esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid DSCNN-GRU Based Surrogate Model for Transient Groundwater Flow Prediction
## 3 Results
### Further Optimization of the DSCNN-GRU Surrogate Model
#### 3.3.3 Optimization of Activation Functions  
After selecting Adam as the optimizer, this study focused on examining the impact of different activation functions on model efficiency and accuracy. Specifically, only the activation function of the convolutional layer was modified, while the other layers remained unchanged. The results, as depicted in Table 1, show that when Softmax is used as the activation function, the model's training time is 3548 s, the MSE value for prediction is  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Different Layers**} & \multicolumn{3}{c}{**Different Optimizers**} & \multicolumn{3}{c}{**Different Activation Functions**} \\ \cline{2-10}  & **5 Layers** & **4 Layers** & **3 Layers** & **Adam** & **Nadam** & **RMSprop** & **Relu** & **Tanh** & **Softmax** \\ \hline Time (s) & 2858.54 & 2914.89 & 3064.91 & 2858.54 & 3212.47 & 2598.3 & 2858.54 & 2876.42 & 3548.15 \\ R\({}^{2}\) & 0.954 & 0.949 & 0.948 & 0.954 & 0.853 & 0.876 & 0.954 & 0.974 & 0.826 \\ MSE & 0.0062 & 0.0069 & 0.0096 & 0.0062 & 0.0685 & 0.0431 & 0.0062 & 0.0052 & 0.0577 \\ SSIM & 0.942 & 0.931 & 0.929 & 0.942 & 0.724 & 0.755 & 0.942 & 0.961 & 0.701 \\ \hline \hline \end{tabular} Note: The values highlighted in red indicate (1) the optimal parameters identified through systematic evaluation, and (2) their corresponding performance metrics that demonstrate the prediction accuracy of the surrogate model under each optimal parameter condition.  
\end{table}
Table 1: Data list of time, R\({}^{2}\) values, and MSE values when using different convolutional layers, different optimizers, and different activation functions for the model with the same remaining data.  
0.057, and the R\({}^{2}\) value is 0.826. However, compared to the other two activation functions, Softmax yielded less favorable results in terms of training time and prediction accuracy; thus, further discussion on this particular activation function will not be pursued.  
In the training set, the mean squared error (MSE) using Tanh as the activation function is 0.0052, which represents a 16% decrease compared to the MSE of 0.0062 when using Relu. Additionally, the R\({}^{2}\) values for Tanh and Relu are 0.974 and 0.954, respectively. These results clearly indicate that using Tanh as the activation function can significantly improve the prediction accuracy of the model in this study. However, it is worth noting that the training time for the Tanh model is 2876 s, whereas it is only 2598 s for the Relu model, resulting in higher computational efficiency. Despite the reduction in training time by 278 s when using Relu, the improvement in prediction accuracy was deemed more important for this study, given that there was no significant difference in model budget efficiency. Therefore, using the Tanh activation function was considered more suitable for this study, as it yielded higher prediction accuracy compared to using Relu, and the reduction in training efficiency is acceptable.
## 4 Simulation and Experimental Verification
### Multi-Objective Optimal Design Option Strength Verification
#### 4.1.2 Dynamic Simulation and Bench Test Verification  
Bench testing is a widely used method for evaluating engineering structures and mechanical equipment. It can simulate load changes under real-world road surface conditions. During the test, random mechanical loads were applied to the samples, and the stress-strain values and fatigue lifetimes at corresponding points were measured to assess reliability.  
According to the 'Pavement vehicles-fifth wheel strength test' related dynamic test standards, as shown in Figure 20, F\({}_{\text{v,t}}\) is applied to the rigid plate, F\({}_{\text{h,t}}\) is applied to the radial of the inner and outer baffle connecting shaft of the fifth wheel, and the load in the dynamic test is circulated sinusoidally. The loading frequency is 2 Hz. The simulation showed that the maximum stress at a certain moment was 250 MPa, and the corresponding stress distribution diagram is shown in Figure 21. The calculated dynamic test fatigue life was greater than 1.0 \(\times\) 10\({}^{7}\) without dynamic failure. The strength and fatigue properties of the dynamic tests were within the permissible limits of the relevant standards.  
