esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 4 Experiments and Results
### Comparison with the State-of-the-Art  
As shown in Table 2, we compare DGGAN with SOTA video anomaly detection methods.  
Experiments on the Ped1 dataset show that our method has a certain AUC improvement in detecting anomalies. Concretely, compared with R-VAE, DDGAN, and Attention Prediction, the AUC performance of DGGAN has increased by \(+10.7\%\), \(+2.9\%\), and \(+1.8\%\), respectively. On the Ped2 dataset, although the two SOTA methods, i.e., ASTNet and SSMTL, reach the AUC of \(97\%\), there are still some differences compared with our method. The detection AUC obtained by other methods is significantly lower than our method. This confirms that our method still has some advantages over these two methods in detecting anomalies in Ped2.  
On the Avenue dataset, the AUC obtained by ASTNet is only \(0.5\%\) higher than that obtained by our method. The detection AUC of SSMTL is significantly higher than our method. We speculate that this is due to the self-supervised learning and teacher-student model used in this method, but this method cannot make the model have a certain awareness of abnormalities in advance during the training phase. The detection of AUCs of other SOTA methods are significantly lower than our method.  
It can be seen from the experimental results that since the reconstruction generator uses pseudo video frames for training during training, it has a certain awareness of anomalies in advance. Therefore, when the model discriminates video frames, it reduces the probability  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Modules** & & & **AUC(\%)** & \\
**NGA** & **NGB** & **SOCA** & **PED1** & **PED2** & **Avenue** \\ \hline ✗ & ✗ & ✗ & 82.3 & 93.5 & 83.7 \\ ✗ & ✗� & ✗ & 84.2 & 95.9 & 84.1 \\ ✗ & ✗ & ✗ & 82.9 & 94.7 & 85.9 \\ ✗ & ✗� & ✗ & 84.9 & 96.8 & 85.3 \\ ✗ & ✗ & ✗ & **85.7** & **97.9** & **86.2** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of AUC with noise generator and SOCA. NGA and NGB represent the noise generator modules A and B, respectively. ✔ and ✗ represent our backbone network with or without specific modules, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**AUC(\%)**} \\  & **PED1** & **PED2** & **Avenue** \\ \hline FastAno [9] & - & 96.3 & 85.3 \\ ASTNet [22] & - & 97.4 & 86.7 \\ R-AVE [30] & 75.0 & 91.0 & 79.6 \\ Attention Prediction [31] & 83.9 & 96.0 & 86.0 \\ SSMTL [3] & - & 97.5 & 91.5 \\ Context-related Prediction [15] & - & 96.3 & 87.1 \\ FFP [5] & - & 95.4 & 85.1 \\ DDGAN [16] & 82.8 & 96.7 & 85.8 \\
**DGGAN(ours)** & **85.7** & **97.9** & **86.2** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of AUC with the SOTA methods.  
of abnormal frames being reconstructed normally and improves the detection accuracy of the overall method for abnormal events.
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 4 Experimental Results and Analysis
### Manually Setting Pruning Rates  
The purpose of this section is to validate the effectiveness of the environmental constraints set through Bayesian optimization. Our experiment compares the results using environmental constraints to those where the pruning rate was manually set for each iteration. We manually set the pruning rates from 0 to 0.9 at 10 points, where a rate of 0 represents the base model without any pruning. The final results are shown in Table 3 and Figure 7. To better compare the effectiveness of our method, we included the scatter points for the best models found in Section 4.2, represented as DINO_Best, Deformable_DETR_Best, and DETR_Best in Figure 7.  
Figure 6: Pruning results of different components. (**a**) Data table of experimental result; (**b**) Comparison of accuracy; (**c**) Comparison of size; (**d**) Comparison of MACs.  
From Figure 7, it is evident that the three best points outperform the models obtained with manually set pruning rates in terms of accuracy. This demonstrates that DETR-like neural networks have varying sparsity levels across layers in remote sensing tasks and applying the same pruning rate to all layers is not optimal. Furthermore, comparing the accuracy trend curves of the three models reveals that the Deformable DETR neural network shows stronger resistance to interference during pruning. As the pruning rate increases, its accuracy decreases at a slower pace, indicating its high potential for pruning.
