esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### VLSI Implementation  
Considering the IEEE 754 standard for floating-point numbers [21], 11-, 16-, 24-, 32-, 53-, and 64-bit multipliers were designed. First, the implementation was performed using the VHSIC hardware description language (VHDL), where the acronym VHSIC refers to a very-high-speed integrated circuit [30]. They were then synthesized using Alliance open-source EDA tools [20].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{y_{2k+1}}\) & \(\mathbf{y_{2k}}\) & \(\mathbf{y_{2k-1}}\) & \(\mathbf{b_{k}}\) & \(\mathbf{PP_{j}}\) \\ \hline
0 & 1 & 0 & +1 & +X \\
0 & 1 & 1 & +2 & +2X \\
1 & 0 & 0 & \(-2\) & \(-2X\) \\
1 & 1 & 0 & \(-1\) & \(-X\) \\
1 & 1 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Radix-4 Booth encoding.  
Figure 5: An 8-bit radix-4 Booth multiplier.  
In contrast to commercial design flows, which typically generate layouts tailored to specific technology nodes, Alliance produces symbolic layouts. This abstraction enables design portability across different technological nodes by decoupling the layout from any manufacturing process. In this methodology, the layout is first drawn using an abstract unit known as \(\lambda\) (lambda). Subsequently, the value of \(\lambda\) is determined for the target technology to ensure compliance with its specific design rules. However, Alliance tools are currently not suitable for advanced deep submicron technologies, specifically those with feature sizes below 180 nm [20]. This approach offers notable advantages such as the ability to share and publish symbolic layouts without requiring non-disclosure agreements. However, this method has several limitations. One is the inability to utilize the most advanced features of the target process node, which results in less area-efficient layouts.  
The synthesis process started with the VHDL analyzer for synthesis (VASY), which converts the RTL design written in VHDL into an understandable language by Alliance. Subsequently, the Boolean minimization (BOOM) tool was executed, which optimized and converted the VASY instructions into simple Boolean equations. The equations were then analyzed with the binding and optimizing on gates (BOOG) tool to obtain the equivalent function using the standard cells provided by the SXLIB library. Additionally, the tool generates a schematic circuit. Subsequently, the circuit was simulated using a simulation tool (ASIMUT) to verify its design. After that, the local optimizations of nets (LOON) tool was used to optimize the critical path by introducing buffers and reducing capacitance. The placer for standards cells (OCP) tool was then used to place the standard cells and establish the physical inputs and outputs. Subsequently, the cell transistors were interconnected with the negotiating router (NERO) tool. Consequently, the circuit layout was obtained. Next, the logical versus extracted (LVX) tool generated a list of nodes to compare the layout with the structural file using COUGAR. Finally, the layout was scaled with the symbolic to real (S2R) tool through a configuration file based on 350 nm CMOS technology.
# Theoretical Description of Carbon Felt Electrical Properties Affected by Compression
## 2 Methodology of Experimental Research
### Characteristics of CF  
In this work, rayon-based CF material made from high-quality carbon fibers, acquired from Mersen Corp (Mersen, Greenville, MI, USA) [33], was used. A micrograph showing the structure of the CF is depicted in Figure 1. Additional characteristics of this CF are tabulated in Table 1[33].  
A special cell for testing was fabricated (Figure 2); the design allows measurements of electrical conductivity for compressed CF. Cell dimensions (Figure 3) for felt samples were of \(30\times 100\) mm. Each felt sample was placed between two identical thin copper sheets of a similar size to that of the felt sample. These copper sheets are placed below and over CF as it seems in Figure 2. Felt thicknesses, denoted as h, diminished from 6.6 to 1.2 mm by tightening four screw nuts. The uniformity of the felt thickness is measured by an electronic caliber having 0.01 mm accuracy.  
