esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling
### Model Calibration and Validation  
#### 3.3.1 Model Calibration  
In PFC, due to the lack of a direct correspondence between microscale parameters and macroscopic mechanical behavior, a trial-and-error method is commonly employed for calibration. This involves continuously adjusting microscale parameters by comparing the macroscopic mechanical parameters obtained from numerical simulations with those from laboratory experiments, including uniaxial compressive strength, elastic modulus, peak strain, and failure modes, to match the results of PFC simulations with laboratory experiments [39]. Based on the mechanical performance relationship between different  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Specific heat capacity/(J kg\({}^{-1}\) K\({}^{-1}\)) & 1015 \\ \hline Thermal conductivity/(W m\({}^{-1}\) K\({}^{-1}\)) & 3.5 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Thermal expansion \\ coefficient/(K\({}^{-1}\)) \\ \end{tabular} } & quartz & \(24.3\times 10^{-6}\) \\ \cline{2-3}  & plagioclase & \(8.7\times 10^{-6}\) \\ \cline{2-3}  & potassium feldspar & \(14.1\times 10^{-6}\) \\ \cline{2-3}  & mica & \(3.0\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal properties of granite [36].  
Figure 4: PFC\({}^{\text{2D}}\) schematic of thermal calculation [35].  
mineral crystals in granite (quartz > feldspar > mica > intergranular), and referring to the range of microscale parameter values used in previous studies, different parameters are assigned to intragranular and intergranular contacts [27; 28]. At room temperature (20 \({}^{\circ}\)C), with a loading speed set to 0.05 m/s, the elastic modulus and bonding strength within and between mineral crystals were adjusted. Suitable parameters were chosen for the uniaxial compression test. The stress-strain curve obtained from simulation was compared with the results from laboratory experiments (Figure 5), revealing a good agreement. Additionally, both exhibited a distinct X-shaped failure pattern, indicating the effectiveness of the parameter calibration.  
To better illustrate the trend of stress-strain behavior with temperature variations, it is necessary to readjust the elastic modulus and bond strength within and between mineral crystals to match the mechanical properties of granite at different temperatures. Typically, the aforementioned process needs to be repeated several times until reasonable microscopic parameters are obtained. The final calibrated results are presented in Tables
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.7 Rate of Change of Frequency over Power Method (ROCOFOP)  
This approach measures the df/dp, where dp is the load power, to identify islanding. The df/dp is more sensitive than the rate of frequency over time when the load and DG have a slight power mismatch. As a result, this approach works effectively when there is a slight power imbalance between the load and the DG [15].
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Carbon to Nitrogen (C/N) Ratio  
In order to optimize biogas production, introducing substrates with an optimal C/N ratio is essential for the entire process and basically for providing sufficient and adequate nutrients levels for microorganisms [133]. Higher concentrations of ammonia are formed with low C/N values, and eventually this will result in hindrance of microbial growth. On the other hand, during the fermentative stage, a C/N ratio above the optimal value will lead to production of large amounts of VFAs. Furthermore, the C/N ratio can reduce the TAN level only if the TAN concentration is slightly more than the threshold inhibitory level; action should be taken promptly, before the inhibition of the process [6].  
Table 2 provides the C/N ratios of different substrates. The optimal C/N ratios of various substrates attained from different AD processes will likely be different, and the AD process is more stable when the C/N ratio ranges from 20 to 30 [14; 134]. During the anaerobic codigestion process, substrates are added to maintain the C/N proportion within digesters [135]. To unify a specific range, including the existing carbon of the easily degradable part and excluding the carbon that is not specifically affected by microorganisms, an available C/N ratio is proposed [126]. By maintaining the C/N ratio at 17:1 [136], enhanced methane generation by around 3.8- and 1.5-fold was achieved compared with perennial ryegrass alone and waste activated sludge (WAS) alone, respectively.  
