esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Adsorption Experiments  
The stock solutions of AV1, AG16, AR18 (\(C_{0}\) = 1000 mg/L) were prepared and the required concentrations were obtained by diluting using volumetric flasks. In the batch studies, influences of dye concentration, phase contact time, auxiliaries presence (salt: Na\({}_{2}\)SO\({}_{4}\), acid: CH\({}_{3}\)COOH, surfactant: SDS) were investigated as factors governing the dye adsorption process. Concentrations of dyes and auxiliary substances in aqueous solutions were selected in such a way as to reflect their content in dyeing baths and sewage [38; 39]. All adsorption experiments were performed at 25 \({}^{\circ}\)C using laboratory shaker Elpin 358+ (Lubawa, Poland) at 180 rpm, amplitude 8 and at the natural pH of dye solutions. After separation of liquid phases by filtration, a single-beam UV-visible absorption spectrophotometer Cary 60 (Agilent, Santa Clara, CA, USA) was used for determination of dye concentrations at the maximum absorbance wavelengths. All adsorption experiments were performed in triplicate with reproducibility \(\pm\) 3%. The mean values of the results have been used for data evaluation. A non-linear method for calculation of isotherm and kinetic parameters in adsorption studies was applied using Microsoft Excel 2013 Solver software add-in. Analysis of the Marquardt's percent standard deviation(MPSD), determination coefficient (\(R^{2}\)) and adjusted R-squared (\(R^{2}_{adj}\)) values was suggested to find the best fitting model. Adjusted R-squared is usually used to compare models of different sizes; in contrast to \(R^{2}\), it can go up or down when a variable is taken out of the model. The above mentioned parameters can be determined using Equations (10)-(12) [40,41]:  
\[MPSD=\ \sum_{i=1}^{n}\Biggl{(}\frac{q_{e\ exp}-q_{e\ cal}}{q_{e\ exp}}\Biggr{)}_{i }^{2} \tag{10}\]  
\[R^{2}=1-\frac{\sum\left(q_{e\ exp}-q_{e\ cal}\right)^{2}}{\sum\left(q_{e\ exp}- q_{e\ man}\right)^{2}} \tag{11}\]  
\[R^{2}_{adj}=1-\Biggl{[}\frac{\left(1-R^{2}\right)\left(n-1\right)}{n-k-1} \Biggr{]} \tag{12}\]  
where \(q_{e\ exp}\) (mg/g) is amount of acid dyes sorbed at equilibrium, \(q_{e\ cal}\) (mg/g) is amount of acid dyes sorbed calculated from the non-linear models, \(q_{e\ mean}\) (mg/g) is measured by the mean of \(q_{e\ exp}\) values, \(n\) is number of points in data sample, \(k\) is number of independent regressors.
## 1 Introduction
LLR calculation when channel coding is considered if the approximation is ingeniously designed and implemented to the optimal LLR. * In order to verify the desirable properties of our proposed simple schemes, the characteristics of the receiver are studied from many aspects with extensive simulations. We organize the rest of this paper as follows. Section 2 focuses on the system model under the fading channel. Section 3 introduces the LLR calculation under the phase noncoherent channel. In this case, the instantaneous fading amplitude is assumed to be available and known exactly at the receiver. Further relaxing this restriction, Section 4 concentrates on LLR calculation when the statistical characteristic for the fading amplitude is available at the receiver. The simulation results are discussed in Section 5. Finally, some conclusions and future work are provided in Section 6.
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 3 Mesh Convergence Analysis for Different Loading Protocols  
### Stress Fields and Estimated Fractional Errors  
The mesh convergence analysis is based on the element quantity in the damper thickness. For each mesh size and number of cycles, the contour plot of the equivalent stress is shown. With the reduction in the size of the elements, the fields of stress, represented by colors, clearly differ in the areas where the maximum equivalent stress is produced (Figure 3). This localized zone is also important when studying the convergence of numerical results, as the gradient should be accurately reproduced and the variation in the results should be minimized when refinement is performed. The maximum stresses between the last two fine-mesh simulations are approximately the same. Note that the number of cycles does not create a significant difference in the stress fields, which is expected since the material is characterized by low strain hardening.  
