esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Study in Short Delay Regime  
### Devices  
In order to investigate the linewidth characteristics of mutually locked lasers with short delay, monolithically integrated coupled DFB lasers were fabricated on identical epitaxial layers. Details about epitaxial structure are described in [36]. As is shown in Figure 6, the integrated device consists of two 500 \(\mu\)m grating regions and a grating-less coupling ridge waveguide. Electrical isolation between electrodes is formed by removing the top InGaAs ohmic contact layer via dry etching. Light from the device is coupled into a lensed fiber at cleaved facet end. In order to avoid reflection from the measurement system, an isolator is employed at the output fiber. On-chip mutually coupled lasers with 0.5, 2, 4 and 6 mm coupling regions are fabricated. The longest delay time is 64 ps, while \(f_{\text{{RO}}}\) of the DFB laser is about 7 GHz, indicating these devices belong to the short delay regime.  
Figure 5: (**a**) Photon lifetime in mutual coupling system with different coupling lengths; (**b**) field distribution in the mutual coupling system. (Laser length \(L=500\)\(\mu\)m, coupling coefficient \(\kappa L=1.5\), absorption coefficient \(\alpha_{i}=10\) cm\({}^{-1}\), injection ratio: 13.5%, group velocity \(v_{g}=9.29\times 10^{7}\) m/s.).  
Figure 6: Diagram and microscopy pictures of integrated mutually coupled devices.
# On the Use of Focused Incident Near-Field Beams in Microwave Imaging
## NF Beams and Results
### Interrogation of Objects by the NF Plate
The dielectric slab has the size of \(\lambda/20\times 2\lambda/3\times\lambda/10\). The description of the excitation and receiver planes are the same as Figure 5. The dielectric slab consists of an OI and two undesired scatterers. The OI is the light green box, and the two undesired scatterers attached to the OI are the dark green boxes. the center with respect to the NF plate. In Case II (Figure 5), both the light and dark green dielectric boxes are present in the system. In both of these cases, the light green dielectric box represents the OI while the dark green dielectric box in Case II represents the undesired scatterer which is located \(\lambda/4\) away from the center. Case III only consists of the undesired scatterer, as shown in Figure A1c. Finally, in Case IV (Figure 6), we have a dielectric slab that consists of an OI and two undesired scatterers. The OI, the light green box in Figure 6, has the same size and dielectric property as the OI in Cases I and II. Two undesired scatterers, the dark green boxes in Figure 6, have the same dielectric property as the OI. (The size of each of these undesired scatterers is \(\lambda/20\times 17\lambda/60\times\lambda/10\).) Having defined our two configurations (i.e., presence and absence of the NF plate) and four case studies per configuration, we now irradiate these four different cases under each configuration, and then collect the resulting fields at the receivers' locations. The receivers are located along the line of intersection between the yellow and brown planes in Figures 5 and 6. This receiver line is parallel to the \(y\)-axis, and is separated from the NF plate by about \(\lambda/7.5\). (Note that the receivers' locations is not on the focal plane; the focal plane, which is \(\lambda/15\) away from the NF plate, passes through the center of the objects.) The fields collected by the receivers in the presence of the objects are referred to as the total NF data. Subtracting the incident NF at the receivers' locations from the total NF data, we find the _scattered NF data_. Analysis of the scattered NF data is the focus of the next section.
# Creep Properties and Creep Modelling of Guilin Red Clay
## 3 The Burgers Creep Model
### Model Validation  
The parameters calculated using different cell pressures can be substituted into the Burgers model to obtain the fitting curve. A comparison of the test curve and the theoretical calculation of the curve is shown in Figure 13, respectively. The correlation coefficient of the two curves under all levels of deviatoric stress is calculated, as shown in Table 8. It can be seen that the correlation coefficient \(R^{2}\) of the theoretical calculation curve and the test curve under each level of deviatoric stress is greater than 0.9, which suggests that the Burgers model can accurately describe the creep properties of red clay.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cell Pressure/kPa**} & \multicolumn{2}{c}{\(\mathbf{E_{1}}\)**/kPa**} & \multicolumn{2}{c}{\(\mathbf{E_{2}}\)**/kPa**} & \multicolumn{2}{c}{\(\mathbf{\eta_{1}}\)**/(kPa-min)**} & \multirow{2}{*}{\(\mathbf{\eta_{2}}\)**/(kPa-min)**} \\ \cline{2-2} \cline{5-7}  & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline
100 & \(1.1\times 10^{-3}\) & \(4169.6\times 10^{-3}\) & 9328.6 & \(9.0\times 10^{-3}\) & \(3.0\times 10^{-6}\) & \(4.0\times 10^{-4}\) & 33,333.3 \\
200 & \(1.0\times 10^{-3}\) & 3533.4\(\times 10^{-3}\) & 21,057.0 & \(9.0\times 10^{-3}\) & \(3.0\times 10^{-6}\) & \(6.0\times 10^{-4}\) & 20,000.0 \\
300 & \(0.7\times 10^{-3}\) & 4925.4\(\times 10^{-3}\) & 14,236.0 & \(7.0\times 10^{-3}\) & \(3.0\times 10^{-6}\) & \(6.0\times 10^{-4}\) & 25,000.0 \\
400 & \(0.7\times 10^{-3}\) & 5256.5\(\times 10^{-3}\) & 17,489.0 & \(7.0\times 10^{-3}\) & \(2.0\times 10^{-6}\) & \(6.0\times 10^{-4}\) & 16,666.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the Burgers model under different cell pressures.  
