esa-sceva/satcom-chunk-collection huggingface dataset:
# Architectural Design for Digital Twin Networks
## 4 Functional DTN Implementation  
### DTN Description  
Figure 2 illustrates our implementation of a Digital Twin Network. Conceptually, it integrates a simulator with graph algorithms and a theoretical link model, creating a versatile and multifaceted system. This interconnected system maintains a continuous connection with the physical network, acquiring information through monitoring mechanisms. Below is a brief overview of the key facets; for a more detailed description, please refer to [16].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Challenge** & **Description** \\ \hline Scalability & \begin{tabular}{l} Efficiently managing the growing complexity and costs of data \\ acquisition, storage, and model implementation. Ensuring the DTN \\ supports large-scale networks without compromising performance, accuracy, or efficiency. \\ \end{tabular} \\ \hline Interoperability & \begin{tabular}{l} Seamless integration of diverse software, hardware, and \\ communication technologies requires standardized interfaces and \\ unified data models. Varying characteristics of wired, wireless, and \\ mesh networks, such as latency, bandwidth, and reliability, can affect \\ DTN accuracy, performance, and functionality. \\ \end{tabular} \\ \hline Data & \begin{tabular}{l} Balancing accuracy, flexibility, and scalability for diverse applications \\ and large networks. While virtualization works for small networks, \\ larger scales require cost-effective solutions like mathematical \\ abstractions or AI. Maintaining this balance adds complexity to \\ developing functional models. \\ \end{tabular} \\ \hline Real-Time & \
\begin{tabular}{l} Minimizing latency and ensuring synchronization between the \\ network and its twin. Real-time demands and simulation delays create \\ performance challenges, requiring automated processing and \\ optimized workflows. Matching real-time needs with suitable \\ computing resources ensures efficient execution. \\ \end{tabular} \\ \hline Security &
\begin{tabular}{l} DTNs face heightened risks due to constant synchronization and \\ third-party integrations, expanding the attack surface. Robust \\ measures like firewalls, intrusion detection, encryption, and \\ authentication are essential to protect data and secure communication \\ across these fronts. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Challenges in building and maintaining Digital Twin Networks.
## 4 Discussion
In line with our results, several studies document a lower top of atmosphere NDVI compared with NDVI at the surface due to the atmospheric scattering effect [70; 71]; however, recent improvements in PlanetScope's surface reflectance products should minimize this effect [71]. Additionally, acquiring UAV imagery that can be used to scale across sensors, space, and time can be challenging, due to differences between sensors and sensor units, ambient weather, and lighting conditions [61; 63]. Narrowband hyperspectral imagery and derived indices are also appropriate for detecting daily changes in plant moisture status, but not yet commonly practical using commercial UAV cameras. Most notably among these is the photochemical reflectance index (PRI), which uses bands at 532 nm and 570 nm and requires a hyperspectral imaging camera [59; 60]. PRI was developed to capture diurnal changes in the xanthophyll cycle of leaves and canopies, which contributes to a plant's ability to efficiently disperse light energy for photosynthesis [72]. Because water limitation affects the light-harvesting capacities and photosynthetic pathways within the leaves of a plant, PRI has become an appropriate index with which to detect the effects of rapid changes in plant health due to lack of water. Additionally, evergreen species, such as coyote brush, alter their chlorophyll:carotenoid ratios in response to water or temperature availability, and this can be detected with PRI on a seasonal or daily timescale [73]. NDVI, however, may be less suited for diurnal detection of water stress impacts on vegetation greenness or photosynthetic capabilities in evergreens, because canopy reflectance remains relatively stable even throughout the beginning and end of a season, when species alter rates of photosynthesis and have access to varying amounts of water. Furthermore, nuanced differences in photosynthetic downregulation during brief or sudden disturbances may not be captured by NDVI. While NDVI is effective in detecting longer-term changes in photosynthesis (such as new growth and leaf development), PRI is more suitable for subtle changes in evergreen leaf pigmentation. However, over longer time scales, NDVI and PRI can be used together to understand rates of photosynthesis, carbon fixation, and net primary production in a plant based on the combination of greenness and light-use efficiency models [72]. New work also highlights the ability to measure solar-induced fluorescence via narrowband remote sensing. About 1% of solar energy captured by plants is re-emitted by chlorophyll as fluorescence. The amount of fluorescence emission is a direct indicator of the photosynthetic activity of a plant and also provides indirect information about the plant stress [74]. Remote sensing of solar-induced fluorescence requires very narrow spectral bands (\(\sim\)0.05 nm) around 690 nm and 760 nm [67; 75].
