esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Methods
#### 2.3.3 Partial Correlation Analysis  
Partial correlation analysis is used to analyze the linear correlation between two variables while controlling for the linear effects of other variables [44]. The judgment index is the \(R\) of the correlation coefficient value.  
\[P_{ij,k}=\frac{P_{ij}-P_{ik}P_{jk}}{\sqrt{\big{(}1-P_{ik}^{2}\big{)}\big{(}1-P_ {jk}^{2}\big{)}}} \tag{7}\]  
Suppose there are 3 variables \(i\), \(j\), and \(k\), while \(P_{ij,k}\) and \(k\) are partial correlation coefficients between independent variable \(k\) and independent variables \(i\) and \(j\); \(P_{ij}\), \(P_{ik}\), \(P_{jk}\) are correlation coefficients between two factors. According to \(R\) and \(P\), we obtain three different significance levels: significant positive correlation (\(R>0\), \(P<0.05\)), no significant correlation (\(P\geq 0.05\)), and significant negative correlation (\(R<0\), \(P<0.05\)).
## 4 Solution of Fatigue Life of Pipeline under Combined Excitation under OTM Frame  
### Dynamic Response Analysis of Hydraulic Pipeline Based on Optimal Transport Model (OTM)  
The previous paragraph extracted the pressure and resonance excitation of fluid pulsations on the inner surface of the pipeline. This section aims to utilize the extracted pulsation excitation and resonance excitation within the OTM framework to calculate the dynamic response of the pipeline, as well as to determine its fatigue life at the maximum stress point.  
The resonant loads and the pressure on the inner wall of the pipeline were previously extracted to introduce both vibration excitation and pulsating excitation of the fluid into the OTM. To account for potential misalignment between sampling points of pipeline inner wall pressure and the OTM grid file, nodes on the inner wall surface are clustered into 100 sets based on their distance from sampling points. A pressure load is then applied to each set in order to incorporate a pre-stress field excited by fluid pulsation in the OTM. Figure 14 illustrates how the inner wall surface is divided into four sets based on distance.  
Figure 14: The nodes along the inner wall surface of the pipeline are clustered based on their proximity to the sampling point. ((**a**–**d**) are four sets of inner wall surfaces).  
Figure 13: Positioning of sampling point along the inner wall of the pipeline.  
The discrete model used in the numerical simulation of OTM consists of 185,932 nodes and 681,965 matter points. Resonant excitation is applied to the starting and ending positions of the lines as well as the bracket contact surfaces. Table 1 provides details on the actual material parameters utilized, while Table 2 outlines key parameters relevant to the numerical simulation algorithm employed. Based on this discrete model combined with initial conditions, boundary conditions, and material parameters, we simulate dynamic stress within a pipeline under compound excitation using our model. Our total simulation time amounts to 0.0016 s (Table 3); Figure 15 displays results for the dynamic stress experienced by pipeline structures under combined stress.  
The simulation results demonstrate that the point of maximum stress in the tube body coincides precisely with the attachment location of the clamp, where a peak von Mises stress of 228 MPa is observed.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 6 Structured Background Models
#### 6.1.2 Signal Subspace Models  
In [113], the anomaly detection method that works within a subspace of the original signal space is presented. The method is called the signal subspace processing anomaly detector (SSPAD), and it operates within a subspace spanned by the spectral vectors in the immediate vicinity of the PUT. SSPAD is able to detect local anomalies without requiring covariance estimation or calculation of its inverse. SSPAD determines the finite impulse response (FIR) filter coefficients for the neighboring spectral vectors that minimize the mean square error of the PUT reconstruction. For selecting the signal subspace, in [113], a guard window is implemented, and then four boxes centered at horizontal and vertical axes originating from the PUT are proposed. The boxes are placed just outside of the guard window to prevent the projection of the target spectra to the signal subspace. The pixels contained in a specific box are the inputs for the FIR filter. The SSPAD detection statistic in the matrix form is derived in [11]:  
\[SSPAD(\underline{x}|\mathcal{B}) =\min_{k=1,2,3,4}\{\|\underline{\varepsilon}_{k}\|\} \tag{39}\] \[=\min_{k=1,2,3,4}\{\|\underline{P}^{\perp}_{\underline{x}}\cdot \underline{x}\|\}\] \[=\min_{k=1,2,3,4}\{\|\left(\underline{I}-\underline{V}_{\underline {x}}\cdot\underline{V}^{\#}_{\underline{x}}\right)\cdot\underline{x}\|\} \begin{array}{c}>\\ <\\ \end{array}\lambda\]  
where \(\underline{P}^{\perp}_{\underline{x}}\) is the projection matrix onto the subspace orthogonal to the signal subspace spanned by the spectral vectors of the \(k\)-th box, \(\underline{V}_{\underline{x}}\) denotes the matrix containing those vectors, and \(\underline{V}^{\#}_{\underline{x}}\) is the pseudoinverse of the \(\underline{V}_{\underline{x}}\).  
