esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Routing Protocols Using Ant-Like Mobile Agents
### AntHocNet  
In a MANET, nodes are mobile and therefore enter and leave the network frequently. It is not reasonable or useful to maintain a complete description of the network. In order to cope with such situations in MANET, the same research group of AntNet has developed an algorithm that builds and maintains routing tables reactively with some proactive actions to refresh the routing information. In a sense, AntHocNet can be seen as the MANET version of AntNet-FA. AntHocNet is a routing algorithm that pursues multiple paths simultaneously [31; 32; 33; 34]. As in AntNet and AntNet-FA, ant-like agents roam in the network seeking routing information; however they are dispatched reactively when a source node wants to start communication with the destination node. The quality of a path is quantified as the inverse of the cost that is the number of hops travelling from the source to the destination, and it is expressed as the pheromone value. When a source node wishes to start a communication session to a destination node, the source node checks whether there is a route from the source to the destination. When there is one, the source node simply uses it; when there is not, the source node must discover a route to the destination. The source node creates an ant-like agent called _reactive forward ant_, to explore the network to seek the route. The _reactive forward ant_ collects the route information as well as looking for the route. When an ant agent reaches the destination node, it becomes the _backward ant_, and travels back to the source node. Along the path, the _backward ant_ updates the routing tables based on the route information collected by the _reactive forward ant_. Data packets are probabilistically distributed into several paths. This is accomplished by probabilistically selecting next node to transmit based on the pheromone value at each intermediate node.  
Even the transmission route is established from the source node to the destination node and the data communication session started, the source node does not remain passive. It creates other ant-like agents called _proactive forward ants_ and distributes them into the network. These _proactive forward ants_ move from node to node based on the pheromone value, and monitor the quality of the route. From time to time, an intermediate node broadcasts additional _proactive forward ants_ to explore new routes to the destination.  
There are a few improvement proposals to AntHocNet. A notable one is Fuzzy Logic Ant Based Routing (FLAR) proposed by [PERSON] _et al._[35]. FLAR has integrated fuzzy logic into AntHocNet. In MANET environments, route failure management is a challenge because of the node mobility and rather narrow bandwidth. [PERSON] _et al._ have proposed an adaptive fuzzy ant-based routing algorithm that combines ACO and the fuzzy logic technique to cope with the route failure problem. A similar proposal can be found in [36]. They have compared average end-to-end delay and packet delivery ration with AntHocNet, and have shown the effectiveness of the integration of fuzzy logic to ACO.
# Shape Optimization of Single-Curvature Arch Dam Based on Sequential Kriging-Genetic Algorithm
## 3 Case Study  
### Basic Information of a Single-Curvature Arch Dam  
To investigate the computational efficiency of the GA-FEM, GA-KSM, and GA-SKSM for shape optimization of a single-curvature arch dam, this study used the Jiancaogou single-curvature arch dam, located approximately 160 m upstream of the Jiancaogou Bridge in Guizhou, China, as a test example. The maximum height of this dam is 55.5 m, upstream water level is 53 m, and sedimentation water level is 38 m.  
In this study, four variables were defined for optimizing the geometry of this single-curvature arch dam. The lower and upper bounds of these design variables required in the optimization process are listed in Table 1.  
Moreover, to determine the optimal shape of the single-curvature arch dam, the mechanical properties of the dam body and foundation rock materials listed in Table 2 were considered.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Design Variable** & \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{B_{c}}\)**(m)** & \(\mathbf{B_{b}}\)**(m)** \\ \hline
**Lower Bound** & 0.2 & 0.5 & 4 & 15 \\
**Upper Bound** & 0.5 & 0.9 & 8 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lower and upper bounds of design variables.  
Figure 2: Schematic flowchart of (**a**) a genetic algorithm with a Kriging surrogate model (GA-KSM) and (**b**) a genetic algorithm with a sequential Kriging surrogate model (GA-SKSM).
