esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 4 Performance Analysis
### Frequency Synchronization Performance Analysis  
To verify the effectiveness and performance of the AGC compensation method, we compare the error vector measure (EVM) of the preamble with and without AGC compensation. The EVM is used to quantify the signal distortion and is defined as  
\[EVM=\sqrt{\frac{\sum\limits_{k=1}^{L}\left[\left(I_{k}-\bar{I}_{k}\right)^{2} +\left(Q_{k}-\bar{Q}_{k}\right)^{2}\right]}{\sum\limits_{k=1}^{L}\left(I_{k}^{ 2}+Q_{k}^{2}\right)}}, \tag{31}\]  
where \(I_{k}\) and \(Q_{k}\) represent the real and imaginary parts of the original training sequence, respectively, and \(\bar{I}_{k}\) and \(\bar{Q}_{k}\) represent the real and imaginary parts of the quantized training sequence, respectively.  
Furthermore, the frequency synchronization performance is evaluated using the normalized mean square error (NMSE) of frequency estimation, which is defined by  
\[NMSE\overset{\Delta}{=}\frac{\sum\limits_{m=1}^{M}\left[\left(\hat{f}_{w,m}- f_{w}\right)\right]^{2}}{M\hat{f}_{w}^{2}}, \tag{32}\]  
where \(M\) is the number of simulation runs, and \(\hat{f}_{w,m}\) is the frequency offset estimate obtained from the \(m\)-th simulation run.
## 5 The Random Grouping Selection Algorithm for the JSCC-IA Scheme in the Asymmetric Networks  
The proposed JSCC-IA scheme can improve the BER performance of the IA system in the large-scale networks with limited antennas. However, for asymmetric networks where the attenuation varies significantly among the users, the way how the users are clustered might have an impact on the BER performance, and the simple grouping strategy in Section 4 might not fully explore the potential of the JSCC-IA scheme. In this section, a random grouping selection algorithm is proposed to search for better clustering combinations which can further improve the error performance of the proposed JSCC-IA scheme.
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result
### Welding Experiment and Analysis  
#### 3.2.1 Experimental Scheme  
A total of 6061 aluminum alloy sheets (size: 300 mm \(\times\) 50 mm \(\times\) 1 mm) were used for the welding experiment. The welding experiment used a direct-current (DC) CMT welding process. The shielding gas was 99.999% high-purity argon gas. The diameter of the ER5356 wire was 1.2 mm. Other welding parameters used in the experiment are shown in Table 2.  
In order to simulate the welding assembly errors made in production, an aluminum-alloy-sheet butt joint form was designed, as shown in Figure 3. In Figure 3a, the weld gap changes from 0.5 mm to 1.3 mm along the welding direction. In Figure 3b, a weld gap of 0.5 mm remains unchanged, but the two workpieces show misalignment, with a value of misalignment of 0.5 mm. In Figure 3c, a change in the weld gap and misalignment of workpieces occur simultaneously.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Welding Parameters** & **Value** \\ \hline Welding current & 53 A \\ Welding voltage & 11.1 V \\ Welding speed & 60 cm/min \\ Wire speed & 3.5 m/min \\ Shielding gas flow & 15 mL/min \\ \hline \hline \end{tabular}
\end{table}
Table 2: Welding experiment parameters.  
Figure 3: Workpiece joint forms. (**a**) Variable gap. (**b**) Misalignment. (**c**) Variable gap and misalignment.  
Figure 2: Experimental setup.  
Two experiments were carried out for each butt joint form in Figure 3. The arc sound, welding current, and arc voltage were collected synchronously during the welding experiments. During the welding process, the melt pool became larger due to a larger weld seam or misalignment, making it prone to burn-through or a lack of fusion. A lack of fusion and burn-through are very serious welding defects that can occur during production. Figure 4 shows the three weld states: normal, burn-through, and lack of fusion.