The anomalies in the SVDD are the pixels that significantly deviate from the local background determined by the four FIR filters. In the algorithm implementation, care should be exercised regarding the appropriate size of the guard window and the signal subspace boxes. In order to improve computational efficiency, the box size should be small, and also, it is important not to employ too many constraints on the projection [11]. If the anomalies are spatially grouped and co-aligned in the vertical or horizontal direction, this could lead to bad SSPAD detection performance. In that case, a different strategy for a spatial sampling of the signal subspace samples should be applied.
# Organic Electrochemical Transistor Microplate for Real-Time Cell Culture Monitoring
###### Abstract  
Human cell cultures provide a potentially powerful means for pharmacological and toxicological research. A microplate with a multielectrode array of 96 organic electrochemical transistors (OECTs) based on the semiconductive polymer poly(3,4-ethylenedioxythio-pheno):poly (styrene sulfonic acid) PEDOT:PSS was developed and fabricated by the screen printing method. It consists of a microplate of a 12 \(\times\) 8 chimney-well array with transistors on the bottom. The OECT is circular with a channel of 1.5 mm\({}^{2}\) in the centre surrounded by the circular gate electrode. The device is designed for electrogenic cell monitoring. Simulations with the electrolyte revealed good electrical characteristics and indicated the setup information of the experimental conditions. A transconductance of \(g=1.4\) mS was achieved in the wide range of gate voltages \(V_{\mathrm{gs}}=\pm 0.4\) V when the drain potential \(V_{\mathrm{ds}}=-0.735\) V was set and the long term relaxation was compensated for. The time constant 0.15 s limited by the channel-electrolyte charge electrical double layer (EDL) capacitance was measured. The device was tested on a 3T3 fibroblast cell culture and the sudden environmental changes were recorded. The living cells can be observed on the channel of the OECT and during electrical stimulation by gate voltage, as well as during the source current response.  
O pacs: OECT; screen printing; organic electrochemical transistor; PEDOT:PSS; microplate; multi-electrode array; cell culture +
Footnote †: journal: Applied Sci. **2017**, 7, 998; doi:10.3390/app700998
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
###### Abstract  
The potential of the Non-Orthogonal Multiple Access (NOMA) approach for wireless communications in the fifth generation (5G) and beyond can not be underestimated. This is because users with favorable channel conditions can serve as relays to improve system performance by employing Successive Interference Cancellation (SIC). Lately, the combination of NOMA and the cooperative relay has attracted the interest of researchers. The analysis of cooperative relay NOMA (CR-NOMA) with a massive multiple-input multiple-output (mMIMO) system is mainly based on theoretical channel models such as the correlated-based stochastic channel model (CBSM) even though the geometric-based stochastic channel model (GBSM) has been found to provide better, practical and realistic channel properties. This, in our view, is due to computational challenges. Again, the performance of CR-NOMA systems using the GBSM channel model with large antenna transmitters and network coding schemes has attracted little attention in academia. Therefore, the need to study mMIMO CR-NOMA that considers channel properties such as path-loss, delay profile and tilt angle has become vital. Furthermore, the co-existing of large antenna transmitters with coding schemes needs further investigation. In this paper, we study the performance of a two-stage mMIMO CR-NOMA network where the transmitter is represented as a uniform rectangular array (URA) or cylindrical array (CA). The communication channel from the transmitter (TX) to the user equipment (UE) through a relay station (RS) is modeled with a 3 GPP's three-dimensional (3D) GBSM mMIMO channel model. To improve the analytical tractability of 3D GBSM, we defined the antenna element location vectors using the physical dimension of the antenna array and incorporated them into the 3D channel model. Bit-error rates, achievable rates and outage probabilities (OP) are examined using amplify-and-forward (AF) and decode-and-forward (DF) coding schemes. Results obtained show with fixed power allocation and SNR of 20 dB, far or weak users can attain a high achievable rate using DF and URA. Again, from the results, the combination of AF and CA presents better outage probabilities. Finally, the results indicate that the performance difference between CBSM and GBSM is marginal, even though the proposed 3D GBSM channel model has a higher degree of random parameters and computational complexities.  
