esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Studies on User Research Based on EEG and Eye Tracking
## 3 Indicators of User Research
### Classification and Characteristics of EEG Signals  
#### 3.1.1 The Reference Value of EEG Research  
An EEG is a spontaneous brain electrical signal measured by electrophysiological methods. It exists in the central nervous system, and its value lies in its ability to directly reflect the activity of neurons. The frequency domain characteristics of EEGs are very obvious, and according to different frequency bands, they can be divided into \(\delta\) (1-3 Hz), \(\theta\) (4-7 Hz), \(\alpha\) (8-12 Hz), \(\beta\) (15-20 Hz), and \(\gamma\) (40 Hz or more) waves, five kinds of rhythm waves that correspond to a sleep state, fatigue state, awake and relaxed state, emotional fluctuation state, and an excited state, respectively [21]. As shown in Figure 3, different waveforms represent different psychological information. \(\alpha\) waves are the main manifestation of cortical electrical activity when the cerebral cortex is awake, quiet and closed eyes. When people open their eyes, think about problems, or receive other stimuli, \(\alpha\) waves disappear immediately. \(\beta\) waves are manifestations of cortical excitation. This  
Figure 2: The research concept framework.  
wave only appears in the frontal lobe, when people are quiet with their eyes closed. If they open their eyes to see things, think about problems, or suddenly hear sounds, \(\beta\) waves will be triggered in other areas of the cortex. \(\theta\) waves appear when subjects are tired, disappointed, or frustrated with emotional stimuli [22]. These different waveforms reflect the psychological state of the subjects. Different brain waves correspond to different frequencies, representing corresponding mental states.  
In actual research, it has been found that the strength of theta waves can reflect the activity of cognitive control mechanisms [23], and the power of alpha waves in the frontal area can serve as an indicator of pleasure [24]. Rhythmic waves in various frequency bands are of great reference value for cognitive research. In research, we need to not only pay attention to the overall activity status of the brain, but also care about specific psychological components related to it, such as the attention level, memory, and emotional response related to tasks. As shown in Figure 4, the specific meanings of user research-related indicators are as follows:  
1. Emotional indicators: The left and right frontal lobes of the brain are sensitive to positive and negative emotions, respectively. By analyzing their lateralization, we can understand the strength changes of users' positive and negative emotions [25].
2. Attention indicators: The left and right parietal lobes of the brain control attention allocation, and can understand users' attention allocation.
3. Memory indicators: The temporal lobe controls our memory, and can encode and recall environmental information. Through analysis of the temporal lobe, we can understand users' memories of products [16].  
Figure 4: Brain indicators and their meanings.  
Figure 3: The waveforms of different brain waves and their states.
## 2 Results
### Electrochemical Behavior of Rhodamine B  
The electrochemical behavior of Rhodamine B was tested by comparing different electrodes. Cyclic voltammetry was performed for 10 mg L\({}^{-1}\) of Rhodamine B using 1 M of H\({}_{3}\)PO\({}_{4}\) as the supporting electrolyte. The prepared sensor TiO\({}_{2}\)-MWCNT-MIP-SPCE displayed higher sensitivity in comparison with the non-selective SPCE, the TiO\({}_{2}\)-MWCNT-SPCE, and the non-modified SPCE (Figure 2). In addition, the MIP-SPCE offers a high sensitivity compared with the NIP-SPCE (Figure 2b). Thus, it can be said that the MIP-SPCE is very useful for the investigation of the electrochemical behavior of Rhodamine B.  
As shown in Figure 2a, the cyclic voltammogram curves obtained for the TiO\({}_{2}\)-MWCNT-MIP-SPCE exhibited an oxidation peak of Rhodamine B, within the potential range tested. It suggests that the oxidation reaction of Rhodamine B on the TiO\({}_{2}\)-MWCNT-MIP-SPCE is totally irreversible. The oxidation current of the TiO\({}_{2}\)-MWCNT-MIP-SPCE (0.9 V) at the TiO\({}_{2}\)-MWCNT-MIP-SPCE was higher than the other compared electrodes (Figure 2). On the basis of the 1H\({}^{+}\) per two electrons stoichiometry, the electrochemical process of Rhodamine B on the TiO\({}_{2}\)-MWCNT-MIP-SPCE is one H\({}^{+}\) and two electrons reaction. A possible oxidation mechanism for Rhodamine B is shown in Figure S2. Differential pulse voltammetry (DPV) was also performed at a low concentration of Rhodamine B (100 ng mL\({}^{-1}\)) using 1 M of H\({}_{3}\)PO\({}_{4}\) as supporting electrolyte (Figure S3).  