Figure 3: Maximum equivalent von Mises stress for the different mesh sizes in convergence analysis.  
The trends of the fractional errors in the simulation results of the damper response at maximum stress levels for different loading protocols as a function of the element size are shown in Figure 4a through Figure 4c. According to the adapted Richardson extrapolation shown by the red curve, the increase in the fractional error stagnates at an element size of 5.6 mm (four elements in thickness). This is validated with the results from six elements.  
The maximum stress levels with an increasing number of elements per thickness are given in Figure 4d. The curve shows \"wave-like\" behavior until reaching an element size of 4.8 mm. The difference in the resulting maximum stress between 4.8 mm and 5.6 mm is negligible. When comparing these results with previous research, similar locations of maximum values of stress and strain are found in the damper; however, there is a notable difference in the through-thickness local behavior. This difference is due to the different number of elements included in the analysis. For example, [PERSON] et al. [24] identified maximum stress values at similar locations of the dampers, but there was no variation in stress along the damper thickness. [PERSON] et al. [27] compared the equivalent plastic strain contours of different dampers, using three elements in the thickness of one damper and two in others. The resulting differences were attributed solely to the shape factors of the dampers, without taking into account the impact of the number of elements through the thickness of the damper, as given in Figures 3 and 4.  
Figure 4: Mesh convergence analysis of (**a**) estimated fractional error (E1) vs. element size for 1 cycle, (**b**) 5 cycles, and (**c**) 10 cycles. (**d**) Maximum equivalent stress, (**e**) AEPS, (**f**) equivalent plastic strain, and (**g**) strain energy vs. number of elements in the damper thickness.
# Signal to Noise Ratio of a Coded Slit Hyperspectral Sensor
## 3 Results and Data Analysis
### The Visualisation of SNR  
From the above analysis it is seen that the SNR of both the conventional spectrograph and the CA multiplexed sensors is a function of both wavelength and integration time. The relationship between these two SNRs can be expressed in the form of \(SNR^{c}=aSNR^{m}/\sqrt{aSNR^{m}+b}\), where \(a\) and \(b\) are wavelength independent. It is found that they are somewhat linearly dependent in the low SNR regime, but it becomes less linear when the SNR is large, as illustrated in Figure 6. To study how the SNRs are scaled to integration time, one specific wavelength at 650 nm and 1700 nm have been chosen for the VNIR and VNIR-SWIR sensors, respectively. The variation of the SNR with the wavelength dependence will be presented in the forthcoming paper. The relationship of the SNRs in the two sensor types have also been studied for frame rates ranging from 15 Hz to a maximum of 340 Hz, and typical results at frame rates of 35 Hz and 200 Hz have been shown in Figure 6. This range of frame rate is sufficient enough to study the sensors with platform speeds for approximately a small fixed-wing commercial UAV and up to the sound speed at sea level.
## 3 Materials and Methods
### Stochastic Models  
When data are presented using stochastic modeling, specific degrees of randomness or unpredictability are taken into account when predicting results. Using random variables, it predicts the likelihood of different outcomes under various scenarios.  
SIMCAT it is considered to be a model based on a deterministic, stochastic, and Monte Carlo approach. It is used for the prediction of various biochemical indices and ion leachate, i.e., BOD, DO, and Cl\({}^{-}\), NH\({}_{4}\)\({}^{+}\) NO\({}_{3}\)\({}^{-}\)[19; 142].  
Monte Carlo, a specific branch of stochastic modeling, is a broad class of computational techniques that rely on periodic random sampling to obtain numerical results. It uses randomness to solve problems that might be deterministic in principle [143].  
Vadose zone flow and transport model (VADOFT) is a 1-D finite-element prediction code applied to chemical transport, flow/fate in the unsaturated zone. It employs parameters such as pressure, water content, and hydraulic conductivity to solve the flow equations. The code, when equipped with a Monte Carlo pre- and post-processor, enables the running of multi-parameter scenarios several hundred times and provides stochastic (probabilistic) outputs [144].
