esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Analysis
### Design of Blind Identification Criterion  
The scheme for nonlinearity cancellation is presented in Figure 3, where \(g(\cdot)\) is assumed as the transfer function of a broadband RF front-end including the linear term with memory \(g_{1}(\cdot)\) and the nonlinear term with memory \(g_{N}(\cdot)\). Hence:  
\[z(n)=g[x(n)]=g_{1}[x(n)]+g_{N}[x(n)] \tag{15}\]  
The input signals \(x(n)\) could be divided into strong signals \(x_{h}(n)\) which produce nonlinear interference when passing through the RF front-end, and weak signals \(x_{l}(n)\) containing nonlinear distortions, low power signals, and noise \(n_{x}(n)\), namely:  
\[x(n)=x_{h}(n)+x_{l}(n)+n_{x}(n) \tag{16}\]  
Substituting Equation (16) into (15):  
\[\begin{array}{rl}z(n)&=g_{1}[x_{h}(n)]+g_{1}[x_{l}(n)]+g_{N}[x_{h}(n)]+g_{N} [x_{l}(n)]+g_{N}[x_{h}(n),x_{l}(n)]+n_{z}(n)\\ &\approx g_{1}[x_{h}(n)]+g_{1}[x_{l}(n)]+g_{N}[x_{h}(n)]+n_{z}(n)\\ \end{array} \tag{17}\]  
Normally, as a result of \(x_{h}(n)>>x_{l}(n)\), \(g_{N}[x_{l}(n)]\) and \(g_{N}[x_{h}(n),x_{l}(n)]\) are much less than \(g_{N}[x_{h}(n)]\) and below the noise floor; thus, they can be neglected in \(z(n)\). If \(h(\cdot)\) is the nonlinear  
Figure 3: Principle of nonlinearity mitigation.  
behavior model and meets the condition that \(h[g_{1}(\cdot)]=g_{N}(\cdot)\), based on the discussion in Section 2.2, we obtain:  
\[\begin{array}{ll}s_{z}(n)&=z(n-\tau_{best})-h[z(n)]\\ &=g_{1}[x_{h}(n-\tau_{best})]+g_{1}[x_{l}(n-\tau_{best})]+g_{N}[x_{h}(n-\tau_{ best})]+n_{z}(n-\tau_{best})\\ &\quad-h\{g_{1}[x_{h}(n)]+g_{1}[x_{l}(n)]+g_{N}[x_{h}(n)]+n_{z}(n)\}\\ &\approx g_{1}[x_{h}(n-\tau_{best})]+g_{1}[x_{l}(n-\tau_{best})]+g_{N}[x_{h}(n- \tau_{best})]-h\{g_{1}[x_{h}(n)]\}+n_{s}(n)\\ &=g_{1}[x_{h}(n-\tau_{best})]+g_{1}[x_{l}(n-\tau_{best})]+n_{s}(n)\end{array} \tag{18}\]  
Since \(x_{h}(n)>>x_{l}(n)\), there is an assumption that the output of the nonlinear behavior model \(h\{g_{1}[x_{h}(n)]+g_{1}[x_{l}(n)]+g_{N}[x_{h}(n)]+n_{z}(n)\}\ \approx h\{g_{1}[x_{h}(n)]\}\). Actually, Equation (18) provides the identification criterion of nonlinear mitigation. To each delay within the searching range, optimization can be employed to make the power residual between \(g_{N}[x_{h}(n-\tau_{best})]\) and \(h\{g_{1}[x_{h}(n)]\}\) achieve its minimal value. The minimal one among these values is acquired at the best delay and, hence, the corresponding coefficients of the nonlinear model are regarded as the optimal solution for nonlinearity mitigation.
# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis  
### Experimental Settings  
#### 4.1.1 Imaging Satellite  
This paper selects the fine strip II mode of the GF3 satellite for regional imaging task planning. The satellite and sensor parameters are shown in Table 1.  
Figure 4: The procedure of ECL-INS-LMOA.
