esa-sceva/satcom-chunk-collection huggingface dataset:
# Information-Aware Secure Routing in Wireless Sensor Networks
## 5 Simulations  
### Scene Setting  
In this study, a Virtual Machine (Ubuntu 18.04.2 LTS) and Network Simulator version 3 (NS-3.28) were employed to assess the performance of the proposed model. Simulations were conducted using a simulated WSN, in which 100 nodes were randomly deployed in a \(100\times 100\) m square and interconnected with a mesh network. Every node was assigned with the same initial energy and communication radius. Nodes can be divided into four categories: Normal nodes, malicious nodes, fault nodes, and dead nodes. To simplify the model, we view the fault nodes and dead nodes as malicious nodes, so all nodes in the WSN are one of two types.  
The energy consumption required for the communication between a pair of nodes follows the radio energy dissipation model, as shown in Figure 4. We can see from the figure that a transmitter has two parts--i.e., the radio electronics and the power amplifier--whereas a receiver only has radio electronics. The signal transmitting power in the wireless transmission is exponentially attenuated as the transmission distance increases. This loss can be compensated by the power amplifier. The free space model is employed when the distance between the transmitter and the receiver is less than a threshold \(d_{0}\). When the distance is greater than or equal to \(d_{0}\), the multipath fading model is adopted. The energy consumed by the sensor node sending the message (\(l\)-bit) to the receiver (the distance between the transmitter and the receiver is \(d\)) is expressed as follows [45]:  
\[E_{Tx}(l,d)=E_{Tx-elec}(l)+E_{Tx-amp}(l,d)=\left\{\begin{array}{ll}lE_{elec} +l_{fs}d^{2},&d<d_{0}\\ lE_{elec}+l_{mp}d^{4},&d\geq d_{0}.\end{array}\right. \tag{10}\]  
The energy consumed to receive the message (\(l\)-bit) is  
\[E_{Rx}(l)=E_{Rx-elec}(l)=lE_{elec}, \tag{11}\]  
where \(E_{elec}\) is the energy consumption required for electronic devices to accept and transmit 1-bit data. \(\varepsilon_{fs}\) and \(\varepsilon_{mp}\) represent the free space factor and multipath fading factor, respectively.  
Figure 4: Radio energy dissipation model.
## 3 Network Model and Acoustic Propagation Model
### Acoustic Propagation Model  
In this paper, the Thorp model [24] is adopted to describe the underwater acoustic propagation model. The path loss of the acoustic link is defined as:  
\[A(d,f)=d^{k}\alpha(f)^{d} \tag{3}\]  
where \(f\) represents the signal frequency, and \(d\) represents the propagation distance. The spreading factor is represented by \(k\). For spherical transmission, \(k=2\); for cylindrical transmission, \(k=1\); and in the actual case, \(k=1.5\).  
In addition, \(\alpha(f)\) represents the sound absorption coefficient. The calculation method of \(\alpha(f)\)is indicated by the empirical Thorp equation as:  
\[10\log\alpha(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4100+f^{2}}+2.75 \times 10^{-4}f^{2}+0.003 \tag{4}\]  
When the length of the transmission path is \(d\), the average SNR (signal-to-noise ratio) is:  
Figure 1: UWSN scenarios based on OR.  
\[SNR(d)=\frac{E_{b}/A(d,f)}{N_{0}}=\frac{E_{b}}{N_{0}d^{k}\alpha{(f)}^{d}} \tag{5}\]  
where \(E_{b}\) represents the average energy consumed to transmit a bit of data, and \(N_{0}\) represents the noise power spectral density under the condition of an additive white Gaussian noise (AWGN) channel.
## 2 Standard Nondestructive Testing and Evaluation Techniques
### Conclusion and Comparison to MNDT  
Descriptive summaries of standard nondestructive techniques are provided in Table 1. Furthermore, according to various sources in the literature, a quantitative comparison between traditional NDT techniques and MNDT techniques is given in Table 2. It should be noted here that the values in Table 2 are approximate as they are dependent on the material under test, equipment, and testing condition.  
