esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optical Indices
### Telescope Aperture and Overall Throughput
#### 3.3.3 Selection of Telescope Aperture and Overall Throughput
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{\(\boldsymbol{\tau}\) (\(D_{tel}\) = 2 m)} & \multicolumn{2}{c}{**CH1**} & \multicolumn{2}{c}{**CH2**} & \multirow{2}{*}{**Average \(N_{transit}\)**} \\ \cline{2-2} \cline{5-6}  & \(SNR_{1}\) & & & & \\ \hline
0.3 & 0.573 & 149 & 0.704 & 99 & 124 \\
0.4 & 0.671 & 109 & 0.819 & 73 & 91 \\
0.5 & 0.751 & 87 & 0.919 & 58 & 73 \\
0.6 & 0.831 & 71 & 1.015 & 48 & 60 \\
0.7 & 0.898 & 61 & 1.095 & 41 & 51 \\
0.8 & 0.93 & 53 & 1.173 & 36 & 45 \\
0.9 & 1.019 & 47 & 1.239 when the aperture and other parameters of instrument are determined, the promotion of overall throughput also contributes to a higher \(SNR_{1}\) and lower \(N_{transit}\). However, the telescope aperture and overall throughput are limited by the actual capacity for processing and assembling [35]. Considering factors of mirror deformation, detector ability, pupil aberration, and requirements for a lightweight and miniaturized space telescope, it is a great challenge for a very large mirror; therefore, a reasonable aperture size should be selected in combination with SNR requirements. Transit times can be up to \(N_{limit}=634\) for GJ 1214 b within a mission lifetime of 1000 days, but the actual number of revisits is far smaller than this value. Therefore, according to the relationship between transit times and instrument characteristics, aperture and throughput can be set as \(D_{tel}\geq 1.5\) m and \(\tau\geq 0.4\), respectively. Parameter settings are based on a challenging case of GJ 1214 b. To verify the feasibility of the parameter settings, simulations for other exoplanets were conducted. As shown in Table 3, \(A_{pt}\) and demanded \(N_{transit}\) for hotter and larger planets were simulated under the condition of \(D_{tel}=2\) m, \(\tau=0.5\). Results indicate that the goal SNR could be reached when combined with several transit observations.
## 2 Related Work
### Uplink Data Transmission  
During the data transmission phase in CF M-MIMO systems, all antennas of all UE simultaneously transmit their data symbols to all APs. The received signal at AP m can be expressed as  
\[\mathrm{y}_{m}=\sum_{k=1}^{K}\mathrm{H}_{mk}\mathrm{s}_{k}+\mathrm{n}_{m} \tag{8}\]  
where \(\mathrm{s}_{k}=\mathrm{F}_{k}\mathrm{x}_{k}\in\mathbb{C}^{N}\) is the transmitted signal of UE \(k\), with \(\mathrm{x}_{k}\sim\mathcal{N}_{\mathbb{C}}(0,\mathrm{I}_{N})\) being the data symbol vector of UE \(k\), and \(\mathrm{n}_{m}\sim\mathcal{N}_{\mathbb{C}}\left(0,\sigma^{2}\mathrm{I}_{L}\right)\) is the additive noise vector.  
The rationale for selecting the Weichselberger Rician fading model is grounded in its ability to more accurately capture realistic wireless channel conditions compared to conventional models such as the Rayleigh model. Unlike simpler models, the Weichselberger model accounts for joint spatial correlation at both the transmitter and the receiver, making it particularly suitable for scenarios involving large-scale antenna arrays. Additionally, it supports both line-of-sight (LoS) and non-line-of-sight (NLoS) components, incorporating phase shifts that are essential for capturing the dynamic behavior of channels in cell-free massive MIMO (CF M-MIMO) systems, especially in mobility-aware environments. Furthermore, the model is consistent with recent research and industry standards, reinforcing its applicability for simulating spatially correlated channels in practical, real-world deployments of massive MIMO networks.
