esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Comparative Analysis of Single-Source Domain and Multi-Source Domain Transfer Learning Task Results
#### 4.3.2 Comparative Analysis of Diagnostic Results  
Figure 10 shows the confusion matrix of the diagnostic results of the single-source domain and multi-source domain migration learning tasks. The images are arranged in the same order as in Figure 9. It can be seen from Figure 10 that the multi-source domain tasks (c), (f), and (i) can reduce the classification errors for the target domain samples compared to the single-source domain tasks (a), (b), (d), (e), (g), and (h).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Task** & **Accuracy** & **Task** & **Accuracy** & **Increase Percentage (\%)** \\ \hline B-C\(\rightarrow\)A & 97.78\% & B\(\rightarrow\)A & 95.09\% & 2.69\% \\ C\(\rightarrow\)A & 96.13\% & 1.65\% \\ A-C\(\rightarrow\)B & 99.65\% & A\(\rightarrow\)B & 98.72\% & 0.93\% \\ C\(\rightarrow\)B & 99.28\% & 0.37\% \\ A-B\(\rightarrow\)C & 99.34\% & A\(\rightarrow\)C & 97.85\% & 1.49\% \\ B\(\rightarrow\)C & 98.84\% & 0.50\% \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of fault diagnosis results between single-source domain and multi-source domain migration.
# A Numerical Analysis on Lateral Resistance of Pile-Bucket Foundation for Offshore Wind Turbines
## 2 Finite Element Model
### Meshing and Interaction  
The element type of the soil adopts the eight-node linear brick reduced integration element (C3D8R) and enhances the hourglass stiffness. This element has high accuracy for the displacement and does not cause shear locking. The element of the pile and the bucket adopt the four-node doubly curved shell (S4), and the finite membrane strain is adopted. The shell is more suitable for analyzing the characteristics of the large-diameter steel pipe pile. The details are shown in Figure 3.  
Assembling the pile and the bucket, the inner and outer surfaces of the pile and bucket follow the Mohr-Coulomb friction law. Contact pair is characterized by small sliding and surface-to-surface contact. The concept of \"elastic slip deformation\" is used in the friction model of the ABAQUS contact surface, allowing for a small amount of relative slip deformation when the pile-soil contact surface is bonded together. This is because under the action of horizontal load, the pile will slide and deform with the soil when it rotates or flexes. The normal contact behavior is selected as the hard contact, and the tangential behavior is selected as the penalty, which both ensure that the pile-soil interaction model can better reflect the actual situation of the pile foundation's working properties. The friction coefficient is determined by the friction angle of the soil, according to tan(0.75\(\varphi\)) [19]. The same is applied to the bucket lower surface of the cover. The master surface is the pile (or bucket), and the soil is the slave surface. The sides of the soil are the ultimate lateral displacement, the bottom surface is fixed, and the top surface is free and unconstrained.
## 4 Starlink as Case Study  
The rationale for focusing on low Earth orbit (LEO) in this study is due to the significantly higher number of orbiting objects of all types in LEO compared to other orbital regimes. It is evident that a greater number of satellites are launched into LEO than other orbital regimes. Figure 5 below illustrates the current number of orbiting objects by type and orbital regime, as reported in the latest space debris environment report by ESA [56].  
Figure 4: Illustration of method for calculating midpoint indicators.  
PL stands for payloads, which refer to space objects designed to perform specific functions in space, excluding launch functionality [56]. This category includes operational satellites as well as calibration objects. Since all operational satellites come under the payloads category, from Figure 5, we can infer that most of the debris in LEO is caused by operational satellites. Hence, in the LEO satellite category, particular focus is given to the Starlink constellation of satellites, and it was taken as our case study. The key reason is because Starlink represents the world's largest low Earth orbit (LEO) internet constellation, slated to encompass a total of 42,000 satellites [57]. The next-largest commercial constellation planned in LEO is by OneWeb, with 7000 satellites [58].
## 6 Conclusions  
This study used atmospherically corrected high-spatial and high-spectral resolution VNIR CASI hyperspectral imagery to construct synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI image datasets with dense coincident surface observations in the form of in situ water quality sonde and laboratory measurements to evaluate the performance of reflectance algorithms for estimating BGA values in a 8.9 km\({}^{2}\) temperate inland reservoir. The study focused on currently operational and near-future imaging satellite constellations that may be suitable contributors to an operational water quality monitoring system for smaller inland reservoirs, lakes and rivers.