Electrical conductivity was measured between copper sheets simulat  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Fiber Diameter, \(\mu\)m**} & **Average** & **Standard Deviation** \\ \cline{2-3}  & **19.2** & **1.66** \\ \hline Felt density, [kg/m\({}^{3}\)], \(\rho_{\mathrm{f}}\) & 88 & - \\ Carbon density, [kg/m\({}^{3}\)], \(\rho_{c}\) & 1954 & - \\ Porosity, [\%], \(\lambda\) & 95.5 & - \\ Relative carbon volume, p.u. [\%], V & 0.045 (4.5\%) & - \\ Specific felt surface, [m\({}^{-1}\)], S & 9.8\(\cdot\)10\({}^{3}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of a CF.  
Figure 1: A magnified picture of CF structure.  
Figure 3: Dimensions of the experimental cell (from [21]).  
Figure 2: Special experimental cell for conductance measurements.  
The electrical conductivity of compressed CFs was initially measured in dry conditions. This was then repeated after immersing CFs in three different liquids of dissimilar electrical permittivity including glycerol, ethanol (96% strength) and cyclohexane. Conductivity estimation was provided by a bench-type four-probe R/L/C METER IX 3131B (Metrix Co., Dubuque, IA, USA) able to measure resistance (R\({}_{\text{ac}}\)) by applying 100 Hz, 120 Hz, 1 kHz, and 10 kHz AC frequencies with an error of \(\sim\)0.6%. In addition, the resistance (R\({}_{\text{dc}}\)), by applying DC voltage, was measured by a Fluke multimeter.
## 2 Materials and Methodology
### Effect of Relative Humidity (40%, 50%, and 60%)
#### 3.3.2 Recycled Concrete Aggregates (RCAs)  
The results of the three RCAs (RCA-RP1, RCA-RP2, and RCA-IW1) studied are presented in Figure 12. In RCA-RP1 and RCA-IW1, the decrease in RH led to a decrease in the CO\({}_{2}\) uptake content, while in RCA-RP2, the CO\({}_{2}\) uptake values were very similar in the three different conditions. In RCA-RP1 and RCA-IW1, the highest value of CO\({}_{2}\) uptake was registered at 60% RH after 12 h of forced carbonation (12.9% and 34.8%). With the reduction in RH to 50% and 40%, the CO\({}_{2}\) uptake value of both aggregates also decreased: to 4.0% and 4.7% for RCA-RP1, and to 18.7% and 11.9% for RCA-IW1.  
The predominant effect of the variation in RH identified in the carbonation of RCA was a decrease in CO\({}_{2}\) uptake. As previously mentioned, water content is essential for the carbonation reaction, since CO\({}_{2}\) is first dissolved (solvation) in water, forming carbonic acid. Then, the carbonic acid is dissociated by reacting with calcium cations, forming calcium carbonate. However, a decrease in the water content will lead to incomplete reactions, leaving some unreacted carbonatable minerals in the RAs [56], impacting the efficiency of CO\({}_{2}\) capture and the formation of calcium carbonate minerals (lower CO\({}_{2}\) uptake). In RCA-RP2, the CO\({}_{2}\) uptake remained similar at 60%, 50%, and 40% RH (5.2%, 6.9%, and 4.3%, respectively), indicating that the reduction had less impact in this aggregate.  
Figure 11: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO\({}_{2}\) uptake was achieved for MRAs at 60, 50, and 40%.  
Figure 12: Weight loss due to decarbonation before and after forced carbonation, considering the time at which maximum CO\({}_{2}\) uptake was achieved for RCAs at 60, 50, and 40%.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 4 LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES
### Covariance Matrix Adaptation Evolution Strategy (CMA-ES)  
Among its many variants, CMA-ES can solve various types of optimization problems efficiently. In CMA-ES, a multivariate normal distribution is used to model the search space. New individuals are produced using Gaussian distribution that accounts for the population's path over successive generations. CMA-ES automatically adapts the mean vector \(m\), covariance matrix C, and step size \(\sigma_{c}\). CMA-ES steps are summarized according to [26]:  
1. Generate an initial population and then calculate the fitness function.
2. Gaussian distribution is used to produce new individuals, thus: \[D_{k}=N\Big{(}T,\sigma_{c}^{2}C\Big{)}\ \forall\ i=1,\ldots,n\] (23)
3. Update \(m\) using the best \(\mu\) individuals according to \(T=\sum_{k=1}^{\mu}W_{k}\ D_{k}\), where \(\sum_{k=1}^{\mu}W_{k}=1\) and \(W_{1}\geq W_{2}\geq\cdots\geq W_{\mu}\).