While the standard NDT techniques are well-established and widely used across various industries, MNDT provides unique advantages. For example, compared to standard NDT, MNDT can achieve greater penetration depths in nonmetallic materials, such as composites, plastics, and ceramics [38], making it particularly useful for inspecting thick or multilayered structures.  
Moreover, microwaves are sensitive to material properties such as permittivity and conductivity, allowing for detailed material characterization and detection of defects and material conditions, which might not be possible with other NDT techniques [39].  
Additionally, MNDT utilizes low-power non-ionizing radiation. This makes MNDT safe for the operator, the specimen under test, and the surrounding areas; thus, no radiation shielding or extensive safety precautions are required [40]. The latter might not be true for traditional NDT methods like radiography testing.  
Furthermore, many MNDT devices are portable and adaptable to various inspection environments, with this versatility proving to be particularly useful for infrastructure and on-site inspections where mobility and adaptability are crucial [41].  
Aside from technical advantages, MNDT is more cost-effective for specific applications than standard NDT, particularly in terms of operational costs, maintenance costs, and inspection speed, which can lead to reduced downtime and increased productivity [42; 43].  
The forthcoming section describes the applications, techniques, and previous work on MNDT techniques for crack detection.  
Figure 5: A demonstration of X-ray radiography. The radiographic film or detector captures the defect’s shadow.
## 3 Results  
### Characterization of Thyroid Nodules  
A total of 60 thyroid nodules were studied here, which included 23 benign (follicular adenoma, colloid nodule, and cyst) and 37 malignant nodules (papillary carcinoma). The size of nodules ranged between 4 to 35 mm with a mean size of 15.3 \(\times\) 10.5 mm\({}^{2}\). Table 1 lists the ultrasonographic features of all patients. It was found that the percent of taller shapes in malignant nodules (59.5%) was much larger than in benign nodules (21.8%). Hypoechogenicity was also a sonographic feature of malignant nodules: The percent of hypoechogenicity of malignant nodules (81.0%) was much higher than that of benign nodules (52.2%). No statistical difference in nodule shape was observed between the malignant and benign nodules. The presence of calcifications of the malignant nodules (70.2%) was much higher than of benign ones (21.7%). However, no significant difference was observed between malignant and benign nodules with regard to the vascularity of Doppler examination.  
A longitudinal B-mode image of the normal thyroid was shown in Figure 1 where homogeneous isoechoic structure and nearly regular well-defined margins could be found in the image. Based on the echogenicity change in B-mode ultrasonography, the pathological change of thyroid gland was determined in clinics. Moreover, thyroid nodules could be classified by boundaries of ROI and texture features in images. Figure 2 shows distinct ultrasound features and accordingly pathologic findings comparing between benign and malignant lesions. A thyroid adenoma (Figure 2a) with ultrasonographic features of hyperechoic echogenicity, no calcification, and wider shape, was benign, finally proven by FNA findings. Its pathologic result showed that the follicular cavity is filled with gelatinous material, and that a papillary structure is partially visible (Figure 2c). On the contrary, hypoechoic echogenicity, microcalcification, and taller shape demonstrated a malignant papillary thyroid carcinoma (Figure 2b), with complex branched papillaries containing fibrous vascular cores (Figure 2d).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Features**} & \multicolumn{1}{c}{**Benign**} & \multicolumn{1}{c}{**Malignment**} \\ \hline \multirow{2}{*}{Shape} & taller & 5 & 22 \\  & wider & 18 & 15 \\ \hline \multirow{2}{*}{echogenicity} & hypoechoic & 12 & 30 \\  & hyperechoic & 11 & 7 \\ \hline \multirow{2}{*}{Calcification} & absent & 17 & 11 \\  & present & 5 & 26 \\ \hline \multirow{2}{*}{Vascularity} & absent & 18 & 32 \\  & present & 5 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of benign/malignant thyroid nodules in US images.