## 2 Materials and Methods
### Experimental Design and Data Treatment  
The optimisation tests were carried out using a solution of standards containing equal amounts of OA and UA (50 \(\upmu\)g/mL each). A central composite design was used to investigate the effects of two independent variables--temperature (\(T\)) and flow rate (\(F_{R}\))--on the separation process. The independent variables were coded on five different levels (\(-\)1.41, \(-\)1, 0, 1, 1.41), using preliminary experimental data as guidance to set the ranges of the factors. The complete design consisted of 13 experimental points, including five replications of the central point (all variables coded as zero). The behaviour of the system was described by the following second-degree polynomial equation:  
\[Y=\beta_{0}+\beta_{i}X_{i}+\beta_{j}X_{j}+\beta_{ii}X_{i}^{2}+\beta_{jj}X_{j}^ {2}+\beta_{ij}X_{i}X_{j} \tag{1}\]  
where \(Y\) is a response, \(\beta_{0}\) is a constant coefficient, \(\beta_{i}\) and \(\beta_{j}\) are linear coefficients, \(\beta_{ii}\) and \(\beta_{jj}\) are quadratic coefficients, \(\beta_{ij}\) is interaction coefficient, and \(X_{i}\) and \(X_{j}\) are coded values of the independent variables [31]. Three responses \(Y_{1-3}\) were considered, including the separation time (\(Y_{1}\), \(ST\)) understood as the retention time of the latest peak (UA), the resolution (\(Y_{2}\), \(R_{s}\)) between the adjacent bands of OA and UA, and a combined response (\(Y_{3}\), \(D\)) described by the overall desirability function according to [PERSON] and Suich [32]:  
\[D=(d_{1}d_{2})^{\frac{1}{2}} \tag{2}\]  
where \(d_{1}\) and \(d_{2}\) correspond to individual desirability function for each response:  
\[d_{1}=\left\{\begin{array}{rl}0&ST>8\\ \dfrac{8-ST}{3}&5<ST<8\\ 1&ST<5\end{array}\right. \tag{3}\]  
\[d_{2}=\left\{\begin{array}{rl}0&Rs<1.3\\ \dfrac{Rs-1.3}{0.4}&1.3<Rs<1.7\\ 1&Rs>1.7\end{array}\right. \tag{4}\]  
The resolution (\(R_{s}\)) was calculated using the following equation:  
\[R_{s}=2.0\times\dfrac{t_{1}-t_{2}}{w_{2}-w_{1}} \tag{5}\]in which \(t_{1}\) and \(t_{2}\) are retention times, and (\(w_{2}+w_{1}\)) is a sum of peak widths at baseline between tangents lines drawn at 50% peak height. The statistical analysis of the data and development of regression equations between the process variables and analytical responses were performed using the software Statistica 12.0 PL (Statsoft, Krakow, Poland, 2015). According to the experimental data, the fitting models were constructed and the statistical significance of the model terms were examined by regression analysis and analysis of variance (ANOVA, \(\alpha\) = 0.05).
# Non-Parametric Model-Based Estimation of the Effective Reproduction Number for SARS-CoV-2 +
## 3 Results
### AKS Performance for Multiple Time-Varying SIRD Model Parameters
In contrast, with increasing dynamic activity in the data, the error bands become smaller and the estimates coincide with the true values. To analyze whether the SIRD-AKS can also handle different time dependencies of the model parameters, for example, rapidly changing parameters including discontinuous functions over time, two further parameter settings were addressed, as shown in Figure 1B,C. For this, the SIRD model was evaluated with time-constant parameters \(\gamma_{t}\) and \(\theta_{t}\) and a time-varying \(\beta_{t}\). In parameter setting II, the time course of \(\beta_{t}\) was chosen as an autoregressive process of order \(p=1\) (AR[1]) with an added exponential decay. The autoregressive process is given by \(x_{i}=0.0022+0.95\,x_{i-1}+w_{i}\) with the noise term \(w_{i}\sim\mathcal{N}(0,0.004)\). Furthermore, a step function with three constant levels was utilized in parameter setting III. Data were simulated (not shown) and the SIRD-AKS algorithm was applied analogously to the previous setting I with sinusoidal \(\beta_{t}\). For all parameter settings I, II and III, the SIRD-AKS results adequately capture the dynamics of the three time-dependent parameters, demonstrating a broad applicability of the method to different function classes of time-dependent-parameter time courses.
