esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
MANET is a mobile ad hoc network with many mobile nodes communicating without a centralized module. Infrastructure-less networks make it desirable for many researchers to publish and bind multimedia services. Each node in this infrastructure-less network acts as self-organizing and re-configurable. It allows services to deploy and attain from another node over the ad hoc network. The service composition aims to provide a user's requirement by combining different atomic services based on non-functional QoS parameters such as reliability, availability, scalability, etc. To provide service composition in MANET is challenging because of the node mobility, link failure, and topology changes, so a traditional protocol will be sufficient to obtain real-time services from mobile nodes. In this paper, the ad hoc on-demand distance vector protocol (AODV) is used and analyzed based on MANET's QoS (Quality of Service) metrics. The QoS metrics for MANET depends on delay, bandwidth, memory capacity, network load, and packet drop. The requester node and provider node broker acts as a composer for this MANET network. The authors propose a QoS-based Dynamic Secured Broker Selection architecture (QoSDSBS) for service composition in MANET, which uses a dynamic broker and provides a secure path selection based on QoS metrics. The proposed algorithm is simulated using Network Simulator (NS2) with 53 intermediate nodes and 35 mobile nodes of area 1000 m \(\times\) 1000 m. The comparative results show that the proposed architecture outperforms, with standards, the AODV protocol and affords higher scalability and a reduced network load.  
MANET; QoS metrics; routing protocol; cluster formation; link failure
# Quantum Biotech and Internet of Virus Things: Towards a Theoretical Framework
## 3 Theoretical Foundation
### QBIoVT Colonnade
#### 3.2.3 IoVT/mRNA Security, Privacy, Risk  
Every wave of cutting-edge technology, including IoT approbation and enactment has its daunting challenges. Implementing and managing IoVT poses challenges, specifically issues of privacy, security, and risk. The IoVT system is dependent on communications, sensors, cloud, storage, and software programs intensifying concerns about privacy, security, and risk. Such concerns prevail still and are not resolved around privacy and security, creating risk within an increasingly IoVT connected world. As per a recent survey by researchers [77], the top hindrances to IoT growth are privacy and security concerns. Scholars [58] listed that the main security challenges in IoT are (i) access control, (ii) privacy, (iii) policy exaction, (iv) trust, (v) mobile and middleware security, (vi) confidentiality, and (vii) authentication. The IoVT vulnerabilities can be mitigated through security best practices.  
To address the security challenges, stakeholders need to organize their thinking in the following manner: (a) security at the design stage, (b)security management to be enhanced, (c) security features built on proven practices, (d) security prioritization, (e) IoVT transparency, and (f) connectivity carefully implemented. As IoT devices proliferate into uncontrolled, complex, and unfavorable environments, securing IoT systems poses unique challenges.  
For example, since numerous devices endeavor points of entry within an IoT system, device authorization and authentication are captious for securing IoT systems. Prior to access gateways, and applications, devices ought to establish their identity. Moreover, it should be noted that many IoT devices fall by using weak password authentication. IoT Platforms should provide device authorization services to resolve which apps, services, or resources that each device has access to throughout the system.  
The following are the common perceptions and safety profile of mRNA vaccines: (i) COVID-19 mRNA vaccines have been rigorously tested for safety before being authorized by regulators; (ii) mRNA technology is a novel concept, and for more than two decades, researchers have been pursuing it; (iii) mRNA vaccines do not carry a risk of causing disease in the vaccinated person and do not contain a live virus; and (iv) mRNA does not affect or interact with a person's DNA.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 3 Measurement Setups
### Offshore Ground-Based Comparative Measurement Campaign
#### 3.1.3 Onshore Nacelle-Based Wake Measurements  
The second and third data set were acquired within the German project \"CompactWind\", in which two of the previously described LiDAR devices have been installed on the nacelle of an eno114 3.5 MW wind turbine with a rotor diameter \(D=114.9\) m and a hub height of \(h=92\) m. The onshore wind farm consists of two wind turbines from the same type and is located near Rostock in the village Brusow. The surrounding terrain is slightly hilly with a compact forest to the east.  
