esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrically Tunable Perfect Terahertz Absorber Using Embedded Combine Graphene Layer
## 4 Simulation Results
In other words, the absorption spectra of structure move upon the absorption band by adjusting Fermi potential without manipulating absorber geometries. Given that the Fermi level is adjustable with external DC voltages, it can be a practical procedure to manufacture a tunable combine graphene perfect absorber based on the presented design. Blueshift and enhancement of absorption intensity are well highlighted in Figure 7 and Table 7 when the Fermi potential of the combine graphene layer changes. In detail, the Fermi level lies in the forbidden gap between the valence and conduction bands from the electronic physics point of view. Therefore, increasing the Fermi energy leads the electrons from the valance band to move to the conduction band. In this state, the Fermi potential increases and becomes close to the conduction band in terms of energy levels and electron density increases, resulting in a plasmonic modes shift [44]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fermi Potential (eV)** & 0.4 & 0.5 & 0.6 & 0.7 & 0.8 & 0.9 \\ \hline
**Plasmonic resonant** & 8.644 & 9.647 & 10.55 & 11.353 & 12.09 & 12.79 \\
**frequency(THz)** & & & & & & \\ \hline
**Amplitude of absorption** & 1.3285 & 1.671 & 1.959 & 2.314 & 2.584 & 2.871 \\
**peak(\(\times\)10\({}^{5}\) nm\({}^{2}\))** & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The variation of absorption parameters under changisng in combline’ Fermi potential. Figure 7: Spectral sweep of the modeled perfect absorber by Fermi level changing. The most important advantage of the proposed combine absorber can be mentioned to the ease of practical implementation and simplicity of fabrication. The designed structure has the potential for simple structure fabrication, high absorption efficiency, and stability, with a bandwidth around 112 GHz, and small size. For the implementation of the proposed structures, it is clear that the standard microelectronic fabrication planar method can be used. Optical lithography at a submicron level and electron beam lithography at a nanoscale or deep submicron level can be used to image the combine layer in the proposed perfect absorber [45]. In further detail, first, an appropriate insulator is prepared, and then a graphene sheet can be implemented by large-scale transfer techniques, and so the photoresist is spin-coated on the graphene sheet. After doing the standard steps in photolithography, a mask is prepared for the combine layer, and then, by photoimaging, the pattern is shaped on the resist. After removing the resist on the graphene layer and developing a suitable solvent, the combine graphene pattern appears on the top of the substrate. Based on the available technologies of Electron Beam lithography(EBL), there is the potential for size miniaturization to reduce down about 5 nm [46]. The proposed perfect absorber can be introduced as a pioneering candidate in terms of application like a refractive index sensor, thermal emission sensor, or a nanosensor to detect bacterial and viral particles.
# Design of an IMCPID Optimized Neural Network for Stepless Flow Control of Reciprocating Machinery
## 3 Controller Design
### Feed-Forward Decoupling  
The undesirable dynamic coupling within the system will affect the stability and control performance of the stepsless control system [29]. This paper introduces a simple and efficient feed-forward decoupling method to eliminate the coupling, and the block diagram for a two-input and two-output system is shown in Figure 3 [24].  
Here, \(r_{i}\) is the set-point value, \(y_{j}\) is the output value, \(C_{i}(s)\) is the proposed controller, \(G_{\rm ij}(s)\) is the process plant and \(D_{\rm ij}(s)\) is the decoupler.  