# Cable Force Identification of Two Bending Cable Networks with Arbitrary Boundary Conditions
## 3 Methodology
### Statistic Method to Remove Outliers  
In this paper, the cable tensions and cross-tie axial stiffness are identified from cable network frequencies that are obtained from acceleration signals at an arbitrary location on the cables of a cable network system under free vibration or ambient vibration. Since some errors may occur during the measurement of cable frequency, estimated cable forces and cross-tie axial stiffness might be biased. Hence, this paper will apply the median absolute deviation (\(MAD\)) method in statistics to remove the outliers and improve the method's accuracy. The median absolute deviation or absolute deviation from the median was discovered and introduced by [PERSON] (2017). The median (\(M\)), similar to the mean value of a data set, has the benefit of being less effective in the occurrence of outliers. The \(MAD\) method helps to determine the range that most data points condense. [PERSON] (2018) defined the \(MAD\) value as follows:  
\[MAD=bM(|x_{i}-M(x_{i})|) \tag{13}\]  
where \(x_{i}\) is the data point and \(b\) is \"a constant linked to the assumption of normality of the data, disregarding the abnormality induced by outliers\" [PERSON] (2018). Constant \(b\) usually equals 1.4826.  
The criteria to detect outliers depend on the researchers' definition and justification. In general, the decision criteria are defined in Equation (14). If a data point \(x_{i}\) is out of the detected range, it is considered an outlier.  
\[M(x_{i})-c\times MAD\leq x_{i}\leq M(x_{i})+c\times MAD \tag{14}\]  
where \(c\), varying from 2.0 to 3.0, is a constant related to the researchers' decision. [PERSON] (1989) strongly recommended a threshold of 2.5 as a reasonable choice. Thus, this paper also uses this \(c\) constant equal to 2.5 to remove outliers.
# Optical Helicity and Chirality: Conservation and Sources
## 4 Macroscopic Sources
### Helicity in Bi-Isotropic Media  
We model a general linear, lossless bi-isotropic medium, where we allow for both a chiral and a magnetoelectric response, using an extension of the \"Drude-Born-Fedorov\" constitutive relations to include the Tellegen parameter \(\alpha\)[45]:  
\[\mathbf{D} =\epsilon\left(\mathbf{E}+\beta\mathbf{\
abla}\times\mathbf{E}\right) +\alpha\mathbf{H},\] \[\mathbf{B} =\mu\left(\mathbf{H}+\beta\mathbf{\
abla}\times\mathbf{H}\right)+ \alpha\mathbf{E}, \tag{28}\]  
where \(\beta\) is the referred to as the chirality parameter. The results of [16] have been extended to include chiral and Tellegen media, as well an anisotropic polarisabilities, in [18], where it has been explicitly shown that [PERSON]'s equations in a medium remain invariant under a duality transformation of the fields when there is a constant ratio \(\epsilon/\mu\) and a Tellegen parameter of zero. The chirality parameter, on the other hand, is free to vary in space. This result in [18] is based on the symmetrized constitutive relations of Condon [45], but it is straightforward to show that the same condition for dual symmetry holds for the Drude-Born-Fedorov relations given above. Helicity Conservation in a Chiral Medium  
Both helicity and energy are conserved in a lossless chiral medium [18; 47]. It therefore follows that, in a situation where the interface between the vacuum and the medium is dual-symmetric, the helicity per photon of light in the chiral medium should remain the same as the vacuum values. This is the situation depicted in Figure 4. In [14], free electromagnetic fields with left- and right-circular polarisation are shown to have a helicity of \(\pm\hbar\) per photon. In [46], the expressions for the helicity density and flux density (1) and (14) are trivially extended to those in a linear medium by replacement of the vacuum electric and magnetic responses, \(\epsilon_{0}\) and \(\mu_{0}\), with \(\epsilon\) and \(\mu\). In considering the propagation of left- and right-circularly polarised light within a chiral medium, is it shown that, although the flux density formed in this way produces the correct helicity of \(\pm\hbar\) per photon, the helicity density does not. This follows from the fact that the energy density within a chiral medium is of the form [48]:  
\[w_{1}=\frac{1}{2}\left[\mathbf{D}\cdot\mathbf{E}+\mathbf{B}\cdot\mathbf{H}- \beta\epsilon\mu(\mathbf{E}\cdot\dot{\mathbf{H}}-\dot{\mathbf{E}}\cdot \mathbf{H})\right], \tag{29}\]containing an explicit \(\beta\)-dependent term. As we know the correct form of the helicity flux density within the medium is:  
\[\mathbf{v}=\frac{1}{2}\left(\sqrt{\frac{\epsilon}{\mu}}\mathbf{E} \times\mathbf{A}+\sqrt{\frac{\mu}{\epsilon}}\mathbf{H}\times\mathbf{C}\right), \tag{30}\]  
we can use this and the condition of local helicity conservation to find the form of the helicity density.
