esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency-Domain Lifting-Line Aerodynamic Modelling for Wing Aeroelasticity
## 3 Numerical Investigation  
The main purpose of this section is twofold: \((i)\) to examine the effects of both the sectional load modelling and steady-unsteady versions of the Kutta-Joukowski theorem on the evaluation of the aerodynamic matrix through the presented LLT-like solver (see Sections 2.1 and 2.2) and \((ii)\) to assess the accuracy of this aerodynamic matrix by comparison with the predictions provided by a BEM computational tool for three-dimensional lifting bodies in arbitrary motion. Note that this computational tool was extensively validated in the past against both the experimental and numerical data available in the literature, concerning both fixed and rotary wing configurations [27; 28; 29; 30; 31; 32; 33; 34]. Finally, in order to obtain from the developed LLT-like aerodynamic operator a reduced-order model of particular interest for aeroelastic applications, the rational matrix approximation of the aerodynamic matrix is carried out and validated.  
The numerical results present the evaluation of the magnitude and phase of the aerodynamic matrix components within the reduced frequency range \(k\in[0,2]\). They concern an untwisted rectangular wing with a chord length \(c=1\) m and three aspect ratios corresponding to half-span lengths \(l=5\) m, \(l=2.5\) m and \(l=1.25\) m. The undisturbed-flow dynamic pressure is assumed to be equal to unity, whereas the pitching and torsion motions are assumed to occur around the local aerodynamic centre line.  
Both the results presented obtained by the LLT-like tool and those provided by the BEM solver are numerically converged ones, in terms of the body and wake discretisation elements (with the latter suitably related to the specific \(k\) value of the analysis).  
Two different cases of increasing complexity are considered for the definition of the bending and torsion shape functions, namely \(\psi\) and \(\gamma\):Spanwise constant shape functions \[\begin{cases}\psi(\xi_{j})=1\\ \gamma(\xi_{j})=1\end{cases}\] (29) for which the generalised forces correspond to the total lift and pitching moment around the aerodynamic centre line;
2. Shape functions expressed as the following approximations of the first bending and torsion natural modes of vibration of a clamped-free beam (named the NMV shape functions, in the following) \[\begin{cases}\psi(\xi_{j})=\xi_{j}^{2}\\ \gamma(\xi_{j})=\xi_{j}\end{cases}\] (30)
###### Abstract  
We demonstrate the feasibility of using a single microring resonator (MRR) as optical notch filter for enabling the direct modulation of a reflective semiconductor optical amplifier (RSOA) at more than tripled data rate than possible with the RSOA alone. We conduct a thorough simulation analysis to investigate and assess the impact of critical operating parameters on defined performance metrics, and we specify how the former must be selected so that the latter can become acceptable. By using an MRR of appropriate radius and detuning, the RSOA modulation bandwidth, which we explicitly quantify, can be extended to overcome the RSOA pattern-dependent performance limitations. Thus, the MRR makes the RSOA-encoded signal exhibit improved characteristics that can be exploited in practical RSOA direct modulation applications.  
direct modulation; microring resonator; optical notch filter; reflective semiconductor optical amplifier 2018
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 3 GAWDO for OFDM Network Optimization  
### The Proposed GAWDO  
The GA and the WDO are optimization techniques that are based on the principles of evolution. They commence with a set of solutions that undergo initialization, selection, mutation, and recombination until the most elite and rational settlement has been attained [22]. This makes them superior to conventional methods. The WDO is a straightforward and fast converging algorithm [23]. However, the GA struggles with the selection of elite individuals which often leads to stalling and untimely convergence [24; 25].  
In this paper, a hybrid genetic algorithm wind-driven optimization (GAWDO) technique is introduced. The main aim of hybridization is to lessen the limitations of an algorithm with diversification aspects of another algorithm. This hybrid algorithm was designed to give better quality results than those produced by the traditional GA and WDO. The GAWDO was created based on incorporating the WDO as part of the GA selection function for the following reasons:  
* To continuously influence the number and quality of individuals that can be created and given the responsibility of reproduction at every evolution stage. This would reduce the loss of good genetic material. Since the GA is a multipath search algorithm, this would reduce the chances of trapping in suboptimal minima [26; 27].
* To select and maintain a healthy population which would ensure search efficiency until termination of the optimization processes. This would also help the algorithm to effectively explore all the search spaces where the probability of finding optimum solutions is highest [10; 24; 28].