## 4 Discussion
### Ankle Joint ROM  
In this study, we identified two key findings: left and right active ankle plantarflexion ROM and left active ankle dorsiflexion ROM showed significant differences between the injury group and non-injury group. Additionally, in the injury group, the right active ankle plantarflexion ROM was significantly greater than that on the left side. These findings are consistent with those reported by [PERSON] and associates [18]. However, our findings regarding right active ankle dorsiflexion ROM between the injury and non-injury groups, as well as left and right active ankle dorsiflexion ROM within the injury group, differ from those of [PERSON] and associates [18], reporting significant differences in these measures. This discrepancy may primarily stem from differences in the definition of injury. Also, the same authors defined the injury group as individuals with a history of at least one lateral ankle ligament injury in the dominant leg within the past two years, while we defined it as male soccer athletes who self-reported an ankle injury within the past year (including injuries that reduced participation time or required medical treatment). These different grouping criteria may have led to variations in the results. Moreover, we consider a two-year injury history to be too broad, as some male soccer athletes in the injury group may have already recovered.  
Additionally, we identified a significant negative correlation between right active ankle plantarflexion ROM and ankle injuries. Past research indicates that approximately 85% of ankle injuries are of the inversion type, with the anterior latofibular ligament (ATFL) involved in about 65% of cases. Specifically, inversion ankle sprains occur when the foot inverts, the ankle rotates outward, and the lateral muscles, tendons, and ligaments of the ankle are stretched beyond their normal range. Among these structures, the ATFL, being the weakest of the lateral ligaments, is typically the first to sustain injury or tearing [30]. Moreover, as the ATFL is relaxed during dorsiflexion and tightened during plantarflexion, its injury or tearing can significantly impair plantarflexion ROM. Since most participants experienced right ankle injuries, this explains the observed significant negative correlation between right active ankle plantarflexion ROM and the occurrence of ankle injuries. Previous studies have indicated that reduced ankle joint ROM may alter ankle joint torque, thereby affecting its function [31]. Additionally, reduced ROM leads to hypermobility of the midfoot, which can contribute to osteoarthritis in adjacent joints [31]. In other words, increased plantarflexion ROM may have a protective effect.
## 1 Methods for the Analysis of Protein Fluorescence Spectra
### Decomposition algorithms  
In this section we will briefly mention two methods for the decomposition of steady-state fluorescence spectra. The first approach implements the time-domain [23-25] or frequency-domain methods [26-28] for the resolution of fluorescence spectra. The methods are based on the fluorescence lifetime measurements, which are in general more complicated than steady-state experiments.  
The second approach implements an iterative non-linear-squares analysis of Stern-Volmer quenching plots [20,29-32]. The method is based on the fact that the fluorescence of tryptophan residues can be quenched by external quencher molecules, such as acrylamide, iodide and cesium ions [33-37]. The probability of fluorescence quenching depends on the rate of collision of the quencher and fluorophores (dynamic quenching). Thus, the emission of tryptophan fluorophores located on the surface of the protein is expected to be quenched more effectively than the fluorescence of the Trp residues buried in the protein matrix, where the quencher has limited access. The dependence of the emission intensity on the quencher concentration _[c]_ is given by the well-known Stern-Volmer equation:  
\[\frac{F_{0}}{F}=\frac{\tau_{0}}{\tau}=1+K_{sv}\cdot c \tag{1}\]  
Figure 2: Indole ring of the tryptophan residue.  
where \(F_{0}\), \(F\) is the fluorescence intensity and \(\tau_{0}\), \(\tau\) is the lifetime in the absence and presence of the quencher, respectively; and \(K_{sv}\) is the Stern-Volmer constant. The significant limitation of this method lies in the fact that it can only resolve a spectrum into two components, corresponding to two classes of tryptophan residues (exposed and buried), while reality might be much more complicated.
# Joint Hybrid Beamforming Design for Millimeter Wave Amplify-and-Forward Relay Communication Systems
## 3 Hybrid Beamforming Design for HD AF Relay Systems
### MO-Based HBF Design
6: Calculate \(\
abla_{\mathbf{G}_{\text{RF}_{1}}}\text{T2}\big{(}\mathbf{G}_{\text{RF}_{1},k}, \mathbf{G}_{\text{RF}_{2},k}\big{)}\) according to (18) and compute \(\mathbf{G}_{\text{RF}_{1},k+1}\) by using the MO method. 7: Calculate \(\
abla_{\mathbf{G}_{\text{RF}_{2}}}\text{T2}\big{(}\mathbf{G}_{\text{RF}_{1},k+1}, \mathbf{G}_{\text{RF}_{2},k}\big{)}\) according to (19) and compute \(\mathbf{G}_{\text{RF}_{2},k+1}\) by using the MO method. 8: Update \(\gamma_{2,k+1},\mathbf{G}_{\text{U},k+1}\) and \(\mathbf{G}_{k+1}\) according to (15) and (16). 9: Calculate \(\
abla\text{T3}(\mathbf{W}_{\text{RF},k})\) according to (23) and compute \(\mathbf{W}_{\text{RF},k+1}\) by using the MO method. 10: Update \(\mathbf{W}_{\text{B},k+1}\) and \(\mathbf{W}_{k+1}\) according to (21). 11:\(k\gets k+1\). 12:endwhile
