esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Comparison and Verification of Typical Landslides
#### 4.3.2 Lianfeng Landslide  
The Lianfeng landslide is located in Lianfeng Village, Donghe Town, Ziyang County; the slope is about 42\({}^{\circ}\), and the elevation is 198 m. A field survey found that a 3-5 m-high scarp was formed on the posterior border (Figure 10d), and staggered scarps and cracks were seen on the flank (Figure 10e). There are many tension cracks on the slope, and the collapse of the toe is obvious (Figure 10f).  
Figure 9: Huijayuan landslide. (**a**,**b**) show annual deformation-rate results from ALOS/PALSAR\(-2\) and Sentinel\(-1\)A, respectively. (**c**) is the optical image on Google Earth; (**d**–**f**) are the field-survey pictures showing the areas in white boxes A, B and C in (**c**), respectively. The P1 and P2 points marked on the figure are the points for time series deformation analysis in Chapter 5.4.1 The field-survey results verified the validity of monitoring landslides using ALOS/PALSAR\(-\)2. The deformation of this landslide is obvious on ALOS/PALSAR\(-\)2, with an average annual deformation rate of 15 mm\(-\)79.8 mm/year; however, the results of Sentinel\(-\)1A show that this area is relatively stable, with an annual deformation rate of \(-\)13 mm\(-\)10 mm/year. The vegetation coverage here is thick, which is why even though the deformation is obvious, the Sentinel\(-\)1A data still failed to detect it.
## 3 Results and Discussions  
### RAFT Polymerization of Azide-Terminated PMMA  
To investigate the efficiency of PMMA chain end transformation, polymerization kinetic studies on the linear PMMA synthesis were executed. The polymerization progress was checked by taking samples from the reaction mixture, which were measured by GPC and GC to estimate the evolution of conversion, molecular weights, and polydispersity index (PDI) with time. As it can be seen from Figure 2, M\({}_{\mathrm{n}}\) is increasing with conversion and PMMA has a small PDI, demonstrating a well-controlled RAFT reaction when using the RAFT agent. At the same time, when the high molecular weight is achieved at a high monomer conversion, the PDI values are still low (around 1.42). Compared with previous work [24], the conversion reaches a higher level (\(\sim\)55%), and the M\({}_{\mathrm{n}}\) of azide-terminatedPMMA increases more quickly with conversion, which suggests a much quicker reaction rate in this work.  
The results on the RAFT polymerization of the azide-terminated PMMA show a linear increase in \(\ln(\mathrm{M_{0}}/\mathrm{M_{t}})\) with time (Figure 3), suggesting a constant radical concentration, i.e., the absence of extensive termination reactions [25]. A conversion of 55% was obtained in 10 h, resulting in an azide-terminated PMMA with \(\mathrm{M_{n}}\) = 22000 g/mol and PDI = 1.50.  
In general, the MWCNTs-\(g\)-PMMA was obtained through the click coupling between MWCNTs-alkyne and azide-terminated PMMA. The grafting PMMA molecular weight was controlled by the prior synthesis of azide-terminated PMMA via RAFT polymerization. The grafting percentage on the MWCNT surface can present the click reaction efficiency with the reaction time. In this work, TGA measurement elucidated the grafting percentage that governed the alkyne/azide click reaction on MWCNT surfaces, as shown in Figure 4. It is evident that the grafting rate of PMMA on the surface of MWCNTs rises with the increase of reaction time. The grafting rate of MWCNTs-\(g\)-PMMA reaches 21.9% after a reaction of 24 h, and when the reaction continues to 30 h, the grafting rate is not changed obviously. The saturation grafting rate after 24 h could be due to the steric demands of the clicked PMMA chains rendering the remaining azido groups inaccessible for the click reaction [26].  
Figure 3: First-order kinetic plot for the RAFT polymerization of PMMA at 60 \({}^{\circ}\)C with AIBN as initiator mediated by DMP-\(\mathrm{N_{3}}\) ([MMA]\({}_{0}\):[RAFT]\({}_{0}\):[AIBN]\({}_{0}\)=250:1:0.2).  