Figure 1: Characterization of TiO\({}_{2}\)-MWCNTs-MIP by Raman spectrum (**A**), and SEM images (**B**), images are presented at magnification of 50K\(\times\) and 100K\(\times\).
## 2 Materials and Methods
### Molecular Detection of Virulence Genes
#### 2.5.2 Detection of Virulence Genes of Recovered Isolates  
All primers used in the current study are shown in Table 1; they were synthesized by a Macrogen company (Gasan-dong, Seoul, South Korea). The _E. coli_ target genes were _Sfa_/_focDE_ (E1), _papC_ (E2), and _fimH_ (E3). The virulence factors of these genes are S and _FIC_ fimbie for _Sfa_/_focDE_, P fimbiae for _papC_, and type 1 fimbiae for _fimH_. Sequences were determined through multiplex PCR with a 60 \({}^{\circ}\)C annealing temperature; the amplified sequences are shown in Table 1[36]. The _K. pneumoniae_ virulence genes used in this study were _rmpA_ (K1), which targets the regulator of mucoid phenotype A; _jbtS_ (K2), which targets siderophore; and _mrkD_ (K3), which targets adhesion type 3 fimbiae[37]. Sequences were determined through multiplex PCR with a 60 \({}^{\circ}\)C annealing temperature; the amplified sequences are shown in Table 1[38].  
The _P. aeruginosa_ virulence genes used in this study were bifunctional enzymes (_exo5_) (P1) [39; 40], hemolysin phospholipase (_pclH_) (P2), and enterotoxin gene toxA (P3). Sequences were determined through multiplex PCR with a 58 \({}^{\circ}\)C annealing temperature; the amplified sequences are shown in Table 1[41]. The _S. enterica_ virulence genes used in this study were aggregative fimbiae (_agfA_) (S1), Salmonella pathogenicity island-3 (_misL_) (S2), and Salmonella-specific invasive (_intrA_) (S3) [42]. The reactions were performed either uniplex (_invA_) with a 57 \({}^{\circ}\)C annealing temperature or multiplex (_nisL_ and _agfA_) with a 63 \({}^{\circ}\)C annealing temperature, as described in Table 1.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Organism** & **Gene** & **Primer 5–3** & **Annealing** & **Size** & **Reference** \\ \hline \multirow{8}{*}{_E. coli_} & _Sfa/facDE_ (E1) & F: CTCCGGAGAACTGGGGCATCTTAC & & 410 bp & \\  & _papC_ (E2) & F: GACGGTGTACTGCAGGGTGTGGCG & & 60 \({}^{\circ}\)C & 328 bp & [36] \\  & _fimH_ (E3) & F: AACGGATGATTCCAGTTTGTG & & 465 bp & \\  & _fimA_ (K1) & R: ATTGCGTACCAGCATTAGCAATGTCC & & \\ \hline \multirow{8}{*}{_K. pneumoniae_} & _rmpA_ (K1) & F: CATAAGAGATATTGGTGTGACG & & 461 bp & \\  & _bptS_ (K2) & F: GACGAGAGACACAGCTAG & & 60 \({}^{\circ}\)C & 242 bp & [43] \\  & _mbD_ (K3) & F: AACGTACTGCCTGACTTTCCGGCA & & 340 bp & \\  & _exo5_ (P1) & GATCTGGA & & 270 bp & \\  & _pcH_ (P2) & F: AAAGCAATGCACTCAGCGGTACATTCC & & \\  & _pcH_ (P2) & F: GAGGAGGAGG & & 307 bp & [41] \\  & _toxA_ (P3) & F: ATGGTGTAGGTAGCGGCACAT & & 433 bp & \\ \hline \multirow{8}{*}{_P. aeruginosa_} & _agfA_ (S1) & F: TCCGCCCGACTCAACG & & 261 bp & \\  & _cisL_ (S2) & F: GACGGTGTAGGTGTGTGCATTCAG & 63 \({}^{\circ}\)C & 986 bp & [42] \\  & _invA_ (S3) & F: CACTTTCCTACTTAACAGGTCTG & 57 \({}^{\circ}\)C & 413 bp & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bacteria virulence genes, primers sequences, amplicon size, and annealing temperature.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 5 Orbiting Microgravity Facilities
### The International Space Station
#### 5.2.5 Experimental Design  
Because of the complexity of the ISS environment and the limited flight opportunities, careful experimental design is even more important for spaceflight research. In general, the scientific objectives should be clear, while experiments should be kept as simple as possible. A reduced number of treatments, carefully designed controls, hardware robustness, and sample resilience are all factors that could increase the success of the experiment. Smaller mass and time and energy requirements will contain costs and increase the chances of having a payload approved for flight onboard the ISS. A simple experimental design, accompanied by all the suitable controls, will instead ensure that the conclusions drawn from results are appropriate.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