Supposing the system is decoupled completely, the transfer function matrix will turn into a diagonal matrix. \(D_{21}(s)\) and \(D_{12}(s)\) satisfy the following relationship:  
\[\left\{\begin{array}{l}u_{1}G_{21}(s)+u_{1}D_{21}(s)G_{22}(s)=0\\ u_{2}G_{12}(s)+u_{2}D_{12}(s)G_{11}(s)=0\end{array}\right.. \tag{14}\]  
Then, we can obtain \(D_{21}(s)\) and \(D_{12}(s)\) as:  
\[\left\{\begin{array}{l}D_{21}(s)=-\frac{G_{21}(s)}{G_{22}(s)}\\ D_{12}(s)=-\frac{G_{21}(s)}{G_{11}(s)}\end{array}\right.. \tag{15}\]
# Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems
###### Abstract
* To guarantee the reliability, the proposed scheme stores its main code parts and the transforming history of retouched videos into the public blockchain system. When they are stored in a blockchain system, they become immutable, whereas they can be changed at any time when they are stored in a centralized server. The proposed scheme stores its main codes in the form of _smart contract_ upon the public blockchain system The smart contract is a digital programming code suitably designed for the public blockchain system. * To evaluate the practical feasibility of the proposed scheme, we implement the scheme upon a commercial public blockchain system and confirm that the scheme verifies the integrity preservation of retouched videos with 100% accuracy within a reasonable execution time. To avoid the maximum size constraint of a single smart contract, the scheme consists of twenty smarts contracts, and they are linked to their blockchain addresses. Figure 1: Overall procedure to trace integrity preservation for retouched but non-tampered video file. The rest of this article is organized as follows. Section 2 surveys the related previous studies and explains the benefit of the proposed scheme compared to the previous works. Section 3 describes the proposed integrity analysis and integrity preservation trace scheme in detail. Section 4 explains the implemented software tool of the proposed scheme and deals with the experimental evaluation results of the implemented tool. Section 5 concludes this article with discussions for further study.
## 3 Data and Methodologies
### Extracting Ice Velocity and Melt Pond Extent  
We utilized cross-correlation feature tracking [43] to derive the ice flow on the selected Sentinel-2 images near-infrared (NIR) band between May and September in 2017. The ice flow was first established by a speckle tracking technique in two dimensions implemented on COSI-Corr software [43; 44]. We then used the Rosenau filter algorithm to remove the speed outliers [45], which is processed by region growth filter, median and directional filter to remove from velocity field. Additionally, the inverse distance weight algorithm is used to interpolate velocity field. Finally, we chose the mean speed in a non-glacier stable region for the accuracy estimation [46].  
Melt ponds were extracted from the floating ice in the Sentinel-2 images by a threshold segmentation algorithm executed on the Google Earth Engine platform [23]. We applied this physically based technique to the pond outlines defined with the normalized difference water index (NDWI) [47], which was set to 0.25 for segmentation threshold. NDWI is calculated with the difference ratio of the red and blue bands, following Equation (1):  
\[NDWI_{ice}=\frac{(Blue-Red)}{(Blue+Red)} \tag{1}\]  
The pixels were classified as water and no-water to count meltwater extent statistics. We visually check manually the results corresponding Sentinel-2 images to excluded evident gross error due to could, shade and coverage void. Finally, we calculated the whole melt ponds area at each scene and acquire the time series of melt ponds evolution.
# A Review of Video Object Detection: Datasets, Metrics and Methods
###### Abstract  
Although there are well established object detection methods based on static images, their application to video data on a frame by frame basis faces two shortcomings: (i) lack of computational efficiency due to redundancy across image frames or by not using a temporal and spatial correlation of features across image frames, and (ii) lack of robustness to real-world conditions such as motion blur and occlusion. Since the introduction of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015, a growing number of methods have appeared in the literature on video object detection, many of which have utilized deep learning models. The aim of this paper is to provide a review of these papers on video object detection. An overview of the existing datasets for video object detection together with commonly used evaluation metrics is first presented. Video object detection methods are then categorized and a description of each of them is stated. Two comparison tables are provided to see their differences in terms of both accuracy and computational efficiency. Finally, some future trends in video object detection to address the challenges involved are noted.  