# A Bayesian Control Chart for Monitoring Process Variance
## 4 The Construction of the EWMA Variance Chart with the Bayesian Approach
### Performance Comparison of the EWMA Variance Control Chart  
In general, the EWMA type control chart performs better than the Shewhart-type chart for small shifts. The single sampling EWMA variance (SS EWMA-V) chart has proved better performance than the NLE, CEW, NP-M, and IRC charts from previous studies [23; 34; 35]. To demonstrate that the proposed Bayesian EWMA variance chart outperforms the existing charts, the SS EWMA-V chart is considered for comparison. Table 5 presents the out-of-control average run lengths of the proposed Bayesian EWMA variance chart and the SS EWMA-V chart with a sample size of 10 (\(n=5\)) and smoothing parameter 0.05 (\(\lambda=0.05\)). The results of Table 5 show that, while \(E_{0}(p)\) and \(E_{1}(p)\) are both equal to \(p_{0}\) and \(p_{1}\), respectively, the \(ARL_{1}\)s of the Bayesian EWMA variance chart
## 3 Results and Discussion
### Validation of the Regression Model  
The predictive performance of the regression model was evaluated by comparing its predictions with experimental data that were excluded from the training dataset. This assessment was essential to confirm the model's ability to generalize to new data while avoiding overfitting, despite its high fit with the training data (R\({}^{2}\) = 0.9733).  
Table 5 presents a comparison between the predicted MPY values from the model and the actual MPY values measured by the commercial LPR sensor and potentiostat. Each predicted value is accompanied by a prediction interval (PI), representing the range within which the actual values are expected to fall with a high degree of confidence.  
The data in Table 5 demonstrate that the predicted MPY values remain within the expected range for all test conditions, as indicated by the prediction intervals (PIs). This consistency shows that the model is capable of making reliable predictions, even when applied to new factor combinations not included in the training dataset. For example, in Test 2, the model predicted 119 MPY (PI: 104-133), while the actual values were 127 MPY from the sensor and 131 MPY from the potentiostat--both comfortably within the predicted range. Although minor discrepancies were observed, such as in Test 1 where the model predicted 48 MPY (PI: 33-63) but the actual readings were slightly lower (38 MPY from the sensor and 45 MPY from the potentiostat), these variations fall within an acceptable margin. This suggests that the model maintains predictive consistency across various operating conditions.  
At lower flow velocities and higher temperatures, as seen in Test 4, the model tends to slightly overestimate MPY values. This behavior indicates that incorporating additional data points at intermediate levels may further enhance the model's accuracy by refining its ability to represent non-linear trends effectively. Overall, the agreement between predicted and observed MPY values validates the robustness of the model. The results confirm that the predictions are reliable and remain within the expected range for new conditions, making the model a practical tool for real-time corrosion monitoring.
## 2 Materials and Methods
### Characterizations  
Mg- or Zn-doped GaN films were characterized by X-ray diffraction (XRD) measurements using a Bruker AXS D8 Discover Diffractometer at room temperature, equipped with a wavelength (Cu K\(\alpha\)) of 1.5406 A. The XRD patterns were obtained in a range from 25\({}^{\circ}\) to 60\({}^{\circ}\), with step-size and step-time of 0.02\({}^{\circ}\) and 1 s, respectively. The X-ray tube operation conditions were 40 kV and 40 mA. The surface morphology and elemental analysis (SEM/EDS) of the Mg- or Zn-doped GaN films were obtained using a JEOL JIB-4500 (SEM+FIB). The profilometry was made using a Dektak 150 Surface Profiler. Photoluminescence spectra (PL) were measured at room temperature with an excitation wavelength of 243 nm and a 310 nm filter using a fluorescence spectrophotometer Hitachi F-7000 FL with a 150 W xenon lamp. The Raman-scattering characterizations of the Mg- or Zn-doped GaN films were obtained using a Horiba Jobin Yvon HR-800 Micro Raman spectrophotometer. X-ray photoelectron spectroscopy (XPS) measurements were taken with an Escalab 250 Xi Brochure, using an energy range from 0 to 1400 eV. Four-point probe measurements were carried out using a Lucas Signatone QuadPro Resistivity System.