```  
**Algorithm 1** HBF-MO Algorithm.
## 3 Results and Discussion
### Characterization of Ag(0) Nanoparticles (NPs) and Polymer Films  
Functionalized Ag(0)NPs were characterized by TEM and EDX. The bright field transmission electron microscopy images (TEM) of films cured using AgSbF\({}_{6}\)/NVK/DPSi (1%/2%/2% _w/w_) system dissolved in different monomers (TMPTA, St or EPOX) are reported in Figure 7. The TEM micrograph (Figure 7) of Ag(0)NPs obtained after thermal polymerization at RT shows that the Ag(0)NPs remain well-dispersed in the polymer matrix and are well dispersed with no significant agglomeration. A spherical geometry was noticed of the particles whose diameters are in the 5 to 25 nanometer range. The TEM images suggest the possibility of a homogeneous polymeric coating on the nanoparticle surface. These morphological results are in agreement with ref [10,62,72,76].  
The formation of Ag(0)NPs created _in situ_ in this hybrid polymer matrix during thermal radical and/or cationic polymerization at room temperature were monitored using energy dispersive X-ray (EDS) spectroscopy analysis, as shown in Figure 8. Indeed, in the resulting EDS pattern, C, O, F, Si, Au, Ag and Sb peaks are clearly shown, demonstrating that silver/polymer composites were successfully prepared by this one-step process. Remarkably, the presence of Au(0) particules is due to the phenomenon of metallization of the polymer matrix before making the measurement.  
Thermal behavior of the cured films was evaluated by DSC measurements. The DSC curves are reported in Figure 9. Remarkably, a clear increase of the glass-transition-temperature (Tg) values after addition of _N_-vinylcarbazole (NVK) in the formulations was noted (_i.e._, 226 \({}^{\circ}\)C for curve 2 _vs._ 212.5 \({}^{\circ}\)C for  
Figure 7: TEM analysis of silver nanoparticles obtained after thermal polymerization using AgSbF\({}_{6}\)/NVK/DPSi (1%/2%/2% _w/w_) system in (**A**) TMPTA, (**B**) St, (**C**) EPOX and (**D**) IPN (TMPTA/EPOX).  
curve 1, Figure 9). The reason for such an increase is due to the formation of polyvinylcarbazole (PVK) in the film of polymer, which leads to (1) a decrease of the free volume; and (2) the impediment of the mobility crosslinked epoxide and/or an acrylate polymer network.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 2 Materials and Methods
### Unsupervised Classification Algorithm FBCCA
#### 2.1.1 Filter Bank Construction  
First, an appropriate filter bank for multi-subband EEG signal filtering need to be built. Generally, digital signal processors include FIR and IIR. IIR filter carries the characteristics of short processing time and large amount of data, which can be used to filter EEG signals. The experimental results show that the Chebyshev type-1 filter in IIR filter can process SSVEP signal well. This work also uses Chebyshev type-1 filter to process SSVEP signal. As shown in Figure 2, we constructed a Chebyshev type-1 filter bank with \(W_{p}\) of 90 Hz and \(W_{s}\) of 100 Hz at high frequency. The \(W_{p}\) and \(W_{s}\) at low frequency are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Filter Bank** & **CF1** & **CF2** & **CF3** & **CF4** & **CF5** & **CF6** & **CF7** \\ \hline \(W_{p}(\mathrm{Hz})\) & 6 & 14 & 22 & 30 & 38 & 46 & 54 \\ \(W_{s}(\mathrm{Hz})\) & 4 & 10 & 16 & 24 & 32 & 40 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 1: \(W_{p}\) and \(W_{s}\) at low frequency of the filter bank.  
Figure 1: The process of FBCCA in EEG signal analysis.  
