esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Coherent Receiver Settings for Optimum Channel Spacing in Gridless Optical Networks
## 3 Proposed Clock Recovery Circuit and Method
### Method Integrated in SDN Framework  
SDN offers a centralized server that oversees the whole network, it can aggregate information about the various links and modems. Industry standards such as Network Configuration Protocol (NETCONF), an Internet Engineering Task Force (IETF) network management protocol, is deployed to collect transponders configuration and measurements. Then software application will store relevant data in dedicated \"Modem database\" (DB), as illustrated in Figure 8, added to typical SDN controller. The \"network optimizer\" actor has a mechanism for viewing snapshots of available maximum concatenated capacity on each link group in the network. Moreover, a real-time view of the network can be supported to collect historical information to feed planning capacity needs and triggers for optimization activity. \"Path Computation\" process, along with up-to-date topology DB, allows for light-path establishment using a constrained-routing signaling protocol.  
In the context of point-to-point DCI DWDM, such as 400 ZR [28] aimed for link up to 120 Km (i.e., one span), nonlinearities of the fiber (such as SPM, FWM and XPM) have negligible role. A similar assumption applies to link OSNR specified to be at least 26 dB (due to the fact that only two Erbium-doped fiber amplifiers (EDFAs) are in the data-path of the optical signal), by Optical Internet Forum (OIF) standard in [28]; therefore, the spacing between the channels has the major  
Figure 8: Model of SDN controller (**left**) and high-level flow of proposed method (**right**).  
role in determining the noise induced in the clock phase error estimation. For Fixed-Grid network configuration or when there is no OSNR margin in Flex-Grid link (beyond the required-OSNR of the modem), there is no room for optimizing spacing between channels; therefore, a pre-defined laser frequency (i.e., spacing) is communicated by the SDN controller to the transponder. The latter can be established using standardized data model such as the Yet Another Next Generation (YANG) model. On the other hand, upon a request for a new service in a Flex-Grid network and where OSNR margin is available (especially in the beginning of life of the modem), a brute force method can be used to optimize receiver parameters by adding the optical channel, i.e., activating the laser at the transmitter, at nominal spacing (pre-defined or at (1+\(\beta\))*symbol-rate). Then run two-dimensional sweep for gain and bandwidth using receiver software/firmware to find the combination that achieves the minimal untracked jitter. If there is margin on untracked jitter, a fitting method, as shown in Figure 9, can be applied to determine the minimal spacing. Last, the SDN controller adjusts the laser frequency to squeeze the channel.
## 4 Discussion
### Simulation Results
The simulation study aims to investigate the significance of the proposed CVB method and its impact on the performance of the MMC. The study presents the operation of the MMC with the CVB method, depicted in Figure 3, while the operation without the CVB method is illustrated in [19]. The proposed CVB method ensures that the SM capacitor voltages remain at their nominal values. In contrast, in [23], the MMC is controlled using VSG and PQ controls without the CVB. Under this control mode, the SM capacitor voltages deviate from their nominal values, either increasing or decreasing depending on their respective duty cycles. The CVB method enhances the output voltage waveform of the MMC, reducing the THD and constraining the fluctuations in the SM capacitor voltages and DC current. Consequently, it effectively prevents issues related to excessive charging or discharging of the SM capacitors. Furthermore, as the fluctuations in the SM capacitor voltages diminish, the circulating current also decreases. These findings unequivocally demonstrate the efficacy of the proposed voltage-balancing algorithm for the MMC.