## 3 Results
### Total Phenolic Content and Antioxidant Capacity
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Species** & **Treatment** & **Roasting** & **S**\(\times\)**T** & **S**\(\times\)**R** & **T**\(\times\)**R** & **S**\(\times\)**T**\(\times\)**R** \\  & **(S)** & **(T)** & **(R)** & **S**\(\times\)**T** & **S**\(\times\)**R** & **T**\(\times\)**R** \\ \hline ORAC & 0.538 & 0.000 & 0.717 & 0.000 & 0.428 & 0.257 & 0.001 \\ TPC & 0.000 & 0.000 & 0.000 & 0.000 & 0.020 & 0.005 & 0.000 \\ RSA—DPPH & 0.000 & 0.000 & 0.004 & 0.121 & 0.000 & 0.001 & 0.022 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Values of significance determined with factorial ANOVA performed on production factors and their interactions on TPC and antioxidant activity in CS samples. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **TPC** & **RSA** & **ORAC** \\ \hline \multirow{4}{*}{Washed} & \multirow{2}{*}{_Canephora_} & Light & 7.88 \(\pm\) 0.21 ab & 36.47 \(\pm\) 2.83 a & 291 \(\pm\) 15 bodef \\  & & Medium & 8.20 \(\pm\) 0.33 bc & 37.49 \(\pm\) 1.10 abc & 283 \(\pm\) 13 bodef \\  & & Dark & 8.09 \(\pm\) 0.07 ab & 36.61 \(\pm\) 2.1 ab & 255 \(\pm\) 15 abc \\ \cline{2-5}  & \multirow{4}{*}{_Arabica_} & Light & 7.14 \(\pm\) 0.08 a & 38.22 \(\pm\) 1.49 abc & 226 \(\pm\) 15 a \\  & & Medium & 8.35 \(\pm\) 0.53 bc & 43.83 \(\pm\) 0.38 de & 248 \(\pm\) 13 ab \\  & & Dark & 9.18 \(\pm\) 0.32 c & 48.33 \(\pm\) 2.16 ef & 263 \(\pm\) 20 abcd \\ \hline \multirow{4}{*}{Natural} & \multirow{4}{*}{_Canephora_} & Light & 10.30 \(\pm\) 0.45 d & 41.27 \(\pm\) 1.99 bcd & 298 \(\pm\) 20 cdef \\  & & Medium & 10.79 \(\pm\) 0.49 de & 41.61 \(\pm\) 1.29 cd & 306 \(\pm\) 24 defg \\  & & Dark & 11.48 \(\pm\) 0.29 ef & 39.52 \(\pm\) 0.80 abcd & 328 \(\pm\) 16 efgh \\ \cline{2-5}  & \multirow{4}{*}{_Arabica_} & Light & 12.92 \(\pm\) 0.36 gh & 49.24 \(\pm\) 1.75 f & 360 \(\pm\) 16 h \\  & & Medium & 11.94 \(\pm\) 0.19 fg & 47.74 \(\pm\) 0.57 ef & 337 \(\pm\) 17 fgh \\  & & Dark & 13.39 \(\pm\) 0.44 h & 50.39 \(\pm\) 0.47 f & 348 \(\pm\) 14 gh \\ \hline \multicolumn{2}{c}{Significance} & *** & *** & *** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Values (mean \(\pm\) standard deviation) of total phenolic content (TPC; mg GAE/g of dry powder), radical scavenging activity (RSA; uM TE/g of sample), and oxygen radical absorbance capacity (ORAC; uM TE/g of sample) detected in CS samples according to species, post-harvest treatment, and roasting intensity. The results of variance analysis with [PERSON]’s test for mean comparison are shown.
## 4 Numerical Simulation and Discussion
### Discussion
#### 4.4.2 Sensitivity Analysis of Hydraulic Conductivity  
The hydraulic conductivity of the rock mass is often difficult to determine because of the complexity of its intrinsic fractured network. Practically, an estimated range of conductivity is often provided for a specific rock stratum instead of an exact value, as shown in Table 4. A sensitivity analysis of the valley deformation is performed considering the hydraulic conductivities of the weathered basalt and limestone strata near the valley.  