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Platform Structure and Details
#### 2.1.1 Hull Structure
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Norm** & **Instructions** \\ \hline Underwater Vision & NeZha Development Board [3] & 85 mm \(\times\) 56 mm; Intel® processor N97 (Alder Lake-N); quad-core SoC clocked at 3.60 GHz, TDP of 12 W; 8 GB of LPDDR5 system memory, up to 64 GB of eMMC storage, onboard TPM 2.0, 40-pin GPIO connector, and support for Windows and Linux operating systems & \\  & Underwater Camera [4] & 1080 P HD pixels; IP67 rated waterproof; 25 FPS image transfer rate \\ \hline Motion Control & STM32 Development Board [2] & Model: STM32F103C8T6; Core: ARM Cortex-M3; Main Frequency: 72 MHz RAM; 20 K (SRAM); ROM: 64 K (Flash); Supply Voltage: 2.0-3.6 V (Standard 3.3 V); \\  & Package: LQFP48 (48 pins) & \\ Module & BeilDou Antenna [2] & GPS/BDS Compatible \\  & Hull Steering [2] & Voltage: 7.4V; Rotation speed: 9000 r \\ \hline \hline \end{tabular}  
* Manufactured by Espressif Systems, Shanghai, China; Purchased via Taobao; 2 Manufactured by DFRobot, Shanghai, China; Purchased via Taobao, 3 Manufactured by Allwinner Technology, Zhuhai, China; Purchased via Taobao; 4 Manufactured by 3 RD EYE, Shenzhen, China; Purchased via Taobao.  
\end{table} 
Table 1: _Cont._human-vessel interaction through the visualization platform on the software side. The payload module is used to carry mission-specific equipment (e.g., cameras, sensors, etc. ).
## 2 Mathematical Model
### Echo Laser Pulse Waveform
Taking into account various attenuations in Equation (5), the mathematical equation of ELPW is obtained by summing the received power corresponding to a grid as follows:  
\[P_{r}(t)=\sum_{i=1}^{N}\frac{P_{t}}{\pi R_{i}^{2}}G_{i}(t)\rho\cos\theta_{i}A_{ i}A_{R}T_{A}^{2}T_{C}^{2}\eta_{T}\eta_{R}\eta_{F}, \tag{7}\]  
where \(N\) is the total number of grids illuminated by the transmitted laser beam, \(T_{A}\) and \(T_{C}\) are the one-way transmittance by the atmospheric attenuation and cirrus clouds, respectively, \(\eta_{T}\) is the transmitting optical efficiency, \(\eta_{R}\) is the receiving optical efficiency, and \(\eta_{F}\) is the optical filter transmittance. In the near-ultraviolet to visible spectral band (0.3\(\sim\)0.7 \(\upmu\) m), the atmospheric attenuation is dominated by aerosol and molecular scattering as well as ozone absorption. In the near infrared band beyond 0.7 \(\upmu\)m, attenuation is subjected to absorption features of atmospheric constituents including water vapor, oxygen and carbon dioxide. Typically, cirrus clouds are overhead about 50% of the time at most locations. The one-way transmittances by the atmospheric attenuation and cirrus clouds are expressed as [31]  
\[T_{A}(\lambda,V,h_{\rm s})=\exp\!\left[-\sigma(\lambda,V,0)h_{SH}\sec(\theta_{ Z})\exp\!\left(-\frac{h_{\rm s}}{h_{SH}}\right)\right], \tag{8}\]  
\[T_{C}=\exp\!\left[-0.14(C_{T}\sec\theta_{z})^{2}\right]\!, \tag{9}\]  
where \(\sigma(\lambda,V,0)\) and \(V\) are the atmospheric attenuation coefficient and the visibility at sea level, respectively, \(\lambda\) is the laser beam wavelength, \(h_{SH}\) is the scale height of 1.2 km, \(\theta_{Z}\) is the zenith angle of space target, \(h_{\rm s}\) is the station height, and \(C_{T}\) is the cirrus cloud thickness.
