esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Probabilistic Numerical Model of SFRC Beam Flexural Behavior
### Probability Distribution of Model Parameters  
Uncertainties in the material properties are quantified using probability distributions. Table 1 presents the probability distribution types and the assumed Coefficients of Variation (CoV). Mean values of the probability distributions of selected material properties can be obtained experimentally or using Equations (22)-(28). The probability distributions and their CoVs characterizing the uncertainties in concrete compressive and tensile strength, concrete elastic modulus, steel yield strength and steel elastic modulus are adopted based on the recommendations by [PERSON] et al. [30]. It is assumed that the CoVs of material properties defined for plain concrete can be used for SFRC, as the CoVs suggested by [30] are similar or lower to the CoVs of the SFRC's material properties obtained experimentally [2; 6].  
Residual tensile strength of SFRC depends on the fiber orientation and the number of fibers crossing the crack, both of which are uncertain as they depend on the distribution of fibers along the specimen [31]. [PERSON] et al. [5] and [PERSON] et al. [6] experimentally investigated the post-cracking behavior of SFRC and quantified the uncertainties in the residual tensile strength for different crack openings, different displacement application rates, and casting directions. Based on these experimental results, a CoV of 35% is assumed for residual tensile strength. Similarly as for tensile strength, a lognormal distribution is assumed. The variance of geometric properties (e.g., concrete cover or rebar area), steel hardening ratio, fiber content and fiber aspect ratio are not considered in this study. Furthermore, the correlation between considered model inputs is not considered, as the [PERSON]' method considers models with independent input variables.
## 4 Implementation
### Proposed Scheme
#### 4.3.2 Undersampling  
When the frame rate of the camera decreases below the packet rate of the transmitter, undersampling occurs. The payload will be lost in undersampling unlike in the case for oversampling. Figure 14 shows the scenario in which the missing payload is created and detected using the SN. In this case, if the SN is long enough, the receiver can detect the missing payload. If one payload is missing, the error can be detected by comparing the SN of the two adjacent DSs which received on the receiver side. The number of SNs in different states depend on the length of SN part in each sub-packet. For example, four missing payloads of the transmitted packets can be detected if we use two bits for the SN length. When one error is detected, the error correction becomes easier. If two consecutive sub-packets have two non-consecutive sequence number values (n - 1 and n + 1), missings occur as shown in Figure 14. In our implementation below, we use the SN length of 4 bits then 16 missing payloads can be detected if the undersampling case occurs.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 1 Introduction
As PPP accuracy is independent of baseline length, improving its convergence and performance is necessary if it is to be considered as a good alternative to RTK technique. The scope of this paper therefore addresses atmospheric constraining in PPP with the aim of improving the solution accuracy and quality. There is a level of progression which can be noticed from the age of dual-frequency multi-GNSS processing through the usage of triple-frequency measurements to the application of atmospheric constraints. Specifically, the following key questions are to be answered: (1) What is the magnitude of improvements observed from the usage of traditional dual-frequency measurements to triple-frequency PPP processing with atmospheric constraints? (2) What are the inherent challenges when constraining PPP solutions with atmospheric corrections either functionally or stochastically? (3) What is the significance of PPP-AR in multi-GNSS PPP atmospheric constrained solution? (4) Finally, what are the key challenges left in obtaining near-instantaneous PPP convergence akin to RTK data processing?