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 3 Results and Analysis
### Unsupervised Soil Contamination Test Results
After using different feature extraction methods, the recognition time required for using the one-class SVM is within 0.1 s. The recognition time using the _Mean_ combined with the one-class SVM is the fastest method, taking only 0.03 s. The slowest one is the use of \(FT\) combined with the one-class SVM, which only takes 0.08 s. The efficiency of this pollution detection is unmatched by commonly used chromatography methods. Furthermore, in order to consider the impact of different sample sizes during the model training stage on the recognition performance of the one-class SVM, different numbers of training samples were used to establish an unsupervised detection model, and the results are shown in Figure 5. As the number of training samples increases, the soil pollution detection effect is better, which is consistent with the traditional supervised learning model [34]. However, it is not difficult to find that the one-class SVM can still achieve good recognition performance when using only five training samples to establish the model. The recognition rate exceeds 96% under different feature extraction methods. Figure 4: Confusion matrix for each detection model using MAX eigenvalues, (**a**) KNN (20–20) and (30–10), (**b**) RF (20–20), (**c**) RF (30–10), (**d**) SVM (20–20), (**e**) SVM (30–10), and (**f**) one-class SVM (40–0). Figure 5: Effect of different numbers of training samples on the recognition of single-classification support vector machines. It can be seen from the above analysis that the proposed unsupervised learning model requires fewer training samples when conducting soil pollution detection, and only five healthy soil samples can be used to at least complete the model's establishment. Its recognition accuracy is comparable to that of traditional supervised learning, but when identifying errors, the proposed supervised learning method does less harm to soil governance and restoration.
## 3 Methodology
### Multi-View Attention  
Extracting periodic fluctuations from the seasonal component sequence is particularly important. Through FFT [32], the seasonal component is mapped to the frequency domain represented by complex values. After mapping the time series to the frequency domain, a time series can be completely represented by three attributes--frequency, amplitude, and phase--which reflect different characteristics of the periodic fluctuations of the sequence. Amplitude usually reflects the maximum distance that a sequence deviates from its equilibrium position at a certain moment, while phase represents the different states of a periodic sequence at different moments. By mapping the learned seasonal representation from the time dimension to the frequency domain, \(F\in\mathbb{R}^{I\times D}\) is obtained, where \(D\) represents the feature dimension. The real and imaginary parts of \(F\) are represented as \(F_{r}\) and \(F_{i}\), respectively. Amplitude and phase are represented by \(\mathcal{A}()\) and \(\Phi()\), respectively, and can be described as  
\[\mathcal{A}(F)=\sqrt{F_{r}^{2}+F_{i}^{2}}, \tag{5}\] \[\Phi(F)=\arctan(\frac{F_{i}}{F_{r}}).\]  
The inputs to MVI-Attention, i.e., queries, keys, and values, are denoted as \(q_{s}\in\mathbb{R}^{T_{x}\times D}\), \(k_{s}\in\mathbb{R}^{T_{x}\times D}\), and \(v_{s}\in\mathbb{R}^{T_{x}\times D}\), respectively. They are mapped to the frequency domain and combined with the sampling strategy:  
\[Q_{s},K_{s},V_{s}=Select(\mathcal{F}(q_{s},k_{s},v_{s})). \tag{6}\]  
Further, amplitude and phase representations corresponding to \(Q_{s}\), \(K_{s}\), and \(V_{s}\), are obtained, respectively. At the same time, similar attention mechanisms are leveraged in three other perspectives, including frequency, amplitude, and phase. Formally,  
\[f=\sigma(Q_{s}\cdot K_{s}^{\top})\cdot V_{s}, \tag{7}\] \[A=\sigma(\mathcal{A}(Q_{s})\cdot\mathcal{A}(K_{s}^{\top}))\cdot \mathcal{A}(V_{s}),\] \[\phi=\sigma(\Phi(Q_{s})\cdot\Phi(K_{s}^{\top}))\cdot\Phi(V_{s}),\]  
where \(\sigma\) is the activation function, such as _Softmax_ or _tanh_. Finally, iDFT is applied to obtain the seasonal representation:  
\[MVI-Attention(q_{s},k_{s},v_{s})=\sum_{i=1}^{I}A_{i}[\cos(2\pi f_{ij}+\phi_{i}) +\cos(2\pi\tilde{f}_{ij}+\tilde{\phi}_{i})], \tag{8}\]where \(A_{i}\) and \(\phi_{i}\) represent the amplitude and phase at the \(i\)-th frequency, respectively, and \(\bar{f}_{i}\) and \(\bar{\phi}_{i}\) represent the corresponding conjugate frequency and amplitude.
