esa-sceva/satcom-chunk-collection huggingface dataset:
# Photodynamic Inactivation of Human Coronaviruses
## 3 Results
### PhytoQuin Treatment Does Not Cause Visible Damage to HCoV Particles  
Photoactivation of PhytoQuin generates ROS that can damage nearby macromolecules, which is thought to be the primary antimicrobial mechanism of PhytoQuin PDI. HCoVs have a lipid envelope studded with viral glycoproteins required for key steps in cellular entry. We previously observed that PhytoQuin PDI did not damage herpesvirus DNA genomes [20] that are well protected by a strong icosahedral capsid [23]; we reasoned that the HCoV RNA genome, which is only lightly protected by viral nucleocapsid (N) proteins, may be more susceptible to PDI. Specifically, we hypothesized that PhytoQuin PDI may damage HCoV lipid envelopes and render the RNA genome more susceptible to damage or degradation.  
We first examined PhytoQuin-treated HCoV virions by transmission electron microscopy (TEM) to determine whether treatment caused any gross alterations in virion morphology that could represent virion damage. HCoV-OC43 virions were treated with 4 \(\mathrm{\mu g/mL}\) of PhytoQuin in the presence of light at 49 J/cm\({}^{2}\), which we previously demonstrated was sufficient to inhibit viral replication (Figure 2B). In parallel, virus preparations were treated with PhytoQuin in the dark or treated with DMSO vehicle control. Following treatment, samples were fixed, negative-stained, and mounted on grids for TEM. We observed that viral particles treated with DMSO or PhytoQuin, irrespective of exposure to light, were consistent in size and shape across all conditions tested (Figure 3). Using our staining procedure, glycoprotein spikes were visible on the surface of some but not all virions, with these being most evident in the DMSO-treated samples (Figure 3). However, our limited sample size made it difficult to quantify these subtle morphological changes following PDI, and no causal relationship could be established between these morphological changes and loss of infectivity. Based on these observations, we concluded that even though PhytoQuin PDI caused a -4-log drop in infectivity, it did not cause any gross structural damage to CoV virions.  
Figure 3: PhytoQuin treatment does not cause overt damage to HCoV-OC43 particles. HCoV-OC43 particles treated with 0.5% DMSO (+/ -LED light) or 4 \(\mathrm{\mu g/mL}\) PhytoQuin (+/ -LED light) were fixed, negative stained with uranyl acetate, and imaged using a transmission electron microscope. Five to seven viral particles are shown per condition. Scale bar = 100 nm. Abbreviations: DMSO, dimethyl sulfoxide; LED, light-emitting diode.
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 2 Design Principle
### Optimization Process  
The optimization process flow is illustrated in Figure 3. Firstly, we generate the initial structure and convert it into the \(\Phi(x,y)\) function using the level set method. Next, we import the structure into Lumerical solution for 3D-FDTD electron magnetic field (EMF) simulations. For each iteration of TE\({}_{0}\) and TE\({}_{1}\) mode, two simulations are performed. One simulation involves the original TE\({}_{0}\) or TE\({}_{1}\) sources at the input port, while the other simulation involves the adjoint TE\({}_{0}\) source at the corresponding output port. After obtaining the electromagnetic field data from the FDTD monitors, the gradients calculated using the adjoint method are transferred to Matlab. These gradients are used to adjust the velocity values at the boundary points in the level set function, thereby evolving the shape of the design region. The updated structure is then imported back into FDTD for the next iteration. This iterative loop continues until either the FOM value exceeds the required threshold, or the maximum number of iterations is reached. Once the loop terminates, the optimized structure is obtained.  
We obtain the optimized structure after 200 iterations from the initial structure. Figure 4 illustrates the 3D surfaces mapping of the initial structure (Figure 4a) and the final structure (Figure 4b) using the level set method. Figure 4c displays the total FOM values throughout the optimized process. The entire process takes about 36 h, with the value of FOM increased  
Figure 3: Schematic diagram of the optimization process of the (de)MUX.  
Figure 2: Schematic mapping of the evolutional surfaces from initial structure. (**a**) The initial structure; (**b**) the structure mapping to 3D surface using the level set method.  
from 0.65 to 0.935. We can see that in the first 25 iterations, the FOM function rapidly rises to around 0.9, while the curve of the function exhibits oscillations with an overall upward trend in subsequent iterations. The value exhibits stabilized after 125 iterations, with a final maximum value of 0.935.