video object detection; review of video object detection; deep learning-based video object detection +
Footnote †: journal: Applied Sciences
## 3 Offshore Platform Overview and Finite Element Model  
### Deep-Water Offshore Platform  
As illustrated in Figure 3, the deep-water offshore platform (designed for water depths reaching 105 m) consists of an upper module and a lower jacket substructure. The substructure utilizes a bucket-supported four-legged jacket configuration to enhance stability in deep-water conditions. Upon the completion of fabrication, installation, and commissioning for the upper module, the entire structure will be transported to the offshore site for ultimate installation. The upper module adopts a four-level configuration with footprint dimensions of 51 m \(\times\) 47 m, an overall height of around 11 m, and the top deck elevation reaching approximately 48 m above mean sea level. Functional modules mounted on the deck--such as power distribution systems, HVAC (Heating, Ventilation, and Air Conditioning) setups, and fire protection apparatuses--have been meticulously integrated into the finite element (FE) model through the application of equivalent mass modeling methodologies.  
Figure 3: Offshore platform layout. (**a**) Oblique view; (**b**) Side view; (**c**) Front view.  
The jacket foundation is composed of four primary legs anchored by twelve open-tip steel pipe piles, featuring six horizontally framed layers strategically placed along its vertical axis. The uppermost layer at +9.0 m mean sea level (MSL) connects to the topside modules, with subsequent layers positioned at \(-11\) m, \(-36\) m, \(-62\) m, \(-90\) m, and \(-103\) m MSL--each layer incorporating a progressively denser X-brace configuration and thicker member walls. At the seabed interface, the substructure forms a 55 m \(\times\) 55 m square footprint, with its legs inclined at a 1:7 slope to enhance lateral rigidity against overturning moments. The piles have a 2.5 m diameter, 40 mm wall thickness, and 80 m total length, while the jacket's tubular components are constructed from S355 steel.
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 5 Some Applications
### Other Applications II: Human Body joints Antifriction  
Human body joints are subjected to friction with coefficients of friction as low as \(10^{-3}\) at pressures exceeding \(10^{2}\) atm, and they can maintain their lubrication for several decades. For this reason, they can be considered as models for the development of low-friction materials. Since human joints have a gel-like structure comprised of proteoglycans and collagen with high water content (75-80 wt%), gel lubricants can be ideal materials to reduce their tribological problems [117,118]. Gel fracture is mainly caused by stress concentration at the cross-linking point. Consequently, it is extremely important to design gels that can disperse stress, improving mechanical strength. For example, cyclodextrin and tetrapolyethylene glycol (PEG) are substances employed to reduce stress concentration [119,120]. Double-network (DN) gels have structures with different polymer networks based on the fact that one polymer network prevents the fracture of the others [121]. Considering the fully explained beneficial properties of ILs, IL-type DN gels, also called DN ion gels, were recently proposed and studied for their low friction under high temperature and vacuum [122].  
Clearly, in vitro and in vivo tests are necessary to reproduce accurate and reproducible measurements of human joint wear. In 2018, friction tests were conducted using a ball-on-plate type to simulate the human body joints and to study new DN ion gels as potential human lubricants. The authors [123] proposed and studied three new IL-type DN gels derived from poly 2-acrylamidomethyl-propanesulfonic acid (PAMPS), polydimethylacrylamide (PDAAm), and 3-ethyl-1-methyl-imidazolium ethylsulfate (EMI-EtSulf), in substitution of water. In particular, among other elements, they observed reduced friction with their DN ion gel with respect to a corresponding DN hydrogel (with water), probably due to an increased polymer adhesion caused by substituting water with the IL. Moreover, this increased lubrication regime may be also justified by the higher viscosity and thermal stability in comparison with DN hydrogels.