## 5 Innovative Control System for Personalized Thermal Comfort Prediction
### Real-Time Skin-Measurement for Smart and Personalized Control
#### 5.1.6 Validation of the Skin Temperature  
The accuracy of the measured skin temperature was investigated by [PERSON] et al. [153] using a PT100-resistance-thermometer fastened to the skin. Using contact-based thermal sensors simultaneously to the contactless thermographic sensor the accuracy of the determined skin temperature improves. Both results are processed by the data server simultaneously. However, the applicability of contactless infrared sensors for clothed body parts is limited since in this case, the measured skin temperature varies dependent on the clothing insulation and fitting.
## 4 Discussion
### Results Validation  
It is difficult to validate the accuracy of the \(\eta_{EQUIA}\) formula using the power data obtained from a real PV system. This is because in the Equatorial region, the temperature rarely remains at 25 \({}^{\circ}\)C throughout the day. Such a condition only occurs early in the morning when the irradiance is very low, i.e., less than 100 W/m\({}^{2}\). However, during that time, the inverter is either in sleep mode or in grid synchronization mode. One practical approach is to validate the \(\eta_{EQUIA}\) (which is formulated at 25 \({}^{\circ}\)C) by comparing the measured energy yield (\(E_{sys\_meas}\)) of a real system to the calculated energy yield (\(E_{sys\_calc}\)). The latter is calculated using  
\[E_{sys}=P_{array}\cdot PSH\cdot f_{temp}\cdot f_{mm}\cdot f_{dirt}\cdot\eta_{ calcle}\cdot\eta_{inv} \tag{12}\]  
To determine \(E_{sys\_calc}\), the number of days is set at 365, \(P_{array}\) at 3 kW, \(PSH\) is fixed according to the designated month or year, \(f_{temp}\) at 98%, \(f_{mm}\) at 95%, \(f_{dirt}\) at 97%, and \(\eta_{calle}\) at 98%. These made the accumulation of errors set at 0.8. These values are widely used and have been verified by reference [2]. While these variables remain fixed, the \(\eta_{inv}\) is varied using either the value \(\eta_{max}\), \(\eta_{EURO}\) or \(\eta_{EQUA}\). This is to compare the computed energy yield when using three types of weighted efficiency formulas. By taking the absolute difference between \(E_{sys\_meas}\) and \(E_{sys\_calc}\), i.e.,  
\[E_{sys\_diff}=\big{|}E_{sys\_meas}-E_{sys\_calc}\big{|}. \tag{13}\]  
The weighted efficiency which results in \(E_{sys\_diff}\) being the closest value to zero can be considered to be the most appropriate setting for the Equatorial region. The real PV system is a 3 kW roof-top grid-connected system with a 1:1 (PV module: inverter) sizing ratio. The array comprises of twelve 255 W monocrystalline modules, facing South at 10\({}^{\circ}\) tilt angle. The strings from the panels are connected to a SB3000 HF inverter. As suggested in references [2; 3], the \(E_{sys}\) calculation is only valid when the data is logged for one year. Figure 10 shows the energy produced, logged throughout the year 2014. The total \(E_{sys\_meas}\) for the year is 4141.7 kWh. The computed values \(E_{sys\_calc}\) using \(\eta_{max}\), \(\eta_{EURO}\), and \(\eta_{EQUA}\) are shown in Table 11. The \(E_{sys\_diff}\) are 99.10, 59.47, and 6.63 kWh, respectively. Clearly, the computation using \(\eta_{EQUA}\) results \(E_{sys\_diff}\) is nearest to zero. Thus, it can be concluded that \(\eta_{EQUA}\) is the best option to represent the inverter efficiency for the Equatorial region.