# Damping Ratio of Sand Containing Fine Particles in Cyclic Triaxial Liquefaction Tests
## 1 Introduction
Moreover, a good understanding of the change in damping ratio due to the progression of shear strain can shed light on (i) the liquefaction response with the dynamic properties and (ii) the constitutive relationship to help simulate the liquefaction behaviour more accurately. Unfortunately, the aforementioned experimental studies so far provide very little information about the influence of fine particles on the evolution of damping ratio. To fill this knowledge gap, the primary objective of this study is to improve the understanding of the influence of fine particles on the damping ratio on account of sand liquefaction. With this aim, a standard silica powder was selected to create different binary sand-fines mixtures at three fines contents (i.e., 5.0%, 10.0% and 15.0%). Three sets of cyclic triaxial tests were then performed to establish cyclic shear resistance curves for clarifying the liquefaction potential with the variation in fines content. The damping ratio was then measured from the obtained stress-strain curves at each fines content to analyse its impact on cyclic degradation. With these experimental results, this paper presents a mechanism behind based on the microscopic perspective to rationally explain why the damping ratio and the fines content might be independent of each other.
## 2 Materials and Methods
### User Interface Device  
We used a typical 2D mouse in the experiment (Figure 1a). The experimenters placed the 2D mouse on a desk and controlled the Kmodstudio using the mouse while sitting on a chair, as per the predefined procedure.  
Similar to the 2D mouse experiment, for the 2D & 3D mice, the experimenters placed two devices on the desk and used while them sitting on the chair. Each experimenter controlled Kmodstudio using the 2D mouse with the right hand and the 3D mouse (Figure 1b) with the left hand. Using the controller, placed at the center of the 3D mouse, the experimenters performed rotation, zoom-in and zoom-out operations in the X, Y, and Z directions.  
Figure 1c shows the VR controller used in this study. The experimenters controlled the position of the mouse cursor while in a standing position by holding the device in both hands and moving the wrist in the VR environment. Click events were implemented using the buttons.  
The Kinect sensor & bend-sensing data glove (Figure 1d) were developed by [PERSON] et al. (2018) as a 3 DUI for use in a VR environment. The experimenters controlled the position of the cursor by tracking the position of the hand and then tracking the movement of the arm using the Kinect sensor, which is a 3D depth measurement camera. Kmodstudio runs the commands listed in Table 2 using the bend-sensing data glove, which can measure the degree of the bending of fingers when each finger is bent. The experimenters can use this device while standing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Condition** \\ \hline User interface device & 2D mouse, 2D \& 3D mouse, VR Controller, Bend-sensing data glove with Kinect sensor \\ \hline Experimenter & 10 people (6 male, 4 female, average 24.8 years old) \\ \hline Mining software & Kmodstudio (Korea Resources Corporation, Wonju, Korea) \\ \hline Task & 3D orebody modeling consisting of 69 operations \\ \hline HMD device & Oculus Rift (Oculus, Irvine, CA, USA) \\ \hline Quantitative evaluation criterion & Total working time, number of total clicks, click accuracy \\ \hline Survey evaluation criterion & Ease of learning, ease of use, level of immersion, level of fatigue \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions and evaluation criteria related to user interface devices, experimenter, software, and the head mounted display (HMD) device.  
Figure 1: Three-dimensional (3D) user interface devices used in this study. **(a)** Two-dimensional (2D) mouse (M185, Logitech, Lausanne, Switzerland); **(b)** 3D mouse (Space Explorer, 3D Connexion, München, Germany); **(c)** virtual reality (VR) controller (Oculus Touch, Oculus, Irvine, CA, USA); and **(d)** bend-sensing data glove with Kinect sensor ([PERSON] et al., 2018).
# Machine Learning-Assisted Secure Random Communication System
## 6 Results and Discussion
### Comparative Analysis  
The proposed ML-RCS can be compared with the previously proposed RCSs in terms of its performance and data rate. Regarding its performance, we have compared the BER of our system with that of other prominent RCSs mentioned in the literature. Similarly, the data rate has also been analyzed by comparing the number of noise samples (utilized \(N\)) required to successfully transmit and receive a unit binary digit. Table 5 summarizes the comparison of the proposed ML-RCS with other RCSs mentioned in the literature.  
