esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Circularly Polarized 5G Antenna Design and Optimization
### Geometry 3  
In geometry 3 the RDRA was energized by adding one more strip, as presented in Figure 2c. The modification is discussed in the next section. The return losses of this design configuration are depicted in Figure 3. The S\({}_{11}\) curve of geometry 3 shows that the degenerate TE\({}^{\rm x}_{\rm 813}\) was excited at 4.16 GHz and TE\({}^{\rm y}_{\rm 183}\) at 4.4 GHz, but still, impedance matching (\(\,\)\(\rm 1S_{11}\)\(\,\leq\)\(\,\)10 dB) was not achieved. As shown in Figure 4, the 3 dB axial ratio bandwidth of \(\sim\)2% was achieved. The provided bandwidth was not enough to cover the targeted 5G band. Moreover, the S\({}_{11}\) of the antenna needed to be optimized further. So, the shape of the feeding strip was further changed and this is discussed in the next geometry.
## 3 Proposed Framework
### Angular Momentum of Water Particle and Advection of Foam Particle
#### 3.2.1 Angular Momentum Calculation in Fluid Particles  
Rotational momentum is generated by the torque(\(\mathbf{\tau}=\mathbf{r}\times\mathbf{f}\)) due to the difference in position(\(\mathbf{r}\)) between the point of action of the force(\(\mathbf{f}\)) acting on the object and the center of the mass of the object. However, since the point of action of the force applied to the water particles is the position of the particle, it has been considered to generate only linear momentum without angular momentum. In this paper, it is assumed that the force applied to the neighbor particle becomes the torque of the given particle, and the obtained torque is as Equation (11).  
\[\mathbf{\tau}_{p}=\sum_{np}((\mathbf{x}_{p}-\mathbf{x}_{np})\times\mathbf{f}_{np})W_{p,np} \tag{11}\]  
where \(\mathbf{\tau}_{p}\) is the torque applied to the particle. \(\mathbf{x}_{p}\) and \(\mathbf{x}_{np}\) are the positions of a given particle and its neighbor particle, \(W_{p,np}\) is the smoothing weight function of the two water particles, and \(\mathbf{f}_{np}\) is the force acting on the neighbor particle. \(\times\) is a cross product operator. We calculated the torque acting on the water particle using Equation (11), and based on this, we update the angular momentum of the water particle by integrating with time as Equation (12).  
\[\mathbf{L}_{p}^{n+1}=\mathbf{L}_{p}^{n}+\mathbf{\tau}_{p}^{n}\Delta t \tag{12}\]  
where \(\mathbf{L}_{p}\) is the angular momentum of the water particle, and \(\Delta t\) is the time-step. The angular velocity of the water particle is calculated with Equation (13).  
\[\mathbf{\omega}_{p}=I_{p}^{-1}\mathbf{L}_{p}^{n+1} \tag{13}\]  
where \(\mathbf{\omega}_{p}\) is the angular velocity of the water particle, and \(I_{p}\) is the scalar inertia moment of the particle. In this paper, the inertia moment was calculated by approximating \(I_{p}=km_{p}\), and the value of \(k=\frac{1}{30}\) was used.
## 1 Introduction
With respect to motion control, recent representative works include a vision-based observer method for multiple cell transport [12], a proportional-derivative (PD) control strategy for single cell transport [13], a saturation controller for swarming motions of cells [14], a combined translational and rotational controller for cells [15], and a motorized stage-optical tweezers cooperative controller for multi-cell manipulation [16]. Other representative methods include a disturbance compensation controller for in vivo cell manipulation [17], a potential field controller for multi-stage cell transport [18], a neural  
Figure 1: Holographic optical tweezers system. network controller for object manipulation in the presence of unknown optical trapping stiffness [19], a model predictive controller for microsphere pattern formation [20; 21], and independent actuation of fifty microrobots using optically generated thermal gradients [22]. Magnetic actuation provides an alternative technique for automated biomanipulation that has attracted a lot of attention. Magnetic manipulation involves either using time varying magnetic fields to move the biological objects directly [23; 24], or using time varying magnetic fields to move microrobots that serve as end effectors for the objects [25; 26; 27; 28; 29]. We refer the interested reader to [30] for a survey of automated planning and control methods across different micromanipulation techniques, to [31] for a recent review of the advances in controlling multiple microrobots, and to [32] for a discussion on various designs of magnetically actuated microrobots. However, as in the case of optical manipulation, not much work has been done to specifically address the problem of microscale object perception from bright field imaging. While there are a handful of methods for bright field image processing, most of them require a set of defocused images at varying heights and/or detect objects of one type only [33; 34; 35; 36]. Moreover, some of the methods are not suitable for real-time implementation [37] or only work for specific cell shapes [38]. We have developed a method to address many of these limitations and perceive objects of varying types and shapes from a series of time-lapse images taken at the same cross-sectional plane [39]. Our method is based on a novel combination of many well-known image processing techniques. Here, we further demonstrate the effectiveness of the method in providing highly accurate and precise perception results for multiple cell types, including human cells, in different fluid media, whose compositions may vary over time. In addition, we present a convolutional neural network model to distinguish the individual beads even in tightly aggregated clusters.