## 4 Mean Squared Performance of Individual MISO APA Filters
### Variance Relation for MISO Filters  
Now, we analyze the energy conservation relation (30) in the mean squared sense. To this end, we take the expectations of both sides of (30) and get:  
\[\mathrm{E}\left\{\left\|\widetilde{\mathbf{w}}_{n}^{(j)}\right\|^{2}\right\}+ \mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_ {n}^{(j),T}\right)^{-1}\mathbf{e}_{an}^{(j)}\right\}=\mathrm{E}\left\{\left\| \widetilde{\mathbf{w}}_{n-1}^{(j)}\right\|^{2}\right\}+\mathrm{E}\left\{ \mathbf{e}_{p}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right) ^{-1}\mathbf{e}_{p}^{(j)}_{n}\right\} \tag{31}\]  
Considering that at steady state (i.e., for \(n\to\infty\)), the weights no longer change, we have that \(\mathrm{E}\left\{\left\|\widetilde{\mathbf{w}}_{n}^{(j)}\right\|^{2}\right\}= \mathrm{E}\left\{\left\|\widetilde{\mathbf{w}}_{n-1}^{(j)}\right\|^{2}\right\}\), so (31) reduces to:  
\[\mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_ {n}^{(j),T}\right)^{-1}\mathbf{e}_{an}^{(j)}\right\}=\mathrm{E}\left\{ \mathbf{e}_{p}^{(j),T}\left(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right) ^{-1}\mathbf{e}_{p}^{(j)}_{n}\right\}. \tag{32}\]  
Using (27), the right-hand side of (32) becomes:  
\[\begin{split}\mu_{j}\mathrm{E}\left\{\mathbf{e}_{p}^{(j),T} \left(\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{p}^{ (j)}_{n}\right\}&=\mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\left( \mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T}\right)^{-1}\mathbf{e}_{an}^{(j)} \right\}-\mu_{j}\mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\mathbf{B}_{n}^{(j)} \mathbf{e}_{n}^{(j)}\right\}\\ &\quad-\mu_{j}\mathrm{E}\left\{\mathbf{e}_{n}^{(j),T}\mathbf{B}_ {n}^{(j)}\mathbf{e}_{an}^{(j)}\right\}+\mu_{j}^{2}\mathrm{E}\left\{\mathbf{e} _{n}^{(j),T}\mathbf{A}_{n}^{(j)}\mathbf{e}_{n}^{(j)}\right\}\end{split} \tag{33}\]  
where:  
\[\mathbf{B}_{n}^{(j)} =\left(\delta_{j}\mathbf{I}+\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{ (j),T}\right)^{-1} \tag{34}\] \[\mathbf{A}_{n}^{(j)} =\mathbf{B}_{n}^{(j)}\mathbf{X}_{n}^{(j)}\mathbf{X}_{n}^{(j),T} \mathbf{B}_{n}^{(j)}. \tag{35}\]  
Replacing (33) in (32), we obtain the variance relation for the \(j^{\text{th}}\) MISO filter:  
\[\mu_{j}\mathrm{E}\left\{\mathbf{e}_{n}^{(j),T}\mathbf{A}_{n}^{(j)}\mathbf{e} _{n}^{(j)}\right\}=\mathrm{E}\left\{\mathbf{e}_{an}^{(j),T}\mathbf{B}_{n}^{(j) }\mathbf{e}_{n}^{(j)}\right\}+\mathrm{E}\left\{\mathbf{e}_{n}^{(j),T}\mathbf{ B}_{n}^{(j)}\mathbf{e}_{a}^{(j)}\right\}. \tag{36}\]
## 2 Materials and Data
### Remote Sensing Data  
The NASA Terra MODIS vegetation index (VI) products (MOD13Q1, Version 6.1) provide consistent spatial and temporal time series comparisons of global vegetation conditions that can be used to monitor the Earth's terrestrial photosynthetic vegetation activity in support of phenolic, change detection, and biophysical interpretations. In this study, NDVI and EVI data from the NASA-produced MOD13Q1 dataset with a temporal resolution of 16 days and a spatial resolution of 250 m were used. The MOD15A2H product provides a terrestrial LAI at 500 m resolution every 8 days, and it has been available since 2000 and allows analysis of LAI time series data over multiple growing seasons over long periods of time.
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.3 Dynamic Route Selection  
OR usually chooses its route dynamically among multiple potential routes to a sink. However, if the potential routes are too closely located, the benefits of dynamic routing are going to be reduced due to the transmission interference among routes. Thus, a good OR needs to form multiple potential routes not so close to each other. We can achieve this by properly selecting a good forwarder set. To this end, we can also consider to properly scheduled wakeup times of ADC in order to obtain a good forwarder set. It is going to be interesting to evaluate the effect of the ADC of nodes on the interference among the potential routes.