## 2 Materials and Methods
### In Vitro Determination of Antioxidant Activity
#### 2.12.3 Determination of the Effective Concentration (EC\({}_{50}\)) of N/S-Carbon QDs  
An appropriate quantity of the obtained N/S-Carbon QDs was dissolved in 10 mL of deionized water to obtain 0.2, 0.4, 0.6, 0.8 % (\(w/v\)) concentration levels, respectively. Thereafter, 1.8 mL of the [DPPH\({}^{\bullet}\)] ethanolic solution (0.037 mg mL\({}^{-1}\), 9.42 \(\times\) 10\({}^{-5}\) mol L\({}^{-1}\)) and 1.2 mL of acetate buffer 100 mM (pH = 7.10) were placed in a cuvette and the absorbance of the [DPPH\({}^{\bullet}\)] radical was measured at t = (A\({}_{0}\) = 1.507). Subsequently, 1 mL of the N/S-Carbon QDs solution (2, 4, 6, and 8 mg of N/S-Carbon QDs) was added to a cuvette along with 1.8 mL of ethanol solution of [DPPH\({}^{\bullet}\)] and 0.2 mL of acetate buffer. The absorbance of the reaction mixture was measured at 517 nm. The % inhibition of [DPPH\({}^{\bullet}\)] was calculated using the following equation:  
\[\rm\%\ Inhibition=A_{t}/A_{0}\ \times 100 \tag{2}\]  
where A\({}_{0}\) is the initial absorbance of the DPPH standard solution and A\({}_{t}\) is the absorbance of remaining [DPPH\({}^{\bullet}\)] free radical after reaction for 20 min with N/S-Carbon QDs samples. The effective concentration of aqueous N/S-Carbon QDs solution that is the specific concentration that could decrease the [DPPH\({}^{\bullet}\)] concentration by 50% was estimated based on the following equation:  
\[\rm y=-4.0179x+66.645;R^{2}=0.9970 \tag{3}\]A blank sample with ethanol and acetate buffer (2:1, \(v/v\)) was used. Reported results are the average \(\pm\) standard deviation values of three replicates (n = 3).
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.10 TetR and AcrR Domain  
The TetR protein family is a group of transcriptional regulators with an HTH DNA-binding motif, which is widely distributed among bacteria [119]. TetR family proteins control efflux pumps and transporters having a role in antibiotic resistance and tolerance to toxic chemicals, synthesis of osmoprotectants, quorum sensing, drug resistance, virulence and sporulation [120; 121]. However, the TetR family is named after its most characterized member, TetR, which has a role in the regulation of the expression of _tet_ genes, involved in conferring tetracycline resistance in the bacterial system [120]. Proteins with the TetR domain maintain its optimal cellular level by feedback control. TetR (dimer) binds to two adjacent DNA major grooves (6 bp each) located in the promoter region of the target gene on both of the strands, where helix \(\alpha\)3 (Gln38 to His44) is involved in a sequence-specific recognition [119; 120]. The Arg28 in helix \(\alpha\)2 strengthens the specific contact with the complementary strand [119; 120]. The hydrophobic core, developed from the contributing residues from the \(\alpha\)1, \(\alpha\)2, and \(\alpha\)3 bundle, stabilizes the TetR DNA binding domain [119]. A highly conserved Lys48, located in \(\alpha\)4,also has an essential role in the TetR-DNA complex formation [119].  
The TetR family protein also works in a complex circuit with other proteins, including AcrR, a transcriptional repressor of the \(acrAB\) operon responsible for encoding a multidrug efflux pump which removes a wide range of antibiotics and confers antibiotic resistance in _E. coli_[121]. The AcrR protein (215 amino acid; dimer) crystal structure showed that it is composed of a three-helix DNA-binding domain and a unique C-terminal domain (large internal cavity) for ligand binding, which is structurally similar to members of the TetR family of transcriptional repressors [120; 122]. It was predicted that ligand (rhodamine 6G, ethidium and proflavin) binding at the C-terminal ligand binding site leads to an alteration in the conformation of the N-terminal DNA binding region and thereby initiates transcription at the corresponding promoter of the target gene [123].
