esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Prediction of Space Large-Scale Membranes Using Energy Flow Analysis
## 1 Introduction
Most works of EFA focus on deterministic structures which means the parameters are known precisely and unchanged. There exist natural uncertainties in practical parameters due to assemblage and manufacturing errors. Uncertainties of vibration responses resulted from uncertainties of parameters will increase with increasing size of structures. For the vibration prediction of large-scale membranes, it is meaningful to take uncertainties into account. There are many methods to deal with uncertainties in structures [32; 33; 34; 35; 36]. And one of the most popular methods is using probabilistic distribution. Parameters with uncertainties are described with random variables that satisfy certain probabilistic distributions. Then the vibration responses are obtained in probabilistic form which is thought to be more feasible than deterministic results. Analytical formulations are usually preferred since they enable to obtain the vibration energy directly without solving differential equations with numerical methods as FEM. And the analytical expressions facilitate the analysis of membranes with uncertainties. However, existing analytical expressions of energy flow only apply to structures of regular shapes since they are usually derived with exact travel distances of waves. In this work, a new analytical formulation of averaged energy for the space large-scale membrane with deterministic parameters is proposed based on the Green kernel of vibrating membranes. The formulation, using mean free path to describe travel distances of energy waves, is applicable to membranes of various shapes. Then the vibration energy formulation for the large-scale membrane with random parameters is developed. A validation is presented with the comparison of the averaged energy response from the proposed formulation and the exact energy response from traditional displacement solution. Considering the length of the membrane as an uncertain parameter, results from both random and deterministic analysis are provided to illustrate the characteristics of random energy responses. The derived analytical formulation of energy response provides more reasonable results for the space large-scale membrane with random parameters and is an instructive representation for membranes with uncertainties of other types. Besides, the work gives a reference for research on the vibration of other large-scale structures from the energy point of view.
# Fractional Order Processing of Satellite Images
## 1 Introduction
### Satellite Image Data  
In this paper, forty-three random images with low nebulosity from ESA's Sentinel-2 satellite [19] were used. The images retrieved from the website were analysed and a ground truth was manually taken using GIMP. The satellite images database, as well as the detailed extended results of this study, are available in an online repository [20].  
Due to space reasons, from the forty three figures, four were chosen to illustrate performance in this paper. The selected images are numbers 12, 18, 27 and 38. These pictures were selected since they present different levels of global performance due to their heterogeneity and are shown in Figures 1-4 together with their corresponding ground truth.  
Figure 1: Image number 12 to be evaluated with corresponding ground truth.  
Figure 2: Image number 18 to be evaluated with corresponding ground truth.
## 2 Architecture
### M2M Gateway Resource Abstraction Layer
#### 2.1.2 M2M Gateway Wireless Communication Protocol
Both should depend on abstractions, and the abstractions should not depend on details; instead, the details should depend on abstractions. Hence, the physical device protocol components are decoupled, and the interaction depends on the abstract interface. When a new data packet is sent from the sensor device to the protocol stack framework, the protocol management framework will publish this new data packet to the protocol stack bus such that the protocol stacks deployed in the protocol container will be allowed to analyze this new data packet in a top-down order. If the H7000 protocol can analyze this data packet, then it will be loaded to the protocol stack as the lowest layer protocol. After the data packet is analyzed by the H7000 protocol, the extracted data payload will be published to the upper layer protocol. This data payload is in fact the complete data packet of the M-Bus and ModBus protocol. The protocol of this layer will then attempt to analyze this data payload. If ModBus can accurately analyze this data packet, then ModBus will be loaded as the second layer protocol (_i.e._, the top layer) of the protocol stack. Thus, the dynamic adaptation of the protocol stack is completed. In addition, this protocol stack is bound to the sensor device that sent this data packet; the applications of this protocol stack will be responsible for analyzing future data packets sent from this sensor device.