## 3 Experiment
### Results  
The experiment without water in the pipe was evaluated based on the records of accelerometers. The influence of air was neglected. Thus, it was possible to determine material properties of the pipes. The values of damping ratio \(\zeta\) and modulus of elasticity \(E\) (see Figure 2) were used for evaluating experiments with water in the pipe. The results are listed in Table 1.  
The speed of sound is different for different pipe dimensions (diameter and wall thickness) and does not depend on the frequency. The second viscosity is shown in Figure 6 as a function of frequency.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **Wall** & & **Speed of** & **Damping** & **Young’s** & **Second** \\
**Length** & **Diameter** & **Thickness** & **Eigenvalue** & **Sound** & **Ratio** & **Modulus** & **Viscosity** \\
**(m)** & **(mm)** & **(mm)** & **(s\({}^{-1}\))** & **(m s\({}^{-1}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(kPa s)** \\ \hline
0.8 & 32.0 & 6.5 & \(-22.5+5411.9i\) & 1369.0 & 702.4 & 227.0 & 2.30 \\
1.0 & 32.0 & 6.5 & \(-37.2+4436.5i\) & 1383.6 & 704.0 & 227.0 & 6.98 \\
1.0 & 50.0 & 3.0 & \(-34.2+4820.8i\) & 1462.8 & 1841.8 & 225.6 & 6.44 \\
2.0 & 32.0 & 6.5 & \(-25.2+2263.6i\) & 1430.0 & 702.4 & 227.0 & 18.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the experiment.  
Figure 5: Determination of the real part of the eigenvalue from the discrete Fourier transform.  
The Figure 7 shows the model sensitivity on the value of the second viscosity. The real part of the system eigenvalue describes the damping of pressure pulsations. The circle marks a value corresponding to the experiment.  
When the second viscosity is supposed to be zero, the shear viscosity has to be changed to obtain the correct eigenvalue. Figure 8 shows the sensitivity on the shear viscosity. The damping corresponds to the experiment when the shear viscosity is 0.19 Pa s. This value is almost 200\(\times\) greater than the physically correct number for water at given conditions (0.001 Pa s).
## 3 Analysis and Discussion of the Results
### Analysis and Discussion of the Experimental Results
\[p_{\rm RSR}=p_{2}-\frac{\rho}{2}u_{2}^{2}c_{\rm p,RSR}. \tag{7}\]  
The pressure ratio can be expressed as follows:  
\[\frac{p_{\rm PS}}{p_{\rm RSR}}=\frac{p_{2}+\Delta p_{\rm GL}-\Delta p_{\rm GL, loss}}{p_{2}-\frac{\rho}{2}u_{2}^{2}c_{\rm p,RSR}}, \tag{8}\]  
The variable \(\Delta p_{\rm GL}\) denotes the pressure increase in the solute, while \(\Delta p_{\rm GL,loss}\) represents the solute's pressure loss. It can be assumed that the proportionality approach \(p_{2}\propto H\propto n^{2}\) applies to the pressure at the impeller outlet [27]. Equation (7) illustrates that \(\Delta p_{\rm RSR}\propto u_{2}^{2}\propto n^{2}\) applies to the pressure drop between the impeller outlet and the measuring point in the back side gap (\(\Delta p_{\rm RSR}\)). It can be assumed that for the increase in pressure at the solute and pressure loss of the solute, \(\Delta p_{\rm GL}\), \(\Delta p_{\rm GL,loss}\propto Q^{2}\)[27]. This  
Figure 8: Ratio of the static pressure at the pressure side (\(p_{\rm ps,exp}\)) to the static pressure in the back side gap (\(p_{\rm RSR,exp}\)) with varying motor frequencies (\(f_{\rm M}\)) and varying inflow without (\(\Delta\alpha_{\rm pb}=0^{\circ}\)), with positive (\(\Delta\alpha_{\rm pb}>0^{\circ}\)) and negative (\(\Delta\alpha_{\rm pb}<0^{\circ}\)) prewhirl angles. shows that \(\Delta p_{\rm GL}\) and \(\Delta p_{\rm GL,loss}\) do not vary when the speed is varied. The increase in flow losses and the associated reduction in hydraulic efficiency can be attributed to the impeller.