Additionally, it is worth mentioning that organic wastes used for biogas production are generally rich in lignocellulosic-type resistant materials [14]. Thus, special pretreatments (Section 3) are required to utilize such wastes under short retention times with anaerobic organisms [138]. Therefore, significant amounts of \"invalid carbon\" in these materials affect the calculation of C/N. Thus, C/N calculation shows only the general characteristics of organic waste materials, not the actual substances utilized by anaerobic microbes.
# Rebar Fabrication Plan to Enhance Production Efficiency for Simultaneous Multiple Projects
## 3 Problem Definition  
The three steps that make up the rebar production process are shown in Figure 2: (1) importing, (2) cutting, and (3) bending. In the rebar fabrication plant, the required length and shape are calculated according to the strength and diameter of the rebar through design drawings. The received raw steel is processed according to the preceding process (Figure 2), the quality of the processed rebar is checked, and it is then shipped to individual sites. Although this process is straightforward, the loss rate is challenging to minimize because several variables associated with the (1) raw steel, (2) machine, (3) rebar shape, and (4) hoist must be considered. Additionally, we first set the variables that have a decisive effect on reducing the loss rate and necessary time. Firstly, the reason why time is considered the most important among these is that the model built is based on a discrete-event simulation. Secondly, considering the priority, time-related variables were set as major variables.  
Imported raw steel is typically 8 m or 10 m long. With the increasing complexity of construction projects, the required lengths of the rebars at sites vary. The steel that is left over after fabrication is regarded as waste, which raises the project's loss rate or reduces the amount of raw materials accessible for other projects. As a result, plants frequently produce rebar in accordance with requests from several projects at once to reduce the loss rate.  
As indicated in Table 2, rebar fabrication involves various types of machines for (1) transporting, (2) cutting, and (3) bending. The cutting machine typically cuts 18 or 24 rebars with a 10 mm diameter at once, or 13 or 21 rebars with a 13 mm diameter at once. There may be a slight delay because the bending machine normally bends five to nine rebars with a diameter of 10 mm, and three to seven rebars with a diameter of 13 mm at a time. Third, the rebar shapes to be fabricated vary across projects, which changes the machine capacities. For instance, the diameter of the raw steel affects how many rebars are produced simultaneously. Finally, depending on the transportation site and rebar shape, the hoist's maximum capacity fluctuates during the rebar transportation operation.  
Therefore, by combining numerous locations to gather and process various types of rebars at once, a production plan must be designed that can decrease the loss rate that happens while processing raw steel with a limited length. This framework can also help ensure rebar quality. Notably, no specific standard exists for processing rebars to be delivered to multiple sites simultaneously. The processing of rebars typically relies on personnel experience. In such scenarios, the loss rate cannot be effectively minimized. In order to determine the loss rate, necessary processing time, and ideal production plans, a simulation model was created in this study based on the basic rebar fabrication process.  
Figure 2: Processing system for rebar fabrication.
# Electromagnetically Induced Transparency in All-Dielectric U-Shaped Silicon Metamaterials
## 3 Results and Discussion
Figure 5a clearly shows that there are two frequency segments in the transmission phase spectrum, which are caused by anomalous phase dispersion and normal phase dispersion respectively [38]. The corresponding group index can reach 2035 due to strong dispersion of the transmission phase, which is shown in Figure 5b. The maximum optical delay indicated in Figure 5c is 0.75 ps in the transmission window. These values indicates that the proposed Si metamaterial can be used in efficient slow-light devices. Figure 4: Simulated transmission spectra with different incident polarization angles. Figure 5: Simulated transmission phase shift (**a**), group index (**b**), and group delay (**c**) of the dielectric metamaterial with geometrical parameters are the same as shown in Figure 1b. Optical sensing is also a potential application of the EIT metamaterial. When the refractive index of the surrounding medium changes (such as different liquids), the transmission peak tends to shift. Figure 6a shows the simulated transmission spectra of the designed dielectric metamaterial which immersed in different media with refractive index ranging from 1 to 1.1. It is clear that the peak position shifts towards the long wavelength despite the index changes very small. The figure-of-merit is an important parameter used to evaluate the sensing property and is determined as [39]  
Figure 2.0  
where \(\text{S}=\delta\lambda/\delta\text{n}\) is the sensitivity towards the environment refractive index, and \(\Delta\lambda\) is the resonance linewidth of the EIT-like transmission window. A linear fit between the wavelength shift of the transmission peak and the external refractive index is shown in Figure 6b, and the slope of the linear fit gives a sensitivity \(S\) of 203 nm/RIU. The corresponding FOM is 29 and is higher than Fano-resonant plasmonic sensors in the previous work [40; 41; 42].