The adopted conductivity values are the upper limit, lower limit, half of the lower limits of the suggested ranges in Table 4. Thus, 9 scenarios with different conductivity combinations were implemented. According to the result in Figure 21, a nearly 60% reduction for valley deformation was observed when the conductivities of the weathered rock strata changed from their upper limits to lower limits.  
Figure 21: The sensitivity of valley deformation considering different conductivities of the weathered rock strata. (1 January 2013–1 January 2015).  
Figure 20: Comparison between thermal-hydro-mechanical (THM) simulated results and hydro-mechanical (HM) simulated results.
# High-Efficiency Adsorption of SARS-CoV-2 Spike 1 Protein by Plasma-Modified Porous Polymers
## 3 Results and Discussion  
### Microstructure and Functionalization of the Modified Porous Polymer  
Figure 1 shows typical SEM images of the materials. After plasma treatment for 20 min, a mass of fresh pores and a regular pore network formed on the surface of the resin. The generated micropores and mesopores could facilitate the S1 protein adsorption process; thus, the new porous resin was named \"modified porous polymer (MPP)\" in this study.  
FTIR and XPS spectroscopy were used to characterize the chemical structure of the MPP resins. The FTIR result of MPP is shown in Figure 2a; there is a weak band at ca. 1140 cm\({}^{-1}\), which can be considered to show the existence of the C\(-\)O stretching vibrations, the band at ca. 1450 cm\({}^{-1}\) represents the C\(-\)C skeletal vibration of the benzene ring, and the peaks around ca. 1610 cm\({}^{-1}\) and 1710 cm\({}^{-1}\) are attributed to C=O stretching vibrations of the ester groups or carboxylic groups [30; 31; 32]. The peak at 1630 cm\({}^{-1}\) was attributed to N-H stretching. It has been suggested that an adsorbent surface implanted with functional groups similar to protein residues (\(-\)NH, O\(-\)C=O, C=O) can bind viral surface proteins more stably and effectively through the interaction between polar molecules. Thus, the oxygen-containing functional groups on the surface of MPP can greatly enhance the adsorption performance.  
Figure 1: Preparation of MPP and its microstructure. (**a**) Plasma modification of MaPR; (**b**) MPP; (**c**) SEM images of MPP magnified to 500 nm.  
Figure 2: Functionalization of MPP. (**a**) FTIR spectrum of MPP; (**b**) XPS spectrum of MPP; (**c**) C1s XPS spectrum of MPP; and (**d**) N1s XPS spectrum of MPP.  
Figure 2b,c demonstrate the XPS result and the C1s spectrum of MPP. The C1s spectrum of MPP can be divided into four peaks with binding energies of 284.6, 285.7, 289.8 and 287.8 eV, representing C\(-\)C, C\(-\)O, O\(-\)C=O and C=O bonds in the benzene ring, respectively (Table 1). Carbon, oxygen and nitrogen were detected in MPP, with contents of 83.97%, 12.81% and 3.22%, respectively, and the O/C ratio was 3.98. Figure 2d shows the XPS spectra of N1s, which can be divided into Pyridinic\(-\)N, Amine/Pyrrolic\(-\)N, Graphitic\(-\)N and Oxidized\(-\)N, with binding energies of 398.5 eV, 399.8 eV, 401.2 eV and 403.1 eV, respectively [33; 34; 35]. In the total N atom of MPP, Graphitic\(-\)N accounts for 79.01%, and Amine/Pyrrolic\(-\)N accounts for 17.05%. These results are consistent with previous research [36]. We introduced oxygen-containing and nitrogen-containing functional groups into the MaPR through non-thermal plasma treatment to improve the adsorption performance.
