esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 5 Performance Analysis
### Simulation Setting
#### 5.1.2 Structure of Feature Extractor  
In order to illustrate the effectiveness of the HDAM and feature reuse structure, three feature extractors are used in HCPL, which are the HAFRNet shown in Figure 4, the feature reuse network (FRNet) shown in Figure 6a that uses only a feature reuse structure without HDAM and the CNN shown in Figure 6b. Therefore, the HCPL-HAFRNet (i.e., the proposed method), the HCPL-FRNet and the HCPL-CNN are denoted, respectively.
## 4 Results and Discussion
### Verification and Discussion of Results
#### 4.2.1 Landslide
These sinkholes are bead-like in arrangement, with generally large diameters, reaching up to 8 m, and depths ranging from 2 to 8 m. Large sinkholes are commonly found in the central area of the loess terrace platform, where they can gather substantial surface runoff and form seepage channels. Based on the field investigation and deformation rate of landslide No. 9, it is evident that the landslide is in a continuous deformation phase. Under the influence of regional tectonic activities, river erosion, and groundwater seepage, the slope structure has been fragmented, with developed cracks and significant deformation at the front edge. Adverse geological phenomena are commonly observed. Subsequently, due to continuous rainfall, seismic activity, and the dynamic load of vehicles at the rear edge, the strength of the  
Figure 6: Field investigation photos of landslide No. 9. (**a**) UAV model and deformation phenomenon distribution map of landslide No.9; (**b**) A field investigation photo of fault F3; (**c**) Small scale collapse phenomenon; (**d**) A crack that penetrates through the house; (**e,f**) represent cracks on the slope; (**g,h**) are sinkholes. rock and soil mass will decrease, resulting in slope instability and ultimately leading to a landslide. Proposed transmission towers 5S29060 and 5S29061 would be located near the lower-middle section of the landslide, close to the front edge. This area is characterized by multiple large upright sinkholes and intense cutting within the terrace. Under the influence of rainfall infiltration, there is a high risk of tower instability. In addition, there is a landslide wedge below the site whose deformation and sliding have led to a decrease in the stability of the slope. Once the towers are built, there will be a risk of deformation and collapse toward the wedge. Therefore, it is recommended to that the proposed transmission towers be relocated to a more stable area, away from the landslide.
# Light Harvesting and Optical-Electronic Properties of Two Quercitin and Rutin Natural Dyes
[PERSON] et al. [29] investigated four \(\pi\)-bridge substituted dyes that were based on kireto-pyrrole-pyrrole in DSSCs. The electrochemical and photovoltaic properties were calculated on the DFT/B3 LYP/6-31G (d, p) level. The results show that dyes that are based on furan as \(\pi\)-bridge have potential as an electron donor in DSSCs. The electron transfer and optical indexes of four bis-\(N\),\(N\)-dimethylaniline-group based molecules (P1-P4) by introducing acyclic and cyclic architectonied \(\pi\)-bridge in DSSCs have been thoroughly analyzed by [PERSON] et al. [30] by the DFT and TD-DFT method, showing that, with the increasing number of ethylene \(\pi\)-bridges, molecular LHE had enhanced, and the electron injection driving force (\(\Delta G^{inict}\)) had decreased and the molecular efficiency was also reduced. However, the inserting cyclic conjugated bridges had an active influence on molecular efficiency. Quercitin (Q) and Rutin (R) belong to flavonoids, which can be obtained from plants. They both have same core groups (as shown in the black region of Figure 1) and multibranch hydroxyl groups, which help to connect with TiO\({}_{2}\). Q and R are environmentally friendly materials and they have a wide range of raw materials resources, which are usually applied in the field of medicine and they are used as food coloring. There is little reported of application in the fields of DSSCs. In this work, Q and R natural dyes were chosen to study their photochemical properties in solar cells. For the purpose of comprehending the photoelectric performances of Q and R, the UV-VIs spectra, Fourier transforms infrared (FT-IR) spectrum, and cyclic voltammograms were measured in the experiment. What is more, the theoretical calculation had been applied to investigate the spectra and photoelectric capabilities. The investigated results can provide a base for the basic photoelectric properties of Q and R and explain why Q had higher \(\eta\) than R in the experiment. The investigation can also help to develop the utility potential of dyes in DSSCs.