# A Review of Sharp-Switching Band-Modulation Devices
## Operating Principles and Models
### Compact Model V-I Formulation  
The main challenge for compact modeling is a unified description of the steady-state hysteresis. This means that the same equations must capture all biasing schemes. This cannot be achieved by the current-voltage \(\mathrm{I_{A}(V_{A})}\) formulation, as proposed in the previous regional model. Instead, the reciprocal voltage-current \(\mathrm{V_{A}(I_{A})}\) characteristics can be analytically constructed from the following set of equations:  
\[\mathrm{V_{AOFF}}=\mathrm{N_{OFF}}\cdot\beta\cdot\ln\left(1+\frac{\mathrm{I_{ A}}}{\mathrm{I_{DiffOF}}}\right) \tag{10}\]  
\[\mathrm{V_{A_{BC}}}=\mathrm{N_{BC}}\cdot\beta\cdot\ln\left(1+\sqrt{\frac{ \mathrm{I_{A}}}{\mathrm{I_{RecP_{N}}}}}\right)+\mathrm{N_{BC}}\left(\psi_{ \mathrm{Gdep}}-\psi_{\mathrm{Ginv}}\right) \tag{11}\]  
\[\mathrm{V_{A_{NEG}}}=\frac{\mathrm{V_{ON}}-\mathrm{V_{OFF}}}{\mathrm{I_{ON}}- \mathrm{I_{OFF}}}\mathrm{I_{A}}+\frac{\mathrm{V_{ON}I_{OFF}}-\mathrm{V_{OFF}I_ {ON}}}{\mathrm{I_{ON}}-\mathrm{I_{OFF}}} \tag{12}\]  
\[\mathrm{V_{A_{PIN}}}=\mathrm{N_{PIN}}\cdot\beta\cdot\ln\left(\frac{2\cdot \mathrm{I_{RecP_{IN}}}-\mathrm{I_{Diff_{PIN}}}+\sqrt{\mathrm{I_{Diff}^{2}+4I_{A}I _{RecP_{IN}}}}}{\mathrm{I_{RecP_{IN}}}}\right)+\mathrm{R_{PIN}I_{A}} \tag{13}\]  
Each of the above four equations is valid in a well-defined region, designated by its subscript: OFF for off-current regime, BC for barrier collapse regime, NEG for negative resistance regime, and PIN for the conductive diode regime. Thanks to the analytical expressions for the boundary conditions between the conductive and blocked regimes (i.e., \(\mathrm{V_{ON}}\) and \(\mathrm{V_{OFF}}\)), Equations (10)-(13) are linked in a common formulation to ensure continuity (mandatory for implementation in Verilog-A). Figure 13 shows a convincing agreement of the model with, both, TCAD results (a) and experimental data (b).
## 2 Material and Methods
### Microhardness Analysis  
The microhardness values of the samples were measured in 3 stages: at the beginning, after the artificial caries lesion was formed on the samples, and after the pH cycle was completed, by adopting the same conditions and steps and using a microhardness measurement device (Matsuzawa HWMMT-X3, TTS, Okasa, Japan) with a Vickers measuring tip.  
Microhardness was measured using the Vickers tip of the device from three different points to eliminate regional differences in each sample and by applying 100 g of force (0.980 Newtons) for 15 s by the square pyramid-shaped diamond tip of the device. The values obtained for each enamel sample were recorded, and the mean microhardness value was determined by averaging the three measurements mentioned above.  
For the five toothpastes for children applied in the pH cycles, the percentages of surface microhardness recovery values (%SMHR) were calculated using the following formula:  
\[\%\text{SMHR}=\frac{\text{Microhardness~{}after~{}remineralization}-\text{Microhardness~{}after~{}demineralization}}{\text{Initial~{}microhardness~{}after~{}demineralization}}\times 100\]
# Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal
## 3 Background
### Fuzzy Logic  
Fuzzy logic is a form of many-valued logic that is able to handle the concept of partial truth, where the truth value may range between completely true and completely false [65]. Fuzzy logic is based on fuzzy sets, which are classes of objects with continuum grades of membership, ranging between zero and one [66].  