## 3 Results and Discussion
### Reversibility Studies  
In the dialysis experiments, the inhibition of MAO-A by **3o** was reversed, and its activity increased from 38.7% (the value of A\({}_{\text{U}}\)) to 79.2% (the value of A\({}_{\text{D}}\)) (Figure 2A). The degree of MAO-A recovery after its inhibition by **3o** was similar to that observed with the reversible reference inhibitor toloxatone (from 29.7% to 84.5%) and contrary to the values obtained with the irreversible reference inhibitor paragline (from 25.2% to 26.1%), from which the enzyme activity did not recover. The inhibition of MAO-B by **3s** was reversed, and its activity increased from 35.0% (A\({}_{\text{U}}\)) to 84.0% (A\({}_{\text{D}}\)) (Figure 2B). The recovery was also similar to that observed with the reversible reference lazabemide (from 35.3% to 87.9%), whereas the inhibition of MAO-B by the irreversible inhibitor paragline was not reversed (from 30.6% to 33.8%). These experiments show that **3o** and **3s** are reversible inhibitors of MAO-A and MAO-B, respectively.  
Figure 2: Reversals of MAO-A inhibition by **3o** (**A**) and MAO-B inhibition by **3s** (**B**) using dialysis experiments.
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 3 The RPL and Problem Description
### RPL Control Messages  
RPL control messages are defined as a type of internet control management protocol version 6 (ICMPv6). The structure of the message is depicted in Figure 1, as in [7]. There are three types of control messages in RPL that are defined as follows:  
* DIO (DODAG information object)
* DIS (DODAG information solicitation)
* DAO (destination advertisement object)  
The DIO message is broadcasted by DODAG root to build a new DAG. It contains the network information that allows a node to discover an RPL Instance, learn its configuration parameters, and maintain the DODAG. The DIS message is used to solicit a DIO message from a node. This message is used to ask for DIO message from neighboring node and normally used when a node joins a stable network. The DAO message is sent by each node to propagate the route information towards the root. It is issued in a unicast manner and used to construct downwards routes (point-to-point and point-to-multipoint). Also, it contains the prefix reachability information.
# A Fuzzy Model for Reasoning and Predicting Student's Academic Performance
## 3 The Experiment
### The Data Sets
#### 3.1.1 The Fuzziness of the Student Performance Data (the Uncertainty)  
The relationship between students' results and absenteeism is difficult to be manipulated or measured by mathematical methods. For example, if we put this relation in the 'if then' rule (if the student attends the class, then he will pass the exam), we can see that this rule does not reflect the relationship between the degree of the attendance and the degree of passing the exam. Thus, the suitable rule in this case can be (if student attends the class to some degree, then he will pass the exam to some degree) which can be abbreviated as  
If A (to some degree x), then B (to some degree y)  
The rule now looks more equivalent to representing the relationship between the class attendance and exam results. However, if we apply this relation using deterministic or stochastic methods, we cannot obtain the correct output because of the following:  
1. The relation is uncertain, as there is no standard output for specific input where different students can obtain varieties of exam results even if they have the same attendance rate, and the opposite is also correct.
2. It is difficult to represent the sets in this rule using the normal set because we need to represent the degree of elements' belongingness (membership degree), not only the elements' belongingness (membership). Accordingly, the suitable way to deal with such a relation is using fuzzy logical concepts, as we proposed in Section 2.1. The fuzzy model of this relation based on our proposed FPM will be as follows:  
* Let A be the fuzzy set of degrees of the students' attendance
* Let B be the fuzzy set of students' results in the final exam
* Then, the relation will be: \((A\times B)\cup(\
eg A\times Y)\) (as explained in Section 2.1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Section 1 (Training Set)**} & \multicolumn{2}{c}{**Section 1 (Test Set)**} \\ \cline{2-5}
**No.** & **Attendance Rate** & **Final Exam Out of 40** & **Attendance Rate** & **Final Exam Out of 40** \\ \hline
10 & 0.89 & 26 & 0.91 & 13 \\ \hline
11 & 0.91 & 29 & 0.91 & 14 \\ \hline
12 & 0.92 & 37 & 0.91 & 13 \\ \hline
13 & 0.92 & 35 & 0.91 & 22 \\ \hline
14 & 0.96 & 20 &
## 2 Methods
### Sensor Modeling
#### 2.2.2 Transfer Function Based Analytical Model  
The model shown in Equation (3) is limited because it does not cater for damping effects, type of bond, shear deformation and axial effects. Similarly, we cannot determine if there is any resonance frequency between the desired ranges. These limitations can be overcome by utilising the transfer function of the cantilever based whisker shaft.  
While analysing the frequency response of the sensor, it is important to understand the loading of the sensing whisker shaft. The beam can be loaded in four possible ways, as shown in Figure 4.  