## 3 Design and Implementation
### Electronics  
This section outlines component integration and peripheral communication protocols. To meet the requirements for multimodal, responsive interaction (Req 1), the RSC must be able to hear and playback audio, which is why we chose the AIY Voice Bonnet v2 [68; 79]. It features a button connector, a dual speaker terminal, integrated left and right microphones, headers to connect servo motors and serial devices, and documentation with examples of  
Figure 4: Process of design thinking and rapid prototyping development. The progression involves (I.) conducting a design study to investigate features of existing social robots, a step that is followed by (II.) brainstorming and concept sketching. Subsequently, (III.) component modelling is carried out in CAD-enabled layout exploration. The initial prototype design was an open-enclosure test assembly to secure all components (IV.). This test assembly helped document additional design insights. The RSC was ready for further development after identified issues had been addressed and additional features incorporated (V.).  
use [80]. The Voice Bonnet's drivers also come pre-packaged with a ready-to-use operating system image [81]. We used a Raspberry Pi single-board computer because it was turnkey and compatible with the AIY Voice Bonnet and drivers. Specifically, we used model 4B with 8 GB RAM [82] (state-of-the-art at the time of development), as it provides shorter compilation times than did previous-generation models.  
We integrated several peripheral devices (Figure 5) to meet RSC requirements, using a custom expansion board to power the connected devices (Figures 6 and 7). This board includes a logic-level shifter circuit, ensuring communication with peripherals operating at different voltage levels.  
The HMI Nextion touch display [83] talks with the RPi using the universal asynchronous receiver transmitter (UART) protocol. It is connected through the expansion board headers and requires logic-level conversion (3.3 V to 5 V). The RGB NeoPixel LED ring [84], connected through the expansion board, also requires logic-level conversion for reliable pulse-width-modulated (PWM) communication. On the other hand, the servo motors [85] work with the 3.3 V level PWM signal from the RPi, routed through the Voice Bonnet to the expansion board, where they connect and receive 5 V power. Finally, the arcade button connects to a dedicated general-purpose input-output (GPIO) header on the Voice Bonnet [79].  
This schematic (Figure 6) outlines the connections and layout for a Raspberry Pi 4B integrated with an AIY Voice Bonnet v2, detailing the connectivity for multiple peripherals. It shows direct speaker connections, the expansion board (Figure 7) with a logic level shifter [86] for interfacing between the Raspberry Pi and the Nextion UART touch display and NeoPixel LED at different voltage levels, and two servos controlled via PWM.  
Additional files are available in the ElectroCircuits folder of the repository [36; 78].
# Design of Capacitive Paddy Moisture Sensor Based on Electrical Impedance Spectroscopy Analysis
###### Abstract  
The intelligent control of the paddy drying process is an prominent development trend and the accurate detection of paddy moisture content is the basis for the intelligent control of the paddy drying process. For the design of a paddy moisture sensor, electrical impedance spectroscopy experiments of the paddy variety Wu Yu Jing 3 (_Oryza sativa_ L.) were conducted to optimize the excitation frequency and establish a paddy moisture detection model. By analyzing the change of paddy capacitance in moisture ranging from 14% to 29% over the frequency ranging from 100 Hz to 1 MHz with temperature ranging from 40 \({}^{\circ}\)C to 80 \({}^{\circ}\)C, 2 kHz is selected as the excitation frequency for paddy moisture detection in this study. Two-thirds of the experimental data points (i.e., 55 experiments points) at 2 kHz were modeled by regression analysis and the remaining one-third of the experimental data points (i.e., 28 experiments points) were used to evaluate the paddy moisture detection models. The R\({}^{2}\) of the paddy moisture detection model was 0.933 and the standard deviation of fitting test was 1.14%. A paddy moisture sensor was designed, and the standard deviation of the detection residual was 1.23%, which was close to the standard deviation 1.14% in the fitting test.  
paddy moisture; impedance spectroscopy; capacitance; frequency; regression models; sensor +
Footnote †: journal: Applied Physics Letters
# Two-Dimensional Metal-Organic Frameworks and Their Derivative Electrocatalysts for Water Splitting
## 3 Application of 2D MOFs and Their Derivatives for Water Splitting
### Oxygen Evolution Reaction
This catalyst requires only 1.58 V to generate 10 mA cm\({}^{-2}\) of current. In a separate study, [PERSON] et al. [158] synthesized a unique hybrid catalyst, FeS\({}_{2}\)-MoS\({}_{2}\)@CoS\({}_{2}\)-MOF, that required only 1.58 V, 1.66 V, and 1.78 V to generate 10, 50, and 100 mA cm-2 of current, respectively. While some bifunctional catalysts may not match the high performance of single-function catalysts, they can substantially simplify the reaction apparatus in the context of electrocatalytic water splitting. Given these benefits, it is evident that increased research focus on bifunctional catalysts is warranted.
