following models and algorithms:  
* Vanilla Preact ResNet-18;
* Preact ResNet-18 with Data Augmentation;
* Algorithm 1 (training Preact ResNet-18 via cross-language transfer learning with the last layer unfrozen);
* Algorithm 2 (training Preact ResNet-18 via cross-language transfer learning with all the layers unfrozen).  
To address the handwritten kanji recognition task for different class counts, 150, 200, and 300, the evaluations were performed based on the previously described metrics for all the examined approaches. Additionally, the recognition accuracy of each individual character was analyzed. Let us now take a closer look at the results of these experiments.
## 3 Materials and Methods
### Location-Aided Routing Protocol (LAR)
Moreover, the intermediate nodes are required to meet some conditions, including the angle, distance, and energy level, to be calculated in the criterion function using the following formula:  
\[\text{Criterion Function}=\text{f}_{\text{node}}=\text{w}_{1}*\text{Angle }(\text{node})+\text{w}_{2}*, \tag{3}\] \[\text{Dis }(\text{node x. destination})+\text{w}_{3}*\text{Energy Consumption }(\text{node}),\]  
where  
\[\text{Angle }(\text{node})=\text{\ }\frac{\text{Angle }(\text{node})}{180}\text{ }\in[0.1] \tag{4}\] \[\text{Distance(node x. destination)}=\text{\ }\frac{\text{Dis }(\text{node x. destination})}{\text{length of environment}^{\prime}\text{ s diagonal}}\text{ }\in[0,1]\] (5) \[\text{Energy Consumption }(\text{node})=\text{\ }\frac{\text{ Initial Battery }(\text{node})-\text{Current Battery }(\text{node})}{\text{Initial Battery }(\text{node})}\text{ }\in[0,1] \tag{6}\]  
Figure 3: Optimized Particle Swarm Optimization- Location-Aided Routing (OPSO-LAR) mechanism. \(w_{1},w_{2},\text{ and }w_{3}\in[0\,1]\) represent the weighting factors of the variables used in the objective function and play a role in the effectiveness of the request zone. After calculating the three conditions, the flooding ratio (FR) decides how many nodes forward the RREQ.
## 4 Sensing Performances  
To adjust the tunable temperature-related characteristics of the here proposed THz thermal sensor, the defect parameters have been further investigated by using simulations. It has been observed that by changing the length and radius of the defect, the peak frequency of the NBDS will shift accordingly. At first, the defect length \(L\) was set to 0.5 \(\Lambda\), and the defect radius, \(R\), was varied from 0.3\(r_{0}\) to 1.1\(r_{0}\). The variations in transmission frequency, as a function of temperature, are shown in Figure 4a. The results are presented for different defect radius, \(R\) (from 0.3\(r_{0}\) to 1.1\(r_{0}\)). To make Figure 4a more intuitive, the transmission frequency, \(f\), value for \(R\) = 0.7\(r_{0}\) and \(T\) = 313 K has been set to zero, and the other \(f\) values have been accordingly adjusted (i.e., these \(f\) values are reported with respect to the one for \(R\) = 0.7\(r_{0}\) and \(T\) = 313 K). As can be seen in Figure 4a, for a constant temperature, the transmission frequency, \(f\), will increase with an increase in defect radius, \(R\). For the situation with a constant defect  
Figure 3: Frequency shifts of the NBDS when the temperature is increased from 273 K to 353 K.  
radius, the frequency will continuously decrease when increasing the temperature from 273 K to 353 K. To obtain the sensitivity of the proposed thermal sensor, a least square linear fit has been performed for each set of simulated data. The sensitivity, averaged over the five different sets, was then found to be \(9.35\pm 0.80\) MHz/K. The highest sensitivity was found for \(R=0.7r_{0}\), with a value of 10.15 MHz/K.  
The next step was to study the effect of various defect lengths, \(L\), (0.2\(\Lambda\), 0.4\(\Lambda\), 0.6\(\Lambda\), 0.8\(\Lambda\), and 1.0\(\Lambda\)) for a constant defect radius, \(R\), (of 0.9\(r_{0}\)). Simulations were thereby performed at temperatures, \(T\), that were increased from 273 K to 353 K (in steps of 20 K). The obtained frequency deviations are shown in Figure 4b (by using the transmission peak for \(L=0.6\Lambda\), at 313 K, as a reference). In this figure, it can be seen that the transmission peak shifts to a lower frequency as the defect length, \(L\), increases (at constant \(T\)). Moreover, at a constant defect length, the frequency decreases with an increase in temperature. The sensitivity, averaged over the five different sets, was found to be \(10.75\pm 0.75\) MHz/K. Moreover, the highest sensitivity was found for both \(L=0.2\Lambda\) and \(L=0.8\Lambda\), with a value of 11.5 MHz/K.  