# Enhanced Antifungal Activity of Engineered Proteins via Swapping between Thiodexin H2 and H3
## 3 Results and Discussion  
### Purification and Identification of AtTrx-H2, -H3, -2N3C, and -3N2C Proteins  
Antimicrobial peptides and proteins (AMPs) including host defense molecules have been widely identified in vertebrates and invertebrates, and extensively investigated as antimicrobial agents against pathogenic microorganisms, especially drug-resistant microorganisms [21]. Plants are constantly exposed to diverse environments harboring various pathogenic microorganisms, such as fungi, bacteria, viruses, protozoa, and nematodes. Therefore, AMPs derived from plants can be used as effective defense molecules against pathogenic microorganisms [22]. The major groups of AMPs found in plants include thinins [22; 23], defensins [22; 23], and lipid transfer proteins [23; 24]; however, reactive oxygen species (ROS)-related proteins were recently included as a new AMP group [6; 19]. Among the several subgroups of AtTrx, including Trx-m, -f, -x, -y, -z, -o, and -h, the most abundant H-type AtTrxs have been extensively studied for their disulfide reductase activity and use as a molecular chaperone [11]. Although the antimicrobial activity of AtTrx-H5 was reported [6], in this study, we sought to compare the antifungal activities and action modes of the recombinant AtTrx-H2 and -H3 proteins, as well as their hybrid proteins 2N3C and 3N2C, obtained by swapping the N- and C-terminal sequences of H2 and H3, respectively (Figure 1A). The H2, H3, 2N3C, and 3N2C proteins were expressed in _E. coli_ and purified using affinity and size-exclusion chromatography (data not shown).
## 3 Micromanipulation by Vibration-Induced Acoustic Wave
### Manipulation by BAW
#### 3.2.1 UTW Devices (Ultrasonic Acoustic Beam Tweezers)
[61]: (**a**) the earliest ultrasonic acoustic beam tweezers, which were large, and the particle could be manipulated within the diameter of the tank. (Reprinted with permission from [60]. Copyright 2013, Acoustic Society of America.) (**b**) Similar to the research of [PERSON] et al. [58; 59; 60] but with a small volume. The chamber is moveable so that the particles can translate with the chamber. (This figure is republished reference [18] of [PERSON] et al. and licensed under CC BY 4.0. Copyright © 2019 The Authors) (**c**) Acoustic beams designed as optical beams. A particle is manipulated at the focus of a concave surface where beam transducers are set regularly. (This figure is republished reference [61] of [PERSON] et al. and licensed under CC BY. Copyright © 2015, Springer Nature)  
operation. Particles can move and rotate in the potential field without moving the units. However, the size of the particles that can be manipulated by such equipment is usually large, and a large energy concentration is generated in the place of the operation area, so it may not be biocompatibile.
# Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data
## 2 Materials and Methods
### Case Study on Sentinel-2
#### 2.2.1 Data Sources  
The spectral signatures of the different types of plastic have been collected from [25]. They provide a dataset of 47 hyperspectral reflectance measurements of plastic litter samples in different conditions: dry, wet, submerged at different depths, and with varying suspended sediment concentrations. Measurements of the spectral reflectance were performed in an optical calibration laboratory and in a water tank and were analyzed in a study conducted by [34]. We extracted plastic materials (plastic bags, plastic bottles, placemats, etc.) and also wood from this dataset. We chose varying conditions: some of the materials are dry while others are wet or submerged (depth from 0 cm to 4 cm). When several colors were available (for placemats for instance), we selected all of them. The data is in open-access and are made available by [25].  
The spectral signature of seawater has been collected from the Sentinel-2 USGS Spectral library v.7 using the seawater signature for coastal waters [35]. The data is available at [36].  
The spectral sensitivity (\(S^{s}(\lambda)\) in Equation (1)) can be downloaded at [37]. Finally, the psf\({}^{s}\) for all of the S2 spectral bands are available at [38] (which also contains the source code of the simulator). The PSFs have been computed knowing the diameter of the pupil, the focal length, the different spectral sensitivity of the 13 bands, and using standard values for MTF due to detectors and satellite orbit, following Section 3.4 of [32].
