esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Identification of Cationic Peptides and Molecular Docking  
Sample preparation, mass spectrometry analysis, bioinformatics, and data evaluation were performed in collaboration with the Proteomics Core Facility at the Indiana University School of Medicine. Methods are described in literature reports published elsewhere [21; 22] and vendor provided protocols. Samples were analyzed using a 5 cm trap column and a 15 cm (2 \(\upmu\)m particle size, 50 \(\upmu\)m diameter) EasySpray (801A) column on an UltiMate 3000 HPLC and Q-Exactive Plus ((ThermoFisher Scientific, Waltham, MA, USA) mass spectrometer. Data analysis, including de novo, were performed using the PEAKS software (Bioinformatics Solutions), with Q-Exactive. From the generated peptides, only peptide with an Average Local Confidence (ALC) >80% were chosen to proceed to the following step. The peptide list was searched against the BIOPEP-UWM Database of Bioactive Peptides [23]. Antihypertensive (AHT) prediction and amphiphilic scores were generated by the AHTPin [24] in silico prediction algorithm of antihypertensive peptides (Supplementary Table S1). Molecular docking was performed using the Accelrys Discovery Studio software and Auto Dock Vina, as previously described [25]. Among all identified peptides, all were predicted as antihypertensive peptides and were docked against ACE, along with the inhibitor drug captopril. For molecular docking, the crystal structure of human ACE bound with Lisinopril (PDB: 108A) was used. Before docking, Lisinopril and water molecules were removed, and polar hydrogen atoms were added in the presence of cofactors (zinc and chloride ions). A binding site with a radius of 15 A and coordinates x: 41.58, y: 37.374, and z: 43.47 was created. All generated docking modes were evaluated according to the affinity energy values.
# Multiscale Superpixelwise Locality Preserving Projection for Hyperspectral Image Classification
## 2 Related Work and Background  
Denote an HSI with \(N\) pixels and \(D\) bands as \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{N}]\in\mathbb{R} ^{D\times N}\), and the connected data set in a low-dimensional space as \(\mathbf{Y}=[\mathbf{y}_{1},\mathbf{y}_{2},\cdots,\mathbf{y}_{N}]\in\mathbb{R} ^{d\times N}\), where \(d\ll D\). LPP is a widely used manifold learning method for HSI dimensionality reduction [16; 17; 18; 19; 26], while the region covariance descriptor is an effective spectral-spatial feature for HSI classification [26; 27; 28]. In the following, they are briefly introduced as backgrounds of our proposed method.
## 3 Architecture of the Satellite-Enabled IoRT Network
### Monitoring Devices Integrated with a LEOBIT Satellite Transceiver  
We developed a ground IoT terminal device for environmental monitoring based on the Xingyun satellite communication protocol. The device could forward monitoring data to the Xingyun IoT satellites during overpass across the site. Figure 2 shows a prototype of the ground IoT terminal device. The developed device included a satellite transceiver and dataloger. The operational frequency of the uplink communication ranged from 1668 to 1675 MHz, and the downlink receiving frequency ranged from 1518 to 1525 MHz. The downlink transmission rate of the transceiver was 2.4 kbps, and the uplink transmission rate was adjustable at 1.2, 2.4, and 4.8 to 9.6 kbps, which satisfied the requirements of conventional monitoring data transmission. Moreover, the satellite transceiver was a low-power device: the transmitting power reached approximately 1 W, and the average power consumption of the whole device was lower than 1.5 W. Table 1 summarizes the power consumption characteristics of the LEOBIT satellite transceiver under different working scenarios.  
Additionally, the employed dataloger was a multichannel universal data collector that was specifically designed to automatically acquire observations in harsh environments based on the power supply excitation channel of the sensor. These characteristics allowed the datalogger to flexibly control the connection time and greatly reduce the system power consumption. Thus, the datalogger could operate with less than 1 W of power supplied by solar panels and was highly suitable for field deployment applications. In the prototype  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working Mode** & **Description** & **Minimum Value** & **Maximum Value** & **Unit** \\ \hline Terminal sleep & LEOBIT is deactivated & 30 & 50 & mA \\ Terminal wake-up & LEOBIT is activated & 180 & 190 & mA \\ Sending uplink instructions. & LEOBIT is activated & 400 & 410 & mA \\ \hline \hline \end{tabular}
\end{table}
Table 1: The power consumption of LEOBIT satellite transceiver.  
of the ground IoT terminal device for the Xingyun satellite IoT, the key components of the terminal device included a LEOBIT satellite transceiver, transmitting and receiving antennas, and RR1016 datalogger. The interaction between the data center and in situ sensors is shown in Figure 3.