## 3 Optimization of the PMPP
### Algorithms Used to Solve the PMPP in the Numerical Example
#### 3.2.2 Nelder-Mead Method  
\(\bullet\)**Algorithm Description**  
For a function of n variables, the algorithm maintains a set of n + 1 points forming the vertices of a polytope in n-dimensional space. This method is often termed the \"simplex\" method, which should not be confused with the well-known simplex method for linear programming. The algorithm performs a transformation sequence to decrease the function values of each vertex. At each iteration, new function values are calculated for several points and compared with the function values of the previous iteration that are at the vertices. This process is complete when the simplex becomes sufficiently weak in a certain direction, or when the function values are sufficiently close in one direction (provided that f is continuous). The Nelder-Mead simplex typically requires one or two evaluations of the function at each iteration. It uses only two types of transformation to constitute a new simplex in each iteration:  
* Reflection: far from the worst peak (the one with the highest value of the function).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(k\) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \(d(k)\) & 15 & 17 & 15 & 15 & 15 & 14 & 16 & 14 & 16 \\ \(k\) & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 \\ \(d(k)\) & 13 & 15 & 14 & 15 & 12 & 15 & 13 & 15 & 11 \\ \(k\) & 18 & 19 & 20 & 21 & 22 & 23 & & & \\ \(d(k)\) & 16 & 13 & 15 & 12 & 14 & 16 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Random demands.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(k\) & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \(U(k)\) & 1 & 12 & 15 & 15 & 15 & 15 & 15 & 15 & 15 \\ \(k\) & 9 & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 \\ \(U(k)\) & 13 & 15 & 14 & 15 & 12 & 15 & 13 & 15 & 12 \\ \(k\) & 18 & 19 & 20 & 21 & 22 & 23 & & & \\ \(U(k)\) & 15 & 13 & 15 & 12 & 14 & 15 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Optimal production plan yielded by exact method.  
* Shrinkage: toward the best vertex (the one with the smallest function value) [31]. In these transformations, the angles between the edges of all of the simplexes remain constant throughout the iterations so that the working simplex can change size, but not shape. The following steps briefly summarize the Nelder-Mead algorithm [32]
* Initial Simplex: Make the initial set of points usually constructed by generating n + 1 vertices.
* Repeat the following tasks until the termination test is satisfied:
* Calculate the transformation for termination test (reflection and shrinkage).
* If the termination test is not satisfied, recalculate the simplex of resolution.
* Return the best solution of the current simplex S and the value of the associated function.
* **Numerical Results*
* When we apply the Nelder-Mead method to the above numerical example, we obtain the following results:
* Total Cost = 315483.7 mu.
* Optimal production plan (see Table 4).
* Optimal number of preventive maintenance actions = \(N^{*}\) = 2.
# Shape Optimization of an Integrated Mast for RCS Reduction of a Stealth Naval Vessel
## 1 Introduction
Although the previous studies were conducted to analyze the RCS of the integrated mast [21] and the simplified battleship equipped with the integrated mast [22] an investigation on the optimization of the integrated mast shape for RCS reduction has not been presented. Therefore, it is of great significance to optimize the mast shape for RCS reduction and to analyze the RCS of the naval ship equipped with the optimized mast. In this paper, we analyze the RCS of an integrated mast and present the optimized mast shape for RCS reduction. To avoid the unnecessary RCS spikes in the RCS pattern of the naval ship equipped with the integrated mast and to minimize the multiple reflections caused by the interaction with the mast and the naval ship we optimize the integrated mast considering the shape of the naval vessel. The stealth performance of the optimized mast is checked through the analysis of the RCS of a naval ship equipped with an integrated mast. The RCS is simulated using commercial electromagnetic (EM) software based on the shooting and bouncing rays (SBR) method and the diffraction fields at the edges are also considered. First of all, we define the threat frequency and the