esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 5 Proposed Method
### Routing Using ED-MORFO
#### 5.2.2 Fitness Function  
The parameters used in the routing optimization are mentioned as follows:  
Queue length  
It deliberates the congestion limit of every node present in WSN. The QL indicated in Equation (21) is regarded as a primary fitness value during routing. This QL is utilized to increase data delivery performance, because the created ED-MORFO requires sending alert messages over the WSN.  
\[\text{QL}=\frac{RP_{k}}{Total\ buffer} \tag{21}\]  
where the received packets at \(k\)-th node is represented as \(RP_{k}\).  
Link quality is exploited to determine effective data transmission among the nodes \(l\) and \(k\). The Equation (22) is utilized to determine link quality.  
\[\text{Link quality}=\frac{1}{f\times r} \tag{22}\]  
where \(f\) and \(r\) define the forward and reverse data transmission among the nodes, respectively.  
The details about the distance (\(D1\) and \(D2\)) and residual energy (\(RE\)) are already defined in the section. Consequently, all the different objective fitness values are in conflict, and they are transformed into a single objective function, which is illustrated in Equation (23):  
\[\begin{split} Routing\ fitness=\delta_{1}\times\text{QL}+\delta_{2} \times\text{Link quality}+\delta_{3}\times D1+\delta_{4}\times D2\\ +\delta_{5}\times RE\end{split} \tag{23}\]  
where \(\delta_{1}\), \(\delta_{2}\), \(\delta_{3}\),\(\delta_{4}\), and \(\delta_{5}\) are the weighted parameters associated with each objective, which is equal to 0.3, 0.2, 0.15, 0.15, and 0.2, respectively [31].
## 2 Design Evolution of FSS Structure  
Figure 1 illustrates the design layout of the proposed miniaturized band-reject FSS, comprising a convoluted circular loop element realized on a lossy FR-4 substrate of thickness 1.55 mm and dielectric constant of 4.4. The unit cell is developed through a four-step procedure as specified in Figure 1. A conventional circular loop of radius R is initially designed. Four U-shaped stubs rotated at 90\({}^{\circ}\) are incorporated with the ring to miniaturize the cell size. The edges are further chamfered at various radii and optimized to achieve the desired frequency response. Finally, four inductive stubs are integrated into the flower-like structure to obtain its response at the desired 10 GHz frequency. The fourfold symmetric cell is designed in Ansys HFSS and has a footprint of 0.2\(\lambda\)\(\times\) 0.2\(\lambda\)\(\times\) 0.05\(\lambda\), where \(\lambda\) is the free-space wavelength at the resonant frequency. Table 1 specifies the optimized values of the design variables of the FSS structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Parameter** & **Value** \\ \hline a & 1.0 & b & 1.0 & c & 0.28 \\ d & 3.9 & e & 0.4 & f & 1.0 \\ g & 0.75 & m & 1.7 & n & 2.85 \\ r\({}_{1}\) & 0.2 & r\({}_{2}\) & 0.5 & r\({}_{3}\) & 0.7 \\ r\({}_{4}\) & 0.5 & r\({}_{5}\) & 0.4 & R & 1.425 \\ P & 6.0 & d\({}_{x}\) = d\({}_{y}\) & 0.6 & \(\alpha\) & 45\({}^{\circ}\) \\ \hline \hline \end{tabular} All units are in mm.  
\end{table}
Table 1: Optimized design variables of the convoluted FSS.  
Figure 1: The design evolution process of the proposed FSS shield: (**a**) Optimized FSS resonant structure; (**b**) FSS element rotated at 45\({}^{\circ}\); (**c**) a 3 \(\times\) 3 element array illustrating operation principle of the FSS, rejecting X-band signals while being transparent to other frequencies.  
FSS Unit Cell Rotation at 45\({}^{\circ}\)  
Figure 1b specifies the FSS element is rotated at an angle of \(\alpha=45^{\circ}\) to evaluate its electromagnetic performance against the polarization of the incident waves. The FSS element, when rotated at 45\({}^{\circ}\), has a performance comparable to that of its unrotated counterpart for various oblique incident angles. However, it can be observed that the resonant frequency shifts to lower frequencies by 6%, along with a 5% increase in the operational bandwidth. Thus, unit cell rotation does not affect its electromagnetic performance because of its structural symmetry along the x- and y-axes.