###### Abstract  
Edge-assisted visual simultaneous localization and mapping (SLAM) is widely used in autonomous driving, robot navigation, and augmented reality for environmental perception, map construction, and real-time positioning. However, it poses significant privacy risks, as input images may contain sensitive information, and generated 3D point clouds can reconstruct original scenes. To address these concerns, this paper proposes a dual-component privacy-preserving approach for visual SLAM. First, a privacy protection method for images is proposed, which combines object detection and image inpainting to protect privacy-sensitive information in images. Second, an encryption algorithm is introduced to convert 3D point cloud data into a 3D line cloud through dimensionality enhancement. Integrated with ORB-SLAM3, the proposed method is evaluated on the Oxford Robotcar and KITTI datasets. Results demonstrate that it effectively safeguards privacy-sensitive information while ORB-SLAM3 maintains accurate pose estimation in dynamic outdoor scenes. Furthermore, the encrypted line cloud prevents unauthorized attacks on recovering the original point cloud. This approach enhances privacy protection in visual SLAM and is expected to expand its potential applications.  
Keywords:simultaneous localization and mapping; privacy preserving; dual-component approach; image inpainting; point dimensionality lifting
## 2 Kinetic Equation
### Order Parameter  
By analogy with the standard QED [10], let us introduce the function \(\Phi(t)=u(t)+iv(t)\) as an order parameter of the system that describes polarization effects in graphene by means of the anomalous averages (20), (21) and (28) which are characteristic for systems with broken symmetry (e.g. [5; 6; 16]). We write the corresponding equation of motion  
\[\dot{\Phi}-\frac{2i\varepsilon}{\hbar}\Phi=\lambda(1-2f), \tag{35}\]  
which follows from Equation (27). The formal solution of this equation with the zero initial condition is  
\[\Phi(t)=\int\limits_{t_{0}}^{t}dt^{\prime}\lambda(t^{\prime})\left[1-2f(t^{ \prime})\right]\exp\left[\frac{2i}{\hbar}\int\limits_{t^{\prime}}^{t}d\tau \varepsilon(\tau)\right]. \tag{36}\]  
Let us consider now a finite electric field which is switched off at the point of time \(t_{\text{off}}\), i.e., \(E(t>t_{\text{off}})=0\) and hence according to Equation (10) \(\lambda(t>t_{\text{off}})=0\). Then, for \(t>t_{\text{off}}\) it follows from Equation (36) that the order parameter is different from zero and oscillates with the frequency \(2\varepsilon_{\text{out}}/\hbar\), i.e.,  
\[\Phi(t>t_{\text{off}})=\Phi_{\text{out}}(\vec{p})\exp\left[\frac{2i\varepsilon _{\text{out}}}{\hbar}(t-t_{\text{off}})\right], \tag{37}\]  
where the asymptotical value of the quasienergy (8) is equal to  
\[\varepsilon_{\text{out}}=\varepsilon(t\to\infty)=v_{F}\sqrt{(\vec{p}-\frac{ \varepsilon}{c}\vec{A}_{\text{out}})^{2}}, \tag{38}\]  
\(A_{\text{out}}^{k}=\lim\limits_{t\to\infty}A^{k}(t)\). In Equation (37) the momentum dependent amplitude  
\[\Phi_{\text{out}}(\vec{p})=\int\limits_{t_{0}}^{t_{\text{off}}}dt^{\prime} \lambda(t^{\prime})\left[1-2f(t^{\prime})\right]\exp\left[\frac{2i}{\hbar}\int \limits_{tt}^{t_{\text{off}}}d\tau\varepsilon(\tau)\right] \tag{39}\]  
is defined by the entire prehistory of the system evolution in a given external field.  