threat region by considering the operational environments of the naval vessel such as the shape of the ship and the radar signals coming from the enemy radar. We then simulate and analyze the RCS of the integrated mast in terms of the threat frequency, the integrated mast shape and the direction and polarization of the incident waves. The integrated mast shape is optimized in the consideration of the defined threat region. As the RCS reduction cannot be done for all incident angles in the spherical coordinate system, cardinal points are defined as exceptional angle ranges. The integrated mast is designed to have low RCS properties in the primary threat sectors except for the exceptional angle regions and the improvement of the RCS of the naval ship equipped with the optimized mast is confirmed.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 4 Results
### Forecast Analysis
#### 4.2.2 Prediction Results of Combined Models
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Models** & **RMSE** & **MAE** & **MAPE (\%)** & **Dstat** \\ \hline \multirow{8}{*}{Homogeneous combination} & \multirow{8}{*}{ARIMA-based} & Mean & 0.1009 & 0.0753 & 2.5245 & 0.6 \\  & & Median & 0.0989 & 0.0739 & 2.5103 & 0.5968 \\  & & PSO-GA & 0.0839 & 0.0633 & 2.1190 & 0.6947 \\  & & Mean & 0.1489 & 0.1081 & 3.6087 & 0.5175 \\  & & Median & 0.1507 & 0.1098 & 3.6555 & 0.5111 \\  & & PSO-GA & 0.1360 & 0.0991 & 3.3173 & 0.6101 \\  & & Mean & 0.1144 & 0.0856 & 2.9431 & 0.5587 \\  & & Median & 0.1167 & 0.0876 & 3.00 & 0.5111 \\  & & PSO-GA & 0.0941 & 0.0709 & 2.3952 & 0.6415 \\ \hline \multirow{8}{*}{Heterogeneous combination} & \multirow{8}{*}{SSA-based} & Mean & 0.0994 & 0.0726 & 2.4552 & 0.6381 \\  & & Median & 0.0946 & 0.0691 & 2.3464 & 0.6349 \\  & & PSO-GA & 0.0871 & 0.0635 & 2.1190 & 0.6384 \\  & & Mean & 0.1425 & 0.1045 & 3.5940 & 0.5714 \\  & & Median & 0.1174 & 0.0851 & 2.8778 & 0.7548 \\  & & PSO-GA & 0.1157 & 0.0847 & 2.8794 & 0.7278 \\  & & Mean & 0.1036 & 0.0790 & 2.7204 & 0.6254 \\  & & Median & 0.0990 & 0.0761 & 2.6548 & 0.6254 \\  & & PSO-GA & 0.0918 & 0.0696 & 2.3808 & 0.6447 \\  & & Mean & 0.1541 & 0.1130 & 3.8139 & 0.4254 \\  & & Median & 0.152 & 0.1125 & 3.8117 & 0.4317 \\  & & PSO-GA & 0.1532 & 0.1129 & 3.7718 & 0.4182 \\ \hline \multirow{2}{*}{Semi-heterogeneous combination} & \multirow{2}{*}{Full models} & Mean & 0.1063 & 0.0785 & 2.6724 & 0.6095 \\  & & Median & 0.1066 & 0.0784 & 2.6809 & 0.6127 \\ \cline{1-1}  & & PSO-GA & 0.0746 & 0.0568 & 1.9209 & 0.7138 \\ \hline \end{tabular}
\end{table} 
Table 8: Evaluation of individual models for wheat price forecasting.
## 3 Data and Methods  
### Data  
Fieldwork was conducted in outcrop sections along the Yan'an River, and seven profiles related to tectonic-sedimentary characteristics were selected from the Yan'an Formation. These profiles were described based on their grain size, color, sedimentary structures, and stratigraphic contacts. A significant amount of well logging and seismic data was collected from oilfield production units, selecting 134 seismic data points from within the basin, along with nearby well logs, which were used for paleogeomorphological restoration and reservoir sandbody analysis.
## 6 Conclusions  
Knowing the spatial distribution of the electromagnetic field in an inductive WPT system is important to improve the coupling between the emitter and receiver coils, such that the transferred power increases. The measurement approach using three orthogonally oriented coils as an EMF sensor allowed an accurate investigation of the field distribution if the following operations were performed:  
* The measurement of sensor coil axis deflections;
* The balancing of the analog amplification of the three output signals from the sensor;
* The correction of the nonlinearity introduced in the analog chain;
* The detection of the field direction;
* Compensation for the field variations due to the generator;
* Error correction due to the axis deviations of the sensor coils.  
Thus, the novelty and main findings of our proposal consisted in a new calibration method which included the initial basic calibration of the measuring path and additional corrections to the measured values.  
In order to demonstrate the feasibility of the proposed procedure, several practical experiments were carried out. The 106,641 values captured during a complete measurement session are summarized in Figure 25.  