# Train Speed Estimation from Track Structure Vibration Measurements
## 4 Verification of the Train Speed Estimation Method
### Discussions
According to previous studies [27; 28; 29; 30; 31], it can be deduced that it is quite possible to estimate train speed from the vibration measurements of both ballastless track and ballast track with this method. Second, the proposed method needs all the passing trains to have the same geometric dimensions.
###### Abstract  
The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency.  
offshore wind energy; installation planning; optimization; Model Predictive Control; Mixed-Integer Linear Programming; decision support; scheduling; resource restrictions
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Application of the Prediction Model
#### 3.3.2 Developing the Predictive Models  
For the prediction method, we used a deep-learning algorithm that follows the following steps:  
1. Accept the training and testing dataset--Table 1 is an example of the training and testing dataset required to input the predicting system.
2. Execute the prediction deep-learning algorithm--The input and output of the prediction procedure: \[\begin{array}{l}X_{m\times n,d,t}\to Y_{m^{*}\times 1,d,t+\Delta t}\left(train\ file\right)\\ X_{\tilde{m}\times n,d^{*},t}\to Y_{\tilde{m}^{*}\times 1,d^{*},t+\Delta t} \left(Test\ file\right)\end{array}\]  
In this section, we denote \(X_{m\times n,d,t}\), a dataset of size \(m\) with \(n\) features collected on day \(d\) and at time \(t\). \(Y_{m\times 1,d,t}\) is the class labels associated with \(X_{m\times n,d,t}\). Therefore, the \(\Delta t\) defines a time variation given a dataset \(X_{m\times n,d,t}\) known for \(d\) and \(t\).  
The following four steps comprise the proposed estimation procedure:  
Collect two samples of data, \(X_{m\times n,d,t}\) and \(X_{m\times n,d,t+\Delta t}\), \(\Delta t>0\);  
Compute a new training set \(Z_{m\times n+1,d}\), using \(X_{m\times n,d,t}\) and \(Y_{m\times 1,d,t+\Delta t}\) by concatenation, that is \(Z_{m\times n+1,d}=[X_{m\times n,d,t};Y_{m\times 1,d,t+\Delta t}]\);  
Collect the test set \(X_{\tilde{m}\times n,d+\Delta t,t}\) (\(\Delta d>0\));  
Compute \(Y_{\tilde{m}\times 1,d+\Delta d,t+\Delta t}\) using the machine-learning algorithm on \(Z_{m\times n+1,d}\) and \(X_{\tilde{m}\times n,d+\Delta d,t}\).
## 2 Material and Methods
### Software Design
#### 2.2.5 Climate Control Application  
A set of open loop and closed loop controllers, as shown in Figure 10, were implemented in Simulink using the application development framework. These constitute PID controller for  
Figure 9: The three user interfaces: (**a**) monitor dashboard; (**b**) diagnose dashboard; and (**c**) control dashboard. They are configured in ThingsBoard platform. Monitor and diagnose dashboards are configured using pre-existing widgets and the control dashboard uses the widgets generated by the application development framework.  
temperature control; hysteresis controller for humidification and CO2 concentration; circadian generator for LED light spectrum; and an additional cyclic controller for CO2 concentration.  