The first measurements were performed from 14.05.2015 02:30 h (UTC) till 14.05.2015 06:00 h (UTC). Here, we show only one LiDAR in measuring horizontal PPI scans with \(0^{\circ}\) elevation at nearly hub height with a total azimuthal opening angle of \(40^{\circ}\) centred in downstream direction. Each of the 571 scans took 20 s, resulting in a repetition period of 22 s, including an initialisation time. We parameterised the Leosphere Windcube 200s with a pulse length of 200 ns respectively 59.96 m (FWHM) and an accumulation time of 200 ms with a pulse repetition frequency of 20 kHz. In this time period  
Figure 8: Histogram of 10 min averaged ultrasonic anemometer inflow conditions from 21.12.2013 15:35 h (UTC) till 19.01.2014 7:55 h (UTC) (**a**) horizontal wind speed in the meteorological reference frame is marked in dark blue, whereas the LiDAR laser beam projected wind speed \(u_{Inf}\) (Equation (17)) is shown in green. The bin width is 1 m/s, (**b**) wind direction with a bin width of \(3^{\circ}\).  
Figure 9: Time series of the 10 min averaged wind direction measured by the ultrasonic anemometer from 21.12.2013 15:35 h (UTC) till 19.01.2014 7:55 h (UTC). Gaps in the plot demonstrate unavailability of LiDAR data. Horizontal lines indicate the wind direction of possible wake shading of the anemometer on FINO1 based on geometrical correlations.  
in which the turbine was operating a significant wake was measureable. Within the framework of \"CompactWind\", we were able to alternate the nacelle mounted LiDAR from the described Leosphere device with a Stream Line XR LiDAR by Halo Photonics. The here used Stream Line XR dataset is shown as an example of general applicability of the dynamic data filtering approach.  
The corresponding third data was captured from 31.10.2016 00:00 h (UTC) till 31.10.2016 00:30 h (UTC). In that time period, the LiDAR was operating in PPI mode using the above mentioned opening angle, accumulation time and scan speed. The measurement was parameterised with a pulse length of 100 ns or 29.98 m and a pulse repetition frequency of 10 kHz.
## 4 Verification and Discussion of the Example  
### Viscoelastic Displacement of the Rod Subject to Uniaxial Tension  
#### 4.1.1 Parameter Specification  
A vertical axial rod is fixed at the bottom and subjected to tensile force \(P\), with material behavior described by the generalized Maxwell viscoelastic model, comprising a spring element and a Maxwell body in parallel. It is assumed that only one-dimensional elements are used. The load \(P=2\) N, cross-sectional area \(A=100\) mm\({}^{2}\), length \(L=500\) mm, and material parameters are \(E_{\infty}=0.65\) MPa and \(E_{1}=3\) MPa. The viscosity coefficient is taken separately as \(\eta_{1}=5.4\) MPa \(\cdot\) s, \(10.8\) MPa \(\cdot\) s, and \(21.6\) MPa \(\cdot\) s. The corresponding relaxation times are \(\tau_{1}=1.8\), \(3.6\) and \(7.2\) s, as shown in Figure 1. The viscoelastic displacement of the rod subject to the uniaxial tension at different relaxation times is solved in this section, where the numerical results are computed using our own programmed algorithms.