## 2 Materials and Methods
### Wettability Determination  
The wettability was determined using semi-automatic home-made system. The mechanized liquid dosing system allowed application of repeatable amounts of water on the surface at a given height. The computer equipped with a microscopic camera acquired images of droplets as a movie; therefore, one could determine wettability angles considering time as a parameter. The contact angle was determined using vector-based analysis software, enabling the measurement resolution below 0.1 deg. For each sample, at least five measurement values were obtained at different locations on the surface to provide a statistical approach. An example of the acquired image is shown in Figure 4.
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 3 Hash-Chain  
### Structure of the Hash Chain  
As a simplified structure of the blockchain, the hash chain uses cryptographic encryption to calculate the data blocks linked before and after. Each block is composed of two parts: a block header and a block body. The content of the block header contains information that forms the link relationship between the front and back blocks. The block body is mainly used to store transaction data and transaction details. Combined with the transmission characteristics of SAGIN, we have redefined the hash-chain templates, namely block template and transaction template [27].  
The block template is shown in Figure 1(a), which contains the local number of the generated block, the identity materials of the source SM (\(R_{this}\)), the hash value of the previous block, timestamp, and the Merkle root [28]. Among them, collect the wireless signal sent by the SM node and extract the RF fingerprint characteristics of the signal of the device, and use it as the \(R_{this}\). The hash value of the previous block is used as the critical data of the block information connected before and after the hash chain. Moreover, the introduced timestamp can be used as an essential basis to ensure the authenticity of the data and traceback at the same time to ensure that the information cannot be tampered with. The hash value, timestamp, root hash, and identity signature of the previous block together generate the hash value of the current block. The Merkle tree structure uses the primary transaction data as the leaf nodes of the hash chain and is constructed in a bottom-up manner. The hash value of the two transactions is calculated to obtain the hash value of the intermediate node. Then the intermediate node hashes in pairs and continues to build up. The final hash result is used as the root hash value of the Merkle tree. Each set of transaction data sets corresponds to a unique Merkle root.  
The transaction template is shown in Figure 1(b), which is different from the transaction information used for financial management such as Bitcoin and Ethereum. In the SAGIN, the cross-regional identity information exchange of vehicle nodes is used as a transaction content. The identity materials of the SM node in the source area and the destination area is represented by the RF fingerprint. The transaction number is the order of the number of transactions collected in a unit of time. The identity materials  
Figure 2: Block template and transaction template.  
encrypted by the vehicle is to ensure the confidentiality of the information. We use the public key of the SM node in the destination area to encrypt. Other SM nodes cannot read the vehicle identity materials. In contrast, the destination area SM node can use the private key to decrypt to obtain the vehicle identity materials about to enter the area. The SM node packs transactions collected in unit time to generate a block and implements a simplified consensus mechanism to verify and back up the records on the block.