The method of applying a load in the biaxial bench test is the same as that in Figure 20. F\({}_{\text{v,t}}\) acts vertically downwards on the rigid plate, and F\({}_{\text{h,t}}\) is applied to the radial of the inner and outer baffle connecting shaft of the fifth wheel. The magnitude of F\({}_{\text{v,t}}\) is 117.72 KN to 353.16 KN, and the magnitude of F\({}_{\text{h,t}}\) is \(-\)120 KN to 120 KN. The load in the dynamic test is circulated sinusoidally. The loading frequency is 2 Hz.  
According to the above finite element simulation results, it can be found the maximum stress point is located at the inner baffles restraining the circular hole. Therefore, eight strain collection points were arranged at the key positions of the outer and inner baffle connections of the fifth wheel in this test, and eight-channel dynamic strain gauges were used to synchronously collect the bench test data.  
In Figure 22a, the left side shows the fifth wheel installed on the line, and in Figure 22b, the right side shows the biaxial bench test conducted after the fifth wheel was installed. The strain results of eight test points on the fifth wheel were collected, and the strain data of eight groups of periods were obtained. According to [PERSON]'s law, the maximum stress of the biaxial dynamic bench test is less than 250 MPa (the maximum stress value of the dynamic simulation test in Figure 21). And the final fatigue life of the fifth wheel bench test is more than 2 million cycles.
# Roles of D-Amino Acids on the Bioactivity of Host Defense Peptides
## 6 In Vivo Testing of d-AA-Containing HDPs  
One of the most important objectives of HDP research is to use them in clinical applications. The development progress of clinically worthy HDPs is slow. As of now, the few instances where HDPs have found mass application in clinical setting were all topical such as gramicidin and polymyxins for primarily treating Gram-positive and Gram-negative bacteria, respectively [106]. Although there are clinically approved peptide-based membrane-disrupting antibiotics available for systemic administration, it is rather questionable whether these antibiotics fit the definition of host defense peptides. An example is daptomycin, which is used as a reserve antibiotic against life-threatening infections such as methicillin-resistant _Staphylococcus aureus_[107]. However, daptomycin is a lipopeptide and works against only Gram-positive bacteria, which is opposite to the equal effectiveness against both Gram-positive and Gram-negative bacteria as displayed by many antimicrobial host defense peptides [7].  
Current clinical trials of HDPs is primarily focused on topical applications [108]. Small-scale clinical trials for developing HDPs that are suited for systemic administration are in existence, especially for HDPs conjugated to homing motifs that further enhance their selectivity. [PERSON] et al. [109] tested a luteinizing-hormone-releasing hormone (LHRH) ligand conjugated to a cationic membrane-disrupting peptide in patients with a range of LHRH receptor-expressing tumor types. However, the results of this study could be described as cautiously optimistic.  
In another work, a Phase I clinical trial was initiated with an N-terminal peptide of human lactoferrin for the treatment of infection and inflammation complications resulting from the hematopoietic stem cell transplantation treatment of hematological malignancies [110]. The same peptide initially tested by [PERSON] et al. [110] was very well tolerated and is now entering Phase II clinical trials [111]. An essential step in the long journey of a drug from laboratory to clinic is in vivo animal testings. To this end, d-AA incorporation as a means for developing HDPs suited for eventual clinical application deserves special attention because a considerable number of solid results were obtained from in vivo testings of these HDPs.
# NIRS-EMG for Clinical Applications: A Systematic Review
## 2 Electromyography
### EMG for Measuring Fatigue  
Fatigue should not be defined as a failure (e.g., in holding an isometric configuration), since fatigue onset should be detected before a macroscopic failure is achieved [35]. In the detection of fatigue, several confounding factors are, however, present, such as the facts that force might not be constant and could be addressed to other muscles, and that biochemical and physiological factors may alter the nature of the contraction. Common methods used for fatigue detection are related to frequency-domain analysis: Metrics that are usually employed are the decrease of the Median Frequency (MF), and/or of the Mean Power Frequency (MPF) [41]. A decrease in MF or MPF in the estimation of the power spectrum, calculated over consecutive time windows, is considered to be a biomarker for fatigue. In some studies, even an increase of the RMS has been considered as a biomarker for fatigue, but this relationship is not always observed. During fatigue, the power spectrum decreases due to two main phenomena: The decrease in the firing behavior of motor units, and the shape of MUAPs [35]. These factors cause a compression of the spectrum of the EMG signal during fatigue [35].