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GPS Tracking
#### 2.6.1 Bit Synchronisation and Navigation Data Decoding from very Weak Signals
Once the subframe is synchronised it is possible to reconstruct the desired data items from the stored data by accumulation down the column. The parity of each word in the matrix can then be checked individually. If the parity check is passed, the word is decoded and its position marked. The choice of 20 entire navigation frames is made so that sub-frames 1 to 3 will be repeated 20 times resulting in a probability of bit error in the accumulated data of less than \(10^{-6}\) at 15 dB-Hz. It is likely that time and ephemeris parameters can be provided by the platform and only frame synchronisation is required. Therefore, we have only decoded the time of the week in our hardware implementation to demonstrate the principle. The success rate of the acquisition engine with signals of different carrier to noise density ratios (C/No) is shown in Figure 12. Here 25 trials were performed at each signal level and the success rate recorded for the acquisition, bit synchronization, and data decoding stages. The signals were generated  
Figure 11: GPS preamble location. with our Spinent GSS800 simulator and the output power is displayed in Figure 12. The decoding of the time of the week is used to determine if frame synchronisation was successful. Using typical derivations from [23; 31] the detection threshold is chosen such that the probability of detection is theoretically 0.95 at 15 dB-Hz and probability of false alarm is \(10^{-3}\).The acquisition results of Figure 12 are slightly worse but within 1 dB of the expected values. Clearly, the receiver is limited by its ability to synchronise and decode the navigation data before the acquisition limit is encountered. Despite this, at 15 dB-Hz the receiver is still able to acquire and decode the navigational data with a success rate of around 60% for each attempt.
## 3 Algorithm Flow
### Polarization Image and Spectral Decoupling and Spectral Reconstruction
#### 3.1.1 Polarization Interference Image Segmentation  
The segmentation of the polarization interference image array is the basis of subsequent data processing. It can be seen from the system principle that the image array obtained by a single polarization channel of the detector should be arranged in a pattern of 8 \(\times\) 8 with 64 image units of the same size. However, the manufacturing accuracy error and assembly error of the multi-stage micro-mirror, the manufacturing accuracy error of the micro-lens array, and the rotation translation error will lead to the alignment error of the optical system, which may cause different widths and order offsets of each interferogram unit, so it is necessary to segment the image units. Therefore, a method of convolution kernel traversal combined with the Canny Edge Detection Algorithm is proposed to realize image segmentation.  
The method of using a single convolution kernel to traverse the image to obtain the rough location set of image elements is relatively effective, but there is also the problem of incomplete detected edges, while the Canny Edge Detection Algorithm has high accuracy in edge pixel location and can effectively suppress noise interference so that more fine edge information can be obtained; therefore, the combination of the two algorithms can improve the accuracy of edge detection so as to achieve more accurate image segmentation. The specific steps are as follows.  
1. Adopt the convolution kernel with the size of the enclosing circle of a single image unit, and set a threshold to traverse the image to obtain the coarse positioning set of the image unit;
2. Carry out Canny edge detection on the image to obtain the image edge information positioning set;
3. Merge the ergodic coarse positioning set through the convolution kernel and the positioning set through Canny edge detection to obtain the accurate edge information set.  
The coarse positioning point set \(W=\left\{Z_{\mathrm{a}}\right\}_{\mathrm{a}=1}^{m_{1}}\) is obtained through the image processing of convolution kernel traversal, where \(m_{1}\) represents the number of feature points. The measured image information positioning set \(V=\left\{Z_{\mathrm{g}}\right\}_{\mathrm{g}=1}^{m_{2}}\) is obtained through Canny edge detection, where \(m_{2}\) represents the number of feature points. The union set of the two is obtained as \(Y\), which is the final precise edge information set, as shown in Equation (7):  
\[Y=W\cup V=\left\{Z_{\mathrm{a}}\right\}_{\mathrm{a}=1}^{m_{1}}\cup\left\{Z_{ \mathrm{g}}\right\}_{\mathrm{g}=1}^{m_{2}} \tag{7}\]