# Inversion of a Thunderstorm Cloud Charging Model Based on a 3D Atmospheric Electric Field
## 3 Experimental Results and Analysis
### Inversion of the Charge Model  
According to the observation data of radar and atmospheric electric field meters, the classical three-layer charging model and the structural inversion of the two-layer charging model were respectively performed on the thunderstorm cloud at a certain time point of a second stage thunderstorm. The sampling interval of the three-dimensional atmospheric electric field meter was 1 s, and the measured data of the three-dimensional electric field at two adjacent sampling moments were 7.299 kV/m, 2.219 kV/m, \(-\)29.560 kV/m and 7.222 kV/m, 2.183 kV/m, \(-\)29.207 kV/m. Due to the extremely short time, the observation point was considered. The relative distance (\(|x-x_{0}|\), \(|y-y_{0}|\), \(z_{0}\)) from the thunderstorm cloud was approximately constant. A set of nonlinear equations between the three-dimensional electric field value and each charge structure parameter was established as per Equations (26)-(31):  
\[\begin{array}{ll}E_{x1}&=\frac{\mathrm{Q}_{1}(x-x_{0})}{2\pi(\epsilon_{0}+ \epsilon_{1})}\Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}+(z_{0}+500)^{2}\Big{]}^{- \frac{3}{2}}+\frac{\mathrm{Q}_{2}(x-x_{0})}{2\pi(\epsilon_{0}+\epsilon_{1})} \Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}\\ &\qquad+(z_{0}+1500)^{2}\Big{]}^{-\frac{3}{2}}+\frac{\mathrm{Q}_{3}(x-x_{0})}{ 2\pi(\epsilon_{0}+\epsilon_{1})}\Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}+(z_{0}+25 00)^{2}\Big{]}^{-\frac{3}{2}}\end{array} \tag{26}\]  
\[\begin{array}{ll}E_{y1}&=\frac{\mathrm{Q}_{1}(y-y_{0})}{2\pi(\epsilon_{0}+ \epsilon_{1})}\Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}+(z_{0}+500)^{2}\Big{]}^{- \frac{3}{2}}+\frac{\mathrm{Q}_{2}(y-y_{0})}{2\pi(\epsilon_{0}+\epsilon_{1})} \Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}\\ &\qquad+(z_{0}+1500)^{2}\Big{]}^{-\frac{3}{2}}+\frac{\mathrm{Q}_{1}(y-y_{0})}{ 2\pi(\epsilon_{0}+\epsilon_{1})}\Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}+(z_{0}+25 00)^{2}\Big{]}^{-\frac{3}{2}}\end{array} \tag{27}\]  
\[\begin{array}{ll}E_{z1}&=\frac{-\mathrm{Q}_{1}\epsilon_{1}(z_{0}+500)}{2\pi \epsilon_{0}(\epsilon_{0}+\epsilon_{1})}\Big{[}(x-x_{0})^{2}+(y-y_{0})^{2}+(z_ {0}+500)^{2}\Big{]}^{-\frac{3}{2}}+\frac{-\mathrm{Q}_{2}\epsilon_{1}  
charge structure was an even-polar structure with a negative polarity, which was consistent with the classical dipole structure.
Sensors_**2010**, _10_, 10571-10600; doi:10.3390/s101210571  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Near-Infrared Sub-Bandgap All-Silicon Photodetectors: State of the Art and Perspectives**  
**[PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON]**  
Istituto per la Microelettronica e Microsistemi (IMM), Consiglio Nazionale delle Ricerche (CNR),  
Via P. Castellino 111, 80131 Napoli, Italy; E-Mails: [EMAIL_ADDRESS] (G.C.);  
[EMAIL_ADDRESS] (M.I.); [EMAIL_ADDRESS] (I.R.); [PERSON]S] (L.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +39-081-613-2345; Fax: +39-081-613-2598.  
_Received: 15 October 2010; in revised form: 10 November 2010 / Accepted: 20 November 2010 / Published: 29 November 2010_  
**Abstract:** Due to recent breakthroughs, silicon photonics is now the most active discipline within the field of integrated optics and, at the same time, a present reality with commercial products available on the market. Silicon photodiodes are excellent detectors at visible wavelengths, but the development of high-performance photodetectors on silicon CMOS platforms at wavelengths of interest for telecommunications has remained an imperative but unaccomplished task so far. In recent years, however, a number of near-infrared all-silicon photodetectors have been proposed and demonstrated for optical interconnect and power-monitoring applications. In this paper, a review of the state of the art is presented. Devices based on mid-bandgap absorption, surface-state absorption, internal photoemission absorption and two-photon absorption are reported, their working principles elucidated and their performance discussed and compared.  
**Keywords:** optoelectronics; photodetector; silicon; waveguide; ring resonator; absorption
# Effect of Tide Level Change on Typhoon Waves in the Taiwan Strait and Its Adjacent Waters
## 2 Numerical Model Description
### Tide Model  
Tides are divided into two aspects: tide level and current. Tides are difficult to combine with waves due to their dynamic and directional characteristics. In comparison, the tide level can be better integrated with the wave simulation. Therefore, this paper simulates typhoon waves by adding tidal levels to the SWAN model.  