4. Update \(\sigma_{c}\) and C.
5. Steps 2 and 3 are repeated until a stop criterion is met.
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 4 Discussion  
In this paper, we have proposed an analytical model that can be used to evaluate the behavior of an e-HARQ prediction based on LDPC subcodes of various lengths and other LLR-based predictors used in 5G transmissions. The model covers parallel processes of e-HARQ and HARQ, and clearly demonstrates the benefit of the combined e-HARQ and HARQ mechanism in comparison with the traditional HARQ in terms of main performance measures, e.g., by constraining the FP and FN probabilities to \(10^{-1}\), we can achieve around 20% and 7.5% of gain at FN = \(10^{-3}\) in terms of a mean spending time until successful delivery. It also provides an efficient and fast tool to analyze the way the system behaves under a range of cost functions aimed at finding optimal parameters for an e-HARQ prediction operation. The given analytical model in compliance with the optimization analysis can be efficiently used by network designers to get a quick estimate of the threshold value used for classification when designing an e-HARQ mechanism without the need to spend considerable time for simulation studies or expensive experimental setup. This work does not take into account scheduling effects due to the presence of multiple users in the network, which is currently under investigation.  
Conceptualization, C.H., B.G., K.S., T.S. and T.R.; methodology, T.R.; software, T.R., B.G.; validation, T.R., B.G. and C.H.; formal analysis, T.R.; investigation, T.R.; writing--original draft preparation, T.R.; writing--review and editing, B.G., T.R.; visualization, T.R.; supervision, C.H. and K.S.; project administration, C.H.; funding acquisition, C.H. and T.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors are sincerely grateful to the reviewers for their helpful comments, which have significantly improved the quality of the paper. The authors declare no conflict of interest.
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Assessment of Drought Hazard and Vulnerability
#### 2.6.2 Weights and Ratings for Evaluating Drought Potential  
Based on the cumulative distribution function of SPEI, each drought severity category was assigned with a particular weight from 1 to 4 by considering the SPEI intervals (Table 1). In detail, the mild (M), moderate (MO), severe (S), and extreme (E) classifications were defined the weights (W) of 1, 2, 3, and 4, respectively, in which a high value indicated a high drought intensity. Thereafter, each weighted category was further divided into four rating scores (R) ranging from 1 to 4 using the Jenks Natural Breaks classification method in ArcGIS 10.5, in which the order is increased through evenly dividing the interval of the cumulative probabilities for each drought range (see Figure 2) (note: the Jenks Natural Breaks method creates ranges based on the average of each range to distribute the data more evenly across the ranges, in which the data values within each range are also fairly close together [29]).
## 3 Methodology to Develop and Execute a Redispatch Model in Real-Time Operation  
The methodology is executed in real time with a redispatch model that is designed to be tested and applied in any standard test power system and is even adaptable for a real power system of any country. This redispatch methodology is applied in real-time operation  
Figure 2: Descriptive statistical graph showing which countries in Europe, South America, and North America are performing or developing real-time redispatch models.  
through the development of a sequence of theoretical foundations and implementation of mathematical equations, which optimally guide the system operator in the technical and economic actions, during the entire time horizon of higher generation uncertainty and that differentiate it from the rest of the traditional unit commitment and redispatch models.  