Figure 4: Structure optimization of the proposed THz thermal sensor. Frequency shifts for different defect radii, \(R\) (**a**), and different defect lengths, \(L\) (**b**).
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 1 Introduction
Versatile fusion models with advanced image processing and machine learning techniques are required to fuse the multispectral high-resolution data. The accuracy level reached by most of the referred decision-making frameworks and models is around 85%. This statement proves that more emphasis must be given to preprocessing, especially in data fusion tasks to improve the data accuracy, which has an impact on enhancing the situation awareness of AVs to improve their accuracy of decision-making. The studies related to data fusion are elaborated on in the related work section. In light of the mentioned limitations, this study suggests a hybrid fusion model that combines traditional data fusion methods with modern technology in order to merge high-spectral and high-resolution images. The study introduces simple and versatile models for extracting significant image features such as color, edge, height, and width. However, the proposed hybrid image fusion model had its own drawbacks. It encountered challenges in managing large volumes of image data and particularly struggled with fusing 3D point cloud data. To address these issues, this study has expanded the research by introducing a GAN model along with customized kernel functions. This transformation converts the proposed hybrid image fusion model into a machine learning model, capable of effectively handling vast amounts of image data, including both 2D images and point cloud data. The proposed GAN model successfully accomplishes tasks such as separating images into spatial and temporal data, extracting features from the separated data, and merging them using customized kernel functions to enhance the accuracy of the image data. To evaluate the proposed models, the nuScenes and GeoTiles datasets were employed. The rest of the paper is organized as follows: Section 2 covers the basics of sensor technology, gives an overview, and discusses the importance of data fusion to improve the accuracy of the data. Section 3 discusses the existing literature and its pitfalls that motivated us to pursue this proposal. Section 4 elaborates on the development of the proposed fusion and feature extraction models. Evaluation of the proposed models along with the outcomes is discussed in Section 5. The overall summary, outcomes, and future directions of this paper are enclosed in Section 6.
# Ku-Band Mixers Based on Random-Oriented Carbon Nanotube Films
## 2 Materials and Methods
### Measurement of Mixer  
#### 2.4.1 Mixing Performance Measurement of the Mixer  
In this work, the active mixer was constructed based on a carbon nanotube field-effect transistor (FET) as the core. Mixer measurement requires a variety of instruments, including a probe table (MPI TS200-THz, MPI Corporation, San Jose, CA, USA), power amplifier (TVTA 1840-10, East Setauket, NY, USA), vector network analyzer (Keysight N5247A, Santa  
Figure 1: Carbon nanotube films and structures of RF transistors: (**a**) SEM image of randomly oriented carbon nanotube films; (**b**) SEM image of the whole two-finger CNT-based RF transistor with GSG electrode pads; the scale of the right illustration is 1 \(\upmu\)m.  
Rosa, CA, USA), semiconductor analyzer, bias Tee (Keysight 11612 BK01), and spectrum analyzer (Keysight) N9020B). The DC bias source (Keysight B2902A) was used to supply the power voltage to the CNT-based RF mixer. Before testing, SOLT calibration of the entire test route was required to compensate for power loss at high-frequency operation, resulting in more accurate mixer performance. The RF and local oscillator (LO) signals were combined into one signal source through a combiner (the combiner was reversed as a divider) to the gate as the input port and the drain as the output port. The intermediate frequency (IF) signal as output was obtained through the spectrometer. The conversion gain (CG) can be calculated by the following equation:  
\[CG=10\log\frac{P_{IF}}{P_{RF}} \tag{1}\]  
where \(P_{\text{IF}}\) is the output IF signal power, and \(P_{\text{RF}}\) is the input RF signal power.