Figure 2: Dependence of number-average molecular weight (\(\mathrm{M_{n}}\)) and PDI (\(\mathrm{M_{w}}/\mathrm{M_{n}}\)) of the grafted PMMA on the conversion for the PMMA RAFT polymerization at 60 \({}^{\circ}\)C with AIBN as initiator mediated by DMP-\(\mathrm{N_{3}}\) ([MMA]\({}_{0}\):[RAFT]\({}_{0}\):[AIBN]\({}_{0}\)=250:1:0.2).
## 3 Experiments and Results
### Performance of U-Net Segmentation and HSV Color Space Extraction
Therefore, using the U-Net model to filter the red light pattern could increase the accuracy of the bleeding area prediction in the TURP surgery. After the total area of the prostate and bleeding was calculated, the four levels of classification described earlier were determined to evaluate the performance of the TURP surgery in diagnosis. The proposed Res-Unet algorithm, which integrated the Resnet-50 and U-Net models, was used to perform the segmentation of the misjudgment of prostate surgery images. To evaluate the performance of the proposed algorithm, it is compared with two efficient algorithms, Autoencoder and Segnet. The comparison of the mean residual coefficient scores are shown in Table 10. The result shows that the mean residual coefficient of Res-Unet, Autoencoder and Segnet models are 0.28, 0.63, 0.70, respectively. Res-Unet achieves the best mean residual coefficient, which illustrates the red light pattern as almost being filtered from the cutting loop. Meanwhile, Res-Unet can significantly remove the misjudgment area and is superior than the other two models. Part of the testing results by these segmentation models is shown in Table 11. Furthermore, the experiments reveal that the Res-Unet model can concatenate the details of feature maps from a low to high level and automatically extract more semantic information, which achieves an acceptable performance on image segmentation. Figure 12: U-Net model training results. (**a**) The accuracy of self-predicted data. (**b**) The loss function of self-predicted data. \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Model**} &
\begin{tabular}{c} **Mean Residual Coefficient** \\ **(EPOCH = 50 LR = 0.00001)** \\ \end{tabular} \\ \hline Res-Unet model & 0.28 \\ \hline Autoencoder model [31] & 0.63 \\ \hline Segnet model [32] & 0.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: The comparison of the mean residual coefficient score with different models. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{**Testing Image**} & \
\begin{tabular}{c} **Bleeding Area without** \\ **Using U-Net Model** \\ \end{tabular} &
\begin{tabular}{c} **Bleeding Area with** \\ **Using U-Net Model** \\ \end{tabular} \\ \hline
1 & & \\
2 & & \\
3 & & \\
4 & & \\
5 & & \\
6 & & \\
7 & & \\
8 & & \\
9 & & \\
10 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The performance of bleeding area extraction using the HSV color space.
# Development of a Lightweight Prosthetic Hand for Patients with Amputated Fingers
## 5 Conclusions  
We developed the lightweight prosthetic hand for amputees who retain their thumb and partial metacarpal. To accommodate the actuators and electrical components within the limited design space, the actuators are installed vertically between the finger modules and the mounting part. Furthermore, for weight reduction, all frames were manufactured from nylon using the MJF 3D printing, except for the shafts. Each prosthetic finger has 4-DOFs with one active and three passive motions. The DIP, PIP, and MCP joints in each finger module are driven using tendons and an actuator. Functions, such as self-adaptive grasping, extension/flexion, and adduction/abduction motions, were achieved, and passive motions were realized using linear and torsional springs. Finally, the performance of the prosthetic hand was verified experimentally, and its weight (152.32 g) is considerably lower than that of conventional prosthetic hands.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/10/3536/s1](http://www.mdpi.com/2076-3417/10/10/3536/s1)]([http://www.mdpi.com/2076-3417/10/10/3536/s1](http://www.mdpi.com/2076-3417/10/10/3536/s1)), Video S1: title: A lightweight prosthetic hand.  
**Author Contributions:** Conceptualization, W.R. and Y.C.; Methodology, W.R. and S.L.; Software, W.R. and Y.C.; Formal analysis, W.R. and Y.J.C.; Writing--original draft preparation, W.R.; Supervision, Y.C., Y.J.C. and S.L.; Project administration, S.L.; Writing--review and editing, Y.C., Y.J.C. and S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Research was supported in part by the convergence technology development program for bionic arm through the National Research Foundation of Korea (NRF) funded by the Ministry of Science & ICT (No. 2015M3C1B2052811), Republic of Korea.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: (a) Natural and (b) self-adaptive grasping motions with diverse objects.