# Multi-Head TrajectoryCNN: A New Multi-Task Framework for Action Prediction
## 3 Methodology  
Given a partially observed motion sequence \(\mathbf{X}_{1}\)= \(\{\mathbf{x}_{1}\),\(\mathbf{x}_{2}\),\(\cdots\)\(\mathbf{x}_{N}\}\in R^{N\times I\times D}\), its corresponding future motion sequence can be represented as \(\mathbf{X}_{2}\)= \(\{\mathbf{x}_{N+1}\),\(\mathbf{x}_{N+2}\),\(\cdots\)\(\mathbf{x}_{N+M}\}\in R^{M\times J\times D}\) and its action semantic denoted by \(y\), where \(N\) is the length of the partially observed motion sequence, \(M\) is the length of the future human motion sequence, \(J\) is the number of joints of the human body, \(D\) is the dimension of the joints (i.e., \(D\) = 3),\(\mathbf{x}_{i}=\{j_{i1},j_{i2},\cdots,j_{il}\}\in R^{l\times D}(i=1,2,\cdots,N+M)\), and \(j_{i*}\) is the \(*\)-th joint of the \(i\)-th pose. The action prediction model aims to predict the future motion sequence and action semantics using on a partially observed motion sequence, which can be formulated by Equation (1).  
\[\{\mathbf{X}_{2},y\}= f(\mathbf{X}_{1}) \tag{1}\]  
where \(f(\cdot)\) denotes the action prediction model.  
In this paper, we parameterize the action prediction model via a multi-task framework, including a regression head and a classification head. The regression head predicts the future dynamics \(\mathbf{X}_{2}\), and the classification head aims to predict the semantic label \(y\).
## 1 Introduction  
Internet of Things (IoT) networks provide fast and reliable data services by deploying a large number of cheap devices [1, 2]. However, massive accesses to wireless devices burden the available wireless spectrum resource. In recent years, unmanned aerial vehicles (UAVs) have been extensively applied to IoT networks for their high mobility, low cost, and on-demand deployment [3]. UAVs can be served as aerial base stations to provide aid in terrestrial network communications [4], and as mobile relays to provide reliable wireless connectivity to remote areas [5]. However, when the ground infrastructure is faulty, a UAV cannot provide long and reliable services due to its limited power and coverage. Therefore, a collaboration framework between high-altitude platforms (HAPs) and UAVs can be designed to form a double-layer three-dimensional (3D) dense coverage network to guarantee the quality of service (QoS) requirements of terrestrial users. Different from traditional base stations, HAPs hovering at high altitude can provide enormous network coverage for terrestrial users [6]. Besides, HAPs have lower construction and maintenance costs compared to the traditional low earth orbit (LEO) satellite [7]. Therefore, an efficient uplink transmission can be achieved in future wireless communications by combining the high coverage of HAPs and high flexibility of UAVs.  
The forthcoming sixth generation (6G) communication puts forward higher requirements on the spectral efficiency and throughput of the system due to the exponential growth of network nodes. Mass connectivity and high spectrum utilization of the non-orthogonal multiple access (NOMA) technique can effectively solve the above problems [8]. Multiple users share the same spectrum to transmit data through NOMA, and the successive interference cancellation (SIC) technology is used to decode the transmitted data at the receiver and ensure the reliability of information transmission [9]. Although the use of a SIC receiver may increase the complexity of the receiver design, it can improve the frequency spectrum efficiency [10].
# Analysis of the Refraction Effect in Ultrasound Breast Tomography
## 3 Results  
Based on the preliminary studies presented earlier, it was concluded that regulation of the temperature of the water in which the breast is immersed during ultrasound tomography examinations may allow the refraction angles of ultrasound wave beam rays to be reduced at the water/skin/fat/breast/fat/skin/water boundaries. This can lead to reduced distortion of reconstructed breast cross-sectional images, as a result. Therefore, in this chapter, a detailed analysis of the variation of water temperature in ultrasound tomography imaging is performed on the basis of appropriate calculations and measurements.