Conceptualization, C.P. and A.T.; methodology, V.T.; software, C.P.; validation, C.P., A.T. and V.T.; formal analysis, A.T.; investigation, C.P.; resources, V.T.; data curation, A.T.; writing--original draft preparation, C.P.; writing--review and editing, A.T.; visualization, C.P.; supervision, V.T.; project administration, C.P., A.T. and V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are unavailable due to privacy.
## System Model  
### Network Model  
In our framework, the assumptions regarding WSNs are as follows:  
* Data are transmitted rarely, once or a few times daily.
* The BS is not subject to energy restrictions and has strong communication and computation capabilities.
* The sensor nodes have enough power to reach the BS.
* The sensor nodes are energy-constrained and have the same capabilities.
* Each sensor node can change its transmission power level dynamically in order to adapt to a certain communication distance.
* A sensor node can switch between run and sleep states under the command of a TDMA order.
* An additional solar-powered battery is used _only_ to relocate/move CH, and not for processing and communication.
* The CH has enough time and enough energy in order to move to the optimal location.
* CH relocation is done occasionally in order for the CH to achieve an optimal distance from all of the other sensors inside the cluster.
## 4 Simulation Results
### Operating Conditions  
To investigate the AdHT performance, the operating conditions under which the technology is working must be defined. These refer to the waste heat temperature, which is recovered from the process as well as to the ambient heat temperature, which is exploited as heat sink for the process.  
Table 3 summarizes the range of operating conditions that are investigated in this paper. The main application field of the present analysis is the exploitation of extremely low-grade temperature source, which is usually dumped into the environment. The investigated ambient heat temperature ranges from extremely low values, typical of operation in cold climates during winter, up to temperature levels commonly encountered in warm climates during summer season. It has to be pointed out that, for the working pairs using water as the refrigerant, the minimum ambient heat temperature that can be considered for the calculation is 5 \({}^{\circ}\)C, if freezing issues inside the system are to be avoided.
## 1 Introduction
### Literature Review  
Experimental methodologies can be employed to design optimal processes; however, limitations related to time and cost restrict their effectiveness in deriving optimal solutions solely through experimentation. Consequently, recent research has shifted its focus toward predictive and optimization studies of induction hardening processes using analytical methods that incorporate various physical factors.  
[PERSON] [6] performed coupled electromagnetic-thermal analysis to investigate the interaction between electromagnetic fields and temperature during induction hardening. This study demonstrates that the geometry of the coil and the associated current parameters significantly influence heating efficiency. Similar investigations employing electromagnetic-thermal coupling have been conducted by several researchers. For instance, [PERSON] et al. [7] and [PERSON] et al. [8] adopted multiphysical approaches to predict temperature distributions and deformations occurring during the heat treatment of metallic materials. Their research provided insights into the thermal effects induced by electromagnetic heating on material microstructures and phase transformations.  
Furthermore, [PERSON] et al. [9] extended this scope by conducting a coupled electromagnetic-thermal-structural analysis to examine the stresses and deformations generated during induction hardening. Their findings highlight the critical impact of temperature-induced residual stresses and deformations on the quality and performance of the final product.  
Recent advancements have also focused on the sensitivity of material parameters and the optimization of process conditions through numerical simulations. [PERSON] et al. [10] performed a sensitivity analysis using a coupled electromagnetic-thermal model to evaluate the effects of material parameters and surface conditions on the induction hardening process. Their study emphasized the importance of accurate material data and surface preparation to achieve optimal hardening outcomes. Additionally, studies on induction cladding, such as those by [PERSON] et al. [11], have provided valuable insights into the thermal and electromagnetic interactions during the coating process, demonstrating the benefits of simulation for optimizing energy efficiency and coating quality. These studies highlight how numerical analysis is evolving to optimize complex physical interactions, improving both simulation accuracy and process efficiency.
## 5 Experimental Validation and Benchmarks
### Applied Solutions in Real-World Networks
#### 5.2.3 Scalability and Real-Time Performance in Urban Networks  
In both the 5G IDS and smart city traffic management systems, scalability was a key consideration, as both systems needed to operate across vast networks with a large number of nodes (5G towers, traffic sensors, etc.). The AI solutions were designed to be scalable, capable of handling tens of thousands of devices while maintaining performance.  