* To select and maintain parents with the best structural variations. This implies more diversity since the probability of replacing a gene with a similar/redundant structure will be small. This would ensure maximum population diversity [12; 17; 18].
* To select parents based on their expectations. The best parents with high expectation values would be given more reproduction chances in descending order. This would mitigate stalling which greatly exists in the conventional GA when scores or expectation values are similar because they are chosen using probabilities [10; 26; 29].  
* To ensure good survival rates of the best individuals. Having the best individuals would give more assurance that all the desired search space would be fully exploited [30; 31]. The hybrid algorithm was developed by merging the two algorithms as per Figure 1.
## 1 Introduction
* Navigation: When the user needs to meet the situation of addresses, positions, directions in an indoor places such as big supermarkets, commercial centers, etc. * Discovery: When it is necessary to find or locate things or persons in indoor places. It is very useful to locate people with Alzheimer or to locate disabled people with very little motion. * Security: It can be used to avoid theft, to move unwanted items, etc. Wireless sensors would be in specific places, when the sensors transfer a position threshold they send an alarm. * Tracking: When it is required to track a device or person inside a building. There are two main methods to estimate the position in indoor environments. On the one hand, there are the so-called deductive methods. These take into account the physical properties of signal propagation. They require a propagation model, topological information about the environment, and the exact position of the base stations. On the other hand, there are the so-called inductive methods. These require a previous training phase, where the system learns the signal strength in each location. The main shortcoming of this approach is that the training phase can be very expensive. The complex indoor environment makes the propagation model task very hard. It is difficult to improve deductive methods when there are many walls and obstacles because deductive methods work estimating the position mathematically with the real measures taken directly from environment in the training phase [12]. In this work we present a hybrid location system using a new stochastic approach which is based on a combination of deductive and inductive methods. This system has been developed for wireless sensor networks using the IEEE 802.11b/g standard in order to use a deployed wireless access network that is also used for internet access and data transfer. On the other hand, the aforementioned technology allows us to cover a hard indoor environment without many base stations. The goal of this work is to reduce the training phase without losing precision. The remainder of this paper is organized as follows. Section 2 presents the best known related work on location methods in sensor networks. Our hybrid location system is described in Section 3. Section 4 shows the efficiency of our system. It shows real measurements and compares our proposal with other systems. A comparison between our proposal and other hybrid systems proposed in the literature is shown in Section 5. Section 6 concludes the paper and discloses our planned future work.
# Remote Control of a Mobile Robot for Indoor Patrol
## 1 Introduction
Images captured by the network camera placed on the robot can be transmitted to the remote PC or smartphone. Intelligent control, wireless communications, and sensor fusion are successfully integrated in this study. The outline of this paper is as follows. In the second section, the discussion of image processing technology for filtering noise on floor, capturing the targets from the image frame, as well as computing the distance and the angle via the camera are given. Section 3 shows the WMR dynamic tracking and indoor patrol schemes; fuzzy steering control rules are also described. The experiments' settings are explained in section 4 and a description of the architecture of the hardware system, which includes sensors, Zigbee, smartphone, camera, and block diagrams of the communications scheme. Section 5 shows five experimental results of the proposed system. Conclusions are then given in the last section.
# Time Series Methods and Business Intelligent Tools for Budget Planning--Case Study
## 2 Methods of Quantitative and Value Strategic Planning of Budget
### Forecasting Methods
#### 2.1.2 Linear Regression  
Linear regression is a statistical technique used to model the relationship between a dependent (explanatory) variable and one or more independent (explanatory) variables [26]. The key assumption is that the relationship between the variables is linear, meaning that changing one causes the other to change proportionally. The values of the independent variables are used to predict the values of the dependent variable when forecasting using this model. This method is widely used in economics, social sciences, and data analysis. It is simple, and the results are easy to interpret.  
The linear regression model defines the straight line equation:  
\[y=\beta_{0}+\beta_{1}x+\epsilon\]  
where  
\(\beta_{0}\)--the intercept;  
\(\beta_{1}\)--the regression coefficient;  
\(\epsilon\)--represents the random error.  
The paper [27] compares three forecasting methods--linear regression, exponential smoothing, and weighted moving average--on the basis of the lowest values of the forecast errors. The results show that linear regression is the most accurate. Analysis of the tracking signal and moving range values confirmed that the method results were within acceptable control limits, indicating its accuracy and reliability. The linear regression method is therefore considered to be suitable for making predictions and providing a basis for making decisions in the future.  