# Driver Intention Recognition for Mine Transport Vehicle Based on Cross-Modal Knowledge Distillation
## 2 Materials and Methods
### Experimental Platform and Procedure  
The experimental platform included a 65-inch display screen (60 Hz refresh rate, 2560 \(\times\) 1440 resolution), an EEG cap, and a driving simulator with a steering wheel and pedals. The EEG cap, a BitBrain device integrated with the ErgoLAB EEG data acquisition system (Kingfar International Inc., Beijing, China), adhered to the International 10-20 system for electrode placement. Sixteen channels (O1, O2, P7, P3, Pz, P4, P8, C3, Cz, C4, F7, F3, Fz, F4, F8, and Fpz) were employed, with signal acquisition at 256 Hz. Pure water ensured electrode impedance remained below 5 k\(\Omega\), and the reference electrode was positioned on the right earlobe. The driving simulator utilized a Logitech G29 force-feedback steering wheel and pedals set to maximum resistance to prevent unintentional movements. The experimental platform is depicted in Figure 3.  
Figure 1: Tunnel scene diagram: (**a**) mine No. 1; (**b**) mine No. 2.  
Figure 2: Video annotation diagram.  
During the experiment, subjects were seated 80 cm from the display screen in a darkened room to eliminate external interference. A 5 min baseline recording was conducted beforehand, with subjects instructed to remain relaxed with their eyes closed. During the driving intention EEG data collection, the screen randomly displayed video stimuli representing different driving scenarios: upcoming left turn, right turn, obstacle avoidance, and normal driving. Subjects were required to focus on the screen while assuming a driving posture, which is gripping the steering wheel with both hands and placing their feet on the pedals. They were instructed to generate specific driving intentions corresponding to the action cues in the videos, such as turning left, turning right, slowing down, or maintaining normal driving. Each trial consisted of a 2 s gray screen followed by a 4 s video stimulus as shown in Figure 4. After every 10 trials, a pause screen was shown, allowing subjects to relax. They could resume the session by pressing a button. Each session comprised 156 trials, and subjects completed 2 sessions, resulting in 312 trials per subject. The entire experiment lasted approximately one hour per subject, yielding 4056 trials across all subjects.  
Figure 4: Temporal structure of a block.  
Figure 3: Experiment platform setup: 1—display screen; 2—steering wheel; 3—pedals; 4—button; 5—EEG cap.
## 1 Introduction
To meet the high requirement for 5G communications, the single element AVA antenna is extended to array versions having two elements, four elements and finally proposed design has eight elements. Gain values of two element antenna of 4.3 dBi, 5.6 dBi, 6.93 dBi and 8.12 dBi at 15 GHz, 18 GHz, 28 GHz, and 38 GHz, respectively. Four elements AVA antenna has gain values of 8.02 dBi, 8.10 dBi, 8.32 dBi and 8.54 dBi at 15 GHz, 18 GHz, 28 GHz, and 38 GHz, respectively. The proposed eight elements AVA antenna obtained high gain values of 10 dBi, 10.5 dBi, 11.5 dBi, and 10 dBi at 15 GHz, 18 GHz, 28 GHz, and 38 GHz, respectively which meets high gain requirement for 5G wireless communications. The proposed eight element antenna is fabricated. The simulated and measured results were observed closely match. The proposed eight element antenna is compared with related work. It is observed that the proposed eight elements AVA is most efficient and has stable higher gain compared to other antennas. Remaining paper is organized in the following sections. Section 2 discusses the design and optimization of the single element and multi-element Vivaldi antennas. Section 3 covers discussion and analysis of the simulated and measured results of the proposed 8-element array antenna. Section 4 compares the proposed antenna with relevant Vivaldi antennas, whereas the work is concluded in Section 5.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 4 Conclusions  
This work presented a successful attempt at the design and performance analysis of a ten-element MIMO antenna system for the n79 (4.4-5.0 GHz) and 5.9-7.1 GHz frequency bands for smartphone integration. Using an FR-4 substrate was affordable yet effective and allowed us to construct the device with standard 158 mm \(\times\) 60 mm dimensions. The design process helped solve the mutual coupling and spatial issues where the isolation level and envelope correlation coefficients (ECC) were high, which is desirable for MIMO operation. Simulation results obtained also validated that the proposed antenna system achieves optimum S11 levels, favorable radiation patterns, and reasonable gain levels in the desired bands of operation. Using criteria like bandwidth, efficiency, diversity gain, channel capacity, etc., it is concluded that the design is optimal for modern demands in wireless communications. The proposed ten-element MIMO antenna system not only guarantees improved data rate and reliability, but can be extended to future smartphone applications with future spectrum developments. Taking FR-4 as the dielectric material also demonstrated the feasibility of this design for manufacturing and its suitability for mass production without compromising greatly on performance. In general, this work contributes to the development of smartphone antennas and presents a potentially novel solution to meet the requirements of advanced, higher data rates and better communication quality for future mobile networks.  