## Acronyms and Notations  
\begin{tabular}{l l} ACS-SSI & average correlation signal based stochastic subspace identification \\ COV-SSI & covariance-driven stochastic subspace identification \\ DOF & degrees of freedom \\ SNR & signal-to-noise ratio \\ SVD & singular value decomposition \\ MED & modal energy difference \\ MAC & modal assurance criterion \\
**A** & state matrix \\
**B** & input matrix \\
**C** & output matrix \\
**D** & feedthrough matrix \\ \(w(k)\) & random input signal \\ \(v(k)\) & measurement noise \\ \(k\) & discrete time value \\ \(y(t)\) & matrix of \(l\)-channel measurement signal \\ \(\dfrac{r_{k}(\tau)}{r_{k}(\tau)}\) & correlation function of original signals \\ \(\dfrac{\lambda_{i}}{r_{k}(\tau)}\) & average correlation function \\ \(\lambda_{i}\) & eigenvalue of matrix **A** \\ \(\psi_{i}\) & eigenvector of matrix **A** \\ \(Z_{\text{b}}\) & bounce motion of vehicle body \\ \(\Theta\) & pitch motion of vehicle body \\ \(\phi\) & roll motion of vehicle body \\ \(Z_{A\ldots F}\) & bounce motion of axles \\ \(\theta_{C}\), \(\theta_{D}\) & pitch motion of balanced suspension \\ \(k_{E}\), \(k_{F}\) & separate stiffness of leaf spring for balanced suspension \\ \(c_{E}\), \(c_{F}\) & separate damping of leaf spring for balanced suspension \\
**M** & mass matrix \\ \(Z\) & vector of vehicle dynamic responses \\
**C** & matrix of vehicle damping \\
**K** & matrix of vehicle stiffness \\
**C\({}_{\text{t}}\)** & matrix of tire damping \\
**K\({}_{\text{t}}\)** & matrix of tire stiffness \\
**Q\({}_{g}\)** & vector of displacement inputs \\ \(x\) & intermediate variable \\ \(u\) & input variable \\ \(dE_{\text{bf}}\) & MED between the left and right suspensions at the front of vehicle in bounce mode \\ \(dE_{\text{br}}\) & MED between the left and right suspensions in rear of vehicle in bounce mode \\ \end{tabular}
## 2 Device Fabrication  
The device structure used in this work is similar to that in [16], except a lateral channel direction considering the compatibility with the state-of-the-art CMOS technology for a sub-5 nm-technology nodes [31] (Figure 1). It is named as a surrounding channel nanowire (SCNW) TFET, since its intrinsic (or lightly doped) channel which is named as tunnel region surrounds conventional nanowire structure. All the materials except for gate oxide are Si. The gate oxide is SiO\({}_{2}\). In TCAD simulation, a channel length (\(L_{\text{CH}}\)) is set by 30 nm to exclude short-channel effect. Considering the latest CMOS technology, a nanowire radius except surrounding channel (i.e., tunnel region) (\(T_{\text{B}}\)) and a gate oxide thickness (\(T_{\text{OX}}\)) are set by 7 nm and 1 nm, respectively. The other important design parameters are summarized in Figure 1 and Table 1. All the parameter variations in this simulation are set in consideration of the fabrication processes [32; 33]. The following models are used for an accurate simulation result: Shockey-Read-Hall recombination, doping and field dependent mobility, and dynamic non-local BTBT after calibration by referring [17]. Since the thickness of tunnel region (\(T_{\text{TUN}}\)) is less than 8 nm, modified local density approximation is also used to consider quantum effect. In addition, the physical characteristics for BTBT is reflected by the calibrated current model based on the fabricated device [34; 35; 36; 37]. For the calculation of BTBT generation rate (G) per unit volume in uniform electric field, [PERSON]'s model is use as follows:  
\[G=A\left(\frac{F}{F_{0}}\right)^{P}\exp\left(-\frac{B}{F}\right), \tag{1}\]  
where \(F0=1\) V/m, \(P=2.5\) for indirect BTBT, \(A=4.0\times 1014\) cm\({}^{-1}\cdot\)s\({}^{-1}\), and \(B=1.9\times 107\) V\(\cdot\)cm\({}^{-1}\) are the [PERSON]'s model parameters and \(F\) is the electric field [34]. The pre-factor \(A\) and the exponential factor \(B\) parameter are calibrated by referring [17].  