The presence of such residual oscillations of the order parameter is a prerequisite for the analogous behavior of the polarization current (see Section 5).  
Thus,  
\[|\Phi(t>t_{\text{off}})|^{2}=|\Phi_{\text{out}}(\vec{p})|^{2}=\text{const} \tag{40}\]  
after switching off the external field, i.e., the long-lived order is formed.  
The amplitude \(\Phi_{\text{out}}(\vec{p})\) of oscillations of the order parameter in the residual state can be defined from the integral of motion (29) by rewriting it in the form  
\[(1-2f_{\text{out}})^{2}+|\Phi_{\text{out}}|^{2}=1. \tag{41}\]The order parameter \(\Phi(t)\) reflects the role of anomalous averages in the kinetics of the excitation process in graphene, that can be considered as a peculiar field induced phase transition [1, 16]. Some other features of this process in graphene will be considered below.
## 3 Theoretical Analysis and Proof
### Robustness Analysis
: _Assuming that the system is disturbed by the random noise \(\psi(t)=\zeta\in\mathbb{R}^{m+n}\), its steady-state computing error is \(\lim_{k\to\infty}\lVert\varepsilon_{k}\rVert_{2}<2l\,sup_{1\leq i\leq k,1 \leq\theta\leq l}\lvert\zeta_{i}^{\theta}\rvert_{2}/(1-\lVert P\rVert_{2})+ O(\sigma^{3})\), with \(l=m+n\)._Proof. : The analysis can be divided into two parts (i.e., the random noise \(\psi(t)=\zeta\) and \(O(\sigma^{3})\)). First, only considering the random noise \(\psi(t)=\zeta\), similarly to Theorem 2, the following equation is obtained:  
\[ \begin{split}\varepsilon_{k+1}^{\theta}=&(2.5-\mathrm{ Y}-\Gamma)\varepsilon_{k}^{\theta}-(2.5-\mathrm{Y})\varepsilon_{k-1}^{\theta}+1.5 \varepsilon_{k-2}^{\theta}-\\ & 0.5\varepsilon_{k-3}^{\theta}+\zeta_{k}^{\theta}-\zeta_{k-1}^{ \theta}.\end{split}  \tag{25}\]  
Define \(\alpha_{k}^{\theta}=[\zeta_{k}^{\theta}-\zeta_{k-1}^{\theta},0]^{\mathrm{T}}\), and thus, (25) is rearranged as  
\[\Phi_{k+1}^{\theta}=P\Phi_{k}^{\theta}+\alpha_{k}^{\theta}. \tag{26}\]  
Therefore, the abovementioned equation can be rewritten as  
\[\begin{split}\|\Phi_{k+1}^{\theta}\|_{2}&\leq\|P \Phi_{k}^{\theta}\|_{2}+\|\alpha_{k}^{\theta}\|_{2}\\ &\leq\|P\|_{2}\|\Phi_{k}^{\theta}\|_{2}+\|\alpha_{k}^{\theta}\|_ {2}\\ &\leq\|P
# High-Resolution Spaceborne SAR Geolocation Accuracy Analysis and Error Correction
## 4 Discussion
### Other Geolocation Error Sources  
Solid Earth effects and approximations in image production may also introduce geolocation errors in addition to satellite orbit error, atmospheric error, and elevation-related error. Among solid Earth effects, solid Earth tides (SETs) have the most significant impact on geolocation accuracy. These are induced by the tidal forces of the Moon and Sun [46], potentially causing elevation changes of up to half a meter in equatorial regions and up to 40 cm in mid-latitude areas. Apart from SETs, ocean tidal loading and atmospheric tidal loading can also introduce a centimeter-level error [47; 48]. Commonly, studies employ programs written by [PERSON] to eliminate these effects [49]. Owing to the relatively minor influence of the solid Earth effects and the fact that they are hardly considered when processing other data, we have not discussed them.  