Conceptualization & Investigation, M.Z.; Methodology, Q.A.; Software, N.B.; Formal analysis, D.P.B.; Writing--review & editing, S.S.; Supervision, Y.A. All authors have read and agreed to the published version of the manuscript.  
This research is partially funded by Wrexham Glyndwr University.  
The data can be shared upon request.  
The authors declare no conflict of interest.
## 3 Results
### Evaluation of Microbial Load Reduction  
Data concerning the evaluation of microbial load in the control and 3 g L\({}^{-1}\) bicarbonate added cultures are reported in Figure 7. Starting from an initial concentration of \(\sim 3.0\times 10^{6}\) UFC/mL microbial load, samples containing NaHCO\({}_{3}\) presented a reduced microorganisms concentration compared to control cultures after 3 h, showing 1 log decrease from \(3.0\times 10^{6}\) CFU mL\({}^{-1}\), to \(3.02\times 10^{5}\) CFU mL\({}^{-1}\). After 48 h, a 34.21% microbial load increase was observed in control, while an 11.62% growth was evidenced in NaHCO\({}_{3}\) samples.  
Figure 6: Maximum photosystem II (PSII) effective absorption (440 nm) cross-section (\(\sigma\)PSII) in _Chlorella sorokiniana_ control cells and in cells grown in bicarbonate 1 g L\({}^{-1}\), 2 g L\({}^{-1}\), and 3 g L\({}^{-1}\) enriched medium. Error bars represent SD (n = 3).
## 2 Background and Conventional Approaches Related to RQ1 and RQ2
### Heterogeneity in Risk Clusters  
The key objective in classical regression-based risk premium modeling is to accurately describe the stochastic relationships between the insured claim amounts and independent, homogeneous risk clusters. Homogeneity within a risk cluster reflects the similar characteristics of policyholders, which aids in managing the inherent variability observed in claim data. Common approaches in this domain include Generalized Linear Models (GLMs), Generalized Additive Models (GAMs), and Multivariate Adaptive Regression Splines (MARSs), among others [3].  
GLMs serve as a fundamental framework for analyzing premiums and risk clusters, owing to their clarity and theoretical robustness. These models effectively tackle heteroscedasticity through the application of Weighted Least Squares (WLS) and accommodate variability across different risk levels by incorporating a class-specific term in the linear predictor. Nonetheless, GLMs operate under the assumption that all heterogeneity stems from known covariates, which may lead to the neglect of unobserved factors and non-linear interactions that significantly contribute to heterogeneity [2]. GAMs build upon GLMs by integrating smoothing functions, which facilitate the capturing of non-linear relationships among risk factors. This approach enhances flexibility by employing a non-parametric modeling strategy that adjusts the functional form based on the underlying data. However, GAMs fall short in capturing variations arising from categorical covariates [10]. MARSs represent another sophisticated advancement, further refining GLMs by applying piecewise linear regressions across data intervals. This characteristic not only enhances computational efficiency compared to GAMs but also makes MARSs particularly adept at handling high-dimensional data challenges [11].  
In the context of risk premium modeling with GLMs, GAMs, and MARSs, addressing heterogeneity necessitates the inclusion of an additional class-specific effect term within the linear predictor. This term aims to capture the latent heterogeneity inherent in each risk cluster, reflecting unique deviations from the cluster mean [12]. However, a notable limitation of these classical risk premium modeling frameworks is that once parameters are estimated, predictions for new risk premiums are typically made solely based on these parameters and the associated covariates, neglecting the latent factors and uncertainty inherent in these estimates. Consequently, overlooking this uncertainty constrains the insurer's ability to make more informed decisions and effectively formulate their financial strategies [13].