Figure 1: Phase-cycle traffic signal diagram.  
A fuzzy set \(A\) is defined as a membership function \(f_{A}(x)\) that maps the elements of a universe of discourse \(X\) with the elements of the interval \([0,1]\): \(f_{A}:X\to[0,1]\), representing the grade of membership of \(x\) in \(A\). The closer the value of \(f_{A}(x)\) to \(1\), the higher the grade of membership of \(x\) in \(A\).  
Input scalar values are mapped to the fuzzy interval \([0,1]\) through a _fuzzification_ process. Many types of curves can be used for fuzzification, of which the triangularly shaped membership functions are the most common.  
Let \(x\) be an element of a set \(A\); then, its triangular membership function is computed by:  
\[f_{A}(x)=max[min(\frac{x-L}{C-L},\frac{R-x}{R-C}),0], \tag{4}\]  
where \(L\), \(C\) and \(R\) are real scalar values that delimit \(A\) (see Figure 2).  
The reversible process in which a fuzzy value is converted to a scalar is called defuzzification. The most common method for defuzzification is the centroid of area, which is the most prevalent and physically appealing [67,68]. Let \(x\) be the sample element and its discrete membership function \(f_{A_{i}}(x)\); then, the defuzzified value is computed by:  
\[WA=\frac{\sum\limits_{i=1}^{n}\bar{x}_{A_{i}}f_{A_{i}}(x)}{\sum\limits_{i=1}^ {n}f_{A_{i}}(x)}, \tag{5}\]  
where \(n\) represents the number of sets to which the element belongs, and \(\bar{x}_{A_{i}}\) is the element with maximum membership function in a set \(A_{i}\). This method, also referred to as weighted average (WA), is the most frequently used in fuzzy applications due to its efficiency [69].
## 6 Results and Discussions
### Access Point 3  
The data traffic and energy consumption of Access Point 3 when using each of the evaluated routing protocols are as follows:  
* Shortest-path routing method: Access Point 3 only participates in the longest (energy-aware) path. Thus, in this routing method, it is idle the whole transmission time. Consequently, the number of bytes transmitted and received as well as the transmission and receiving energies are close to zero, as shown in Figures 7c and 8c as well as Figures 9c and 10c, respectively.
* Proposed SDN-based energy-aware routing method: Access Point 3 is idle from 0 to 60 s since the SDN controller selects the shortest path (Access Point 2). Then, from 60 to 160 s, the SDN controller decides on the energy-aware path. At 160 s, the SDN controller prefers to revert back to the default routing path (shortest path) until 240 s. Finally, the energy-aware path is chosen again between 240 and 400 s. Whenever the energy-aware path is chosen, Access Point 3 receives bytes from Access Point 1, as shown in Figure 8c, and the consumed receiving energy increases, as shown in Figure 10c. Then, Access Point 3 transmits the bytes to Access Point 5 as shown in Figure 7c, and the consumed transmission energy increases, as shown in Figure 9c. The rest of the time (from 0 to 60 s and 160 to 240 s), Access Point 3 neither receives nor transmits data, and the corresponding energies are close to zero.
###### Abstract  
Scent-marking is the most effective method of communication in the presence or absence of a signaler. These complex mixtures result in a multifaceted interaction triggered by the sense of smell. The objective was to identify volatile organic compound (VOC) composition and odors emitted by total marking fluid (MF) associated with Siberian tigers (_Panthera tigris altaica_). Siberian tiger, an endangered species, was chosen because its MF had never been analyzed. Solid phase microextraction (SPME) for headspace volatile collection combined with multidimensional gas chromatography-mass spectrometry-olfactometry for simultaneous chemical and sensory analyses were used. Thirty-two VOCs emitted from MF were identified. 2-acetyl-1-pyrroline, the sole previously identified compound responsible for the \"characteristic\" odor of _P. tigris_ MF, was identified along with two additional compounds confirmed with standards (urea, furfurral) and four tentatively identified compounds (3-methylbutanamine, (\(R\))-3-methylcyclopentanone, propanediolic acid, and 3-hydroxybutanal) as being responsible for the characteristic aroma of Siberian tiger MF. Simultaneous chemical and sensory analyses improved characterization of scent-markings and identified compounds not previously reported in MF of other tiger species. This research will assist animal ecologists, behaviorists, and zookeepers in understanding how scents from specific MF compounds impact tiger and wildlife communication and improve management practices related to animal behavior. Simultaneous chemical and sensory analyses is applicable to unlocking scent-marking information for other species.  