Figure 1: Schematic structure diagram and definitions of design parameters in SCNW TFET.
## 5 Results and Discussion
If it is truth that the concept of QoS in communication systems is closely related to the network performance of the underlying routing system [69], these strategies, with the obtained results, can be considered as service related primitives allowing for the implementation of high-level differentiation. For instance, considering the curves for burst loss in Figure 14, it is possible to exploit the \"no loss\" bound of the SBPR algorithm to guarantee no burst loss at all until an average load of 54% of the capacity, and a burst loss ratio of 1 burst in 100,000 for an average traffic load of 80% of the capacity. Obviously, other policies are possible taking advantage of these optimally routed traffic patterns that can be exploited for end-to-end QoS provisioning. \begin{table}
\begin{tabular}{l l c c c} \hline  & & MCL & MEC & SBPR \\ \hline \multirow{3}{*}{ARPANET} & number of constraints & 442 & 1299220 & 462 \\  & number of variables & 1141 & 1299601 & 1161 \\  & total time spent in sec. & 0.02 & \((\approx 3600)\) & 0.01 \\ \hline \multirow{3}{*}{NSFNET} & number of constraints & 224 & 297934 & 238 \\  & number of variables & 547 & 298117 & 561 \\  & total time spent in sec. & 0.02 & \((\approx 1800)\) & 0.02 \\ \hline \multirow{3}{*}{RANDOM12} & number of constraints & 174 & 156684 & 186 \\  & number of variables & 397 & 156817 & 409 \\  & total time spent in sec. & 0.01 & \((\approx 1000)\) & 0.01 \\ \hline \multirow{3}{*}{COST239} & number of constraints & 162 & 108790 & 173 \\  & number of variables & 331 & 108901 & 342 \\  & total time spent in sec. & 0.02 & \((\approx 1200)\) & 0.02 \\ \hline \end{tabular}
\end{table} 
Table 2: Sizes of instances and reported time spent in the optimization problems. Figure 11: Proposed strategies _vs._ SP for Arpanet. Figure 12: Proposed strategies _vs._ SP for Nsfnet. Figure 14: Proposed strategies SP for Cost239. Figure 13: Proposed strategies _vs._ SP for Random12.
# Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond
## 3 Results and Discussion
### Reported Application of AI to RT
#### 3.2.7 Planning Quality Assurance, Commissioning, and Machine Performance Checks  
Planning QA, metrics, and chart reviews are essential components to ensure safety and high quality in RT treatments [98]. The advances made in OIS technology, including \"record and verify\" modules, have led to a growing collection of patient data, images, and reports. This huge quantity of information is not manageable for individuals or groups, thus, and could potentially represent a barrier to error identification or could lead to gaps in quality [99].  
For this reason, several ongoing studies highlight the potential of AI approaches to strengthen and speed up the QA processes, including daily metric and chart reviews collected in the RTOISs. The recently published AAPM TG-275 report [98] lists new recommendations for comprehensive and minimum initial chart checks, which require significant human resources if manually performed [100]. The development of an initial chart check automation process for radiotherapy informative systems dramatically will improve the practicality and efficiency of implementing the above TG recommendations, yielding significant reductions in both manual check times (by 44-98%) and residual detectable errors (by 15-85%) [101].  