The paper [28] presents one-dimensional models for short-term electricity demand forecasting using linear regression and daily cycle patterns in time series. These patterns simplify the problem by eliminating trend, seasonality, and mean and variance nonstationarity, allowing local modeling of relationships between variables. Techniques that reduce the number of predictors and facilitate data visualization include stepwise regression, lasso, principal components, and PLS. The high accuracy of the proposed approaches is confirmed by comparison with other methods such as ARIMA, neural networks, and exponential smoothing.
## 3 Results and Discussion
### Case Study 1

Table 4: Minimization of machinability indices using evolutionary algorithms for Case study 1. Figure 3: Pseudocode for optimization algorithms (**a**) MFO (**b**) GHO (**c**) GA (**d**) GWO (**e**) PSO. Figure 4: Convergence plot for case study 1 (**a**) Cutting Force (**b**) Surface Roughness (**c**) Cutting Temperature. Similarly, the convergence plots for the other two machinability indices are given in Figure 4b,c, respectively. The order of preferences of algorithms based on their performance in obtaining the minimum response values is MFO-GWO-GHO-GA-PSO for SR and MFO-GHO-PSO-GWO-GA for CT. Chattered vibrations are generally existing in the machining process, and this is significantly affecting the SR. The SR values are lower in the cryogenic machining than the dry and wet machining from the experimental value and predicted data. Further, the cutting temperature is directly related to CF and SR. CT increases with an increase in \(v_{\text{c}}\). The flow of cryogenic LN\({}_{2}\) (\(-196\)\({}^{\circ}\)C) between tool and workpiece interface greatly reduces the CT in the machining zone compared with dry and wet machining [42].
## 4 Results and Discussion
### Energy Considerations for Industrial Application
#### 4.7.2 Energy Demand for WCO Mixing and Binder Preparation  
The WCO and AB were blended into the bitumen at 160 \({}^{\circ}\)C using high-speed mixing. According to [PERSON] et al. [44], maintaining this temperature in a batch-type asphalt plant consumes an average of 3.24 kWh/ton of electricity. Additionally, [PERSON] and [PERSON] [45] proposed a temperature-based model for natural gas consumption in asphalt plants (Equation (3)):  
\[NG\ \left(m^{3}/ton\right)=0.0935\ *\ T_{mix}-7.663 \tag{3}\]  
Using this model, mixing at 160 \({}^{\circ}\)C results in 14.3 m\({}^{3}\)/ton of natural gas consumption. Assuming a conversion factor of 9.5 kWh per m\({}^{3}\) of natural gas, the total thermal energy input amounts to 135.85 kWh/ton. Combined with electrical demand, the total energy required for mixing reaches approximately 139 kWh/ton.
# Surface Quality Evolution Model and Consistency Control Method of Large Shaft Multi-Pass Grinding
## 1 Introduction
Firstly, the influence of the surface quality of the previous grinding pass is not considered by existing research. In multi-pass grinding, the same grinding parameters will result in different grinding qualities under different initial surface qualities, which makes the surface quality model ineffective. Secondly, the research on the formation mechanism and control method of surface quality consistency is insufficient. Even if better grinding parameters are selected to obtain higher grinding quality, there will still be large differences in the grinding quality indexes along the whole shaft; in other words, the grinding quality consistency is poor. Thus, the surface quality consistency is hard to guarantee through selection of grinding parameters alone. In this study, the formation mechanism of surface quality consistency is revealed by qualitative analysis of the influence of grinding parameters on surface quality. Then, the surface quality evolution models for surface roughness and glossiness based on Elman neural network are developed, which build regressions between process parameters, surface quality indexes of the previous grinding pass, and surface quality indexes of the current grinding pass. Moreover, a consistency control method of surface quality is proposed by adjusting the actual grinding depth within the dimensional accuracy tolerance range at the rough grinding stage. A case study is performed to verify the effectiveness of the proposed method.
###### Abstract  
With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency.  
Vehicular networks; V2X communication; deep reinforcement learning; vehicle clustering
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion
### Quantum Efficiency  
Quantum efficiency (QE) was calculated using the slope of the optical power versus current curves, as defined in Equation (11) [19]. Figure 3a-c illustrate QE performance for each diode, highlighting significant differences in electrical-to-optical conversion efficiency.  