geometric-based stochastic model (GBSM); correlated-based stochastic model (CBSM); cooperative relay-noma (CR-NOMA); massive MIMO (mMIMO); network coding schemes +
Footnote †: journal: Electronics Letters
## 4 Conclusions  
In conclusion, optical bistability, tristability and multistability in arrays of graphene have been studied in the work. The transmission spectra show there are many resonant modes in this periodic structure and a saltus step point. Optical bistability can be achieved by modulating the input intensity of the light wave or the incident wavelength. The bistable thresholds and the interval between the lower and upper thresholds can also be changed by the external voltage on graphene. Furthermore, optical tristability and multistability are achieved as the intensity of the incident light keeps on increasing. This study may present an application for all-switches, optical storages and logic gates.  
Figure 7: (**a**) Nonlinear transmittance for optical tristability and multistability. (**b**) Input-output relation of intensity for optical tristability and multistability.  
**Author Contributions:** Conceptualization, X.Z.; Formal analysis, X.K. and D.Z. ([PERSON]): Funding acquisition, B.X., D.Z. ([PERSON]) and D.Z. ([PERSON]); Investigation, X.Z., B.X., M.F. and D.Z. ([PERSON]); Project administration, X.K.; Resources, X.K.; Software, X.Z., B.X. and D.Z. ([PERSON]); Supervision, D.Z. ([PERSON]); Writing--original draft, X.Z., M.F. and D.Z. ([PERSON]); Writing--review and editing, M.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Natural Science Foundation of China (NSFC) (51975542); the Scientific Research Project of Hubei University of Science and Technology (BK202017, HKCXTD-001); the Science and Technology Plan Research Project of Hubei Education Department (B2019162); and the Industry-University-Research Innovation Foundation of Science and Technology Development Center of Education Ministry of China (2020 QT02).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Classification Accuracies  
The classification accuracies based on SBERT were higher than those based on TF-IDF for all the classifiers, but the accuracy of the different classifiers varied considerably. Figure 4 shows the F1 scores of the seven classifiers for classifying normal and abnormal participants, with the dashed line indicating the F1 score of the baseline classifier (0.56). Error bars represent standard errors of the mean, and the asterisk indicates a significantly higher F1 score compared to that of the baseline (one-tailed \(t\)-test, \(p\) < 0.05). Using TF-IDF (light-gray bars in Figure 4), only SVC and random forest classifiers outperformed the baseline classifier significantly. Using the SBERT (dark-gray bars in Figure 4), LDA, SVC, random forest, and KNN classifiers outperformed the baseline classifier significantly. For both embedding models, the random forest classifier produced the highest F1 scores, 0.68 (TF-IDF) and 0.74 (SBERT), with corresponding recall scores of 0.57 (TF-IDF) and 0.61 (SBERT).  
Figure 4: F1 scores for classifying normal and abnormal readers using sentence embeddings. The dashed line shows the F1 score of the baseline classifier. Error bars represent standard errors of the mean. The asterisk indicates a significantly higher F1 score compared to that of the baseline (one-tailed \(t\)-test, \(p\) < 0.05).  