Finally, we need to determine the subband's range. In general, when a single channel's EEG signal is filtered by \(m\) filter banks, \(m\) EEG signals processed by the subband filter may be produced. Finally, when the channel count is \(n\), the \(m\) filter banks can be filtered to generate an \(n\times m\) matrix. The (1) represents the EEG data for \(n\) channels.  
\[C=\left(\begin{array}{c}\mathrm{channel_{1}}\\ \mathrm{channel_{2}}\\ \vdots\\ \mathrm{channel_{n-1}}\\ \mathrm{channel_{n}}\end{array}\right) \tag{1}\]  
After processing each channel with a Chebyshev type-1 filter bank consisting of m subbands, the processed subband filtered EEG signal produced is as follows:  
\[C_{nm}=\left(\begin{array}{c}\mathrm{channel_{11}}\cdots\mathrm{channel_{1m }}\\ \mathrm{channel_{21}}\cdots\mathrm{channel_{2m}}\\ \vdots\\ \mathrm{channel_{(n-1)1}}\cdots\mathrm{channel_{(n-1)m}}\\ \mathrm{channel_{n1}}\cdots\mathrm{channel_{nm}}\end{array}\right) \tag{2}\]
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions  
Renewable energy is an environment-friendly energy source. Such carbon-free technology helps to combat climate change and has become an essential alternative to current petrochemical energy sources. However, renewable energy often has diverse characteristics, which lead to uncertainties  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature** & **Years** & **Sources of Energy** \\ \hline [PERSON] et al. [8] & 2020 & Solar \\ [PERSON] et al. [5] & 2019 & Solar, Wind \\ [PERSON] et al. [9] & 2019 & Solar, Wind, Hydropower \\ [PERSON] et al. [10] & 2019 & Solar, Wind, Hydropower \\ [PERSON] and [PERSON] [11] & 2019 & Solar, Wind \\ [PERSON] et al. [7] & 2018 & Solar, Wind \\ [PERSON] et al. [12] & 2018 & Solar \\ [PERSON] et al. [13] & 2017 & Solar \\ [PERSON] et al. [14] & 2016 & Solar, Wind, Wave \\ [PERSON] et al. [15] & 2016 & Solar, Wind \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review studies of applying machine-learning techniques to renewable energy.  
of renewable energy power systems. Therefore, the prediction of renewable energy is an important way to deal with this problem. Machine learning is a data-driven process used to establish an intelligent outcome. Basic steps of machine learning contain data collection and preprocessing, feature selection and extraction, model selection, and model verification. [PERSON] et al. [16] categorized machine-learning models in renewable energy into statistical models, artificial intelligence techniques, and hybrid methods. This study collected 130 recent papers and Figure 1 shows the pie chart in terms of seven renewable energy sources. It can be noticed that both solar energy and wind energy are close to 40 percent each; and each of the other five renewable energy fields are less than five percent in the literature of this study.
# Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications
## 3 Requirements for Blocks of All-Digital Multiband Frequency Synthesiser
This noise component increases when the frequency of DCO's output signal decreases, because being together decreases \(\Delta\Sigma\) dithering frequency. The case of \(\Delta\Sigma\) quantization and shaping noise, when frequency of the DCO signal is decreased to 1 GHz and all other parameters are the same as in first case (shown in Figure 4) is shown in Figure 6. In this case the maximum of \(\Delta\Sigma\) shaping noise increased by 23 dB/Hz and this maximum moved towards lower frequency offsets, but it is still out of frequency synthesiser's passband. This noise component has to be lower than the oscillator's component of DCO's noise. Figure 5: Calculated phase noise and its components, induced by quantization of digitally controlled oscillator, when\(f_{\mathrm{DCO}}\) = 6 GHz,\(f_{\mathrm{REF}}\) = 40 MHz,\(f_{\Delta\Sigma}\) = \(f_{\mathrm{DCO}}\)/4, \(\Delta f_{\mathrm{RES}}\) = 560.64 kHz, \(W\) = 8, \(n\) = 3. Another property of DCO for multiband frequency synthesiser for IoT and IoV applications, besides phase noise performance, is the tuning range. As mentioned above, the frequency of synthesisers output signal has to range from hundreds of megahertzes up to 6 GHz. But frequency of the DCO does not have to be tuneable in whole range--frequency range can be extended by using frequency dividers, such as in reference [24]. Therefore, we will assume that for multiband applications, the tuning range of DCO has to be at least 1 GHz. According to the discussion in this and previous chapters, requirements for main blocks of an all-digital multiband frequency synthesiser for IoT and IoV applications can be defined:  
* the resolution of TDC must be better than 12 ps to ensure a phase noise of less than \(-90\) dBc/Hz when frequency of DCO's signal is 6 GHz;
* the out-of-band phase noise of frequency synthesiser is dominated by noise induced by DCO, therefore its phase noise in this region has to be less than \(-120\) dBc/Hz;
* total DCO's quantization and \(\Delta\Sigma\) shaping noise has to be lower than noise induced by TDC in synthesiser's passband and oscillator's component of DCO's noise;
* the frequency tuning range of the DCO has to be wide (at least 1 GHz);
* the digital synthesiser's filter should consist of a proportional and integral gain parts and additional IIR filters in order to change the type and order of the frequency synthesiser and be able to precisely control the bandwidth of the synthesiser.