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 6 Implementation  
Contiki 3.0 OS [40] was selected to validate the HARE protocol stack, mainly due to its ability to easily execute multiple processes concurrently and its powerful COOJA network simulator [41]. While COOJA was mainly used in first development stages and early simulations, ZolertiaTM RE-Mote nodes [42], whose main features are depicted in Table 5, were used for preliminary testing and operational validation. All results compiled in the current article were obtained by means of ZolertiaTM RE-Mote nodes working at 868 MHz. Preliminary testing was also conducted on MEMSICTM TelosB 2.4 GHz nodes [43], proving that HARE protocol stack is platform-independent. (See [44] for a comprehensive list of hardware compatible with the Contiki OS code tree.).  
Figure 9: STA’s decision flowchart prior to a new transmission window.  
HARE protocol stack was fully programmed as a new hardware independent module for Contiki 3.0 OS, adding all the described functionalities in Section 5 to the already available upper communication layers (link and network), and including a novel transport layer. Specific interactions of HARE with PHY layers of the aforementioned hardware were programmed separately.  
Three different testbeds were designed to test the functionalities of HARE protocol stack running in ZolertiaTM RE-Mote nodes: Testbed #1 was used to analyze the performance of main HARE functionalities in a controlled area with easy access to devices, Testbed #2 established the maximum range coverage of the system in line-of-sight condition, and Testbed #3 reproduced the conditions of an environmental monitoring application.
## 1 Introduction  
Music is a main source of the world's cultural heritage that, since ancient times, has been transmitted and stored by handwritten and printed music scores thanks to the inherent bond between the sound of music and its written representation. This is why the automatic processing, analysis and interpretation of music scores, Optical Music Recognition (OMR), has been a research topic of interest for decades [1, 2, 3] and it is still of great relevance thanks to the desire to preserve our cultural heritage and, more specifically, our musical heritage by means of the creation of digital databases with musical content, including digitized scores.  
In this context, the need for transcription of musical scores to aid their distribution, understanding and dissemination is clear. Unfortunately, manual transcription is a very much time-consuming task. This fact leads to the interest on the development of OMR algorithms and related tools to facilitate search, analysis and interpretation of the scores.  
Commonly, OMR systems include a series of stages for image pre-processing [4, 5], staff detection and removal [6, 7], musical symbol isolation and recognition [8, 9] and musical information reconstruction and representation [3, 10].  
Music information reconstruction includes the interpretation of the meaning of extracted musical symbols and, specifically, their relative position with respect to the corresponding staff. The detection and classification of musical symbols has been faced by using different approaches [11, 12] with neural-network-based approaches coming out recently [8, 13, 14], which often require large datasets for training [15]. However, only recently some of these works specifically consider the full extraction of staff lines [16] or the precise identification of their location [17].  
For the correct interpretation of musical symbols, their location with respect to the staff is of main importance and, consequently, the staff lines need to be detected as accurately and completely as possible. This is a task that is not often considered in OMR systems. Conversely, the actual objective related to the processing of staff lines is commonly their removal in the first stages of the processing schemes to facilitate the later identification of other musical objects [6; 18] since they are often considered an obstacle for symbol segmentation recognition [19].  
In this paper, we propose a processing scheme that performs the reconstruction of the staves in processed images of actual ancient scores containing partially deleted staves, the remaining or portions of symbols from a previous symbol extraction scheme and other artifacts.  
The paper is organized as follows. In the next section, the context that defines the type of images we are dealing with is established. In Section 3, the complete scheme for staff detection and tracking aimed at staff reconstruction is presented. Section 4 contains an analysis of the behavior of the proposed scheme, including results and a discussion. Finally, conclusions are drawn in Section 5.
# Edge Currents Induced by AC Electric Field in Two-Dimensional Dirac Structures
## 1 Introduction  
The discovery of graphene and other two-dimensional (2D) crystals opened a new page in the physics of low-dimensional systems [1; 2] and triggered the research aimed at the development of efficient sources and detectors of terahertz radiation based on 2D Dirac materials [3; 4]. In small-size samples, e.g., flakes obtained by mechanical exfoliation, the important and sometimes decisive role in the formation of photoelectric response is played by edges and nearby regions [5; 6]. At the edges, the translational and space inversion symmetries are naturally broken, which gives rise to edge-related mechanisms of the photogalvanic effect [5; 6; 7; 8; 9; 10; 11] and the second harmonic generation [12; 13; 14].  