Figure 3: The similarity scores (**left**) and \(p\)-values of comparison between normal and abnormal groups (**right**) based on TF-IDF (**A**) and SBERT (**B**) as function of sentence ID. The asterisk (**left**) represents significant difference between normal and abnormal groups. Blue dashed line (**right**) represents a significance level of 0.05.
# A Self-Supervised One-Shot Learning Approach for Seismic Noise Reduction
## 2 Method
### FCN Architecture  
The fully convolutional network (FCN) architecture is exclusively composed of convolutional layers. In this configuration, the network processes an input of arbitrary size and produces an output with a size corresponding to the inference. This type of network architecture is particularly applicable to image segmentation and denoising because of its efficiency and adaptability to the final structure size.  
In the development of the models, two distinct FCN topologies, FCN3 and FCN9, were established. The less complex model, FCN3, incorporates a structure with three convolutional layers, all of which employ 9 \(\times\) 9 filters with identical padding and ReLU activation layers. The first convolutional layer has 32 filters, followed by a second layer with 64 filters and a third layer with 32 filters. The final layer of this network consists of a convolutional layer containing 1 \(\times\) 1 filters. The total number of parameters was 334,529. A diagram illustrating the architecture of the network used in this study is presented in Figure 2. The input images will contain masks, as exemplified in the figure, and the target value will be the original, noisy image.  
In contrast, the FCN9 topology consists of a structure that shares many similarities with a DnCNN network. This network begins with a first convolutional layer coupled with a ReLU activation function. Following this layer, seven more convolutional layers are added, all equipped with 9 \(\times\) 9 filters, using'same padding', 'batch normalization', and a ReLU activation function. Each of these intermediate layers has 64 filters. The final network configuration is a convolutional layer equipped with 1 \(\times\) 1 filters and a linear activation function. The total number of parameters was 2,328,576. Figure 3 presents a schematic representation of the FCN9 topology used in this study.
# A 5G V2X Ecosystem Providing Internet of Vehicles +
## 4 Proposed 5G V2X Ecosystem
In this direction, 5G infrastructure based on software defined concepts can support IoV applications [4]. For this purpose, Figure 4 shows the logical structure for entertainment services in 5G V2X ecosystem, divided into three layers, following the concept of SDN. The infrastructure layer consists of the vehicles, UPF and other service provider elements to the users. In the control layer, the SDN controller manages gNodeB and Hypertext Transfer Protocol based (HTTP) Adaptive Streaming (HAS). The application layer represents the abstraction provided by the controller through the entertainment services delivery. Figure 4: Logical view of SDIoV entertainment services. In the SDN structure, the data plane includes vehicles and gNodeBs and is responsible for collecting data. The control plane includes gNodeBs and V2X-servers and is responsible for deriving control instructions. The SDN controller must count on the data collection module based on the data plane, besides the network monitoring module. Finally, the application plane is responsible for the rules in the SDN structure. The general scheme of the proposed architecture operation is shown in Figure 5. Initially, gNodeB receives the Internet-based video services requests from each vehicle connected to it. The network condition monitoring is performed on the controller. When the video server receives the HTTP GET, it pushes the video to the V2X-Server and notifies vehicles by sending an HTTP REDIRECT. Dynamic Adaptive Streaming over HTTP (DASH) is considered one of the most successful streaming technologies (TCP stream) for video on demand. DASH was deployed on the V2X-Server using the MEC concept, allowing a processing that is closer to the end-user. Through adaptive algorithms, DASH allows the dynamic adjustment of the video representation for the network conditions. Then, the vehicle sends another HTTP GET request for the video request to the V2X-Server, which answers by providing the appropriate video segment, after processing. The vehicles can implement the following functions for the various V2X services: vehicle and environmental information collection module through sensors; positioning module; and communication module including V2I and V2V. The V2X association with MEC (V2X-Server that is used for HAS application) counts on a cache module to save recent and popular contents, as well as to process edge information after orchestration decisions by network function virtualization. It can also be used for the SDN application plane processing, such as security and efficiency services. This section presented the proposal of a 5G V2X ecosystem based on TS 23.501. The proposed ecosystem supports IoV communications and entertainment services delivery.