Circadian Rhythm Generator  
The chamber does not include sensors for measuring the radiant flux. Therefore, any disturbance in this flux due to external source cannot be measured and is not compensated. If this disturbance is avoided, the light spectrum, and thus the spectral power, inside the chamber can be individually varied and the necessary radiant flux can be achieved. To achieve this, a configurable open-loop controller for circadian rhythm generation was designed. This uses a sinusoidal function to generate the base ON amplitude signal  
\[A_{\text{ON}}(t)=\sin\left(\pi\frac{(2t-t_{\text{offset}})}{2t_{\text{on}}} \right), \tag{1}\]  
where \(t_{\text{on}}\), \(t_{\text{off}}\), and \(t_{\text{offset}}\) are the ON, OFF, and offset times in seconds (s). Using the given set-points for minimum and maximum percentage radiation power, the input for individual LED channels are generated as  
\[u_{\text{I}i}=\begin{cases}I_{\text{mini}}+I_{\text{maxi}}A_{\text{ON}}(t)&t \in[\frac{t_{\text{off}}}{2},\frac{2t_{\text{on}}+t_{\text{off}}}{2}]\\ I_{\text{mini}}&\text{otherwise}\end{cases}, \tag{2}\]  
where \(I_{\text{mini}}\) and \(I_{\text{maxi}}\) are the minimum and maximum spectral power in percentage (%) of the \(i\)th LED color channel and \(i=1,2,\ldots\), 6 corresponds to white, red, green, blue, far-red, and far-blue, respectively.
## 3 Methods  
### Reference and Test Sample  
In this study, a reference sample and test a sample with artificial defects were applied. Each PEEK sample was processed by purchasing commercially available materials. The reference sample was the same size and thickness as the test sample and had no defects. The test sample applied in this study was a 10 mm thick square plate sample made of PEEK material. Figure 3 shows the geometric details of the subsurface defect sample. The PEEK test sample size was 220 \(\times\) 220 mm, and an artificial defect was simulated on the back side. The diameter of the designed defects ranged from 2 mm in diameter to 20 mm in 2 mm intervals, and the defect depth ranged from 5 mm to 9 mm in 1 mm intervals. Each row (A to E) of the test sample had the same defect depth, and each column (1 to 10) had the same defect size. Many defect conditions were simulated in this study because when reviewing the types of actual defect examples, defects with various sizes and different depths occur. Therefore, the purpose was to precisely analyze the level of defect detectability when applying thermal imaging non-destructive testing to PEEK material. Table 1 shows thermomechanical properties of the PEEK material utilized in this study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Density** & **Thermal Conductivity** & **Heat Capacity** \\  & \(\rho\) (kg/m\({}^{3}\)) & \(k\) (W/m-K) & \(C_{p}\) (J/kg\(\cdot\)K) \\ \hline Polyetheretherketone & & & \\ (PEEK) & 1300 & 0.25 & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Thermomechanical properties of PEEK materials.
## 5 CGE-SSA Multi-Objective Optimization
### Improved Sparrow Algorithm
#### 5.2.3 Dynamic Adaptive Weight Improvement Follower with Uniform Distribution  
During the optimization process for the traditional SSA, defects such as weak global search ability, weak local development, and easily falling into local optimization result in insufficient search accuracy. Therefore, drawing on the traditional idea that inertia weights can improve an algorithm's global search capability [35], uniformly distributed dynamic adaptive weights are proposed to coordinate the algorithm's global search and local exploitation capabilities. The improved follower position update formula is as follows:  
\[X_{i,j}^{t+1}=\left\{\begin{array}{l}\beta\cdot Q\cdot\exp\left(\frac{X_{ours }^{t}-X_{i,j}^{t}}{\hat{r}^{2}}\right)i>\frac{n}{2}\\ X_{p}^{t+1}+\left|X_{i,j}^{t}-X_{p}^{t+1}\right|\cdot A^{+}\cdot L\leq\frac{n} {2}\end{array}\right. \tag{9}\]  
\[\beta=\delta\left(\beta_{initial}-\left(\beta_{initial}-\beta_{final}\right) \frac{1}{\exp(1)-1}\right)\left(\exp\left(\frac{t}{T}\right)-1\right) \tag{10}\]  
where \(X_{p}\) is the optimal position found via the sender; \(X_{ours}\) is the worst position found via the discoverer; \(\beta\) is the dynamic weight; \(\delta\) is a uniformly distributed random number from 0 to 1; \(\beta_{initial}\) and \(\beta_{final}\) are the initial and ending values of the weight, which are taken as 1 and 0, respectively; and T is the maximum number of iterations.  