# Performance Analysis of Interferometer Algorithm under Phase Measurement Error
## Appendix C Derivation of  
\[\begin{array}{l}E\left[\left(\Delta\phi_{0}\sin a_{0}+\Delta\phi_{1}\sin a_ {1}+\cdots+\Delta\phi_{M-1}\sin a_{M-1}\right)^{2}\right]\\ =E\left[\Delta\phi_{0}^{2}\sin^{2}a_{0}\right]+E\left[\Delta\phi_{1}^{2}\sin^{ 2}a_{1}\right]+\cdots+E\left[\Delta\phi_{M-1}^{2}\sin^{2}a_{M-1}\right]\\ =E\left[\Delta\phi_{0}^{2}\right]\sin^{2}a_{0}+E\left[\Delta\phi_{1}^{2}\right] \sin^{2}a_{1}+\cdots+E\left[\Delta\phi_{M-1}^{2}\right]\sin^{2}a_{M-1}\\ =\sigma_{\phi}^{2}\sin^{2}a_{0}+\sigma_{\phi}^{2}\sin^{2}a_{1}+\cdots+\sigma_{ \phi}^{2}\sin^{2}a_{M-1}\\ =\left(\sin^{2}a_{0}+\sin^{2}a_{1}+\cdots+\sin^{2}a_{M-1}\right)\sigma_{\phi}^{ 2}=\frac{M}{2}\sigma_{\phi}^{2}\end{array} \tag{12}\]  
where the last identity follows from the second equation of (11). The first identity can be obtained from (32) with \(m\
eq n\) and the third identity can be obtained from (32) with \(m=n\)Similarly, \(E\Bigg{[}\left\{\sum\limits_{m=0}^{M-1}\Delta\phi_{m}\cos(a_{m})\right\}^{2}\Bigg{]}\) can be obtained as follows:  
\[\begin{array}{l}E\left[\left(\Delta\phi_{0}\cos a_{0}+\Delta\phi_{1}\cos a_{1 }+\cdots+\Delta\phi_{M-1}\cos a_{M-1}\right)^{2}\right]\\ =E\left[\Delta\phi_{0}^{2}\cos^{2}a_{0}\right]+E\left[\Delta\phi_{1}^{2}\cos^ {2}a_{1}\right]+\cdots+E\left[\Delta\phi_{M-1}^{2}\cos^{2}a_{M-1}\right]\\ =E\left[\Delta\phi_{0}^{2}\right]\cos^{2}a_{0}+E\left[\Delta\phi_{1}^{2}\right] \cos^{2}a_{1}+\cdots+E\left[\Delta\phi_{M-1}^{2}\right]\cos^{2}a_{M-1}\\ =\sigma_{\phi}^{2}\cos^{2}a_{0}+\sigma_{\phi}^{2}\cos^{2}a_{1}+\cdots+\sigma_{ \phi}^{2}\cos^{2}a_{M-1}\\ =\left(\cos^{2}a_{0}+\cos^{2}a_{1}+\cdots+\cos^{2}a_{M-1}\right)\sigma_{\phi}^ {2}=\frac{M}{2}\sigma_{\phi}^{2}\end{array} \tag{10}\]  
where the last identity holds due to the first equation of (11). The first identity and the third identity are valid due to (32) with \(m\
eq n\) and \(m=n\), respectively.
# Explainable Machine-Learning Predictions for Peak Ground Acceleration
## 2 Establishment of the Dataset  
The KiK-net strong motion network covers 661 stations throughout Japan, and each of its stations are equipped with three-component, high-sensitivity acceleration sensors on the uphole and downhole [40]. The network has extremely rich ground motion records and obtains site information through drilling for external sharing.  
Based on the seismic records of the KiK-net, the seismic records between 1 March 1996 and 1 November 2020 were selected and filtered as follows:  
(1) We only selected the horizontal site stations with strong seismometers on the surface. Many stations in KiK-net are located on a hillside with large slopes rather than a horizontal site, which is why topography has a certain influence on PGA. The aim of this study was to predict the PGA of horizontal sites, mainly by taking the one-dimensional horizontal site as the research object. The influence of terrain was not within the scope of this study, so it was excluded.  