Mathematically, the redispatch model methodology performs a multinodal optimization for any electrical system, including busbars, lines, transformers, substations, and generators, by means of an objective function that minimizes operating, start-up, outage, and non-supplied energy costs. The redispatch results are obtained with fast convergence times and help the system operator to make timely decisions. Finally, the structure, modeling, and implementation of the redispatch model are novel compared to the classical static economic dispatches and the economic merit list. The redispatch differs by integrating unique technical and economic constraints, such as the polynomial modeling of the heat rate for thermal power plants, modeling of gas volume stock and hydro reservoirs, uncertainty of solar-wind renewable energies, and the maximum dispatch power that avoids underfrequency load shedding.
# Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
## 2 Materials and Methods
### Five-Dimensional Model
#### 2.1.2 Physical Entity of the Log Cutting System  
The physical entity refers to the actual log rotary-cutting line in the physical workshop, including equipment such as the log centering machine, log debarker, and veneer peeling machine. Additionally, the physical entity of the log rotary cutting system includes various sensors that support the collection and transmission of information such as the log point cloud data, motor speed, and status information. The log rotary cutting system is capable of uploading data and information, as well as executing instructions. It provides support for real-time interconnection and iterative interaction between physical and virtual entities.  
In this study, the physical entity specifically refers to the log-centering machine experimental platform (Figure 3). The platform consists of a head-and-tail card shaft mechanism for log clamping, with a motor driving the rotation through a synchronous belt pulley. Control is implemented using a hierarchical mode with an upper computer and a lower computer. The lower computer, an embedded terminal, communicates with a laser ranging sensor via a UART serial port to collect depth information concerning the log segments. It also communicates with a motor servo driver via a CAN bus to control the motor speed and obtain rotation angles. The embedded terminal and upper computer communicate through RS485 serial communication. Sensor data, identification numbers, and log rotation angles are sent from the embedded terminal to the upper computer for processing, enabling virtual communication between the physical entity and the virtual model.
# Heuristic Method for Decision-Making in Common Scheduling Problems
## 5 Characteristic of the Algorithm Class That Generates the Final Parts of the Trajectory
### Generating a Single Trajectory  
The presented method consists of consecutive construction trajectories, starting from the initial state \(s_{0}=(x_{0},t_{0})\) for whole trajectories or chosen state for the final part of the trajectory.  
The trajectory generation process using the algebraic-logic model as follows. In each newly designated process state \(s\) a decision has to be chosen from a set of possible (sensible) decisions in the given state. Then, for the given state and chosen decision a new process state \(s^{\prime}=(x^{\prime},t^{\prime})\) is determined, with both the process of the proper state and the corresponding moment of time. They are calculated using the transition function of the process \(f(x,t,u)\). If the new state belongs to the set of goal states \(S_{G}\), the generation of the trajectory is completed successfully and its assessment can be made. If the new proper state or the corresponding moment of time does not meet the limitations, then this process state belongs to the set of non-admissible generalized states \(S_{N}\). Then the trajectory generation is stopped (the trajectory is non-admissible). Figure 1 shows single trajectory constructing.  
Figure 1: Single trajectory constructing.
## 2 Materials and Methods
### Density Analysis for Extracting Main Maritime Traffic Routes
In addition, \(V_{n}\) denotes an attribute value of a vessel of \(n\), and \(V_{n+1}\) denotes an attribute value of a vessel of \(n+1\). Ship density analysis is used and applied in a variety of contexts. High and low levels of traffic can be identified in the relevant sea area as a measure of the vessel's traffic volume. The density analysis result of 60 m or more, excluding small ships and fishing vessels, is a suitable method for selecting the main route rather than analyzing the routes in all sea areas.
## 3 Results and Analysis
### Analysis of the Influence of Water Injection on Settlement and Moisture Content  
Figure 10 illustrates the relationship between moisture content, settlement, and the amount of water injected at different monitoring locations of the model.  
As shown in Figure 10, both the moisture content and settlement deformation increase with increasing water injection. The most significant change in moisture content occurs at a depth of 20 cm, where the change between points II-I is the greatest, reaching 7.3%.  