1. A point force applied at the tip of the whisker. The transfer function for this case can be expressed as [42; 43]: \[G_{11}(L,s)=\frac{\sinh(2\lambda L)-\sin(2\lambda L)}{EI\lambda^{3}D(s)},\] where \[D(s)=2[2+\cos(2\lambda L)+\cosh(2\lambda L)],\] and \[\lambda(s)=\sqrt[4]{\frac{cs+ms^{2}}{4 EI}},\] where \(c\) denotes the damping factor and \(s\) is the (complex) Laplace variable.
2. Point force applied at the slope of the whisker. The transfer function for this case can be expressed as [42; 43]: \[G_{12}(L,s)=\frac{\cosh(2\lambda L)-\cos(2\lambda L)}{EI\lambda^{2}D(s)}.\]
3. Distributed force applied per unit length to the tip of the whisker. The transfer function for this case can be expressed as [42; 43]:\[G_{11}(L,s)=\frac{-1}{EI\lambda^{4}}\frac{[\cosh(\lambda L)-\cos(\lambda L)]^{2}}{D (s)}.\]
4. Distributed force applied to the slope of the whisker. The transfer function for this case can be expressed as [42; 43]: \[G_{11}(L,s)=-2\frac{\cosh(\lambda L)\sin(\lambda L)-\cos(\lambda L)\sinh( \lambda L)}{EI\lambda^{3}D(s)}.\]
## 4 Limitations of OCTA  
OCTA is relatively new and has some limitations that might affect its usage in clinical practice. We review in brief some of these limitations and potential advancement of OCTA manufacturers to address these limitations.  
OCTA's field of view is much smaller than that of FA. However, this limitation has been solved in many commercial OCTA devices (wide field OCTA) by using multiple techniques such as image montaging [18; 178; 179], OCT-based microangiography [180], akinetic swept source [104] and extended field imaging technique [122; 181], which enabled up to \(12\times 12\) mm scanning areas. Commercial OCTA devices often struggle to obtain high-quality images through small pupils or moderate media opacities through which FA and OCT are able to transit. Image artifacts are common in clinical practice. OCT angiography can also mask laser scars since they are \"no flow\" areas [182]. As mentioned above, OCTA does not show active leakage either.  
In addition, OCTA is unable to detect blood flow below the minimum threshold. This leads it to miss microaneurysm if blood flow within is below that detected by OCTA [12]. Furthermore, OCTA can mask laser scars since they are eminent \"no flow\" areas [182].  
A limitation of SD-OCTA is its inability to demonstrate choroidal diseases such as CNV, but this limitation is not present in SS-OCTA, which has longer wavelength with deeper penetration into the choroid.  
On the other hand, different types of artifacts, such as segmentation, blinks, motions, and projection, may influence interpretation and measurements in OCTA images [183]. Segmentation artifacts occur in many situations, so there could be a need for manual manipulation of images in these situations to optimize the vascular details to ensure optimal OCTA interpretation [184]. Blink artifact appears as a black line because the OCT signal could not reach the retina or the software; accordingly, no movement was detected. Motion artifact appears as white lines, which represent signal decorrelation over the entire b-scan. These artifacts usually appear in areas where there is heavy subject movement when the patient moves or loses fixation. This motion artifact has been corrected in many OCTA devices by implication of the motion tracking system [185].  
Projection artifact happens when the large retinal vessels of the superficial vessel complex cast shadow on the deep vessel complex of the retina, making identification of the latter difficult. Moreover, blood vessels in the inner retina give a false shadow of blood vessels in the outer retina, which is normally avascular. A projection-resolved algorithm solved this problem to some extent [162]. One of the limitations of OCTA in the assessment of the optic nerve vasculature is that it detects overall disc flow measurements without differentiating retinal circulation from the posterior ciliary artery [186].