## 3 Network and Partial Networking Coding Models
### The Partial Network Coding Model
With random partial network coding, the coefficients of the unselected data blocks at the intermediate nodes are likely to be zero and consequently the generated coding coefficient matrix at a receiving node is likely a sparse matrix. As it is easier to generate the inverse of a sparse matrix, the decoding rate at the receiving node is higher. If the matrix becomes full rank, the receiving node would be ready to perform decoding. But the coefficient matrix produced by random partial network coding is usually not full rank. As a result, the encoding vector would have a linear correlation and the receiving node would consequently need more than N encoded data blocks to perform decoding, which increases the transmission of invalid information and leads to more network consumption. As the signal propagation speed in an underwater acoustic channel is very low, the delay in data transmission between nodes is significant in UWSNs. Furthermore, the error rate in data transmission is high, so some redundancy packets are required at a receiving node, which increases the actual energy consumption at intermediate nodes as it is proportional to the number of the forwarded packets. To tackle these issues, our GPNC protocol adopts partial network coding to reduce retransmissions, transmission delays, and energy consumptions. When an intermediate node receives an irrelevant linearly encoded packet, it knows it is a new packet and does not necessarily need to know what packet it is. Therefore, even if there is a packet loss, it is unnecessary to retransmit the lost packet, which greatly reduces the transmission of redundant data packets as well as energy consumption.
# Within and Across-Language Comparison of Vocal Emotions in Mandarin and English
###### Abstract  
This study reports experimental results on whether the acoustic realization of vocal emotions differs between Mandarin and English. Prosodic cues, spectral cues and articulatory cues generated by electroglottograph (EGG) of five emotions (anger, fear, happiness, sadness and neutral) were compared within and across Mandarin and English through a production experiment. Results of within-language comparison demonstrated that each vocal emotion had specific acoustic patterns in each language. Moreover, normalized data were used in the across-language comparison analysis. Results indicated that Mandarin and English showed different mechanisms of utilizing pitch for encoding emotions. The differences in pitch variation between neutral and other emotions were significantly larger in English than in Mandarin. However, the variations of speech rate and certain phonation cues (e.g., CPP (Cepstral Peak Prominence) and CQ (Contact quotient)) were significantly greater in Mandarin than in English. The differences in emotional speech between the two languages may be due to the restriction of pitch variation by the presence of lexical tones in Mandarin. This study reveals an interesting finding that occurs when a certain cue (e.g., pitch) is restricted in one language, other cues were strengthened to take on the responsibility of differentiating vocal emotions. Therefore, we posit that the acoustic realizations of emotional speech are multidimensional.  
emotional speech; pitch variation; Mandarin; English Article
# Oscillation Suppression Method of Digital Proportional Valve Based on Fuzzy Intelligent PID Control
## 5 Simulation Results and Discussion
### Effectiveness Analysis  
To verify the effectiveness of the proposed DPV control method, firstly, the valve was simulated and analyzed under the no-load condition, and the pressure compensation valve was ignored in this case.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 3 Results
### Quantization  
Comparing the BER performance of different quantization schemes. The shared-NNMS decoding network with 22 layers (equivalent to 10 iterations) is employed to meet the hardware requirements for UAV flights by reducing system storage and computational complexity. To achieve this, a codebook-based quantization method is utilized. We evaluate the BER performance for various quantization parameters while considering the trade-off between performance and hardware consumption. In Figure 10, the codebook sizes range from 1 to 3, the quantization bits range from 3 to 5.  
Based on the BER performance and storage space considerations, different quantization bits and codebook sizes are chosen. As shown in Figure 10, when the size of the codebook is c = 1, the BER performance deteriorates as the number of quantization bits increases. When the codebook size c is greater than 2, the greater the number of quantization bits, the better the BER performance. Furthermore, it can be seen that using the quantization scheme described in this article to quantify the weights of the NNMS decoding network, its BER performance may be even better than that of the unquantized BER performance.  
In the context of drone power inspection scenarios, where reliable communication transmission between UAV relays is sought, careful selection of suitable weight types and precision is necessary to strike a balance between BER performance and computational efficiency. Through experimental evaluation, the optimal quantization parameter settings (c = 2, q = 4) can be determined to achieve reduced weight precision and types in the decoding network, enabling low-complexity and high-reliability communication transmission on UAV-to-UAV data links.  
Figure 9: BER performance of different decoding schemes.