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 3 Construction of Guaranteed Cost Strategy with Perfect Information  
To solve the orbital pursuit-evasion problem given in the second section and enable the players to achieve their individual goals, we should construct a feedback control for the players. However, for the orbital pursuit-evasion problem with bounded control, it is difficult to get optimal feedback control by solving the HJI equation. Therefore, researchers have focused on constructing near-optimal feedback control. The existing near-optimal feedback control strategies [15; 16] are based on solving numerous open-loop solutions, which is time-consuming and cannot guarantee capture.  
To overcome this shortcoming, we derive a guaranteed cost strategy by adopting the Lyapunov-like function-based method [17] and the matrix analysis theory. Besides, we propose a hybrid method combining the homotopy method and [PERSON]'s method to calculate the unknown time-to-go in this strategy. This guaranteed cost strategy can guarantee the miss distance and does not need to solve numerous open-loop solutions. The detailed procedure is shown as follows.
# Evaluation of Shear Effect on Deflection of RC Beams
## 4 Discussions  
### Flexural Strength at First Flexural Yield and Ultimate State  
Table 4 compares the experimental and analysis results of the flexural strength at the first flexural yielding and peak load of each specimen. The experimental result of the yield moment is the applied moment when the strain gauge attached to the tension reinforcing bars first reaches the yield strain, as measured in Figure 1b. In addition, the experimental results of the ultimate moment were taken as the moment applied to the specimens at the peak load. The yield and ultimate moment of specimens, \(M_{y}\) and \(M_{u}\), can be obtained analytically from the following equations using the equilibrium and compatibility conditions [20]:  
\[M_{y}=0.5f_{c}bkd\left(d-\frac{kd}{3}\right)+A_{s}^{\prime}f_{s}^{\prime}(d-d ^{\prime}) \tag{1}\]  
\[M_{u}=0.85f_{c}ab\left(d-\frac{a}{2}\right)+A_{s}^{\prime}f_{s}^{\prime}(d-d ^{\prime}) \tag{2}\]  
where \(f_{c}\) is the compressive stress of concrete, \(b\) is the beam width, \(kd\) is the depth of the neutral axis at flexural yielding, \(d\) is the depth of the beam, \(a\) is the depth of the equivalent stress block, \(f_{c}\) is the design strength of the concrete, \(A_{s}^{\prime}\) and \(f_{s}^{\prime}\) are the amount and stress of the compression reinforcement, respectively, and \(d^{\prime}\) is the distance between the centroid of the compression reinforcement and the extreme compression fiber.  
As shown in Table 4, the ratios of the analytical results to the experimental results of the yield moment were an average of 0.99 and the COV of 3.06%, indicating that the analytical results predicted the experimental results accurately. The ratio between the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{2}{c}{**Experimental**} & \multicolumn{2}{c}{**Experimental/Analytical**} \\ \cline{2-5}  & \(M_{y,exp.}\) & \(M_{w,exp.}\) & \(M_{w,exp.}\) & \(M_{w,exp.}\) \\  & **(kNm)** & **(kNm)** & \(\frac{M_{w,exp.}}{M_{w,em.}}\) & \(\frac{M_{w,exp.}}{M_{w,em.}}\) \\ \hline B3-2.5a & 124.8 & 156.0 & 0.94 & 1.18 \\ B3-2.5b & 131.1 & 153.4 & 0.99 & 1.16 \\ B3-2.5c & 129.5 & 150.7 & 0.97 & 1.14 \\ B6-2.5a & 197.6 & 222.1 & 1.01 & 1.17 \\ B6-2.5b & 195.5 & 214.3 & 1.00 & 1.13 \\ B6-2.5c & 204.3 & 227.6 & 1.05 & 1.20 \\ B6-4.0a & 192.6 & 210.9 & 0.99 & 1.11 \\ B6-4.0b & 188.1 & 209.4 & 0.96 & 1.10 \\ B6-4.0c & 191.9 & 216.2 & 0.98 & 1.14 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimens**} & \multicolumn{2}{c}{Mean} & 0.99 & 1.15 \\  & COV (\%) & 3.06 & 2.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of experimental and analytical results for flexural moment.  
experimental and analysis results for the ultimate moment was 1.15 on average and the COV was 2.78%, which was found to sufficiently exceed the flexural strength required by the ACI building code.