Figure 11: Confusion matrix for the ILR; \(\alpha=1.1\), \(N=26\), and \(\mathrm{MSNR}_{\mathrm{db}}=-11\). This figure shows the inability of the ILR to retrieve binary information.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**RCSs** & **Achieved BER = \(10^{-3}\) at \(\mathrm{MSNR}_{\mathrm{dB}}\)** & **Utilized \(N\)** \\ \hline \hline [PERSON], [PERSON] [44] & \(-6\) & 2000 \\ [PERSON], [PERSON]; [PERSON], [PERSON] [45] & \(-6\) & 1000 \\ [PERSON], [PERSON]; [PERSON] [46] & \(-8\) & 500 \\ [PERSON] [47] & \(-6\) & 1600 \\ [PERSON]; [PERSON] [48] & \(-5\) & 1000 \\ [PERSON]; [PERSON] [50] & \(-4\) & 1000 \\ [PERSON] et al. [51] & \(-12\) & 2000 \\ [PERSON]; [PERSON] [52] & \(-5\) & 500 \\ Proposed ML-RCS & \(-12\) & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 5: BER and data rate comparison.  
It can be observed that the proposed ML-RCS achieved a BER = \(10^{-3}\) at an MSNR\({}_{\text{dB}}\) = \(-12\), which is the minimum achievable value among all the RCSs. It reflects the proposed ML-RCS's capability to successfully transmit and receive information under severe channel impairments, which gradually arises when we operate with a lesser MSNR\({}_{\text{dB}}\). Most importantly, the proposed ML-RCS achieved this performance with \(N\) = 35 only. This reflects that the ML-RCS can transmit more bits (approximately \(\frac{2000}{35}\) = 57 more bits) in comparison to the closest competitor, the system of [PERSON] et al. [51], which utilized \(N\) = 2000 for a unit binary digit. This comparison shows the proposed ML-RCS's capability to provide better performance with an increased data rate.
# Cross-Section Shape and Asymmetric Support Technology of Steeply Inclined Thick Coal Seam Roadway
## 1 Introduction
However, there is limited research on the cross-sectional shapes of roadways and asymmetric support technologies in steeply inclined thick and extremely thick coal seams. Additionally, the impact of variations in coal seam dip angles on roadway stability remains unclear. Therefore, this study takes the 1502 level roadway in the steeply inclined 5\(\#\) coal seam of Dayuan Coal Industry as a case study. Through an integrated theoretical analysis, numerical simulation, and field monitoring, we systematically investigate the deformation and failure mechanisms of the surrounding rock under varying cross-sectional geometries and coal seam dip angles. An asymmetric support strategy tailored to the optimal cross-sectional shape is proposed and validated through industrial-scale trials. The performance of the support system is rigorously monitored and analyzed, aiming to establish a novel theoretical framework and practical guidelines for roadway surrounding rock control under analogous steeply inclined thick coal seam conditions.
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
## 1 Introduction  
Once, there was an argument about the source of the modulating variations in radar echoes from birds. Radar ornithologists believed that respiration movement could modulate the radar echoes from birds and produce a 10 dB-level signal fluctuation [1]. The primary empirical evidence confirmed that the periodic signal fluctuation could be as high as approximately 10-20 dB [2, 3, 4], sometimes even over 40 dB in some radar bands [5]. In addition, researchers have verified that the flapping patterns over the respiration movement of birds are believed to be the primary cause [6, 7, 8, 9, 10] because the signal fluctuations were correlated with the flapping movement of the bird's wings [11, 12]. Moreover, the radar signatures posed by the flapping wings can be extracted from the radar signals of birds and introduced to recognize and track the radar echoes of birds [13, 14, 15, 16, 17]. Nevertheless, those researchers provided no direct measurement of the fluctuating radar signals produced by the respiration movement of birds in labs but measured the radar signals of flying birds in the outfield and made connections between the variations and flapping gaits.  
We first investigated radar signals modulated by respiration movement using direct and quantitative measurements of such signal fluctuations in a microwave anechoic chamber. As described in Section 2, we theoretically modelled the signal fluctuations modulated by respiration after introducing the water sphere model. We also performed experiments to measure the weak signal fluctuation using dynamic measurements composed of a network analyzer and a high-speed camera. The results are provided and analyzed in Sections 3 and 4 using both time series analysis and frequency analysis. Finally, the conclusions are provided in Section 5.