Figure 10: Sedimentation and moisture content with water injection. (**a**) At a depth of 40 cm in the foundation; (**b**) at a depth of 20 cm in the foundation; (**c**) interface between the subgrade and foundation.  
Figure 9: Temperature curve.  
This is because the monitoring point is closest to the side ditch and is most affected by water flow infiltration. At a depth of 40 cm and at the interface between the subgrade and foundation, changes in the moisture content are less affected by water injection due to the soil compaction, which reduces the pore ratio and inhibits water flow spread. Furthermore, the primary direction of seepage is vertically downward due to gravity. Settlement deformation at each layer shows that the closer the horizontal distance to the side ditch, the more pronounced the settlement. The settlement at 40 cm ranges from 0.23 to 0.81 cm, at 20 cm from 0.62 to 0.69 cm, and at the interface between the subgrade and the foundation from 0.32 to 0.68 cm. The settlement at a depth of 20 cm is most strongly influenced by water injection.
## 2 Analysis Method
### Electromagnetic-Mechanical-Acoustic Coupling
#### 2.4.2 Mechanical-Acoustic Coupling  
The mechanical and acoustic domains are coupled by the diaphragm of driver and passive radiator. For the acoustic domain, the input is the velocity, which is in a direction normal for the diaphragm. The velocity is described as  
\[\dot{X}=\omega X\sin(\omega t+\varphi) \tag{7}\]  
where \(\dot{X}\), \(X\) and \(\varphi\) are the velocity, displacement, and displacement phase, respectively. By solving the standard acoustic governing equation, the sound pressure in every node of air region can be solved. The pressure is calculated in the following equations  
\[\begin{array}{l}F_{lower}=\int_{S}p_{lower}(x,y)dxdy\\ F_{upper}=\int_{S}p_{upper}(x,y)dxdy\\ F_{air}=F_{lower}-F_{upper}\\ \\ F=P_{air}+F_{current}\end{array} \tag{8}\]  
The lower part and upper part air pressure forces are defined as \(F_{lower}\) and \(F_{upper}\), respectively. \(F_{lower}\) and \(F_{upper}\) are obtained by integrating the node pressure force over the diaphragm surface. The difference between the lower part and upper part air pressures is defined as \(F_{air}\). The air-pressure force is depicted in Figure 5. In addition, a current force drives the unit, except for the air-pressure force. This current force is defined as \(F_{current}\). The total force acting on the vibration system is \(F\). It is concluded that the total force influences the mechanical velocity. Therefore, the mechanical and acoustic domains are considered to be coupled with each other.  
Based on this description, the simulation tool of the speaker box with the passive radiator is depicted in Figure 6. The electromagnetic and mechanical parameters are listed in Table 1, based on the parameter-identification method described in [13]. The inductance and force factor are calculated by electromagnetic FEM. The effective mass and stiffness are calculated by mechanical FEM. The SPL simulation results of the enclosure are obtained after the parameters are applied in the equivalent circuit. The experiment is conducted after the sample is manufactured. In the previous method, the acoustic  
Figure 5: Air region and air-pressure force.  
domain is modeled by the piston radiation model [11]. In the proposed method, the acoustic domain is modeled by finite element method which can consider the enclosure inner shape.  
According to the SPL comparison of the simulation and experimental results shown in Figure 7, the simulation tool is verified experimentally and can be used to analyze the performance of the speaker enclosure with the passive radiator. From 100 Hz to 3 kHz in the SPL curve comparison, the previous method and proposed method both can predict the SPL, because there is no standing wave in the enclosure. In addition, the wavelength of sound in low frequency satisfies the requirement of piston sound source model. After 3 k, the previous method cannot predict the SPL of the speaker enclosure with a passive radiator, whereas the proposed method can. This is because the proposed method considers the acoustic geometry, whereas the previous method cannot.