## 2 Flared SBF Antenna  
### Antenna Configuration  
This study aims to further increase the gain and bandwidth of the antenna presented in [4; 20] while maintaining its cross-polarization ratio. The flared SBF antenna shown in Figure 1 consists of several parts and is fed by a waveguide feed because of its high gain and high-power handling abilities. The conventional SBF antenna consists of only four parts: the main reflector, the rim, the sub-reflector, and the feed antenna. The choke portion was included in the work presented in [4; 20] as a way of increasing the gain. In this study, to further increase the gain, we decided to flare the upper diameter of the antenna's rim, similar to the design of the horn antenna. Like the horn antenna, the flare angle has a significant effect on the gain. As the flare angle increases, the gain also increases until it reaches the optimum flare angle. Although much of the research that has been conducted shows that antenna flaring does not affect the impedance bandwidth, we observed a significant improvement in the impedance bandwidth of the proposed antenna design when the rim is flared as a result of resonance merging. Several parametric studies have been conducted to find the optimum flare angle and rim height by varying the flare angle (bf) and rim height (Hr) and examining the behavior of the gain and the bandwidth.  
The SBF antenna's main reflector, choke, and rim are fabricated using 3D printing technology, after which the entire surface is then metalized using a conductive spray. To model this in HFSS, the entire structure is first designed with a PLA plastic material (\(\epsilon_{\rm r}\) = 2.1, tan\(\delta\) = 0.005), and an impedance boundary condition is then applied all over the surface of the structure to simulate the surface resistance of the silver metal coating (0.05 \(\Omega\)/sq). The sub-reflector is made of aluminum metal; therefore, it is designed using the perfect electric conductor (PEC) material, while the waveguide feed is modeled as an open-ended waveguide structure (PEC) with a wave port feed. The optimized dimensions of the flared SBF antenna are provided in Table 1, and are also expressed in terms of the free space wavelength (\(\lambda\)). \(\lambda\) is equal to c/f, where c = 3 \(\times\) 10\({}^{8}\) is the speed of light, and f = 5.5 GHz is the operating frequency.
## Appendix A Derivation of Arrival Time to Circular Plumes  
In this appendix, we evaluate the arrival time distribution for a two-dimensional random walker to reach a trapping disk of radius \(r_{\text{mate}}\), i.e., \(\Omega=\left\{(x,y)\ \mid\ x^{2}+y^{2}\leq r_{\text{mate}}^{2}\right\}\). In Cartesian coordinates, the probability density function \(g(x,y,t)\) for a walker to occupy position \(\mathbf{x}=(x,y)\) solves the parabolic exterior problem:  
\[\frac{\partial g}{\partial t} =D\
abla^{2}g, x^{2}+y^{2}>r_{\text{mate}}^{2}, t>0, \tag{11a}\] \[g =0, x^{2}+y^{2}=r_{\text{mate}}^{2}, t>0,\] (11b) \[g =\delta(x-x_{0})\delta(y), x^{2}+y^{2}>r_{\text{mate}}^{2}, t=0. \tag{11c}\]  
Without loss of generality, in Equation (11b) we set the initial location of the moth to be at \((x_{0},0)\), so that its distance from the female is \(r_{0}=x_{0}\). To solve Equation (11), it is convenient to first transform to dimensionless variables and a polar coordinate system. Accordingly, we write  
\[x=r_{\text{mate}}r\cos\theta,\qquad y=r_{\text{mate}}r\sin\theta,\qquad t= \frac{r_{\text{mate}}^{2}}{D}\tau,\qquad R=\frac{r_{\text{mate}}}{r_{0}}, \qquad g(x,y,t)=p(r,\theta,\tau),\]  
so that the problem for \(p(r,\theta,\tau)\) satisfies  
\[p_{\tau} =p_{rr}+\frac{1}{r}p_{r}+\frac{1}{r^{2}}p_{\theta\theta}, r>1, \theta\in[0,2\pi), \tau>0, \tag{12a}\] \[p =0, r=1, \theta\in[0,2\pi), \tau>0,\] (12b) \[p =R\delta(r-R^{-1})\delta(\theta), r>1, \theta\in[0,2\pi), \tau=0. \tag{12c}\]  