(2) In terms of PGA selection, all records greater than 0.01 g were included in the dataset as much as possible.  
According to the above principles, a total of 40 KiK-net stations and 3104 sets of uphole and downhole seismic records were collected (the uphole and downhole seismic records represent the acceleration records of the surface and bedrock, respectively). To test the applicability of machine learning in different site classification tasks, we divided the sites into different classes. The site classification methods of different countries can reflect the extent of soft and hard areas of the site, although their methods are different. For example, China divides sites into 5 classes, America divides sites into 6 classes, and Japan divides sites into 3 classes. Considering that the classification in Japan is slightly rough and that the plasticity index is required as a parameter for class E and class F in the American code, but these parameters are not included in the station profile, we classified the sites according to \"GB 50011-2010: Code for seismic design of buildings\" in China [41]. The classification criteria are shown in Table 1. Table 2 shows the number of stations corresponding to the site and the number of seismic records, and the distribution of PGA is shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Shear Wave Velocity of Rock Vs or Equivalent Shear Wave Velocity of Soil Yes (m/s)**} & \multicolumn{4}{c}{**Site Class and Overburden Thickness**} \\ \cline{2-6}  & **I\({}_{0}\)** & **I\({}_{1}\)** & **II** & **III** & **IV** \\ \hline \(V_{s}>800\) & 0 & & & & \\ \(800\geq V_{\rm s}>500\) & & 0 & & & \\ \(500\geq V_{\rm w}>250\) & & \(<\)5 & \(\geq\)5 & & \\ \(250\geq V_{\rm w}>150\) & & \(<\)3 & 3-50 & \(>\)50 & \\ \(V_{\rm w}\leq 150\) & & \(<\)3 & 3-15 & 15-80 & \(>\)80 \\ \hline \hline \end{tabular} I\({}_{0}\) and I\({}_{1}\) are subclasses of site class I.  
\end{table}
Table 1: Chinese code for classification of sites.
# Switchable Chiral Metasurface for Terahertz Anomalous Reflection Based on Phase Change Material
## 4 Conclusions  
In conclusion, we proposed a switchable chiral metasurface that can switch between the RCP mirror and LCP mirror by making use of the phase transition of GST. As \(\sigma=0\) S/m, ASRR_1 will reflect 76% incident RCPL and perfectly absorb incident LCPL at 0.76 THz. When \(\sigma\) is equal to 3 \(\times\) 10\({}^{5}\) S/m, the response of ASRR_1 to circularly polarized light will be reversed. It reflects the incident LCPL and absorbs the incident RCPL at 0.66 THz. Moreover, the performance of ASRR_2, the mirror structure of ASRR_1, is also investigated. Then, we simulate the reflected spectra with different inductivities and the surface current distribution of ASRR_1 to understand the root cause of switching. Finally, we demonstrate that this chiral metasurface can realize anomalous reflection by using the geometric phase and generalized Snell's law. Consequently, this work offers a new idea for realizing active and tunable optical chirality and will find applications in polarization-dependent fields.  
Conceptualization, Z.T.; validation, J.C. and X.C.; formal analysis, Z.T., J.C. and X.C.; investigation, J.C. and X.C.; writing--original draft preparation, J.C.; writing--review and editing, Z.T.; supervision, Z.T.; project administration, Z.T.; funding acquisition, Z.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2017 YFA0701004), Tianjin Municipal Fund for Distinguished Young Scholars (Grant No. 20 JCJJC00190), and Key Fund of Shenzhen Natural Science Foundation (Grant No. JCYJ20200109150212515).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 6: Simulated electric distributions of anomalous reflection with conductivities of (**a**,**b**) 0 S/m and (**c**,**d**) 3 \(\times\) 10\({}^{5}\) S/m.