## 4 In Situ Testing
### Concept and Methodology  
The dynamic characteristics of structures can be determined using various in situ methods, each with its own potential inaccuracies. Detailed information and an introduction to the identification of the dynamic characteristics of structures can be found in the literature [22; 43; 44; 45; 46; 47].  
In this study, the time signals of the ambient excitation were used to determine the natural frequencies. Averaged frequency spectra were generated and analysed using the Python software environment [47]. Eigenmodes were visualised using Artemis through operational modal analysis (OMA) [48]. Damping characteristics were assessed by analysing the integrated lines of the decay curve resulting from pulse excitation. Stochastic subspace identification in Artemis and decay curves from train crossings were used for comparison purposes only [48; 49].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bridge** & **L** & **d** & \(t_{WDL}\) & \(A_{B}\) \\
**[ID]** & **[m]** & **[m]** & **[m]** & **[m\({}^{2}\)]** \\ \hline
12,842 & 17 & 1 & 1 & 84 \\
12836 & 17 & 1 & 1 & 84 \\
12829 & 9.5 & 0.6 & 0.8 & 75 \\
13871 & 9.5 & 0.6 & 0.8 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the investigated concrete portal frame bridge.  
Figure 7: General view of concrete portal frame bridge 12,842.  
The concept incorporates 22 geophones for the clear identification of the first three eigenmodes, including bending and torsional modes, as well as rigid body modes and abutment movements. The arrangement is illustrated in Figure 8. To ensure the continuous operation of the railway line during measurements, the entire measurement setup was installed beneath the superstructure without any artificial excitation of the structure. As all four bridges have a similar structure and design, identical setups were used for all measurements.
## 4 Our Modeling Approach  
Our modeling approach is based on extracting transitions through the data received from the real system in the form of log files and on generating sequences of the transitions obtained to form the overall model. In this section, we provide the details. Some background knowledge is needed beforehand.  
When modeling a system as a Colored Petri net, the overall system is represented by tokens, places, and transitions. Here, we must add some extensions to the definitions given in the introductory section. A _token_ is not just an object as in PN, but it is an instance of an object (objects are also referred to as _entities_ in the CPN formalism), which can be distinguished from other instances based on the current values of its characteristics. For example, an entity employee may be described as a set of characteristics: <First Name, Last Name, Education, Years of Experience> and, at a time, there may be two tokens: <[PERSON], [PERSON], High School, 1> and <[PERSON], [PERSON], University, 10>. The two tokens are instances of the same entity, yet they differ based on their values.  
As in PN, a _place_ is represented by a circle and it is a container of tokens. However, in the CPN formalism, a number of different (in terms of their values) tokens inside a place can define a network's state. For example, a state: project in process may contain the tokens <[PERSON], [PERSON], High School, 1> and <[PERSON], [PERSON], University, 10>. This shows that the system is in a state where the two employees are working on a project. Finally, a transition is a system's change, from one state to another but in a CPN model, it can be enabled by tokens with different values.  
The main properties of the CPN formalism are presented below:  
1. The CPN formalism allows for distinguishing the tokens, that is, there may be tokens that correspond to the same entity but the attributes (values of the token) differ. In this regard, all the data retrieved about members of an entity will be placed to different tokens instead of one that represents the entire entity.
2. The CPN formalism takes into consideration the actual timing of the events, not just their sequence (as in the basic PN formalism). In this regard, our proposed model will use the timing of the actual events to generate the model.
3. The CPN formalism can employ guard values to define conditions under which an event can (or cannot) occur at a certain time. These conditions can be extracted from records of the actual events that have taken place.  
In the following subsections, we present the most important aspects of our CPN modeling approach: sequencing transactions from data, transaction modeling, timing considerations, guardian expressions, and the hierarchical modeling.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## Appendix A The Structural Similarity Index (SSIM)
### The Feature Similarity Index (FSIM)  
The last of the used metrics has been the FSIM [12]. It is a full reference metric based on the determination of two characteristics of the compared images: the phase congruence (PC) and the gradient map of the image (GM). The PC is a dimensionless measure of the local structure [38]. It is used as a main feature in FSIM. The theory on PC function offers a simple but biologically plausible model of how mammalian visual systems detect and identify features in an image [39; 40]. Considering that the PC is invariant to the contrast but considering that the local contrast of the image affects the perception of the HVS on the quality of the image, the magnitude of the GM is used as a secondary feature to encode contrast information. The PC and GM play complementary roles in the characterization of local image quality. After obtaining the local similarity map, the PC is again used as a weighting function to obtain a unique quality score at each point in the image. According to [PERSON] et al. [12], the FSIM metric can achieve a correlation with subjective evaluations higher than all IQA metrics do.  