## 3 RS Applications in Ocean
### Ocean Surface Current (OSC)
Table 2 also summarizes these systems along with their advantages and limitations for OSC studies. More details about the applications of each system are also provided in the following subsections. Figure 3: The global ocean currents, including warm currents (red line), cold currents (blue line), and neutral current (black line) adopted from [[https://commons.wikimedia.org/wiki/File:Corrientes-oceanics.png](https://commons.wikimedia.org/wiki/File:Corrientes-oceanics.png)]([https://commons.wikimedia.org/wiki/File:Corrientes-oceanics.png](https://commons.wikimedia.org/wiki/File:Corrientes-oceanics.png)) (accessed on 8 October 2022). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{6}{*}{Passive} & \multirow{3}{*}{Optical} & Provides high spatial resolution images & Calibrating issues due to defining several input parameters, limited by cloud cover, requires a reliable operational procedure for feature tracking, not suitable for nighttime limited by cloud cover, edge-of-scan distortions, hard for features to evolve due to degradation of their surface signature \\  & & Can measure under clouds and in all \\  & & weather conditions except for rain, OSC \\  & & estimation at a global scale \\  & & Not limited by cloud cover or daytime, \\  & & contains physical properties, high \\  & & spatial resolution, different data \\  & & acquisition modes are available, ability \\  & & to detect small leads, penetration \\  & & capability \\  & & Almost daily global coverage, accurate \\  & & topography for SI thickness \\  & & measurement, ability to map \\  & & small leads \\  & & & \\  & HF radar & Suitable for global-scale studies \\  & & & \\  & Marine radar & Not limited by cloud cover and \\  & & daytime, long-time data archive \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different RS systems for OSC estimation along with their advantages and limitations. Figure 4: Different methods for ocean current estimation.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 3 Structure and Properties of Kirchhoff Migration  
### Introduction to Kirchhoff Migration and Its Mathematical Structure  
The main purpose of this problem is to identify unknown locations \(\mathbf{x}_{\text{ANO}}^{(m)}\) from measured far-field pattern data without any _a priori_ information of targets, e.g., permittivity, permeability, size, shape, etc. For this, we present the Kirchhoff migration technique for a real-time identification of the anomalies \(\mathbf{A}_{m}\). For simplicity, we will ignore the term \(k^{2}(1+i)/4\sqrt{k\pi}\) in (1). To introduce this topic, we first consider the MSR matrix \(\mathbb{K}\):  
\[\mathbb{K}=\left[\begin{array}{cccc}u_{\infty}(\boldsymbol{\vartheta}_{1}, \boldsymbol{\theta}_{1})&u_{\infty}(\boldsymbol{\vartheta}_{1},\boldsymbol{ \theta}_{2})&\cdots&u_{\infty}(\boldsymbol{\vartheta}_{1},\boldsymbol{ \theta}_{N})\\ u_{\  
where \(\mathbf{c}_{n}\in\mathbb{R}^{1\times 3}\backslash\{\mathbf{0}\}\), \(n=1,2,\cdots,N\). With this, we can introduce the following imaging function adopted by the Kirchhoff migration:  
\[\mathfrak{F}_{\text{KIR}}(\mathbf{x}):=\left|\mathbf{W}(\mathbf{x})^{*}\mathbb{ K}\overline{\mathbf{W}}(\mathbf{x})\right|=\left|\sum_{n=1}^{N}\tau_{n} \langle\mathbf{W}(\mathbf{x}),\mathbf{U}_{n}\rangle\langle\mathbf{W}(\mathbf{ x}),\overline{\mathbf{V}}_{n}\rangle\right|. \tag{4}\]  
Then, the map of \(\mathfrak{F}_{\text{KIR}}(\mathbf{x})\) will contain peak of large magnitude at the location \(\mathbf{x}\in\Lambda\cup\text{Y}\), so that it will be possible to identify unknown anomalies. A detailed description of Kirchhoff migration is discussed in Appendix A. **Remark 1** (Selection of test vector). : _It is worth mentioning that the selection of \(\mathbf{c}_{n}\) of (2) is highly rely on the shape of \(\mathbf{A}_{m}\) and \(\mathbf{R}_{s}\). Unfortunately, we have no a priori information on targets \(\mathbf{A}_{m}\) and \(\mathbf{R}_{s}\) so that it is impossible to select optimal vectors \(\mathbf{c}_{n}\). Thus, with motivation from several researches [14; 15; 41; 42], we adopt following unit vector \(\mathbf{W}(\mathbf{x})\) instead of (3) such that_  
\[\mathbf{W}(\mathbf{x})=\frac{1}{\sqrt{N}}\left[e^{i\mathbf{k}\theta_{1}\cdot \mathbf{x}},e^{ik\theta_{2}\cdot\mathbf{x}},\cdots,e^{ik\theta_{N}\cdot \mathbf{x}}\right]^{T}\in\mathbb{C}^{N\times 1}. \tag{5}\]  
The feasibility of the Kirchhoff migration can be explained based on the discussion in Appendix A. However, sometimes it is impossible to obtain good results via the Kirchhoff migration. Furthermore, appearance of various unexpected results via Kirchhoff migration cannot be explained. Hence, a careful investigation of the mathematical structure of the imaging function \(\mathfrak{F}_{\text{KIR}}(\mathbf{x})\) must be considered. For this purpose, we establish the mathematical structure of imaging function. The result is following. **Theorem 1**. : _For sufficiently large \(N\) and \(k\), \(\mathfrak{F}_{\text{KIR}}(\mathbf{x})\) can then be represented as follows:_  