## 3 Case Studies
### Effect of Impact Velocities  
In this section, the effect of impact velocities was investigated by increasing the initial impact velocity with 2.0 m/s, 4.0 m/s, 6.0 m/s, 8.0 m/s, 10 m/s, and 15 m/s. The striking mass was assumed as 100 tons with a hemisphere indenter type. It is evident that the impact energy is proportional to the  
Figure 7: Force- radial displacement curves with various strain-rate hardening definitions.  
square of impact velocity \(v\). Thus, the energy to be dissipated will result in larger deformations for larger impact velocities. Moreover, the strain rate is also linearly proportional to impact velocity \(v\). The force-displacement relations for different impact velocities are displayed in Figure 8. When the impact velocities increase larger than 8 m/s, the increase in stiffness due to strain rate effect is clear. It is interesting to note that this difference is not only at the initial stage of loading but also remains throughout the impact until the whole impact energy is dissipated. Another important point is that when the impact velocity increases further, the resistance of the ring-stiffened cylinder against denting decreases due to tripping of ring-stiffeners near the impact area.
# How Time Window Influences Biometrics Performance: An EEG-Based Fingerprint Connectivity Study
## 3 Results  
As summarized in Figures 1-3 for the EEG Motor Movement/Imagery dataset and in Figure 4 for the DREAMER dataset, overall, the results of the present study clearly show an evident increase in the biometric performance (as expressed in terms of EER and AUC)with an increase in the time window, for all the experimental conditions, for both beta and gamma frequency bands and, even though with different magnitudes, for both the PLI and the PLV method. In particular, for the EEG Motor Movement/Imagery dataset, the best performance was obtained for the PLV approach in the gamma band for the eyes-open resting state condition with a minimum time window equal to at least 10.5 s (EER = 0.018) and the PLV approach in the beta band for the eyes-closed resting state condition again with a minimum time window equals to at least 10.5 s (EER = 0.035). In more detail, Figure 1 shows the effect of using different time windows with a step of 0.5 s on the EER for both the PLV and PLI, the beta and gamma frequency bands, and the two resting state conditions.  
Figure 1: The effect of using different time windows from 0.5 s to 12 s, with a step of 0.5 s, on the EER for both the PLV and PLI, the beta and gamma frequency bands, and the two resting state conditions, namely eyes-closed (EC) and eyes-open (EO).  
Figure 2: The effect of using different time windows from 0.5 s to 12 s, with a step of 0.5 s, on the AUC for both the PLV and PLI, the beta and gamma frequency bands, and the two resting state conditions, namely eyes-closed (EC) and eyes-open (EO).  
Moreover, Figure 2 shows the effect of using different time windows, always with a step of 0.5 s, on the AUC, again for both the PLV and PLI, beta and gamma frequency bands, and the two resting state conditions. Finally, to better represent the effect of the time window on the computed performance, Figure 3 shows the score distributions for the two more extreme windows, the shorter of 0.5 s and the larger of 12 s, for the PLV methods in the gamma frequency band for the eyes-open resting state condition. This latest result clearly indicates how the time window affects the overall performance in terms of the total overlap between genuine and impostor score distributions. As for the DREAMER dataset, as depicted in Figure 4, similar to what was already reported for the EEG Motor Movement/Imagery dataset, the results still show a clear trend towards an increase in the performance using longer time windows, with an EER equal to 0.09 and an AUC equal to 0.03 for time windows of at least 9 s.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 4 LCoS for Augmented Reality Displays
### Augmented Reality Head-Mounted Displays
#### 4.1.1 Response Time  
The LCoS response time greatly affects the AR viewing experience. To avoid the flickering effect [99], a refresh rate of 60 Hz is the minimum requirement, while >144 Hz is highly favorable. However, the relatively slow LC response time could impair grey level accuracy at high frame rates, as Figure 13 illustrates. Taking 120 Hz as an example, the frame time is 8.3 ms. The green curve shows the applied voltage at the two consecutive frames. To switch from dark to bright state, the voltage is raised from the minimum to the maximum value, as the green curve denotes. The LC molecules respond gradually so that the reflectance increases from 0 to 100% during the first frame (0-8.3 ms) (blue curve). As a result, the _average_ reflectance in the first frame is much lower than the expected 100%, as the red dashed lines mark. Similarly, if the voltage is released at the second frame, it only generates a relatively poor dark state. The relatively slow response time (tens of milliseconds) of conventional LC materials cannot satisfy the >60 Hz refresh requirement (>180 Hz color field rate). To overcome the slow response issue in the above-mentioned analog driving scheme, a digital driving scheme, say, pulse width modulation (PWM), is widely adopted in an LCoS intensity modulator [100]. By applying binary voltages with various pulse widths within one frame, the LC molecules respond to the root-mean-square (RMS) voltages and human eyes perceive the average reflectance. Although digital driving lays less burden on LC response time and provides better grey level accuracy, stronger FFE may appear with small grey-level changes. In 2000, [PERSON] et al. designed a bit-splitting method to overcome the FFE in digital driving by sub-frame rate increasing [101]. The LC response time in digital driving scheme needs to be carefully selected: it should be less than the frame time, but longer than the pulse width to avoid a perceivable light intensity fluctuation [102]. In theory, digital driving is not suitable for phase-only LCoS because phase information cannot be averaged with an RMS value. In practice, phase flickering has been suppressed to an acceptable level [103]. Jointly with its merits of fast response time, high repeatability and good phase vs. gray level linearity, digital driving is widely adopted in commercial phase-only LCoS devices [104]. In general, fast-response LC materials help improve the performance of both analog-driven and digital-driven LCoS devices. Therefore, extensive efforts have been made to obtain submillisecond response LCs [46; 57; 58; 59].  