## 4 Experimental Testing and Result Analysis
### Experimental Results and Analysis  
After training to obtain the optimal model, we conducted a comprehensive evaluation of the model on an independent test set. The test set included a variety of situations with large perspective distortions to verify the model's adaptability and accuracy in different scenarios. As shown in Figure 7, the model was able to accurately identify the connection relationships between terminals and cables under complex perspective conditions. This indicates that the YOLOv8n model performs well in handling large perspective distortions, effectively addressing the misalignment issues between terminals and cables, thereby enhancing the accuracy and reliability of recognition.  
To further quantify the model's performance, we calculated several evaluation metrics, including precision, recall, mean Average Precision (mAP), etc. These metrics indicate that the YOLOv8n model's performance on the test set has achieved the expected effect, with high detection accuracy and robustness. The specific evaluation results are shown in Table 2 below:To assess the model's performance in practical applications, we also conducted field tests in multiple scenarios, including different lighting conditions, varying background complexities, and various cable and terminal arrangement methods. The field test results show that the YOLOv8n model's performance in real environments is consistent with laboratory test results, capable of stably and accurately identifying the connection relationships between terminals and cables.  
In summary, the YOLOv8n model performs excellently in identifying the connection relationships of terminals and cables, especially under conditions of large perspective distortion, providing reliable technical support for the automatic inspection system of substations. Future research will further optimize the model structure and training methods to improve its performance in more practical application scenarios and explore its potential application value in other industrial automation fields.
## 4 Analysis of Influencing Factors
### Effect of the Support Structure Insertion Ratio
#### 4.4.2 Tunnel Vertical Displacement  
Figure (b)b shows the tunnel's maximum vertical displacement upon completion of the foundation pit excavation under varying insertion ratios. According to the diagram, when the insertion ratio increases from 0.2 to 1.0, the uplift decreases by 5.32%, 8.51%, 9.57% and  
Figure 16: Tunnel maximum displacement at various insertion ratios.  
Figure 15: Maximum structural stress of the tunnel for various dimensions of the support pile wall.  
9.57%, respectively, and the settlement decreases by 15.9%, 22.97%, 28.62% and 28.98%, respectively. The uplift and settlement of the tunnel decrease exponentially with increasing insertion ratio. When the insertion ratio exceeds 0.8, further increase has little effect on the tunnel's vertical displacement. The tunnel's vertical displacement under each insertion ratio exceeds the warning value given in the relevant specification.
## 3 Probabilistic Forecasting of ALSC
### Probabilistic Indices for ALSC  
In order to accurately evaluate ALSC under uncertainties, five probabilistic evaluation indices are defined as follows.  
1. Expectation of ALSC: \[E_{\text{ALSC}}=\frac{1}{N_{M}}\sum_{i=1}^{N}\lambda_{i}\] (41) where \(N_{M}\) is the number of the Monte Carlo simulation and \(\lambda_{i}\) denotes the ALSC value of the \(i\)th Monte Carlo simulation.
2. Standard deviation of ALSC: \[S_{\text{ALSC}}=\sqrt{\frac{1}{N_{M}}\sum_{i=1}^{N}\left(\lambda_{i}-E_{\text {ALSC}}\right)^{2}}\] (42)
3. Variation coefficient of ALSC: \[\beta_{\text{ALSC}}=\frac{S_{\text{ALSC}}}{E_{\text{ALSC}}}\] (43)  
Figure 4: Flowchart of probabilistic ALSC forecasting.  
(4) Probability of exceeding threshold for ALSC: \[P_{\text{ET}}=\frac{M_{\text{ALSC}<\overline{\lambda}}}{N_{M}}\] (44) where \(M_{\text{ALSC}<\overline{\lambda}}\) denotes the number at which ALSC is less than the given threshold \(\overline{\lambda}\). (5) Probability of ALSC equal to zero: \[P_{\text{EZ}}=\frac{M_{\text{ALSC}=0}}{N_{M}}\] (45) where \(M_{\text{ALSC}<\overline{\lambda}}\) denotes the number at which ALSC is equal to zero. \(E_{\text{ALSC}}\) gives a general estimation of the ALSC. \(S_{\text{ALSC}}\) and \(\beta_{\text{ALSC}}\) represent the fluctuation of the ALSC. \(P_{\text{ET}}\) and \(P_{\text{EZ}}\) are the risk indices of power system.