\[
## 2 Drone Visible Light Communications with Differentiated LED Beams
### Drone Visible Light Communications with Lambertian LED Beam
On the other hand, when a Lambertian optical beam of the drone and the aerial user with upward orientation receiver is applied to configure the drone aerial to aerial (A2A) user link, the A2A geometry relationship of \(\phi_{\rm A2A}\) and \(\theta_{\rm A2A,up}\) could be expressed as:  
\[\cos(\phi_{\rm A2A})=\cos\big{(}\theta_{\rm A2A,up}\big{)}=\frac{h_{\rm A2A}}{d _{\rm A2A}}=\frac{H-h_{\rm alt}}{\sqrt{r_{\rm A2A}^{2}+\left(H-h_{\rm alt} \right)^{2}}}, \tag{7}\]  
where \(\phi_{\rm A2A}\) denotes the irradiance angle for the optical source to the aerial user; \(\theta_{\rm A2A,up}\) denotes the incident angle to the aerial user receiver with upward orientation receiver, \(h_{\rm alt}\) denotes the altitude of aerial user, and \(r_{\rm A2A}\) being the radial distance between drone and aerial user on the ground plane, as shown in Figure 1b. Similarly, by referring (3) and (7), the baseline Lambertian optical channel gain for drone A2A VLC with upward orientation receiver \(H_{\rm A2A,up}^{\rm Lam}\) could be renewed as:  
\[H_{\rm A2A,up}^{\rm Lam} =\left\{\begin{array}{l}\frac{A_{\rm T}}{d_{\rm A2A}^{2}}R_{ \rm Lam}(\phi_{\rm A2A})T_{s}\big{(}\theta_{\rm A2A,up}\big{)}g\big{(}\theta_{ \rm A2A,up}\big{)}\cos\big{(}\theta_{\rm A2A,up}\big{)},\ \ \ \ \ 0\leq\theta_{\rm A2A,up}\leq\theta_{\rm FOV}\\ 0,\ \\[\eta^{\text{Lam}}=\frac{C^{\text{Lam}}}{P}=\frac{1}{2P}\log_{2}\biggl{(}1+\frac{e} {2\pi}\biggl{(}\frac{\eta PH^{\text{Lam}}}{\sigma_{w}}\biggr{)}^{2}\biggr{)}, \tag{12}\]  
where \(H^{\text{Lam}}\) could be explicitly given by (6), (8) and (10) for the Lambertian A2T user link, the Lambertian A2A user link with upward orientation receiver and the Lambertian A2A user link with horizontal orientation receiver, respectively.
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 3 Methodology
### BLE-Based Positioning  
In this paper, we propose a weighted centroid positioning method, which is an extension of the simple centroid algorithm with RSS-related weights added to the anchor tags evolving in the positioning.
# Efficient Resource Management for Sum Capacity Maximization in 5G NOMA Systems
## 2 System Model and Problem Formulation
Constraint in (8) guarantees the minimum capacity of each user, where \(\bar{C}_{\text{min}}\) is the threshold of minimum user capacity. In addition, the maximum and minimum power limits are defined in constraints (9) and (10), respectively. Due to binary variable for sub-channel allocation and interference terms in (5) and (8), the problem (P1) is a non-convex optimization problem which is difficult to handle. Therefore we employ sub-optimal techniques to obtain the efficient solutions.
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
## 2 Equipment
### Mechanism of Magnetorheological Polishing Related to Subsurface Damage Removal  
In magnetorheological polishing technology, the rheological properties of the magnetorheological polishing fluid in a magnetic field can be utilized to polish the workpiece, with the relevant principle shown in Figure 5[17]. Before the magnetorheological fluid entered the polishing zone, the magnetic moments of magnetically sensitive particles were randomly arranged since there was no external magnetic field. The magnetorheological fluid would not show magnetism to the outside. At this moment, the magnetic sensitive particles and polishing abrasive particles were uniformly dispersed in the base carrier liquid. After the magnetorheological fluid was brought into the polishing area by the polishing wheel, the magnetically sensitive particles were magnetized to generate the dipole moment under the action of the high-intensity gradient magnetic field. In order to minimize the energy, the magnetically sensitive particles were connected into chains to form a \"flexible polishing film\". Under the magnetic buoyancy of the magnetic field, magnetorheological fluid was precipitated from non-magnetic polishing particles, which were \"embedded\" on the surface of the \"flexible polishing film\". For the reason that the magnetorheological fluid had the properties of Bingham medium in the magnetic field, the rigid \"core\" formed by Bingham medium would generate a wedge-shaped zone in the polishing area after the optical element was pressed into a magnetorheological forging belt. The polishing abrasive particles would adhere to the surface of the optical element under the action of hydrodynamic pressure and pass through the wedge-shaped zone at a high speed, thus achieving higher polishing efficiency.  