## 5 Connectivity Limitations of Smart Farming in Remote Areas
### Case Study 1: Meshed LoRaWAN Gateways for Smart Farming in Remote Areas
As a consequence, these satellites are prone to interruptions, especially for areas closer to the equator such as the gulf country of northern Australia [82]. Another category of satellite communications is the geostationary satellites, which are placed directly over the equator at approximately 35,800 km high [82] and revolve in the direction of earth's rotation. These satellites appear to 'hover' in the sky and hence can be accessed using a directional antenna pointing at a fixed direction in the sky. One of the major Australian telecommunication providers, namely Optus, operates a fleet of 5 geostationary satellites over Australia and New Zealand with access to another nine third-party satellites in the Pacific and Indian Ocean regions [86]. IoT and machine to machine (M2M) applications are supported by these satellites. A British satellite telecommunications system, Inmarsat [87], provides data and voice communications via 12 geostationary satellites. Inmarsat provides satellite connectivity to Vodafone's IoT platform all over Australia  
Figure 4: Meshed IoT gateways adapted from [78]. including regional areas. Another group of geostationary satellites are the Sky Muster satellites (NCN Co 1A and NBN Co 1B) of NBN [88], which maintain each of their 101 fixed spot beams on specific regions over Australia. However, these particular satellites do not support IoT directly as access is provided to satellite dishes located on residential and business premises. Additionally, a network named Thuraya, covers 140 countries and relies on two geostationary satellites [89]. Their reception exists in LOS areas in Australia. Thus, the available satellite communication systems provide connectivity to IoTs and UAVs in some rural parts of Australia. For our future work, we set a milestone to extensively analyse on which type of connectivity option is suitable for farming in different parts of Australia depending on several criterion, such as location, network coverage, communication delay, energy consumption etc.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis
### Variations in Measurements During Dry Period
#### 5.1.2 Variations at a Large Scale  
To investigate large-scale variations, it is critical to mitigate small-scale fluctuations. In this study, a one-hour averaging method is employed. To further smooth the fluctuations and highlight the overall trend from a broader perspective, a low-pass filter with a normalized bandwidth of 0.3 is adopted. The lowpass filter uses a minimum-order filter with a stopband attenuation of 60 dB and compensates for the delay introduced by the filter. Using this narrower bandwidth facilitates better visualization of long-term trends in the data. It is important to note that this filtering process is intended solely for enhancing the visualization of the overall trend; the spectrum analysis is conducted on the averaged data, not the filtered data. Specifically, the Fourier transform \(\mathcal{F}\) of \(L\)-length hourly average measurement sequence \(x(t)\) is utilized, and the Power Spectral Density (PSD) estimate \(P(f)\), calculated by Equation (2), is employed to analyze the distribution of power across different frequency components.  
\[P(f)=\frac{2|\mathcal{F}(x(t))|^{2}}{f_{s}L} \tag{2}\]  
The sample interval for hourly average measurements is set to one hour, corresponding to a sampling frequency \(f_{s}\) of \(1/3600\) Hz. For simplicity, the frequency unit is defined as  
Figure 7: Signal measurements taken within one hour during dry period: (**a**) signal measurements in Dataset1. (**b**) signal measurements in Dataset3.  
The PSDs for the four signal quality and strength metrics show a declining trend at higher frequencies. The SNR PSD estimate in Dataset1 does not have a peak at 0.0417 (\(\times 1/3600\)) Hz while the RSRP PSD estimate in Dataset3 does not show this peak. Since the datasets are collected at different locations and on different dates, the observed daily pattern in signal measurements is not a unique phenomenon. This may be attributed to adjustments in signal transmission power from the LTE BS or variations in network congestion at different times.  
When analyzing the impact of rainfall on signal measurements at a large scale, it is important to separate the daily pattern from the effects of rainfall. To mitigate repetitive effects, the entire day is divided into four non-overlapping time windows [15]. However, based on the collected data in this study, the timing and magnitude of drops and increases  
Figure 9: Signal measurements during dry periods: (**a**) 3–6 March in Dataset1. (**b**) 31 March–1 April in Dataset3.  
in value vary from day to day. Typically, values begin to decline between 5 p.m. and 7 p.m., and then generally stop declining, starting to rise again between 5 a.m. and 7 a.m.