# Influence of Cavitation and Shaft Deformation in the Analysis of Lubrication of the Stern Bearing
## 2 Governing Equations of Lubrication Model
### Force Balance Model of Shaft  
To facilitate the analysis of lubrication characteristics of the bearing, a two-dimensional section of the bearing is taken under consideration. The force on the journal is shown in Figure 1. Ignoring the influence of inertia in oil film, the force balance condition of the bearing journal in stable operation is formulated as follows:  
\[\int_{0}^{L}\int_{0}^{2\pi}p\cos(\theta+\phi)\mathrm{d}\theta\mathrm{d}z+F_{x} +W_{x}=0 \tag{1}\]  
\[\int_{0}^{L}\int_{0}^{2\pi}p\sin(\theta+\phi)\mathrm{d}\theta\mathrm{d}z+F_{y} +W_{y}=0 \tag{2}\]  
where \(p\) is the oil film pressure; \(F_{x},W_{x}\) and \(F_{y},W_{y}\) are the components of the external load and the contact force on the bearing in the \(x\) and \(y\) directions, respectively; and \(W_{A}\) is defined as the module of the contact force, which can be calculated as  
\[W_{A}=\sqrt{{W_{x}}^{2}+{W_{y}}^{2}} \tag{3}\]  
where \(W_{A}\) is determined by the contact model of the micro-convex body between the bearing and the stern shaft. The contact stiffness of a single micro-convex can be calculated through the Hertz elastic contact theory. The elastic contact force on a single micro-convex is calculated as  
\[p_{m-c}=\frac{4E_{\mathrm{eq}}\sqrt{R}}{3}\delta_{a}^{3/2} \tag{4}\]  
where \(E_{eq}\) represents the equivalent elastic modulus. Assuming that the height of a micro-convex on rough surfaces follows a Gaussian distribution with a standard deviation, the normal contact load and normal contact stiffness of the joint can be obtained using probability theory and statistical methods.  
\(F_{e}\) is defined as the module of external load on the stern shaft.  
\[F_{e}=\sqrt{{F_{x}}^{2}+{F_{y}}^{2}} \tag{5}\]  
Its size is determined by the working condition of the bearing. \(\phi\) represents the angle between the bearing center and the connecting line of the shaft center and the \(Y\) axis. The position relation is shown in Figure 1a, and the distribution of the fluid film pressure and the contact force of the micro-convex body is shown in Figure 1b.  
The liquid film force in the \(x\) and \(y\) directions (\(F_{oilx}\) and \(F_{oily}\)) can be calculated as  
\[F_{oilx}=\int_{0}^{L}\int_{0}^{2\pi}p\cos(\theta+\phi)\mathrm{d}\theta\mathrm{d}z \tag{6}\]  
\[F_{oily}=\int_{0}^{L}\int_{0}^{2\pi}p\sin(\theta+\phi)\mathrm{d}\theta\mathrm{d}z \tag{7}\]
## 3 Precise Positioning Using Uncombined PPP (UPPP) Algorithm
### Analysis of GNSS Observations from Different Logging Apps
#### 4.1.1 Inconsistency between Pseudorange, Carrier-Phase and Doppler Observations
Figure 8: First-order differentiation of GPS pseudorange and carrier-phase observations as well as Doppler observations on L1 frequency for PRN 01 (Google Pixel 5).
## 3 Results
### Resonant Raman Scattering (RRS) Spectroscopy  
Figure 3 shows the evolution of the resonant Raman line shapes as a function of RMS surface roughness values, also indicated in the figure. The experimental spectra, represented by solid color lines, are plotted in the 125-375 cm\({}^{-1}\) frequency range. For comparison, in all cases, the spectra have been normalized to 1 LO phonon intensity to render features more evident.  
The spectra in Figure 3 show two prominent main peaks, which have been labeled as 1 LO and 2 LO for the first-order longitudinal optical phonon at 165.7 cm\({}^{-1}\) and its overtone located at 332.5 cm\({}^{-1}\), respectively [27, 28]. An overtone is a phonon whose frequency (\(\omega_{n}\)) is a multiple of the fundamental frequency (\(\omega\)(LO)) so that it can be expressed as \(\omega_{n}=n\omega\)(LO), with \(n\) being the overtone number [29].  
Figure 3 displays a vertical dashed line indicating an additional phonon mode in CdTe, attributed to the first-order transverse optical (TO) phonon at approximately 141 cm\({}^{-1}\)[27]. Under resonant conditions, the TO Raman scattering cross-section is determined solely by the deformation potential. In contrast, the LO scattering cross-section arises from contributions of both the deformation potential and the Frohlich interaction. Thus, the LO peak exhibits significantly higher intensity compared to the TO mode [27]. Moreover, unlike the sharp 1 LO phonon peak with an FWHM of 11.85 cm\({}^{-1}\), the 2 LO phonon mode exhibits a broader profile. As shown in the inset of Figure 3, the intensity of the 2 LO phonon mode is correlated with increasing surface roughness.  