## 3 Factor Graph Integrated Navigation Framework
### IMU Pre-Integration Factor Node
The update equation for the Jacobian matrix of the system state variables with respect to the system error state variables, which calculates the first derivative, can be expressed as:  
\[J_{k-1,t}=\Phi_{t}J_{k-1,t-1} \tag{22}\]  
where \(J_{k-1,t-1}\) is the initial Jacobian matrix and is an identity matrix. To avoid repeated integration, the pre-integrated measurements are approximated using a first-order expansion based on the Jacobian and covariance matrices. The updated pre-integrated measurement values are calculated as follows:  
\[\left\{ \begin{array}{l}\triangle\hat{p}_{b(k)}^{b(k-1)}\approx\triangle p_{ b(k)}^{b(k-1)}+J_{b\omega}^{p}\delta b_{\omega}+J_{ba}^{p}\delta b_{a}\\ \triangle\hat{p}_{b(k)}^{b(k-1)}\approx\triangle p_{b(k)}^{b(k-1)}+J_{b\omega }^{p}\delta b_{\omega}+J_{ba}^{p}\delta b_{a}\\ \hat{q}_{b(k)}^{b(k-1)}\approx q_{b(k)}^{b(k-1)}\otimes\left[\begin{array}{ c}1\\ \frac{1}{2}J_{b\omega}^{\phi}\delta b_{\omega}\end{array}\right]\end{array} \right. \tag{23}\]  
where \(J_{b\omega}^{p},J_{ba}^{p},J_{ba}^{p},J_{ba}^{p},J_{ba}^{p}\) is a submatrix of \(J_{k-1,k}\), representing the Jacobian matrix of the first derivative of the superscript with respect to the subscript. For example, \(J_{b\omega}^{p}\) corresponds to the position of \(\delta p_{b(k)}^{b(k-1)}/\delta b_{\omega}\). Based on the previous derivation, the IMU pre-integration factor residual can be expressed as:  
\[r_{imu}(z_{b(k)}^{b(k-1)},X)= \begin{bmatrix}\triangle p_{b(k)}^{b(k-1)}- \triangle\hat{p}_{b(k)}^{b(k-1)}\\ \triangle p_{b(k)}^{b(k-1)}-\triangle\hat{p}_{b(k)}^{b(k-1)}\\ 2{(q_{b(k)}^{(k)})}^{-1}\otimes q_{n(k)}^{n(k)}\otimes q_{n(k-1)}^{n(k-1)} \otimes\hat{q}_{b(k)}^{b(k-1)}\\ b_{\omega(k)}-b_{\omega(k-1)}\\ b_{a(k)}-b_{a(k-1)}\end{bmatrix}  \tag{24}\]
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
## 3 Problem and Algorithm Description
### DC-KH Algorithm Description
#### 3.3.2 KH Main Cluster Head and Vice-Cluster Head Selection Phase  
When the dynamic layer and non-uniform clustering stage is completed, the master cluster heads and vice-cluster heads are selected with the improved KH algorithm. First of all, a threshold is set. Once the radius of the cluster is less than this threshold, the candidate cluster head becomes the cluster head. On the contrary, the two cluster heads are selected in the first phase according to the KH algorithm.  
1. Fitness Function  
The performance of cluster head in the cluster routing algorithm depends on the choice of the fitness function. To prolong the survival time of the master cluster head, the condition that the energy loss of the main cluster head is significantly more than that of the ordinary node should be considered. The selection of the location of the master cluster head should be considered as well to collect the information of other nodes in cluster. The distance between the two nodes is hoped to reach the minimum. To select the optimal cluster head, the formula of the adaptive function shown below is adopted:  
\[f=\varepsilon\times f_{1}+(1-\varepsilon)f_{2} \tag{27}\]  
\[f_{1}=E(H)/\sum_{i=1}^{m}E(n_{i}) \tag{28}\]  
\[f_{2}=(m-1)/\sum_{i=1}^{m}d_{i\to H} \tag{29}\]  
where \(f_{1}\) is the ratio of cluster head node to total energy, the reciprocal of \(f_{2}\) is the average distance between the two, \(m\) is the total number of nodes in a cluster, \(E(n_{i})\) is the energy of the \(n_{i}\) node in the cluster, and \(d_{i\to H}\) is the distance from the main cluster head to node N.  
Figure 1: Sketch map of node dynamic hierarchical and non-uniform node clustering.  
According to the main cluster head fitness function, the values of \(\varepsilon\) for controlling the proportions of \(f_{1}\) and \(f_{2}\) should be adjusted so that the maximum value of \(f\) is the optimal cluster head. The sub-cluster head should then be selected according to the principle of distance from the base station mainly to achieve two purposes: the distance from the base station should be near and must have high energy. The fitness function can be denoted as follows:  
\[g=\lambda g_{1}+(1-\lambda)g_{2} \tag{30}\]  
\[g_{1}=E(H)/\sum_{i=1}^{m}E(n_{i}) \tag{31}\]  
\[g_{2}=d_{H\to BS}/\sum_{i=1}^{m}d_{i\to BS} \tag{32}\]  
where \(g_{1}\) is the ratio of cluster head node to total energy, and \(d_{H\to BS}\) is the distance between the vice-cluster head and base station. \(\sum_{i=1}^{m}d_{i\to BS}\) is the sum of the distances between all nodes in cluster and base station; and \(g_{2}\) is the ratio between the two nodes. The method is consistent with the master cluster head selection method. Once \(g\) is the largest among all variables, the optimal pair is selected as the optimal sub-cluster head.