## 5 Experimental Results and Discussion
### Jammer DoA Determination  
Here, the classification methods are used to identify jammer threat direction of arrival (DoA). In this experiment, both right-hand and left-hand jammer positions with respect to the transmitter have been considered for 8-ary LG formats only (i.e., worst results reported in the previous sub-section). The dataset comprises 28,800 images created from the two jammer directions with a capturing duration of 200 s for each mode, and for SJR ranging from \(-5\) to 3 dB with 1 dB step (i.e., total dataset size: 200 images \(\times\) 2 directions \(\times\) 8 modes \(\times\) 9 SJR values). Two classifiers have been trained and tested using 70% and 30%, respectively, of the obtained dataset. The first classifier uses the CNN network; however, the output layer contains two nodes, which denote direction of jammer either right or left. In the second technique, a simple classifier based on LDA is used. Using this technique, each image with size (28 \(\times\) 28) is flattened into one vector, resulting in an input dimensionality of \(N\) = 784. The LDA has the ability to reduce the dataset into only 1D. Then a threshold value \(i_{\text{th}}\) is obtained to classify between left or right DoA.  
Figure 9 shows the classification performance in terms of the recognition accuracy for LDA- and CNN-based classifiers. The CNN-based method is able to identify the left and right jammer DoA with 100% accuracy for all SJRs. This could be attributed to the clear and frequent incidents of the left-hand jammer on the right-side of the received mode (see Figure 4) and the vice versa is expected for the right-hand jamming. However, exploiting LDA-based algorithm (i.e., low-complexity classifier) reduces the DoA identification to a maximum of 97% at SJR of 2 dB. Inset in Figure 9b shows the distribution of data on LDA space. It is observed that much useful information of data was preserved in 1D space, with the existence of a clear separation between the two DoA jammers, except a small region of interference. While LDA classification accuracy is less than the CNN classifier, nevertheless it gives acceptable results, especially at SJR \(\geq\) 0 dB, where the accuracy reaches more than 95%.  
Figure 8: Average accuracy for 16-ary LG+Mux-LG and 32-ary LG+Mux-LG+HG modulation schemes.
# Intelligent Fusion Imaging Photonics for Real-Time Lighting Obstructions
## 4 Methods
### Quantitative Metrics  
Image quality was quantified using three quantitative metrics: PSNR, SSIM and RMSE. Firstly, PSNR, modification of MSE, was used as a metric because this metric focused on maximal lighting obstructions, as well as overall residual lighting obstructions. As PSNR focused on the maximal difference between the input and reference image, making this metric much more robust to noise and sensitive to errors. PSNR was calculated as  
\[PSNR(I(I_{IF}(x,y),\ I(x,y))=10\log_{10}\left(\frac{I_{MAX}x^{2}}{MSE(I_{IF}(x,y ),\ I(x,y))}\right) \tag{6}\]  
where \(I_{MAX}\) is 255, \(MSE(I_{IF}(x,y),\ I(x,y))=\frac{\sum(I(I_{IF}(x,y)-I(x,y))^{2}}{N}\), and \(N\) is the number of pixels in the image. Secondly, SSIM was used as a metric because this metric is a perception-based model that considered image degradation. SSIM was calculated as  
\[SSIM(x,y)=\frac{\left(2\mu_{x}\mu_{y}+C_{1}\right)\left(2\sigma_{xy}+C_{2} \right)}{\left(\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\right)\left(\sigma_{x}^{2}+ \sigma_{y}^{2}+C_{2}\right)} \tag{7}\]  
where \(\mu_{x},\mu_{y},\sigma_{x},\ \sigma_{y},\sigma_{xy}\) are the local means, standard deviations, and cross-covariance for images \(x,y\) and \(C_{1}=\left(0.01\ I_{MAX}\right)^{2}\), \(C_{2}=\left(0.03\ I_{MAX}\right)^{2}\). Thirdly, RMSE provided a measure of the differences between the input and reference image. RMSE was calculated as  
\[RMSE(I_{IF}(x,y),\ I(x,y))=\sqrt{\frac{\sum(I_{IF}(x,y)-I(x,y))^{2}}{N}} \tag{8}\]  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s23010323/s1](https://www.mdpi.com/article/10.3390/s23010323/s1)]([https://www.mdpi.com/article/10.3390/s23010323/s1](https://www.mdpi.com/article/10.3390/s23010323/s1)).  