# The FaaS-Based Cloud Agnostic Architecture of Medical Services--Polish Case Study
## 4 Results
### Results For Single-Request Experiments  
The load profile (A) was generated by executing an HTTP request using the Postman ([[https://www.postman.com/use-cases/api-testing-automation/](https://www.postman.com/use-cases/api-testing-automation/)]([https://www.postman.com/use-cases/api-testing-automation/](https://www.postman.com/use-cases/api-testing-automation/)), accessed on 17 May 2022) tool. To describe the behavior of the system for such a test load, the authentication functionality was considered. The criteria, such as the mean time of request executions and the capacity of RAM used by a _Pod_, were taken into account. The experimental results for the authentication functionality are presented in Table 1. They allow for comparing the classic Kubernetes solution to the new one based on Fission with respect to the times of execution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No**} & **Classic ACS** & **FaaS ACS** & **FaaS ACS** & **FaaS ACS** \\  & **Microservice** & **Warm Start** & **Cold Start without** & **Cold Start with** \\  & **[ms]** & **[ms]** & **Image in Cache [ms]** & **Cached Image [ms]** \\ \hline
1 & 52 & 57 & 7360 & 3140 \\
2 & 48 & 56 & 3150 & 4160 \\
3 & 50 & 60 & 6180 & 3150 \\
4 & 51 & 52 & 3170 & 4150 \\
5 & 50 & 58 & 4160 & 3240 \\
6 & 53 & 55 & 12,360 & 4180 \\
7 & 51 & 65 & 11,250 & 4150 \\
8 & 55 & 54 & 3540 & 3170 \\
9 & 50 & 56 & 4540 & 4160 \\
10 & 54 & 53 & 12,740 & 3170 \\ \hline mean & 51.4 & 56.6 & 6845 & 3665 \\ std & 2.12 & 3.78 & 3885.61 & 518.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental results for authentication functionality—duration of request executions.  
Applying Fission to cloud healthcare services allowed us to obtain the solution with improvements in many areas. First, the scale-out from 0 feature was successfully implemented, and it took only a few seconds for the cold start (mean 3.78 s) due to applying the image caching mechanism. Another benefit is the significant decrease in memory consumption. The volume of RAM used by standard _Pods_ was 320 MB, while the dedicated Function Pods (Fission variant) consumed only 19.4 MB. In addition, the time of the warm start of service deployed as a function (56.6 ms) was very close in comparison to the response time of plain _Pods_ (51.4 ms). The Student's t-test was used to confirm that there is no statistically significant difference between the means for the assumed confidence level of 0.95. All of that was achieved with compliance to Kubernetes technology and with the preservation of cloud provider agnosticism.  
It is worth mentioning that the mean time of the cold start of FaaS ACS provided from the cached image was 6.8 s and was significantly lower than the mean time of instantiating the classic ACS microservice, which was 61 s.  
Similar beneficial conclusions regarding execution times and memory consumption were obtained due to applying the FaaS approach based on Fission in implementing other aforementioned functionalities, i.e., authorization and defining/submitting patient surveys.
# Application Layer Protocol Identification Method Based on ResNet
## 2 Related Work
### Protocol Feature Matching-Based Approach  
To enhance accuracy, [PERSON] [14] proposed the GramMatch method, which improves the accuracy of feature extraction by enhancing the n-gram feature word extraction method. This method provides features for the protocol feature library and utilizes fingerprint library matching to achieve protocol identification. Additionally, research in the literature [15; 16; 17] has proposed methods for protocol feature word filtering, including improved FP feature extraction algorithms, fusion algorithms combining pattern matching and clustering, and LDA feature word inference algorithms, to improve the accuracy of feature words. Furthermore, [PERSON] [18] introduced the use of the EPS-neighborhood hit algorithm to extract more accurate feature words.  
[PERSON] [19] proposed a new method for feature recognition and classification clustering of unknown network protocols. First, a protocol feature library is constructed. Then, the features of the protocols to be detected are vectorized, generating a 0-1 matrix. The PCA algorithm is applied to perform dimensionality reduction on the vectorized protocol features. Combined with the CPFV algorithm, clustering optimization is performed. Based on the clustering results, new protocol formats are generated, and unknown network protocols are classified and identified. However, this method can be affected by the number of types, the choice of cluster centers, and data loss during dimensionality reduction, which may lead to a decrease in protocol recognition accuracy.  