Several examples are now available on the ability of ML and DL approaches [102; 103; 104] to distinguish introduced RT treatment delivery errors based on the collected 2D/3D data from devices (diode or chamber arrays) available for patient-specific quality assurance (PS-QA). In the future, patient-specific QA and machine performance prediction processes can hopefully be fully automated.
## 4 Methods for Improved Reliability and Robustness
### Model Improvement Techniques  
We denote model improvement (MI) techniques as those methods that directly address the limitations of DL-based approaches leading to poor reliability or robustness. Differently from QC techniques, where an external algorithmic tool flags problematic situations, MI techniques solve the lack of reliability or robustness by explicitly correcting the model. Another key difference w.r.t. QC tools, which can be plugged in most of the segmentation models as an external module, is that MI techniques imply modifications to the models or the overall analysis pipelines. In the following, we first present MI techniques for improved reliability and robustness classifying them based on the specific problem they tackle (Section 3). The section concludes with an ablation analysis of the presented MI techniques to illustrate their contributions to the performance of CMR segmentation methods.
###### Abstract
The problem that a lenslet cannot adjust its focal length to image the corresponding pixel in different positions arises here, because it is hard to detect the 3D position before the 3D point is formed. While there are lenticular sheets with adjustable lenslets [19; 20; 21; 22], the focal length of the lenslets is adjusted for extending the viewing distance of the AD, not for imaging the corresponding pixels. As we know, lenslets in the lenticular sheet are used for collimating and redirecting the light beams emitted from the pixels on the AD, not for imaging the pixels at the expected 3D point positions. Actually, there is no point imaged at the expected 3D point position. The 3D point is only virtually formed in the viewer's brain due to the fusion of the stereo pair. Therefore, the monocular models in References [17; 18] are far more than sufficient to quantify the effect of lens aberration on SD. We believe that the binocular vision theory is the right way to solve the problem. The key point to the fusing in the brain is the disparity formed in the left and right eyes. Therefore, we propose a binocular model to sidewise assess the perceived SD by using the similar factor of spots at the viewing plane. In this paper, based on the principle of binocular vision, we implement a theoretical model to quantize the reduction effect of lens aberration to SD in ADs. A stereo pair of spots, represented by two pixels in the display panel and emits light rays through two different lenslets, and the two light beams reach the viewing plane and form two light spots with an interpupillary distance. By comparing the disparity of the two light spots in the viewing position, the reduction effect of SD caused by lens aberration can be obtained. We only consider the stereoscopic vision and neglect the psuedoscopic vision that can cause the object flip inside out. The rest of the paper is organized as follows. In Section 2, the models of SD for ADs are presented by using the principle of binocular vision, and in Section 3, simulations are performed to investigate the relationships between each parameter and SD. Section 4 presents the experiments to verify the influence of the viewing distance and viewing angle by SD reduction. Finally, the conclusions are presented in Section 5.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### MDP Model for Expected Total Rewards  
Our goal is to maximize the total rewards over N slots through a relay power control scheme. However, due to the limited battery capacity, the relay power selection results in each slot will affect the initial battery capacity at the next moment. So, power decisions on different time slots are mutually influential. The MDP is a useful model to handle such decision problems, and backward induction is an effective algorithm to solve this problem [24].  
Therefore, we formulate \(P_{2}\) as a Markov decision process (MDP) problem, which can also be expressed as:  
\[\text{P}_{2}:\underset{\pi\in\prod}{\max}E^{\pi}\sum_{i=1}^{N}R(i), \tag{27}\]  
where \(\pi\) is a feasible relay power policy, \(\prod\) denotes the set of all feasible policies. \(R(i)\) is the reward of slot i, which is given by \([\beta_{i}D_{i}-(1-\beta_{i})H_{i}]\).