## 2 System Model
### Interference Scenario
#### 2.3.1 Energy Harvesting Protocol for Interference Scenario  
HT harvests energy only from the jammer and WiFi access point. The harvested energy is defined by the following equation:  
\[E_{\rm{active}}^{3}=P_{\rm{}}\big{|}g_{\rm{}}\big{|}^{2}\beta_{1}+P_{\rm{WiFi }}|g_{\rm{W-HT}}|^{2}(\beta_{0}+\beta_{1}). \tag{14}\]  
Since the HT and BS are passive for \(\beta_{2}\) period, the jammer cuts off the signal. The power of the signal sent by HT in active data transmission is given as  
\[P_{\rm{active}}^{3}=\frac{E_{\rm{active}}^{3}+E_{\rm{}}-E_{\rm{ss}}-E_{\rm{c}} ^{3}}{\beta_{1}}. \tag{15}\]  
where \(E_{\rm{c}}^{3}=P_{\rm{c}}\ \beta_{1}\). In the interference scenario, HT not only fails to deliver its information to the IR, but also leaks information to the ED.
## 4 Applications of 3D PFFT-BEM
### Model Comparisons of the PFFT-BEM with the 3D Parabolic Equation Method
#### 4.1.2 Higher-Order Term Effects: Acoustical Society of America (ASA) Wedge  
The first benchmark example is the ASA wedge, as shown in Figure 11. The sound source is fixed at (0, 0, 100 m) with a frequency at 75 Hz. The wedge meets free surface (\(z=0\)) at \(y=\) -2000 m. The slope angle of the wedge is \(\pi\)/36. The computational domain size covers the range of 28 km \(\times\) 4 km with a maximum depth at 400 m. The medium properties are defined in Figure 11, with sound speed \(c_{1}\) = 1500 m/s and density \(\rho_{1}\) = 1 g/cm\({}^{3}\) in the water and \(c_{2}\) = 1700 m/s, density \(\rho_{2}\) = 1.5 g/cm\({}^{3}\) and medium attenuation \(\alpha=0.5\) dB/\(\lambda\) in the bottom. The boundary element model uses quadrilateral element with \(\Delta l\ =\ \lambda\)/12. We compared the 3D PFFT-BEM with 3D cylindrical coordinate PE results based on the nonlinear operator Equation (32). The far-field solution of 3D PFFT-BEM and a 3D PE method are compared with benchmark solutions. From Figure 12a, quantitative validation of large-scale 3D sound field prediction by direct numerical computation is obtained. In contrast, phase error is observed in the far field of 3D PE results. The approximation of the square root operator in Equation (21) is the main reason for this phase error. Figure 12b shows the phase differences between PFFT-BEM and the 3D PE method. As expected, the phase errors of 3D PE increase as a function of distance. It should be noted that higher-order terms can be included to make the 3D PE solutions more accurate [23; 24]. However, the small marching steps of this higher-order 3D PE method will introduce large computational costs, especially in the cylindrical coordinates. We show the 3D feature of the TL contour for the ASA wedge in Figure 13.
# An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks +
## 4 Description of Our Proposed Cross-Layer Mobile Data-Gathering Protocol
### Distributed Clustering Phase
\begin{split} E_{i}(t+1)=\Bigg{(}OP\times\phi\times\bigg{(}\Big{(} \sum_{N\in\lambda_{i}}D_{i}(N)+D_{i}(t+1)\Big{)}\times\\ (E_{rec}+E_{tran})+(|\Delta|\times E_{tran})\Bigg{)}\end{split}  \tag{13}\]  
while the expected energy, \(Ex(\lambda,i)\), is obtained by  
\[Ex(\lambda,i)=Degree(i)\times(\lambda+1)\times OP\times\phi\times E_{rec} \tag{14}\]where \(OP\) denotes the operational period/phase, \(\phi\) indicates the traffic (data generated) rate, and \(E_{rec}\) signifies the receiving energy. In the proposed cross-layer protocol, the sensor degree to elect a CH is applied through a timer-based. Following this principle, each sensor can set a timer upon starting its scheduling process (i.e., its own time), which should be different than its 1-hop neighboring nodes. The timer of each sensor node depends on its priority value, which has an inversely proportional relation with its degree, distance, and energy. A sensor that has higher degrees (1-hop neighbors), higher energy, and lower distance to the sink, within its neighborhood, should be able to begin its schedule process sooner than other nodes. Conversely, when a sensor has fewer degrees, lower energy, and higher distance to the sink, it should be delayed for a longer time to start its scheduling process. The timer for each sensor, \(T_{node}\), is given by  
\[T_{node}=(1-P(\lambda,i))\times T_{sch}, \tag{15}\]  
where \(T_{sch}\) is the scheduling interval set prior as a constant value for all sensor nodes during the deployment process. It should be noted that the priority value of each node \(P(\lambda,i)\) is between 0 and 1, where 0 means it has the highest chance to be a CH and 1 denotes it has the lowest chance to be a CH.