# Copernicus Global Land Cover Layers--Collection 2
## 3 The CGLS-LC100 Product and Accuracy Assessment
### Quantitative Comparison of CGLS-LC100 and Existing Global LC Datasets  
When compared against the combined reference dataset, the CGLS-LC100 2015 Collection 2 map shows at global scale a higher agreement (by 3%) to the combined reference dataset than the other tested four global LC maps (Table 4) [39]. Similarly, at a continental scale, the CGLS-LC100 2015 Collection 2 map has consistently higher agreements than the other maps (Table 4) [39]. Only in South America, MODIS 2010 map has slightly higher accuracy than the CGLS-LC100 2015 Collection 2 map. Please note, that the objective of this comparison is only to indicate the generic quality of the CGLS-LC100 2015 Collection 2 map as compared to commonly used previous global LC maps. One must be cautious to compare directly the overall agreement of these maps due to the differences in the reference years and legend definitions and other differences between the combined reference dataset and the global LC maps [40]. Anyhow, this result indicates that the CGLS-LC100 2015 Collection 2 map has a comparable, if not better, quality as previous LC maps. Additional comparison studies verified this indication [20; 21; 22; 23].
# Wind Speed Spectrum of a Moving Vehicle under Turbulent Crosswinds
## 2 Wind Speed Spectrum Models  
### Wind Speed Spectrum of a Static Vehicle  
The correlation characteristics of two points in a turbulent flow field can be expressed in terms of the correlation functions \(f(r)\) and \(g(r)\), where \(f(r)\) is the cross-correlation function of the wind speeds for two points apart from \(r\) along the axis of the two points (Figure 1a), and \(g(r)\) is the cross-correlation function of two points apart from \(r\) which are perpendicular to the axis (see Figure 1b) [20].  
\[f(r)=\langle u_{1}(x)\;u_{1}(x+r)\rangle/u_{1}^{2}(x) \tag{1}\]  
\[g(r)=\langle u_{2,3}(x)\;u_{2,3}(x+r)\rangle/u_{2,3}^{2}(x) \tag{2}\]  
where \(u_{1}\), \(u_{2}\) and \(u_{3}\) are the fluctuating wind speeds in the \(x\), \(y\) and \(z\) directions, at time t, for a point in the spatial turbulent flow field, respectively. \(x\), \(y\) and \(z\) directions are the longitudinal, lateral and vertical directions.  
The longitudinal (\(S_{11}(k_{1})\)) and lateral (\(S_{22}(k_{1})\)) wind speed spectra can be obtained by the two following formulas [20]:  
\[S_{11}(k_{1})=4\sigma_{11}^{2}\int_{0}^{\infty}f(r)\cos(k_{1}r)dr \tag{3}\]  
\[S_{22}(k_{1})=4\sigma_{22}^{2}\int_{0}^{\infty}g(r)\cos(k_{1}r)dr \tag{4}\]  
where \(\sigma_{11}\) and \(\sigma_{22}\) are the root mean squares of the fluctuating wind speeds in the longitudinal and lateral directions, \(\sigma_{11}^{2}=\int_{0}^{\infty}S_{11}(k_{1})dr\), \(\sigma_{22}^{2}=\int_{0}^{\infty}S_{22}(k_{1})dr\); \(k_{1}=w/U\), \(w=2\pi f\), \(f\) is the frequency value and \(U\) is the incoming mean wind speed.  
[PERSON] [20] derived the relationship between \(g(r)\) and \(f(r)\) based on the spherical symmetry assumption.  
\[g(r)=f(r)+(r/2)\frac{d}{dr}f(r) \tag{5}\]  
[PERSON] [21] derived the mathematical models of the main correlation functions based on the isotropic turbulence theory:  
\[f(r)=\Big{[}(r/a)^{n}/2^{n-1}/\Gamma(n)\Big{]}K_{n}(r/a) \tag{6}\]  
\[g(r)=\big{[}(r/a)^{n}/2^{n}/\Gamma(n)\big{]}[2K_{n}(r/a)-(r/a)K_{n-1}(r/a)] \tag{7}\]  
where \(a\) and \(n\) are the parameters controlling the shape and scale, respectively; \(K_{n}\) and \(K_{n-1}\) are the second type of Bessel function; and \(\Gamma\) is the Gamma function.  