semiconcls; scent-markings; marking fluid; odor; _Panthera tigris altaica_; Siberian tiger; multidimensional GC-MS-olfactometry; 2-acetyl-1-pyrroline; solid-phase microextraction; volatile organic compounds +
Footnote †: journal: molecules
## 4 Next Forwarding Node Selection in UWSNs
### Energy Efficiency
Moreover, it uses local depth information only, without any expensive GPS or beacons. However, the disadvantages of EEDBR are discussed as follows. EEDBR utilizes only residual energy for selecting the next forwarding nodes and does not use any link quality metrics, which does not ensure energy-efficient and reliable routing. Moreover, it selects the node that has less depth than the sender based on residual energy, ignoring the depth of neighbors, which leads to an increase in the number of retransmissions and the number of hops. Furthermore, the knowledge acquisition phase should be updated with a short interval of time, which leads to high network overhead. Lastly, EEDBR does not take into account the communication void, which is considered a critical issue in selecting the next forwarding nodes. Based on layering, Localization-Free Multi-Layered Routing Protocol (MRP) has been proposed by [49]. The author has introduced a new architecture based on Super Nodes. Super Nodes are connected directly with sink nodes placed on the water surface, as illustrated in Figure 12. Ordinary nodes are deployed at the bottom level of the water. These super nodes divide the area into layers using beacon messaging based on different transmission power. Thus, selecting the next forwarding nodes is based on layer ID (layer-by-layer), and each node calculates the holding time based on residual energy until reach super nodes. All the nodes that overhear the same packet will drop this packet and so on. MRP is related to H\({}^{2}\)-DAB in that it uses the same layering technique. Moreover, layer ID in MRP is equivalent to Hop-ID in H\({}^{2}\)-DAB. It produced a better result than H\({}^{2}\)-DAB. Moreover, MRP solves communication void problem by introducing a new architecture which has been designed based on super nodes. However, MRP suffers from some problems. The proposed architecture consumes high energy consumption due to the use of super. Moreover, the super node uses max power to send the data packets to another Super Node that placed above. Thus, it results in high energy consumption. Furthermore, MRP did not take the link quality parameter into its account which has a direct effect on packet delivery ratio.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 2 LIDAR Theory
### WindCube Principle
#### 2.2.2 Doppler Beam Swinging (DBS) Principle  
Each laser direction is defined by the angles \(\varphi\) et \(\theta\), and the normalized vector \(\overset{\rightarrow}{n}(D_{LOS})\) pointing from the LIDAR towards the particles is defined by:  
\[\overset{\rightarrow}{n}(D_{LOS})=\left(\begin{array}{c}-\cos\theta sin \varphi\\ -\sin\theta sin\varphi\\ -cos\varphi\end{array}\right) \tag{5}\]  
The \(LOS\) radial wind speed measurements \(V_{r}(h)\) are defined for each beam direction in the opposite sens of the \(\overset{\rightarrow}{n}(D_{LOS})\) vector, as a function of the wind vector at the given altitude \(V(h)\). They are geometrically characterized by:  
\[V_{r}(h)=V(h)-\overset{\rightarrow}{n}(D_{LOS})=u(h)sin\varphi cos\theta+v(h )sin\varphi sin\theta+w(h)cos\varphi \tag{6}\]  
The \(V_{r}\) measured at each fixed positions \(D_{LOS}=(N,E,S,W,Z)\) are combined to determine the wind speed vector at the altitude \(h\):  
\[\begin{array}{c}u(h)=\frac{V_{V_{N}}(h)-V_{V_{S}}(h)}{2 sin\varphi}\\ v(h)=\frac{V_{V_{E}}(h)-V_{W}(h)}{2 sin\varphi}\\ w(h)=V_{r_{Z}}(h)\end{array} \tag{7}\]  
Therefore, DBS LIDARs combine radial wind speed measurements made at different positions and times to derive a velocity vector. Two main measurement errors are introduced by this principle: The filtering of smaller scales due to the large size of the probe volume [14], and the two-point correlation contamination between the wind field components, known as variance contamination or cross-contamination [10].
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 3 Case Study
### Model Validation  
The accuracy of the model proposed in this paper was examined by comparing the prediction results with the measured data along the strike observation line at four prediction times for the working panel 22101. In addition, the prediction results using the time functions in Section 2.2.4 at the same prediction times were also compared in order to provide a clearer description of the prediction accuracy and advantages of the MMF time function. The comparison curves are shown in Figure 15.  
A comparison of the results predicted by four time functions in Figure 15 shows that the difference between the progressive surface subsidence predicted by MMF time function and measured subsidence is much smaller, particularly at the extreme values of subsidence for each time period and near the boundaries.  