## 2 Materials and Methods
### Characterization of AA.AgNP and SD.AgNP Nanoparticles  
The AA.ext, the SD.ext and the reacted solutions fabricating silver nanoparticles after 8 h were diluted fivefold to scan with a UV-vis spectrophotometer (Shimadzu UV-1800, US). DIW was used to adjust the background in the UV-vis measurement. The wavelength range was set from 350 to 750 nm.  
For FTIR spectroscopy, the lyophilized AA.ext, SD.ext, AA.AgNPs and SD.AgNPs were respectively mixed with KBr, and then pressed into pellets. The FTIR (Perkin Elmer, Waltham, Massachusetts, US) was operated in the wavenumber range of 400-4000 cm\({}^{-1}\) versus the transmittance mode.  
Figure 1: The brief description of the green synthesis procedure of the _Achyranthes aspera_ leaf broth (AA.ext)-mediated silver nanoparticle (AA.AgNP; **a**) and _Scoparia dulcis_ leaf broth (SD.ext)-mediated silver nanoparticle (SD.AgNP; **b**).  
The elemental compositions of AA.AgNPs and SD.AgNPs were determined by energy dispersive X-ray analysis (EDXA, model H-7593, Horiba, Northampton, UK). Their morphology and dimension were observed by a TEM (JEM-1400, JEOL, Tokyo, Japan) and SEM (FE-SEM S4800, Hitachi, Ibaraki, Japan).
# Key Recovery for Content Protection Using Ternary PUFs Designed with Pre-Formed ReRAM
## 2 One Way Unclonable Functions with Ternary States
### One Way Unclonable Functions with PUFs
#### 2.2.4 ReRAM-Based PUFs  
This PUF is discussed in detail in this paper as it has interesting tamper-resistant features [14; 15]. Each cell of a resistive random-access memory (ReRAM) has a unique resistance value that is compared to a median value. The value, 0 or 1, is generated from each cell depending on whether the resistance has been lower or higher than the median. The set of addresses **A** generated from **T** can point to a set of addresses in a particular order. The set of instructions **I** can point to a subset of the array. The responses of the PUF become the stream **K**. Other random access memory circuits such as DRAMs [16], Flash memories [17; 18; 19], and MRAMs [20; 21] can also be used to design PUFs.
## 3 TLE Parameter Determination
### Reliability Verification of the TLE Modeling  
To investigate the simulation accuracy of the TLE method, we performed a modal analysis of threaded structures with different pitch and thread lengths using the TLE method. From Equation (6), the tangential moduli of the TLE with different pitch and thread lengths were obtained. The Young's modulus could be obtained according to \(E=2(1+\
u)\,G\)[16; 28], where \(G\) is the tangential modulus and \(\
u\) (\(\
u=0.269\)) is Poisson's ratio, as indicated in Table 5.  
[PERSON] and [PERSON] [29] proposed a new concept for the contact influence area. It is assumed that the joint effects are extended to a region on both sides of the contact interface, which is referred to as the 'joint affected region'. This study extends the thread contact influence area \(t/2\) in the normal direction according to the actual thread tooth height, and a TLE with a thickness of \(t\) was used to simulate the thread influence area. The thread of the threaded tubes was an ISO metric triangular thread. For the convenience of modeling, the TLE thickness was determined to be 2 mm according to the height of the thread teeth, and the TLE length was the thread length. An FE model of the threaded structure was created in MD/NASTRAN, consisting of three parts: an internal thread tube, external thread tube, and TLE, as shown in Figure 7. The internal and external thread tubes and TLE were created using an eight-node solid element (CHEXA), and the contacts between the TLE and threaded tubes were defined as 'tied' constraints. Table 6 lists the material parameters of the threaded tubes [24]. The threaded tubes and TLE were described using a linear elastic material model. The TLE material parameters for different specifications of the threaded structures are listed in Table 5.  
Table 7 lists the first three order frequencies of the threaded structures obtained in the experiments and with the TLE method. Compared with the experiments, the maximum error of the TLE modeling method is 3.6%, and the minimum is \(-\)0.08%. All of the errors are within 5%, which satisfies the precision requirements. In summary, the theoretical determination method for the TLE material parameters is reasonable and reliable for threads with different pitches and thread lengths.