In the isotropic turbulence field, the relationship [20] between the turbulence integral scale \(L\) and the parameter \(a\) is satisfied as follows:  
\[L=\bigg{[}\sqrt{\pi}\cdot\Gamma(n+\frac{1}{2})/\Gamma(n)\bigg{]}\cdot a \tag{8}\]  
Respectively inserting Equations (6) and (7) into Equations (3) and (4) yields  
\[S_{11}(k_{1})=\frac{4\sigma_{11}^{2}L/U}{\Big{[}1+(ak_{1})^{2}\Big{]}^{n+1/2}} \tag{9}\]  
\[S_{22}(k_{1})=\frac{2\sigma_{22}^{2}(L/U)\big{[}1+2a^{2}k_{1}^{2}(n+1)\big{]} }{\Big{[}1+(ak_{1})^{2}\Big{]}^{n+3/2}} \tag{10}\]  
Figure 1: Correlation functions.  
When \(n=1/3\), the von Karman wind spectrum [22; 23] is as follows:  
\[\frac{fS_{11}(k_{1})}{\sigma_{11}^{2}}=\frac{4X_{u}}{[1+70.8X_{u}^{2}]^{5/6}} \tag{11}\]  
\[\frac{fS_{22}(k_{1})}{\sigma_{22}^{2}}=\frac{2X_{u}[1+188.8X_{u}]}{[1+70.8X_{u}^ {2}]^{11/6}} \tag{12}\]  
where \(X_{u}=k_{1}L/2\pi\).
# Challenges Associated with Implementing 5G in Manufacturing
## 3 Use Cases of 5G in Manufacturing
### Cloud Control of Machines  
The capabilities afforded by 5G will enable manufacturers to design robots that benefit from increased data integration and real-time decision making. For a considerable length of time, manufacturing plant computerization has depended on programmable logic controllers (PLCs) that were introduced to control the machines they were connected to and afterwards hardwired into computing systems to implement control management. Cloud control management is mostly virtualized computing resources, storage, applications, and services that are managed by software so the data generated can be accessed on-demand. Cloud management is a combination of software, automation, policies, governance, and algorithms that determine how those cloud computing services are offered.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Use-Case Category** & & **Scenario** & **Impact** \\ \hline Time-critical processes & \(\bullet\) & Real-time, closed-loop & \\  & \(\bullet\) & robotic control & \\  & \(\bullet\) & Video-driven & \\ Time-critical processes & & machine-human interaction & Increased efficiency and yields; safety \\  & \(\bullet\) & Augmented Reality/Virtual & \\  & & Reality (AR/VR) for & \\  & & maintenance and training & \\ \hline Non real-time processes inside the factory & \(\bullet\) & Tracking products and & \\  & & machine inventory & \\  & \(\bullet\) & Non-real-time sensor data & Optimized management of \\  & \(\bullet\) & Remote inspection & production facilities \\  & & and diagnostics & \\ \hline Enterprise communication & \(\bullet\) & Logistics and warehousing & \\  & \(\bullet\) & Employee and & \\  & & back-office communications & Improved business operations \\  & \(\bullet\) & Tracking & \\  & & goods post-production & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Use cases in manufacturing [2].  
When 5G reliably meets its initial specifications, the PLC will be able to be virtualized in the cloud, empowering machines to be controlled remotely and continuously with reduced expense to the company or end-user.