Figure 2: Photoluminescence (PL) spectra of CdTe with different root-mean-square (RMS) values. The inset shows the correlation between the full width at half maximum (FWHM) and the RMS value of roughness.
## Case Study
### Application of Fast Optimization Approach
#### 6.2.3 Prior Map  
We utilize log-permeability fields, well spacing maps, and dynamic saturation fields as the foundational elements for prior image construction. These are then transformed into a probability distribution range through a 0-1 standardization method and serve as the crossover probability. The process is shown in Figure 7.
# Integration of Freeplay-Induced Limit Cycles Based On a State Space Iterating Scheme
## 6 LCO Analysis of Time Integrations with the SSI Scheme
### LCO Results Varying with the Airspeed and a Comparison to Henon-RK45  
Similar procedures are implemented for the 18 cases of airspeeds, which increase from 12.4 to 26.0 m/s. Two branches of LCOs are found, as illustrated in Figures 24 and 25: (1) A 3-D LCO arises at 12.4 m/s, with \(\overline{A}_{\text{LCO}}\) and \(f_{\text{LCO}}\) fluctuating within 2.48-2.85 and 4.29-4.51 Hz, respectively, depending on the I.C., and ends at about 26.0 m/s, where \(\overline{A}_{\text{LCO}}\) surges over 100 and \(f_{\text{LCO}}\) increases to 5.22 Hz; (2) a 2-D LCO exists at the airspeeds between 12.2 and 23.0 m/s, where \(\overline{A}_{\text{LCO}}\) increases from 0.082 to 0.487 while \(f_{\text{LCO}}\) decreases from 4.74 to 3.76 Hz. It is also found that the amplitude and the frequency of the 3-D LCO are quite sensitive to the I.C. at lower airspeeds (about 12.2-13.5 m/s), while such sensitivity almost disappears at higher airspeeds. However, the I.C. almost has no influence on the 2-D LCO. The SSI scheme also confirms that all LCOs are stable with respect to any system state.  
The LCO results derived from time integrations with the Henon-RK45 method and the SSI scheme are compared in Figures 24 and 25. The Henon-RK45 method uses 7300 I.C.s (see Figure 15), and the LCO results are analyzed based on a simulation time of 15-20 s, which means that the frequency resolution is 0.25 Hz according to Fast Fourier Transformation (FFT), leading to the discontinuity of frequency shown in Figure 25. In the case of airspeed \(U~{}>\) 24 m/s, the system response can be a stable LCO, quasi-periodic motion, or chaotic motion, so the points on Figure 24 after \(U>\) 24 m/s have a somewhat irregular distribution. However, with respect to the stable 3-D and 2-D LCOs, the results from the SSI scheme agree well with those from the Henon-RK45 method.  
Figure 23: LCO amplitudes and dominant frequencies versus pitch velocity for the fourth iteration. The airspeed is 20 m/s.
# Joint QoS and Congestion Control Based on Traffic Prediction in SDN
## 2 Related Work
The mentioned approaches support at most three different traffic classes. [PERSON] et al. [21] define path weight as a function of QoS parameters, and they use an ant colony system (ACS) to minimize it. Similarly, The paper [22] exploits a genetic algorithm (GA) for minimization of delay and packet loss. The authors of [21; 22], instead of defining a mathematical formulation, propose a function that is minimized by GA heuristic methods. In [23], authors focus on critical links that are the links specifying the maximum throughput of each path. In this regard, they select a path based on its critical link weight. [PERSON]'s approach not only guarantees end-to-end delay but also end-to-end bandwidth. The work [24] follows a similar approach by minimizing (traffic load)/capacity of each link. A resource allocation approach with three classes that focus on the delay of high priority traffic is introduced in [25]. The Ghosh scheme [26] prevents flow from violating a predefined threshold by minimizing end-to-end delay and maximizing network throughput. [PERSON] [27] uses Integer Linear Programming (ILP) to formulate the network traffic routing in a way that minimizes delay and packet loss. All the reviewed QRA algorithms just focus on the current network traffic while they are unaware of future pattern of demands.