## 3 Attacks on HWMP
### HWMP Security Requirements
For example, Identity Based Signature (IBS) schemes can be used on the NMF of the PREQ and RANN frames. The identity used in the schemes can include MAC addresses and the root permission as the input. With a permission level implemented, all mesh STAs will be able to determine whether the mesh STA is a true root or not. Of course, using this type of scheme requires other considerations, such as key distribution methods and computational overhead, but implementing the root permission level provides one possible security service. Internal attackers have the required security keys to authenticate the HWMP frames in a point-to-point manner; thus, they can easily forge the path metric. End-to-end-based integrity services will not be able to stop internal attackers from forging path metrics because the MF will be updated whenever the HWMP frames are propagated. Therefore, an efficient forgery detection method is required to prevent intermediate mesh STAs from transmitting HWMP frames with fake path metrics. For example, mesh STAs can check the frames propagated by their neighbors. When a mesh STA transmits an HWMP frame, it should check the frames propagated by its neighbors by comparing them with the frame it transmitted. The path metric in the frames propagated by its neighbors should not be less than the path metric of the original frame. Of course, this is just one example of how the forgery detection method can be implemented; there will be many factors to consider in designing a real-life forgery detection method, such as the computational overhead and the storage resources. IBC-HWMP provides one way of implementing this type of forgery detection method, and the working principle of this detection method is explained in the next section. In short, Table 9 can be divided into two parts. The first two columns, authentication and integrity (point-to-point) and encryption (point-to-point), are required to protect the HWMP frames against external attacks. The last three columns, authentication and integrity (end-to-end), root permission level, and forgery detection method, are required to protect the HWMP frames against internal attacks. A security protocol can protect HWMP against both external and internal attacks if it can satisfy all of the requirements mentioned in the table. Existing authentication protocols in 802.11s provide the necessary link-based security key, such as PTK/GTK. In other words, point-to-point-based security services for HWMP frames are easily implemented. The difficult part involves the security service requirements for internal attacks. Futuredevelopment of HWMP security protocols should therefore concentrate on security service requirements for internal attacks.
# Characterization of the Solution of the Seismic Source Mechanism in Southeastern Sichuan
###### Abstract
Multidisciplinary evidence reveals systematic correlations between the spatial distribution of induced seismicity and active well pads [17], temporal clustering of seismic events and hydraulic fracturing cycles [18], and statistical parameters and earthquake sequence evolution stages [19]. This mechanism aligns with global cases of induced seismicity, including Mw 5.7 events in Oklahoma (USA) [20], Mw 4.6 tremors in Alberta (Canada) [21], M\({}_{1}\) 5.5 earthquakes in Pohang (South Korea) [22], and the 2012 Emilia seismic sequence (Italy) [23], all linked to unconventional hydrocarbon extraction. As China's largest shale gas production base, the Sichuan Basin exhibits a marked temporal correlation between escalating seismicity rates and intensive hydraulic fracturing activities. While causal attribution remains complex in tectonically active regions, mounting observational constraints increasingly implicate anthropogenic fluid injection as a key modulator of contemporary seismic hazards in this region [3, 5, 6, 7]. While prior research has offered valuable insights into the seismic activity of this region, the current study seeks to deepen our understanding by integrating moment tensor solutions, stress field inversion, and Mohr circle analysis. Our focus is on unraveling the intricate interplay between tectonic processes and human-induced activities, such as shale gas extraction, in triggering seismic events. Specifically, we aim to provide a detailed spatial characterization of the stress field and its perturbations resulting from fluid injection. This approach not only sheds new light on the mechanisms driving induced seismicity in southeastern Sichuan but also enhances our comprehension of how natural and anthropogenic factors jointly influence local seismicity.
# Uni-Cycle Genetic Algorithm as an Adaptation Engine for Wireless Channel Equalizers
## 4 Results
### Simulation Scenarios  
Two simple but realistic GSM propagation models with 6 and 12 delay taps, respectively, accurate for an urban area, are considered [16]. The power delay profiles of such channels are presented in Tables 2 and 3, respectively. In order to get a wider scope on the UCGA-AE capabilities, a 6-tap hilly-terrain model, characterized by the power delay profile from Table 4 is also taken into account. The sampling rate of 1.5 MHz is assumed. Respective sample time of ca. 0.67 us, compared with all power delay profiles, guarantees a selective kind of fading for every considered channel profile. The impact of the Doppler effect on the equalizer performance is studied for every channel model; numerous Doppler shift values are assumed one by one: \(f_{d}=0,\;3,\;15,\;50,\;200\) Hz.  