# A Detailed One-Dimensional Hydrodynamic and Kinetic Model for Sorption Enhanced Gasification
## 3 Results and Discussion
### Validation with Experimental Data
#### 3.2.2 Effect of Gasification Temperature on LHV  
As seen in Section 3.2.1, the gas composition is strongly affected by gasification temperature, due to the temperature dependence of (i) products released from pyrolysis, (ii) Arrhenius approaches to describe the gasification reactions, and (iii) carbonation/calcination equilibrium. Based on the lower heating values (\(LHV\)) of pure syngas components, the \(LHV\) of the gas mixture was calculated and compared with experimental data. In Figure 8, simulation results are shown over a temperature range of 600-850 \({}^{\circ}\)C and for different make-up flow rates.  
Figure 8: Simulated lower heating value (\(LHV\)) of syngas for gasification temperatures between 600 \({}^{\circ}\)C and 850 \({}^{\circ}\)C (lines for limestone make-up of 0.2, 6.6, and 15 kg/h); comparison with experimental results from a 200 kW (Experiment 1) and 20 kW (Experiment 2) DFB system.  
The results show a good correlation with the experimental data and only a slight variation for different make-up flow rates is noticed. Furthermore, the effect of CO\({}_{2}\) capture and its limitation at around 750 \({}^{\circ}\)C, recognizable as an inflection, can be described with this model. For higher temperatures, the \(LHV\) remains the same at a value of 10.9 MJ/m\({}^{3}\) @STP. The highest values of \(LHV\)--around 14.5 MJ/m\({}^{3}\) @ STP--can be reached at temperatures lower than 650 \({}^{\circ}\)C.
## 4 Discussion
### SMAP/CYGNSS TB Uncertainties
#### 4.6.3 SMAP/CYGNSS TB Algorithm Uncertainties  
Near-zero and positive \(\beta\) values, generally in areas with poorly correlated SMAP emissivity and CYGNSS reflectivity observations, are likely the greatest source of uncertainty in SMAP/CYGNSS TBs. While we will attempt to reduce some of this uncertainty by replacing poorly correlated \(\beta\) values with representative values (Section 4.3), an incorrect \(\beta\) value has the potential to create significant uncertainty in calculated TBs. If we only consider \(\beta\) values with R \(<\) -0.4 (Section 4.3 & Figure 6b), the standard deviation of \(\beta\) values is 0.006 dB\({}^{-1}\). Using 5 th to 95 th percentile ranges in \(\beta\), T\({}_{\mathrm{s}}\), and \(\Delta\Gamma\), we determined that the potential RMSE in TBs based on \(\beta\) errors of \(\pm\) 0.006 dB\({}^{-1}\) is \(\sim\)3.5 K.  
Other SMAP/CYGNSS TB sources of uncertainty include seasonal fluctuations in surface water not captured by our water mask, and uncertainty in surface temperature data. No water mask is perfect, and some TB uncertainty is expected near water bodies that fluctuate seasonally. Uncertainty related to surface temperature is primarily due to its coarse resolution. For consistency, we used the same surface temperature data as SMAP (GMAO GEOS-5), which has a native resolution of 0.25 \(\times\) 0.3125-degrees. This coarse spatial resolution does not adequately capture the spatial heterogeneity of surface temperature on the landscape. Using a finer spatial resolution surface temperature would likely improve our downscaling algorithm and will be considered in the future.
## 4 Numerical Verification of Innovative Designs
### Load-Carrying Capacity
As shown in Figure 9b, the maximum tensile stress of the main girder is 59 Mpa, and its control load condition is the combination of constant load + vehicle load + overall temperature fall + foundation settlement load. The maximum compressive stress is 71 Mpa; the control load condition is the combination of constant load + vehicle load + crowd load + foundation settlement + cross wind load, and the maximum tensile and compressive stresses occur near 3/8 of the span of the main girder. The maximum shear stress is 30 Mpa, and its control load condition is the combination of constant load + car load + crowd load + foundation settlement + cross wind load. According to the strength design value of steel in Table 3.2.1 of specification [33], the ultimate state stresses of the steel structure carrying capacity are less than 320 Mpa and 185 Mpa, which meet the design requirements. A conventional arch bridge susender is symmetrically designed in the cross-bridge direction and longitudinal direction, so the susender also carries the force symmetrically. However, the suspenders of special-shaped arch bridges are arranged heterogeneously in space, and the susender forces are relatively complex. The susender forces of the main and secondary arch collaborative Y-shaped thin-walled steel box arch bridge in the service stage are shown in Table 6, and the distribution trend of the susender forces is gradually decreasing from the two ends of the bridge to the middle of the span. The susender force is larger at both ends of the bridge; the maximum force appears at the short susender 1\({}^{\text{st}}\)(1\({}^{\prime\prime}\)), 29\({}^{\text{st}}\)(29\({}^{\prime\prime}\)); the maximum force is 1468 kN; and the breaking force value of the susender is 4972 kN. The safety factor is greater than 3.0, and therefore, the susender model selection meets the design requirements. The suspenders 4\({}^{\text{st}}\)(4\({}^{\prime\prime}\))-24\({}^{\text{st}}\)(24\({}^{\prime\prime}\)) in the middle of the span have a relatively uniform force of 876.3 kN, compared with the suspenders 11\({}^{\text{st}}\)(11\({}^{\prime\prime}\))-29\({}^{\text{st}}\)(29\({}^{\prime\prime}\)) in the double arch rib segment, and the suspenders 1\({}^{\text{st}}\)(1\({}^{\prime\prime}\))-10\({}^{\text{st}}\)(10\({}^{\prime\prime}\)) in the single arch rib segment have a slightly smaller force. Figure 9: Load-bearing capacity limit state load combination stress (unit: MPa): (**a**) arch ribs; (**b**) main girder.