The photocurrents flowing along the sample edges (the edge photogalvanic effect) were observed and studied in single-layer and bilayer graphene samples excited by terahertz radiation [5; 6; 10], also in an external magnetic field in the conditions of cyclotron resonance [9] and in the regime of the quantum Hall effect [15]. It is found that the edge photocurrent is induced by both linearly and circularly polarized radiation. Moreover, the photocurrent direction is controlled by the polarization of the incident radiation: the electric field direction with respect to the edge for the linearly polarized field and the photon helicity for the circularly polarized field. The edge photogalvanic effect in 2D structures can be considered as a low-dimensional analog of the surface photogalvanic effect studied in bulk semiconductor crystals and metal films and recently in nanocomposite films [16; 17; 18; 19; 20; 21; 22].  
The microscopic theory of the edge photogalvanic effect in the spectral range of intraband transport has been developed so far for 2D systems with parabolic energy spectrum of charge carriers [6; 8]. Here, we generalize the theory to the class of 2D Dirac materials. We present a comprehensive theoretical study of the edge currents for 2D systems with arbitrary dispersion \(\varepsilon(p)\) and arbitrary type of electron scattering in the 2D bulk. We show that the edge current is determined by the dispersion of carriers and the relaxation times of the first and second angular harmonics of the distribution function and compare the results for single-layer and bilayer graphene, which are examples of 2D systems with parabolic and linear energy spectra.
## 2 Results
### AtADF3 Is Involved in the Parasitism and Pathogenicity of RWTNs  
After _A. thaliana_ was inoculated with RWTNs, compared with uninfected _A. thaliana_, the expression of AtADF3 increased significantly from 1 dpi to 3 dpi. It then decreased from 5 dpi to 7 dpi, with the highest transcription level recorded at 3 dpi (Figure 7a). Histopathological analysis detected GUS activity in transgenic leaves expressing AtADF3 pro:GUS (Figure S2). The results showed that AtADF3 was expressed in the veins of the leaves, and AtADF3 expression in RWTN-infected leaves was higher than that in healthy leaves (Figure 7b). Subcellular localisation analysis of AtADF3 showed that AtADF3 was located in the cytoplasm and nucleus of _A. thaliana_ protoplasts, consistent with the localisation of Ab-FAR-1 (Figure 7c). The homozygous _A. thaliana_ AtADF3 mutant (_adf3_, SALK_139265) was screened (Figure 7d) and used for the RWTN-infection assays. After 21 days, the symptom severity of _adf3_ was significantly higher (\(p<0.05\)) than that of the WT plants. The number of nematodes on _adf3_ was also significantly increased (\(p<0.01\)) by 35.27% compared with that on WT plants (Figure 7e). These results suggest that AtADF3 is involved in the interaction between _A. thaliana_ and RWTNs and that the _adf3_ mutant was more susceptible to RWTNs.  
Figure 6: Ab-FAR-1\({}^{\mathrm{Asp}}\) interacts with AtADF3. (**a**) The yeast two-hybrid interaction between Ab-FAR-1\({}^{\mathrm{Asp}}\) and AtADF3. Left column, cotransormants grown on SD-TL (-trp -leu) agar medium demonstrate that both bait and prey plasmids are present in yeast (_Saccharomyces cerevisiae_); right column, only yeast cells containing the Ab-FAR-1\({}^{\mathrm{Asp}}\) bait plus the AtADF3 prey or the positive control interaction of LargeT plus p53 grew and turned blue on the selective medium SD-TLHA (-trp -leu -his -ade) + x-\(\alpha\)-gal agar medium. Ab-FAR-1\({}^{\mathrm{Asp}}\), p53 and LaminC were cloned into the pGBKT7 vector, while AtADF3 and LargeT were cloned into the pGADT7 vector, respectively. LargeT was cotransformed with p53 as a positive control. LargeT/LaminC, AtADF3/LaminC and LargeT/Ab-FAR-1\({}^{\mathrm{Asp}}\) cotransormations were negative controls. (**b**) Co-immunoprecipitationprecipitation of AtADF3 with Ab-FAR-1\({}^{\mathrm{Asp}}\). Ab-FAR-1\({}^{\mathrm{Asp}}\):eGFP or eGFP was transiently co-expressed with AtADF3:Flag in tobacco leaf cells. The isolated protein was analysed by immunoblotting with anti-Flag antibodies to detect AtADF3 and anti-GFP antibodies to detect Ab-FAR-1\({}^{\mathrm{Asp}}\). (**c**) Bimolecular fluorescent complementation (BiFC) visualization of the Ab-FAR-1\({}^{\mathrm{Asp}}\) with AtADF3 interaction. Ab-FAR-1\({}^{\mathrm{Asp}}\): nYFP and AtADF: cYFP were co-expressed in tobacco leaves. Controls were performed by co-expressing nYFP and AtADF3:cYFP, Ab-FAR-1\({}^{\mathrm{Asp}}\):nYFP and cYFP. Bars = 100 \(\upmu\)m as shown. Three independent experiments were performed with similar results.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