The calculation of the FSIM index consists of two stages. In the first stage, the local similarity map is calculated, and then in the second stage, the similarity map is grouped into a single similarity score. Given the X and Y images, the FSIM value for a pixel \(\mathbf{x}=(i,\ j)\) of these images is given by:  
\[\begin{array}{l}\text{FSIM}_{\text{X,Y}}(\mathbf{p})=\frac{\sum_{\mathbf{p}} \left[\text{S}_{\text{PC}}(\mathbf{p})\cdot\text{S}_{\text{C}}(\mathbf{p}) \right]\text{PC}_{\text{m}}(\mathbf{p})\right]}{\sum_{\mathbf{x}}\text{PC}_{ \text{m}}(\mathbf{p})}\\ =\frac{\sum_{\mathbf{x}}\left[\left(\frac{2\text{ PC}_{\text{X}}(\mathbf{p})\cdot\text{PC}_{\text{Y}}(\mathbf{p})+T_{1}}{ \text{PC}_{\text{X}}^{3}(\mathbf{p})+\text{PC}_{\text{Y}}(\mathbf{p})+T_{1}} \right)\cdot\left(\frac{2\text{ GM}_{\text{X}}(\mathbf{p})\cdot\text{CM}_{\text{Y}}(\mathbf{p})+T_{2}}{ \text{CM}_{\text{X}}(\mathbf{p})+T_{2}}\right)\cdot\text{PC}_{\text{m}}( \mathbf{p})\right]}{\sum_{\mathbf{x}}\text{PC}_{\text{m}}(\mathbf{p})}\\ \end{array} \tag{12}\]  
where \(\text{PC}_{\text{m}}(\mathbf{x})=\text{max}(\text{PC}_{\text{X}}(\mathbf{x}), \text{PC}_{\text{Y}}(\mathbf{x}))\). T\({}_{1}\) and T\({}_{2}\) are two positive constants used to increase the stability of the \(\text{S}_{\text{PC}}(\mathbf{p})\) and \(\text{S}_{\text{G}}(\mathbf{p})\) functions [12].
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 4 xPON Activation Processes  
The activation process describes the steps in which an inactive ONU connects or reconnects to a PON [26]. The activation process includes three phases, specifically: parameter learning, serial number acquisition and ranging. During the learning parameter phase, the ONU acquires the operational parameters that are needed in the upstream transmission. During the serial number acquisition phase, OLT discovers a new ONU (by serial number) and assigns an ONU identifier (ONU-ID) to it.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **GPON** & **EPON (Type 2)** \\ \hline Standard & ITU-T G.984 & IEEE 802.3 ah \\ Bitrate downstream & 1.244/2.488 Gbit/s & 1.25 Gbit/s \\ Bitrate upstream & 1.244/2.488 Gbit/s & 1.25 Gbit/s \\ \(\lambda\) for downstream & 1480–1500 nm & 1490 nm \\ \(\lambda\) for upstream & 1260–1360 nm & 1310 nm \\ Split ratio & 64 (up to 128) & 32 \\ Network reach & 20 km & 20 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic comparison of GPON with EPON.  
The ONU round trip delay (RTD) is the time interval between the downstream frame transmission and the corresponding upstream transmission burst from the given ONU. The RTD consists of a propagation delay that is directly proportional to the length of the fibers from the ONU and the response of the ONU. To ensure that transmission bursts from different ONUs are ordered at the interface of the same upstream GPON transmission convergence layer (GTC) frame, the delay time is assigned to each ONU to postpone the transfer of the upstream burst to the time not used for a common response time. This response time is called the equalization delay (EqD), and for each given ONU, the OLT is calculated based on the RTD measurement and consequently transmitted during the ranging state.  
To avoid collisions with the upstream bursts transmitted during acquisition of the serial number and the range of the newly-connected ONU, the OLT must temporarily suppress the upstream transmission of the active ONU for the time that the arrival of upstream bursts from the new ONU is assumed. This time interval is referred to as the quiet window.