Figure 3: **(a)** Quantum efficiency (%) of the 650 nm red laser as a function of current (\(\eta_{max}=0.551,l_{threshold}=85\) mA). **(b)** Quantum efficiency (%) of the 405 nm violet laser as a function of current (\(\eta_{max}=0.340,l_{threshold}=100\) mA). **(c)** Quantum efficiency (%) of the 532 nm green laser as a function of current (\(\eta_{max}=0.199,l_{threshold}=135\) mA).  
Performance highlights:
* 650 nm (Red Laser): Achieved the highest peak QE at 55.1%, with optimal performance between 90 and 150 mA. Its efficiency curve shows a relatively smooth rise to this peak, followed by a gradual decline at higher currents due to thermal effects and carrier recombination losses.
* 405 nm (Violet Laser): Reached a peak QE of 34.0%, indicating moderate conversion efficiency. It exhibited greater sensitivity to thermal loading, resulting in steeper post-peak degradation.
* 532 nm (Green Laser): Displayed the lowest peak QE (19.9%), consistent with its higher operating threshold and reduced spectral compatibility with silicon photodetectors.  
Across all wavelengths, the decline in QE at elevated current levels was attributed to non-radiative recombination, thermal loading, and carrier leakage. These factors reduce the proportion of injected current converted to useful optical output. The results emphasize the importance of current regulation and thermal management, particularly in systems employing continuous-wave operation or high current drive.  
The clear advantage of the red laser in both energy conversion and photodetector compatibility supports its use in power-sensitive, visible light-based FSO systems.
## 2 Submillimeter-Level Microwave Ranging
### MWR Payload Development
#### 2.3.3 Microwave Channel  
The differential frequency phase measurement method is adopted in order to achieve high-precision phase measurement in the MWR system. The phase measurement frequency of the Ka band is designed as 600 kHz. By making the reference frequency source of the USO from two spacecraft differ by 66 Hz, the carrier phase measurement can be completed with identical MWR equipment on-board the two spacecraft. The method uses the local transmitting carrier as the local oscillator for mixing, down-converts the receiving carrier, directly generates a phase measuring frequency point of about 600 kHz (i.e., the direct mixing technology of the transmitting carrier), and then sends it to the digital signal processing unit for carrier phase measurement.  
The MWR microwave channel consists of an RF module, a local oscillator, and a secondary power supply. (a) The RF module uses a local oscillator signal to convert the 32.7 GHz RF signal to an 600.098 kHz intermediate frequency (IF) signal with a certain gain. The RF module gain can be adjusted by telecontrol command, with gain state parameters telemetry output. (b) The local oscillator uses the input reference signal in order to generate the local oscillator signal for RF module frequency down-conversion. (c) The secondary power provides the required secondary voltage for the RF module, and it controls the ON/OFF switch of the equipment through the telecontrol command, and it provides telemetry information on the switch state.  
Fundamental wave mixing is adopted for the RF module's down-conversion, which has the advantages of low frequency conversion loss (usually less than 10 dB) and low noise coefficient. Moreover, it can better suppress high-order mixing by-products when a single-balance or double-balance method is used. The central frequency of the fundamental mixing local oscillator signal is chosen as 32.7 GHz, according to the frequency relationship between input and output signals.
## 2 Materials and Methods
### Method Qualification  
Method qualification was carried out with a fit-for-purpose approach [8]. The qualification run contained duplicate STDs at eight concentrations and QCs at three different level concentrations. The in-house acceptance criteria for STDs and QCs in the qualification run were within \(\pm\)25% of the precision and accuracy values. A quadratic regression (weighted 1/concentration), with an equation y = ax\({}^{2}\) + bx + c, was used to fit the calibration curves. In addition, two blank plasma samples were run. The intra- and inter-day run for the accuracy and precision assays were calculated at each QC concentration.  
Preliminary stability tests were conducted in rat plasma samples under different conditions, such as short-term, long-term, and freeze-thaw stability at three different levels of QC. The short-term stability was determined at room temperature for 6 h. The long-term stability was determined by analyzing QC samples kept frozen at \(-\)80 \({}^{\circ}\)C for 3 months. For the freeze-thaw stability, the samples were subjected to three freeze-and-thaw cycles at \(-\)80 \({}^{\circ}\)C. The in-house acceptance criteria for all preliminary stability tests were also within \(\pm\)25% of the precision and accuracy values.