## 4 Results and Discussion
### Strain-Driven Comparison of PoD\({}_{A}\) and PoD\({}_{E}\)
Figure 9 indicates the trend lines of the average PoD\({}_{\text{A}}\) and PoD\({}_{\text{E}}\) values of AE activities based on the concrete strain. As seen, PoD\({}_{\text{E}}\) exhibits a low variation ranging from 45% to 60% in both low and high strain values and shows insensitivity to detectability at turning points TP1 and TP2. Conversely, in the PoD\({}_{\text{A}}\) curve, PoD values start from around 15% at lower strain values, increase with the increase in strain in accordance with mechanical behavior expectations, and show inflection points,  
Figure 8: Comparison of amplitude- and energy-based detectability approaches: (**a**) PoDs vs. amplitude, (**b**) PoD\({}_{\text{A}}\) and PoD\({}_{\text{E}}\). particularly where detectability rises at TP1 and TP2. This circumstance renders \(\mathrm{PoD_{A}}\) more meaningful, as it aligns more closely with concrete strain values and AE hit rates, which are directly related to the formation of damage. As the increase in the AE hit rate signifies mechanisms such as crack growth and propagation, an increase in detection probability is expected. By examining the trends of PoD values based on both strain rate and AE hit rate, as shown in Figure 10, it is observed that \(\mathrm{PoD_{A}}\) demonstrates the expected increasing behavior in both cases. Regressively fitted curves also reveal a consistent exponential function change, particularly in alignment with the AE hit rate, where \(\mathrm{PoD_{E}}\) remains constant in contrast. Therefore, throughout the monitoring of strain or hit rate, amplitude-based PoD calculation provides reasonable detectability levels.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 3 Methods  
We explored six academic databases due to their relevance to the topic. These databases were IEEE Xplore, Google Scholar, ANU SuperSearch, Scopus, Pubmed Central, and ResearchGate. In order to search these databases, a set of keywords was derived in consultation with university librarians. These keywords were robot*, emotion recognition, and sensor* (where * denotes wildcard characters). For consistency, the six databases were searched to find papers that contained all three terms in any meta field. As technology is rapidly evolving and to keep our research accurate-to-date, our search results were narrowed down to papers published in the last seven years; from 1 June 2015 to 1 August 2022.  
If the databases searched this way had less than 200 results, all papers were added to be screened. In the case where there were more than 200 results (Google Scholar, ANU SuperSearch, and Scopus), the results were sorted by the engine's definition of relevance, and the top 200 results were added to the screening pool. This resulted in a collection of 1141 articles in total, of which 885 were unique.  
Following this, the records were screened in order to provide papers relevant to our research interests. Articles were excluded if they were either: (1) publications that were not original peer-reviewed papers, (2) not related to emotion recognition, (3) do not mention the applicability of research to improving robots or machines or agents, or (4) do not state research applicability in a mental health context. For the validation of excluded results, two reviewers acted independently on the 885 unique papers. There were 17% mismatched papers from each reviewer. By discussing with the third reviewer, 80 papers were finally included for detailed analyses. The process and milestones are displayed in Figure 3. Then, both quantitative and qualitative data were extracted from the 80 papers. The aim of each paper, the number of participants, the physiological data used, the methods used for emotion classification, the emotional category type, and the outcome of each paper were recorded.  
Figure 3: Screening process for the review.
# Electrically Tunable Propagation Properties of the Liquid Crystal-Filled Terahertz Fiber
###### Abstract  
A bandgap-guiding microstructured fiber for terahertz (THz) radiation was designed by infiltrating the cladding air holes with nematic liquid crystal. Structural parameter dependence of the photonic bandgaps, polarization-dependent bandgap splitting, and electrically tunable propagation properties of the designed fiber were investigated theoretically by using the finite-element method. An external electric field applied across the designed fiber can broaden the effective transmission bandwidth and achieve single-mode single-polarization guidance. Flattened near-zero group-velocity dispersion of \(0\pm 1\) ps/THz/cm was obtained for the y-polarized fundamental mode within a broad frequency range. Our results provide theoretical references for applications of liquid crystal-filled microstructured fiber for dynamic polarization control and tunable fiber devices in THz frequency.  
Keywords:terahertz; microstructured optical fiber; liquid crystal; photonic bandgap; electrical tunability; finite-element method +
Footnote †: journal: Applied Physics Letters