Figure 12: The schematic of LCoS based AR-HMD optical system. PBS: polarizing beam splitter; PRM: partially-reflective mirror; FM: focusing mirror; blue lines: p-polarized light; red lines: s-polarized light; purple lines: p and s polarized light.
# CRISPR-Cas Systems: Prospects for Use in Medicine
## 3 Genome Editing
### Early Genome Editing Systems  
TALEN nucleases were first discovered in the pathogenic bacterium _Xanthomonas_[35]. TALE proteins contain about 30 amino acid repeats flanking the DNA-binding region. The region of DNA binding is called RVD and consists of repetitive variable di-residues capable of specific DNA binding. Based on these proteins, chimeric nucleases have been developed, including the TALE and Fokl nuclease domains [36].  
ZFN are proteins, which include the so-called zinc fingers, which are a structural protein motif comprising two zinc ions linked to two histidines and cysteines (Cys2-His2). ZFNs were also fused to the nonspecific Fokl endonuclease domain to obtain zinc finger proteins (ZFPs) [37]. ZFPs require a specific site, located at a 200 bp distance from the cleavage site. The choice of these sites is limited. This technique is suitable for deleting or knocking out a gene, but cannot be used for gene insertion or correction.  
TALEN and zinc fingers have several disadvantages (in particular, low transfection efficiency) but are still used. TALEN, which are more specific and less cytotoxic than ZFN, are difficult to pack into viral delivery systems due to their large size [38].  
CRISPR-Cas systems, in turn, due to their efficiency, accuracy, and wide range of applications, are recognized as the most promising genome editing technology [39]. Their specificity and targeting are determined only by RNA sequence, the design of which is much simpler than that of a protein. CRISPR-Cas systems also allow targeting multiple genes simultaneously. This is a significant advantage over TALEN and ZFN.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 3 Theoretical Analysis of Prestress Loss of BFRP Tendons
### Results and Discussions
#### 3.2.3 Total Prestress Loss of BRRP Tendons  
The total prestress loss of BFRP tendons at various distribution angles and slab widths is summarized in Table 7. The smallest prestress loss of the BFRP tendons was 12.9%, given the slab width of 3.0 m and distribution angle of 20\({}^{\circ}\), while the highest prestress loss was 20.1%, given the slab width of 12.75 m and distribution angle of 20\({}^{\circ}\). For the slab width of 9.0 m and 12.75 m, the prestress loss decreased with the rise of the distribution angle, while for the slab width of 4.5 m, the prestress loss decreased first and then increased with the minimum loss of 12.9% when the distribution angle was 30\({}^{\circ}\). In the case of a 3.0 m wide slab, the prestress loss experienced a continuous increase with the rise of the distribution angle.  
As expected, given the same distribution angle, the total prestress loss varied with the slab width as well. With a distribution angle of 20\({}^{\circ}\), the greater the slab width, the higher the total prestress loss. However, when the distribution angle was more than 20\({}^{\circ}\), the prestress loss experienced a decrease first and then increased with the increase in slab width, given the fact that the higher the distribution angle of the BFRP tendon, the faster the prestress loss decreased.