## 1 Introduction
In particular, orthogonal time-frequency space (OTFS) has been recently proposed for high-mobility communications [25]. OTFS modulates information in the delay-Doppler domain rather than in the time-frequency domain of classic OFDM modulation, providing a strong delay and Doppler resilience, enjoying the potential of full diversity [26]. However, since OTFS modulation is an emerging technique, there are compatibility and standardization challenges compared to more established modulation technologies that are already part of V2V communication standards. Additionally, in highly dispersive channels, like V2V channels with high mobility, OTFS modulation may face difficulties in achieving efficient performance due to the additional complexity in channel profile estimation and compensation. In this article, we propose to use VC as a means to reduce ICI and at the same time add diversity to the received signal. We use a low-complexity data detector based on maximum ratio combining (MRC) to be able to exploit the diversity in the data during the detection process. The simulation results show a significant improvement in the system performance in terms of BER compared to conventional OFDM systems with linear detectors. The OFDM receiver developed here maintains its structure and computational complexity lower than the conventional OFDM receiver with linear detection. The proposed system demonstrates high robustness against uncertainties in channel statistics, achieving a low BER even at high levels of Doppler frequency dispersion. Based on these results, the compromise is evident between the loss in spectral efficiency and the computational cost required by the MRC estimation. The rest of the paper is organized as follows. Section 2 describes the transmission system model with virtual carriers. Section 3 describes the detection algorithm using the MRC method for OFDM systems with VC. Section 4 presents the computational structure of the proposed transmitter and receiver. Section 5 shows the computational complexity of the main detection schemes for wireless communication systems. Sections 6 and 7 present the simulations and discussion of the results obtained. Finally, Section 8 concludes the work.
## 6 Current Problems and Research Difficulties
### QoS with Routing  
Many of the existing QoS-enabled routing techniques are restricted to certain applications and take just a few metrics under consideration. A lack of harmony exists between maintaining QoS and preserving energy. In this sense, future research in several applications or multiple UWSNs should focus on energy-efficient routing with QoS.
## 2 Theoretical Modal and Transfer-Matrix Method for TM-polarized Surface States
The Bloch wave number \(K\) can be derived through the following equation [12]:  
\[e^{iKL}=\frac{1}{2}\Bigg{[}\left(M_{p}^{11}+M_{p}^{22}\right)+\sqrt{\left(M_{p}^{ 22}-M_{p}^{11}\right)^{2}+4M_{p}^{12}M_{p}^{21}}\Bigg{]}, \tag{7}\]  
where \(M_{p}^{11}\), \(M_{p}^{12}\), \(M_{p}^{21}\), \(M_{p}^{22}\) are the four components of the \(2\times 2\) matrix \(\hat{\textbf{M}}_{p}\). To fulfill the condition of surface confinement of SWs, the Bloch wave number \(K\) should contain a positive imaginary part, and hence the field will decay in the MMDM. The impedances of the Bloch waves can be evaluated as:  
\[Z_{MMDM}=\frac{e^{iKL}-M_{p}^{11}}{M_{p}^{12}}. \tag{8}\]  
For the homogenous isotropic medium, the impedance can be expressed as:  
\[Z_{i}=-\frac{z_{0}\sqrt{\varepsilon_{i}-\beta^{2}}}{\varepsilon_{i}}. \tag{9}\]  
From Equations (8) and (9), and according to the tangential field continuity, the dispersion relations of the TM-polarized SWs can be obtained by:  
\[Z_{MMDM}=Z_{i}. \tag{10}\]  
In view of Equation (10), we can numerically calculate the group velocities of the SWs by using:  
\[v_{g}=\frac{d\omega(\beta)}{d\beta}=\frac{\omega_{p}d\Omega(\beta)}{d\beta}. \tag{11}\]