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 2 Dual Nuclear Magnetic-Field Measurement Method  
### PLL-Based Dual Nuclear Magnetic-Field Measurement Method  
Figure 1a shows a typical experimental arrangement of the NMRRS. A vapor temperature-controlled glass cell is mounted in the NMRRS. The vapor cell contains two noble gas nuclei (\({}^{129}\)Xe and \({}^{131}\)Xe), an alkali-metal atom(\({}^{87}\)Rb), and buff gas. A circularly polarized pumping beam, whose wavelength is equal to the D1 line of the Rb atoms, orients parallel to an applied magnetic field \(B_{0}\). Thereby, the valence electrons of the alkali metal are spin-polarized. The collisions between the alkali-metal atoms and noble gas atoms transfer some of the electron-spin polarization to the nuclei of the noble gas, which produced the coherency of the nuclear spin.  
The nuclear spins are made to precess about \(B_{0}\) by the oscillating magnetic field \(B_{+}\) Xe applied along the x-axis, producing a net nuclear magnetization. The linear polarized probe light to read out the spin precession is detected by a balanced detector after passing through the vapor cell. The signal is demodulated in a lock-in amplifier to obtain the two nuclear spins. The frequency of the nuclear spin precession can be observed by PLL circuits. When the sensor is rotating about the axis of the applied field \(B_{0}\) at a frequency \(\omega_{r}\), the measured Larmor precession frequency is  
\[\omega_{N}=\gamma_{N}B_{0}\pm\omega_{r} \tag{1}\]  
where \(\gamma_{N}\) is the gyromagnetic ratio of the nucleus.  
Figure 1b shows a conventional block diagram of the PLL-controlled nuclear spin. The PLL controls the coherent spin resonance with the oscillating magnetic field and observes the Larmor frequency of the nuclear spin.  
From Equation (1), it is not possible to distinguish between changes in \(\omega_{N}\) caused by fluctuations in \(B_{0}\) from changes solely due to rotation. It will be necessary for any practical version of the NMRRS to maintain the magnitude of \(B_{0}\) precisely or, alternatively, to employ techniques that effectively compensate for any variations in the value of \(\omega_{N}\) caused by \(B_{0}\) fluctuation.  
As shown in Figure 1, two isotopes precessing in Larmor frequency (\(\omega_{a}\) and \(\omega_{b}\)) are introduced to determine the rotation rate. The rotation rate is independent of the magnetic field \(B_{0}\).  
\[\omega_{r}=\frac{\frac{\gamma_{a}}{\gamma_{b}}\omega_{a}-\omega_{b}}{1-\frac {\gamma_{b}}{\gamma_{a}}} \tag{2}\]  
\[B_{0}=\frac{\omega_{a}+\omega_{b}}{\gamma_{a}+\gamma_{b}} \tag{3}\]  
where \(\gamma_{a}\), \(\gamma_{b}\) are the gyromagnetic ratios of the two isotopes.
## 3 Results
### Biological Pretreatment Performance
#### 3.1.2 COD Removal  
As for COD removal, the effect of biological pre-treatment using Eco-flushTM indicated that the process also effectively reduced the organic matter load present in the raw PSW; albeit, with a lowly COD removal efficiency of 38 \(\pm\) 8.4%. However, this was done under ambient temperature conditions in which the biological pre-treatment can be proven to be a challenge because of the high concentration of organic matter present in the PSW. However, with an increased organic loading rate, the total removal of COD contributing constituents in the PSW can increase proportionally. As shown in Table 1, a COD product discharge of 5896 mg/L and COD removal efficiency of 38 \(\pm\) 8.4% showed that there was a need of a subsequent treatment stage that would further reduce the COD to prescribed discharge standard of wastewater (5000 mg/L) as per the City of Cape Town by-laws, 2013 [17]. Overall, this study proved to be more efficient as compared to a similar study whereby the biological pre-treatment of poultry product processing wastewater removed only 24% tCOD in an aerated equalization tank [18].