## 4 Results and Analysis
### Rotation Speed  
Under the conditions of control oil pressure of 1.6 MPa, and the rotation speeds are set to 888 rpm, 1688 rpm, and 2488 rpm. The corresponding oil film thickness response curves are illustrated in Figure 4a-c, respectively. The gap deviation coefficient can be calculated by Equation (18), and the corresponding result is illustrated in Figure 4d. With the variable of the control oil pressure, the gap deviation coefficient successively undergoes a process of increasing, stabilizing, and then decreasing to a constant value. The results of the engagement time, disengagement time, and gap deviation coefficient after the friction element separation for different rotation speeds are presented in Table 2.  
Figure 4: The response curves of oil film thickness for (**a**) 888 rpm, (**b**) 1688 rpm, and (**c**) 2488 rpm. (**d**) The gap deviation coefficient.  
As the rotation speed increases, the engagement time increases from 0.8789 s to 1.3126 s, an increase of 0.4337 s (49.3%); the disengagement time increases from 2.7044 s to 2.7065 s, an increase of 0.0021 s (0.08%); and the gap deviation coefficient after the friction element separation reduces from 0.6267 to 0.6198, a reduction of 0.0069 (1.1%). Thus, the rotation speed has a negligible effect on the disengagement time. The higher the rotation speed, the higher the relative shear speed of the oil film, and the higher the hydrodynamic pressure, which helps the friction elements separate.
The e-nose technology, which works as a human olfactory system, is widely used for different applications in many industrial production processes that range from environmental monitoring (air contaminants detection) to food safety and medical applications [13]. In regards to the food industry, the e-nose is one of the best solutions to monitor and analyze the quality of food and agro-products and to assess the freshness through shelf-life investigation, as reported in this review [14]. During the storage period, the odor emitted from food products is an important indicator of quality and product conformity, also considering the potential of an electronic nose to perform odor assessment with a minimal cost, thus providing a rapid and predictive response compared to the laboratory methods which require more time [15]. Regarding honey, in the past years, e-nose technology has been mainly used for the classification and assessment of botanical or geographic origin [16; 17; 18]. In the present study, it is assumed that the application of a combined analytic approach focused on VOCs and odor analysis could help the comprehensive evaluation of honey quality and freshness in time. Thus, the aims of this study were first set to investigate the trend of VOCs of _Robinia pseudoacacia_ L. honey samples during storage at different temperatures by HS-SPME-GC-MS. A second objective was to assess the feasibility use of the e-nose to recognize and analyze the quality of the same honey in order to confirm this technique to be involved in rapid quality control.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 4 Comparison of Direct and DTNbox Approaches for Bulk Data Transfers
### Direct Approach  
In the DTN experiments, data generation and dispatch to destination are usually coupled. The application that generates the data is also sends the data to the destination node using the BP the advantage of this direct approach is that it ensures the minimum delivery time; the disadvantage is that the application that generates the data must be \"DTN aware\", i.e., it must be designed to interface directly with the API of BP.
# A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids
## 4 Test Results and Discussions
### Scenario 1: Operation at Sea State
Nevertheless, as mentioned in Section 3, due to uncertainties of the sea and weather conditions it would be necessary to consider disturbances as external inputs to the NARX-NN for learning procedure to recognize the sudden changes in order to obtain better results.