# Experimental Investigation of a Device to Restrain the Horizontal Sliding of U-FREIs
## 8 Test Results and Observations
### Compression Test Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline \multicolumn{6}{c}{\(K_{V}\) (kN/mm)} \\ \hline  & **Cycle I** & **Cycle II** & **Cycle III** & **Cycle III Average** & \(\Delta\)**K\({}_{\text{V}}\)-0 (\%)** & \(\Delta\)**K\({}_{\text{V}}\)-1 (\%)** \\ \hline Q1-0 & 359.59 & 500.03 & 517.00 & \multirow{3}{*}{508.27} & \multirow{3}{*}{\(-2.27\)} \\ Q2-0 & 438.01 & 483.10 & 499.54 & & \\ \hline Q1-1 & 418.87 & 679.86 & 504.88 & \multirow{3}{*}{496.71} & \multirow{3}{*}{\(-2.27\)} \\ Q2-1 & 414.09 & 479.75 & 488.55 & & \\ \hline B1-0 & 262.11 & 293.73 & 295.62 & \multirow{3}{*}{313.38} & \multirow{3}{*}{\(-0.59\)} \\ B2-0 & 283.22 & 358.31 & 331.14 & & \\ \hline B1-1 & 266.16 & 284.92 & 295.58 & \multirow{3}{*}{311.52} & \multirow{3}{*}{\(-0.59\)} \\ B2-1 & 308.03 & 336.63 & 327.47 & & \\ \hline B1-2 & 175.72 & 177.54 & 177.00 & \multirow{3}{*}{211.19} & \multirow{3}{*}{\(-32.61\)} & \multirow{3}{*}{\(-32.21\)} \\ B2-2 & 239.17 & 243.99 & 245.39 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: K\({}_{V}\) values for all the tested specimens and configurations and percentage variations in the presence of the constraints. Figure 9: Force–displacement curves of compression tests for specimens B1 and B2: (**a**) without constraint; (**b**) with constraint on one contact surface; (**c**) with constraint on both contact surfaces. Considering the average \(K_{V}\) values in the presence of the constraints, the following observations can be made:  
* For both specimens Q and B, the presence of one constraint had negligible influence on the vertical stiffness; indeed, the \(K_{V}\) percentage variations were lower than 3%. * For specimens B, in the presence of two constraints, there were decreases in the stiffness by about 33% and 32% with respect to the cases without and with one constraint, respectively; however, in this case, the results were influenced by the damage induced in the specimens by the shear tests. For greater clarity, these results are shown also in graph form in Figure 10.
## 3 The Inversion Strategy of FPAR
### The Strategy of Inversion  
#### 3.2.1 Spectral Convolution  
The PROSAIL model was the first model used in the inversion of FPAR from FY-3C MERSI data. The simulated reflectivity was then convoluted in order to meet the characteristics of the FY-3C data. Thus, the final reflectance data was obtained by convolving the simulated spectral curve with the MERSI sensor's spectral response function (as shown in Figure 2) with the blue, green, red, and near infrared bands using the reference formula [31]:  
\[\rho_{i}^{*}\ =\ \frac{\int_{i}p^{*}(\lambda)R(\lambda)E_{0}(\lambda)d\lambda} {\int_{i}R(\lambda)E_{0}(\lambda)d\lambda}, \tag{9}\]  
where \(\rho_{i}^{*}\) is the reflectivity on the specified band \(i\), which is obtained by convolving the simulated spectral curve through the sensor spectral response function; \(R\); \(p^{*}\) refers to the simulated reflectivity; \(R(\lambda)\) is the spectral response function corresponding to the i-th channel; and \(E_{0}(\lambda)\) refers to the outer layer of the atmosphere, known as the Solar irradiance (solar constant). The solar constant is the 1366.1 Wm-2 [32].