Figure 13: Comparison between Burgers model curve and test curve under different cell pressures. (**a**) Cell pressure of 100 kPa. (**b**) Cell pressure of 200 kPa. (**c**) Cell pressure of 300 kPa. (**d**) Cell pressure of 400 kPa.  
As can be seen from Figure 13, when the applied deviatoric stress is 50-70% of the failure deviatoric stress, the fitted value is in good agreement with the experimental value. When the applied deviatoric stress is 80-90% of the failure deviatoric stress, the axial strain of the sample is obviously increased, which shows the phenomenon of delayed strain growth. The experimental curve and the fitted curve have an obvious growth trend, and the final axial strain value is close. The difference between the experimental curves and the fitted curves at 80-90% of the destructive deviatoric stress may be due to the strain \(\varepsilon_{3}\) generated with the Kelvin model when calculating the modulus of elasticity \(E_{2}\) and the viscous kettle coefficient \(\eta_{1}\) as well as the time required to enter the steady state creep phase being too small. In addition, the model has the characteristics of fewer model parameters and is easy to obtain, which makes it easier to use in engineering practice. It also provides a reference for the analysis of post-construction settlement control.
# Digital Twin Conceptual Model within the Context of Internet of Things
## 1 Introduction  
Smart factories and smart manufacturing are some of the important emerging smart cities' applications. Objects, products, systems, processes, services, and things in general terms are utilizing the emerging technologies such as Internet of Things (IoT) and cyber physical systems (CPS) to monitor their lifetime cycles. Nowadays, a product can be equipped with a mini microcontroller with a set of sensors and actuators along with internet connectivity that empowers manufacturers and users to track it from anywhere at any time. Industry 4.0 coins such product as a smart product.  
A smart object, product, system, and process consists of both a physical part and its complementing digital counterpart, also referred to as virtual and cyber part, and more recently referred to as its digital twin. NASA [1] describes digital twin as a simulation of a system or product mirroring the physical model into a virtual model, i.e., a highly detailed simulation model replicating physical semantics and behavior of a system into a flying twin. Although a lot of work has been steered in the direction of digital twin in various applications, a holistic, end-to-end conceptual model of a digital twin is yet in early stages of development. The creation of a digital twin model consists of several stages such as dynamic data collection from mobile sensors, data communication, real-time datasimulation and modeling, machine learning and data analysis, visualization and business intelligence, and security. Although a lot of work has been done in each of these stages in isolation, there is a lack of interconnectivity between these stages for designing a digital twin model that presents the impact of each stage for a digital twin in a holistic manner.  
The rest of the paper is organized as follows. Section 2 elaborates on the related work and proposes an end-to-end conceptual model for digital twin. The physical space layer is presented in Section 3. Section 4 presents the communication network layer followed by virtual space layer in Section 5, the data analytics' visualization layer is described in Section 6. Application and security layers are discussed in Section 7 collectively. Two use cases are proposed in Section 8 and followed by the conclusion in Section 8.
# Optical-OFDM VLC System: Peak-to-Average Power Ratio Enhancement and Performance Evaluation
## 3 Assessment Criteria Employed to Judge the Effectiveness of MCM Approaches
### Power Efficiency  
As previously stated, the data rate depends on the arrangement of symbols in each approach, with the H.S principle being a necessary condition to generate real bipolar samples following the IFFT operation. The conversion from bipolar to unipolar involves two methods. First, a DC bias is added to the bipolar signal, followed by clipping it to zero, as seen in DCO-OFDM and even part of ADO-OFDM. Consequently, DCO and ADO-OFDM are considered power-inefficient O-OFDM techniques. The amount of additional DC bias controls how well these techniques work [13; 40].  
Second, various schemes clipped the real output bipolar samples to zero, as employed in ACO-OFDM, FLIP-OFDM, ASCO-OFDM, and LACO-OFDM. The utilization of zero-clipping operations, made possible by the symbol H.S structure of these techniques, enhances symbol detection in the receiver, resulting in superior performance compared to DCO-OFDM and ADO-OFDM. Ultimately, FLIP, ACO, LACO, and ASCO-OFDM stand out as power-efficient OFDM techniques [2; 19; 44].