The prediction accuracy was evaluated in order to compare the prediction effectiveness of each time function more intuitively. The root mean square error (RMSE) (Equation (27)) and relative root mean square error (RRMSE) (Equation (28)) of the progressive surface subsidence for each time period were calculated.  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{m}\left(W_{Pi}-W_{Mi}\right)}{m}} \tag{27}\]  
\[\text{RRMSE}=\frac{\text{RMSE}}{W_{\text{max}}} \tag{28}\]  
Figure 15: Comparison of the prediction results based on different time functions and measured data.  
where \(W_{Pi}\) is the prediction subsidence of the \(i\)th point; \(W_{Mi}\) is the measured subsidence of the \(i\)th point; \(W_{max}\) represents the maximum value of the measured subsidence for each time period; \(m\) is the number of observation points (\(m\) = 26).  
The accuracy of the progressive surface subsidence predicted by four time functions is shown in Table 3.  
With surface subsidence increases, the RMSE and RRMSE gradually increase. The MMF time function outperforms other time functions in all time periods in terms of prediction accuracy. The mean values of RMSE-Logistic and RRMSE-Logistic are 80.57 mm and 7.66%, respectively, the mean values of RMSE-Gompertz and RRMSE-Gompertz are 79.77 mm and 7.73%, respectively, and the mean values of RMSE-Weibull and RRMSE-Weibull are 90.61 mm and 8.62%, respectively. While the mean values of RMSE-MMF and RRMSE-MMF are 46.65 mm and 4.63%, respectively, the accuracy of RMSE is improved by 33.92 mm, 33.12 mm, 43.96 mm, respectively, and the accuracy of RRMSE is improved by 3.03%, 3.10%, 3.99%, respectively.  
Combined with the analysis of the subsidence data above, the average RMSE of the progressive surface subsidence predicted by MMF time function is 46.65 mm and an average RRMSE of 4.63%. This meets the requirements of practical engineering applications. It can be used fully for dynamic prediction of mining subsidence.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 2 System Model
### Channel Model and Received Signals
Then, the channel gains can be rewritten as \(h_{a,k}=\alpha_{a,k}\exp(\mathrm{j}\phi_{a,k})\), for \(a=1,2,...\), \(A\), and \(k=1,2,...\), \(U\), where \(\alpha_{a,k}\) is the fading amplitude modeled by a Rician random variable, whose PDF is defined by [37]  
\[f_{\alpha}(\alpha)=\frac{2(K+1)}{P}\alpha\exp\left(-\frac{K+1}{P}\alpha^{2}-K \right)I_{0}\left(2\sqrt{\frac{K(K+1)}{P}}\alpha\right),\quad\alpha\geq 0, \tag{2}\]  
where \(I_{0}(\cdot)\) is the modified Bessel function of the first type and order zero [38] (Equation (9.1.10)), and  
\[K=\frac{\mu^{2}}{2\sigma^{2}}, \tag{3}\]  
is a shape parameter and represents the ratio of the power of the LOS path and the power of the remaining non-LOS multi-path. Further, the scale parameter  
\[P=\mu^{2}+2\sigma^{2}, \tag{4}\]  
indicates the power received on all paths. In addition, \(\phi_{a,k}\) is the random channel phase. By employing the joint distribution of \(\alpha\) and \(\phi\)[37] (Equation (6-74)), the marginal PDF of \(\phi\) can be obtained as  
\[ \begin{split} f_{\Phi}(\phi)&=\int_{0}^{\infty}f( \alpha,\phi)\mathrm{d}\alpha\\ &=\frac{1}{2}\exp\left(-\frac{K}{1+K}\right)\left[1+\sqrt{\pi} \exp\left(\frac{K}{1+K}\cos^{2}(\theta)\right)\right.\\ &\qquad\times\left.\mathrm{erfc}\left(-\sqrt{\frac{K}{1+K}}\cos( \theta)\right)\sqrt{\frac{K}{1+K}}\cos(\theta)\right],\end{split}  \tag{5}\]  
where \(\mathrm{erfc}(\cdot)\) is the complementary error function [38] (Equation (7.1.2)). Finally, in (1), \(\mathbf{n}\) is the additive white Gaussian noise (AWGN) vector, whose elements are independent and identically distributed complex Gaussian random variables with zero mean and variance \(\sigma_{n}^{2}\), that is, \(\mathcal{CN}(0,\sigma_{n}^{2})\), where \(\sigma_{n}^{2}=N_{0}/(2T_{s})\) is the noise variance, \(N_{0}\) is the unilateral noise power spectral density, and \(T_{s}\) is the symbol duration.