Approximations applied to SAR image production can lead to geolocation errors. For Sentinel-1 images, the bistatic effect, Doppler shift in range, and Doppler FM-rate mismatch are three typical error sources [18; 50]. The bistatic effect arises when the \"stop-and-go\" model assumes that the sensor receives all signals corresponding to the same LOS direction simultaneously. However, they are not simultaneous in reality, introducing an error of about 2 m to 3 m in azimuth. Doppler shift results from neglecting the variation in the Doppler frequency matching the same LOS, leading to a range error as large as  
Figure 17: Registration procedure using the ISCE. The DEM is projected to the radar center coordinate system as z data and is used to generate a simulated amplitude image. The orbit can be corrected using the rgshift calculated by registering the SAR image and the simulated image.  
Figure 18: The registration’s SNR: (**a**) TerraSAR-X image; (**b**) Tianhui-2 reference image.  
0.4 m. Doppler FM-rate mismatch affects the azimuth geolocation accuracy, and the error is typically less than 1.5 m. We have not discussed these factors because these approximations are not applied to TerraSAR-X images [18].
# Integration of Wavelet Denoising and HHT Applied to the Analysis of Bridge Dynamic Characteristics
###### Abstract  
When the dynamic characteristics of a bridge structure are analyzed though Hilbert-Huang transform (HHT), the noise contained in the bridge dynamic monitoring data may seriously affect the performance of the first natural frequency identification. A time-frequency analysis method that integrates wavelet threshold denoising and HHT is proposed to overcome this deficiency. The denoising effect of the experimental analysis on the simulated noisy signals proves the effectiveness of the proposed method. This method is used to perform denoising pre-processing on the dynamic monitoring data of Sutong Bridge, and the denoised results of different methods are compared and analyzed. Then, the best denoising data are selected as the input data of Hilbert spectrum analysis to identify the structural first natural frequency of the bridge. The results indicate that the wavelet-empirical mode decomposition (EMD) method effectively reduces the interference of random noise and eliminates useless intrinsic modal function (IMF) components, and the excellent properties of the signal evaluation index after denoising make the method suitable for processing non-stationary signals with noise. When Hilbert spectrum analysis is applied to the denoised data, the first natural frequency of the bridge structure can be identified clearly and is consistent with the theoretical calculation. The proposed method can effectively determine the natural vibration characteristics of the bridge structure.  
Hilbert-Huang transform (HHT); empirical mode decomposition (EMD); wavelet threshold denoising; dynamic characteristic; first natural frequency +
Footnote †: journal: Applied Physics Letters
## 3 Test Results and Analyses
### Influence of the Repair Thickness on the Load-Bearing Performance of Repaired Pipes  
The load-displacement curves of specimens F-1 to F-3 are shown in Figure 5. The improvement of the bearing capacity of F-2 and F-3 compared to F-1 in their initial state is shown in Table 7. Here, it can be seen that the change trend of the load-displacement curve of each specimen after repair is basically the same: The larger the repair thickness, the greater the rate of the load increase, while the greater the crack load of the specimen, the greater the ultimate load of the specimen. As seen from Table 7, compared to the initial state of specimen F-1, the crack load of specimens F-2 and F-3 increased by 58.32% and 148.95%, respectively, while the ultimate load increased by 110.23% and 201.28%, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Group** & **Ultimate Load D\({}_{\textbf{u}}\)/kN** \\ \hline F-2 & 24.87 \\ F-5 & 21.73 \\ F-8 & 14.96 \\ F-3 & 35.64 \\ F-6 & 31.08 \\ F-9 & 27.09 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Plate load test results of pipe sections with the same corrosion thickness.  