# Dynamic Voltage Optimization Based on In-Band Sensors and Machine Learning
## 6 Machine Learning in Voltage Optimization
### Reinforcement Learning  
Reinforcement Learning is a learning method focused on goal-directed learning from interactions to maximize a numerical reward signal [43]. Reinforcement learning is a method that acts as an agent that functions in a reward-punishment environment and learns by trial and error. Most of the reinforcement methods use a Markov Decision Processes (MDPs) that helps the model establish states through its trial and error learning-curve. The two main forms of reinforcement learning are by model-based and model-free approaches. The model-based methods rely on planning as their primary component, while model-free methods primarily rely on learning [43]. Fundamental mechanisms of both methods of the reinforcement learning provide an action to the environmental state based on the current state that evaluates what was observed and the associated reward as seen in Figure 4. A characteristic example of the implementation of reinforcement learning is for dynamic pricing [44].  
Table 2 shows a sample of the applications and models in ML classified within their own learning methods. Standard practice in finding the best fitting model is to apply the known data to different models that fit the desired application outcomes. The approach to apply ML to \(\overline{\text{DVO}}\) is identify the key parameters for the model that can provide predicted outcomes at high accuracy levels without overfitting the model. An ideal ML model for \(\overline{\text{DVO}}\) applications consist of evaluating inputs at a defined current state and predict the next state from previous knowledge of the network and information from the sensors.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Application** & **Models** \\ \hline Supervised Learning & Classification & Neural Networks \\  & & Naive Bayes \\  & & Nearest neighbor \\  & & Support Vector Machines \\ \hline  & Regression & Linear Regression \\  & & Decision Trees \\  & & Neural Networks \\  & & Gaussian Linear Models \\ \hline Unsupervised Learning & Dimensionality Reduction & Structure Discovery \\  & & Big Data Visualization \\  & & Compression \\  & & Feature Elicitation \\ \hline  & Clustering & kMeans \\  & & Fuzzy C-Means \\  & & Gaussian Mixture \\  & & Hidden Markov Model \\  & & Neural Networks \\ \hline Reinforcement Learning & Model-Based & Policy Iteration Based \\  & & Value Iteration Based \\ \hline  & Model-Free & Q-learning \\  & Dyna-Q \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machine Learning Methods [42; 45; 46]  
Figure 4: Reinforcement Learning Overview of Reward-Action System.
# Energy Blockchain for Public Energy Communities
## 3 Field-Test Implementation and Results
### On-Site Installation  
The on-site implementation of the BSMs showed some installation problems which were hard to identify on the laboratory testing. The experienced communication issues and failures, especially in one of the test sites, suggested the need for a backup connection procedure, not needed on laboratory testing. For this reason, a backup GSM connection was implemented in the devices that created further issues in the data lifecycle management.  
In particular, each blockchain component has been initially designed for working as a blockchain full node [25]. This means that each device stored all the blockchain data in its memory, thus exchanging a high amount of data with the blockchain P2P network. This approach ensured better data security and reliability. However, the GSM connection does not provide enough bandwidth to sustain this approach, so the architecture has been changed to switch to a light node approach when working with a GSM connection [31]. The light nodes do not have the total blockchain saved on their memory, so it was impossible to validate the state of the network in total security. In addition, the light nodes exchange only the proofs of the transactions in the Merkle tree form [31], which is, at least in principle, less secure. On the other hand, the exchange of the Merkle tree data requires far less bandwidth and local memory.  
Thus, the architecture of the BSMs has been changed so that it could switch between full and light node when the available connection was cabled or GSM, respectively. This change in architecture allowed reaching better QoS and stability.  
The distributed ledger used for test purposes is a private test version to avoid the definition of a public ledger at this point in the project with the intention to identify a safe and cost-effective solution among the available public chains.  
As a first result, the smart meter measurements, extracted from the blockchain and related to the measurement campaign of 15 July 2020 are shown in Figure 6.  
Figure 5: (**a**), a picture of the blockchain-enabled Smart Meters (BSM) used in this paper: a prototype of Bhitiatec PRO blockchain-enabled smart meter. (**b**), a picture of the on-site installation of the device.  