In Figure 10, it can be seen that, in the pre-search phase of the algorithm, the dynamic weight is high, and the follower performs a global search using Equation (9). As the number of iterations increases, the value of dynamic weight becomes smaller, and the follower performs local exploitation. It is concluded that dynamic adaptive weighting improves the algorithm's global search and local development capabilities.  
Figure 9: Function \(f_{10}\) iteration curve.
## 1 Introduction
Such a system does not seem to be significantly affected by occlusion problems, as it ensures body tracking even when several body joints and segments are temporarily occluded, so that only a portion of the body is framed in the video [18]. Several studies validated its accuracy by comparing one person's skeleton tracking results with those obtained from a Vicon system. All found a highly negligible relative limb positioning error index [45; 46]. Many studies exploited OpenPose for several research purposes, including Ergonomics. In particular, several studies carried out both in a laboratory and in real life manufacturing environments, suggest that OpenPose is a helpful tool to support worker posture ergonomics assessment based on RULA, REBA, and OCRA [18; 47; 48; 49]. However, deep learning algorithms used to enable people tracking using RGB images usually require hardware with high computational requirements, so it is essential to have a good CPU and GPU performance [50]. Recently, a newer open-source machine learning pose-estimation algorithm inspired from OpenPose, namely Tf-pose-estimation [51], has been released. It has been implemented using Tensorflow and introduced several variants that have some changes to the CNN structure it implements so that it enables real-time processing of multi-person skeletons also on the CPU or on low-power embedded devices. It provides several models, including a body model variant that is characterized by 18 key points and runs on mobile devices. Given its low computational requirements, it has been used to implement mobile apps for mobility analysis (e.g., Lindera [52]) and to implement edge computing solutions for human behavior estimation [53], or for human posture recognition (e.g., yoga pose [54]). However, as far as we know, the suitability of this tool to support ergonomic risk assessment in the industrial context has not been assessed yet. In this context, this paper introduces a new low-cost and low-computational markerless motion capture system, based on frame images from RGB cameras acquisition, and on their processing through the multi-person key points detection Tf-pose-estimation algorithm. To assess the accuracy of this tool, a comparison among the data provided by it with those collected from a Vicon Nexus system, and those measured through a manual video analysis by a panel of three expert ergonomists, is performed. Moreover, to preliminary validate the proposed system for ergonomic assessment, RULA scores obtained with the data provided by it have been compared to (1) those measured by the expert ergonomists and (2) those obtained with data provided by the Vicon Nexus system.
## 7 Research Challenges, Open Issues, and Future Directions
### Enabled Network Intelligence  
In the future, cars will be equipped with many sensors, and the edge cloud will gather data and preprocess it before sharing it with other portions of the network. In such a network, incoming data packets are classified based on their importance, e.g., time-critical events and non-real-time events. Advanced Federated Learning and Blockchain algorithms must preprocess the incoming data and efficiently spread it accordingly.  
**Scalability:** The implementation of blockchain and federating learning in the VANET environment produce different scalability issues. Blockchain is a decentralized technology [168; 169]. Therefore, its applications should be implemented on the underlying medium. Blockchain and federating learning cannot be implemented if the scalability and performance of the systems are not significant and sufficient. Based on decentralization and data security, the blockchain scalability issues are categorized into the following different aspects: performance limitations, network delays, and consistency. For ensuring blockchain security, end nodes required transaction data consensus [170].  
**Collaboration between Off-Chain and On-Chain Data Storage:** The two main ways of data storage, blockchain and traditional information-based, are used in VIoT [171]. The performance of blockchain can be improved with the help of computing systems and off-chain storage. On the other hand, blockchain systems confirm the data's credibility and safe sharing from traditional information systems [172]. Although all of the above required combining traditional information methods and blockchain technology, data consistency and relevancy are the most critical points from the off-chain and data on-chain perspectives [173].