The receiver in any real communication system is not capable of predicting exact channel parameters, e.g., time dispersion. Hence, the number of equalizer taps is assumed to be 15 for both the UCGA-AE and the RLS-AE, regardless of the considered channel model. To ensure the accuracy of the results, the data frame contains 100,000 uncoded BPSK symbols from which 500 initial symbols belong to the training sequence.  
Table 1 presents the most important settings of the MATLAB GA solver, used to evaluate the performance of UCGA-AE. Regarding the reference RLS algorithm, some preliminary tests have been carried out to find the most accurate value of the forgetting factor--it is set to 0.95.
# Learning Better Representations for Audio-Visual Emotion Recognition with Common Information
## 1 Introduction
The correlation loss is used to extract common information between audio data, visual data, and the corresponding emotion labels. We adopt common information here to describe the effectiveness of combining emotional information from different modalities by correlation analysis. It is implemented by a deep learning version of Hirschfeld-Gebelein-Renyi (HGR) maximal correlation [31; 32; 33], a well-known measure of dependence, to learn the maximally correlated feature representations of different modalities. The classification loss is used in order to extract discriminative information from each modality for emotion prediction. Further, we generalize our framework to the semi-supervised learning scenario. We conduct experiments on three public audio-visual datasets: eNTERFACE'05 [34], BAUM-1s [35], and RAVDESS [36]. The results demonstrate that, by capturing common information with HGR maximal correlation, our deep learning approach can significantly enhance the stability of features that are learned from different modalities and improve the emotion recognition performance. To summarize, our main contributions are as follows:  
* We design a deep learning framework to learn the discriminative feature representations from the audio and visual data for emotion recognition. * We propose a correlation loss that is based on HGR maximal correlation to capture common information between audio data, visual data, and the corresponding emotion labels. * We generalize our framework to the semi-supervised learning scenario with common information. * We conduct experiments on the eNTERFACE'05, BAUM-1s, and RAVDESS datasets to demonstrate the effectiveness of our system. To the best of our knowledge, our method is the first work to utilize HGR maximal correlation in order to extract common information between audio data, visual data, and the corresponding emotion labels for audio-visual emotion recognition. The remainder of this paper is organized, as follows. In Section 2, we describe the related works. In Section 3, we explain HGR maximal correlation. In Section 4, we introduce our approach in detail. Subsequently, we perform extensive experiments on the eNTERFACE'05, BAUM-1s, and RAVDESS datasets in Section 5. Finally, we draw conclusions and point out some future works in Section 6.
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 3 Recent Reference Works on the Topic of Ventricular Fibrillation Detection and Prediction
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**DETECTION** & **Se (\%)** & **Sp (\%)** & **Acc (\%)** & **Win (s)** & **Database** & **Author and Year** \\ \hline Signal compression algorithm (SCA) & 71.2 & 98.5 & 96.2 & 8 & AHA CUDB VFDB & Amann 2005b [51] \\ \hline Bandpass filter and auxiliary count & 94.4 & 95.9 & 94.7 & 10 & AHA CUDB VFDB & Jekova 2004 [52] \\ \hline Improved filter and counts (IFC) & 97 & 98 & 98 & 6 & CUDB VFDB MITDB & Fokkernood 2007 [53] \\ \hline Improved filter and counts (evaluation) & 95.84 & 96.2 & 95.96 & 5 & CUDB VFDB MITDB & Fokkernood 2007 [53] \\ \hline Improved filter and counts (evaluation) & 93.6 & 94.8 & 94.2 & 2 & CUDB VFDB MITDB & Fokkernood 2007 [53] \\ \hline Convolutional neural network (CNN) & 91.04 & 95.32 & 93.18 & 5 & CUDB VFDB MITDB & Acharya 2018 [54] \\ \hline Variation mode decomposition (VMD) & 96.54 & 97.97 & 97.23 & 5 & CUDB VFDB MITDB & Tripathy 2016 [55] \\ with random forest (RF) classifier & 90.97 & 97.86 & 97.02 & 5 & CUDB VFDB & Mohanty 2018 [56] \\ \hline Adaptive variational mode and boosted & 97.32 & 98.95 & 98.29 & 5 & CUDB VFDB MITDB & Xu 2018 [57] \\ \hline SVM and bootstrap resampling scheme & 96.8 & 97.3 & 97.07 & 5 & CUDB VFDB MITDB & Li 2014 [58] \\ (evaluation) & 95.71 & 95.46 & 95.61 & 2 & CUDB VFDB MITDB & Li 2014 [58] \\ \hline Logistic regression model & 95 & 97 & N/A & 3 & CUDB & Plesinger 2018 [59] \\ \hline Digital Taylor–Fourier transform & 86.38 & 93.97 & N/A & 8 & VFDB & Tripathy 2018 [60] \\ \hline Random forest algorithm & 90.3 & 75.4 & 82.8 & 4 &