## 3 Results and Discussion
### Validation of coSIF and Comparison with Simpler Methods
The RASPE for cokriging and kriging is slightly lower than that for the trend-surface-only method, suggesting that leveraging the spatial dependence identified in Section 3.1 yields slightly improved prediction accuracy. While prediction accuracy is important, prediction uncertainty must also be considered when performing probabilistic prediction. We summarize the distribution of our predictions using the average interval score (INT) and the average Dawid-Sebastiani score (DSS), whichoffer different assessments on the quality of the prediction uncertainties. Between the two validation blocks, INT differs slightly as follows: the 95% prediction intervals are best calibrated when using cokriging for the Corn Belt validation block and when using kriging for the Cropland validation block. However, cokriging is found to offer the best characterization of uncertainty with respect to DSS in both blocks. While cokriging and kriging are similar with respect to INT and DSS, the performance of the trend-surface-only method is noticeably worse. In different ways, the BIAS, RASPE, INT, and DSS are all summary statistics of the validation standardized residuals given in Equation (21), averaged over all the residuals in the validation block. Thus, these metrics assess the individual predictive distributions at each validation location, and they weight each result equally. However, because spatial dependence is present in the residuals (see Figure 15 in Appendix B), we also consider a multivariate extension of the Dawid-Sebastiani score (MDSS) to evaluate the multivariate predictive distribution at all validation locations simultaneously. The MDSS metric clearly favors the methods that leverage spatial dependence (cokriging and kriging), which provide superior multivariate predictive distributions. Compared to kriging, cokriging achieves a slightly better MDSS in both validation blocks, suggesting there is an advantage to leveraging the dependence between SIF and XCO\({}_{2}\) identified in Section 3.1. The validation metrics presented in Table 1 suggest that proper characterization of uncertainty requires that the prediction method accounts for spatial dependence in the data. Cokriging and kriging both account for spatial dependence, but cokriging offers smaller prediction uncertainty when there is cross-dependence between SIF and XCO\({}_{2}\). The validation metrics in Table 1 are available for each of the other three months considered in this study in Tables 15-16 in the Supplementary Material. There, we find that when there is weak dependence between SIF and XCO\({}_{2}\), there is little advantage in cokriging. Instead, modeling the univariate SIF covariance function and predicting using kriging is more efficient and provides comparable results to cokriging when there is weak dependence between SIF and XCO\({}_{2}\).
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Energy Conservation  
Although energy consumed for sensing and computation in WSNs is much lower than for communications, this is not the case in CR-WSNs. In CR-WSNs, CR-WS nodes have to sense and monitor channels, maintain the available-channel information, predict channel status for future use, relay the sensing report to the CH or BS and its neighbors, and listen to the signal of the CH, BS, and neighbor nodes for spectrum information. Therefore, CR-WS nodes have a higher duty cycle. CR-WS nodes may have multiple transceivers to work on multiple channels, especially one for a control channel and another for data channels. These transceivers must switch channels frequently; hence, they consume more energy.  
Because a frequent role change for a CH is not always possible in CR-WSNs, as described in Section 3, preserving the energy of the CHs as well as the CMs, is necessary.  
In clustering, inter-clustering communications is performed by the cluster head. Therefore, the number of sensor nodes performing long distance communications decreases. Furthermore, as described in Section 3, clustering encourages multihop communications, which conserves energy. Therefore, clustering is an effective way to conserve energy in CR-WSNs.
# A Game Player Expertise Level Classification System Using Electroencephalography (EEG)
## 2 Proposed Methodology
### Preprocessing  
The EEG data recorded by the wireless EMOTIV headset are affected by different kinds of artefacts and need to be preprocessed to improve the quality of signals. These artefacts include noise from electrical lines, muscle movement, heart beat, sweating, electrode movement, and so on. The EEG signals have a bandwidth between 0.2 and 100 Hz and are recorded with 128 samples per second. In order to remove the DC offset, the mean value is subtracted from the entire data. For noise removal, a two-step process is applied including filtering and independent component analysis (ICA). The artefacts caused by physical movements such as heart beat and eye blink appear in the frequency range between 1.2 Hz and 5 Hz. The muscular movements effect the EEG oscillations above 45 Hz. The high gamma band is shown to have the highest correlation with motor-evoked potentials (MEPs) [25]. A first-order band-pass Butterworth filter with a pass-band of 5-45 Hz is used to remove artefacts caused by biological movements. The filtering process removes part of the delta and gamma waves, but the experimental procedure does not include any audio source. The selected game for this study does not rely on memory, as the game play is random for each turn. The filtering process is also useful in removing the power interference that occurs at 50 Hz. The proposed method also finds the significant channels that can be used for classification of player expertise level. It is a known fact that EEG recordings from multiple channels represent information that is a mixture of underlying sources from different brain areas. Hence, ICA is used to estimate independent sources from highly correlated EEG data [26]. ICA is a blind source separation technique, which statistically separates uncorrelated signals. ICA is performed using EEG lab [27] on the raw EEG data, and the separate components are used for feature extraction.