## 3 Implementation of RUL Prediction Based on TCNMABG
### Evaluation Indicators  
To better verify the effectiveness of the proposed method, four evaluation indexes are used to assess the prediction results, which include the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination R\({}^{2}\).  
\[MSE=\frac{1}{N}{\sum_{i=1}^{N}{\left(Y_{i}-P_{i}\right)}^{2}} \tag{4}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}{\left(Y_{i}-P_{i}\right)}^{2}}{N}} \tag{5}\]  
\[MAE=\frac{\sum_{i=1}^{N}{\left|Y_{i}-P_{i}\right|}}{N} \tag{6}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{N}{\left(Y_{i}-P_{i}\right)}^{2}}{\sum_{i=1}^{N}{ \left(\overline{Y}_{i}-Y_{i}\right)}^{2}} \tag{7}\]  
where \(Y_{i}\) is the real value of remaining useful life, \(P_{i}\) is the prediction value of the proposed method, and \(N\) is the total number of data samples.  
The smaller the values of RMSE, MSE, and MAE, the higher the prediction accuracy of the proposed method. The coefficient of determination \(R^{2}\) is used to measure the fitting of the prediction methods based on the observed data, whose value ranges from 0 to 1. The closer \(R^{2}\) is to 1, the better the fitting of the prediction methods based on observed data.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## Materials and Methods
### Methods
#### 3.2.3 NDVI Time Series-Based Classification  
Relationship between the NDVI time series and vegetation activities has been well established theoretically and empirically. The NDVI curve properties and characteristics can generate a set of metrics (such as the onset of greening/senescence, timing of maximum NDVI, the growing season length, rates of NDVI changes, _etc._) that measure the timing and magnitude of NDVI response and summarize the spatial and temporal distribution of crop growth and phenology, therefore being widely used as an input for classification. A range of classification algorithms has been developed based on NDVI time series, such as the supervised classification methods, spectral matching techniques, decision tree algorithms, neural network methods, support vector machines, and so on [21; 22; 51; 52; 53].
## 5 Results and Discussion
### Configuration WDH Simulation  
In this section, the study expands the research of SCH nonlinear model. A comprehensive investigation is conducted in a new simulation route by considering the elevator and aileron in Configuration WDH. The newly added components have been introduced in Section 3, such as the T-style vertical/horizontal stabilizer, the elevator on the horizontal stabilizer and the aileron function in wing component. The coordinated turn is a systematic control route. The turn should coordinate elevator control, aileron control and yaw control to perform a perfect turning circle. The structured \(H_{\infty}\) control method was adopted in the simulation. The control requirements were:  
\[\left\{\begin{array}{c}w=0\\ v_{b}=0\\ u_{b}=\text{constant}\\ \phi=\text{constant}\\ r_{b}=\frac{g}{u}\tan\phi\\ q_{b}=\text{constant}\\ \end{array}\right. \tag{46}\]  
where \(\phi\) was controlled by aileron. \(r\) was still controlled by tail rotor collective pitch. \(q\) was controlled by the elevator on the horizontal stabilizer. The simulation was conducted on \(u_{b}=20\) m/s.  
In the simulation, as shown in Figure 13, the Configuration WDH performs a perfect coordinated turn in a semi-circle. The error of height is less than 2 m. In Figure 14, variations of multiple variables during the turn are plotted. The control of \(\theta_{a}\) and \(\theta_{e}\) is effective with small oscillation and overshoot to obtain the target attitude. Such as in Figure 14a, a 3 degree \(\theta_{a}\) can produce 0.6 rad/s roll angular velocity \(p\) (Figure 14d). Then the helicopter reaches \(-20^{\text{o}}\) roll angle \(\phi\) (Figure 14e) within 2 s and performs little overshoot and fluctuation. The control of aileron and elevator consumes small amount of power in this process. However, in the real flights, the helicopter needed longer time to stable during the turning point under main rotor control and large amount of power was consumed. The control performance of main rotor cannot be as good as the performance by \(\theta_{a}\) and \(\theta_{e}\) in Configuration WDH. The Configuration WDH benefits from a clear flow condition of the wings. It is noteworthy that during the lateral control of aileron, the main rotor also produces a damping moment (\(L_{main\ rotor}\)). This is a symbol of stability but it indeed lowers the maneuver efficiency. The main rotor reacts more like a burden in this high-speed flight. In addition, an extra \(q_{b}\) helps to make the helicopter fly at a stable height.  
Figure 13: 3D plot of coordinated turn simulation by NMM under Configuration WDH.  
The simulation in coordinated turn proves the feasibility of the elevator component and the differential maneuver of the aileron-wing. The simulation confirms the effectiveness of SCH nonlinear model in a wider route setting.