\begin{tabular}{l} Brescia medical \\ database \\ \end{tabular} & [PERSON] [61] \\ \hline \end{tabular}
\end{table} 
Table 1: A comparison of the provided methods in recent papers for ventricular fibrillations detections with the specification of sensitivity (Se), specificity (Sp) and accuracy (Acc).
## 2 Materials and Methods
### Sensor Selection  
Through the analysis of potential fault causes, vibration signals of the water pump were monitored and its vibration data under different working conditions were obtained for experimental analysis. To collect these data, we chose the HG-ZD-20B integrated vibration sensor, whose detailed parameters are listed in Table 2.  
The sensor was installed on the circulating water pump in the IRAS test area, using a magnetic suction base to affix it to the pump's metal shell. The water pump model is 50 WQ9-22-2, with a rated voltage of 380 V and a rated speed of 3000 r/min. This magnetic suction method is convenient for installing and quickly positioning the sensor at various monitoring points. By comparing different monitoring points, more distinctive signal data could be obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Description** \\ \hline Use environment & Temperature \(-40\)\({}^{\circ}\)C\(-85\)\({}^{\circ}\)C \\ Range & 0–20 mm/s \\ Output & 4–20 mA \\ Working voltage & DC 12–24 V \(\pm\) 10\% \\ Wiring mode & Two-wire system \\ Measurement direction & Vertical or horizontal \\ Accuracy & \(<\)F.S \(\pm\) 1\% \\ Resolving power & 0.1 mm/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed parameters of the vibration sensor used in this paper.  
Figure 1: Schematic structure of the newly proposed CNN-transformer-BiGRU model to detect water pump faults in industrialized recirculating aquaculture systems.
# HYDRUS-1D Simulation of Nitrogen Dynamics in Rainfed Sweet Corn Production
###### Abstract  
Nitrogen loss from agricultural fields results in contamination of ground and surface water resources due to leaching and runoff, respectively. Nitrogen transport dynamics vary significantly among agricultural fields of different climates, especially in the tropical climate. This study intended to evaluate the rainfall impact on nitrogen distribution and losses under tropical rain-fed conditions. The study was carried out in a sweet corn field for two growing seasons at the Malaysian Agricultural Research and Development Institute (MARDI) research field. The HYDRUS-1D numerical model was used to simulate nitrogen transport dynamics in this study. The observed nitrogen concentrations were used for calibration and validation of the model. Total nitrogen input to sweet corn was 120 kg/ha for both seasons. Nitrogen losses through surface runoff and leaching were dominating pathways. Surface runoff accounted for 35.3% and 22.2% of total nitrogen input during the first and second seasons, respectively. The leaching loss at 60 cm depth accounted for 4.0% (first season) and 18.5% (second season). The crop N uptake was 37.5% and 24.9% during the first and second seasons, respectively. Nitrate was the dominant form of N uptake by the crop that accounted for 83.6% (first season) and 78.5% (second season). The HYDRUS-1D simulation results of nitrogen concentrations and fluxes were found in good agreement with observed data. The overall results of simulation justified the HYDRUS-1D for improved fertilizer use in the tropical climate.  
Keywords:rain-fed conditions; nitrogen transport; contamination; runoff and leaching losses; HYDRUS-ID simulation +
Footnote †: journal: Applied Sciences