[PERSON] [20] proposed a method for feature extraction of unknown wireless network protocols based on sequence associations. This method mainly consists of two steps: inter-frame feature extraction and intra-frame feature extraction. Specifically, it extracts protocol frame-related feature information by comparing and analyzing the relative positions of different protocol frames; it extracts protocol function-related feature information by analyzing the positional relationships of various fields within the protocol frame. Different protocols may have varying characteristics and complexities, so this method cannot determine whether it is applicable to all known and unknown types of wireless network protocols.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems
#### 2.5.3 Transmission Range  
TRDC [22] transmits the signal up to a range of 10 m, while the transmission ranges of ML-SC-IBFD [23] and A-IBFD [24] achieve an effective transmission range of 7.2 m. Apart from that, the HSIC [41] system only transmits signals up to 0.5 m, while the A-SIC [25] system provides no detail about the transmission range. The transmission range of BPSK-based FD-UWAC systems including RLS-adaptive [17;25] and ANE [60] has been tested for low transmission range. However, in [29], the FSK-based FD-UWAC system was evaluated for a longer transmission range of 5 m to 1000 m while maintaining significant energy efficiency and battery life. A Hybrid-SS device based on spread spectrum (SS) [33] achieves 3 km efficient FD-operation between the FD-UWAC modems. More research is required, however, to enhance its robustness by reducing multipath interference.
# Electrification in Maritime Vessels: Reviewing Storage Solutions and Long-Term Energy Management
## 2 Electrification of Marine Vessels
### Energy Management System (EMS) for Marine Vessels
An AI-driven EMS further enhances these advantages by minimizing operational costs, improving equipment lifespan through predictive maintenance tailored to maritime environments, and enabling data-driven decision making through real-time monitoring. As AI continues to evolve, the integration of advanced machine learning techniques will unlock even greater efficiencies, allowing for more sustainable, reliable, and cost-effective maritime operations.
###### Abstract  
A dual-band dual-polarized antenna with high isolation characteristics is proposed for polarimetric synthetic aperture radar (PolSAR) applications. The antenna consists of four dipole antennas and 2 \(\times\) 2 patch antenna arrays operating at the P-band (450-730 MHz) and Ka-band (34-36 GHz), respectively. The dipole antennas and the patch antenna arrays need dual-linear polarization characteristics to acquire PolSAR data. Improvements in the isolation characteristics at the P-band are achieved by inserting a metamaterial absorber with a fractal geometry between the transmitting (Tx) and receiving (Rx) dipole antennas. Without the absorber, the simulated isolation characteristics between the Tx and Rx antennas are lower than 19.2 dB over the target band. On the other hand, with the absorbers, the simulated isolation characteristics are higher than 23.44 dB over the target band, and remarkable improvement is achieved around the resonance frequency of the absorber. The measured results are in good agreement with the simulated ones, showing that the proposed antenna can be a good candidate for PolSAR applications.  
s +
Footnote †: journal: Applied Sciences  
article  
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as high as 10 cm can be achieved by Ka-band SAR systems [4]. Thus, multiband quad-pol antennas are required in order to carry out various missions with PolSAR systems. Multiband antennas were reported for SAR applications in [5; 6]. In [5], the proposed antenna consists of a patch antenna and an 8 \(\times\) 8 patch antenna array operating at the P-band and Ku-band, respectively; the P-band antenna has dual-linear polarization characteristics, but the Ku-band antenna has only single-linear polarization characteristics; therefore, it is not appropriate for use in PolSAR applications. In [6], dual-polarized patch antenna arrays operating at the X-band and Ku-band were proposed; although the proposed antenna is suitable for PolSAR systems due to the quad-pol characteristic, it cannot be utilized for detecting targets in forests or underground.  
In this paper, a dual-band dual-polarized antenna with improved isolation characteristics is proposed for PolSAR applications. The antenna consists of Tx and Rx dipole antennas and 2 \(\times\) 2 patch antenna arrays. The dipole antennas and patch antenna arrays operate at the P-band and Ka-band, respectively. Fractal-shaped metamaterial absorbers are located between the Tx and Rx antennas to enhance the isolation characteristics at the P-band.