Figure 4: Load–displacement curves of pipe sections with the same repair thickness: (**a**) Repair thickness of 10 mm; (**b**) repair thickness of 20 mm.  
The load-displacement curves of specimens F-7 to F-9 are shown in Figure 7. The improvement of the bearing capacity of the initial defect pipeline F-7 by specimens F-8 and F-9 is shown in Table 9. From Figure 7, it can be seen that the load-displacement curves of specimens F-7 to F-9 are approximately the same as those of the above specimens. As can be seen from Table 9, compared to the initial state of specimen F-7, the crack load of the specimens F-8 and F-9 increased by 121.06% and 196.45%, respectively, while the ultimate load increased by 94.54% and 252.28%, respectively.  
The above test results show that ultra-high-performance concrete material can effectively improve the load-bearing performance of pipes by repairing reinforced concrete pipes. As the repair thickness increased, the inhibiting effect of the lining layer on crack expansion increased, and the longer the whole pipeline lining in the state of a superimposed structure [18]. The crack and ultimate loads of the original pipes increased significantly, the bearing effect of the lining layer was given full play, and the load-bearing performance of the existing pipes greatly improved.
# Application of Organic-Inorganic Hybrids in Chemical Analysis, Bio- and Environmental Monitoring
## 2 Classification and Synthesis of Organic-Inorganic Hybrids (OIH)  
### Classification  
Depending on the nature of the organic-inorganic interface, hybrid materials can be classified into two main group: (i) hybrid systems where components have weak interaction (Van der Waals, electrostatic or hydrogen bonds); and (ii) organic-inorganic components that are linked by covalent or ionic-covalent chemical bonds [17].  
The first type or class of OIH (Type/Class I) is characterized by the physical interaction (\(\pi\)-\(\pi\) interaction, hydrogen bonds formation etc.,) between organic and inorganic components. Notably, OIH materials of Class I occur only when organic and inorganic components both have some specific functional groups on their surface. Otherwise, OIH materials are in an inhomogeneous phase that results in phase separation between organic and inorganic components [38]. This class is mostly presented by various types of core-shell structures (organic-inorganic or vice versa), inorganic particles, or other nanostructures embedded into an organic (e.g., polymer) matrix which found application in biosensors [39], photocatalysis [40], and optoelectronics [41].  
Type/Class II. In this type of hybrid materials, chemical bonding occurs between two different phases. The building blocks for class II OIH possess at least two distinct functional groups: metal-to-carbon links which are stable in the hydrolysis reactions and alkoxy groups (R-OM bonds) which may experience hydrolysis-condensation reactions in the presence of water [7]. This class of OIH has found a wide application in batteries [42], filtration [43], hydrophilic/hydrophobic materials [7]. For example, class II can be prepared by alkoxysilane (-Si(OR)3)-functionalized organic compound and telechelic organic component with functional moieties by co-condensation reaction. Other examples of OIH of class II are perovskites, metal-organic frameworks (MOF), and bimetallic organophosphate oxides.  
Remarkably, both types/classes of OIH are intensively used for the development of biosensing tools (Figure 1). For biosensing, the design of OIH is determined by the requirements of the analytical task and operational modes of the final device.  
Figure 1: Classification of organic-inorganic hybrids (OIH) on the basis of the interaction between organic and inorganic components (original image).
## 1 Introduction
Different hole transport materials (HTMs) with different energy levels, such as poly(3-hexylthiophene-2,5-diyl) (P3 HT), N2,N2,N2\({}^{\prime}\),N7,N7,N7\({}^{\prime}\),N7\({}^{\prime}\)-octakis(4-methoxyphenyl)-9,9\({}^{\prime}\)-spirobi 9H-fluorene-2,2\({}^{\prime}\),7,7\({}^{\prime}\)-tetramine (spiro-OMeTAD) and poly((2,3-bis(3-octyloxyphenyl)-5,8-quinoxalinediyl)-2,5-thiophenenediyl) (TQ1) are studied to maximize the solar cell efficiency. The results show that the energy level of HTM is critical for the hole extraction and therefore also the solar cell photovoltaic performance since the gold contact might not be selective towards any of the charge carriers [28].