The smart meter can register measured data with 1s step but was programmed to record and save data every 15 min, and, as depicted in Figure 6, the power generation shows the typical profile of PV generator, which increase its production during the middle hours of the day, particularly in summer. Although the power generation of the whole energy community has a profile well aligned with the typical production profiles, the power consumption is extremely variable and barely predictable due to the nature of the analyzed buildings.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 4 Design Implementation
### Queue Scheduler  
As we discussed in Section 4.1, different kinds of packets have different retransmission requirements. According to the degree of significance, packets can be divided into four priorities from high to low: The I frame packets have highest priority, P frame packets have high priority, lost I frame packets that need retransmission have low priority, and lost P frame packets that needs retransmission have lowest priority. Figure 4 shows the packet storage and transmission process at a sensor node. When a node generates or receives a data packet, the packet is moved to the I-Frame packet queue or P-Frame packet queue depending on its attribute. These two queues are used to store the unconfirmed packets temporarily, waiting for the retransmission if needed. I frame packets are of significant importance in supporting the real time traffic. Therefore, we give the maximum priority to I frame packets, because our main aim is to protect I frame packets. If I-frame packet is lost, it goes into lost I frame queue, and if a P frame packet is lost it goes into lost P frame queue. In our experiments, in order to deal with traffic classes with different priorities in an efficient way, weighted round-robin scheduler is used [42]. In the weighted round-robin scheduler, we allocated a weight \(w_{i}\), \(i\in\{I\;Frame,\;P\;Frame\}\), that is, it is assigned \(w_{i}\) slots during each round. A traffic source with a higher weight receives more network bandwidth than a traffic source with less weight.
# Efficient QC-LDPC Encoder for 5G New Radio
## 2 5G NR QC-LDPC Codes  
The NR access technology marks a transition in FEC coding for the 3 GPP of cellular technologies [22]. In this section, the QC-LDPC codes are reviewed, and the characteristics of standard 5G QC-LDPC codes are summarized. In addition, procedures are presented for the construction of the parity check matrix of the target LDPC codes.
## 2 Literature Review and Assumptions
### Linear Structural Relation (LISREL) Model  
The LISREL model is a structural equation modeling (SEM) analysis that was developed by [PERSON] and [PERSON] [33] in 1993. The purpose of this model was to investigate the correlation between multiple variables and single variables. The members of the LISREL model include covariance structure analysis, latent variable analysis, confirmatory factor analysis, LISREL analysis. The SEM integrates multiple regression and factor analysis and can apply to the analysis of the relationship between correlated dependent variables [34]. A structural equation includes two types of variables which are respectively latent variable and manifest variable. Social behavioral science deals with variables that cannot be directly observed and they belong to latent variables. The effect of a latent variable can only be reflected by the observed variables, which belong to the manifest variables. The theoretical structure of the LISREL model includes the structural model and the measurement model [35].  
For the estimation of model parameters, [PERSON] and [PERSON] [33] proposed the maximum-likelihood approach for the LISREL model. This approach is different from the typical approach of least squares for path analysis. The LISREL applies the maximum-likelihood to estimate the parameters and the approach is to compare the goodness of fit of the variance matrix of the model and the variance matrix that is derived from the sample data. It can also be used to estimate the chi-square and the degree of freedom. If the chi-square is significant, it indicates a significant difference between the sample data and the model structure, which means the proposed model is not suitable for the real observed data. However, since the chi-square is very sensitive to the sample size, it tends to generate results with significant differences. Therefore, it is not enough to determine whether a model presents the goodness of fit from the chi-square. Instead, the common rule includes the ratio of the chi-square to the degree of freedom. A ratio that is less than five (or three, is even better) can serve as a criterion whether the model fits. In addition, there are other studies that proposed different models with the goodness of fit for researchers to determine whether a model is good such as GFI, AGFI, CFI, RMS, NFI, and NNFI [34]. Therefore, we applied the LISREL model to the analysis in this study.
## 2 Materials and Methods  
### Data Source  
GPS data provided by the IGS and the POLENET (Polar Earth Observing Network) were used in this study. Figure 1 shows the geographical locations of GPS tracking stations in the polar regions, including the IGS stations (blue dots), the POLENET stations (red dots), and the verification stations (green circles) which are used for verifying the ionospheric TEC models. In the Arctic, there are 46 IGS stations available, while there are 10 IGS stations and 44 POLENET stations in the Antarctic. Most POLENET stations have provided measurements since 2010. Before 2010, only a few stations with non-uniform distribution were available in the Antarctic, which were insufficient to map the distributions and variations of the TEC; as a consequence, the modeling of the ionospheric TEC in the Antarctic region started only from 2010. The sampling rate of the GPS measurements was 30 s. The satellite positions were calculated according to the broadcast ephemeris file (brdc) provided by the Crustal Dynamics Data Information System (CDDIS).  
Figure 1: The geographical locations of global positioning system (GPS) tracking stations, including the International GNSS (global navigation satellite system) Service (IGS) stations (blue dots), the Polar Earth Observing Network (POLENET) stations (red dots), and the verification stations (green circles) in the Arctic (**left**) and Antarctic (**right**).