## 3 Methodology
### Testing and Relaxing the Classical Assumptions
#### 3.3.5 Diagnostics of the Homoscedasticity Assumption
One of the best is the HC\({}_{3}\) estimator [45] which uses the square of the predicted residual (\(\varepsilon_{i,HO}\)) at the \(i\)th record of the training set as a surrogate for \(\sigma_{\varepsilon_{i}}^{2}\):  
\[\hat{\mathbf{K}}_{\overset{\rightarrow}{\varepsilon}}^{HC_{3}}=diag\Big{(}\sigma _{1,HO}^{2},\sigma_{2,HO}^{2},\ldots,\sigma_{n,HO}^{2}\Big{)} \tag{44}\]  
The same source proves that a robust unbiased heteroscedasticity consistent HC\({}_{3}\) estimator of the covariance matrix of the model parameters is:  
\[\mathbf{K}_{\overset{\rightarrow}{b}}^{HC_{3}}=\frac{n}{n-p}\Big{(}\mathbf{X}^{T}\bm {X}\Big{)}^{-1}\mathbf{X}^{T}\hat{\mathbf{K}}_{\overset{\rightarrow}{\varepsilon}}^{ HC_{3}}\mathbf{X}\Big{(}\mathbf{X}^{T}\mathbf{X}\Big{)}^{-1} \tag{45}\]  
In light of the discussion about the multicollinearity assumption (see Section 3.3.1) we will modify (45) and use the computationally robust form of the robust HC\({}_{3}\) estimator:  
\[\mathbf{K}_{\overset{\rightarrow}{b}}^{HC_{3}}\approx\frac{n}{n-p}\sum_{ \begin{array} []{c}j=1\\ s_{j}^{cor}>0\end{array} }^{p}\frac{\overset{\rightarrow}{v_{j}}\overset{ \rightarrow}{v_{j}}\overset{\rightarrow}{v_{j}}\overset{\rightarrow}{v_{j}} }{\Big{(}s_{j}^{cor}\Big{)}^{2}}\mathbf{X}^{T}\hat{\mathbf{K}}_{\overset{\leftarrow}{ \varepsilon}}^{HC_{3}}\mathbf{X}\sum_{ \begin{array}{c}j=1\\ s_{j}^{cor}>0\end{array} }^{p}\frac{\overset{\rightarrow}{v_{j}}\overset{ \rightarrow}{v_{j}}\overset{\rightarrow}{v_{j}}}{\Big{(}s_{j}^{cor}\Big{)}^{2}} \tag{46}\]  
The corrected singular values \(s_{j}^{cor}\) (for \(j=1,2,\,\ldots,p\)) in (46) are identified using PCCSV. As a result, step 5 of MHTRA should be modified as follows:  
5\({}^{\prime}\). Calculate the resubstitution OLS residuals (15), the standard error estimate (17), the covariance matrix of parameters (46), the predicted residuals (21) and form \(\overset{\rightarrow}{e}_{HO}=\left(e_{1,HO},e_{2,HO},\ldots,e_{n,HO}\right)^ {T}\). Similarly, step 9 of the MSRA should be modified to:  
9\({}^{\prime}\). Calculate the \([p\times p]\)-dimensional covariance matrix of the model parameters using (46). There are other useful applications of (46) in the MLR model. For example, a powerful bootstrap heteroscedasticity test based on the difference between (45) and (18) is proposed in [52]. In [53], several general information matrix tests for model misspecification in regression models are proposed, based on the difference of the two matrix estimates (18) and (45). According to [43] the robust estimate (45) should be used always, because it is an unbiased estimator for the parameter's covariance matrix when the model is either heteroscedastic or homoscedastic. However, when the multicollinearity assumption is violated, then the robust estimate (46) is biased and should not be used (see [54]). Luckily, that is not the case in our MLR model because it uses only cross-sectional data, and never time series.