During the CMP of quartz glass, the force exerted on the polishing disc was transmitted to the surface of optical elements through polishing particles. The positive pressure of a single polishing particle on the surface of optical elements was about \(10^{-3}\) N [18]. Meanwhile, during the magnetorheological polishing of quartz glass, the positive pressure exerted by polishing particles on the surface of optical elements was composed of three parts, including gravity (G), magnetic buoyancy (Fz), and normal component of hydrodynamic pressure (Fwn). Among them, gravity (G) can be ignored, as shown in Figure 6. According to the parameters of magnetorheological polishing and the measured hydrodynamic pressure in the polishing area, it can be found that the positive pressure exerted by a single polishing particle on the surface of optical elements in magnetorheological polishing was about \(10^{-7}\) N, which was far less than that exerted by polishing particles in traditional polishing. Therefore, magnetorheological polishing can effectively remove subsurface plastic scratches introduced by traditional polishing.  
Figure 5: Schematic diagram of magnetorheological polishing principle.
# Optimization of BP Neural Network Model for Rockburst Prediction under Multiple Influence Factors
## 6 Engineering Application
The buried depth of a water diversion tunnel is generally 1500-2000 m, and the maximum buried depth is 2525 m. The tunnel rock mass is relatively complete, and the uniaxial compressive strength of the rock is 55-114 MPa, with the maximum major principal stress of 46 MPa measured. The rockburst phenomena have occurred several times since the tunnel construction. The LM-BP neural network model is used to predict rockburst on the 3 + 390 working face of the Jinping II hydropower station. The prediction results are consistent with the SVM model, cloud model and the actual situation, as shown in Table 8.  
In this paper, the rockburst prediction results of the Qinling tunnel (case 4) and the highway tunnel of Zhongnan mountain (case 5) by the LM-BP neural network model is as shown in Table 9. The prediction results are completely consistent with the actual engineering situation, while the SVM model predicts case 4 from level III to level IV. Based on the prediction results of the above five engineering cases, the prediction results of the LM-BP model are completely consistent with the situation in the field. However, the SVM model misjudged case 2 from level III to II and case 4 from level III to IV, as shown in Figure 6.
###### Abstract  
The amount of suspended sediment transported from rivers to the ocean fluctuates over time, with a substantial increase occurring during storm events. This surge in sediment poses numerous challenges to coastal areas, highlighting the importance of accurately assessing the sediment load to address these issues. In this study, we developed and experimentally verified a novel method for suspended-sediment-discharge quantification in estuaries and coasts using underwater imaging. Specifically, red clay samples with different particle sizes were introduced into separate tanks containing clean water. After adequate mixing, the concentration, particle size, turbidity, and water quality were measured and analyzed using LISST-200x and EXO2 Multiparameter Sonde sensors. To maintain constant lighting conditions, a camera box was created for filming. Based on the experimental results, a turbidity-concentration relationship formula was derived. The proposed regression equation revealed that the relationship between the turbidity and estimated suspended-sediment concentration was significantly affected by the particle size, and the prediction results were underestimated under high-concentration conditions. Using blue, green, and gray band values, a multiple regression model for estimating suspended-sediment concentrations was developed; its predictions were better than those obtained from the turbidity-concentration relationship. Following efficiency improvements through additional approaches considering underwater-image filming conditions and characteristics of actual streams, estuaries, and coasts, this method could be developed into an easily usable technique for sediment-discharge estimation, helping address sediment-related issues in estuaries and coastal regions.  
suspended sediment; underwater image; turbidity; grain size; multiple regression analysis Article