The governing equations of the numerical simulation of power flow include a continuity equation and two momentum equations. The basic equations are:  
\[\frac{\partial\xi}{\partial t}+\frac{\partial p}{\partial x}+\frac{\partial q }{\partial y}=0 \tag{2}\]  
\[\frac{\partial p}{\partial t}+\frac{\partial}{\partial x}\bigg{(}\frac{p^{2}} {h}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}\frac{pq}{h}\bigg{)}+gh\frac{ \partial\xi}{\partial x}-\Omega p-fVV_{x}+gp\frac{\sqrt{p^{2}+q^{2}}}{C^{2}h^{ 2}}=E\bigg{(}\frac{\partial^{2}p}{\partial x^{2}}+\frac{\partial^{2}p}{ \partial y^{2}}\bigg{)}-\frac{h}{p_{w}}\frac{\partial}{\partial x}(P_{a}) \tag{3}\]  
\[\frac{\partial p}{\partial t}+\frac{\partial}{\partial x}\bigg{(}\frac{pq}{h} \bigg{)}+\frac{\partial}{\partial y}\bigg{(}\frac{p^{2}}{h}\bigg{)}+gh\frac{ \partial\xi}{\partial x}+\Omega p-fVV_{x}+gp\frac{\sqrt{p^{2}+q^{2}}}{C^{2}h^{ 2}}=E\bigg{(}\frac{\partial^{2}q}{\partial x^{2}}+\frac{\partial^{2}q}{ \partial y^{2}}\bigg{)}-\frac{h}{p_{w}}\frac{\partial}{\partial y}(P_{a}) \tag{4}\]  
where \(\xi\) is the tidal level; \(p\) and \(q\) are the single-width flows in the \(x\) and \(y\) directions, respectively; \(h\) is the water depth; \(\Omega\) is the Coriolis force parameter; \(p_{w}\) is the water pressure; \(C\) is the Chezy coefficient; \(P_{a}\) is the standard atmospheric pressure;\(f\) is the wind friction coefficient; \(V\), \(Vx\), and \(Vy\) are the components of wind speed in the \(x\) and \(y\) directions, respectively; \(E\) is the eddy viscosity coefficient.  
The initial conditions are \(\zeta(x,y)|_{t=0}=\zeta_{0}\); \(p(x,y)|_{t=0}=0\); \(q(x,y)|_{t=0}=0\).
# Internet of Things for Sensing: A Case Study in the Healthcare System
## 6 Results and Discussion
To further validate our point, we looked into the CI signatures generated by various activities considering the calibrated phase information and analyzed the data shown in Figure 3.  
Figure 3: Raw channel information (CI) phase and calibrated phase information obtained during the experiment. Analyzing the calibrated phase information for corresponding human activities, we can clearly see that each human behavior has generated a particular registration as far as calibrated phase is concerned. A cluster of stationary data can be seen in Figure 3a that indicate sitting. Looking at activities such as motion or sitting, we are able to differentiate them with raw data and also with an SVM. Unlike a sitting posture, significant variances in the calibrated CI phase information can be seen in Figure 3b when a person starts walking within the area of interest. When the subject makes the transition to push-ups, two chunks of data can be seen in Figure 3c. Similarly, the phase information changes when the person is squatting. Finally, Figure 3e shows the stationary data when a person experienced sleepiness. In order to discuss the time history of narcolepsy sleep attacks, we analyze the acquired signal, as seen in Figure 4.  
Figure 4 shows that 1400 packets in total were received for 1400 s when the experiment was being performed. A frequency # 15 was selected for amplitude-vs.-time history analysis as in [44]. We can see that the amplitude levels for sleep episodes fluctuate between 5 dB to 10 dB from 1150 s to 1350 s are clearly different from the other four activities. However, the power levels for walking and push-ups remain the same for a certain period of time. Similarly, there are moments where the power level of push-ups, walking and sitting activity are similar. Thus, to classify the different human activities and detect the ND sleep episodes accurately, we use the support vector machine.
## 3 Results and Discussion
### Enhanced Microfluidic Immunoassay by Applying MET
#### 3.2.2 Frequency Effect on Binding Reaction Efficiency  
We have learned that harmonic electric fields of different frequencies have effects on stirring eddies and pump streamlines produced by SWET and TWT, and then we investigate the influence that the standing-wave and traveling-wave field frequencies on the enhancement factor of antigen concentration absorbed on the reaction surface (Be(t = 600 s)). In this situation, the voltage amplitudes are kept constant at Vsw = 6 V and Vtw = 6 V, and when varying one of the dual electric field frequencies, the other one is set at the ideal operating frequency. The voltage frequency exerts a substantial influence on the interfacial binding kinetics, mainly through an adjustment in the specific flow state of electrothermal induced convection.  