**Author Contributions:** Conceptualization: S.E.L. and Y.P.; Methodology: S.E.L. and H.D.; Investigation: H.D., C.Y., Y.L., X.Z., J.G. and A.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Air Force Office of Scientific Research (AFOSR FA9550-16-1-0272, FA9550-19-1-0186, FA9550-22-1-0285), National Science Foundation (NSF 1454188), and academic research fund at the University of Michigan.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Correspondence and requests for data should be addressed to S.E.L.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Changing the NDR Length of the Same Gene Can Regulate Gene Expression  
To further verify the effect of NDR length on exogenous gene expression, we compared GFP gene expression under L-NDR and NDR-, as well as S-NDR and NDR+ conditions, using RT-qPCR, ELISA, and Western blotting. GFP mRNA levels were reduced by 19, 22, and 9 times, and protein levels by 3, 6, and 4 times under L-NDR and NDR- regulation in _YBL025W_, _YBL032W_, and _YBL071W-A_ genes, respectively (Figure 7A,B). Western blot analysis detected consistent 27 kDa products (Figure 7C).  
Figure 6: Analysis of GFP expression levels in _S. cerevisiae._ (**A**) The relative mRNA expression levels of GFP in L-NDR and S-NDR groups were determined by RT-qPCR. (**B**) The GFP expression levels in L-NDR and S-NDR groups were determined by an ELISA kit. (**C**) The expression of GFP protein in L-NDR and S-NDR groups was detected by Western blotting using anti-GFP antibodies. The data for the mean \(\pm\) SEM come from three NDRs with three replications in each NDR group. An independent _t_-test was down (two-tailed). \({}^{**}p\) < 0.01, \({}^{**}p\) < 0.001. RT-qPCR, reverse transcription quantitative polymerase chain reaction.  
Conversely, GFP mRNA levels increased by 11, 33, and 4 times, and protein levels by 6, 15, and 3 times under S-NDR and NDR+ regulation in _YAR008W, YAR027W_, and _YAL030W_ genes, respectively (Figure 7D,E). Western blot analyses also indicated 27 kDa products, corroborating the mRNA expression trends (Figure 7F). These findings demonstrated that alterations in NDR length significantly influenced exogenous gene expression, with shorter NDRs downregulating gene expression and longer NDRs upregulating it.
## 6 Results and Discussion
### Assessment of Artifact-Induced Disruption of SSVEP  
As a first step, it was verified whether the jaw clenching artifact actually disrupts SSVEP. For this purpose, during stimulation at the frequency of 25 Hz, 10 windows with artifacts and 10 windows without artifacts were selected. Figure 7 presents the signal spectrum at electrode O2 for participant S24, comparing artifact-free time windows with windows containing muscle artifacts. In both cases, steady-state visually evoked potentials were elicited at 25 Hz. As shown, jaw-clenching artifacts severely disrupt the SSVEP signal, preventing effective detection. This underscores the importance of thorough EEG preprocessing to remove artifacts.  
Figure 8 illustrates the spectrogram of EEG signals recorded during stimulation at the frequency of 25 Hz during jaw clenching at 4, 10, 16, 22, and 27 s, also recorded from participant S24 at electrode O2. For most of the recording, 25 Hz SSVEP is clearly visible. However, during jaw clenching, muscle activity strongly propagates to O2 and disturbs the 25 Hz frequency associated with the SSVEP. Similar trends were observed in other participants. At lower stimulation frequencies (5-20 Hz), these artifacts were less disruptive to SSVEP than at higher frequencies (>20 Hz).
## 3 Results and Discussion
### Pressure and Viscosity Distributions  
Prior to discussing the resulting velocity and force profiles, the effects of the parameters on the pressure distribution, and hence the viscosity distributions, will be presented. Plots of the pressure across the channel are not included as they vary linearly according to Equation (22).  
In this work viscosity is taken to be a function of pressure, as defined by Equation (29), wherein parameters \(n\) and \(\alpha\) are the viscosity distribution control parameters. Therefore, as the pressure varies due to hydrostatic forces, the fluid's viscosity also varies across the channel. In practice, the values of \(p_{0}\) and \(\mu_{0}\), \(n\), and \(\alpha\) can be adjusted to reproduce the appropriate variation of the viscosity for the problem at hand. In the present study, their chosen ranges guarantee realistic viscosity values yet are varied enough to illustrate their effects on the flow. Other than the permeability, \(k\), the variations in all of the other parameters listed in Table 1 have a direct influence on the viscosity across the channel, as discussed in the following sections.