* Scalability Test: For the 5G IDS, the model was scaled across 1000 network nodes, and it still maintained a detection accuracy of 97% without significant degradation in performance.
* Real-Time Processing: Both systems were tested for real-time processing capabilities. For traffic management, the reinforcement learning model made decisions within 2 s of receiving real-time data, ensuring that traffic signals were adjusted dynamically in response to changing conditions.  
These examples demonstrate the potential of AI to provide practical solutions to complex challenges in communication networks and urban environments, addressing both performance and privacy concerns.
## 1 Introduction
However, it is difficult to deal with the problems of GMPPT under frequent, continuous, and rapid changes in irradiance, because the iterative process is usually slow and can be easily trapped in a local optimal solution. In addition, the accuracy of the steady-state solution is not high. In summary, a large number of scholars have conducted in-depth studies on the local shadowing in PV GMPPT. However, most of the algorithms still have problems such as slow global search speed, local optimality, and over-dependence on system models. In addition, the verification methods for these algorithms are based on the static multi-peak problem, and they ignore the possible MPPT failures caused by irradiance changes. To the best of the authors' knowledge, there has been no published paper presenting a framework using an intelligent hybrid method for PV systems in the grid-connected mode. The major contributions of this paper, toward overcoming the drawbacks of the above references, are outlined below:  
1. The output characteristics of the PV arrays are analyzed using the PV array model under PSC, and the \(V\)-\(P\) curve is found to demonstrate a multi-peak trend. The reasons for the failure of the conventional MPPT algorithm are analyzed under the premise of rapid and continuous irradiance changes. 2. For the MPPT failures caused by the timings of the irradiance changes, this paper proposes a hybrid MPPT method that uses an artificial neural network (ANN) to estimate the MPP and an augmented state feedback precise linearization (AFL) controller for the DC/DC converter to meet the fast and frequent changes in irradiance. 3. Considering the influence of the multi-peak output of the PV array on the small signal of the DC/DC converter under PSC, this paper designs a nonlinear control method with precise state feedback linearization. The remainder of this paper is organized as follows: Section 2 presents the structure of the PV system proposed in this paper, a nonlinear mathematical model of the PV array under PSC, and the reason for the failure of GMPPT under PSC. Section 3 introduces the proposed training results of the ANN structure used for MPPT, based on the environment information, and analyzes the error in the network. Section 4 presents an AFL nonlinear controller to track the output reference voltage of the ANN over a wide range, based on the nonlinear mathematical analysis of the DC/DC converter, and designs the controller of the DC/AC converter to accomplish the whole scheme of PV system connection to grid. Section 5 illustrates the performance of the proposed solution using detailed simulations executed under various conditions to validate the effectiveness of the hybrid MPPT method proposed in this paper. Simulations for the comparison of the proposed MPPT method with the conventional P & O method and the improved P & O method are also illustrated. The conclusions are presented in Section 6, which is the last part of this paper.
## 3 Materials and Methods
### Identification of the Wastewater Chemical Components by HPLC  
The wastewater extracts were dissolved in water (1 mg/mL) and injected into a high-performance liquid chromatograph (Shimadzu Nexera LC-40, Shimadzu, Kyoto, Japan) with a UV-Vis detector. Separation was performed at 35 \({}^{\circ}\)C using a Phenomenex C18 reversed-phase column (250 mm \(\times\) 4.6 mm, 5 \(\upmu\)m; Torrance, CA, USA) with a binary gradient mixture of solvent A (ultrapure water/85% \(o\)-phosphoric acid = 99.8/0.2, \(v/v\)) and solvent B (methanol/acetonitrile = 1/1, \(v/v\)) at a flow rate of 1 mL/min: 0-16 min, 4-15% B; 16-50 min, 15-35% B; 50-62 min, 35-4% B; 65 min, 4% B. Phenolic compounds were identified by comparing retention times and absorption maxima at 220 and 320 nm with data obtained for corresponding standards and by sample spiking. Concentrations were expressed as mg of compound per gram of dry extract (mg/g).