The solution to Equation (12) is now determined with the goal of developing closed form expressions for the following three quantities:  
\[\text{Probability moth still free at time }\tau:\quad P(\tau)=\int_{\theta=0}^{2\pi}\int_{r=1}^{ \infty}p(r,\theta,\tau)\,r\,drd\theta,\] \[\text{Probability moth trapped by time }\tau:\quad C(\tau)=1-P(\tau),\] \[\text{Arrival time distribution of moths to trap }:\quad S(\tau)=-P^{\prime}(\tau).\]  
The solution begins by defining the Laplace transform:  
\[g =0, x^{2}+y^{2}=r_{\text{mate}}^{2}, t>0,\]0The quantity \(\hat{p}(r,\theta;s)\) satisfies the associated elliptic equation :  
\[g =0, x^{2}+y^{2}=r_{\text{mate}}^{2}, t>0,\]1 \[g =0, x^{2}+y^{2}=r_{\text{mate}}^{2}, t>0,\]2  
where \(c^{2}=s\). In practice we are only interested in the radial component of the solution which describes the arrival time distribution. For completeness, we describe the full solution obtained by seeking a separable solution of Equation (100a) that is continuous, periodic, even in \(\theta\), bounded as \(r\to\infty\), and satisfies the boundary condition \(\hat{p}=0\) on \(r=1\). This series is  
\[\hat{p}(r,\theta)= \begin{cases}\sum\limits_{n=0}^{\infty}A_{n}\left[I_{n}(cr)- \frac{I_{n}(c)}{K_{n}(c)}K_{n}(cr)\right]\cos(n\theta),\qquad r<R^{-1},\\ \sum\limits_{n=0}^{\infty}A_{n}\left[\frac{I_{n}(cR^{-1})}{K_{n}(cR^{-1})}- \frac{I_{n}(c)}{K_{n}(c)}\right]K_{n}(cr)\cos(n\theta),\qquad r\geq R^{-1}, \end{cases}  \tag{101}\]  
where \(A_{n}\) are constants to be found from incorporation of the Dirac source term. The functions \(I_{n}(z)\) and \(K_{n}(z)\) are modified Bessel functions of the first and second kinds, respectively.
## 3 Evaluation Index  
The measurement of tracking performance of an SDC system can be divided into a certain moment, day, month or year from the perspective of time dimension. Generally, we use the tracking error to measure the tracking performance of an SDC system at a certain time. An effective tracking factor (ETF) was previously proposed to measure the tracking performance of an SDC system in a one-day operation cycle [29]. However, the ETF measured the tracking performance of the SDC system under a set of operating conditions (one radial angle and one tangential angle). It cannot measure the tracking performance of the SDC system in a certain area, nor can it measure the tracking performance of the SDC system over a long running period. Therefore, the average effective tracking factor \(\overline{\sigma}\) and standard deviation (SD) are introduced to represent the average value of the effective tracking factor and the dispersion degree (stability) of the average effective tracking factor of an SDC system under multiple working conditions, respectively. For example, when the azimuth axis radial angle is constant, \(\sigma_{i}\) is used to represent the tracking performance of the SDC system at different tangential angles. When \(i=1\), \(\sigma_{1}\) represents the tracking performance of the SDC system when the azimuth axis tangential angle \(\phi_{1}=0^{\circ}\) (due south). the tangential angle is divided into n equal parts (n = 24), the corresponding effective tracking factors are \(\sigma_{1}\), \(\sigma_{2}\),..., \(\sigma_{i}\),..., and \(\sigma_{n}\), respectively, and then the average effective tracking factor is obtained, which is calculated by Equation (17). The larger the average effective tracking factor, the better the tracking performance of the SDC system. The smaller the SD, the better the stability of the tracking performance of the SDC system Figure 3. \(\overline{\sigma}_{d}\), \(\overline{\sigma}_{m}\), and \(\overline{\sigma}_{y}\) represent the average effective tracking factors of one day, one month, and one year, respectively. \(\mathrm{SD_{d}}\), \(\mathrm{SD_{m}}\), and \(\mathrm{SD_{y}}\), respectively, represent the standard deviations of the average effective tracking factor of one day, one month, and one year (i.e., tracking performance stability).  