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Optimization of the Number of Graphene Layers
Then, single-layer graphene provides the best balance across these metrics, supporting efficient SPR interaction without the excessive losses and broadening seen in multilayer graphene configurations.
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 2 Model and Method
### Locating the Epidemic Source in Tree Network
#### 2.2.1 Infected Time Modeling  
Consider the case of a tree network. Obviously, studying the epidemic spread problem in tree has a huge advantage because there is only one path between the source and each observer. Recall that \(O=\{\theta_{k}\}_{k=1}^{K}\) is a set of \(K\) observers with infected time \(T_{O}=[t_{o_{1}},t_{o_{2}},...,t_{o_{K}}]^{T}\). Since the time delay \(\{\theta_{vu}\}\) along edges are random variables, the infected time \(\{\tau_{sk}\}\) of observers can also be seen as random variables. Suppose that the value of \(\theta_{vu}\) can be all positive integers \(\theta_{vu}\in\mathbb{Z}_{+}\) and we next discuss the distribution of random variable \(\theta_{vu}\).  
\[\left\{\begin{aligned} P(\theta_{vu}=1)&=\frac{1}{Z} \cdot\beta\\ P(\theta_{vu}=2)&=\frac{1}{Z}\cdot\beta(1-\beta)(1- \gamma)\\ &\vdots\\ P(\theta_{vu}=k)&=\frac{1}{Z}\cdot\beta(1-\beta)^{k-1}(1 -\gamma)^{k-1}\end{aligned}\right. \tag{1}\]  
where \(\beta\) and \(\gamma\) denote infection probability and recovery probability respectively and \(Z\) means normalized factor, which can be calculated directly:  
\[Z =\beta+\beta(1-\beta)(1-\gamma)+\beta(1-\beta)^{2}(1-\gamma)^{2} +...\] \[=\frac{\beta}{\beta+\gamma-\beta\gamma} \tag{2}\]Let \(\alpha=\beta+\gamma-\beta\gamma\), so the distribution of random variable \(\theta_{vu}\) can be simplified to \(P(\theta_{vu}=k)=\alpha(1-\alpha)^{k-1}\) that is: \(\theta_{vu}\) is a random variable obeying geometric distribution \(\theta_{vu}\sim Geo(\alpha)\).  
Suppose one node \(s\) in network and it does not belong to observers \(s\in V\backslash\mathcal{O}\). If node \(s\) is the epidemic source in network, for one observer \(o_{k}\), its infected time \(\tau_{sk}\) should be the sum of the initial time \(t_{0}\) and all time delay \(\theta_{vu}\) on the path from node \(s\) to node \(o_{k}\).  
\[\tau_{sk}=t_{0}+\sum_{\overline{vu}\in\mathcal{P}(s,o_{k})}\theta_{vu} \tag{3}\]  
where \(\mathcal{P}(s,o_{k})\subset E\) denotes the path from node \(s\) to node \(o_{k}\). Thanks for the tree we consider, the path is unique.  
Due to all random variables \(\theta_{vu}\) with independent identical distribution \(\theta_{vu}\overset{i.i.d.}{\sim}Geo(\alpha)\), we can find the infected time \(\tau_{sk}\) of node \(o_{k}\) actually obeys a Pascal distribution after translation transformation, i.e., \(\tau_{sk}\sim t_{0}+NB(d_{sk},\alpha)\), its probability density function shows:  
\[P(\tau_{sk}=t_{0}+r_{sk}|s,t_{0})=C_{r_{sk}-1}^{d_{sk}-1}a^{d_{sk}}(1-\alpha)^{ r_{sk}-d_{sk}},r_{sk}\geq d_{sk} \tag{4}\]  
where \(C_{b}^{a}=\frac{b!}{a!(b-a)!}\) represents the number of combinations and \(d_{sk}\) denotes the length of path \(\mathcal{P}(s,o_{k})\).