# Intramode Brillouin Scattering Properties of Single-Crystal Lithium Niobate Optical Fiber
###### Abstract
Compared with the traditional silica optical fiber, the high refractive index difference between the core material and the cladding material of the lithium niobate optical fiber yielded the fiber waveguide with a strong binding ability to the optical field, which can enhance the SBS. Moreover, LiNbO\({}_{3}\) has some natural advantages, including strong electro-optic effect (largest \(r_{33}\) = 27 pm/V at 1500 nm), wide transparency wavelength (from 400 nm to 5 \(\upmu\)m) [29], and stable physical and chemical characteristics, which can significantly enhance the interaction of light and matter and excite strong photon-phonon interactions [30]. In 2013, [PERSON] et al. [31] demonstrated a way to make single-crystal LiNbO\({}_{3}\) optical fibers with silica glass cladding with a one-step drawing process with the CO\({}_{2}\) laser-heated pedestal growth technique. The transmission loss of 0.89 dB/cm and the effective electro-optic coefficient of 23.6 pm/V was achieved in the single-crystal LiNbO\({}_{3}\) optical fiber with core diameter of 9 \(\upmu\)m. These excellent characteristics make LNOI very promising for Brillouin optical communication sensing devices. In this paper, to our knowledge, for the first time, we report the coupling of optical and acoustic modes during backward Brillouin scattering in lithium niobate optical fibers. We numerically fond the BGS using elastodynamic equations by considering all the phase-matched intra-mode interactions that occur inside a 40 \(\upmu\)m diameter lithium niobate optical fiber and analyzing in detail the optical and acoustic modes supported by this fiber. The BFS, Brillouin gain, and spectrum in each mode are characterized, and the frequency shift of Brillouin scattering in the range of 20.73-21.17 GHz can be achieved, and a large BSBS gain is shown. The simulation results provide a theoretical reference for distributed Brillouin sensing and improving the transmission capacity of optical fiber communication systems.
## 2 Demodulation Principles
### Demodulation Mechanism  
Assuming that the electric field of the received optical signal can be expressed as a superposition of two orthogonal polarization components, \(E_{x}\) and \(E_{y}\):  
Figure 1: Concept of PolSK communication and sensing deep integration. (**a**) PolSK transmitter module; (**b**) optical transmission with vibration influence; (**c**) PolSK receiver module. AM: amplitude modulator; PBS: polarization beam splitter; PBC: polarization beam combiner; PD: photodetector.  
\[E_{x} =E_{0}cos\theta, \tag{1}\] \[E_{y} =E_{0}sin\theta, \tag{2}\]  
where \(E_{0}\) is the total electric field amplitude of the incident light, and \(\theta\) is the polarization angle of the incident light.  
The voltage signals outputs are proportional to the received optical intensities.  
\[V_{x}=kl_{x}=kE_{x}^{2}, \tag{3}\] \[V_{y}=kl_{y}=kE_{y}^{2}, \tag{4}\]  
where \(I_{x}\) and \(I_{y}\) are the optical intensities along the \(x\) and \(y\) directions, and \(k\) is a proportionality constant.  
The output voltage \(V_{out}\) of the BPD is the difference between the output voltages of the two detectors:  
\[V_{out}=V_{x}-V_{y}=k\Big{(}E_{0}^{2}cos^{2}\theta-E_{0}^{2}sin^{2}\theta \Big{)}, \tag{5}\]  
For small angles relative to the \(x\)-axis, further simplification yields:  
\[V_{out}\approx kE_{0}^{2}cos(2\theta)=V_{max}\cos(2\theta), \tag{6}\]  
Hence, the output differential voltage signal from the BPD is proportional to the maximum voltage (\(V_{max}\)) observed and the cosine function of the polarization azimuth.