\[\mathrm{S}=\mathrm{S_{1}}+\mathrm{S_{2}}+\mathrm{S_{3}}+\mathrm{S_{4}}=\int_{ \mathrm{t_{0}}}^{\mathrm{t_{1}}}\mathrm{e_{i}}\mathrm{d}t \tag{15}\]\[\sigma=\frac{S_{2}}{S}=\frac{\int_{t_{0}}^{t_{1}}{[e_{f}-e(t)_{2}]dt}}{ \int_{t_{0}}^{t_{1}}{e_{f}dt}}=1-\frac{\int_{t_{0}}^{t_{1}}{e(t)_{2}dt}}{\int_{t_ {0}}^{t_{1}}{e_{f}dt}} \tag{16}\] \[\overline{\sigma}=\frac{1}{n}\cdot\sum_{i=1}^{n}\sigma_{i}\] (17) \[\mathrm{SD}=\sqrt{\frac{1}{n}\cdot\sum_{i=1}^{n}(\sigma_{i}- \overline{\sigma})^{2}} \tag{18}\]
## 2 Materials and Methods
### Optimal Weighted Data Fusion  
Assume the acquired sensor data deviations of the \(n\) sensor nodes are \(\sigma_{1}^{2},\sigma_{2}^{2},\cdots,\sigma_{n}^{2}\), respectively; the actual sensor data value is \(X\); the acquired sensor data are \(X_{1},X_{2},\cdots,X_{n}\), respectively, which are independent of each other; and the weight coefficients are \(W_{1},W_{2},\cdots,W_{n}\), respectively. The estimate sensor value \(\hat{X}\) and overall mean square error \(\sigma^{2}\) after data fusion satisfy the following Equations (1) and (2):  
\[\hat{X}=\sum_{i=1}^{n}W_{i}X_{i}\ \,\ \ \sum_{i=1}^{n}W_{i}=1 \tag{1}\]  
\[\sigma^{2}=E\Big{[}\big{(}X-\hat{X}\big{)}^{2}\Big{]}=E\Bigg{[}\big{(}X-\sum_ {i=1}^{n}W_{i}X_{i}\big{)}^{2}\Bigg{]}=\sum_{i=1}^{n}W_{i}^{2}\sigma_{i}^{2} \tag{2}\]  
According to Equation (2), the overall mean square error \(\sigma^{2}\) is the multivariate quadratic function that has the minimum value. The weight coefficient \(W_{i}\) (\(i=1,2\cdots,n\)) and the minimum overall mean square error \(\sigma_{\min}^{2}\) are shown in Equations (3) and (4).  
Figure 1: Diagram of the sensor node and network coordinator hardware. GPRS, general packet radio service.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### Hold Operation  
During the hold state, when the cell stores logic '1' (Q = S1 = 0, QB = S0 = 1), no read or write access is performed during hold. WL is held LOW and WWL is held HIGH so that P7, P8, N7, and N8 remain deactivated. As a result, the storage nodes are isolated from bit lines, and cross-coupled inverters retain the data.  
Figure 1: Schematic of the proposed WRTU-16T.  
Figure 2: The cell layout of the proposed WRTU-16T.
## 2 Mathematical Modeling for UCAV Path Planning  
### Threat Resource Model in UCAV Path Planning  
Modeling of threat resources is key for solving the UCAV path-planning problem. In this model, \(S\) is defined as the starting point, and \(T\) is the target point (Figure 1) [18,19]. There are some threat resources in UCAV battlefield, for example, radars, missiles, and artillery, the effects of which all are shown in the form of a circle. The extent to which UCAV is threatened is proportional to the fourth power of the distance from the threat center. The flight task is to generate an optimal or suboptimal path, so that the UCAV can avoid the threat area from starting point \(S\) to the destination \(T\).  