Figure 7A shows the variation trend of Be at varying SW frequencies. The SWET flow field is dominated by the Coulomb force at low frequencies (fsw < f\({}_{\text{ACET}}\)), with significant electrothermal eddies, and Be adopts its maximum value in the low-frequency range. However, since the dielectric force governs the flow behavior of SWET at high frequencies (fsw > f\({}_{\text{ACET}}\)), the electrothermal whirlpools not only reverse but also decay appreciably with respect to the low-frequency situation, and Be starts to decrease and then remains constant in the high-frequency limit. Therefore, to enhance the microfluidic immunoassay as far as we can, the SW field frequency used in MET ought to be close to the low-frequency limit.  
In Figure 7B, the value of Be at t = 600 s decreases when the field frequency of the TW signal deviates from the ideal pump frequency, and is at a high value near the intermediate critical frequency. The peak of Be does not occur exactly at the ideal frequency, but rather at slightly below f\({}_{\text{MW}}\). This is because under this condition, TWET not only induces a strong pump motion, but also arouses in-phase electrothermal vortex flow to a certain extent, and their synergistic action tends to maximize the Be value within this special frequency range. Accordingly, the performance of MET-enabled immunoassay exhibits a somewhat gentle peak in the middle frequency band with a change in slope around f = 100 MHz (Figure 7B), due to a collaboration of the frequency-dependent electrothermal swirlingvortex and pump motion in a quite complex manner. Even so, in practice, we still choose to engender the TWET pump force at the central ideal frequency to achieve the most effective sample transport along the channel length direction.
## 3 New Adaptable Strategy for RT and NRT troposphere Monitoring
### Estimating High-Resolution ZTDs and Horizontal Gradients  
The extended model for the GNSS slant tropospheric delay (\(\delta\)T) for a single receiver and a satellite is defined as a function of Zenith Hydrostatic Delay (ZHD), Zenith Wet Delay (ZWD) and horizontal tropospheric gradients pointing to North (G\({}_{\mathrm{N}}\)) and East (G\({}_{\mathrm{E}}\)) using an azimuth (A), which direction is counted from the North [43]  
\[\delta\mathrm{T}=\mathrm{m_{H}ZHD}+\mathrm{m_{W}ZWD}+\mathrm{m_{G}}[\mathrm{G_ {N}\cos(A)}+\mathrm{G_{E}\sin(A)}] \tag{1}\]  
The ZHD and the ZWD are hydrostatic and wet contributions to the ZTD which represents a vertical delay due to the neutral atmosphere in a profile above the station. Note that ZHD, ZWD and horizontal gradients are projected to any satellite direction by using \(\mathrm{m_{H}}\), \(\mathrm{m_{W}}\) and \(\mathrm{m_{G}}\) mapping functions, respectively. In our software, the Global Mapping Function [44] is used for this mapping. The a priori ZTD is represented by the ZHD in (1) which is calculated from the Global Pressure and Temperature model, GPT [45] or from an augmentation tropospheric model exploiting actual NWM analysis [41]. In (1), the ZWD represents then a correction of the estimated ZTD parameter. The ZTD and horizontal tropospheric gradients are estimated every epoch with applying the random-walk process. All these epoch-wise model parameters are recalculated during the backward smoothing.  
Figure 9 demonstrates the behavior of the ZTD from different solutions during the fast change in the troposphere, indicated with a sudden decrease of ZTD by 6-7 cm during a 2.5-h interval at POTS station on 10 May 2013. Precise IGS orbit and clocks are used for the demonstration. Obviously, the post-processing 15-min ZTDs from the GFZ solution using the EPOS software (gray points) and the daily smoothed 30-s stochastic ZTDs from our software (black dots) are in a very good agreement. Note that ZTDs from our solution are resampled to 5 min in the figure. Due to the use of past observations only, the real-time Kalman filter (red points) shows a delay in the change of estimated parameter. The random walk for ZTD was set to 5 mm/sqrt(hour), a commonly used value for any change in the atmosphere. The figure reveals a characteristic behavior for the real-time processing when using past observations in stochastic parameter estimation. We can observe significant improvements mainly in reducing the systematic behavior for the 1-h backward smoothing (green points). The main improvement can be reached within 15-30 min, as indicated for the ZTD from the backward smoothing at 12:00, 13:00 and 14:00. Interestingly, the ZTD from the 2-h smoothing already shows a very good agreement with the post-processing solution. It shows a potential improvement discussed in the previous section and postponing the NRT smoothing by at least 30 min if RT observations are available.  
Figure 9: Real time (red), near-real time (green, blue) and reference (gray, black) ZTD estimates during a fast change in the troposphere.