There are three main steps in the path planning. First, connect the starting point Sand the target point \(T\) into a line segment _ST_. Second, divide the line segment _ST_ into \(D\) + 1 equal parts. At each segment point, draw the vertical line of _ST_, denoted as \(L\)1, \(L\)2,...,_L_k,...L_D. Third, take a discrete point in each vertical section _L_k, these points constitute a collection of discrete points [19,20]  
Figure 1: Typical Unmanned combat air vehicle (UCAV) Battle Field Model.  
\[C=\{S,L_{1}(x(1),y(1)),L_{2}(x(2),y(2)),\ldots,L_{k}(x(k),y(k)),\ldots L_{D}(x(D),y( D)),T\}\.\]  
Fourth, connect these discrete points in order to form a path. In this way, the path-planning problem is transformed into the optimal orthogonal coordinate system to achieve the optimization of the objective function.  
In order to speed up the search speed of the algorithm, we can take line segment \(ST\) as the \(x\)-axis and carry on the coordinate transformation on each discrete point (\(x(k)\),\(y(k)\)) according to Equation (1), where is the angle between the original \(x\)-axis and the line segment \(ST\), while (\(x_{\mathrm{s}}\),\(y_{\mathrm{s}}\)) represents the coordinates of the original coordinate system.  
\[\begin{bmatrix}x^{\prime}(k)\\ y^{\prime}(k)\end{bmatrix}=\begin{bmatrix}\cos\theta&\sin\theta\\ -\sin\theta&\cos\theta\end{bmatrix}\begin{bmatrix}x(k)-x_{s}\\ y(k)-y_{s}\end{bmatrix} \tag{1}\]  
Thus, the \(x\) coordinates of each discrete point can be calculated by a simple formula \(x^{\prime}(k)=\dfrac{\left|ST\right|}{D+1}\cdot k\). The set of discrete points \(C\) can be simplified to \(C=\{0\),\(L_{1}(y)^{\prime}(1)\)),\(L_{2}(y^{\prime}(2)),\ldots,L_{k}(y^{\prime}(k)),\ldots L_{D}(y^{\prime}(D)),0\}\), which can greatly reduce the cost of computation.
###### Abstract
Since air drying has lower operating costs than freeze drying to obtain high flavonoid content and maximum antioxidant activity, they recommended air drying for mulberry leaves. In contrast, freeze drying of leaves was preferred for _Carica papaya_ L. [17], _Streblus asper_ Lour [18], green tea (_Camellia sinensis_ L.) [19] and guava (_Psidium guajava_ L.) [20] leaves to obtain the desired amounts of polyphenols and flavonoids. For nettle (_Urtica diola_ L.) leaves, the total phenol content and antioxidant activity were higher in oven-dried leaves than in freeze-dried leaves [21], while for _Annesela fragrans_ leaves [22], shade drying is the preferred drying method. This once again underlines the need to optimize the drying method for each leaf type and for each bioactive compound. In the case of ginkgo, drying methods for ginkgo seeds have been studied [23; 24; 25; 26; 27], but little attention has been paid to the influence of the drying method on the bioactive compounds in ginkgo leaves. Considering the growing interest in biflavonoids and the fact that yellow ginkgo leaves are a potential source of biflavonoids [6], we wanted to test different drying methods for yellow ginkgo leaves and investigate how they affect biflavonoids and other bioactive compounds. As far as we know, this is the first report on the influence of drying method on the form of dimeric flavonoids in yellow ginkgo leaves and on biflavones overall. We compared three drying methods for plant material: freeze drying, air drying and oven drying for yellow ginkgo leaves. After drying, we determined the browning index and the total content of phenols, flavonoids, phenolic acids and pigments (chlorophyll \(a,b\), total chlorophylls and carotenoids) spectrophotometrically and the content of the five most abundant biflavonoids in the ginkgo leaves (amentoflavone, bilobetin, ginkgetin, isoginkgetin, sciadopitysin) by HPLC-DAD. We also measured the antioxidant scavenging capacity using the DPPH method.